In medical mycology, epigenetic mechanisms are emerging as key regulators of multiple aspects of fungal biology ranging from development, phenotypic and morphological plasticity to antifungal drug resistance. Emerging resistance to the limited therapeutic options for the treatment of invasive fungal infections is a growing concern. Human fungal pathogens develop drug resistance via multiple mechanisms, with recent studies highlighting the role of epigenetic changes involving the acetylation and methylation of histones, remodeling of chromatin and heterochromatin-based gene silencing, in the acquisition of antifungal resistance. A comprehensive understanding of how pathogens acquire drug resistance will aid the development of new antifungal therapies as well as increase the efficacy of current antifungals by blocking common drug-resistance mechanisms. In this article, we describe the epigenetic mechanisms that affect resistance towards widely used systemic antifungal drugs: azoles, echinocandins and polyenes. Additionally, we review the literature on the possible links between DNA mismatch repair, gene silencing and drug-resistance mechanisms. Candida genus are the most prevalent agents of hospital-acquired invasive fungal infections, while Aspergillus, Cryptococcus and Penumocystis spp. also contribute to life-threatening fungal infections in healthcare settings worldwide .,13.FKS gocandins ,13.ERG genes, including ERG2, ERG3, ERG6 and ERG11 in Candida species .,13.11,13 species ,10,13. Tectively ,26. Compectively ,10,13.Candida spp.Since the genetic and molecular basis of antifungal drug-resistance mechanisms is well-studied and reviewed in detail in recent articles ,13,22,23Epigenetic modifications refer to the heritable and stable cellular changes that are not caused due to alterations in the DNA sequence . These mMucor circinelloides (a cause of mucormycosis), the RNAi-based epigenetic drug-resistance mechanism, epimutation, has been shown to confer resistance to FKBP12 (peptidyl-prolyl isomerase)-binding antifungal agents, FK506 and rapamycin, which block calcineurin and TOR signaling pathways, respectively [fkbA gene codes for the FKBP12 protein. The degradation of fkbA mRNA, due to the core RNAi machinery-dependent endogenous expression of sRNAs against the fkbA gene, was found to be the mechanism underlying FK506- and rapamycin-resistant strains of M. circinelloides [pyrF/pyrG (URA5/URA3) gene in M. circinelloides accounted for resistance against the antifungal compound 5-fluoroorotic acid (5-FOA), as products of pyrF/pyrG genes are involved in the conversion of FOA into a toxic compound [M. circinelloides [The RNAi pathway is governed by small RNAs (sRNAs) that are generated by RNA-dependent RNA polymerases and processed by the Dicer endonuclease . The incectively , the fkbelloides . This meelloides . Similarcompound . These relloides ,31. Furtelloides ,32, the Schizosaccharomyces pombe through transcriptional interference [TGP1 (codes for a glycerophosphodiester transporter 1) gene expression is controlled by the lncRNA, ncRNA.1343, via increased nucleosome density at the TGP1 locus, with ncRNA.1343 deletion leading to susceptibility to many drugs, including hydroxyurea and caffeine [LncRNAs have been reported to modulate drug resistance in the fission yeast rference . It has caffeine . HoweverA eukaryotic cell compacts its genetic material inside the nucleus as nucleosomes, the basic unit of eukaryotic chromatin, by wrapping 146 bp of negatively charged DNA around a positively charged octameric protein complex of four types of histone proteins: H2A, H2B, H3 and H4 ,35. A caChromatin modifications that involve chemical or structural changes in the chromatin are represented by modifications of DNA (the methylation of cytosine and adenine bases), post-translational modifications of histones  and conformational changes owing to the reconfiguration of chromatin by ATP-dependent chromatin-remodeling complexes ,36. ThesThe two major histone post-translational modifications, the methylation and acetylation of lysine residue in the N-terminal tail of histone proteins, were assessed for their roles in antifungal resistance and virulence in human fungal pathogens ,37. BothThe lysine acetyltransferases (KATs) and histone acetyltransferases (HATs) perform the acetylation of the lysine residue in cellular and histone proteins, respectively, while lysine methylation in histone proteins is mediated by histone methyltransferases (HMTs) ,36,37,38C. albicans and C. glabrata [C. albicans, which was attributed to elevated ROS production due to caspofungin [HAT1 or HAT2, which code for regulatory subunits of the chromatin assembly associated acetyltransferase complex NuB4, led to voriconazole and itraconazole resistance [Histone acetylation has been implicated in antifungal resistance in glabrata ,40,41,42CgPDR1-network genes and azole resistance in C. glabrata [Interestingly, a reduced histone H4 dosage has also been associated with enhanced caspofungin sensitivity, which was rescued by the exogenous addition of vitamin C, probably due to its antioxidant properties . Two redglabrata . Moreoveglabrata . CollectRTT109 deletion in C. albicans led to an elevated susceptibility towards two echinocandins, caspofungin and micafungin, and nucleic acid synthesis-inhibitory antifungal 5-fluorocytosine [RTT109 deletion in C. glabrata was also reported to result in increased caspofungin sensitivity, as identified in a Tn7 transposon-insertion mutant library screen for altered caspofungin susceptibility [C. albicans, RTT109 deletion in C. glabrata was found to be associated with increased sensitivity towards fluconazole [C. albicans and have selective growth-inhibitory activity against the Candida species belonging to the CTG clade [Furthermore, the histone H3K56 acetyl transferase Rtt109, which acetylates histone H3 at the lysine 56 residue, was implicated in antifungal drug resistance, as cytosine ,44,45. Rtibility . Howeverconazole ,44. ReceTG clade .C. albicans [gcn5Δ/Δ mutant towards caspofungin was not due to high intracellular ROS levels [C. albicans, with an ada2Δ/Δ mutant displaying reduced levels of H3K9 acetylation at the MDR1 locus, and impaired activation of fluconazole-induced MDR1 gene expression [CgADA2 gene, which catalyzes H3K9 acetylation in C. glabrata, resulted in sensitivity to all three drugs, azoles, polyenes and echinocandins, the expression of CgPdr1-dependent multidrug-resistance genes was not perturbed in the Cgada2Δ mutant [The fungal lysyl acetyltransferase, Gcn5, a component of many multi-subunit regulatory complexes, including SAGA and SLIK complexes, was recently implicated in caspofungin resistance, but not azole resistance in albicans . MoreoveS levels . In thispression . SimilarΔ mutant . C. glabrata was also recently implicated in the development of drug resistance. CgGCN5 deletion led to both fluconazole and micafungin sensitivity, and mitigated against the emergence of drug resistance towards these drugs [CgGCN5 deletion was also found to be lethal with gain-of-function CgPDR1 alleles that conferred fluconazole resistance to C. glabrata cells [Furthermore, Gcn5 in se drugs ,52. Of nta cells , underscC. albicans azole-resistant isolates were found to have an increased expression of histone deacetylase-encoding genes, HDA1 and RPD3 [HDA1 and RPD3 expressions in fluconazole-resistant strains were reversed upon the development of stable azole resistance during the course of acquired fluconazole resistance in vitro [S. cerevisiae, as HDA1 and RPD3 deletions led to the diminished functioning of the heat-shock protein, Hsp90, with the acetylation of Hsp90 controlling the activity of this highly conserved cellular chaperone [C. albicans, the simultaneous loss of four KDACs  was required to abolish Hsp90-dependent fluconazole resistance [A. fumigatus [In addition to HATs, HDACs have also been shown to govern antifungal resistance . C. albiand RPD3 . Elevatein vitro , therebyhaperone . In C. asistance . Of notesistance . Consistsistance ,54,55. Fumigatus .C. glabrata, the loss of the NAD+-dependent histone deacetylase Hst1 led to fluconazole resistance, which was abrogated upon the deletion of CgPDR1 or CgCDR1 (encodes a major multidrug efflux pump) genes in the Cghst1Δ mutant, indicating an essential requirement for CgCDR1 or CgPDR1 in the CgHst1-dependent cellular response to azoles [Cghst1Δ mutant was found to have elevated CgCDR1 and CgPDR1 transcript levels, suggesting that CgHst1 acts as a negative regulator of the CgPDR1 network genes [CgCDR1 and CgPDR1 genes, and rendered C. glabrata cells resistant to fluconazole [CaHST3 gene that codes for a NAD+-dependent histone deacetylase, led to echinocandin resistance in C. albicans [C. albicans cells against caspofungin and amphotericin B drugs [In o azoles . Furtherrk genes ,58. Consconazole . Similaralbicans . In addi B drugs .C. albicans cells to azoles, due to the highly diminished transcriptional activation of ERG1, ERG11, CDR1 and CDR2 genes in response to azole exposure [Candida, Fusarium, Aspergillus, Rhizopus and Mucor) [Importantly, and consistent with the genetic analysis, the HDAC inhibitor trichostatin A was found to increase the sensitivity of exposure . Similard Mucor) . CollectC. glabrata cells more and less susceptible to azoles, respectively [ERG genes, including the azole target-encoding gene ERG11, a slight increase in the expression of PDR1-network genes in the Cgset2Δ mutant was observed that may contribute to decreased fluconazole susceptibility of the mutant [C. glabrata was found to control the expression of PDR1-network genes, with the Cgrph1Δ mutant exhibiting a low basal expression of CgPDR1 and CgCDR1 genes, as well as an increased susceptibility towards fluconazole [C. albicans and C. glabrata are summarized in In comparison to HATs and HDACs, the role of HMTs and KDMs in drug resistance in pathogenic fungi is just beginning to be studied. Two recent studies have shown that the deletion of histone H3K4 methyltransferase (CgSet1)- and H3K36 methyltransferase (CgSet2)-encoding genes rendered ectively ,62, highe mutant ,62. Consconazole . The comCandida species. However, the role of histone H3 methylation in drug resistance in other fungal pathogens remains to be examined. In this context, it is noteworthy that heterochromatin-mediated gene silencing in the yeast S. pombe has recently been shown to confer unstable resistance to caffeine, with caffeine-resistant strains also exhibiting fluconazole and clotrimazole resistance [S. pombe, which was found to be dependent on the presence of the H3K9 methyltransferase Clr4 [Collectively, these studies suggest that the methylation of histone H3 at lysine 4 and 36 residues is important for the control of the most prevalent azole-resistance mechanism in the sistance . Additioase Clr4 ,64. ThesAlthough the field of histone modification-based control of antifungal drug resistance is still in its infancy, there is growing evidence that this cellular process is an important resistance determinant in fungal pathogens. ATP-dependent chromatin-remodeling complexes play a key role in the establishment and maintenance of precise global nucleosome positioning, which is pivotal to gene-regulatory processes, including transcription initiation and DNA repair . ChromatMDR1 gene in C. albicans [CaSNF2 deletion abolished elevated CaMDR1 expression and led to the attenuation of fluconazole resistance in mutants carrying gain-of-function mutations in the CaMRR1 gene [CaSNF2 deletion was also found to be associated with a modest increase in fluconazole susceptibility [The SWI/SNF complex has been implicated in antifungal resistance, with ATP-dependent chromatin-remodeling complexes acting as co-activators of transcriptional factors governing the expression of multidrug-resistance genes ,67. CaSnalbicans . ConsistRR1 gene . CaSNF2 tibility .CDR1 gene promoter and control the azole-induced expression of PDR-network genes in C. glabrata, with Cgrtt106Δ and Cgsnf2Δ mutants also displaying increased azole susceptibility [Candida pathogens: C. albicans and C. glabrata.Recently, CgSnf2 and CgRtt106 (histone chaperone) have been shown to bind to the Cgtibility . TherefoThe exposure to fungicidal antifungals could lead to DNA damage due to elevated ROS production , with DNC. albicans and formation of disomies in multiple chromosomes in C. neoformans, with these outcomes being associated with the development of fluconazole resistance due to an increase in the copy number of key antifungal resistance-conferring genes, including CDR1 and ERG11 [C. albicans [Fluconazole exposure has been shown to result in a loss of genetic heterozygosity, aneuploidy, isochromosome formation in nd ERG11 ,72. Theralbicans ,74. In talbicans .C. glabrata msh2∆ mutant, with the drug-resistant clinical isolates of C. glabrata containing nonsynonymous mutations in the CgMSH2 gene, resulting in a hypermutable (increased mutation rate) phenotype [CgMSH2 mutations and resistance to antifungal drugs in clinical isolates collected from India, France, Spain and Korea was not observed in subsequent studies [C. glabrata. The DNA repair proteins that are associated with antifungal drug resistance in C. albicans and C. glabrata are listed in Furthermore, the emergence of resistance to all three classes of drugs, caspofungin, fluconazole and amphotericin B, occurred at an elevated rate in the henotype . However studies ,77,78,79Cryptococcus spp. and A. fumigatus [POL3 gene led to a hypermutator phenotype in Cryptococcus deneoformans, with this mutation being mapped to the exonuclease proofreading domain [S. pombe reported that MMR proteins, Pms1, Mlh1 and Msh2, are pivotal to epigenetic silencing at the mating-type HMR locus and telomeric silencing [pms1Δ, mlh1Δ and msh2Δ mutants was not due to increased mutation frequency [Notably, MMR pathway proteins have also been associated with drug resistance and hypermutable phenotypes in other important fungal pathogens, umigatus ,82,83,84g domain . These rilencing . This liilencing . Importarequency . These fRNA forms, numbers of histone proteins, dynamic acetylation and methylation of histone proteins, acetylation of Hsp90 and probably the precise and timely functions of DNA repair proteins upon the recruitment of gene-silencing complexes aid in controlling resistance to cell-wall- and cell-membrane-targeting antifungal drugs in medically important fungi. This is consistent with epigenetic processes playing an essential role in adaptation to varied stress conditions in diverse organisms ,18,87,88


Recent studies have demonstrated that long non-coding RNAs (lncRNAs) are involved in regulating tumor cell ferroptosis. However, prognostic signatures based on ferroptosis-related lncRNAs (FRLs) and their relationship to the immune microenvironment have not been comprehensively explored in clear cell renal cell carcinoma (ccRCC).In the present study, the expression profiles of ccRCC were acquired from The Cancer Genome Atlas (TCGA) database; 459 patient specimens and 69 adjacent normal tissues were randomly separated into training or validation cohorts at a 7:3 ratio. We identified 7 FRLs that constitute a prognostic signature according to the differential analysis, correlation analysis, univariate regression, and least absolute shrinkage and selection operator (LASSO) Cox analysis. To identify the independence of risk score as a prognostic factor, univariate and multivariate regression analyses were also performed. Furthermore, CIBERSORT was conducted to analyze the immune infiltration of patients in the high-risk and low-risk groups. Subsequently, the differential expression of immune checkpoint and m6A genes was analyzed in the two risk groups.A 7-FRLs prognostic signature of ccRCC was developed to distinguish patients into high-risk and low-risk groups with significant survival differences. This signature has great prognostic performance, with the area under the curve (AUC) for 1, 3, and 5 years of 0.713, 0.700, 0.726 in the training set and 0.727, 0.667, and 0.736 in the testing set, respectively. Moreover, this signature was significantly associated with immune infiltration. Correlation analysis showed that risk score was positively correlated with regulatory T cells (Tregs), activated CD4 memory T cells, CD8 T cells and follicular helper T cells, whereas it was inversely correlated with monocytes and M2 macrophages. In addition, the expression of fourteen immune checkpoint genes and nine m6A-related genes varied significantly between the two risk groups.We established a novel FRLs-based prognostic signature for patients with ccRCC, containing seven lncRNAs with precise predictive performance. The FRLs prognostic signature may play a significant role in antitumor immunity and provide a promising idea for individualized targeted therapy for patients with ccRCC.The online version contains supplementary material available at 10.1186/s12920-022-01418-2. Renal cell carcinoma (RCC), accounting for more than 90% of renal cancers, is a urological malignancy originating from renal tubular epithelial cells. Clear cell renal cell carcinoma (ccRCC) is the most common subtype among over ten histological and molecular subtypes of RCC, making up about 65–75% of RCC , 2. The Ferroptosis, a novel programmed cell death process, is triggered by extra-mitochondrial lipid peroxidation caused by an increase in iron-dependent reactive oxygen species . Ferropthttps://xena.ucsc.edu) [https://www.gencodegenes.org) [http://www.zhounan.org/ferrdb; Additional file The high throughput sequencing data ccRCC from TCGA and the related clinical parameters were acquired from the UCSC Xena genome browser (csc.edu) . The annnes.org) . Clinica2-fold change (FC)|> 1 and p value < 0.05, adjusted by the Benjamini–Hochberg method, were defined as the criteria to identify differentially expressed genes (DEGs) or differentially expressed lncRNAs (DE-lncRNAs). DEGs were intersected with 259 ferroptosis-related genes to screen out ferroptosis-related DEGs. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis are widely used enrichment analysis methods [mRNA and lncRNA data were extracted from the total expression profile of TCGA RNA-sequencing data, and the “limma” package was applied for differential expression analysis . The |lo methods . In this methods .2|> 0.4 at p < 0.01 was considered significant. A total of 65 lncRNA-gene pairs  and low-risk (<median number) groups. Then we separately compared the overall survival of the two groups in the training and validation cohorts and presented them in the form of Kaplan–Meier survival curves (KM curves) through the “survminer” package. Similarly, KM curves were also used to evaluate the prognostic value of seven FRLs constituting the prognostic signature in patients with ccRCC. In addition, univariate and multivariate Cox regression analyses of the clinical variables and risk models were conducted to assess the effectiveness of the risk model. Then, the excellent predictive efficiency of the risk model, compared with other clinical parameters, was demonstrated as time-dependent ROC curves. Finally, DCA, a method widely used to evaluate performance of models, was used to assess the clinical utility of our prognostic models. The DCA results depend on the “survival” and “dcurves” packages presented as decision curves. All of the above analyses were conducted independently in the training and testing sets. The presentation of univariate and multivariate Cox analyses and the ROC curves were realized with “forestplot” and “timeROC” packages.https://cibersort.stanford.edu/) obtained from the official website of CIBERSORT; the LM22 signature gene file and 1000 permutations were employed as the reference. The difference in immune cell infiltration levels between the high-risk and low-risk groups was calculated by the Wilcoxon signed-rank sum test (p < 0.05).The CIBERSORT algorithm was used to assess the immune cell infiltration rates in the two risk groups. The reference dataset for the CIBERSORT algorithm is a matrix named LM22 consisting of 547 leukocyte gene signatures. Based on LM22, CIBERSORT can robustly characterize 22 immune cells from expression profiles; thus, CIBERSORT is widely used in the analysis of tumor microenvironment (TME) . We analPDCD1 (PD-1), CD274 (PD-L1), CTLA4, ADORA2A, C10orf54 (VISTA), HAVCR2 (TIM-3), ICOSLG, NT5E, CD27, IDO2, LAG3, TIGIT, TNFRSF18, and TNFRSF9) in tumor immunotherapy between high-risk and low-risk patients [METTL3, METTL14, and WTAP), two erasers (FTO and ALKBH5), and five readers , from the literature and analyzed their expression in the high-risk and low-risk groups [p < 0.05 indicates a significant difference in gene expression between the two groups.To explore the potential significance of the FRLs signature in immunotherapy, we analyzed the differences in the expression of 14 candidate immune checkpoint genes extracted from literature , and significance was set at After extracting the mRNA data of the training set for differential expression analysis, 2757 DEGs, consisting of 1470 upregulated and 1287 downregulated genes, were obtained  was constructed  was an independent prognostic factor. In addition, age , grade , AJCC stage , T stage , and M stage  were independent prognostic risk factors , AJCC stage , and risk score , monocytes , activated CD4 memory T cells , CD8 T cells , follicular helper T cells , and M2 macrophages , activated CD4 memory T cells, CD8 T cells, and follicular helper T cells were higher in the high-risk group Fig. A. We alsADORA2A, C10orf54, CD274 (PD-L1), HAVCR2, ICOSLG, and NT5E, whereas the other eight immune checkpoint genes , TIGIT, TNFRSF18, and TNFRSF9) were highly expressed in the high-risk group  between the two risk groups is presented in Fig. p = 0.309). These results may provide a novel viewpoint for researching m6A-related genes in ccRCC.T cells are the main immune effector cells in the tumor microenvironment of ccRCC  and are oup Fig. A. These ccRCC, the most common type of renal cancer worldwide, has a high mortality rate . PrimaryIn the present study, a novel FRLs signature consisting of seven lncRNAs  was established for ccRCC. Based on this signature, the high-risk group had a significantly worse overall survival than the low-risk group. Univariate and multivariate Cox regression analyses revealed the risk score as a significant risk factor. Moreover, the current study provides the first comprehensive analysis of the crucial roles of FRLs in the prognosis, immune cell infiltration, and m6A modifications in patients with ccRCC. We first elucidated the correlation between the FRLs signature and the immune landscape; the differential immune analysis results demonstrated that risk score was closely linked to the proportion of infiltrated immune cells in this study. Moreover, the analysis of immune checkpoint and m6A genes of patients with ccRCC in the two risk groups indicated that the risk score was significantly associated with immune checkpoint and m6A genes; the differential expression of these genes correlated with the survival of ccRCC patients in different risk groups. These results may provide a promising strategy with important clinical implications for guiding individual therapies.As a recently discovered unique form of iron-dependent non-apoptotic cell death, ferroptosis is involved in regulating the occurrence and development of various diseases, including tumors , 47. TheAs a form of iron-dependent programmed cell death, ferroptosis is regulated by various lncRNAs, miRNAs, and genes. Recent studies on the regulation of ferroptosis by lncRNAs have been conducted in various cancers, including ccRCC , bladderCurrently, an increasing number of signatures composed of FRLs have been used in the prognosis and diagnosis of various diseases, including sepsis, glioma, liver cancer, and bladder cancer –58. IncrThere is complex crosstalk between ferroptosis and tumor immunity. A previous study suggested that dying ferroptosis cells can release and activate damage-associated molecular patterns or lipid oxidation products in immune cells, such as macrophages, monocytes, and neutrophils, through different intracellular signaling pathways, resulting in different immune and inflammatory responses . AnotherPDCD1 (PD-1), CD274 (PD-L1), CTLA4, ADORA2A, C10orf54 (VISTA), HAVCR2 (TIM-3), ICOSLG, NT5E, CD27, IDO2, LAG3, TIGIT, TNFRSF18, and TNFRSF9) and nine m6A regulators  were differentially expressed between the two groups of patients with ccRCC. Tang et al. have demonstrated that the induction of ferroptosis in combination with immune checkpoint inhibitors cooperatively enhances antitumor activity [In the present study, a total of 14 immune checkpoint genes , RP11-298J20.4 (B), RP5-994D16.9 (C), CTD-2510F5.4 (D), CTB-41I6.2 (E), AC006129.2 (F), and TUG1 (G) in the training set.Additional file 5: Fig. S2. Survival analysis of seven FRLs constituting the prognostic signature in the validation sets. The KM curves indicate that there were significant survival differences between patients with high and low expression of CTD-2396E7.11 (A), RP11-298J20.4 (B), RP5-994D16.9 (C), CTD-2510F5.4 (D), CTB-41I6.2 (E), AC006129.2 (F), and TUG1 (G) in the validation set.Additional file 6: Fig. S3. DCA analysis of the training and validation sets. The 1 (A), 3 (B), and 5 (C) years decision curves of the training set indicate that the clinical utility of the prognosis model is superior to other clinical parameters. The 1 (D), 3 (E), and 5 (F) years decision curves of the validation set indicate that the clinical utility of the prognosis model is superior to other clinical parameters.Additional file 7: Fig. S4. Analysis of immune infiltration in ccRCC patients in the validation set. (A) Boxplots showing infiltration of immune cells in ccRCC patients from the two groups in the validation set. (B) Bubble plots representing the correlations between immune cells and the risk score. Scatter plot of the correlation between the risk score and various immune cells, including regulatory T cells (Tregs) (C), monocytes (D), activated CD4 memory T cells (E), CD8 T cells (F), follicular helper T cells (G), and M2 macrophages (H). *p < 0.05, **p < 0.01, ***p < 0.001.


SERCA3), a member of the SERCA protein family, is located at the endoplasmic reticulum. Its main function is to pump Ca2+ into the endoplasmic reticulum and is involved in maintaining intracellular calcium homeostasis and signal transduction, which are very important factors impacting cancer development and progression. However, the specific role of SERCA3 in cancer remains unclear. Our study, for the first time, comprehensively analyzed the SERCA3 expression profile in multiple cancers and its prognostic value in different cancers using bioinformatics. Furthermore, TCGA database was applied to evaluate the certain correlation of SERCA3 expression with immune modulator genes, immune checkpoints, immune cell infiltration, TMB, and MSI. The results revealed that in many cancers, SERCA3 expression was markedly decreased, which was related to poor prognosis. Additionally, we noticed that SERCA3 expression was correlated with TNM classification and WHO cancer stages in some cancer types. The Pearson correlation analysis showed that SERCA3 expression was closely associated with chemokines, chemokine receptors, MHC, immune activation genes, and immunosuppressive genes. In most cancer types, SERCA3 expression was also associated with immune checkpoints, including PDCD1 and CTLA-4. Further analysis suggested that SERCA3 was significantly correlated with CD8+ T cells, and regulatory T cells. Additionally, pan-cancer analysis confirmed that SERCA3 expression was related to TMB and MSI. In conclusion, these results offer a new insight into the functions and effects of SERCA3 in pan-cancer, and further provide some basis for considering SERCA3 as a potential cancer treatment target and biomarker.The sarcoendoplasmic reticulum calcium adenosine triphosphatase (ATPase) 3 ( Cancer, a major cause of death worldwide, imposed a heavy burden on society –4. CanceSERCA3) enzyme belongs to the SERCA protein family and is found in the endoplasmic reticulum. It pumps calcium ions (Ca2+) from the cytoplasm into the endoplasmic reticulum, which is the main calcium-storing organelle. In most cells, it is mainly involved in maintaining homeostasis of endoplasmic reticulum Ca2+ and the intracellular Ca2+ concentration [2+ is an important regulator of cellular signaling activities, and intracellular Ca2+ disorders can affect gene expression, proliferation, differentiation, and cell death [2+ signal transduction is crucial for cancer development. The growth, proliferation, invasion, death, and drug resistance of cancer cells are regulated by Ca2+ [2+ concentration and duration of Ca2+ elevation may promote breast cancer cell proliferation and invasion [The sarcoendoplasmic reticulum calcium adenosine triphosphatase (ATPase) 3  classification, and World Health Organization (WHO) cancer stages was also detected. The relationship between SERCA3 with immune modulator pathways, immune checkpoints, and immune cell infiltration levels was analyzed. Finally, we examined the correlation of SERCA3 expression with cancer mutation burden (TMB) and microsatellite instability (MSI). We provided a study of SERCA3 in pan-cancer, focusing on the role of SERCA3 in cancer immune functions and the potential mechanisms of cancer immunotherapy.Intracellular calcium homeostasis is a crucial factor that affects the occurrence and development of cancers. N = 10535, G = 60499, year: updated in 2022) was downloaded from the UCSC Cancer Genome Browser (https://xenabrowser.net/), from which SERCA3 expression data for each cancer type were extracted [SERCA3 expression between different cancer types. The final cancer expression data were obtained after eliminating cancer types from less than three sample. All expression data were standardized by log2 conversion. SERCA3 expression in different cancers was calculated using R software (version 3.6.4) [SERCA3 expression in normal and cancer tissues in humans.The Cancer Genome Atlas (TCGA) pan-cancer database  . AdditioSERCA3 expression data from TCGA-LAML (year: updated in 2022) and Primary Tumor databases. The final cancer expression data were obtained after eliminating cancer types from less than three sample. Using R software to correlate SERCA3 expression with TNM classification and WHO cancer stages in various types of cancer. All expression data were standardized via log2 conversion.We selected In addition to extraction of data from TCGA-LAML, TCGA-SKCM (year: updated in 2022), and Primary Tumor databases, prognostic data for TCGA within 1 month of follow-up were also obtained from a previously published TCGA prognosis study , and panSERCA3 expression data and data on five immune modulator pathways, including chemokines, chemokine receptors, major histocompatibility complex (MHC), immune activation genes, and immunosuppressive genes, were extracted from TCGA. Further, we excavated TCGA-LAML and Primary Tumor data and plotted the Spearman correlation analysis heat map of SERCA3 expression and five immune modulator pathways.The SERCA3 level and two immune checkpoint pathways was calculated.Moreover, we extracted expression data on two immune checkpoints, including 24 immune checkpoint inhibitors and 36 immune checkpoint stimulators, from a previous study . We screSERCA3 expression data of each cancer type to Gene Symbol, using CIBERSORT [Mapping the obtained IBERSORT , 29 in RIBERSORT . Immune SERCA3 expression and TMB data were extracted from TCGA and Primary Tumor. Downloaded TCGA level 4 simple nucleotide variation data processed by MuTect2 software from GDC [SERCA3 expression and TMB data were integrated. The final cancer expression data were obtained after eliminating cancer types from less than three sample. All expression data were standardized via log2 conversion. Spearman's correlation between SERCA3 expression and TMB was then compared.from GDC . TMB forSERCA3 expression data were integrated; less than three samples of cancer types were eliminated, and the final cancer expression data was acquired. All expression data were standardized via log2 conversion. Spearman correlation between SERCA3 expression and MSI was then compared.Subsequently, we obtained the MSI score of each cancer type from a previous study , and theSERCA3 in various cancer types was evaluated using Student's t-test. Kruskal–Wallis test and Mann–Whitney U-test were used to calculate the relationship of SERCA3 expression with TNM classification and WHO cancer stages. HR and p-values for overall survival were assessed using the log-rank test. Spearman correlation and Pearson's correlation were applied to detect the correlation between SERCA3 expression and immunity. All analyses were performed using R software . p ≤ 0.05 was considered a statistically significant difference.Differential expression of SERCA3 expression in various cancer types based on TCGA database. The results showed inconsistent expression of SERCA3 in different types of cancer; it had significantly low expression in 13 cancers, including GBM, GBMLGG, LGG, COAD, COADREAD, KIRP, KIPAN, PRAD, LUSC, THCA, READ, BLCA, and KICH. Contrastingly, two cancers, including BRCA and CHOL, showed significantly high SERCA3 expression , GBMLGG (p=0.02), LGG (p=0.02), and COADREAD (p=0.05) , BRCA (p=0.05), LGG (p=0.04), and GBM (p=0.02) (To understand the association of (p=0.05) . Subsequ(p=0.02) .SERCA3 and the OS in cancer patients was evaluated. SERCA3 is a protective factor in most cancers, HR and 95%CI for cancers were PAAD , CESC , SKCM , SARC , BLCA , SKCM-M , COADREAD , HNSC , KIRC , COAD , OV , while SERCA3 is an adverse factor in KIPAN , GBMLGG , TGCT , UVM , LGG . The pan-cancer results were found using cox regression analysis , TGF-B1, CTLA-4, TIGIT, and ICOS in most cancer types  cells, M1 macrophages, and naïve B cells, while negatively correlated with M0 macrophages, M2 macrophages, and eosinophils , LGG (0.0006), OV (0.0035), COAD (0.0360), ESCA (0.0007), and GBMLGG (<0.0001), while it was negatively associated with TMB in LIHC (0.0002), TGCT (0.0431), PAAD (0.0016), PRAD (<0.0001), LAML (0.0131), GBM (0.0089), THCA (0.0004), STAD (0.0018), THYM (7.87e-11), KIRP (0.0126), LUSC (0.0403), and KIRC (0.0137) , LUAD (<0.0001), COAD (<0.0001), and UCEC (0.0017), and was negatively correlated with MSI in TGCT (0.0224), STAD (0.005), LIHC (0.0202), DLBC (0.0136), KIPAN (2.16e-15), GBMLGG (0.0003), SARC (0.0231), HNSC (0.0161), and KIRP (0.0251)  . Moreove(0.0251) .2+ homeostasis is crucial for protein storage and transport, signal transduction, and various cellular activities [2+ levels have been reported to affect cancer progression [SERCA3, a protein that maintains Ca2+ homeostasis in the cytoplasm and endoplasmic reticulum, remains unknown. In this study, the pan-cancer analysis revealed an association between SERCA3 expression and cancer prognosis, immunoregulatory genes, immune infiltration, and mutations.Calcium-dependent cell signal transduction was involved in a variety of life activities including proliferation, differentiation, secretion, and death . Maintaitivities . Abnormagression , 35, 36.SERCA3 expression varied among different cancer types. SERCA3 was expressed at low levels in 13 types of cancers, including GBM, GBMLGG, LGG, COAD, COADREAD, KIRP, KIPAN, PRAD, LUSC, THCA, READ, BLCA, and KICH. Comparative analyses revealed high SERCA3 expression in two cancer types, including BRCA and CHOL. Moreover, SERCA3 expression is association with WHO cancer stages and TNM classification in a few types of cancer. For instance, the expression of SERCA3 is different for WHO cancer stages of GBM, GBMLGG, LGG, and BRCA. Furthermore, the expression of SERCA3 is related to metastasis stages of GBMLGG, LGG, and COADREAD. Cox regression analysis showed that SERCA3 is a protective factor against some cancers, including PAAD, SKCM, SARC, SKCM-M, HNSC, COADREAD, BLCA, COAD, CESC, KIRC, and OV. However, it acts also as a risk factor for GBMLGG, LGG, UVM, KIPAN, and TGCT. These results indicated that SERCA3 has a low level of expression in most cancers compared with its expression in normal tissues and plays a protective role in most cancer types.We found that SERCA3 was correlated with the chemokine receptors CCR4, which plays a significant role in immune regulation and is regarded as a potential therapeutic target in bronchial asthma. CCR4 is also highly expressed in adult T-cell leukemia/lymphoma (ATLL) and cutaneous T-cell lymphoma (CTCLs) [CCR4 mediates the chemotactic response of breast cancer cells to CCL17 and accelerates the growth and metastasis of breast cancer [SERCA3 and immune-activating and immunosuppressive genes, including PDCD1 (PD-1), CTLA-4, TIGIT, and ICOS. By analyzing the correlation between SERCA3 expression and immune checkpoints we found that SERCA3 expression was related to immune checkpoints, including CTLA-4, PDCD1, and ICOS in most types of cancer. PDCD1 and CTLA-4 antibodies, which are immune checkpoint inhibitors, have been approved for the treatment of cancers including non-small cell lung cancer (NSCLC) and melanoma, and have improved the prognosis of patients with these cancers [SERCA3 might partially affect immune checkpoints.Analysis of the results of the TCGA database revealed that the expression of  (CTCLs) . Li et at cancer . Our res cancers , 40. TheSERCA3 expression and levels of immune cell, we detected that SERCA3 expression was positively associated with M1 macrophages and CD8+ T cells levels, whereas it showed a negative correlation with the levels of M0 and M2 macrophages. Cytotoxic CD8+ T cells are the main immune cells against pathogens and neoplastic cells. The cancer immunotherapy partially strengthens CD8+ T cell activity leading to the reduced escape of cancer cells from the immune system and then establishing durable and efficient anti-tumor immunity [SERCA3 may play a protective role in most cancers by increasing T cell infiltration. Previous research reported that an increased M2/M1 macrophage ratio promotes cancer progression [SERCA3 expression was positively correlation with M1 macrophage levels while negatively correlation with M2 macrophage levels, further providing a basis for the protective role of SERCA3 in most cancer types. These results suggest that SERCA3 may interfere with the prognosis of various cancers by regulating the expression of multiple immune cells.The tumor microenvironment (TME) is pivotal in regulating cancer progression and can predict treatment outcomes –43. The immunity , 49. SERgression . SERCA3 SERCA3 expression, TMB, and MSI. The more somatic mutations in tumors, the newer antigens that may form, and TMB can be used to evaluate the number of new tumor antigen loads [SERCA3 expression correlated with TMB and MSI, providing evidence for SERCA3 as a potential predictor of ICB therapy.Finally, we assessed the correlation among en loads . MSI is en loads , 53. By SERCA3 expression with prognosis, immune modulator genes, immune checkpoints, immune cell infiltration, TMB, and MSI, which can provide information to further understand the role of SERCA3 in cancers and its relationship with the immune responses. It also provides a basis for considering SERCA3 as a potential cancer treatment target and biomarker. A potential challenge in the future will involve the development of new therapeutic methods related to the specific targeting of SERCA3 to limit the development and progression of cancer.However, our study had some limitations. First, it was based on bioinformatics and different databases; methods of generating data may have impacted the results. Second, TCGA database lacks data on immunotherapy; hence, we cannot further analyze the indications for immunotherapy. Overall, our study systematically analyzed the association of SERCA3 expression was significantly decreased in most types of cancer, cancer patients with reduced SERCA3 expression tend to have a poor prognosis. Moreover, we analyzed the correlation of SERCA3 expression with immune regulatory gene expression, immune checkpoints, immune cell infiltration, TMB, and MSI. We speculated that SERCA3 might affect cancer progression by regulating the TME, especially immune cells. These results provide new ideas for the function and role of SERCA3 in pan-cancer and provide a theoretical basis for considering SERCA3 as a potential cancer treatment target and biomarker.This research revealed that


This study is aimed at screening prognostic biomarkers in cholangiocarcinoma (CHOL) based on competitive endogenous RNA (ceRNA) regulatory network analysis. Microarray data for lncRNAs, mRNA, and miRNAs were downloaded from the GEO and TCGA databases. Differentially expressed RNAs (DERs) were identified in CHOL and normal liver tissue samples. WGCNA was used to identify disease-related gene modules. By integrating the information from the starBase and DIANA-LncBasev2 databases, we constructed a ceRNA network. Survival analysis was performed, and a prognostic gene-based prognostic score (PS) model was generated. The correlation between gene expression and immune cell infiltration or immune-related feature genes was analyzed using TIMER. Finally, real-time quantitative PCR (RT-qPCR) was used to verify the expression of the 10 DERs with independent prognosis. A large cohort of DERs was identified in the CHOL and control samples. The ceRNA network consisted of 6 lncRNAs, 2 miRNAs, 90 mRNAs, and 98 nodes. Ten genes were identified as prognosis-related genes, and a ten-gene signature PS model was constructed, which exhibited a good prognosis predictive ability for risk assessment of CHOL patients . Four genes, ELF4, AGXT, ABCG2, and LDHD, were associated with immune cell infiltration and closely correlated with immune-related feature genes  in CHOL. Additionally, the consistency rate of the RT-qPCR results and bioinformatics analysis was 80%, implying a relatively high reliability of the bioinformatic analysis results. Our findings suggest that the ten-signature gene PS model has significant prognostic predictive value for patients with CHOL. These four immune-related DERs are involved in the progression of CHOL and may be useful prognostic biomarkers for CHOLs. Cholangiocarcinoma (CHOL) is an aggressive epithelial malignancy that frequently arises from cholangiocytes or the biliary tract. It is the second most common liver cancer after hepatocellular carcinoma (HCC) worldwide and accounts for approximately 3% of gastrointestinal tumors . The incmiR-144-3p plays a fundamental role in CHOL genesis [COL18A1-AS1 and SLC6A1-AS1 [lncRNA TTN-AS1 promotes CHOL progression through miR-320a/neuropilin-1 axis [The competing endogenous RNA (ceRNA) is a novel regulatory network that has been proven to be a major factor in cancer development. The regulatory patterns and crosstalk between miRNAs, lncRNAs, and mRNA have been widely uncovered in recent studies , 7. In a genesis . Based oC6A1-AS1 . The potn-1 axis . There iIn this study, we downloaded microarray datasets from public databases and analyzed the expression levels of miRNAs, lncRNAs, and mRNA between CHOL and normal tissue samples. The optimal pairwise gene was screened to construct a ceRNA regulatory network, along with survival analysis. We established ten signature genes to predict the prognosis of patients with CHOL. Finally, the correlation between prognostic genes and immune cell infiltration was analyzed to identify immune-related genes. These findings proved that the ten-gene signature model can be applied to predict the prognosis of CHOLs.Microarray datasets were derived from the GEO database in May 2019 by setting “cholangiocarcinoma, homo sapiens” as the keywords. The screening criteria were as follows: the dataset should be whole-genome sequencing profiles, including CHOL and normal tissue samples, and the sample number should be more than 100. Finally, we obtained the GSE26566 dataset , which ihttps://gdc-Portal.nci.nih. gov/). After corresponding barcode information to the samples, we screened 45 samples with matched mRNA and miRNA, including nine normal samples and 36 CHOL tumor samples.The mRNA and miRNA samples associated with CHOL were downloaded from The Cancer Genome Atlas database , including probes, gene symbols, and RNA types. The Limma package [First, the expression levels of ceRNAs in TCGA samples were reannotated according to the corresponding information. We extracted the annotation profiles of GSE26566 from the Ensembl genome browser 96 ( package  (Versionhttps://cran.cran.project.org/package=pheatmap), and a heatmap was generated based on the Encyclopedia of Distance [Hierarchical clustering analysis was performed using the pheatmap package   to screen DElncRNA and DEmiRNA pairs, and only the pairwise gene with opposite expression status was conserved for ceRNA network construction.We researched the DIANA-LncBasev2 database  (http://http://starbase.sysu.edu.cn/), which provides five algorithms  to explore miRNA-mRNA interaction maps from CLIP-sequencing data.In addition, we researched the conversed miRNA target site to characterize predictive target genes according to starBase  . Prognosis-related DERs  were screened based on prognosis information  of 36 CHOL samples in TCGA. A log-rank P value less than 0.05 was selected as a threshold. In addition, we developed a prognostic score (PS) model for prognosis prediction based on the prognostic coefficient value of each RNA and expression value. For these ceRNAs in the regulatory network, we conducted univariate and multivariate Cox regression analyses using the survival package   curve analysis.The correlation between target gene expression and the infiltration of immune cells or immune-related feature genes  was analyzed using the online tool TIMER , which iThe obtained ten DERs with independent prognosis correlations, including eight mRNAs, one lncRNA, and one miRNA, were selected for RT-qPCR verification. Two cell lines, normal human intrahepatic bile duct epithelial cells (HIBEC) and cholangiocarcinoma cells (HuCCT1), were purchased from Shanghai Fuheng Biotechnology Co., Ltd. . They were maintained in Roswell Park Memorial Institute 1640 , supplemented with 10% fetal bovine serum , and cultured in an incubator with 5% carbon dioxide at 37°C.–ΔΔCT method.After two passages, the cells were harvested to extract total RNA using RNAiso Plus reagent , following the manufacturer's instructions. Then, the PrimeScript™ II 1st Strand cDNA synthesis Kit  was used to reverse transcribe total RNA to cDNA according to the manufacturer's protocols. The level of miR-25-3p was measured using the stem-loop method, with U6 serving as a reference gene. For the expression of lncRNA MIR99AHG and the eight mRNAs, GAPDH was used as the housekeeping gene. The sequences of all primers are listed in Supplementary Table t-test was used for comparative analysis between the two groups. P < 0.05 was considered as the statistical significance.Data are expressed as mean ± standard deviation. GraphPad Prism 8  was used for statistical analysis. The Student's After data preprocessing, we screened 4675 and 1521 DERs from TCGA and GSE26566 datasets, respectively, using the criteria of FDR value < 0.05 and |log2FC| > 1. Volcano plots and hierarchical clustering heatmaps are shown in First, the correlations of gene expression between the two datasets and network connectivity correlations were analyzed. The power value was set to 12, and the median connectivity of genes satisfied the scale-free network .Z score > 5 and P value < 0.05, including modules 1, 2, 6, and 7 , whereas in the multivariate Cox regression analysis, the prognosis of CHOL was significantly associated with age (P = 4.65E − 02) and prognostic score status , which was consistent with the actual disease status. The area under the curve (AUC) was 0.975, indicating good predictive ability for the risk assessment system in patients with CHOL.A ten-gene signature PS model was constructed according to the coefficient and expression values of the DERs. The predictive ability of the PS model was validated using TCGA and HCC validation sets. Samples in the two datasets could be divided into high- and low-risk groups based on the threshold of the median PS value. Survival analysis revealed that CHOL or HCC patients in the high-risk group exhibited a poor prognosis (ELF4 was correlated with CD4+ cells (P < 0.05), CD8+ cells (P < 0.05), and neutrophil infiltration (P < 0.05). AGXT was correlated with CD8+ cell infiltration (P < 0.05). Our results indicate that these four genes might be prognostic immune-related genes in CHOL progression.The correlation between tumor-infiltrating immune cells and target gene expression was analyzed for eight DEGs. The results showed that most genes were significantly correlated with tumor-infiltrating immune cells in HCC samples . HoweverABCG2 was positively correlated with CD14, CD163, CD33, CD34, and CD4 (P < 0.05); AGXT and C6 were positively correlated with the expression of CD14 (P < 0.05), while it was negatively correlated with CD44 and CD80 (P < 0.05). CKAP2L expression was not significantly correlated with these immune-related genes (P > 0.05). For ELF4, it was positively correlated with CD163, CD22, CD27, CD33, CD4, CD80, CD86, FOXP3, and TLR2 (P < 0.05), whereas it was negatively correlated with CD24 (P < 0.05). LDHD was negatively correlated with CD27, CD38, CD80, CD86, FOXP3, and TLR2 (P < 0.05), while LZTS1 had positive correlation with CD163, CD34, and KIT (P < 0.05). Furthermore, PARPBP expression was negatively related with the expression of CD34 (P < 0.05).In addition, the correlation between immune-related feature genes and the expression of the eight DEGs was also analyzed. As shown in ELF4, CKAP2L, LZTS1, and PARPBP) and five downregulated  DERs, were chosen for RT-qPCR verification. It was found that the lncRNA MIR99AHG expression level was significantly lower in HuCCT1 cells than in HIBEC cells , whereas the expression of LDHD and C6 was evidently downregulated  by Person's correlation analysis. Tumor microenvironment (TME) consists tumor cells, surrounding stroma, and various infiltrating immune cells, which contributed to tumor heterogeneity [ELF4 was significantly correlated with CD4+ cells, CD8+ cells, and neutrophils. In addition, ELF4 was positively correlated with CD163, CD22, CD27, CD33, CD4, CD80, CD86, FOXP3, and TLR2 while negatively correlated with CD24. ELF4 or myeloid ELF1-like factor (MEF) is a member of the ETS family. The general feature of ELF4 is critically associated with cellular processes, such as immune response, osteogenesis, and cancer cell quiescence [ELF4 controls CD8+ T cell homing and proliferation through the tumor suppressors KLF4 and KLF2. –/–ELF4 mice displayed accumulated CD8+ T cells during steady-state conditions, an increased memory T cell population after immunization, and redistribution of T cells to non-lymphatic tissues, indicating the important role of ELF4 in immunity. Transcript fusion of BCORL1-ELF4 has been identified in HCV-positive HCC patients based on genome sequencing profiling [ELF4 and HBV integration at the TERT promoter, which might result in TERT activation in HCC pathogenesis [ELF4 fusion in liver cancer occurrence remains to be clarified. Combined with our results, we assume that ELF4 might be a potential prognostic immune-related gene in CHOL.Furthermore, ogeneity . Tumor-iogeneity , 42. Notiescence . Yamada iescence  reportedrofiling . A recenogenesis . HoweverELF4, and LDHD, in the pathogenesis and progression of CHOL require verification by more experiments in vitro and in vivo.Our study has some limitations. The number of CHOL samples derived from TCGA or GEO databases was small because CHOL is a rare cancer type, and our results might be biased. Second, the mechanisms of prognostic immune-related genes, such as lncRNA MIR99AHG, hsa-miR-25-3p, lncRNA-MIR99AHG may regulate the progression of CHOL by interacting with hsa-miR-25-3p. The ten-signature gene model provided an effective and reliable method for prognostic prediction in patients with CHOL. ELF4, ABCG2, AGXT, LDHD, CKAP2L, LZTS1, PARPBP, and C6 may be prognostic immune-related genes that modulate the subsequent development of CHOL. Our findings would improve our understanding of the progression and recurrence of CHOL and lay the foundation for potential biomarkers or targets for the diagnosis and treatment of patients with CHOL.In conclusion, based on ceRNA network analysis, we identified that


Progesterone is a steroid hormone that performs important functions in mammals. However, studies on its physiological functions in plants have gradually increased in recent years. Therefore, this review summarizes the regulatory functions of progesterone on plant growth and development, as well as its response to stress. Moreover, the plant metabolic processes of progesterone are also discussed. Overall, progesterone is ubiquitous in plants and can regulate numerous plant physiological processes at low concentrations. Since progesterone shares similar characteristics with plant hormones, it is expected to become a candidate for plant hormone. However, most of the current research on progesterone in plants is limited to the physiological level, and more molecular level research is needed to clarify progesterone signaling pathways. Progesterone was also subsequently found in Digitalis purpurea, Nicotiana tabacum, Inula helenium, and Juglans regia, and the progesterone concentrations in these species were comparable to previous studies [Progesterone is a steroid hormone that is synthesized in the ovaries, placenta, and adrenal glands. Although it is often considered an important female reproductive hormone, progesterone is also involved in endometrial cancer , breast −1 . Since tay (RIA) . Progestectively  found tho et al. . Moreove studies ,13. It i3H or 14C-labeled precursors. By treating Digitalis lanata with 14C-labeled sitosterol, it was found that sitosterol could be converted into progesterone [Cheiranthus cheiri showed that progesterone can be derived from cholesterol [5-3β-hydroxysteroid dehydrogenase (3βHSD), whereafter isoprogesterone is isomerized to progesterone  under the action ofΔ5-Δ4-ketosteroid isomerase (3KSI) [The processes of progesterone synthesis  are studlesterol . Additiolesterol ,17. Therlesterol . In mammlesterol ,20. Howelesterol . In addie (3KSI) ,22. Unlie (3KSI) .Digitalis lanata, named DlP5βR1 and DlP5βR2. After down-expression of these two genes by RNA interference (RNAi) technology, shoot cardenolide content decreased significantly. The gene encoding progesterone 5β-reductase in Arabidopsis was identified as VEP1 (At4g24220) [Arabidopsis VEP1 in Digitalis purpurea could thus significantly increase cardenolide production. Compared with non-transgenic plants, digitoxin and digoxin content could increase up to 3.8-fold and 2.2-fold in transgenic plants cultivated in vitro, respectively [P5βR2 in Digitalis purpurea was significantly induced by wounding, heat shock, cold shock, and salt stress [Progesterone can be reduced for use in secondary metabolism in two ways . One met4g24220) ; overexpectively . Researcectively . Therefot stress . TherefoArabidopsis protein DET2 shares about 40% sequence identity with SRD5α in mammals, and has been determined to have the activity of steroid 5α-reductase. The reason for this is that, when DET2 is expressed in human embryonic kidney 293 cells, DET2 can catalyze the 5α-reduction of several steroids, including progesterone, testosterone, and androstenedione [Solanum lycopersicum) has 76% homology with the DET2 protein in Arabidopsis thaliana. Similar to AtDET2, it is active on the substrates progesterone, testosterone, and rostenedione, but has a very low reducing activity on campestenone [Solanum malacoxylon, which can perform 5α-reduction with progesterone and campestenone as substrates, but not testosterone and androstenedione [The other way in which progesterone is reduced is by conversion into 5α-pregnane-3,20-dione under the action of progesterone 5α-reductase (SRD5α) . The Araenedione . The 5α-estenone . Two sepenedione . DET2 miArabidopsis, a putative membrane steroid binding protein 1 (MSBP1) was identified, which can bind progesterone and other molecules  with different in vitro affinities, and among them, MSBP1 has the highest affinity with progesterone [AtMSBP1 in Arabidopsis, AtMSBP2 and AtSBP (steroid binding protein), and cloned OsMSBP1, OsMSBP2, and OsSBP in rice. Furthermore, progesterone-binding membrane proteins were found to be widely distributed in plants by aligning and analyzing EST date in a variety of plants [MSBP1, MSBP2, and SBP were expressed in various tissues [AtMSBP1 was weakly expressed in roots and difficult to detect in mature flowers and siliques, and that the expression level of MSBP1 could be significantly suppressed by darkness [Mammals have multiple progesterone receptors (PR), such as intracellular receptors, membrane-associated progesterone receptor component 1 (PGRMC1), and membrane progesterone receptors (mPR), which can mediate various physiological processes by activating downstream genes or proteins . In Arabesterone . Iino etesterone  soon disf plants . Tissue  tissues , whereasdarkness .MSBP1 can regulate hypocotyl growth and stimulate the anti-gravitropism and gravitropism of hypocotyl and roots, respectively  signaling. MSBP1 could interact with BAK1 (BRI1 associated receptor kinase 1) to promote BAK1 endocytosis, thereby reducing BR response [MSBP1 have been found through electrophoretic mobility shift assay (EMSA) and chromatin IP (ChIP) assay. HY5 (Long Hypocotyl 5) and HYH (HY5 Homolog) can bind to the GAGA-box in the promoter to activate the transcription of MSBP1, thereby inhibiting the elongation of the hypocotyl [MSBP1 was found to stimulate root gravitropism and antigravitropism of the hypocotyl by regulating auxin redistribution [Further studies revealed that ectively . MSBP1 ol growth . Furtherresponse . Additioypocotyl . Moreoveribution .Triticum aestivum) cells. In non-vernalized and vernalized wheat cell membrane extracts, the specific binding of progesterone was 31.0 and 18.1 fmol/mg protein, respectively. However, the specific bindings of progesterone were 4.9 and 21.3 fmol/mg protein in the non-vernalized and vernalized wheat cell cytosolic fractions, respectively [Progesterone also has specific binding sites in the membrane and cytoplasm of wheat , while a low progesterone concentration (0.1 μg/plant) induces the opposite [−4–10−15 M) can promote chickpea (Cicer arietinum) shoot and root growth, with 10−4 M having the best effect [−4 and 10−5 mM improve shoot formation from cotyledons explants and callus formation of sainfoin (Onobrychis sativa), respectively [−8 mM progesterone treatment, but the REC rate decreased significantly under the 10−6 and 10−4 mM progesterone; 10−6 mM progesterone could increase RE, while RE was inhibited at the concentrations of 10−8 and 10−4 mM [The regulation of progesterone on plant growth is concentration-dependent. For example, low progesterone concentrations (0.01–1 μM) stimulate hypocotyl elongation, while high concentrations (100 μM) inhibit hypocotyl growth in bidopsis . A high opposite . Numerout effect . Progestectively . The res 10−4 mM . These r−9 M significantly increased the activities of SOD, POD, and CAT during bean (Phaseolus vulgaris) seed germination [Zea mays), where progesterone (10−4–10−15 M) significantly increased the germination rate of maize seeds and enhanced root and coleoptyle length, while the activities of α-amylase, SOD, CAT, peroxidase (POX), and polyphenol oxidase (PPO) were simultaneously induced [CS, COX19, Pdh1, and ATP6) related to mitochondrial respiration were upregulated [Progesterone can significantly accelerate the rate of chickpea seed germination, which is accompanied by increased α-amylase, superoxide dismutase (SOD), and catalase (CAT) activities, but decreased seed malondialdehyde (MDA) content . It was mination . Similar induced . Furtheregulated . Thus, tActinidia deliciosa) pollen increases gradually with pollen germination [Nicotiana tabacum) male gametophyte growth [Arabidopsis confirmed that progesterone induced plant flowering and promoted reproductive growth [CYP11A1 in the bovine adrenal cortex, which catalyzes the conversion of cholesterol into pregnenolone, was transformed into tobacco, and CYP11A1 overexpression-transgenic plants had shortened vegetative periods (early flowering and maturation of bolls) [Endogenous progesterone content in kiwifruit  ,56. TherAlthough progesterone is not yet recognized as a plant hormone, it has similar functions to plant hormones, which mediate plant growth and development and also regulate biotic and abiotic stress .2O2) content, thereby alleviating salt-induced inhibition of wheat seedling growth [Phaseolus vulgaris, Zea mays, and Poa pratensis, and, in addition to increasing antioxidant enzyme activity to alleviate salt stress, progesterone can ease the salt-reduced K/Na ratio and pigment content (including chlorophyll and carotenoids) [Progesterone at an appropriate concentration can increase the activities of SOD, POD, CAT, ascorbate peroxidase (APX), and nitrate reductase (NR), and reduce lipid peroxidation and hydrogen peroxide (Hg growth . Improvetenoids) ,59,60.2O2, and MDA content) induced by chilling stress can be lessened by progesterone through increased relative leaf water and chlorophyll content, as well as antioxidant enzyme activities, and the application of progesterone lowered the freezing point of chickpea seedlings from −4 to −5.5 °C. These physiological changes contribute to the protective effect of progesterone on chickpeas when exposed to chilling stress [2•− and H2O2, and upregulate the transcription and protein levels of AOX in postharvest bananas (Musa nana Lour). Notably, the antioxidant system, activated by progesterone, also played a crucial role in chilling injury protection [Ipomoea batatas) tuberous roots [AOX gene, is involved in the alleviating effect of progesterone on chilling stress. Additionally, the protective effect of progesterone on wheat seedlings exposed to low temperature was related to its effects on lipid membrane structures [In chickpea, certain oxidative stress indicators  ,67.Pseudomonas bacteria in Arabidopsis leaves [CYP11A1-overexpressing transgenic tobacco plants exhibited resistance to infection by the fungal pathogen Botrytis cinerea, and the average leaf affected area of transgenic plants was about 12 times smaller than non-transgenic plants [Progesterone diminished necrotic symptoms and electrolyte leakage caused by s leaves . Mammalic plants .In general, the current research on the regulation of progesterone on plant biotic/abiotic stress responses mainly focuses on its effects on traditional stress indicators, such as ROS content, antioxidant enzyme activities, photosynthesis, and respiration rate. However, little is known about how progesterone transmits signals into cells to regulate physiological processes, and the key regulatory genes in the progesterone signaling pathway are barely identified. In addition, plant hormones are recognized to be involved in plant stress regulation, and whether there is a correlation between progesterone and these stress hormones has not yet been studied. There is still a long way ahead to explore the molecular regulation mechanism of progesterone in response to plant biotic/abiotic stress.Arabidopsis leaves infected with Pseudomonas syringae, but this inhibition was partially restored by progesterone [2 binding enzyme) activity—trilostane, a progesterone synthesis inhibitor, significantly decreased Rubisco activity in wheat leaves under drought stress, but its activity was restored by the application of exogenous progesterone [Photosynthesis is the basis of crop yield, and progesterone is known to play an important role in the regulation of photosynthesis, which is mainly reflected by three aspects: (1) Improved chlorophyll content—progesterone can significantly increase the chlorophyll content of wheat leaves under salt stress , and impesterone . The damesterone ,67. Wheaesterone . (3) Incesterone . The incArabidopsis [MSBP1 (a progesterone receptor) alters Arabidopsis plant sensitivity to BR [MSBP1 inhibits cell expansion and BR responses, but this phenomenon is restored by co-overexpression of BAK1 in Arabidopsis; MSBP1 could specifically interact with the extracellular domain of BAK1 in vivo, and thus negatively regulates BR signaling [Both progesterone and brassinosteroid (BR) are steroids. Firstly, progesterone and BR are cross-linked in metabolic processes. Deletion of the steroid 5α-reductase DET2, which can reduce progesterone to 5α-pregnane-3,20-dione, resulted in decreased BR accumulation in bidopsis . Secondlbidopsis , but ovety to BR . Overexpignaling  . Secondly, this paper summarized the regulatory role of progesterone in plant growth, development, and stress responses. These properties of progesterone are very similar to plant hormones , and theAlthough plant progesterone has gradually attracted scholarly attention, and its metabolism and physiological functions have been increasingly explored, many unsolved mysteries remain. For example, the side-chain-cleavage processes of the progesterone precursor and its related enzymes have not yet been identified. Progesterone has a binding site in the cytoplasm, so what is the progesterone receptor in the cytoplasm? What is the mechanism between progesterone and its membrane receptor (MSBP1) to regulate downstream physiological functions? Apart from the above-mentioned, does progesterone have other physiological functions, and are these regulatory functions generally applicable to the plant kingdom? These questions require more research. Moreover, from previous studies on the regulation of plant growth, development, and stress response by progesterone, our understanding of the regulatory functions of progesterone generally remains at the physiological level, and rarely involves molecular mechanisms. More studies on progesterone receptors and response genes will thus greatly contribute to our understanding of the progesterone signaling pathway. Based on this, we reviewed plant progesterone in the hope of attracting the attention of more researchers, and to promote the current understanding of progesterone in plants.


Adipogenesis-osteoblastogenesis balance-rupture is relevant in multiple diseases. Current human mesenchymal stem cells (hMSCs) in vitro differentiation models are expensive, and are hardly reproducible. Their scarcity and variability make an affordable and reliable method to study adipocyte-osteoblast-equilibrium difficult. Moreover, media composition has been inconstant throughout the literature. Our aims were to compare improved differentiation lab-made media with consensus/commercial media, and to identify a cell-line to simultaneously evaluate both MSCs differentiations. Lab-made media were compared with consensus and commercial media in C3H10T1/2 and hMSC, respectively. Lab-made media were tested on aged women primary pre-osteoblast-like cells. To determine the optimum cell line, C3H10T1/2 and hMSC-TERT cells were differentiated to both cell fates. Differentiation processes were evaluated by adipocytic and osteoblastic gene-markers expression and staining. Lab-made media significantly increased consensus medium induction and overcame commercial media in hMSCs differentiation to adipocytes and osteoblasts. Pre-osteoblast-like cells only properly differentiate to adipocyte. Lab-made media promoted adipocyte gene-markers expression in C3H10T1/2 and hMSC-TERT, and osteoblast gene-markers in C3H10T1/2. Oil Red O and Alizarin Red staining supported these findings. Optimized lab-made media were better at differentiating MSCs compared to consensus/commercial media, and evidenced the adipogenic commitment of pre-osteoblast-like cells from aged-women. C3H10T1/2 is an optimum MSC line by which to study adipocyte-osteoblast differentiation balance. According to the World Health Organization, musculoskeletal pathologies (MSPs) are the main cause of physical disability in western countries. They affect one in four people older than 20 years old . Even thThe rupture of the bone remodeling balance (the relationship between bone formation and bone resorption) results in pathological situations such as osteoporosis and osteopetrosis . InteresToday, it is well known that adipogenesis and osteoblastogenesis are competitive and mutually controlled processes . As a reDulbecco’s modified Eagle’s medium (DMEM), DMEM supplemented with HAM’S-F12 (DMEM-F12), penicillin/streptomycin, L-glutamine, foetal bovine serum , and trypsin were purchased from Sigma-Aldrich . Insulin growth factor-1 (IGF1) was purchased from PeproTech Inc . Unless referenced, all reagents were purchased from Sigma-Aldrich.The murine mesenchymal stem cell line C3H10T1/2 was donated by Dr. Pardo (IDIS Institute). Bone marrow human mesenchymal stem cell line hMSC-TERT was donated by Dr. Campana . Human mesenchymal stem cells (hMSCs) were purchased from Thermo Fisher Scientific Inc. .To determine the consensual protocol of adipogenic medium, the term “adipogenesis protocol” was searched for in the PubMed database. After a selection of representative paper samples , a consensus adipogenic differentiation media was established. Moreover, in order to determine IGF1 addition into adipocyte differentiation medium, the term “adipogenesis IGF1” was searched for in the PubMed database. With no date limitation, all the available papers were reviewed. All the protocols studied in both searches were considered to elaborate on an adipogenic consensus medium.To establish an osteoblastogenic medium consensual protocol, the term “osteoblastogenesis protocol” was searched for in the PubMed database. All the results obtained were analyzed to determine the optimal reagents for an osteoblastogenic consensus medium.Human pre-osteoblast-like cells were obtained from total knee replacement surgery of aged women (between 60 and 75 years old) as previously described in Helfrich, Miep H., Ralston, 2003 . The Eth4 cells were seeded per well in a 24-well plate and differentiation media was added 6 h after seeding.C3H10T1/2 cells were grown in DMEM high glucose, with 10% FBS, 4 mM L-glutamine, and 100 U/mL penicillin/streptomycin. For their differentiation, 10Lab-made AD differentiation medium comprised DMEM, 2 μM rosiglitazone, 20 nM IGF1, 1 μM dexamethasone, 60 μM indomethacin, 0.5 mM IBMX, 10 μg/mL insulin, 4 mM L-glutamine, 100 U/mL penicillin/streptomycin, and 10% FBS. Medium was renewed every two days. To obtain an efficient differentiation, three cycles, each one with 96 h of adipocyte differentiation medium and 72 h with maintenance medium, were performed. Maintenance medium consisted of culture medium supplemented with 10 μg/mL insulin. Vehicle concentrations used to solubilize the adipogenic factors were maintained below 0.1% to avoid toxic effects.Lab-made OB differentiation medium consisted of DMEM high glucose, 10 nM dexamethasone, 5 mM β-glycerol phosphate, 50 μg/mL ascorbic acid-2-phosphate, 4 mM L-glutamine, 100 U/mL penicillin/streptomycin, and 10% FBS. Medium was renewed every two days.3 cells were seeded per well in a 24-well plate and maintained during 72 h in culture medium in order to achieve an hyperconfluent or confluent starting point, respectively. Then, cells were differentiated to adipocyte with the same procedure referred to for C3H10T1/2. For osteoblast differentiation, 3 × 105 hMSC-TERT cells were seeded, per well, in a 6-well plate. Cells were not seeded in a 24-well plate to avoid detachment during the last days of the differentiation process. The following day, osteoblastogenesis was initiated as described for C3H10T1/2. Bone marrow human mesenchymal stem cells were cultured in MesenPro RS Medium  supplemented with MesenPRO RS Growth Supplement . To induce their differentiation, 45 × 103 cells were seeded per well in a 24-well plate. The following day, two differentiation protocols were used: one according to manufacturer differentiation instructions  StemPro Osteogenesis Differentiation Kit , referred to as commercial media, and the other one, as indicated above for C3H10T1/2.hMSC-TERT cells were cultured in DMEM, 2 mM L-glutamine, 100 U/mL penicillin/streptomycin, and 10% inactivated FBS. To differentiate hMSC-TERT cells to adipocyte, 5 × 103 cells were seeded per well in a 24-well plate. The next day, adipocyte and osteoblast differentiation media were added as formerly referred to for C3H10T1/2.Human pre-osteoblast-like cells were cultured in DMEM-F12, 4 mM-glutamine, 200 U/mL penicillin/streptomycin, and 10% FBS. To differentiate them, 45 × 10Every differentiation process was performed for 21 days, as long as cells remained attached.After differentiation, cells were lysed with Tri-Reagent  and RNA was isolated with a E.Z.N.A. total RNA kit I , according to manufacturer’s instructions. The samples employed were those with a 260/280 nm ratio higher than 2 and a 260/230 nm ratio higher than 1.7. A High-Capacity cDNA Reverse Transcription Kit  was used to obtain cDNA from 50 ng of RNA. mRNA expression levels of the marker genes fatty acid binding protein (FABP4), enhancer-binding protein alpha (CEBPA), perilipin 2 (PLIN2), adiponectin (ADIPOQ), peroxisome proliferator activated receptor gamma (PPARγ), alkaline phosphatase (ALPL), osteopontin (SPP1), osteoactivin (GPNMB), runt-related transcription factor 2 (RUNX2), and bone morphogenic protein 2 (BMP2) were assessed by real-time quantitative PCR using BioRad  MasterMix and the primers  showed in w/v) was added for 10 min. After four washes with distilled water, photographs were acquired. Conversely, to evidence calcium deposits related to mineralisation, the cells were incubated with Alizarin Red  for 25 min, and then washed with distilled water. Photos were acquired after drying.Colorimetric techniques, such as Alizarin Red and Oil Red O, were performed on fixed cells. When differentiated to adipocytes, fixation was performed with formalin 4% for 1 h at room temperature, and when differentiated to osteoblast, fixed with 70% cold ethanol for 5 min at −20 °C. To dye lipid droplets in adipocyte differentiation, the cells were washed with distilled water and 60% isopropanol. Once the cells were dry, Oil Red O 21% (p  <  0.05 was considered significant.Data are presented as the mean ± standard error of the mean (SEM) for at least three independent experiments. Significant differences were calculated using a Student’s t test. All statistical analyses were performed using Prism software , and To study osteoblast and adipocyte differentiation balance in a reliable manner, commercial primary mesenchymal stem cells (MSCs) and media are often used. The alternative to this is the use of cell lines and lab-made media. These media are less expensive and have known compositions, but there is no consensus regarding their specific components and their optimization for different cell lines. Therefore, we aimed to investigate more affordable and reliable media with a known and consensual composition throughout the literature.Comprehensive analysis of a representative sample of articles A revealeThe accumulated variability caused by different reagents, concentrations, additives, cells, and differentiation cycles make it difficult to determine the most convenient media and protocol for proper adipocyte differentiation. In addition, after carefully reading the literature, we observed that certain specific additives, like IGF1, which could be easily incorporated into the culture media to improve the differentiation process ,17,18, wThus, in response to the literature analysis, we designed and tested a consensus protocol for adipocyte differentiation that pooled the best reagents and concentrations described in the literature. As shown in In contrast to adipogenic medium, thorough analysis of the literature regarding osteoblastogenesis differentiation medium revealed that the majority of media have the same constituents, comprising DMEM, low concentrations of dexamethasone, which has been referred as an osteoinducer when used in these conditions ; β-glyceAfter improving the composition of the media and by pulling the most frequent media components in the literature, we compared the lab-made media/consensus media (adipogenic (AD) or osteoblastogenic (OB) medium) with commercial media using standard differentiation protocols on commercial primary human MSCs.After 7 days of differentiation, lab-made AD medium induced hMSC adipogenesis. This was evidenced through the significant augment of FABP4, CEBPA, PLIN2, ADIPOQ, and PPARγ gene expressions. Commercial medium also induced the expression of the same marker genes, even though, due to its variability, only PPARγ and ADIPOQ induced expression were statistically significant compared to day 0 A. UnlikehMSC differentiated with lab-made OB medium for 7 days increased the expression of osteoblast marker genes SPP1, GPNMB, and BMP2. This expression was higher than commercial medium-induced expression, suggesting a more efficient differentiation process B. IntereOsteoblast-adipocyte equilibrium rupture has been associated with several pathologies and physiologic conditions, including ageing  and menoOsteoblastic-adipogenic equilibrium in primary MSCs cultures is the ideal set up. Nonetheless, these cultures are expensive and donor variability often hinders the identification of changes in the osteoblastic-adipogenic balance. Thus, two mesenchymal stem cell lines with a proven capacity to differentiate to both cell fates  were selected. The differentiation induction ability of the proposed media was studied for 21 days in these cell lines. We used mRNA expression of both cell fates’ marker genes (osteoblast and adipocyte) to confirm whether the differentiation processes of MSCs were successfully accomplished.In confluent cultures of C3H10T1/2 cells, the adipogenic lab-made medium significantly induced the adipogenic marker genes from day 7, peaking at day 21 A. ConsisAs in adipogenesis, osteoblastogenesis was studied in both mesenchymal stem cell lines using a lab-made consensus medium. Data obtained revealed that the C3H10T1/2 osteoblast marker genes  significantly augmented during the entire differentiation process A. AlizarIn this work, we aimed to establish a consensus and reliable method by which to study the balance between osteoblast and adipocyte differentiation processes. We developed lab-made MSCs differentiation media with known composition, obtained after pooling the most frequent and active reagents used in the literature for osteoblastogenic and adipogenic differentiation. In addition, we improved adipogenic media composition with IGF-1, a known pro-adipogenic factor. We tested them in C3H10T1/2 cells, in hMSC-TERT cells, and in commercial primary hMSCs. In these cell cultures, we proved that these media were reliable, affordable, and efficient, even more so than commercial ones. We confirmed their usefulness through the determination of the pro-adipogenic commitment of pre-osteoblasts-like cells from aged post-menopausal women.MSC adipogenic-osteoblastogenic differentiation balance disruption is a common feature across different bone alterations. An unbalance towards adipogenesis has been associated with bone fragility . ConversDespite adipogenic differentiation media composition having multiple similarities in the literature ,27,28, tTo test the lab-made media efficiency, several marker gene expressions were assessed. Regarding adipogenesis, FABP4 is present in mature adipocytes and has been also described as a marker for adipocyte progenitors , PLIN2 iTo test the validity of our lab-made media, we compared our osteoblastic and adipogenic media with commercial media on primary hMSCs. As revealed by their better induction of the expression of differentiation marker genes, we obtained a more affordable, reliable, and efficient differentiation media. Even though commercial media marker gene induction seemed greater, their results were associated with high variability. Notwithstanding, when referring to osteoblastogenic induction, commercial medium induction was null.When studying the comparison between adipogenic and osteoblastogenic differentiations, glucose concentration could be biased. In order to control this, both lab-made media comprised high-glucose concentrations. A concern about glucose concentrations in osteoblastogenic medium has been previously discussed . NeverthAccordingly, these lab-made media, used in combination with standard differentiation protocols, facilitated the detection of the pro-adipogenic commitment of osteoblast precursor cells obtained from aged women. Similarly, they also identified an impaired osteoblastic differentiation of precursor cells, which is consistent with their origin from postmenopausal aged women .Interestingly, these data not only showed the sensitivity of our differentiation method to changes in the osteoblast-adipocyte differentiation balance, but also confirmed that MSCs from aged donors are not an adequate cellular model to work with. Hence, it seems that the use of primary MSCs to study this balance should be limited to MSCs from healthy young donors. However, these cells are scarce and not affordable. As a result, in order to avoid the use of primary MSCs, we searched for an MSC cell line that could be differentiated both to osteoblast and adipocyte. We selected two cell lines, mouse C3H10T1/2 and human hMSC-TERT, optimized their differentiation protocols according to their proliferation rate, and compared their differentiation potential with our improved differentiation media. The number of MSCs lines is not as wide as expected. Among human cells, most of them are MSCs immortalized through TERT, and among mice cell lines, the highest number comes from C3H mice. Therefore, we selected two cell lines, mouse C3H10T1/2 and human hMSC-TERT, with proven capacity to differentiate to either osteoblast ,48,49,50Accordingly, we suggest that C3H10T1/2 is a better cellular model to study adipocyte-osteoblast differentiation equilibrium. Despite this, it is evident that using a mouse cell line could limit the validity of the data obtained. Nonetheless, we claimed that this cell line might reduce the use of primary hMSCs and that it could be a convenient first approach to study the adipocyte-osteoblast balance, prior to primary cultures from young donors.In this work, using optimized lab-made media, we provide and prove an easy, affordable, and efficient MSCs differentiation method to study the balance between adipogenesis and osteoblastogenesis. Moreover, we propose an alternative cellular model (C3H10T1/2 cell line) to primary MSCs from young donors that can be efficiently differentiated to osteoblast and adipocyte even better than commonly used human cell lines.The described composition of proadipogenic and proosteoblastogenic media in the literature was analyzed in this work. After optimization of these media, using lab-made media, we provided an easy, affordable, and efficient MSCs differentiation method to study the balance between adipogenesis and osteoblastogenesis. Moreover, we proposed an alternative cellular model (C3H10T1/2 cell line) to MSCs that can be efficiently differentiated to osteoblast and adipocyte, and which performed even better than commonly used human cell lines.


Background: Colon adenocarcinoma (COAD) is a common aggressive malignant tumor. Heterogeneity in tumorigenesis and therapy response leads to an unsatisfactory overall survival of colon adenocarcinoma patients. Our study aimed to identify tools for a better prediction of colon adenocarcinoma prognosis, bolstering the development of a better personalized treatment and management.Method: We used the least absolute shrinkage and selection operator (LASSO) Cox model to analyze the prognosis-related gene datasets from the Gene Expression Omnibus (GEO) database and verified them using The Cancer Genome Atlas (TCGA) database. The area under the curve (AUC) was calculated using the receiver operating characteristic (ROC) curve to evaluate the predictive ability of the risk score model. Gene Set Enrichment Analysis (GSEA) was used to identify the significantly enriched and depleted biological processes. The tumor immune dysfunction and exclusion (TIDE) algorithm was taken to explore the relationship between the risk score and immunotherapy. The observations collectively helped us construct a nomogram to predict prognosis. Finally, the correlation between drug sensitivity and prognostic gene sets was conducted based on the Cancer Therapeutics Response Portal (CTRP) analyses.Results: We constructed a scoring model to assess the significance of the prognosis risk-related gene signatures, which was relative to common tumor characteristics and tumor mutational burdens. Patients with a high-risk score had higher tumor stage and poor prognosis (p< 0.05). Moreover, the expressions of these genes were in correlation with changes in the tumor microenvironment (TME). The risk score is an independent prognostic factor for COAD (p< 0.05). The accuracy of the novel nomogram model with a risk score and TNM-stage prediction prognosis in the predicting prognosis was higher than that of the TNM stage. Further analysis showed that a high-risk score was associated with tumor immune rejection. Patients with a low-risk score have a better prognosis with chemotherapy than those with a high-risk score. Compared to patients in the high-risk group, patients in the low-risk group had a significant survival advantage after receiving chemotherapy. In addition, the prognostic gene sets aid the assessment of drug sensitivity.Conclusion: This study establishes a new prognostic model to better predict the clinical outcome and TME characteristics of colon adenocarcinoma. We believe, our model also serves as a useful clinical tool to strengthen the functioning of chemotherapy, immunotherapy, and targeted drugs. Colon adenocarcinoma (COAD) is the main pathological type of colon cancer and the second leading cause of cancer deaths worldwide . Approxivia surgery, causing advanced COAD patients to receive further treatment with adjuvant chemotherapy and radiotherapy , and transcriptome profiling (TCGA-COAD-RNAseq) and single-nucleotide variant (TCGA-COAD-SNV) datasets from The Cancer Genome Atlas (TCGA) database (https://www.tcga.org). Single-nucleotide variant (SNV) datasets from TCGA. TCGA-COAD-RNAseq contains 515 samples, including 473 tumor tissue samples and 41 normal solid tissue samples. TCGA-COAD-SNV contains 896 samples, including 448 tumor tissue samples and 448 normal samples. GSE39582 contains 585 samples, containing 566 tumor tissue samples and 19 normal tissue samples. We carried out quantile normalization for expression profiles with the preprocessCore package. Then, we carried out survival analysis and univariate Cox regression analysis for every gene in GSE39582 to obtain the overlap genes as prognostic genes  with survival packages. Using the Human Protein Atlas (HPA) database (https://www.proteinatlas.org), by immunohistochemical (IHC) staining, we tested normal intestinal tissue and performed prognosis in COAD organization gene expression differences in the protein level (The expression profiles were downloaded from two platforms: the GSE39582 dataset from the Gene Expression Omnibus (GEO) database  on the foundation of the risk score model. The risk-scoring model obtained from GSE39582 was validated by TCGA data via Kaplan–Meier survival analysis and ROC curves. Taking the median risk score as the cut-off point, the survival analysis was carried out.We took GSE39582 as the training dataset to construct a risk-scoring model based on these prognostic genes; the robust prognosis risk-related genes were selected from all prognostic genes http://www.gsea-msigdb.org/gsea/index.jsp). Moreover, 14 gene sets  were obtained from CancerSEA (http://biocc.hrbmu.edu.cn/CancerSEA/). We performed a gene set variation analysis about immune signatures and tumor signatures and analyzed the relationship between the risk score and GSVA score. Also, we analyzed immune infiltration with different tools to know about the status of immune filtration in different risk groups. Combining the clinical information, we explored the difference between high-risk and low-risk groups in the TNM stage and drug reaction. Apart from these, we combined the TCGA-COAD-RNAseq dataset and the TCGA-COAD-SNV dataset to analyze the genetic background behind the two groups with the maftools package.To analyze the biological process based on the risk score group, we carried out Gene Set Enrichment Analysis (GSEA) with the clusterProfiler package. Seven gene sets  were obtained from GSEA  algorithm was used to explore the relationship between the risk score and immunotherapy. We also analyzed the relationship between the risk score and chemotherapy. Moreover, we combined the risk score and TNM stage to construct a novel nomogram model with the rms package to improve the model value in predicting prognosis.50 values. An FDR-adjusted p-value was used in all the analyses  and merged the mRNA expression and drug sensitivity data. Pearson correlation analysis was performed to obtain the correlation between mRNA expression and drug ICanalyses .ATOH1, C4orf47, CPA4, DNASE1L1, ERFE, F2RL2, FBXO39, FZD3, HPCAL4, ICOS, INHBB, ITLN1, KIF7, KLHL26, LINC00629, LRRC29, MMP12, MYL6B, NPM3, PCBD1, PLEC, POLR2F, POU5F1P4, PRRX2, PTPRU, PTTG3P, RNF112, SERPINB7, SLCO1A2, TH, TMEM39B, TRDV3, and ZDHHC1. The detailed characteristics of these prognostic genes in this study are given in ATOH1, HPCAL4, ITLN1, POLR2F, RNF112, SERPINB7, SLCO1A2, TH, and TMEM39B were significantly downregulated in COAD tissues (p < 0.05) compared to those in normal tissues. C4orf47, CPA4, DNASE1L1, ERFE, F2RL2, FBXO39, FZD3, INHBB, KLHL26, LRRC29, MMP12, MYL6B, NPM3, PCBD1, POU5F1P4, PRRX2, PTPRU, PTTG3P, TRDV3, and ZDHHC1 were significantly upregulated in COAD samples (p < 0.05) compared to those in normal tissues. Here, we also used the Human Protein Atlas (HPA) database to validate the expression of these prognostic genes at the protein level . Moreover, the heatmap in p < 0.05). Also, we carried out a GSEA to analyze the enriched biological processes based on the risk score group. The GSEA showed enrichment of the GO biological processes like cell cycle , DNA replication , ECM–receptor interaction , neutrophil extracellular trap formation , and necroptosis  in TCGA dataset when comparing the high-risk group with the low-risk group (p = 1E-10), DNA replication , and ECM–receptor interactions   . The ris= 1E-10) . A p-valTTN was the most significantly mutated gene in the high-risk group, while APC was the most significantly mutated gene in the low-risk group. SYNE1, MUC16, OBSCN, and DNAH5 mutations almost co-occurred with TTN mutations (p < 0.01), while ZFHX4 co-mutated with OBSCN, FAT4, MUC16, and DNAH5 (p < 0.01). Moreover, TP53 and MUC16 mutations were almost mutually exclusive in the low-risk group (p < 0.01), which had a higher tumor mutational burden (TMB) than the low-risk group (p = 0.041) (BRAF mutations showed higher scores in the prognostic gene sets than the wild-type mutations (p = 0.011)  . In addi= 0.011) . Moreoveecreased . Moreoveecreased . The numecreased . When thICOS, TRDV3, PTPRU, PCBD1, NPM3, LINC00629, KLHL26, ITLN1, INHBB, HPCAL4, ERFE, DNASE1L1, and C4orf47 were associated with a disease-free interval (DFI). Patients may change the disease-free survival (DFS) when PRRX2, ZDHHC1, PTPRU, and LRRC29 have copy number variations. In addition, CNVs in PRRX2 and ICOS change the OS of patients. CNVs in ICOS, ZDHHC1, TMEM39B, PTPRU, POLR2F, LRRC29, KLHL26, HPCAL4, F2RL2, and ATOH1 genes were associated with a progression-free survival.Furthermore, differences in prognostic gene sets regarding tumor copy number variation and patient prognosis were also investigated. CNVs in p = 0.007), as determined by TCGA analysis (p< 0.001) (p = 0.0022) (p < 0.05) , more tumor nodes (N), and higher tumor node metastasis (M) stages . In TCGAanalysis . Moreove< 0.001) . Adenoma 0.0022) . The ris < 0.05) . TogetheFurthermore, a nomogram integrating the genetic risk score (high risk vs. low risk) and TNM stage was constructed to provide quantitative methods to predict a patient’s probability of OS to the clinician . The totp < 0.0001), as the same in patients without adjuvant chemotherapy  (p = 0.028) (p < 0.05)  ( 0.0001) . Patient= 0.028) . Subsequ < 0.05) . These rPOLR2F, KLHL26, ICOS, ITLN1, HPCAL4, NPM3, TMEM39B, TH, SLCO1A2, FZD3, and ATOH1 genes were resistant to drugs. The high expression of PLEC, CPA4, SERPINB7, DNASE1L1, KIF7, C4orf47, F2RL2, and PCBD1 genes with an elevated expression was more sensitive to drugs. PLEC, CPA4, SERPINB7, DNASE1L1, C4orf47, KIF7, and F2RL2 were prognostic genes positively associated with the classical antitumor drug fluorouracil. In contrast, ATOH1, FZD3, SLCO1A2, TH, TMEM39B, and NPM3 were prognostic genes negatively related to fluorouracil (p <0.001). The expressions of PLEC, CPA4, and SERPINB7 positively correlated with belinostat sensitivity (p <0.001). The expressions of FZD3, TMEM39B, and NPM3 negatively correlated with narciclasine sensitivity (p <0.001). The higher the expression of PTPRU, the lower the drug sensitivity of afatinib and PD153035 is (p <0.001). Thus, the prognostic gene set is a useful clinical tool for guiding drug use.We analyzed the correlation between drug sensitivity and predictive gene sets to further explore the value of prognostic gene sets in a clinical treatment. Most of the genes in the prognostic gene set had correlations between gene expression levels and drug sensitivity . The higCOAD is a common aggressive malignant tumor, with a high mortality rate worldwide . The etiIn this study, we used a bioinformatically developed and validated novel prognostic gene set that was significantly associated with OS in COAD patients. A risk score model was also constructed to divide COAD patients into high- and low-risk groups. The Kaplan–Meier survival analysis with the log-rank test and ROC was used to establish the prognostic ability of the model. More importantly, by establishing a validation set, we further verified the reliability of this risk score model.Moreover, the novel prognostic gene set is closely correlated with pro-tumorigenic signatures, including the cell cycle, DNA damage, DNA repair, angiogenesis, metastasis, proliferation, differentiation, stemness, apoptosis, hypoxia, EMT, invasion, inflammation, and quiescence. Many studies showed that these hallmarks of cancer and the immune microenvironment dictate the disease prognosis in COAD. Furthermore, the correlation between the risk score model and gene mutation was also illustrated. The gene mutation probability was significantly higher in a high-risk group than in a low-risk group, which to the best of our knowledge, substantially contributes to cancer progression. Further investigations are necessary to determine the potential functional mechanisms underlying these prognosis risk-related genes. Collectively, our risk score model might be reliable in predicting the prognosis of COAD based on these results.COAD patients treated with 5-fluorouracil, oxaliplatin, irinotecan, and folinic acid  have a better objective response and survival outcome . 5-FluorCAF promotes cancer progression by inducing an immunosuppressive tumor microenvironment, rendering resistance to immunotherapy  for COAD cancer . Li et aFurthermore, we generated a nomogram to quantify the risk assessment and survival probability. Compared to TNM, the nomogram exhibited the highest accuracy and discrimination in OS prediction. In addition, the new prognostic gene set can guide the clinical application of chemotherapy, immunotherapy, and targeted drugs. Therefore, our predictive model may help avoid unnecessary overtreatment of indolent disease and select the best management strategy. However, there are still some limitations to this study. Pure bioinformatics analysis is the main drawback of this study. Second, the interaction between genes in the prognostic gene set should be investigated better to understand the molecular mechanism of COAD occurrence and progression.Our study profiled a novel risk score model based on 33 genes for predicting the overall survival in COAD patients. More importantly, the risk score model is significantly associated with the unfavorable clinical outcome of COAD and might monitor its development to provide more effective personalized therapeutic decision-making. A nomogram model might aid in identifying high-risk COAD patients and selecting appropriate clinical follow-up plans accordingly.