STAT3, a pleiotropic transcription factor, plays a critical role in the pathogenesis of autoimmunity, cancer, and many aspects of the immune system, as well as having a link with inflammatory bowel disease. Changes caused by non-synonymous single nucleotide polymorphisms (nsSNPs) have the potential to damage the protein's structure and function.STAT3 and predicted structural changes associated with mutants that disrupt normal protein–protein interactions using different computational algorithms.We identified disease susceptible single nucleotide polymorphisms (SNPs) in in silico tools, such as SIFT, PolyPhen v2, PROVEAN, PhD-SNP, and SNPs&GO, were used to determine nsSNPs of the STAT3. Further, the potentially deleterious SNPs were evaluated using I-Mutant, ConSurf, and other computational tools like DynaMut for structural prediction.Several STAT3 were identified, 6 of which are considered deleterious by in silico SNP prediction algorithms. Amino acid changes in V507F, R335W, E415K, K591M, F561Y, and Q32K were identified as the most deleterious nsSNPs based on the conservation profile, structural conformation, relative solvent accessibility, secondary structure prediction, and protein–protein interaction tools.417 nsSNPs of STAT3 may serve as potential targets for different proteomic studies, large population–based studies, diagnoses, and therapeutic interventions.The in silico prediction analysis could be beneficial as a diagnostic tool for both genetic counseling and mutation confirmation. The 6 deleterious nsSNPs of  Signal transducer and activator of transcription factors (STAT) regulate key aspects of cell growth, survival, and differentiation. STAT pathway is linked to the Janus kinases (JAK) family of proteins, i.e., STATs are activated by phosphorylation by JAKs. STAT1, STAT2, STAT3, STAT4, STAT5a, STAT5b, and STAT6 are members of the mammalian STAT family [STAT3 include hyper-IgE recurrent infection syndrome 1, autosomal dominant and autoimmune disease, multisystem, and infantile-onset. Genome-wide association studies (GWAS) have revealed the role of STAT3 in diseases like mean corpuscular volume, multiple sclerosis, inflammatory bowel disease, Crohn disease, ankylosing spondylitis, psoriasis, ulcerative colitis, and sclerosing cholangitis. Constitutive activation and regulation of STAT3 induce cellular transformation and can exert various types of tumors including head and neck, prostate, breast, and leukemia [The major disorders associated with STAT3 can enhance cancer stem cell self-renewal and differentiation by altering the gene expression via the epithelial–mesenchymal transition (EMT) [STAT3 induces VEGF expression in the association of hypoxia-inducible factor 1-alpha (HIF1A) to promote tumor angiogenesis [STAT3 has been found to be significantly associated with autoimmune thyroid diseases (AITDs) and further suggests that this may alter the level of thyroid autoantibodies in AITD patients [STAT3 has been associated with a lower risk of gastric cancer in the Chinese population [on (EMT) . STAT3 iogenesis . An exampatients . The rs7Single nucleotide polymorphism (SNP) is considered for over 90% of sequence variations in the human genome  and playSTAT3 gene and to identify their structural and functional impact on the STAT3 protein.In this study, detailed investigations have been carried out using several in silico tools to evaluate the potentially detrimental effects on nsSNPs of the STAT3 gene.Various in silico tools were used to predict the variations in the structure, stability, and function of the STAT3 gene (Accession: NP_003141.2) was collected from the National Center for Biotechnology Information (NCBI) dbSNP database (https://www.ncbi.nlm.nih.gov/projects/SNP) and the protein sequence was retrieved from the UniProt database (UniProtKB – P40763 (STAT3_HUMAN)).The nsSNP distribution of the To estimate the functional repercussions of nsSNPs in the coding area acquired from the dbSNP database, 7 best-performing web tools were employed sequentially.https://sift.bii.a-star.edu.sg/) [Sorting Intolerant From Tolerant   predictshttp://genetics.bwh.harvard.edu/pph2/) [Polymorphism Phenotyping v2   uses thehttp://provean.jcvi.org/index.php) [Based on the alignment score of a protein, Protein Variation Effect Analyzer   analyzeshttps://www.pantherdb.org/tools) [Based on evolutionary relationships, molecular activities, and interactions with other proteins, Protein Analysis through Evolutionary Relationship   predictshttp://snps.biofold.org/snps-and-go/snps-and-go.html) [SNPs&GO (go.html)  used thehttp://snps.biofold.org/phd-snp/phdsnp.html) [Predictor of human Deleterious Single Nucleotide Polymorphisms   is an onhttp://gpcr2.biocomp.unibo.it/cgi/predictors/I-Mutant3.0/I-Mutant3.0.cgi) [I-Mutant 3.0 (3.0.cgi)  was used3.0.cgi) . A ΔΔG vhttp://consurf.tau.ac.il) [The ConSurf web server (u.ac.il)  analyzesNetsurfP-2.0  predictshttp://www.cmbi.ru.nl/hope/) [HOPE (l/hope/)  was usedhttps://npsa-prabi.ibcp.fr/cgibin/npsa_automat.pl?page=npsa_sopma.html) [SOPMA (ma.html)  was usedhttps://string-db.org/) database predicts the functional linkages between proteins as well as their associations by combining data from physical interactions and curated biological pathway databases.The Search Tool for the Retrieval of Interacting Genes/Proteins  predicts the gene–gene interaction using a large set of functional association data including protein and genetic relationships, pathways, co-expression, co-localization, and protein domain similarity. STAT3 was the input query entered into the software.GeneMANIA   is the most accurate technique for predicting protein structure by neural network–based model. The AlphaFold model includes the coordinates of every heavy atom in a protein as well as “its confidence in the form of a projected LDDT-C score (pLDDT) per residue” [AlphaFold (residue” . The LDDresidue” . The Unihttp://biosig.unimelb.edu.au/dynamut/). DynaMut employs normal mode analysis (NMA) to compare the free energy change (ΔΔG) between the wild-type and mutant structures. DynaMut additionally provides structure-based predictions for mCSM [The impact of a single point mutation on the stability, conformation, flexibility of proteins, and the visualization of protein dynamics, were evaluated using the DynaMut server , 307 synonymous, 3 inframe deletions, and 2 inframe insertions. We exclusively chose nsSNPs for further analysis in this study as they change the encoded amino acid.All the SNP data for the Table 1.Various in silico prediction tools such as SIFT, Polyphen-2, PROVEAN, SNP & GO, PANTHER, and PhD–SNP were used to analyze disease-associated SNPs. Initially, all the 417 nsSNPs were loaded to the SIFT server, which predicted 160 nsSNPs as tolerated or deleterious, whereas the remaining SNPs were not found. Out of this, 67 nsSNPs were classified as deleterious with a SIFT score ≤0.05 and the remaining were tolerated. The nsSNPs were then submitted for PolyPhen2 analysis. To increase the accuracy of the prediction, the combined prediction of both SIFT and PolyPhen, such as the nsSNPs with SIFT score ≤0.05 and PolyPhen score >0.90, was selected and 9 nsSNPs were identified as deleterious. The selected 9 nsSNPs were subjected to other in silico tools, namely PROVEAN, SNP & GO, PANTHER, and PhD–SNP, and the results are given in STAT3 with a RI. Finally, 6 nsSNPs were determined as deleterious by all the above online software. rs145786768, rs193922716, rs193922717, rs193922719, rs1064116, and rs1803125 with a change in amino acid V507F, R335W, E415K, K591M, F561Y, and Q32K were picked as the most deleterious nsSNPs since they showed a mutation in a majority of the in silico prediction tools. In addition, we used the I-Mutant server to analyze the effects of nsSNPs on protein stability. The result showed that 6 nsSNPs have a ΔΔG value < −0.5, which indicates decreasing stability of the STAT3 protein (Table 1).According to PROVEAN results, 6 nsSNPs were predicted as disease-causing and 3 were neutral. Through the PANTHER tool, 6 nsSNPs were predicted as probably damaging, 1 was possibly damaging, and 1 was found to be benign. Moreover, SNP & GO predicted 6 of these 9 nsSNPs as disease-relevant mutations in Figure 1. The result of ConSurf is shown in Table 2.Through the ConSurf web server, the evolutionary conservancy of 6 nsSNPs of STAT3 protein was analyzed, together with the identification of putative, structural, and functional residues. The results showed that all the 6 nsSNPS, namely V507F, R335W, E415K, K591M, F561Y, and Q32K, were highly conserved and the variants are indicated in black boxes in Table 3.The variants with high conservation scores in ConSurf output were assessed for solvent accessibility, stability, and secondary structure prediction by NetsurfP-2.0. The results are displayed in th position the mutant introduced an opposite charge; further, the mutant also introduced a charge at position 32. The variation in charge can cause a loss of interaction with other molecules. Figure 2 illustrates the structural images of 6 deleterious nsSNPs showing both wild-type and mutant residues at the specific protein site.HOPE showed the difference between the wild-type and mutant amino acids in connection with their physical and chemical properties, hydrophobicity, spatial structure, and function. Project HOPE server revealed that the mutant residues V507F, R335W, E415K, F561Y, and Q32K were bigger than the wild-type residues whereas the mutant residue of K591M was smaller than the wild-type. In addition, the mutant residue of R335W, K591M, and F561Y is more hydrophobic than the wild-type. The change in the size and hydrophobicity of the mutant residue can disrupt the H-bonding interaction with neighboring molecules. Moreover, the mutation at 335, 591 positions the wild-type residue charge was lost, whereas at 415Figure 3. SOPMA secondary structure elements calculations showed that 50.39% of sites were in alpha helixes, 33.38% in random coils, 2.86% in beta twists, and 13.38% in extended strands. Out of the 6 high-risk nsSNPs, 2 were found in random coils, 3 in alpha helixes, and 1 in extended strands. The STRING maps were used to depict the protein–protein interaction of STAT3. The STRING results showed the functional interaction pattern of the STAT3 protein with other proteins in a cell, which is useful in interpreting the genotype–phenotype consequences of mutations. Histone acetyltransferase p300 (EP300), E3 SUMO-protein ligase (PIAS3), interleukin-10 receptor (IL10RA), JAK1, JAK2, epidermal growth factor receptor (EGFR), heat shock protein HSP 90-alpha (HSP90AA1), proto-oncogene tyrosine-protein kinase (SRC), and homeobox protein NANOG were all found to have substantial functional connections with STAT3 in the STRING prediction .SOPMA predicted the secondary structure of STAT3, which explained the distributions of alpha-helix, beta-sheet, and random coil. The secondary structure prediction of STAT3 by SOPMA is shown in Figure 5 shows the structure predicted by the RaptorX Binding server.STAT3 ligand binding sites were predicted using the RaptorX Binding server. A pocket multiplicity number greater than 40, according to the RaptorX Binding server, suggests an accurate prediction. In the STAT3 protein, there were only 1 processed domain and 2 predicted pockets, one with a multiplicity of 11 that binds to R382 L430 I431 S465 N466 Q469 and the other with a multiplicity of 3 that binds to M331 H332 I467. The server predicted that 128 locations were disordered. RaptorX also predicted the secondary structure showing 45% helix, 13% sheets, and 41% coil. STAT3, which is shown in Figure 6. According to the predicted results, STAT3 interacts physically and genetically with nuclear factor kappa B Subunit 1 (NFKB1Z) and mitogen-activated protein kinase kinase 5 (MAP2K5), EGFR, and STAT1, and is co-expressed mostly with EGFR, protein disulfide-isomerase A3 (PD1A3), interleukin 6 receptor (IL6R), and STAT5B.GeneMANIA server predicted the functional gene–gene interaction network of Figure 7).The overall confidence for the whole protein chain is shown by the average pLDDT scores across all residues The Alpha-Fold algorithm produces a pLDDT score for each individual residue that ranges from 0 to 100. In STAT3 3D structure, a very high degree of confidence (pLDDT >90) was obtained and majority of the 3D structural area belongs to α-helical domains (Table 4. DynaMut identified the ∆∆G prediction of F561Y as destabilizing; however, NMA-based predictions revealed that all 6 deleterious nsSNPs reduce structural stability when compared to the wild-type protein. Figure 8 depicts the difference in vibrational entropy and interatomic interactions between the wild-type and mutant.DynaMut calculates the effect of point mutations on protein stability and flexibility based on interatomic interactions, as summarized in STAT3 using a variety of computational methods determines the effects of deleterious nsSNPs in STAT3 and their association with various diseases.In mammals, several STAT proteins play an important role in host defense. Despite its importance, STAT3 is linked to the JAK family of proteins and is capable of integrating signals from several signaling pathways. STAT3 that is persistently active has been associated with various malignancies, though it is most commonly associated with head and neck cancer and multiple myelomas . The occIn this study, by merging numerous in silico-based SNP prediction algorithms, we projected 6 deleterious SNPs that are considered high-risk and valid, and they were further examined. The nsSNPsrs145786768, rs193922716, rs193922717, rs193922719, rs1064116, and rs180312 with amino acid changes V507F, R335W, E415K, K591M, F561Y, and Q32K were considered deleterious using 6 in silico SNP prediction tools SIFT, Polyphen-2, PANTHER, SNP&GO, PROVEAN, and PHD -SNP. Based on the conservation profile, structural conformation, relative solvent accessibility, secondary structure prediction, and protein–protein interaction, all of the 6 nsSNPs were identified as the most deleterious nsSNPs. Protein stability is essential for a protein's structural and functional activity. The I-Mutant tool revealed that the predicted 6 nsSNPs on the STAT3 protein had decreased stability. Protein stability determines the conformational structure and function of the protein. Misfolding, degradation, or abnormal protein conglomeration can be affected by changes in protein stability .Conserved residues of the protein were involved in biological system management, including stability, folding, or both of these. Functional amino acids that are present in enzymatic sites interact with the proteins in a significant manner and when compared to other residues in the protein, these residues are found to be more conserved . The evoThe exposed variants were located on the surface of the protein, which might lead to loss of interactions and structural alterations, particularly in the transmembrane domains. Repulsion, misfolding, and loss of interactions may occur from the addition or removal of charge or hydrophobicity. SOPMA secondary structure calculations showed that the deleterious nsSNPs are mainly found in helix and coil regions rather than β turns. RaptorX predicted the high-risk nsSNPs as ligand binding locations.STAT3-targeted genes associated with oncogenesis to minimize therapeutic toxicity [STAT3 gene might influence and disrupt the proper functioning of related genes, which highlights the importance of these linked and co-expressed genes in immune pathways and inflammation-associated tumorigenesis. The need for the acquisition of a higher quality of targeted protein structure and the validation of experimental models in turn necessitate further investigation into analysis of the nsSNPs-induced deteriorative changes in STAT3. In AlphaFold, pLDDT computes the degree of prediction and experimental structure of STAT3. The DynaMut tool provides both the stability change and the difference in entropy energy between wild-type and mutant structures. The 6 nsSNPs were found to affect the molecular flexibility of the STAT3 protein by altering its structural conformation.Protein–protein interaction network is an important factor in understanding biological processes. Using STRING, the functional genomics data and structural assessment, functional, and evolutionary features of STAT3 protein were analyzed . EP300, toxicity . Based oSTAT3 gene are capable of regulating the expression, stimulation, and predisposition in inflammatory and neoplastic diseases [STAT3 activation was detected in various tumors in association with the proliferation, invasion, and angiogenesis of malignant cells and the inhibition of anti-tumor immunity [Deleterious SNPs of the diseases . STAT3 aimmunity . Althougimmunity . The stuSTAT3 exerts important effects on tumorigenesis and tumor-related inflammations. Variations in STAT3 are associated with various human tumors. The present study screened 417 nsSNPs of the STAT3 gene using different computational tools and scrutinized 6 nsSNPs with amino acid changes, namely V507F, R335W, E415K, K591M, F561Y, and Q32K, as most deleterious. The deleterious variants may affect structural and cellular function. STAT3 is involved in different tumors and the identified deleterious nsSNPs from our study could be important candidates and could be used as diagnostic markers. Based on our result, we can conclude that these 6 nsSNPs should be considered as potential biomarkers in causing diseases related to STAT3 variations. However, in silico tools cannot replace conclusive trials, and their conclusions should be double-checked by additional biological evaluation.The transcription factor


CIC) is a gene that is frequently mutated in several cancer types, including stomach cancers and certain subtypes of brain tumours and sarcomas. CIC, the protein encoded by the CIC gene, has been shown to play a multitude of roles in both normal and cancer cell functions; however, most studies exploring these roles focus on a single aspect of CIC function and may therefore overlook complex interconnected activities in which CIC is involved. In this study, we have used multiple complementary approaches to obtain a broader view of CIC’s complex functional networks. We observed novel interactions  between CIC and genes/proteins involved in various aspects of cellular function, including regulation of cell division and processing of RNA molecules. Altogether, our work characterises the complexity of CIC’s functional network and expands our understanding of its potential roles in cancer.Capicua (CIC encodes a transcriptional repressor and MAPK signalling effector that is inactivated by loss-of-function mutations in several cancer types, consistent with a role as a tumour suppressor. Here, we used bioinformatic, genomic, and proteomic approaches to investigate CIC’s interaction networks. We observed both previously identified and novel candidate interactions between CIC and SWI/SNF complex members, as well as novel interactions between CIC and cell cycle regulators and RNA processing factors. We found that CIC loss is associated with an increased frequency of mitotic defects in human cell lines and an in vivo mouse model and with dysregulated expression of mitotic regulators. We also observed aberrant splicing in CIC-deficient cell lines, predominantly at 3′ and 5′ untranslated regions of genes, including genes involved in MAPK signalling, DNA repair, and cell cycle regulation. Our study thus characterises the complexity of CIC’s functional network and describes the effect of its loss on cell cycle regulation, mitotic integrity, and transcriptional splicing, thereby expanding our understanding of CIC’s potential roles in cancer. In addition, our work exemplifies how multi-omic, network-based analyses can be used to uncover novel insights into the interconnected functions of pleiotropic genes/proteins across cellular contexts. CIC alterations and/or loss of CIC activity have been associated with inferior survival outcomes in several cancer types [Capicua (CIC) is a transcriptional repressor that is impacted by somatic mutations or rearrangements in several cancer types, including undifferentiated small round cell sarcomas (~60% of cases) , oligodeer types ,13,14,15er types  and resier types ,17,18. TPTPN9, SHC3/4) and members of regulatory feedback mechanisms  [ETV1, ETV4, and ETV5 [Drosophila, where Cic function was originally described [AURKA, CDK4, BUB1) across multiple cancer types [CIC is a transducer of receptor tyrosine kinase (RTK) signalling and functions through a default repression mechanism in which RTK activation leads to reduced CIC activity and subsequent de-repression of its target genes . TargetsED1/2/3) ,22,23,24and ETV5 ,9,10. Inescribed , Cic inhescribed ,27. Althescribed , loss ofescribed ,17,22,28er types . CIC haser types . It has er types , ATXN1L er types , and SINer types  to mediaer types  demonstrCic knockout (KO) mouse model, we demonstrate that loss of CIC is associated with an increased frequency of mitotic defects. Finally, motivated by the observation that CIC’s genetic and proteomic networks include several RNA processing and splicing factors, we perform a differential splicing analysis in which we compare CIC wild type (WT) and KO human cell lines and find differential exon usage patterns predominantly at 3′ untranslated regions (UTRs). Overall, our systematic characterization of CIC’s complex functional network reveals the effects of its loss on cell cycle regulation, mitotic integrity, and transcriptional splicing stability, thus providing new insights into CIC’s potential role(s) in different biological contexts, including cancer. We argue that, in addition to revealing novel CIC functions, our study provides a blueprint for multi-omic characterization of pleiotropic genes to uncover novel connections and functions at play across cellular and subcellular contexts.The increasing frequency of CIC-related publications in recent years appears to indicate a heightened level of interest in this apparently pleiotropic protein that, to date, is understood to be involved in transcriptional regulation and signal transduction to mediate phenotypes across a wide array of developmental and oncogenic contexts. Many of these CIC-related studies focus on a singular aspect of its function  in a unique context . The rapid development of genomic techniques and technologies in recent years , along with access to large public datasets generated by them, have enabled powerful investigations of complex biological relationships from different angles through layering of multiple -omic technologies  heat-inactivated (HI) FBS . Stable cell lines expressing F-CIC-S, F-CIC-L, or F-vec were cultured in DMEM + 10% HI FBS with Zeocin (Thermo Fisher Scientific) selection. Cell culture was performed in a humidified, 37 °C, 5% CO2 incubator.The IDH1-stable NHA cell line was obtained from Applied Biological Materials (ABM) Inc. . The HEK293A cell line was obtained from Dr. Gregg Morin  and authenticated by Genetica DNA Laboratories . The HOG cell line was obtained from Dr. G. Dawson . Cells were cultured in Dulbecco’s Modified Eagle Medium (DMEM) supplemented with 10%  and HEK293A (HEK-CICKO-A9 and HEK-CICKO-D10) cells as described previously [CIC knockout status was verified by Western blot and Sanger sequencing. N-terminal FLAG-CIC-short form (FLAG-CIC-S) and long-form (FLAG-CIC-L) plasmids were prepared as described previously [CRISPR-Cas9 sgRNA sequences were designed to target exon 2 of the eviously . CIC knoeviously .Single-cell 3′ RNA-seq libraries were generated using the Chromium Single Cell 3′ Library & Gel Bead Kit v1  following the manufacturer’s protocol. Full details can be found in the quickCluster function was used to cluster cells for normalisation (with min.mean = 0.1 as suggested for UMI data), and the resulting clusters were used as input to the computeSumFactors function (with min.mean = 0.1). These factors were then used in the normalise function of the scater R package (v1.10.0).The CellRanger software (v1.2.1 and 2.1.1) was used to create fastq files  and to perform alignment, filtering, barcode counting, and unique molecular identifier (UMI) counting . The CellRanger hg19-3.0.0 reference was used for alignment. Data preprocessing was performed in the R statistical software (v3.5.0) , based oestimate_Schwabe_stage function implemented in the tricycle R package [CICWT cells, 554 NHA-CICKO-A2 cells, and 532 NHA-CICKO-H9 cells; S, 478, 186, and 270; G2, 237, 66, and 94; G2/M, 200, 226, and 234; M/G1, 660, 111, and 155. For the differential expression analysis, differential expression was calculated using the DESeq2 R package [CICWT vs. NHA-CICKO samples. Genes with a Benjamini Hochberg (BH)-corrected p-value < 0.05 and an absolute fold-change > 1.5 were considered to be significantly differentially expressed. To visualise these expression differences, DESeq2-normalised expression values were obtained using the counts function with normalised = TRUE. By default, DESeq2 performs independent filtering based on mean expression across samples in order to select a set of genes for multiple test correction that maximises the number of significantly differentially expressed genes found at the specified false discovery rate [p-value of “NA”); these comparisons are indicated with “NC”  in the relevant figures.Cells were scored for cell cycle phases as described by Macosko et al.  and Schw package  (v1.2.1) package  with defery rate . TherefoThe IP-MS analyses of nuclear fractions from HEK293A cells using anti-endogenous CIC or anti-Myc (for N-Myc-CIC-S) antibodies  were perhttps://depmap.org/portal/download/; accessed on 10 February 2020). Briefly, co-essentiality mapping uses fitness effect scores that were derived from normalised and copy number-corrected whole-genome CRISPR-Cas9 knockout screens targeting 18,333 genes performed in 739 cancer cell lines [CIC and each screened gene, and a gene pair was considered co-essential if its BH-adjusted p-value was <0.05 and its coefficient was >0.178 (the inflection point). Given the BH adjustments, we expect a <5% false discovery rate in our analysis. For non-syntenic co-essential maps, genes on chromosome 19 were removed and the thresholds were recalculated (with an inflection point coefficient of 0.138).Co-essentiality mapping and in silico genetic interaction screening were performed using the R statistical software (v4.0.2) package GRETTA (v0.5.0) , with Dell lines ,47. GRETCIC-mutant lines were identified using genomic data from the 739 DepMap cancer cell lines. We identified two lines (HCC366 and JHUEM1) with homozygous deleterious CIC alterations (CICHomDel), five lines  with potentially trans-heterozygous CIC mutations , 53 lines with a single heterozygous deleterious CIC mutation or partial copy number loss (CICHetDel), and 557 lines with no CIC alterations  [CIC and 17,067 genes from over 10,485 human samples assayed using RNA-seq (“hsa-r.6”), where a z-score of zero is a random level and a score of three is a false positive rate of 0.1% for any species or version. In this study, we used the top 50 genes co-expressed with CIC.Genes co-expressed with b (v8.1) . COXPRESb (v8.1)  and Wainb (v8.1)  to benchCIC ChIP-seq peaks for NHA cells were obtained from Lee et al.  . Interestingly, many of these genes have roles that have previously been linked to CIC, as described in Given that iability —that weriability ,46,47. WCIC, we next identified genes that were co-expressed with CIC [ERF, BICRA, and PLEKHG2 . ERF is located on the same chromosome band as CIC (chromosome 19q13.2) and is positioned directly adjacent to and transcribed in the opposite direction of CIC. Similar to CIC, ERF is an ETS domain transcriptional repressor and an ERK-dependent kinase that functions as an effector of the RTK signalling pathway [ETV1 expression in prostate cancer cells [BICRA encodes the SWI/SNF complex member GLTSCR1 [To complement our efforts to identify genes with fitness effect scores similar to with CIC . To do twith CIC , as perfwith CIC . The topregation ,67,68. N pathway ,70. Simi pathway , develop pathway . ERF ander cells , furtherer cells , the co- GLTSCR1 , further GLTSCR1 , cell cy GLTSCR1 , and cel GLTSCR1 , these rCIC. Specifically, we screened for genetic perturbations that, in CIC mutant cells, resulted in decreases or increases in cell viability relative to CICWT control cell lines ; 53 lines with heterozygous deleterious CIC mutations ; and 557 CICWT lines  were defined as candidate genetic interactors of CIC. We identified 70 such genes, all of which were predicted to be SL interactors of CIC . We have previously shown that CIC exerts distinct functions depending on its subcellular location  [CICWT). The immunoprecipitates were then analysed using liquid chromatography followed by tandem mass spectrometry . Given CIC’s known function as a transcriptional repressor, we were particularly interested in investigating whether candidate interactions with proteins involved in transcription regulation and chromatin organisation could be replicated. We therefore performed a variation of the IP-MS technique in which ‘trigger peptides’ [p-value < 0.05) that clustered in 26 groups of terms with similar enrichment memberships, including the groups summarised in CIC  ; howeveretry MS; . This yietry MS; , includieptides’  were used in CIC . These rCIC interacting with members of the SWI/SNF complex , several members of the SWI/SNF complex were identified among the high-confidence candidate interactors of CIC, including SMARCA2, SMARCC1, SMARCC2, and PBRM1 (CICWT line (CICKO HEK cells stably expressing a FLAG-tagged construct of the short CIC isoform (HEK-CICKO-D10 + F-CIC-S) and observed a striking apparent colocalisation between F-CIC-S and ARID1A, ARID2, SMARCA2, and SMARCC1  [CICKO2 + F-CIC-S cells also displayed similar localisation dynamics as endogenous CIC  , observinous CIC . To detenous CIC . CIC didCICKO cells appeared to display a higher frequency of defective mitosis  compared to CICWT cells. To investigate this further, additional IF assays in the NHA lines were used to confirm that NHA-CICKO lines had an increased frequency of mitotic defects compared to the parental NHA-CICWT cells (CICKO lines (NHA-CICKO-A2 and NHA-CICKO-H9) showed 2.2–2.5-fold increases in metaphase alignment defects  and 2–2.3-fold increases in chromosome segregation defects  at telophase/cytokinesis . We also observed a significant increase in metaphase defects in HEK-CICKO cell lines compared to HEK-CICWT cell lines  and from heterozygous control mice  [Cic-null cells in the mitotically active ventricular zone displayed an increased frequency of mitotic defects, including lagging chromosomes and micronuclei  . ConsistCICWT, NHA-CICKO-A2, and NHA-CICKO-H9 cells. To characterise the transcriptional profiles of CIC-proficient and CIC-deficient cells at different phases of the cell cycle, we first scored cells for defined cell cycle phases  [CICWT and CICKO cells assigned to that phase (ETV1/4/5 were among the genes found to be significantly more abundantly expressed in CICKO cells at each phase (ETV4) or some phases , supporting the validity of our approach. Genes overexpressed in CICKO cells were also consistently found to be enriched for targets of RAS/MAPK signalling , as has previously been shown using bulk transcriptome profiling of CIC-deficient cell lines and tumours [CICKO cells in at least one phase included HIPK2, which encodes a serine-threonine kinase that regulates the activity of a wide range of transcription factors and chromatin regulators [TNRC6B and DGCR8, both of which are involved in RNA processing [ATF4, CREB3L2, FOSL1, MLXIP, RXRA, SMAD3, SOX9/12, SREBF1/2, and STAT6, were also found to be more abundantly expressed in CICKO cells, especially at the G1/S and S phases. Conversely, genes encoding proteins involved in transcriptional repression were also identified among those overexpressed in CICKO cells, including, e.g., the polycomb repressive complex 1 (PRC1)-related genes CBX6 and PHC2, the nucleosome remodelling and deacetylase (NuRD) complex members CHD3, MBD3, and GATAD2A, and the histone deacetylase HDAC7.Given CIC’s dynamic pattern of nuclear localisation and the association between its loss and increased mitotic defects, we hypothesised that CIC’s effects on gene expression regulation may depend on the cell cycle. Since dysregulated expression of dynamically expressed genes may not be detectable in asynchronous bulk cell populations and synchronisation through either chemical or physical means can introduce confounding artefacts ,108, we  phases)  using ap phases) ,40  of all significantly differentially expressed genes displayed lower expression in CICKO cells (CCNB1 (significantly less abundant in S and G2/M CICKO cells), CDK1 (G1/S), MAD2L1 (S and G2/M), CENPK (M/G1), and CCNE2 . Echoing the results of our IP-MS analysis, we also observed an enrichment of genes involved in RNA processing and splicing among genes with lower expression in CICKO cells. These include the small nuclear ribonucleoprotein core protein family members SNRPB2 and SNRPD2, the heterogeneous nuclear ribonucleoproteins HNRNPH3 and HNRNPA2B1, the splicing factor 3B subunit SF3B4, and the spliceosome component PRPF31, all of which were significantly under-expressed in G1/S-phase CICKO cells. Interestingly, our results also indicated that NPM1 had significantly lower expression in G2/M-phase CICKO cells, although it showed a similar trend at all phases, especially in the CICKO-A2 cell line , ribosome biogenesis, the p53-mediated stress response, and inhibition of cell growth and proliferation [Although elevated expression of target genes in KO cells a. This iE2 G1/S; c. YWHAQ activity  and whicactivity , were alell line c. NPM1 iferation . NotablyCICKO cells might be directly regulated by CIC, we compared our gene list with a set of 150 high-confidence CIC binding sites that our group previously identified in the NHA-CICWT line using chromatin immunoprecipitation followed by sequencing (ChIP-seq) [DUSP4, DUSP5, ETV4, ETV5, FOSL1, HMGA1, MAFF, OSGIN1, TBC1D14, and TBC1D22A—were differentially abundant between NHA-CICWT and NHA-CICKO cells in at least one cell cycle phase in our scRNA-seq data  . Of the seq data . All of contexts . Severalcontexts , which iOverall, these results indicate that CIC appears to regulate the expression of genes involved in a wide range of functions, including MAPK signalling, transcriptional regulation, mitotic checkpoint activity, and RNA processing. Notably, our results also indicate that CIC regulates the expression of similar groups of genes across different phases of the cell cycle. Furthermore, candidate direct targets of CIC transcriptional regulation appear enriched for genes involved in functions that we have repeatedly identified in this study, including MAPK signalling, AP-1 activity, and apoptosis, further implicating CIC activity within these functional groups.CCND1/Ccnd1, DUSP4/Dusp4, ETV4/Etv4, ETV5/Etv5, GPR3/Gpr3, LRP8/Lrp8, PTPN9/Ptpn9, SPRED2/Spred2, and SPRY4/Spry4. Of the 108 peaks identified in the mESC CIC ChIP-seq dataset (see p = 1.38 × 10−12), 60 with BRD9 peaks (p = 1.71 × 10−40), 84 with BRD4 peaks (p = 2.26 × 10−38), and 102 with SMARCA4 peaks (p = 8.66 × 10−32). Notably, of the 100 genes associated with these 108 mESC CIC peaks, the human orthologues for 10 of them were found to be differentially expressed in at least one cell cycle phase in our scRNA-seq data . The 14 peaks associated with these 10 genes all overlapped with SWI/SNF peaks . We did not observe differences in the overall distribution of entropy scores between the NHA-CICWT line and either the NHA-CICKO-A2 or NHA-CICKO-H9 lines , TSSs , and retained introns  [CICKO cells affects NRG1 isoforms 3, 6, 7, and 8 , which share a common TSS and which belong to types II  and III  [Given that UTR length and intron retention events have both been shown to affect gene expression ,130,131,CWT line d, showedKO cells f, was siKO cells . We alsols  . The altneurons) . Since tneurons) ,137, dysIn this study, we used bioinformatics, mass spectrometry, genomics, single-cell RNA-seq, and microscopy techniques to expand our understanding of CIC function in human and mouse cells and tissues. Results from these orthogonal approaches converged to implicate CIC in cell cycle regulation and splicing in addition to its known function as an effector of MAPK signalling  with a wide range of proteins and genes involved in RNA processing, splicing, and mRNA stability. For example, the list of 156 proteins we identified as high-confidence candidate physical interactors of CIC included at least 38 proteins annotated as being involved in functions related to RNA splicing, rRNA/mRNA metabolism and processing, and ribosome biogenesis, which is consistent with the notion that CIC physically interacts with RNA processing complexes. Furthermore, genetic interactions were also observed between  3′ UTRs ,79,80 an 3′ UTRs  and whicteractor . ThroughCIC’s co-essential network, which is composed of genes with which CIC is likely to share functionality, included several known regulators of MAPK signalling, including DUSP6, RASA1, and ERF. Notably, CIC and ERF have been shown to co-regulate the expression of ETV1 in prostate cancer, where their combined loss and the subsequent increase in ETV1 expression contribute to disease progression [It is worth noting that, in addition to the putative novel roles discussed above, our study also reinforced CIC’s known role as an effector of MAPK signalling and provided clues as to interactions between this established function and CIC’s possible novel ones. For example, gression . As mentgression , suggestCIC mutant lines included a lung cancer and an endometrial carcinoma line; CIC wild-type lines included 557 lines from 29 cancer contexts) and protein interactors (NHA and HEK cell lines). We also highlighted not only interacting genes/proteins but also the broad functional pathways that were enriched in multiple assays , which are known to be more conserved [While our multi-omic analysis uncovered several putative CIC functions, we acknowledge that there are limitations to such analyses. For example, both genetic interaction networks  and protonserved . While tOverall, our study provides a rigorous exploration of CIC’s genetic and proteomic interaction networks and considerably expands our understanding of the diverse cellular processes in which CIC appears to play a role. Beyond the insights it provides into CIC function specifically, this work also presents a model for how multi-omic, network-based analyses that leverage both public and novel datasets can reveal novel and interconnected roles for pleiotropic genes/proteins across cellular and subcellular contexts. For instance, our results raise the intriguing possibility that CIC may be required to ensure faithful mitotic division and that this function may be related to its interactions with SWI/SNF complexes and/or with RNA processing and spliceosomal complexes. This work thus introduces novel avenues through which CIC can act as a pan-cancer tumour suppressor, and we anticipate that it will catalyse further innovative investigations into this intriguing protein’s varied roles.


Pistacia lentiscus L. var. chia resin (Chios Mastiha), the first natural chewing gum, is widely used in Mediterranean cuisine and has been used in traditional medicine from ancient times. Regarding its chemical composition, Chios Mastiha is known to be rich in triterpenes. Triterpenes have a similar structure to glucocorticoids (GCs), the steroid hormones that exert strong anti-inflammatory activities and play crucial roles in the regulation of cellular metabolism. To simplify the characterization of the bioactive compounds of Mastiha resin, three different polarity fractions were isolated and were further analyzed regarding their main chemical composition and an assessment of their biological activities. The biological assessment focused on the evaluation of the potential anti-proliferative, anti-inflammatory, and apoptotic activities as well as the possible interference of the three different polarity Mastiha fractions with the glucocorticoid receptor signaling, with the aim of characterizing the biochemical mechanisms of the actions of the Mastiha fraction. Applying MTT cell viability assay, luciferase/β-galactosidase assay, and Western blot analysis showed that Chios Mastiha apolar, medium-polar, and polar fractions reduced the HEK293 cell viability in a dose-dependent manner, possibly by mitochondrial-mediated induction of apoptosis. Medium-polar and polar Mastiha fractions also suppressed the GR and NF-κΒ transcriptional activation and the p65 protein levels. These activities were accompanied by the modulation of protein levels of regulatory molecules playing a crucial role in cellular energy homeostasis, such as GR, phosphoenolpyruvate carboxykinase (PEPCK), and/or peroxisome proliferator-activated receptor alpha (PPARα), and by the induction of phosphorylation and the activation of the AMP-activated protein kinase (AMPK). The medium-polar fraction was found to be enriched in triterpenes, such as lupeol, 24Z-masticadienonic acid methyl ester, and 24Z-isomasticadienonic acid methyl ester, and it was the most active one, so we propose that triterpenes in medium-polar fraction are possibly the bioactive compounds responsible for Mastiha’s regulatory actions on energy metabolism and anti-inflammatory activities via interference with GR, NF-κΒ, and AMPK signaling. This highlights its potential applications in many fields of pharmaceutical, cosmetic, and nutraceutical interest. Pistacia lentiscus L. var. chia (Mastiha tree), which is an endemic plant cultivated exclusively in the Southern part of the Greek Island of Chios. Chios Mastiha is the first natural chewing gum and is widely used in traditional Mediterranean cooking and beverages due to its distinctive aroma and taste. Mastiha is also an important dietary supplement, especially in cases of a lack of trace elements [Chios Mastiha is the resin obtained from the elements . It has elements . Moreoveelements . In receelements ,4, anti-elements .The composition of Chios Mastiha has been widely studied, revealing that 25–30% of crude Mastiha consists of a sticky polymer β-myrcene, while triterpenes represent approximately 65–70% of its composition. Thus, triterpenes such as moronate, oleanolate, oleanonate, masticadienonate, masticadienolate , and theNatural glucocorticoids (GCs) like cortisol are steroid hormones, which are being secreted from the adrenal glands, and which exert their actions through the glucocorticoid receptor (GR). These hormones are essential for many physiological processes, such as metabolism, growth, development, immunological responses, apoptosis, and mental health . The gluPistacia lentiscus L. var. chia leaves interfere with GR signaling via repression of GR transcriptional activation and the reduction of its protein levels in a proteasomal-dependent manner [In this context, plant origin compounds, such as triterpenoids, have raised the interest of many researchers due to their structural similarities with glucocorticoids and their promising biological properties, pointing out their potential as selective glucocorticoid receptor modulators. Experimental results from our and, also, from other groups have revealed that plant origin triperpenoids, such as echinocystic acid and its 3-O-glucoside derivative , protopat manner .In light of this, and considering the enrichment of Mastiha in triterpenoids, in this study we investigated the biological activities of different polarity fractions isolated from the resin of the Chios Mastiha tree. Emphasis was given to activities related to the actions of GCs. Thus, the possible anti-inflammatory and anti-proliferative properties, and the interference with the regulation of apoptosis, GR-transactivation and -transrepression, and GR-associated metabolic activities of three different polarity Mastiha fractions  were evaluated, applying Western blot analysis, MTT assays, and/or luciferase reporter gene assays in the GR-positive human HEK293 cell line.® 545 particle size 0.02–0.1 mm, CAS 68855-54-9, pH 10 , neutral alumina Alugram®, and RP C-18 silica gel were used and obtained from Macherey-Nagel . 1 H (400 MHz) spectra were measured on Bruker 400 NMR spectrometers. Chemical shifts were referenced to the residual solvent signal (CDCl3: δH = 7.26). Purifications were monitored by TLC on Merck 60 F254 (0.25 mm) plates, visualized by staining with 5% H2SO4 in EtOH and heating. HPLC JASCO Hichrom, 250 × 25 mm, silica UV−vis detector-2075 plus  was used. Dexamethasone (DEX) and all the other solvents and chemicals used were obtained from Sigma-Aldrich . Finally, the Chios Mastiha was a kind donation from the Mastiha Shop and the Chios Mastic Growers Association.Dulbecco’s modified Eagle medium (DMEM) (4.5 g/L glucose), fetal bovine serum (FBS), and molecular weight protein markers were obtained from Thermo Fischer Scientific . Recombinant Human Tumor Necrosis Factor alpha (TNFα) was obtained from Peprotech . A cocktail of protease inhibitors was obtained from Roche . Reporter lysis buffer and luciferin were purchased from Promega Corporation . For chromatography studies, silica gel 60, CelitePistacia lentiscus L. (10 g) was dissolved in acetone  upon stirring for 2 h. Filtration of the suspension was followed to get rid of the insoluble material and to provide 9.5 g (95%) of a yellow, creamy acetonic extract after evaporation of the solvent.Resin from A minimum amount of acetone was then used to dissolve the dried extract and, subsequently, silica gel was added (1:3 weight/weight g). This latter suspension was completely evaporated at reduced pressure. The powder obtained after evaporation was stratified on a Celite layer, as previously described . Three dP. lentiscus resin, was purified by applying low-pressure chromatography analysis on silica gel  to provide 235.5 mg of the polymer 1,4-poly-β-myrcene  to afford, once again, 261.5 mg of the oleanolic aldehyde C, 218.5 P. lentiscus resin has been analyzed by 1H NMR spectra, revealing the presence of phenolic compounds and the lack of triterpenoids.The polar fraction (0.56 g) of acetonic extract of Mouse monoclonal antibodies against glyceraldehyde-3-phosphate dehydrogenase (GAPDH), human glucocorticoid receptor (GR), peroxisome proliferator-activated receptor alpha (PPARα), and rabbit polyclonal antibodies against p65 subunit of NF-κB and phosphoenolpyruvate carboxykinase (PEPCK) were purchased from Santa Cruz Biotechnology . Monoclonal antibodies against β-actin , procaspase-9 , and rabbit polyclonal antibodies against bcl-2, AMPKα, phosphorylated AMPKα at Thr172 (pAMPKαThr172), and procaspase-3  were also used.2 humidity, in DMEM, supplemented with 10% FBS, 2 mM L-glutamine, and 100 units/mL penicillin/streptomycin (pen/strep). 48 h before the addition of resin fractions, cells were cultured in DMEM medium free of phenol red and supplemented with 10% charcoal stripped FBS (CSF), 2 mM L-glutamine, and 100 units/mL pen/strep.The human embryonic kidney 293 (HEK293) cell line was purchased from the American type culture collection (ATCC). HEK293 cells were used because of their high efficiency in transfection experiments and their endogenous GR expression. HEK293 cells were cultured at 37 °C and 5% CO4 cells/well for 24 h in DMEM, supplemented with 10% FBS, 2 mM L-glutamine, and 100 units/mL pen/strep, as previously described [HEK293 cells were plated in 96-well plate at a density of 1.5 × 10escribed . Cells w4 cells/well, in no phenol red DMEM medium supplemented with 10% CSF, 2 mM L-glutamine, and 100 units/mL pen/strep. After 24 h, cells were transiently co-transfected using calcium phosphate, with a Glucocorticoid response elements (GRE) or a NF-κΒ response elements (NF-κB-RE) promoter-driven luciferase construct and a β-galactosidase reporter construct. At 14–16 h upon transfection, the medium was replaced with fresh growth medium. After 24 h, cells were triggered either by 1 μM DEX diluted in EtOH or 20 ng/mL TNFα diluted in ddH2O for the evaluation of GR or NF-κB activity, respectively, with or without the presence of increasing concentrations of Chios Mastiha fractions for 6 h. DMSO/EtOH treatment (1:1000) was used as control condition. Subsequently, reporter lysis buffer was used for the lysis of the cells. Finally, a chemiluminometer  was used to measure the enzymatic activities of the produced luciferase and β-galactosidase in the cell extracts. β-galactosidase activity measurement was assessed to normalize luciferase activity (RLU).GR and Nuclear factor-kappa B (NF-κB) transcriptional activation was evaluated, applying luciferase reporter gene assay, as previously described . Briefly5 cells/well in DMEM medium without phenol red supplemented with 10% CSF, 2 mM L-glutamine, and 100 units/mL pen/strep for 48 h. Cells were then treated with the indicated amounts of Chios Mastiha fractions for an additional 24 or 48 h, with or without 10nM DEX. DMSO/EtOH treatment (1:1000) was used as control condition. Cells were then washed in PBS 1X, lysed in lysis buffer A , and supplemented with cocktail protease inhibitors, DTT, and PMSF. Bradford assay was then applied for protein measurement. Subsequently, cell extracts were electrophoresed in discontinuous SDS-PAGE and Western blotted using specific antibodies against PEPCK, GR, PPARα, p65, AMPKα, pAMPKαThr172, procaspase-9, procaspase-3, and bcl-2, as previously described [HEK293 cells were plated on 6 well plates at a density of 2 × 10t-test or by a One-Way analysis of variance (ANOVA) or a Two-Way ANOVA, followed by Tukeys’s post hoc test using SPSS or Stat Plus software, respectively. Differences were considered significant at a two tailed p-value < 0.05.All results are expressed as mean ± SD. Data were analyzed by an independent P. lentiscus resin to obtain three Mastiha fractions of different polarity. Following this procedure, the apolar fraction corresponds to a neutral fraction, while acid triterpenoids were concentrated in the medium-polar fraction. In this frame, we followed the purification, looking for triterpenoids in the apolar and the medium-polar fractions. The polar fraction, instead, was analyzed only by 1H NMR to achieve a qualitative characterization of the major compounds.In this study, with the aim of simplifying the analysis of the bioactive compounds, a fractionation protocol was applied to the acetonic extract from The apolar fraction (ap) of Mastiha is concentrated in the 1,4-poly-β-myrcene  detectabAs expected, the most abundant yield of triterpenoid acids is in the medium-polar (mp) fraction, in which lupeol  has been1H NMR analysis of the polar (p) fraction revealed the presence of phenolic compounds as demonstrated by chemical shifts between H = 7.26–7.57 ppm (The 7.57 ppm .Pistacia lentiscus L. var. chia (Chios Mastiha) for 6 h  in the presence or absence of 1 µM DEX for 6 h. Applying luciferase reporter gene assay, the effect of the fractions on GR transcriptional activation was assessed. As shown in HEK293 cells were transiently co-transfected with GREs and β-galactosidase constructs as described in the Materials and Methods section, and they were treated with increasing concentrations of ap, mp, and p resin fractions from The suppressive effect of the Chios Mastiha fractions on GR transcriptional activation was further investigated by assessing the effect of Mastiha fractions on the protein levels of GR and its target genes PEPCK and PPARα ,28, applThe most active fractions, as regards suppression in GR transcriptional activation, mp, and p, and their effect on the regulation in GR, PEPCK, and PPARα protein levels prompted us to examine the effect of medium-polar and polar Mastiha fractions on the regulation of AMP-activated protein kinase (AMPK) activity. AMPK regulates cellular metabolism through energy homeostasis balance. Specifically, AMPK senses cellular energy deprivation. Elevated AMP levels cause AMPK phosphorylation and its sequential activation. Activated AMPK, then, promotes catabolic procedures to provide energy supply for the cells . GCs areHEK293 cells were transiently co-transfected with NF-κΒ-RE-luciferase and β-galactosidase constructs as described in the Materials and Methods section, and they were subsequently treated with increasing concentrations of apolar (ap), medium-polar (mp), and polar (p) fractions from Chios Mastiha (1 μg/mL–80 μg/mL), with or without the addition of 20 μg/mL TNFα for 6 h. Applying luciferase reporter gene assay, the effect of the fractions on NF-κΒ transcriptional activation was evaluated to assess the potential of Mastiha fractions regarding anti-inflammatory actions. As shown in To examine whether the reduction in NF-κΒ transcriptional activation was linked with the reduction in its protein levels, Western blot analysis of the p65 subunit of NF-κΒ was applied inHEK293 cell extracts treated with 10, 20, and 40 μg/mL of Mastiha fractions, with or without 10 nM DEX or DMSO/EtOH for 24 h  and 48 hPistacia lentiscus L. var. chia on apoptotic mechanisms was examined by applying Western blot analysis of apoptotic and anti-apoptotic molecules, such as procaspase-3, procaspase-9, and bcl-2 in HEK293 cell extracts treated with 10, 20, and 40 μg/mL of Mastiha fractions with or without 10 nM DEX or DMSO/EtOH for 24 h  were investigated regarding their chemical composition, anti-proliferative, anti-inflammatory, apoptotic actions, and interference with GR signaling. Studies in the past have identified keto-oleanolic aldehyde, oleanolic aldehyde, lupeol, 24Z-masticadienonic acid methyl ester, and 24Z-isomasticadienonic acid methyl ester and phenolic compounds as components of Mastiha tree resin [1H NMR analysis to be present in the apolar Mastiha fraction, as expected. A subfraction of phenolic compounds was also achieved, since 1H NMR analysis revealed plenty of phenolic compounds and a lack of triterpenes in the polar Mastiha fraction.In this study, different polarity resin fractions from ee resin ,9,48,54.ee resin ,7,48,54 The characterization of the biological activities of the isolated fractions was also assessed. Due to triterpenes enriching Chios Mastiha, we focused on the potential cross-talk of resin fractions with GR signaling. We showed for the first time that medium-polar and polar fractions from Chios Mastiha suppressed the DEX-induced GR transcriptional activation in HEK293 cells in a dose-depended manner C,D. ThisPistacia lentiscus L. var. chia and can cause a dose-dependent suppression of the DEX-induced GR transcriptional activation and a reduction in GR protein levels in HEK293 cells, with the medium-polar and the polar fractions showing the highest activity. The latter activity was attributed to the leaves fractions inducing proteasomal activation [Reduction in GR transcriptional activation by the polar Mastiha fraction was accompanied by a decrease in GR protein levels C. Thus, tivation . Reductitivation . A reducIn accordance with the reduction in GR transcriptional activation, Mastiha fractions affect PEPCK and PPARα protein levels, which constitute GR target genes ,28. SpecPPARα is also a GR target and plays an important role as a regulator of lipid metabolism . PPARα iMoreover, Chios Mastiha medium-polar and polar fractions induced an increase in AMPKα phosphorylation at Thr172, while no notable changes in AMPK protein levels were induced . AMPK coThe medium-polar fraction exhibited the highest anti-inflammatory actions among the three different polarity Mastiha fractions, too, as indicated by the suppression of the TNFα-induced NF-κΒ transcriptional activation in a dose-dependent manner and the reduction in the p65 NF-κΒ subunit. These effects may be attributed to the medium-polar enrichment in triterpenes, such as lupeol, 24Z-masticadienonic acid methyl ester, and 24Z-isomasticadienonic acid methyl ester. Lupeol is known to exhibit a wide range of anti-inflammatory activities, as indicated by both in vitro and in vivo studies. Lupeol reduces inflammation in arthritic and asthmatic mouse models and decreases the levels of proinflammatory cytokines, such as TNFα, IL-4, IL-5, and IL-13. Interestingly, lupeol has been found to exhibit equal anti-inflammatory activity to dexamethasone . An addiThe apolar fraction showed anti-inflammatory activity, as well, albeit less than the medium-polar one, possibly due to the presence of the sticky polymer 1,4-poly-β-myrcene and/or to a different composition of triterpenoids. Anti-inflammatory activity of the whole extract from Chios Mastiha powder has been previously reported. Thus, it has been observed that Chios Mastiha powder reduced the serum TNFα levels in male rats with gastric mucosal inflammation . MoreovePistacia lentiscus L. var. chia caused a reduction in procaspase-3, procaspase-9, and bcl-2 protein levels in HEK293 cells [Apoptotic and anti-proliferative activities of Chios Mastiha have been previously demonstrated. Chios Mastiha inhibited the proliferation of human colon cancer HCT116 cells in a dose-dependent manner . In the 93 cells . Thus, oTo sum up, Chios Mastiha different polarity fractions inhibited the cell viability of HEK293 in a dose and time-dependent manner, possibly by activating the mitochondrial dependent apoptotic pathway. Moreover, Chios Mastiha medium-polar and polar fractions suppressed the GR and NF-κΒ transcriptional activation, and caused a reduction in the PEPCK and p65 protein levels, substantiating their potential anti-hyperglycemic and anti-inflammatory activities. These actions may be associated with the effect of the medium-polar and the polar fractions on AMPKα phosphorylation and activation, which subsequently could directly or indirectly interfere with GR signaling. Medium-polar enrichment in triterpenes and especially in lupeol, 24Z-masticadienonic acid methyl ester, and 24Z-isomasticadienonic acid methyl ester that show structural similarities with GCs indicates that these three compounds are possibly the bioactive compounds responsible for the interference of Mastiha fractions with the GR signaling, leading to Mastiha’s anti-glycemic and anti-inflammatory actions. This hypothesis is interesting and needs further investigation. Moreover, the reduction in GR transcriptional activation by the polar fraction may be attributed to the polar fraction’s suppressive effect on GR protein levels, which lead to the reduction in PEPCK and PPARα protein levels, and which may be associated with the anti-hyperglycemic and anti-hyperlipidemic activities of Mastiha. The apolar Mastiha fraction had less effect on GR and NF-κΒ transcriptional suppression, possibly due to the presence of the polymer 1,4-poly-β-myrcene, which hinders the maximum biological effect of the Mastiha’s bioactive triterpenes and/or to the differential triterpene composition compared to the medium-polar fraction. Thus, the interference of Mastiha triterpenes with glucocorticoid, NF-κB, and AMPK signaling is proposed to be responsible for its anti-hyperglycemic, ani-hyperlipidemic, and anti-inflammatory activities highlighting its potential applications in many fields of pharmaceutical, cosmetic, and nutraceutical interest.


Larvae at 48 hpf were anesthetized by immersion in E3 culture medium containing 2 μg/ml propofol, the reasonable anesthetic concentration for zebrafish embryos, which caused significant caudal fin dysplasia, light pigmentation, edema, hemorrhage, and spinal deformity, and decreased the hatchability, body length, and heart rate. The numbers of apoptotic cells in propofol-treated 12, 48 and 72 hpf embryos increased significantly, and the mRNA expression levels of intrinsic apoptosis pathway-related casp3a, casp3b, casp9, and baxb genes were upregulated, mainly in the head and tail. Propofol decreased apoptosis in the head and back of 24 hpf zebrafish, which was consistent with the mRNA expression analysis. Our findings demonstrated that zebrafish embryos and larvae exposed to propofol experienced developmental toxicity, which correlated with the intrinsic apoptosis pathway with casp3a, casp3b, casp9, and baxb as the key genes.General anesthetics can cause neurological damage and long-term behavioral/cognitive impairment during fetal and early postnatal life. However, the adverse influence on embryo development induced by propofol is unclear. We used embryonic zebrafish to explore the effects of propofol on embryonic and larval growth and development, and the related apoptotic mechanism. Zebrafish embryos were immersed in propofol  dissolved in E3 medium from 6 to 48 hours post fertilization (hpf). The survival rate, locomotion, heart rate, hatchability, deformity rate, and body length were analyzed at defined stages. Terminal deoxynucleotidyl transferase nick-end-labeling was used to detect zebrafish embryo apoptosis, and the expression levels of apoptosis-related genes were determined using quantitative real-time reverse transcription PCR and whole-mount  Internapropofol –9.in utero and after birth, exhibiting depression- and anxiety-like behaviors and cognitive impairment. Kinesin family member 17 (KIF17) is a neuron-localized directional motor protein involved in dendrite-directed material transport [Apoptosis, the first discovered type of programmed cell death (PCD), is an important means of removing excess cells during the early development of the fetus and plays an important role in the normal development of organisms ,11. Experansport . In addiransport . HoweverAn ideal model system to investigate developmental toxicology is provided by the zebrafish embryo, because their external post-fertilization, quick development, transparency of embryonic stage, and rapid maturation make studying their embryogenetic mechanism easier than studying mammalian fetuses . In recein situ hybridization (WISH) can be used to observe the distribution of apoptotic cells and the trend of related genes, which effectively makes up for the deficiency of mammalian models [The gene homology between zebrafish and human is as high as 87%, and the apoptosis-related genes are highly conserved ,30. Termn models ,32. As tn models ,28, thusAbove all, previous studies primarily evaluate the effects of propofol on neuronal apoptosis in mammalian fetus. Therefore, our goal was to comprehensively observe the effects of propofol exposure on the growth and development of embryonic zebrafish, and identify key apoptosis genes using TUNEL staining, quantitative real-time reverse transcription PCR (qRT-PCR) with two reference genes, and WISH techniques. Our was to provide potential molecular targets for the clinical prevention of anesthetic toxicity on fetal and neonatal development, and enhance the clinical safety of propofol.The wild-type zebrafish Tuebingen (TU) strain, aged 3–4 months, was purchased from China Zebrafish Resource Center . In a controlled environment with water at 28.5°C (14h day/10h night), separated by sex, zebrafish were fed with fresh brine shrimp at 8:00 am and 8:00 pm. Once a week, after 8:00 pm, male and female zebrafish (1:1) were placed in breeding boxes, which were separated by a transparent baffle. The following day, the light was turned on at 8:00 am, the water in the breeding boxes was changed, and the baffle was removed. After 15 minutes, zebrafish embryos were collected in petri dishes with E3 culture medium and placed at 28.5°C in biochemical incubator . All treatments and protocols in this study were carried out strictly in accordance with the guidelines of CZRC and the Animal Experiment Ethics Committee of Yangzhou University (No. YZUDWSY201611-201).At 6 hours post fertilization (hpf), dead embryos (identified by the formation of white flocculent clumps or no heartbeat) were removed from the petri dishes and others were transferred to one petri dish for mixing, which reduced the impact of different F1 generations. Under a stereoscopic microscope , 6 hpf embryos at the gastrula period of development, which forms the germ ring and embryonic shield, resembling a semi-ring structure, were transplanted into 90 mm petri dishes . With 50A total of 2548 embryos were utilized in this study. In the optimal concentration gradient experiment for propofol, six groups  were assessed, with three replicates. To assess the survival, injury, and deformity rates, three independent experiments were conducted with 50 embryos in each group. Hatchability observations were performed with four replicates. In this process, the heart rate and body length were recorded for seven embryos in each group, while the twisting number and movement after needling were recorded for 28 embryos in each group. Additionally, qRT-PCR experiments were conducted at 12, 24, 48, and 72 hpf with three independent experiments with 50 embryos in each group. WISH was performed at 48 hpf with 12 embryos in each group, using three replicates for each gene. The TUNEL assay was conducted at 12, 24, 48, and 72 hpf with 12 embryos in each group, also using three replicates.He et al. has reported that exposure of 6 hpf zebrafish embryos to propofol until 48 hpf induced significant cell apoptosis in the brain in a dose-dependent manner . MeanwhiDevelopmental toxicity in zebrafish embryos and larva were observed as spinal curvature, hemorrhage, pericardial edema, light pigmentation, and yolk sac edema ,37. At eFrom 24 hpf, zebrafish were housed in E3 medium with 0.003% PTU  to prevent melanin deposition. Peeled using ophthalmic forceps if necessary, embryos were fixed in 4% paraformaldehyde  at 4°C overnight, followed by gradient dehydration in methanol, and stored at −20°C for at least 2 hours. Zebrafish were rehydrated with serial dilutions of methanol. The apoptotic cells were then examined using a One Step TUNEL Apoptosis Kit ; the nuclei were counterstained using 4′,6-diamidino-2-phenylindole (DAPI). Images were captured using a stereoscopic fluorescence microscope , and analyzed using Image J . Moreover, nonspecific uptake of fluorescent substances or autofluorescence in the yolk, iridophore, hatching gland, and neuromast represented false positive TUNEL staining and were excluded .https://www.ncbi.nlm.nih.gov/), and primers were designed using Primer3 (https://bioinfo.ut.ee/primer3-0.4.0/) , and then reverse-transcribed to cDNA in accordance with instructions of the FastQuant RT Kit (With gDNase) (TIANGEN). Each qPCR reaction was detected in a 7500 Real-Time PCR System . The cDNA sequences of 18 related genes were obtained through NCBI (-0.4.0/) . For eacce genes , and thecasp3a, casp3b casp9, and baxb were generated from the zebrafish cDNA sequences using primer sequences, in which the T7 polymerase promoter was added to the 5′ end of the reverse primers  or repeated measures ANOVA, according to the data type. By contrast, after the data transformation attempt failed, data in the form of medians with lower and upper quartiles were subjected to two independent samples nonparametric tests and multiple independent samples nonparametric tests. Post-hoc multiple comparisons between groups were performed using Bonferroni tests. All statistical analyses were performed using SPSS 23 . Significance was set at P < 0.05 (two-tailed).Compared with the control group, in which less than two larvae died, the survival rate of embryos in the 1 μg/ml and 2 μg/ml propofol groups did not change significantly within 96 hpf . HoweverWe next investigate whether the process of development was affected by 2 μg/ml propofol. Compared with that in the control group, the hatching rate in zebrafish exposed to propofol was markedly lower at 48 and 72 hpf (P < 0.05), while no significant difference was found between the two groups at 96 hpf . MoreoveFrom 24 to 96 hpf, at least 30% of the zebrafish exposed to 2 μg/ml propofol had obvious damage and deformity, including caudal fin dysplasia, hemorrhage, light pigmentation, and spinal deformity, among which the difference between the groups was the most remarkable at 48 hpf . The devWe then explored the potential correlation between apoptosis and the developmental disorders and tissue impairment induced by 2 μg/ml propofol. In the propofol group, more apoptotic cells gathered at the tail and some apoptotic cells showed at the head, while they were concentrated on the back and head in the control group Figs  and S2. casp3a, casp3b, casp6, casp7, casp8, casp9, fadd, bida, tp53, pmaip1, bbc3, baxa, and baxb) and five anti-apoptotic genes  in the extrinsic or intrinsic pathway were evaluated using qRT-PCR in propofol-exposed and control embryos. Generally, after propofol exposure, significantly increased expression levels were observed for four key pro-apoptotic genes  of the extrinsic and intrinsic apoptosis pathways, from 12 hpf to 3 dpf, but particularly at 2 dpf  The 1 day post fertilization (dpf) zebrafish.  The 2 dpf zebrafish.  The 3 dpf zebrafish. (O-P) The 4 dpf zebrafish.  The zebrafish in the control group.  All the zebrafish in the 2 μg/ml propofol group showed less pigmentation and shorter body length than the normal zebrafish at the same developmental stage. (A) The dead embryos were organized into a flocculent mass. (B) The caudal fin did not develop. (F) The non-hatching 3 dpf embryos in the 2 μg/ml propofol group showed serious malformation of heart development and edema. (H) The 2 dpf embryos exposed to propofol were manually decapsulated under the microscope to show yolk sac hemorrhage. (K) Yolk sac hemorrhage and edema occurred in the 3dpf larva after propofol exposure. (M-N) 3dpf larvae displaying abnormal spine curvature from a back and lateral view. Black arrows represent yolk sac hemorrhage and hollow arrows represent edema. Scale bar = 1 mm. Magnification = ×63 (A-F), ×32 (G–N), ×25 (O-P).(TIF)Click here for additional data file.S2 FigFluorescein isothiocyanate (FITC) (nucleus), 4′,6-diamidino-2-phenylindole (DAPI) (apoptotic cells) and merged images are shown. The white scale bar (1 mm) belongs to the 72 hours post fertilization (hpf) larvae; the black scale bar (1 mm) belongs to the others. Magnification = ×40.(TIF)Click here for additional data file.S3 Fig(A) Pro-apoptotic gene expression. (B) Anti-apoptotic gene expression. An independent sample t-test was used to compare the mRNA expression in the two groups at the same time point. Three independent experiments were carried out. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.(TIF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.


Chromatin structure plays a fundamental role in regulating gene expression, with histone modifiers shaping the structure of chromatin by adding or removing chemical changes to histone proteins. The p53 transcription factor controls gene expression, binds target genes, and regulates their activity. While p53 has been extensively studied in cancer research, specifically in relation to fundamental cellular processes, including gene transcription, apoptosis, and cell cycle progression, its association with histone modifiers has received limited attention. This review explores the interplay between histone modifiers and p53 in regulating gene expression. We discuss how histone modifications can influence how p53 binds to target genes and how this interplay can be disrupted in cancer cells. This review provides insights into the complex mechanisms underlying gene regulation and their implications for potential cancer therapy. Chromatin is the complex of DNA, histone proteins, and other associated proteins that make up the structure of chromosomes within the nucleus of eukaryotic cells. It is a well-organized and dynamic structure that plays a fundamental role in the packaging and regulating of DNA. The chromatin structure can either promote or inhibit gene expression, depending on the specific modifications present on the histones ,2,3,4. Ep53, as a transcription factor, plays a crucial role in controlling the expression of the various genes involved in cell cycle regulation, DNA repair, apoptosis, and other cellular processes ,7. It acThe interplay between histone modifiers, such as histone acetyltransferases/deacetylase and histone methyltransferases/demethylase, and p53 is crucial for regulating gene expression, maintaining genomic stability, facilitating chromatin remodeling, and promoting DNA repair. The synergy between the two ensures proper cellular responses to DNA damage, stress signals, and other regulatory cues ,14,15,16We implemented a systematic methodology to ensure a thorough literature review. Our approach involved searching the PubMed database using keywords pertinent to our research topic. Additionally, we scrutinized the reference lists of relevant articles to uncover any additional studies of relevance. To mitigate potential bias, we strived to encompass papers published in peer-reviewed journals until May 2023. Multiple mechanisms contribute to the interplay between histone modifiers and p53. Firstly, histone modifiers indirectly influence p53 function by modifying the structure and accessibility of p53 target genes within the chromatin . By estaPackaging DNA into chromatin affects gene expression by making specific genes accessible to transcription factors, which are proteins that bind to DNA and control gene expression. Histone modifications affect chromatin structure by altering the interactions between histones and DNA. Each of these modifications can affect chromatin structure and gene expression differently. Modifications that are associated with active transcription, such as the acetylation of histone three and histone four (H3 and H4) or the di- or trimethylation (me) of H3K4, are commonly referred to as euchromatin modifications  Figure . ConversThe enzymes responsible for regulating post-translational epigenetic modifications on histones have been categorized into four groups based on their roles: writers, erasers, readers, and movers  28]. Wr. Wr28]. Furthermore, histone modifications can be classified into major groups based on the type of modification and the amino acid residue being modified, including acetylation, methylation, phosphorylation, ubiquitination, sumoylation, and ADP-ribosylation . Among tThe primary amino acids that are susceptible to methylation are arginine and lysine B. ArginiSET7/9, encoded by the SETD7 gene, was independently discovered in 2001 by Reinberg’s lab (named SET9) and Zhang’s lab (named SET7) ,34,35. ISET7/9 was also reported to methylate non-histone proteins, including p53 ,35, and Ivanov et al. used chromatin immunoprecipitation (ChIP) assays to analyze the levels of p53 binding and histone H4 acetylation at the promoter of the p21 gene . Methyla-/- mice [Interestingly, SET7/9-mediated methylation of p53 is required for the binding of acetyltransferase Tip60 to p53 and the subsequent acetylation of p53, and the lack of SET7/9 expression leads to defective cell cycle arrest upon DNA damage in mice, suggesting the coordination of two different modifications in p53 activation. However, the loss of SET7/9 is not as sufficient in contrast to p53-/- mice ., suggesting that methylation must precede acetylation for a positive interplay between methylation and acetylation. Of note, several lysine residues near the site where SET7/9 methylates p53 can also be acetylated by CBP/p300 [A similar interplay between p53 and lysine methyltransferase has already been described . Pre-metCBP/p300 . These lG9a, a Set domain-containing protein, serves as the major histone lysine methyltransferase. It methylates histone H3 at both H3K9me1 and H3K9me2 modifications, earning its name G9a . By addiHuman G9a (hG9a) can regulate the expression of p21 in a manner that is independent of p53 and its methylation activity [Similarly, hG9a stimulates p53’s activity independently of methylation by interacting with histone acetyltransferase CBP/p300, resulting in increased histone acetylation at the promoter of pro-apoptotic genes, including PUMA, thus inducing p53 transcriptional activity .On the contrary, the mouse one (mG9a) blunted P53-dependent transcription in a methylation-specific manner . The difPRMTs have crucial roles in various cellular processes, including transcriptional regulation, chromatin regulation, signal transduction, and DNA damage repair. They catalyze the transfer of a methyl group from S-adenosylmethionine to the guanidine nitrogen of arginine residues in proteins ,39. PRMTIn addition to the methylation of p53, PRMT5 has been shown to regulate chromatin structure and gene expression through its interaction with histones. By controlling the alternative splicing of crucial histone-modifying enzymes, such as TIP60 and KMT5C, PRMT5 can affect chromatin structure and, ultimately, impact DNA repair  Figure . TherefoWhile p53 is a target for epigenetic modulators, it can also target histone modifiers. The Jumonji C domain, containing the histone demethylase 2 (JMJD2) family of proteins, selectively demethylates H3K9me3 and H3K36me3. JMJD2B/KDM4B is a p53-inducible gene in response to DNA damage . p53 regEZH2, also known as Enhancer of Zeste Homolog 2, is a vital protein involved in epigenetic regulation. It belongs to the Polycomb group protein family and serves as the catalytic subunit of the Polycomb Repressive Complex 2 (PRC2) . FunctioSeveral studies provided valuable insights into the dynamic relationship between p53 and EZH2. Tang et al. demonstrated that activated p53 downregulates EZH2 gene expression by repressing the EZH2 gene promoter . Additio43−) to specific amino acid residues in proteins—typically serine, threonine, or tyrosine  Similarly, other studies reported that Ser20 undergoes phosphorylation following exposure to ionizing radiation, which could potentially weaken the binding of p53 to Mdm-2 to save p53 from ubiquitin-mediated degradation ,51,52. TMAP kinase cascade is one of the major UV response pathways . This pap38 can directly phosphorylate and activate p53. Upon activation, p38 phosphorylates specific serine residues on p53, such as Ser15 and Ser392 ,58,59, lp38 can also indirectly influence histone modifications through various mechanisms. p38 phosphorylates and activates downstream targets, including kinases and transcription factors, which can, in turn, modulate histone modifications. For example, MSK1, a downstream target of the MAPK pathway, can be activated by p38 MAPK . Upon acWhen activated by the p38 MAPK pathway, MSK1 interacts with p53 and is recruited to the p21 promoter, where it phosphorylates histone H3 in a p53-dependent manner. Therefore, MSK1 plays a role in activating the expression of the p21 gene . This enRSK2 is a p90 ribosomal S6 kinase family member that is activated by growth factors, peptide hormones, and neurotransmitters via MAPK/ERK signaling (ERK1 and ERK2). It is critical in regulating gene transcription by phosphorylating CBP at Ser133 . AdditioAcetylation plays a significant role in modulating the transcriptional activity of p53, serving as a substantial modification  67,68,6,668,69. CREB-binding protein (CBP/p300), in mediating p53 acetylation and its consequential effect on p53 activity, has been illuminated in previous studies ,73,74,75The ability of p53 to be acetylated was subsequently confirmed using acetylation-specific antibodies . SakagucSIRT1 was the first enzyme identified to target p53, which is not a histone, for deacetylation . SIRT1 pTip60 is another histone acetylase linked to DNA damage repair and apoptosis ,89,90,91K120 mutation, found in human cancers, further suggests that defective K120 acetylation may contribute to tumorigenesis . LikewisLi et al. generated mutant mice with lysine to arginine mutations at one  or three  sites in the p53 . The resThe above-mentioned results indicate that histone modifications are vital for regulating p53 function, with specific enzymes responsible for acetylation and lysine methylation playing a role in activating and stabilizing p53. These findings significantly impact our comprehension of p53 and its involvement in cancer, as aberrant p53 activity is frequently observed in various cancer types.The relationship between p53 and histone modifiers is intricate and bidirectional, with both factors capable of modifying each other. Upon DNA damage or cellular stress, p53 recruits histone modifiers to specific genomic loci, influencing chromatin structure and gene expression. Conversely, histone modifiers can impact p53 activity by altering its post-translational modifications or how it binds to DNA. This interplay between p53 and histone modifiers is crucial for regulating gene expression, maintaining genomic stability, facilitating chromatin remodeling, and supporting DNA repair. It ensures appropriate cellular responses to DNA damage, stress signals, and regulatory cues, although their interaction varies depending on the specific cellular environment or stimulus.Dysregulation of histone modifiers and p53 pathways can contribute to treatment resistance in cancer cells. This involvement of histone modifiers and p53 in treatment resistance has prompted the development of multiple epigenetic anticancer drugs approved by regulatory authorities. Epigenetic therapies have emerged as a promising avenue for cancer treatment. Notably, the U.S. Food and Drug Administration (FDA) has granted approval for drugs targeting diverse epigenetic categories, including HDAC, DNMT, and EZH2 . HDAC inCombination therapies in cancer treatment involve utilizing multiple drugs or treatment approaches, simultaneously, to enhance the effectiveness of treatment. These therapies have the potential to overcome drug resistance and improve treatment outcomes for individuals with cancer ,101. By Despite the extensive individual research on histone modifiers and p53, their interplay has received relatively less attention. However, the relationship between p53 and histone modifiers plays a critical role in gene expression regulation, genomic stability, chromatin remodeling, and DNA repair. Understanding the intricate mechanisms of p53 and histone modifiers is highly relevant in cancer therapy, especially considering the frequent occurrence of p53 mutations and dysregulation. Overall, comprehending the interplay between p53 and histone modifiers is crucial for understanding treatment resistance and holds promise for developing innovative cancer therapies that can effectively target and modulate these critical factors.


Background: In this study, we compared the prognosis, tumor immune microenvironment (TIM), and drug treatment response between left-sided (LCC) and right-sided (RCC) colon cancer to predict outcomes in patients with LCC and RCC.Methods: Based on identified differentially expressed genes and using single-cell RNA sequencing data, we constructed and validated a prognostic model for LCC and RCC patients in the TCGA-COAD cohort and GSE103479 cohort. Moreover, we compared the differences of TIM characteristics and drug treatment response between LCC and RCC patients.P = 2.6×10-5), N stage (P = 0.012), advanced pathological stage (P = 1.4×10-4), and more stable microsatellite status (P = 0.007) but not T stage (P = 0.200). For LCC patients, the risk scores were not significantly associated with tumor stage and microsatellite status (P > 0.05). Additionally, immune infiltration by CD8 and regulatory T cells and M0, M1, and M2 macrophages differed significantly between LCC and RCC patients (P < 0.05). APC and TP53 mutations were significantly more common in LCC patients (P < 0.05). In contrast, KRAS, SYNE1, and MUC16 mutations were significantly more common in RCC patients (P < 0.05). In addition, tumor mutation burden values were significantly higher in RCC patients than in LCC patients (P = 5.9×10-8). Moreover, the expression of immune checkpoint targets was significantly higher in RCC patients than in LCC patients (P < 0.05), indicating that RCC patients maybe more sensitive to immunotherapy. However, LCC and RCC patients did not differ significantly in their sensitivity to eight selected chemicals or target drugs (P > 0.05). The average half-maximal inhibitory concentrations for camptothecin, teniposide, vinorelbine, and mitoxantrone were significantly lower in low-risk than in high-risk RCC patients (P < 0.05), indicating that the lower risk score of RCC patients, the more sensitive they were to these four drugs.Results: We constructed and validated a five-gene prognostic model for LCC patients and a four-gene prognostic model for RCC patients, and both showed excellent performance. The RCC patients with higher risk scores were significantly associated with greater metastasis (Conclusions: We investigated the differences in prognosis, TIM, and drug treatment response between LCC and RCC patients, which may contribute to accurate colon cancer prognosis and treatment of colon cancer. Colon cancer (CC) is a common gastrointestinal malignant tumor with high mortality worldwide . NumerouSingle-cell next-generation RNA sequencing (scRNA-seq) can be used to investigate gene expression profiles at the single-cell level, facilitating the dissection of previously hidden heterogeneity in cell populations . TherefoBy combining scRNA-seq data and bulk RNA-seq data, our study established and verified the prognostic models for LCC and RCC patients, and compared their differences of prognosis, microenvironment and drug treatment response. Our findings may facilitate more accurate predictions of the prognosis and potential treatment benefits for CC patients.https://www.ncbi.nlm.nih.gov/geo/). The original data included 23,828 genes and 49,859 cells, of which 18,273 cells were from normal samples, and 31,586 were from tumor samples. And the cells from tumor samples were used for further analysis, of which 16,448 cells were LCC cells and 15,138 were RCC cells.The scRNA-seq dataset GSE200997, comprising 7 normal and 15 tumor samples, was downloaded from the US National Center for Biotechnology Information’s Gene Expression Omnibus (GEO) database . Only samples with survival and location information were included. Similarly, based on the published literature  >1), while three  were protective factors . The areas under the ROC curve (AUC) for the prognostic model at 3-, 4-, and 5-year OS are 0.721, 0.844, and 0.926, respectively  was a risk factor (HR >1), and three  were protective factors , the most common SNP classification was missense, and the most common SNP class was C>T (APC) and tumor protein p53 (TP53) mutation rates were significantly higher in LCC patients (P < 0.05). In contrast, Kirsten rat sarcoma virus proto-oncogene GTPase (KRAS), spectrin repeat containing nuclear envelope protein 1 (SYNE1), and mucin 16 cell surface associated (MUC16) the mutation rates were significantly higher in RCC patients . And the expression of immune checkpoint targets in RCC patients was significantly higher than in LCC patients , cytotoxic T-lymphocyte associated protein 4 (CTLA4), and T cell immunoreceptor with Ig and ITIM domains (TIGIT) expression were significantly higher in the low-risk group than in the high-risk group. However, lymphocyte activating 3 (LAG3), programmed cell death 1 (PDCD1), and hepatitis A virus cellular receptor 2 (HAVCR2) expression did not differ significantly between the high- and low-risk groups . In addition, drug response sensitivity did not differ significantly between high-risk and low-risk LCC patients (P > 0.05, 50 values for camptothecin, teniposide, vinorelbine, and mitoxantrone were significantly lower in low-risk than in high-risk RCC patients, indicating that low-risk patients will be more sensitive to them (Additionally, we found that CC patients were sensitive to 44 drugs (average IC to them –7J. Sens to them –7N.Extensive studies have identified various signatures for predicting CC prognosis, diagnosis, and treatment –23. HoweREG1A and CD16 were significantly differential expressed between LCC and RCC samples, which may indicate that DEGs obtained from bulk RNA-seq data cannot reflect the true expression of genes in different cell types. According the prognostic markers’ expression in each cell type (HSPA1A and FOSB are up-regulated in type 1 and down-regulated in type 3. GDF15 is up-regulated in type 2 and down-regulated in type 3.The common prognostic models are mainly based on gene-sequencing dataset. For example, Liu et al.  found thell type , the annC11orf96 and FOSB expression levels were associated with lower risk scores and longer survival time. In contrast, the high expression levels of REG1A, RPL35, and TESC were associated with higher risk scores and shorter survival time. These findings indicate that C11orf96 and FOSB are risk factors, while REG1A, RPL35, and TESC are protective factors, which is consistent with the results shown in HSPA1A, CD69, GDF15, and LGALS2. HSPA1A was a risk factor for RCC, while CD69, GDF15, and LGALS2 were protective factors. REG1A, RPL35, TESC, HSPA1A, and GDF15 have been associated with CC prognosis in all signatures [In the five-gene LCC prognostic model, low gnatures –33.APC and TP53 mutation rates were significantly lower in RCC patients than in LCC patients, KRAS, SYNE1, and MUC16 mutation rates were instead significantly higher. In addition, TMB values were significantly higher in RCC patients than in LCC patients. These TME differences may cause differences in prognosis or treatment between LCC and RCC patients.Furthermore, we found many significant TME differences between LCC and RCC. First, while the infiltration degree of M0 macrophages was significantly higher in LCC patients than in RCC patients, the infiltration degrees of CD8 T cells and M1 macrophages were instead significantly lower. Secondly, while Additionally, consistent with the results found in previous study , patientIn summary, we systematically investigated differences in prognosis, TME, and treatment between RCC and LCC patients by integrative analysis of scRNA-seq and bulk RNA-seq data, providing insights into LCC and RCC heterogeneity. We also explored immunotherapy and drug chemotherapy differences between LCC and RCC patients, which may be conducive to providing patients with more accurate treatment.Supplementary FiguresSupplementary Table 1Supplementary Table 2Supplementary Tables 3 and 4


Protein regulator of cytokinesis 1 (PRC1) is involved in cytokinesis. Growing evidence suggests the association of PRC1 with multiple cancers. Here, we unveil that, in 28 cancer types, PRC1 is higher expressed in tumor tissues than in non-malignant tissues. Overexpression of PRC1 indicates unfavorable prognostic value, especially in ACC, LGG, KIRP, LICH, LUAD, MESO, PAAD, SARC and UCEC, while methylation of the PRC1 gene at sites associated with its inactivation has a favorable prognostic value in ACC, KIRP, LUAD, MESO, KIRP and LGG. Differentially expressed genes (DEGs) associated with high (> median) PRC1 expression contribute to key signaling pathways related with cell cycle, DNA damage and repair, EMT, cell migration, invasion and cell proliferation in most cancer types. More specifically, the DEGs involved in RAS/RAF/MAPK, PI3K/AKT, WNT, NOTCH, TGF-β, integrin, EMT process, focal adhesion, RHO GTPase-related pathway or microtubule cytoskeleton regulation are upregulated when PRC1 expression is above median, as confirmed for most cancers. Most importantly, high expression of PRC1 appears to be associated with an overabundance of poor-prognosis TH2 cells. Furthermore, positive correlations of PRC1 and some immune checkpoint genes  were observed in several cancers, especially BLCA, BRCA, KIRC, LUAD, LIHC, PRAD and THCA. These findings plead in favor of further studies validating the diagnostic and prognostic impact of PRC1 as well as the elaboration of pharmacological strategies for targeting PRC1.The online version contains supplementary material available at 10.1186/s12943-023-01802-1. PRC1, is critical for cytokinesis (https://www.ncbi.nlm.nih.gov/refseq/). PRC1 is widely located in nucleus, cytoplasm, cytoskeleton, extracellular space, and plasma membrane, based on the information from COMPARTMENTS (https://compartments.jensenlab.org/Search), a subcellular localization database. Among the 19 transcripts of PRC1 , overall survival (OS), and progression-free interval (PFI), was initially assessed in 33 cancer types listed in TCGA. The results indicate that PRC1 might function as a prognostic biomarker in 8 cancers, including ACC, GBM&LGG, KIRP, LIHC, LUAD, MESO, PAAD and SARC, since high expression of PRC1 predicted poor DSS, OS and PFI  and PRC1low (< median) samples from 36 cancer types . A total of 58 genes were found overexpressed in the PRC1high vs. PRC1low tumors in at least 18 cancer types, while 20 genes were significantly underexpressed in PRC1high vs. PRC1low groups (Table S2).At single-cell (sc) RNAseq resolution, PRC1 was significantly and positively related to cell cycle, DNA damage, DNA repair, EMT process, cell invasion and proliferation in multiple cancer types . Based on bulk RNA-seq data from TCGA datasets, differential gene expression (DGE) analysis of 56,493 genes was performed by confronting PRC1high status in all 36 cancer types  . Consistent with the scRNAseq results , cell cycle-related genes were enriched in PRC1pes Fig. A-I. More) Figure -2 A-E, s2 (TMP + 1)) from TCGA database, the Spearman correlations of PRC1 with 56,493 genes in the 36 cancer types were explored . In total, 163 genes were positively associated with PRC1 expression (r > 0.3 and P < 0.05) in all 36 cancer types, contrasting with the fact that not a single gene negatively correlated with PRC1 expression in all cancer types (Table S2). Combined with the 58 genes that correlated with PRC1high status of at least 18 cancer types, 40 genes were defined as PRC1- co-expressed if they satisfied two conditions: log2FC > 1 & P < 0.05 and r > 0.3 & P < 0.05  validated the functional and physical protein associations of PRC1 and the 40 co-expressed genes, with an average local clustering coefficient of 0.944 and a PPI enrichment P value < 1.0e-16 Figure A. Gene oDNA methylation is an essential epigenetic factor that influences gene activities. Intriguingly, we observe that the methylation of two CpG sites, cg14398957 and cg01407062, might lead to PRC1 silencing, as the methylation level of these two sites negatively correlated with PRC1 mRNA expression in the majority of cancer types . More importantly, methylation of these two CpG sites indicated favorable prognostic in several cancers including ACC, KIRP, LUAD, MESO, KIRP and LGG  regulating PRC1 expression were predicted by 6 databases, including HumanTFDB, PROMO, CistromeDB Toolkit, hTFtarget, Genecard, and JASPAR  were used to explore micro RNAs (miRNAs) targeting PRC1  in all the 36 cancer types . In contrast, PRC1 was positively or negatively associated with the infiltration of other immune cells depending on the cancer type  . PRC1 was significantly related with most TH2 cell markers  in some cancers, including KIRC, LGG, LIHC, PRAD, THCA and THYM  has been pinpointed as an unfavorable prognostic factor in several solid tumors, including breast cancer, hepatocellular carcinoma (HCC), prostate cancer (PCa), lung adenocarcinoma (LUAD), and gastric cancer –6. Recen2/M cell cycle arrest and apoptosis, in LUAD and esophageal cancer cells, through interruption of the Wnt/β-catenin pathway [high tumor from all the 36 cancer types. Furthermore, PRC1 was positively associated with DNA damage and DNA repair in all cancer types except COAD. Strong positive associations were also identified between PRC1 and pathway relevant to tropic signals, as well as to cell migration and invasion in the majority of cancer types.PRC1 functions as a tumor promoter in cancers –7, 10–12 pathway . In addi pathway , 12. Our pathway –7, 10–12To date, there is evidence that transcription factor EWSR1-FLI1 can hijack PRC1 to promote tumor growth of Ewing sarcoma, while p53 directly inhibits PRC1 to regulate cytokinesis in several cancer cells lines  , 11. HerTumor development and progression are dynamic and complex processes, which are reliant on the crosstalk between tumor and its immune microenvironment. Notably, we found that PRC1 correlates with the enrichment of TH2 cells in all 36 cancer types. Indeed, TH2 cells are closely associated with chronic inflammation, which is believed tumor promoting factor . InteresHomo sapiens, Mus musculus, or Rattus norvegicus based on the information from the Comparative Toxicogenomics Database . However, such drugs have not been evaluated yet for their potential anticancer effects. It would be particularly interesting to test such putative PRC1 inhibitors for their effects on the immunosuppressive TH2-driven microenvironment, alone or in combination with currently used immune checkpoint inhibitors.Thus far there are no therapeutic agents directly targeting PRC1. PLK1 and CDK16 function by phosphorylating PRC1 , meaningPRC1 is highly overexpressed in the vast majority of cancers, and this overabundance has a poor prognostic impact especially in ACC, LGG, KIRP, LICH, LUAD, MESO, PAAD, SARC and UCEC. Correlative evidence suggests PRC1 to be involved in key signaling pathways associated with cell cycle, DNA damage, DNA repair, EMT, migration, invasion and proliferation. The upstream mechanisms responsible for regulating PRC1 expression involve various TFs, miRNAs and DNA methylation. The correlation of PRC1 with a TH2-rich immunosuppressive tumor microenvironment may encourage future attempts to target PRC1 in the context of immunotherapies.Below is the link to the electronic supplementary material.Supplemental text file 1: Materials and Methods.Additional file 1: Differentially expressed genes (56493 genes) in tumor tissues compared by PRC1: High vs. Low in 36 cancer types, the data is separately shown in sheets 1–36 in 36 cancer types; Additional file 2. GSEA-based pathway analysis of the differentially expressed genes (56493 genes) in 36 cancers, the data is separately shown in sheets 1–36 in 36 cancer types; Additional file 3. The correlation analysis of PRC1 with the 56493 genes in 36 cancers, the data is separately shown in sheets 1–36 in 36 cancer types; Additional file 4. The predicted and experimentally validated transcription factors upstream of PRC1, Additional file 5. The predicted and validated miRNAs targeting PRC1, and Additional file 6. Chemicals that affect the expression/phosphorylation of PRC1.