PNC, YUC, and IAA that regulate auxin synthesis and metabolism, CYCD and CYCU that regulate cell differentiation and proliferation, and GASA that regulates seed size and pod elongation via gibberellin pathway. It is plausible that MPK3 promotes integument cell division and regulates mitotic activity through phosphorylation, and the interactions between these genes form a network of molecular pathways that affect peanut pod size. Furthermore, two variant sites, GCP4 and RPPL1, were identified which are stable at the QTL interval for seed size attributes and function in plant cell tissue microtubule nucleation. These findings may facilitate the identification of candidate genes that regulate pod size and impart yield improvement in cultivated peanuts.Pod size is one of the most important agronomic features of peanuts, which directly affects peanut yield. Studies on the regulation mechanism underpinning pod size in cultivated peanuts remain hitherto limited compared to model plant systems. To better understand the molecular elements that underpin peanut pod development, we conducted a comprehensive analysis of chronological transcriptomics during pod development in four peanut accessions with similar genetic backgrounds, but varying pod sizes. Several plant transcription factors, phytohormones, and the mitogen-activated protein kinase (MAPK) signaling pathways were significantly enriched among differentially expressed genes (DEGs) at five consecutive developmental stages, revealing an eclectic range of candidate genes, including  Arachis hypogaea L.) is one of the most important cash crops, providing vegetable oil and proteins to consumers in many parts of the world  than in wild soybean (Glycine soja)  . Severalne soja) . For exane soja) , and MYBne soja) . In addine soja) , ubiquitne soja) , and phyne soja) , have bespikelet .In this study, the developing pods of two peanut varieties with contrasting pod sizes, together with those of their derivative recombinant inbred lines (RIL), were used for transcriptome analysis. The in-depth transcriptome dynamics and transcriptional network that are associated with pod development were explored, in order to identify the molecular regulatory elements and functional genes that are differentially expressed between the genotypes with large or small pod sizes during seed development. The overlapping of known QTLs and the differentially expressed genes (DEGs) with respect to pod size and the identification of single nucleotide polymorphisms (SNPs) allowed for the selection of candidate genes that regulate pod size. This study may shed more light on the underlying molecular mechanisms of peanut pod development, providing a set of pod size-associated key genes that could be explored and utilized in future breeding programs and genetic modifications for peanut yield improvement.12) of 329 lines was derived from a cross between YH15  and W1202  by single-seed descent (SSD) (A RIL population (Fnt (SSD) . YH15 isP < 0.05.In this study, the cell developmental morphology of seed-35, seed-45, shell-35, and shell-45 developmental stages in YH15, W1202, S181523, and S181517 were observed. The plant samples were first fixed in FAA fixative, dehydrated, and embedded in paraffin. Then they were dewaxed, treated with toluidine blue, and sealed with neutral gum. Finally, the process was observed under an optical microscope, and the morphological characteristics of each period were recorded and photographed. The image analysis software ImageJ was used to quantify and graphed in GraphPad Prism 8. The significance level was set to PRJNA847769).Total RNA was extracted with TaKaRa MiniBEST Plant RNA Extraction Kit . RNA quality was checked using a 2100 Bioanalyzer  to ensure all the RNA samples meet the following criteria: OD260/OD280 ≥ 1.8, the 28S/18S ratio closer to 2. RNA integrity was determined by 1.5% agarose gel electrophoresis. The high quality RNA samples were then fractionated, purified, screened, and finally used to construct cDNA libraries for sequencing on the Illumina Hiseq platform in Beijing Genome Institute . The RNA-seq raw data have been submitted to the NCBI database , Swissprot , KEGG (Kyoto Encyclopedia of Genes and Genomes), and GO (Gene Ontology). For TF annotation, EMBOSS function getorf was used to find gene open reading frames (ORFs) that were aligned to identify the TF protein domains using Hmmsearch , and fin6 . The E-vP-value < 0.05 and | log2 (foldchange) | > 1. The DEGs of YH15 vs. W1202 and S181523 vs. S181517 at each developmental stage were taken to intersect and recognized as the final DEGs since S181523 and S181517 share the same genetic background as their parents YH15 and W1202. The DEGs were functionally identified based on the GO/KEGG annotation and categorization, and the Phyper function of R software7 was used for enrichment analysis to determine P-value, which was subsequently corrected by the False Discovery Rate (FDR). Significant enrichment was defined as functional when P-value was less than 0.05. STRING . ADH3 was chosen as a reference gene and at least three technical replicates were performed for all the genes in each pool  using the reserved RNA samples. First, RNA was reverse transcribed to 1st strand cDNA using the PrimeScript™ II 1st Strand cDNA Synthesis Kit (TaKaRa). Primers were designed using Oligo7 Primer Analysis Software  and their gene specificity was evaluated using Primer-BLAST.ach pool . Differet method  and corrSignificant variations in the pod sizes and seed sizes were found between S181523 and S181517, and between their parents, YH15 and W1202 as well. As shown in A total of 2.29 billion high-quality clean reads with Q20 ≥ 96.04%, GC ≥ 43.50% were generated for all four genotypes, including YH15, W1202, S181523, and S181517, with the mapping rate of 76.57∼98.43% . Due to A total of 67,005 expressed genes were detected, which gradually declined as the seed developed toward maturity. For example, in the pod tissue differentiation stage (Pod-25), the expressed genes were significantly more abundant than in the middle and late pod maturation stages. However, there was no discernible variation in the number of expressed genes between genotypes at each developmental stage . PCA, coFor functional annotation, all the sequences were blasted with the sequences in Nr, Swissprot, Eggnog, GO, and KEGG public databases. As a result, a total of 21,919 genes were commonly annotated by all the five above databases, accounting for 32.67% of the expressed genes, and 66,465 genes were annotated in at least one database, accounting for 99.07% of the expressed genes . Genes aTo identify candidate genes implicated in pod size, DEGs were analyzed between YH15 and W1202, and S181523 and S181517 at five developmental stages including Pod-25, Shell-35, Seed-35, Shell-45, and Seed-45. For YH15 and W1202, a total of 16,875 DEGs were identified at five pod developmental stages, including 5,032 DEGs at the Shell-35 stage, followed by 4,238, 3,774, 2,986, and 2,731, in Shell-45, Seed-45, Pod-25, and Seed-35, respectively . The higGene Ontology enrichment analysis was performed on the intersected DEGs of 1,788, 763, 827, 325, and 457 in Shell-35, Shell-45, Pod-25, Seed-35, and Seed-45, respectively . During In KEGG enrichment analysis, plant-pathogen interaction (ko04626), alpha-Linolenic acid metabolism (ko00592), metabolic pathways (ko01100), arginine and proline metabolism (ko00330), and diterpenoid biosynthesis (ko00904) pathways were most significantly enriched in Pod-25, Seed-35, Shell-35, Seed-45, and Shell-45 period, respectively . RegardiIn addition, PPIs of DEGs were analyzed using the search tool for the retrieval of the interacting genes database. The DEGs identified within the GO terms of plant organ development, signal transduction, and hormone metabolism were subjected to PPIs analysis based on the Swissprot annotation results . As a reWRKY and MYB DEGs were screened out in Pod-25 period, such as arahy.2613M1 (WRKY), arahy.5V6QPU (WRKY), arahy.A8EX91 (WRKY), arahy.ALL85F (WRKY), arahy.46VNPQ (MYB), arahy.D1YJVB (MYB), and all the WRKY family were down-regulated at this stage , arahy.2KRW4X (bHLH), arahy.02IZMF (bHLH), and arahy.KF94VL (MYB)  , 6.arahy.VSM3NT (PNC1), arahy.2CFU61 (PNC2), arahy.WRP4Q5 (YUC2), arahy.6PM354 (YUC4), arahy.5PTI62 (IAA14), and arahy.IPD4BK (IAA31), are related to auxin synthesis (arahy.45C8U1 (LAX3) (arahy.RFF6WQ (PER53), arahy.ID0B6N (PER3) are associated with auxin metabolism (arahy.RRC0V1 (CYCU2-1), arahy.0ZFN4L (CYCU4-1), and arahy.69JDQK (CYCD3-2). Furthermore, DEGs were also enriched to multiple GASA gene families that have been reported to play important roles in gibberellin-regulated pathways (arahy.F1TUFL (GASA4), arahy.VJ6NUA (GASA6), and arahy.V4B8PK (GASA10). As aforementioned, GASA expression is regulated by BR, IAA, and ABA, indicative of the complexity and intertwined regulatory relationship of phytohormone related pathways in regulating peanut fruit development.It has been recognized that the phytohormones, including auxin (such as IAA), brassinolide (BR), cytokinin (CTK), gibberellin (GA), and abscisic acid (ABA), are critical for pod size regulation . Severalynthesis , arahy.41 (LAX3)  are assotabolism . CTK propathways , such asAs a part of the MAPKKK-MAPKK-MAPK cascade, MAPKs play a crucial role in plant growth and development. In light of the KEGG enrichment analysis, the MAPK signaling pathway was significantly enriched in the Pod-25, Shell-35, and Seed-45 phases. A total of 65 DEGs were found to be relative to the MAPK pathway, of which arahy.RQ7QZ9 (MPK3) plays a redundant role with MAPK6 in biological function, arahy.ISVC9H (MAPKKK17), arahy.EJ82H8 (WRKY33), arahy.9C1IJD (WRKY40), and arahy.77CZVH (WRKY70) have been reported to interact with MPK3 and function in concert in the MAPK signaling pathway .YH15, W1202, S181523, and S181527, a total of 567,600 variant sites were acquired on the genome-wide scale after indexing the reference genome, two sequence alignments of the transcriptome sequencing data, and variant identification using GATK software, and 445,360 variant sites were obtained after filtering (arahy.D5VDWJ (VariationPos: 99575149), arahy.2IZ2Y4 , arahy.TC9I43 (VariationPos: 99122494), and arahy.9HBE4Z (VariationPos: 99148219), which were annotated by the Swissprot database, have been reported to be relevant to plant growth and development , arahy.ISVC9H (MAPKKK17), arahy.2KRW4X (MYC4), arahy.EJ82H8 (WRKY33), arahy.EBLC22 (ARF2a), arahy.G65HTE (DIM), arahy.YH7XEC (GAPC), arahy.N7MBXE (PYL5), and arahy.V4B8PK (GASA10). Despite small variations in expression levels, the qRT-PCR results of almost all the nine genes showed expression patterns that are consistent with RNA-seq analysis, validating the results obtained by RNA-seq analysis.In order to validate the expression patterns of DEGs obtained by RNA-Seq analysis, the expression levels of nine selected DEGs in the four varieties at five developmental stages were analyzed by qRT-PCR and displayed by two developmental types, including Pod-25 to Shell-45 and Pod-25 to Seed-45 . These DSeed size is an important agronomic trait of crops, which plays a crucial role in determining seed yield . DicotylWRKY is one of the largest transcriptional regulatory gene families in plants  exhibited gradual decline along with the peanut seed development over time, and also it showed relatively lower expression level in large-grain genotypes than in small-grain genotypes at a given developmental stage, suggesting a negative regulatory role during pod development stages. Likewise, ARF, which is known to be involved in auxin signal transduction, plays an important role in regulating the expression of auxin-responsive genes (ARF2A (arahy.EBLC22) that encodes an auxin signaling component implicated its potential role in fruit ripening and chloroplast formation  expression decreased along with pod development, with significantly lower expression in large-pod genotype than small-pod genotype at each developmental stage, showing a clear negative regulation on pod development  and PNC2 (arahy.2CFU61) expression played a greater role in the shell than that in the seed. IAA14 (arahy.5PTI62), IAA31 (arahy.IPD4BK), auxin-responsive proteins, SAUR50 (arahy.8B51UP), and auxin-responsive factor play negative regulation functions in auxin production and signal transduction (IAA14 (arahy.5PTI62) and IAA31 (arahy.IPD4BK) gene expression in the large-grained genotype was lower than that in the small-grained genotype, in the Shell-35 and Shell-45 period of shell development, but the difference was not conspicuous in other periods , which encodes an auxin influx protein and has positive feedback on auxin transport , CYCD3-1 (arahy.RN6XTC), CYCD3-2 (arahy.69JDQK), and CYCD6-1 (arahy.L9YRC6) as D-type cyclins (CYCD) are overtly differentially expressed concurrently with peanut fruit development. Interestingly, the cell area of the seed increased and was more closely aligned with each other from 35 DAF to 45 DAF. Observing the longitudinal-section of the seed, the cell count of large-grain was significantly higher than that of small-grain peanuts. The above CYCD/CYCU gene family genes were expressed in higher amounts in the early and middle stages, and much higher than in the middle and late shells, which is consistent with the result that cytokinins regulate cell numbers during seed development  have been reported to play a prominent role in seed development by regulating the number of cells during seed development . CYCD1-1elopment . Notablylignment . IRX1 apelopment . Their pGASA) expression is regulated by GA and known to be involved in plant hormone signal transduction that affects seed size  and GASA10 (arahy.VJ6NUA) exhibited substantially higher gene expression in the large-pod genotypes at Pod-25 and Seed-35 than in small-pod genotypes, suggestive of their positive regulatory roles in pod development in promoting pod enlargement , WRKY33 (arahy.EJ82H8), WRKY40 (arahy.9C1IJD), and NPR1 (arahy.KS67M3) were identified as key DEGs derived by KEGG enrichment, suggesting that they may occupy an important place in the protein-protein interaction networks that govern peanut pod/seed development. It has been reported that MPK3 modulates fiber initiation and elongation in cotton  exhibited a lower expression level in the large-pod peanuts than in small-pod peanuts. GCP4 is an important component of ɣ-tubulin in microtubule nucleation in plant cells, transgenic overexpression of which gives rise to dwarf phenotypes and smaller organs  and GCP4 (arahy.D5VDWJ) differed significantly between large-pod and small-pod peanut genotypes.Variant site mining has seldomly been conducted in prior transcriptome investigations, which can not only explore the molecular mechanisms that underpin the traits of interest by virtue of the expression profiles of the candidate genes but also help to narrow down or even pinpoint the key genes by leveraging the segregation of relevant phenotypes among the genotypes with similar genetic backgrounds. In the A02 chromosomal interval relevant to seed size, four genes were found to have stable genotyping, among which r organs , which iGiven that these genes are implicated in resistance to Fusarium wilt and downy mildew in plants , are pothttps://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA847769 or through the SRA accession number: PRJNA847769.The data can be accessed through the following link: YW, ZS, and XZ conceived the study and drafted the manuscript. YW, FQ, WD, BH, and ZZ discussed the writing plan. MT, JW, RZ, XWa, and XWu performed the experiments. XS and HL obtained the experimental materials. YW analyzed the data. All authors have read, reviewed, and approved the submitted version.


MYD88 gene has a physiological role in the innate immune system. Somatic mutations in MYD88, including the most common L265P, have been associated with the development of certain types of lymphoma. L265PMYD88 is present in more than 90% of patients with Waldenström’s macroglobulinemia (WM) and IgM monoclonal gammopathy of undetermined significance (IgM-MGUS). The absence of MYD88 mutations in WM patients has been associated with a higher risk of transformation into aggressive lymphoma, resistance to certain therapies (BTK inhibitors), and shorter overall survival. The MyD88 signaling pathway has also been used as a target for specific therapies. In this review, we summarize the clinical applications of MYD88 testing in the diagnosis, prognosis, follow-up, and treatment of patients. Although L265PMYD88 is not specific to WM, few tumors present a single causative mutation in a recurrent position. The role of the oncogene in the pathogenesis of WM is still unclear, especially considering that the mutation can be found in normal B cells of patients, as recently reported. This may have important implications for early lymphoma detection in healthy elderly individuals and for the treatment response assessment based on a L265PMYD88 analysis.The  MYD88) gene encodes a cytosolic adaptor protein that plays a central role in the innate and adaptive immune responses mediated by the interleukin (IL) 1 receptor (IL-1R) family and toll-like receptors (TLRs) [The myeloid differentiation primary response 88 (s (TLRs) , a toll-s (TLRs) , and an s (TLRs) ,5, leadis (TLRs) ,7,8,9,10MYD88 can also act as an oncogene associated with lymphoma development, and somatic mutations have been identified in numerous patients. The most recurrent, a single nucleotide change from T to C, resulting in a change from leucine to proline at position 265 (L265P)  , is presIn normal physiology, MyD88 is required for full function of the innate immune response as a signaling adaptor in the canonical NF-κB pathway . Upon liL265PMYD88 mutation was a turning point for WM, providing valuable insights into the signaling pathways involved in this malignancy [In the context of cancer and, particularly, in hematology, one can hardly speak about MyD88 without thinking of WM. WM is an indolent hematological malignancy that makes up approximately 1% of non-Hodgkin’s lymphomas (NHL). Patients present both abnormal cells in their bone marrow biopsy and abnormal IgM macroglobulin in their blood . Such aslignancy .L265PMYD88 include increased NF-κB activity; Janus kinase (JAK)/signal transducer and activator of transcription (STAT) signaling; and the production of proinflammatory cytokines, such as IL-6, IL-10 and interferon (IFN)-β, as well as the enhanced growth and survival of lymphoma cells [The L265P mutation makes the TIR domain of MyD88 more activate compared to the wild-type protein, which increases the formation of the ‘Myddosome’ complex and the downstream signaling ,24,25. Tma cells ,26.MYD88 mutations. In normal conditions, the BCR signaling activates NF-κB, phosphoinositide 3-kinase (PI3K), MAPK, and nuclear factor of activated T cells (NFAT) pathways. Bruton’s tyrosine kinase (BTK), a protein of the BCR signaling cascade, has been shown to preferentially form complexes with mutated MyD88 and not with wild-type MyD88 in WM cells. L265PMYD88 triggers BTK and downstream NF-kB signaling independent of IRAK1 and -4 [L265PMYD88 is common in patients with mutations in BCR-associated protein CD79B, and these patients benefit most from the treatment with BTK inhibitors [In addition to the NF-κB pathway, the B-cell receptor (BCR) pathway also plays an important role in the oncogenesis of B-NHL with hibitors ,28,29. Thibitors .There is also evidence of cross-communication with the BCR pathway triggered by mutated MyD88 through the activation of the BCR-signaling component SYK (spleen tyrosine kinase) in WM and activated B-cell diffuse large B-cell lymphoma (ABC DLBCL) cells . SYK canTo complete the picture, NF-κB activity not only activates B-cell proliferation and survival-related genes but also results in IL-6 and Il-10 autocrine signaling, which, via JAK/STAT or HCK, increases the transcription of genes involved in several signaling cascades, such as PI3K/AKT/mTOR, JAK/STAT, or NF-κB .MYD88 alteration has been found at high frequencies in cutaneous DLBCL (69%), primary testicular lymphoma (68%), primary central nervous system lymphoma (38–86%), or ABC DLBCL (30%), indicating its role in the pathogenesis of lymphoid neoplasia [MYD88 mutations. These patients present a different genomic landscape, including other NF-κB-activating mutations, epigenomic dysregulation, and impaired DNA damage repair, which leads to inferior outcomes compared to those with MyD88 mutations—specifically, a shorter overall survival (OS) and a higher risk of histological transformation [MYD88 wild-type genotype to be an entirely separate entity, proposing the presence of the MyD88 mutation as a WM-defining feature [L265PMYD88 in IgM-MGUS suggests that it is an early oncogenic factor, but most IgM-MGUS patients never progress to WM or other lymphoproliferative disorders, so this mutation cannot be considered a unique pathogenic factor in WM [MYD88 mutations are not sufficient for the development of WM or other MyD88-driven lymphomas, and additional alterations in tumor cells and/or the host response are needed [The eoplasia ,37,38,39ormation ,41,42,43 feature ,45. Few or in WM ,47. Thise needed ,49.L265PMYD88 mutation has become the hallmark of WM, as it is present in more than 90% of patients. Therefore, numerous clinical applications have emerged concerning its diagnosis, management, and treatment.The L265PMYD88 is absent or less frequent in other related B-cell (including IgM-secreting) disorders, such as mucosa-associated lymphoid tissue lymphoma, splenic marginal zone lymphoma, nodal marginal zone lymphoma, IgM-secreting multiple myeloma, and chronic lymphocytic leukemia, therefore representing a useful tool for discriminating WM from other related B-cell disorders at diagnosis , and hairy cell leukemia (V600EBRAF) [Currently, the diagnosis of WM is contingent on demonstrating a lymphoplasmacytic cell infiltrate, usually by a bone marrow biopsy, which has several disadvantages, such as patient discomfort, unforeseen complications, high cost, and delay in the diagnosis. In contrast to WM and IgM-MGUS (>90%) ,14,15,46iagnosis  [23,24,5IgM-MGUS ,59,60,61AFV600E) ,63,64.L265PMYD88 detection in the cerebrospinal fluid by dPCR is also useful to diagnose Bing-Neel syndrome and central nervous system (CNS) lymphomas as an alternative to a cerebral or retinal biopsy [L265PMYD88 in these diseases makes this mutation a perfect candidate for liquid biopsy to enter clinical practice [l biopsy , since, l biopsy . Furtherpractice .L265PMYD88 as a predictive biomarker of the therapy response in the bone marrow and peripheral blood compartments [L265PMYD88 testing has been demonstrated to be more useful than serum IgM in estimating the underlying disease burden, especially with agents affecting the serum IgM levels, either by inducing an IgM flare or by blocking IgM secretion, such as rituximab, bortezomib, everolimus, and ibrutinib [L265PMYD88 testing to estimate the underlying disease burden in patients undergoing those treatments could help guide the clinical management and avoid the repetition of bone marrow biopsies to clarify IgM discordance [L265PMYD88 in the response assessment still needs to be validated in a larger series of treated WM patients and, ideally, across multiple therapeutic regimens. In addition to peripheral blood samples, circulating tumor DNA may represent another attractive, noninvasive alternative to bone marrow [Although minimal residual disease (MRD) monitoring in certain lymphomas, such as follicular lymphoma, has been gradually established, it has only started to be explored in WM . Severalartments ,15,57. Tbrutinib . Increase marrow .L265PMYD88 was associated with a higher risk of disease progression in IgM-MGUS and asymptomatic WM [L265PMYD88 would not represent a transformation event [MYD88 status should be included in the initial work-up of all patients with IgM-MGUS/asymptomatic WM to help clarify whether mutated patients show a higher risk of evolution to WM. Furthermore, patients with wild-type MYD88 should be followed closely because of their higher risk of histological transformation and development of therapy-related myelodysplastic syndrome [Data regarding the prognostic value of MyD88 mutation are quite controversial. The initial studies showed that matic WM ,13,70,71matic WM ,73. Theron event . Therefosyndrome ,43,44,74MYD88 mutation on the disease outcome is also unclear. Although it is fairly well-established that the wild-type genotype exhibits an increased risk of death  [L265PMYD88 and wild-typeMYD88 patient populations [The impact of d MYD88) ,75, otheulations ,76.MYD88 status in other diseases has not been fully elucidated either. This is very well-exemplified by CLL, where studies have shown strong contradictions, both in the frequency of MYD88 mutations (from 1.5 to 10% of patients) [L265PMYD88 on patient survival seems evident, but it is still considered a matter of debate [The prognostic significance of the atients) ,77 and iatients)  to neutratients)  or unfavatients) . In ABC f debate ,81,82,83L265PMYD88 may help identify those patients who are more suitable for treatments targeting the MyD88-driven pathways, as described below.There are multiple treatment options for WM patients, such as chemotherapy, monoclonal antibodies, proteasome inhibitors, and BTK inhibitors. The choice for therapy should consider the clinical presentation, comorbidities, and preferences. However, there is increasing evidence that the genomic profile may provide insightful information for treatment selection. Thus, the detection of L265P-CXCR4wild-typeMYD88 (97%) and L265P-CXCR4WHIMMYD88 (68%) compared to patients with the MYD88 wild-type genotype (0%) [L265P-CXCR4wild-typeMYD88 patients [wild-typeMYD88 WM patients, including 27% of the very good partial responses and 50% of the major responses [L265P-CXCR4WHIMMYD88 patients (59%) compared to wild-typeMYD88 (25%) [Thanks to the discovery of the MyD88 mutation in WM, BTK inhibitors have become an important treatment option. Much of their efficacy is due to the presence of this alteration, which has been shown to serve as a predictor of the response in patients treated with a BTK inhibitor-based regimen. In previously treated WM, the major response rate to ibrutinib therapy was found to be substantially higher for ype (0%) ,84,85. Ipatients . Neverthpatients . Zanubrupe (25%) . Acalabrpe (25%) .® Guidelines for WM (version 2.2022) include L265PMYD88 testing of the bone marrow and a genomic-based treatment approach to symptomatic treatment-naïve and relapsed or refractory WM [wild-typeMYD88 patients [The National Comprehensive Cancer Networkctory WM . Concordpatients .MYD88/CD79B double mutant shows an intense sensitivity to BTK inhibitors, while patients with mutated MyD88 but without BCR mutations  are not responsive [In ABC DLBCL, the sponsive ,92. The sponsive . In WM, sponsive ,94.L265PMYD88 is also significant given the interest in therapies targeting the components of pathways activated by this mutation  that are able to recognize L265P-containing neoantigens and elicit human leukocyte antigen (HLA) class I-restricted cytotoxic T-cell responses (when presented by HLA-B*07 and -B*15), supporting the potential for TCR-based immunotherapy. The peptide-specific cytolytic activity of CD8+ T cells was not observed when wild-type MyD88 peptides were presented [L265PMYD88-specific T cells in the tumor niche, identify specific major histocompatibility complex haplotypes for TCRs that could be used for immunotherapy, and quantify the extent to which mutant MyD88 is a target of specific immunity [A novel personalized therapeutic strategy are resented ,117. Nevimmunity ,119.Finally, chimeric antigen receptor (CAR) T cells have provided a new opportunity to specifically exploit T-cell-intrinsic TLR functions, i.e., activating TLR signaling in tumor-recognizing T cells ,122,123.L265PMYD88 mutation is a disease-defining genetic alteration of WM (95–97%) and IgM-MGUS (90%) that can be used for the diagnosis and monitoring of the disease [The  disease ,13,14,15 disease ,49. It h disease .L265PMYD88 was shown to be present in B-cell precursors in 7/10 patients and in residual normal B cells in 6/6 patients [MYD88 mutations were present in pre-B progenitors, being detected in more than 20% of the progenitor cells, and in the nonclonal B cells of WM patients [MYD88 mutation can occur early in lymphopoiesis in WM, before the expansion of the malignant B-cell clone, and that it is not restricted to the clonal population. They also reinforce the idea that L265PMYD88 alone does not conduce a malignant transformation. Other genetic changes , CXCR4, CD79B, ARID1A, TNFAIP3, TP53, and BCL2) are required to cooperate with L265P,MYD88 providing an advantage for B-cell clonal selection and leading to the different types of B-cell malignancies [MYD88 mutation in hematopoietic progenitors is associated with changes in the immune microenvironment that lead to progressive growth and evolution of the B-cell clone [L265PMYD88 mutation still require TLR signals to maintain their proliferation and continued survival [L265PMYD88 should always be considered a preneoplastic event and, therefore, a biomarker for the early detection of B-cell lymphomas. The presence of mutations in earlier progenitors opens up new lines of investigation, which should be extended to other MYD88-mutated lymphomas.Recent works have provided interesting and unexpected findings about the mutation. patients . Anotherpatients . These dgnancies ,127,128.ll clone . In addisurvival ,129,130.L265PMYD88 in ‘normal’ cells of patients could lead to false-positive results in MRD monitoring. An analysis of MYD88 has shown a limited prognostic value for evaluating the treatment efficacy when compared to multiparametric flow cytometry (MFC) [L265PMYD88 is present in phenotypically normal B cells, a positive PCR result does not imply the persistence of clonal tumor cells in the B-cell compartment. Nevertheless, further studies are required to confirm these results, increasing the sensitivity of MFC to ensure that real residual clonal cells are detected and assessing both B-lymphocyte and plasma cell populations [Another important consequence derived from these findings is whether the presence of ry (MFC) . As MYD8ulations ,132,133.MYD88 is an important oncogene that can be recurrently mutated in several types of lymphoma. An assessment of the L265PMYD88 alteration has been shown to improve lymphoma diagnosis and treatment and is becoming increasingly requested for certain entities, such as IgM monoclonal disorders, among all B-cell lymphoproliferative disorders. Its role in disease pathogenesis is not yet well-established, since recent findings have reported the presence of the mutation in earlier progenitors and mature B lymphocytes, suggesting that further alterations or changes in the tumor immune microenvironment are needed to drive the oncogenic transformation. Finally, MyD88-mutant progenitors may be an interesting target for therapy to improve WM curability.


Hepatoid adenocarcinoma of the stomach (HAS) is a subset of gastric cancer (GC) histologically characterized by hepatocellular carcinoma-like foci with or without alpha-fetoprotein secretion, which is easily misdiagnosed. Genomic alterations and potential targets for this population are still largely unknown. Additionally, treatment regimens of HAS are mainly based on GC guidelines, which is not reasonable for diseases with great heterogeneity. The present study comprehensively depicts the genomic features of HAS, and they are significantly different from GC, AFP-producing GC (AFPGC), and liver hepatocellular carcinoma (LIHC). Multiple aggressive behavior-related amplificated or deleted regions in HAS are firstly reported. Moreover, reliable and practicable clinically actionable alterations for HAS are identified, providing evidence for making personalized therapy based on the genomic characteristics of HAS instead of GC.TP53 was the most frequently mutated gene (66%). Hypoxia, TNF-α/NFκB, mitotic spindle assembly, DNA repair, and p53 signaling pathways mutated frequently. Mutagenesis mechanisms in HAS were associated with spontaneous or enzymatic deamination of 5-methylcytosine to thymine and defective homologous recombination-related DNA damage repair. However, LIHC was characteristic of exposure to aflatoxin and aristolochic acid. The copy number variants (CNVs) in HAS was significantly different compared to LIHC, GC, and AFPGC. Aggressive behavior-related CNVs were identified, including local vascular invasion, advanced stages, and adverse prognosis. In 55.26% of HAS patients there existed at least one clinically actionable alteration, including ERBB2, FGFR1, CDK4, EGFR, MET, and MDM2 amplifications and BRCA1/2 mutations. MDM2 amplification with functional TP53 was detected in 5% of HAS patients, which was proved sensitive to MDM2 inhibitors. A total of 10.53% of HAS patients harbored TMB > 10 muts/Mb. These findings improve our understanding of the genomic features of HAS and provide potential therapeutic targets.Hepatoid adenocarcinoma of the stomach (HAS) is a rare malignancy with aggressive biological behavior. This study aimed to compare the genetic landscape of HAS with liver hepatocellular carcinoma (LIHC), gastric cancer (GC), and AFP-producing GC (AFPGC) and identify clinically actionable alterations. Thirty-eight cases of HAS were collected for whole-exome sequencing. Significantly mutated genes were identified.  Hepatoid adenocarcinoma of the stomach (HAS) is a subset of gastric cancer (GC) histologically characterized by hepatocellular carcinoma-like foci with or without alpha-fetoprotein (AFP) secretion . It is aERBB2 expression (score 3+/2+: 21%/19%), as well as overexpression of p16 and IMP3. Additionally, there were also studies showing that TP53 was highly mutated and its abnormal function might affect HAS differentiation ..11]. MitTP53 mutation was observed in 66% of HAS patients, and it was the common node of p53, Wnt/β-Catenin, and the DNA-repair pathway indicating that these pathways might function simultaneously in the development and progression of HAS  pathway and PHLDA1 in the TNF-α/NF-κB pathway, and the co-mutations of PKHD1 in the apical surface pathway and DMD in the G2M checkpoint pathway, indicating that many aberrant pathways tend to co-exist. We also found that multiple genes in a pathway simultaneously mutated in a patient, such as LAMA1, LAMA2, and COL4A2 in the EMT pathway , followed by C > A (12.33%), C > G (10.14%), T > G (7.44%), T > C (7.33%), and T > A (5.09%) A, which APOBEC-induced mutagenesis, which was typical of enrichment of C > T or C > G changes at TCW motifs, in which W was A or T  database v3; B 12]. T. T12]. Ts A or T C and wass A or T D, which s A or T C. The aeAll these SBS COSMIC signatures in HAS except SBS2 were shared in AFPGC, and most of them were associated with defective DNA repair indicating the instable genomic features in AFPGC . In GC, q-value < 0.05; p < 0.05; p < 0.05; p > 0.05; We then evaluated the copy number variants in HAS and found extensive CNVs, including 22 significant amplifications and 50 deletions . Additionally, deletions of the left four TSGs .CNVs in oncogenes and tumor suppressor genes (TSGs) could cause cell growth disorder and contribute to the aberrant proliferation of tumor cells. In the COSMIC gene list, we identified a total of six oncogenes and 14 TSGs located in these frequently altered regions A. OS anaERBB2 amplification was the most recurrent clinically actionable alteration, followed by amplifications in MDM2, FGFR1, MET, CDK4, and EGFR, and oncogenic mutations in genes such as BRCA1/2, NF1, and ALK. Clinically, several ERBB2-amplified GC patients could benefit from trastuzumab target therapy. Therefore, we were greatly interested in the higher proportion of ERBB2 amplification in HAS (21.05%), while it was approximately 14% in GC, 0.6% in LIHC, and 25.8% in AFPGC, indicating that anti-ERBB2 treatment in HAS is also a promising option  were identified A. Specifg option . AdditioLRN-6924 ,19, an iup to 5% .p < 0.001). There were no significant differences in HAS vs. GC  and HAS vs. AFPGC . Microsatellite instability analysis showed all HAS patients were microsatellite stable , high expression of which could inhibit the survival and metastasis of gastric cancer and served as a TSG [PHLDA1 downregulation was proved to promote acquisition and maintenance of drugs resistance targeting receptor tyrosine kinase, which might suppress the effect of trastuzumab treatment on ERBB2 amplification cancers [Among the SMGs, n of HAS ,23. Addias a TSG . PHLDA1  cancers .APOBEC was only identified in HAS, indicating genomic specificity [The mutation spectrum in the context of 96 types of base constitution varied a lot, and the pattern could reflect mutagenic mechanisms in tumorigenesis. The deficiency of DNA mismatch repair was common in all the cancers, but the preference for the TCW mutation pattern mediated by cificity . For LIHcificity , which hCCNE1 locus in this region was synthetic lethal related, targeted by PKMYT1 kinase inhibition, and it was also a promising target in AFPGC, which warranted further research in HAS [Increasing evidence reveals that CNVs induced by various endogenous and exogenous stimuli existed in tumor tissues and were critical in genetic heterogeneity , transfoh in HAS ,33. Deleh in HAS ,35,36,37h in HAS , indicath in HAS , and in ERBB2 amplification was present in more than 20% of HAS patients who had a shorter OS tendency  and BRCA2 (8%) were detected in HAS. An increasing number of clinical trials have shown that various malignancies with BRCA mutation may potentially be sensitive to PARP inhibitors [Additionally, mutations in hibitors , which ohibitors . In the There was a limitation in our study. The incidence of HAS was very low, resulting in a small sample size, which may lead to some deviations in the results. Some novel variations related to the malignant characteristics of HAS were identified. Further functional validation of the variations may have improved the reliability of the conclusion, but the scarcity of samples made the experimental verification difficult.ERBB2 amplification and MDM2 amplification with functional TP53, which are helpful for establishing a treatment scheme based on its own genetic background.HAS has its specific molecular variant characteristics, and it is different from LIHC, GC, and AFPGC. We have identified progression-associated alterations, and provided an overview of clinically actionable alterations of HAS, including


Higher immune scores were also observed for the C2 subtype. A signature containing four MRGs  which can accurately predict the prognosis of HCC patients was then identified. This four-gene signature exhibited a predictive effect in five other cancer types, namely glioma, uveal melanoma, acute myeloid leukemia, adrenocortical carcinoma, and mesothelioma. The mitophagy-associated subtypes of HCC were closely related to the immune microenvironment, immune checkpoint-related gene expression, cancer stem cells, ferroptosis status, m6A, prognosis, and HCC progression. The established MRG signature could predict prognosis in patients with HCC.Globally, hepatocellular carcinoma (HCC) is the sixth most common cancer. In this study, the correlation between mitophagy and HCC prognosis was evaluated using data from The Cancer Genome Atlas (TCGA). Clinical and transcriptomic data of HCC patients were downloaded from TCGA dataset, and mitophagy-related gene (MRG) datasets were obtained from the Molecular Signature Database. Then, a consensus clustering analysis was performed to classify the patients into two clusters. Furthermore, tumor prognosis, clinicopathological features, functional analysis, immune infiltration, immune checkpoint (IC)-related gene expression level, tumor stem cells, ferroptosis status, and N6-methyladenosine analysis were compared between the two clusters. Finally, a mitophagy-related signature was developed. Two clusters (C1 and C2) were identified using the consensus clustering analysis based on the MRG signature. Patients with the C1 subtype exhibited upregulated pathways with better liver function, downregulated cancer-related pathways, lower cancer stem cell scores, lower Tumor Immune Dysfunction and Exclusion scores (TIDE), different ferroptosis status, and better prognosis compared with the patients with the C2 subtype. The C2 subtype was characterized by the increased grade of HCC, as well as the increased number of immune-related pathways and m Liver cancer is a major global health challenge, and according to the World Health Organization, it is estimated to affect more than 1 million individuals annually by 2030 ,5,6. AmoMitophagy is a special type of autophagy occurring in mitochondria that specifically clears damaged and old mitochondria, thereby ensuring the quantity and quality of the mitochondrial population in cells ,14. WhenIn this study, the mitophagy profile and clinical value in HCC patients were investigated using The Cancer Genome Atlas (TCGA) sequencing data. Based on the consensus clustering analysis of gene expression profiling, the patients were classified into two robust clusters having significant differences in molecular features and tumor prognosis. Moreover, a mitophagy-related gene (MRG) signature was developed to evaluate the prognosis of these patients in TCGA dataset. Our analysis showed a significant association between the MRG signature and the prognosis, which could act as an independent clinical pathological prognostic factor. In summary, our study revealed that mitophagy-related biomarkers are strongly correlated with the clinical prognosis of patients with HCC.To identify the functional roles of mitophagy status in HCC patients, 3621 HCC prognostic genes were identified through the single factor Cox analysis, and the MRGs were downloaded from the Molecular Signature Database. Next, Venn diagrams were generated to obtain 21 prognosis-related MRGs between these two gene sets .p = 4.39 × 10−6, To further explore whether the expression of 21 MRGs was associated with HCC subtypes, these 21 genes were applied to classify TCGA-HCC patients into different subtypes through a consensus clustering analysis. When the clustering variable (kappa) was defined as 2, TCGA-HCC patients could be divided into two clusters based on the results from the principal component analysis (PCA) and a consensus clustering matrix analysis B,C. Furtp value < 0.05)  and downregulated genes  are labeled in the volcano plot  . The sevano plot A. The geano plot B.GO and KEGG enrichment analyses were performed based on these upregulated and downregulated genes (C1 vs. C2). As shown in CCNB1, CCNE1, CDK1, CDK2, CDK4, CHEK1, GTSE1, RRM2, SERPINE1, and SFN, led to better OS of HCC patients from the low-expression group compared with those from the high-expression group  algorithm was used as a panel of gene expression signatures to assess distinct immune escape mechanisms in tumors, including the tumor-infiltrating cytotoxic T lymphocyte (CTL) dysfunction and CTL rejection by immunosuppressive factors. A high TIDE score indicated the poor efficacy of the IC blockade (ICB) therapy and short survival following ICB therapy. As shown in Cancer stem cells (CSCs) are small subpopulations of cancer cells with stem-like features, such as self-renewal, dedifferentiation, high tumorigenic capacity, and distant metastases, leading to cancer recurrence ,31. BaseHSPA5, EMC2, SLC7A11, NFE2L2, HSPB1, FANCD2, CISD1, FDFT1, SLC1A5, SAT1, TFRC, RPL8, NCOA4, LPCAT3, GLS2, CS, CARS1, ATP5MC3, ACSL4, and ATL1, were highly expressed in the high-risk C2 subtype compared with the low-risk C1 subtype  accumulation and oxidative damage. Dysfunction of mitochondrial iron transport has a crucial role in mitochondrial diseases and cancers ,31. Mito subtype . Similar subtype . These r6A), the most abundant RNA modification in eukaryotic cells, is closely related to cancer progression, including tumorigenesis, metastasis, and angiogenesis [6A-related genes was further analyzed between the two HCC clusters. Strikingly, the heatmap and boxplots revealed that all m6A-related genes in the high-risk C2 HCC subtype had higher expression than those in the low-risk C1 HCC subtype  + (0.2609 × SQSTM1 expression) + (0.52 × ATG9A expression) + (−0.194 × GABARAPL1 expression). Based on the median risk score, the HCC patients were equally stratified into the high- and low-risk groups of poor prognosis  network was constructed to optimize the top 10 genes with relatively high confidence, namely ytoHubba . We perfrognosis B. Patienrognosis B. The KMWe further examined the immunohistochemical expression of four genes in normal human tissues and HCC tissues from the HPA database. PGAM5 and SQSTM1 were overexpressed in HCC tissues, and the expression of ATG9A was low in HCC tissues, whereas the expression of GABARAPL1 was absent in HCC tissues but low in normal tissues A–D. In aTo ensure the prediction value of the four genes identified, we performed the step regression analysis for validating the survival-related genes in pan-cancer TCGA cohorts. The four-gene signature was found to exhibit a predictive effect in the other five types of cancer, namely glioma, uveal melanoma, acute myeloid leukemia, adrenocortical carcinoma, and mesothelioma.PGAM5 expression) + (0.8062 × SQSTM1 expression) + (−0.4834 × ATG9A expression) + (−0.6285 × GABARAPL1 expression). The glioma patients were equally stratified into the high- and low-risk groups of poor prognosis  + (−1.2004 × ATG9A expression) + (−2.0815 × GABARAPL1 expression). The uveal melanoma patients were equally stratified into the high- and low-risk groups of poor prognosis  + (−0.2004 × GABARAPL1 expression). The acute myeloid leukemia patients were equally stratified into the high- and low-risk groups of poor prognosis  + (0.8342 × ATG9A expression). The adrenocortical carcinoma patients were equally stratified into the high- or low-risk group of poor prognosis (p = 0.00144)  . AUCs fo0.00144) .PGAM5 expression) + (1.545 × ATG9A expression). The mesothelioma patients were equally stratified into the high- and low-risk groups of poor prognosis (p = 0.000291)  . AUCs fo.000291) .Hepatocytes are rich in mitochondria, accounting for 13–20% of the liver volume . MitochoMetabolic reprogramming in tumors is a cancer hallmark. Tumor cells usually prefer aerobic glycolysis (Warburg effect) over oxidative phosphorylation (OXPHOS) for cell metabolism. A crosstalk occurs between mitophagy and metabolic rewiring in cancer cells ,41,42. Ip53 is a TP53-encoded tumor suppressor, which is mutated or deleted in nearly 50% of all human cancers. The tumor suppressor functions of p53 are attributable to the execution of DNA repair, cell cycle arrest, senescence, and apoptosis [PARK2 has a role in cyclin E degradation [p53- and cell cycle-related genes were extracted to verify the survival curve, which also exhibited a similar patten as the enrichment analysis.The gene poptosis . The mutpoptosis . Moreovepoptosis ,46,47. Ipoptosis . Moreoveradation ,26. KEGG+ T cells, and memory NK cells [The damage-associated molecular patterns  from mitochondria could activate immune responses of innate immune cells . Thus, bNK cells ,28. FuncNK cells . These rCSCs are small subpopulations of cancer cells, characterized by stem-like features, such as self-renewal, dedifferentiation, high tumorigenic capacity, and distant metastases, resulting in cancer recurrence ,31. MitoMany cellular processes require iron homeostasis; however, excessive iron accumulation induces ROS accumulation and oxidative damage. Dysfunction of mitochondrial iron transport is crucial in the development of mitochondrial diseases and cancers ,31. Mito6A is the most abundant RNA modification in eukaryotic cells. This modification is closely related to cancer progression, including tumorigenesis, metastasis, and angiogenesis [6A plays a dual role in cancer depending on cancer types [6A regulates the expression of oncogenes or tumor suppressor genes, thereby influencing cancer progression. On the other hand, m6A levels and m6A enzyme expression and activity can be modulated, thereby affecting m6A’s role in cancer [6A modification [6A-related biomarkers such as METTL3 and YTHDF2 are upregulated in HCC patients and promote HCC progression [METTL14 have an adverse effect on HCC development [6A-related genes with HCC prognosis was observed. This study was the first to report an association between mitophagy and m6A modulation, warranting additional studies in the future.mogenesis ,33. Increr types ,33,34. Ofication ,33. Somegression . HoweverATG9A, PGAM5, SQSTM1, and GABARAPL1 were differentially expressed at the transcriptomic level between tumor and normal tissues. ATG9 is the only multi-transmembrane protein of the core autophagy machinery delivered to the formed autophagosome by vesicular transport from the trans-Golgi network and/or endosomes, and ATG9A-mediated lipid scrambling is involved in autophagosome expansion [Because of the strong association between mitophagy and clinicopathological features in HCC patients, the patients were stratified into high- and low-risk groups of poor prognosis with an established signature. In this study, combined with the use of a step regression analysis, the signature of four genes strongly affected survival prediction. Of the 21 MRGs, xpansion ,52,53.Phosphoglycerate mutase 5 (PGAM5) is a mitochondrial serine [Ser)/threonine [Thr) phosphatase usually located in the inner mitochondrial membrane and regulates mitochondrial dynamics and programmed cell death . PGAM5 a6A-related genes. This deepened our understanding of the molecular mechanisms underlying low- and high-risk HCC populations. Third, as the three studies were based on pure biological information analysis without experimental validation, false-positive results with those numbers of genes may be inevitable. Their results also revealed that the types of genes in one study were quite different from another study, except PGAM5. In our study, all four selected genes overlapped with those in the three studies, especially PGAM5, unexpectedly appearing in two of those studies. This indicates that our results are reproducible and reliable to some extent. A recent study validated PGAM5 function, thereby providing sufficient evidence to support the reliability and reproducibility of our results [We summarized the similarities and differences between three studies, which have reported the results similar to those of our study. All these studies included patients with HCC. However, our study had some major differences compared with these three studies. First, we used a different method for selecting genes. Xu et al. used 16 survival MRGs to construct the prognosis model . Chen et results . In summThe present study also has some limitations. The specific role of MRGs in HCC should be further elucidated in vitro and in vivo. Additionally, the specific mechanism of action of mitophagy genes in HCC progression should be explored in further studies, which may provide new strategies for HCC treatment.In conclusion, mitophagy was found to be closely related to the immune microenvironment, IC-related genes, CSCs, and prognosis in HCC patients. The MRG signature was developed to predict the survival of these patients. In the era of precision medicine, this signature would be an effective strategy to meet the clinical requirements of HCC management to a certain extent.https://portal.gdc.com (accessed on 12 March 2022)).Data of 371 HCC patients, including RNA-sequencing expression profiles and the corresponding clinical information, were downloaded from TCGA dataset ). After the removal of overlapping genes, 65 MRGs were retrieved. The Venn online tool was used to select the genes overlapping between 3621 HCC prognostic genes and MRGs. The consensus clustering analysis was then conducted using the “ConsensusClusterPlus” R package (version 1.54.0), with the maximum cluster size set to 6. This heatmap was generated using the “pheatmap” R package (version 1.0.12), which helped visualize the expressions of relatively highly expressed MRGs. Kaplan–Meier (KM) survival curves were generated using the R package “survival.”Based on the p value < 0.05) were established to reveal the abundant differential changes by using the “ggplot2” R package. The heatmap was plotted to visualize the expression profiling of DEGs with the top 50 differential changes. To better comprehend the underlying functions of potential targets, we performed enrichment analyses, such as Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG), using the “ClusterProfiler” R package (version 3.18.0). Moreover, the OCLR algorithm was used to determine the relationship between mitophagy and cancer stem cells (CSCs) [Differentially expressed genes (DEGs) between groups, which were identified through consensus clustering, were explored, and volcano plots  .CD274, PDCD1, PDCD1LG2, CTLA4, LAG3, HAVCR2, TIGIT, and SIGLEC15) were selected [p value < 0.05 was considered significant.Based on the clusters formed through consensus clustering, immune infiltration of the clusters was performed with EPIC algorithms using the “immunedeconv” R package and visualized in the form of a heatmap and boxplot. Moreover, IC-related genes .Ferroptosis-related genes were obtained from a previous study . Aberranresearch  on the mhttps://www.proteinatlas.org (accessed on 15 May 2022)).Protein–protein interactions of MRGs were detected using STRING, and the top 10 of these genes were selected using the built-in CytoHubba App in Cytoscape software. Through univariate Cox proportional hazards regression analysis, we analyzed the HCC dataset to determine whether MRGs could be used to predict overall survival (OS) in HCC patients. Additionally, the multi-factor Cox regression was used to analyze the data, and the step function was used to perform iterations. The optimal model was selected using the “glmnet” R package. KM survival curves were plotted for OS and were compared using the log-rank test. Hazard ratios (HRs) with 95% confidence intervals (Cis) were calculated through a Cox proportional hazards analysis. The HCC gene signature was explored for its predictive effects in pan-cancers. Furthermore, the immunohistochemical expression of four genes were examined in normal human tissues and HCC tissues from the HPA database (


Precision nutrition is an emerging area of nutrition research, with primary focus on the individual variability in response to dietary and lifestyle factors, which are mainly determined by an individual’s intrinsic variations, such as those in genome, epigenome, and gut microbiome. The current research on precision nutrition is heavily focused on genome and gut microbiome, while epigenome  is largely neglected. The epigenome acts as the interface between the human genome and environmental stressors, including diets and lifestyle. Increasing evidence has suggested that epigenetic modifications, particularly DNA methylation, may determine the individual variability in metabolic health and response to dietary and lifestyle factors and, therefore, hold great promise in discovering novel markers for precision nutrition and potential targets for precision interventions. This review summarized recent studies on DNA methylation with obesity, diabetes, and cardiovascular disease, with more emphasis put in the relations of DNA methylation with nutrition and diet/lifestyle interventions. We also briefly reviewed other epigenetic events, such as non-coding RNAs, in relation to human health and nutrition, and discussed the potential role of epigenetics in the precision nutrition research.  Nutrition is among the most important factors affecting human health, and healthy diet intervention has become a mainstream approach in the prevention and treatment of human diseases, such as obesity, type 2 diabetes (T2D) and cardiovascular disease (CVD). The inter-individual variability in response to dietary intervention has long been noted . It is nCurrently, precision nutrition research is mainly focused on the genome and gut microbiome, while the epigenome is largely uncharted. Epigenomic modifications include the hereditable and reversible changes in gene function that occur without changing the DNA sequence, such as DNA methylation, histone modification, and non-coding RNA regulation. Among them, DNA methylation has been the most-studied epigenetic event so far. DNA methylation is a covalent chemical modification of DNA, in which the hydrogen H5 of cytosine is replaced by a methyl group, under the catalyzation by DNA methyltransferases (DNMTs). DNA methylation regulates gene expression, either by impeding the binding of transcriptional proteins or by recruiting proteins that are involved in gene repression. DNA methylation may provide a mechanistic link between environmental factors and gene expression and, therefore, potential targets for diseases prevention, through modifications of environmental factors, such as diets ,5,6.To date, epigenome-wide association studies (EWASs) have identified various DNA methylation markers, associated with various diseases , and emeThe global prevalence of obesity nearly tripled between 1975 and 2016 . It is eHIF3A), were successfully replicated in MARTHA and KORA. A year later, Demerath et al. conducted a cross-ethnic investigation on DNA methylation (leukocyte DNA samples assayed by the Illumina HumanMethylation450 array) with obesity among 2097 African American adults in the Atherosclerosis Risk in Communities (ARIC) study [CPT1A, which has been previously reported to be associated with BMI by Aslibekyan et al. [ABCG1. Moreover, a total of 18 CpG sites were associated with BMI in the adipose tissue, including markers near the previously identified CPT1A and ABCG1 loci, and a number of novel adiposity-related loci, such as LYS6GE, KDM2B, RALB, PRRL5, LGALS3BP, C7orf50, PBX1, EPB49 and BBS2. Of note, the HIF3A probes, previously reported by Dick et al. [SREBF1 locus, demonstrated evidence of a causal effect on BMI. However, the cross-sectional nature of the study limited the definitive causal determination [NFATC2IP), was shown to be causally associated with BMI by Mendelian randomization. Such association was confirmed by the followed analysis in longitudinal analyses. Of note, the study also found that a Methylation Risk Score (MRS), calculated by summing up the methylation values at each of the markers associated with T2D and weighted by marker-specific effect size, could predict future risk of T2D, independent of traditional T2D risk factors, such as BMI, waist-to-hip ratio, glucose, and HbA1c. In addition, this study supported the view that DNA methylation was a consequence of BMI. A recent EWAS, performed among 1192 17-year-old adolescents from the Raine Study, found that DNA methylation at RalGDS/AF-6, RAPH1, MSI2, and SLC25A10 was associated with concurrent BMI and waist circumference [RAPH1, MSI2, and SLC25A10 was also found to be associated with some early life factors, such as maternal smoking, pre- and early pregnancy BMI, and gestional weight gain, suggesting that these could be potential robust markers across lifecourse. It is worth noting that previous EWASs were predominantly performed in European ancestries. A recent cross-sectional analysis identified 116 CpG sites associated with BMI and 8 CpG sites with waist circumference among an Asian popuplation, with moderate consistency compared with other ethnic groups, indicating potential unique DNA methylation markers in the Asian population [The first large-scale EWAS of the association between adult body mass index (BMI) and DNA methylation was conducted by Dick et al., in 2014 . The stuC) study . Replican et al. , and CpGk et al. . The stumination . Anothermination . One of pulation . A briefThe prevalence of T2D is paralleled with that of obesity, which is a major risk factor for T2D ,17. It iTCF7L2, FTO, and KCNQ1, differentially methylated in T2D islets. In addition, 102 of the differentially methylated genes, including CDKN1A, PDE7B, SEPT9 and EXOC3L2, exhibited differential gene expressions in the T2D islets. Previous studies also reported DNA methylation in peripheral blood, which offers easier assessment due to its noninvasive nature. Florath et al. conducted an EWAS among 1515 German older adults [TXNIP gene, remained significantly associated with T2D at replication stage. A lower level of methylation at cg19693031 was associated with increasing fasting glucose and HbA1c concentration. A recent large meta-EWAS of prevalent T2D in a European population, using peripheral blood DNA methylation, confirmed three previously identified CpG sites at TXNIP, ABCG1, and CPT1A, and also reported three novel CpG sites associated with prevalent T2D among Europeans, namely cg00144180 (HDAC4), cg16765088 (near SYNM) and cg24704287 (near MIR23A) [VDR genes are differently methylated between T2D patients and controls [So far, many CpG sites have been discovered to be associated with prevalent T2D, although in relatively modest sample sizes  27,28,2,30,31,32 MIR23A) . Besidescontrols  Moreovercontrols .ABCG1, PHOSPHO1, SOCS3, SREBF1, and TXNIP) and risk of future T2D incidence among Indian Asians and Europeans. In addition, an MRS combining the five CpG sites was associated with incident T2D, independent of established T2D risk factors . Another prospective study was performed among 1264 participants from the EPIC-Norfolk, with replications in LOLIPOP (1074 incident T2D and 1590 controls) and in cross-sectional data from FHS  [TXNIP, ABCG1, and SREBF1, which have been identified previously. One CpG site, cg00574958 at CPT1A, was found to have a possible causal role in T2D. More recently, a meta-analysis of EWASs in blood samples, collected 7–10 years prior to T2D diagnosis, was conducted by Fraszczyk et al. [TXNIP, ABCG1, SREBF1, and CPT1A. After adjustment for BMI, only four CpG sites  remained genome-wide significant, indicating that obesity explained much of the association between DNA methylation and incident T2D. Future studies with larger sample size are warranted to examine the DNA methylation makers predictive of T2D incidence, independent of BMI.Evidence from EWASs in prospective cohorts also reported differentially methylated CpG sites associated with T2D incidence ,37,38. Fcipants) . DNA metk et al. . DNA metIL6R, FASLG, and CCL18. Nakarochi et al. performed an EWAS among an elderly Japanese population [ZFHX3 and SMARCA4, remained significant, even after adjustment for traditional MI risk factors, indicating possible epigenetic mechanisms through these DNA methylation sites. In addition, a recent cross-sectional EWAS of carotid intima-media thickness  reported one CpG, cg05575921(AHRR), was associated with cIMT [Cardiovascular diseases remain the leading cause of death worldwide and impose huge burdens on morbidity, quality of life, as well as substantial economic cost on public health systems ,41. It ipulation . Epigenoith cIMT . ResultsSLC9A1, SLC1A5, and TNRC6C, were found to be associated with CVD risk. Another study worth mentioning was conducted by Agha et al. [UBR4, HR per 5% increase in DNA methylation: 0.54; 95% CI: 0.42, 0.69), while the risk increased 65% for DNA methylation at cg05820312 , independent of traditional risk factors. Furthermore, Mendelian randomization analyses supported a causal relationship between DNA methylation at two CpG sites (cg26470101 and cg07289306) and incident CHD. Together, the current data suggest a possible role of DNA methylation regulation in the pathways to CVD risk.In recent years, several EWASs have been conducted in prospective settings, linking DNA methylation with incident CVD  44,50,5,52,53,54Nutrition plays a pivotal role in modulating DNA methylation. The main methyl donor for DNA methylation is S-adenosylmethionine (SAM), which is a substrate derived from the one-carbon metabolism. The one-carbon metabolism is catalyzed by several enzymes in the presence of dietary micronutrients, such as folate, methionine, choline, and other B vitamins, making DNA methylation highly dependent on the availability of nutrients .IGF2) gene 60 years later, compared to their unexposed, same-sex siblings [INSR and CPT1A, respectively) [Nutrition status at various life stages may impact DNA methylation. The most sensitive time window for epigenetic changes in response to the environmental factors is the period of developmental plasticity, when epigenetic marks undergo critical modifications . During siblings . IGF2 issiblings . A folloctively) . More rectively) . Similarctively) ,64,65,66IGF2 expression, particularly among male infants [IGF2 gene among infants up to 17 months. Godfrey et al. conducted an analysis to examine macronutrient composition in early pregnancy and found that lower intake of carbohydrates was associated with significantly higher methylation RXRA gene, which was related to increased childhood body mass index and child fat mass [In addition to malnutrition, deficiencies of several nutrients or nutritional alterations during pregnancy have also been related to DNA methylations in the offspring ,70,71,72 infants . Similarfat mass .TAPBP gene in offspring. Further analyses found that pre-pregnancy maternal obesity-related CpG sites were associated with BMI z-score and blood pressure in offspring [TFCP2), was associated with GDM at false discovery rate (FDR) 1.38 × 10−2. Additional FDR-significant interactions of maternal methylation and GDM was also reported, with the strongest one at the same cg22790973 (TFCP2), followed by cg03456133, cg24440941 (H3C6), cg20002843 (LOC127841), cg19107264, and cg11493553, located within the UBE3C gene, and cg17065901 in FAM13A, both susceptibility genes for T2D and BMI, and cg23355087 within the DLGAP2 gene, known to be involved in insulin resistance during pregnancy. A recent study meta-analyzed eight birth cohorts, investigating relations between cord blood DNA methylation and fetal exposure to maternal glucose, insulin, and area under the curve of glucose (AUCgluc), following an oral glucose tolerance test [gluc was found to be inversely associated with cord blood DNA methylation at two neighboring CpG sites (cg26974062 and cg02988288), located within the exon of TXNIP. Again, stratified analyses showed these associations were attenuated among participants with GDM. A lower level of DNA methylation at these two sites was associated with several glycemic traits.Maternal overnutrition (such as obesity and diabetes) is also associated with DNA methylation changes. Various evidence has shown that maternal obesity is associated with increased offspring birthweight and cardiometabolic health in early childhood ,74,75. Dffspring . Anotherffspring . An EWASffspring . Anotherffspring . The stu843 LOC1241, cg191GFI1 gene could explain 12–19% of the association between maternal smoking and low birthweight observed in the study participants [There is also evidence showing the association between maternal obesity and DNA methylation changes in several genes involved in leptin/adiponectin systems, the cytokines secreted from adipose tissue ,80. For icipants . Taken tp-value < 1.1 × 10−7, which annotated to genes, including AHRR, F2RL3, FLJ43663, HDAC4, GFI1 and PHGDH. Among them, cg14476101 was shown to be related to PHGDH expression and risk of fatty liver disease. No DNA methylation marker was found for tea consumption. Another study, conducted among young women (age 20–30 years), found that moderate folate depletion might cause a decrease in global DNA methylation [MTHFR 677 TT genotype had a better improvement in DNA methylation status, compared to women with the CC genotype [SOCS3) was associated with higher scores of both MDS and AHEI, as well as lower risk for all-cause mortality. Functional analyses also demonstrated the role of many of the identified CpG sites in diet-associated pathways, involving fatty acid metabolism and insulin signaling. These findings showed that diet quality was associated with differential blood DNA methylation levels, which may reveal novel insights into the epigenetic mechanisms governed by dietary factors. Future studies with deeper coverage of DNA methylation and more precise assessment of dietary intakes are needed to validate the findings.After birth, when the cell or tissue is fully developed, although the plasticity of epigenetic changes is less sensitive to environmental stimuli, DNA methylation is still labile in response to nutritional status . For exahylation . Followigenotype . Recentlgenotype . A totalp = 0.048), among the older participants with median age of 48 years [n = 39) than the weight-loss failures  [n = 75), compared to participants with fatty liver  [Weight-loss diet and lifestyle interventions have been shown to have a profound effect in reducing body weight, as well as other metabolic risk factors, through changes in insulin signaling, fat storage, energy expenditure, and appetite control . It has 48 years . Similar48 years . In addi n = 81) . A simil n = 45) .TXNIP as the average methylation level over CpG sites, within 500 bp of cg19693031, one of the most robust sites associated with insulin resistance [TXNIP were associated with lower levels of fasting glucose (p < 0.001), insulin (p < 0.001), and homeostatic model assessment for insulin resistance  . Among participants with the highest tertile of regional DNA methylation at TXNIP, average protein intake (15%) was associated with greater reduction in insulin and HOMA-IR compared to high protein intake (25%), while no association was found among those within the lower tertiles. Of note, such relationship was independent of concurrent weight-loss. The interaction pattern was attenuated and became non-significant at 2 years, presumably due to the decreased adherence to the diet interventions [In addition, several studies have shown that pre-treatment DNA methylation profiles may serves as biomarkers that may predict individual responsiveness to weight-loss intervention  80,94,99,101,103sistance , islet dsistance ,107, hypsistance , and T2Dsistance ,29,38. W = 0.03) . In addiIn addition, although in small sample sizes, some, but not all, of the studies found that after gastric bypass surgery, the DNA methylation profiles of individuals with obesity were similar to those of normal-weight individuals ,108,109.5-fold), whereas no elevation was detected among healthy individuals. Further, miR-499 also showed comparable diagnostic values with high-sensitivity cardiac troponin T (hs-cTnT), with areas under the receiver operating characteristics curves of 0.97. In addition to miRNA, the role of long non-coding RNA, in the diagnosis, prognosis, and clinical management of obesity, T2D, and CVD, has also been evidenced in growing studies [Besides DNA methylation, other epigenetic events, such as non-coding RNAs, are also involved in the pathogenesis of obesity, T2D, and CVD . Non-cod studies ,124,125. studies ,127,128.Emerging evidence has shown that diet may alter the miRNA profiles ,141,142.In summary, current data support the view that epigenetic events, particularly DNA methylation, might play an important role in the pathogenesis of obesity, T2D, and cardiovascular diseases. Although there are few overlaps of DNA methylation markers in previous studies, compared to the number of single nucleotide polymorphisms identified in genomic studies, the current evidence collectively suggest that DNA methylation is associated with cardiometabolic disorders. The growing evidence has also shown that DNA methylation is closely related to nutrition status at various life stages, including fetal development, early life, and adulthood. Considering the dynamic and modifiable nature of DNA methylation, it could be a target for future precision dietary interventions in the prevention and treatment of cardiometabolic diseases. Emerging data have lent support to the potential effectiveness of diet and lifestyle interventions on changes in epigenetic events, especially DNA methylation and miRNAs. Once validated, these identified epigenetic markers could serve as novel targets for dietary and lifestyle interventions. More future studies, with larger sample size, prospective design, high-coverage and repeated measurements on DNA methylations and other epigenetic events, are still warranted. Of note, since epigenetic events are cell specific, it is essential to investigate the tissue-specific epigenetic modifications in future studies. It is also important to examine more complex interactions of epigenetic markers and dietary factors with other lifestyle factors, such as physical activity, sleep behaviors, and other environmental factors, in determining the individual variability in relation to precision health.


Dear Editor,+ plasmacytoid dendritic cells (pDC) led to the recognition of BPDCN as an independent entity within the WHO classification of myeloid neoplasms [+ SIGLEC6+ DCs (AS-DCs), suggesting a heterogeneous cellular ontogeny [TET2, ASXL1, EZH2), RAS signaling , splicing  and tumor suppressors . Recently, mutations in epigenetic regulators were shown to be a recurrent feature of clonal hematopoiesis, underlying BPDCN [Blastic plasmacytoid dendritic cell neoplasm (BPDCN) is a rare and aggressive blood cancer. In the era of conventional chemotherapy prognosis was poor with high rates of relapse and refractory disease despite consolidating allogenic or autologous stem cell transplantation . Therapyeoplasms . Althougontogeny . Panel aontogeny . Syn- anontogeny . This isng BPDCN .n = 25), lymph node (n = 11), bone marrow (n = 9) and others (n = 2)). For details on case selection, extraction of nucleic acids, WES, RNA-seq, somatic copy number aberration (SCNA) analysis and data processing please see supplementary materials and methods. Baseline clinicopathological characteristics are summarized in Supplementary Tables MutSigCV we identified 41 significant candidate driver genes , RTK-RAS , NOTCH  and WNT signaling pathways   and activating GTPases   and CMML (n = 76) samples. Apparently, CMML is most closely related to BPDCN, with shared mutational drivers including BRAF, CSFR1, EP300, MET and ZRSR2. In addition, several mutations co-occur between all three entities including TET2, SRSF2, SF3B1, NRAS, KRAS, and IDH1/2. Only TP53 mutations were found to be an exclusive commonality between BPDCN and AML but not CMML, potentially reflecting the aggressive nature of the former two  and 12 SCNAs as likely pathogenic according to X-CNV. The genes within these SCNAs recurrently led to the loss of tumor-suppressor genes  and copy number gains in oncogenes   from healthy donors [BCL2, MYB, and others [PDGFRA/B, EGFR, FGFR1, and others as well as a down-regulation of inflammatory mediators such as IL2, IL22, and CXCL8. Despite the results from our MutSigCV analysis in which PDGFRA mutations were shown to be events of only borderline significance (p = 0.05), we found a pathobiological relevance of these mutations reflected in its simultaneous significant upregulation, contributing to the phenotype of BPDCN. PDGF signaling, NCAM1 interactions and cell cycle accelerators were over-expressed. In addition, several processes associated with interactions with the extracellular matrix as a recurrent feature in BPDCN were significantly upregulated  status, gender and age. A significant enrichment of mutations affecting ns. Fig. . Beyond nts Fig. . We furttwo Fig. . FurtherH2) Fig. . From RNy donors . DiffereISH Fig. .CLEC9A and CD1C beyond typical pDC markers such as CLEC4C, GZMB which shape the C1 phenotype. As expected from the report by Renosi et al., we observed an additional enrichment in DC5 signatures (AS-DCs) in more than half the samples. These were, however, equally distributed across both subtypes as a putative epiphenomenon [EP300, ARID2, NF1, NOTCH2, and SF3B1 mutations, whereas atypical C2 cases were enriched for DNMT3A and SRSF2 mutations  and C2 patients showing a trend toward lower survival, albeit in a limited subset of patients, was observed (Supplementary Fig. Thirdly, deconvolution of our bulk gene expression profiles using single-cell transcriptome data predicted abundances of cell types within the mixed cell population of our BPDCN biopsies, independent of localization or tumor cell content/purity. We focused on the distribution of dendritic cell and monocyte subtypes for each case using signatures from scRNA-seq data . Subsequons Fig. . AdditioSupplemental MaterialSupplemental Table 5Supplemental Table 6Supplemental Table 7Supplemental Table 8Supplemental Table 9


MACC1), is a biomarker predictive of both metastatic and metastasis-free survival in patients with colon cancer and other solid tumors. However, the underlying mechanism by which MACC1 affect COAD progression and metastasis remains unknown. In this study, we analyzed the expression level and prognostic value of MACC1, as well as their correlation, in patients with various types of cancer included in The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. High MACC1 expression was found to be significantly associated with poor prognosis in patients with COAD. Analysis of the potential upstream miRNA of MACC1 showed that miR-642a-5p was downregulated in COAD and was negatively correlated with MACC1 expression. Analysis of the upstream regulators of miR-642a-5p showed that the long non-coding RNA (lncRNA) ZFAS1was the most likely upstream regulator of miR-642a-5p. In addition, the expression of MACC1 correlated positively with tumor immune cell infiltration, as well as with the levels of biomarkers of five kinds of immune cells. In summary, these findings suggest that MACC1 contributes to COAD progression and immune cell infiltration via the ZFAS1/miR-642a-5p/MACC1 axis.Colon adenocarcinoma (COAD) is the most common pathologic type of colon cancer. Metastasis is responsible for the high mortality rate of patients with COAD. The gene, metastasis-associated in colon cancer 1 ( Colon cancer is the third most common type of cancer  and the MACC1) gene, which is located on chromosome 7p21.1, was first identified in 2009. Genome-wide analysis of genes differentially expressed in primary colon cancer, metastatic tumors and normal tissues has shown that MACC1 is an independent prognostic indicator of metastasis-free survival [MACC1 plays a key role in the transition from adenoma to carcinoma in mice and humans [MACC1 increases tumor cell proliferation and motility, supports cell survival, and regulates metabolism, thereby promoting metastasis. MACC1 has also been shown to be involved in tumor immunity. For example, MACC1 was found to regulate the expression of PDL1 and tumor immunity through the c‐Met/AKT/mTOR pathway in gastric cancer [MACC1 in COAD and its correlation with immune cell infiltration into tumors remain incompletely understood.The metastasis-associated in colon cancer 1  is a cancer genomics program designed to identify and classify major oncogenic genomic changes to enhance our understanding of cancer [MACC1 mRNA and relevant clinical data across 33 cancer types and normal tissues in the TCGA and GTEx database were downloaded from the UCSC XENA website (https://xenabrowser.net/datapages/). Data for various tumor cell lines were downloaded from the Cancer Cell Line Encyclopedia (CCLE) database . RNA-seq data on gene expression in the FPKM format were converted into TPM format and log2 transformed for further analysis. The levels of expression of MACC1 in tumors and corresponding normal tissues in the data downloaded from the TCGA and GTEx databases were analyzed, as were the associations between MACC1 expression and clinical characteristics in patients with COAD. Differences in expression were analyzed using the Mann-Whitney U test, with p < 0.05 defined as statistically significant. Results were visualized using the R package ggplot2.The Cancer Genome Atlas (TCGA) database (f cancer . The Genf cancer . RNA-seqhttps://gepia2.cancer-pku.cn/#index) is a web-based tool for interactive analysis based on normal and cancer samples from the TCGA and GTEx databases. GEPIA2 offers a series of functions such as differential expression analysis, profile plotting, correlation analysis, survival analysis, detection of similar genes, and dimensionality reduction analysis, all of which may be customized [MACC1 and lncRNAs in COAD tissues from the TCGA normal and GTEx databases as controls were analyzed using the “Expression DIY-Box Plot” module of GEPIA2 using the settings, p-value cutoff = 0.001, |log2FC (Fold change)| cutoff = 1, and “Match TCGA normal and GTEx data.” Correlations between MACC1 expression and immune cell biomarkers in COAD were analyzed in the “Correlation Analysis” mode of GEPIA2, with correlation coefficients calculated using Spearman analysis. The selection criteria were set as |R| > 0.1 and p < 0.05 for identifying statistically significant.Gene Expression Profiling Interactive Analysis 2  is a centralized repository of publicly available proteomic sequence datasets. CPATC identifies the protein composition and characteristic proteome of each tumor sample by mass spectrometry [https://ualcan.path.uab.edu) is a user-friendly interactive web-portal designed to facilitate analysis of the correlation between gene expression in tumor subgroups and patient survival [MACC1 total protein from CPTAC database, by entering “MACC1” and selecting the dataset of colon cancer and MACC1 proteomic expression profile based on sample types.The Clinical Proteomic Tumor Analysis Consortium  curves. The areas under the ROC curves (AUCs) were between 0.5 and 1, with AUCs >0.9 defined as being highly accurate. The relationships between MACC1 expression and indicators of patient prognosis, including overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI), were evaluated in 33 types of cancers, with the results presented as Forest plots and Kaplan-Meier curves. A uniformly standardized pan-cancer dataset from the UCSC XENA database (https://xenabrowser.net/datapages/), with samples from patients followed-up for less than 30 days and those with fewer than 10 samples in a single cancer species excluded. Hazard ratios (HRs) with 95% confidence intervals (CIs) were calculated by univariate regression analyses. Survival rates in patients with different levels of MACC1 expression were determined by the Kaplan-Meier method and compared by log-rank tests. The optimal cut-off value for MACC1 expression that differentiated patients into subgroups with high and low expression was calculated by the minimum p-value approach using the R package maxstat (Version 0.7–25). Statistical analyses were performed using the survival package in R software (version 3.6.3), with the forestplot and survminer packages used for visualization. A p value <0.05 was considered statistically significant.The diagnostic value of https://starbase.sysu.edu.cn/) allows systematic identification of RNA-RNA and protein-RNA interaction networks [MACC1 were predicted by the starBasev2.0 database in the miRNA-mRNA panels. Predicted miRNAs appearing simultaneously in more than two programs were considered candidate miRNAs for MACC1 and included in subsequent analyses. Correlation analyses for miRNA and MACC1, lncRNA and hsa-miR-642a-5p, and lncRNA and MACC1 in COAD samples in the starBasev2.0 database were performed in the miRNA-Target CoExpression or RNA-RNA CoExpression panels. The levels of expression of hsa-miR-642a-5p in COAD and normal tissue were also analyzed in the miRNA Differential Expression panels, as well as to predict candidate lncRNAs that could potentially bind to hsa-miR-642a-5p. The miRNA-mRNA and lncRNA-miRNA interactions networks were visualized by Cytoscape (version 3.8.2).The starBasev2.0 database  allows a comprehensive analysis of gene expression and tumor infiltrating immune cells in multiple cancers [MACC1 expression levels and tumor infiltration by immune cells, including B cells, CD8+T cells, CD4+T cells, Tregs, NK cells, macrophages, neutrophil cells, and dendritic cells, in COAD were selected. The p-values and Rho values were obtained via the purity adjustment Spearman's rank test. The correlations were shown as scatter plots, with p < 0.05 considered statistically significant.The TIMER2.0 web server ( cancers . The “ImR software (V3.6.3), although some of the statistical analyses were performed automatically using the online databases described above. In all statistical analyses, p < 0.05 was considered statistically significant for all statistical analyses.Statistical analyses were performed using MACC1 in carcinogenesis, its level of expression was determined in normal tissues, tumor cell lines and various primary human cancers. Assessment of MACC1 expression levels in 31 normal tissues from the GTEx database showed that MACC1 was expressed at low levels in most normal tissues, although its expression was higher in bone marrow , cholangiocarcinoma (CHOL), COAD, lung adenocarcinoma (LUAD), rectum adenocarcinoma (READ), stomach adenocarcinoma (STAD), thyroid cancer (THCA), and uterine corpus endometrial carcinoma (UCEC), while significantly lower in five types of cancer, namely head and neck squamous cell carcinoma (HNSC), kidney chromophobe tumors (KICH), liver hepatocellular carcinoma(LIHC), lung squamous cell carcinoma(LUSC), and prostate adenocarcinoma(PRAD) , cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), CHOL, COAD, diffuse large B-cell lymphoma (DLBC), esophageal carcinoma (ESCA), glioblastoma multiforme(GBM), kidney renal papillary cell carcinoma (KIRP), lower grade glioma (LGG), LUAD, ovarian serous cystadenocarcinoma (OV), pancreatic adenocarcinoma (PAAD), READ, STAD, testicular germ cell tumors (TGCT), THCA, thymoma (THYM), UCEC, and uterine carcinosarcoma (UCS). In contrast, MACC1 expression was significantly lower in eight other types of cancer, namely adrenocortical carcinoma (ACC), HNSC, acute myeloid leukemia (LAML), LIHC, LUSC, pheochromocytoma and paraganglioma (PCPG), PRAD, and skin cutaneous melanoma (SKCM). The levels of expression of MACC1, however, did not differ significantly in kidney chromophobe (KICH) and kidney renal clear cell carcinoma (KIRC) samples  . Because samples . Taken tMACC1 expression in patients with 33 types of human cancers from the TCGA database was analyzed. The correlations between MACC1 expression with overall survival (OS), disease-specific survival (DSS) and progression-free interval (PFI) were determined using the R Package survival (version 3.2–7), with forest plots were used to evaluate the relationship between MACC1 expression and patient prognosis. Higher expression of MACC1 mRNA was indicative of shorter OS in patients with LGG , PAAD , UVM , BRCA , COAD , and BLCA , but correlated with an increased OS in patients with KIRC , KIRP , SKCM , KICH , CHOL , PCPG , and STAD  , PAAD , UVM , ESCA , COAD , and BLCA , but were associated with a longer DDS in patients with KIRC , KIRP , SKCM , HNSC , CHOL , KICH , and STAD  , PAAD , UVM , DLBC , GBM , COAD , and BLCA , but correlated with a longer PFI in patients with KIRC , KIRP , KICH , HNSC , CHOL , and SKCM   . Higher  p=0.01) . Higher  p=0.03) . The leve tumors .MACC1 mRNA was upregulated in COAD. To further understand the importance of MACC1mRNA and protein levels in COAD, MACC1 expression data from the TCGA and CPTAC databases were analyzed. Unpaired analysis of samples from the GEPIA2 database showed that MACC1 mRNA levels were significantly higher in 275 COAD samples than in 349 normal colon tissue samples  in distinguishing COAD from control samples. The optimal MACC1 mRNA cut-off, 2.756, had a sensitivity of 0.976, a specificity of 0.933 and an accuracy of 0.909, suggesting that MACC1 may be a potential biomarker to distinguish COAD from normal tissues , race, carcinoembryonic antigen (CEA) levels, T stage, N stage, M stage, pathologic stage, perineural invasion and lymphatic invasion  are an important class of gene regulators responsible for regulating the expression of many key genes at the transcriptional and post-transcriptional levels. miRNAs are mainly involved in the negative regulation of gene expression. To assess whether  network . Because in COAD . Further in COAD  and that in COAD . These fMACC1 mRNA. Thus, the levels of these lncRNAs should correlate negatively with the levels of miRNA, but positively with the levels of MACC1 mRNA. Determination by GEPIA2 of the levels of expression of the predicted lncRNAs in COAD and corresponding normal tissues showed that, of the 113 possible lncRNAs, only four, MIR4435-2HG, LINC00511, MAFG-AS1, and ZFAS1, were expressed at significantly higher levels in COAD than in corresponding normal tissue samples , M1 macrophages (TLR2 and IRF5), M2 macrophages , neutrophils , and dendritic cells  in COAD  cells [MACC1 has an important role in CRC carcinogenesis and progression through the β-catenin signaling pathway and mesenchymal-epithelial transition [MACC1 plays a carcinogenic role in COAD.Although  cancers –26, the findings . MACC1 kC) cells . These fansition . CombineMACC1 could be predicted using the program starBasev2.0, which includes PITA, RNA22, miRmap, microT, miRanda, PicTar, and TargetScan[MACC1. No study to date, however, has assessed the role of hsa-miR-642a-5p targeting of MACC1 in CRC.Non-coding RNAs (ncRNAs), including small microRNAs (miRNAs), lncRNAs, and circular RNAs (circRNAs), were shown to be involved in multiple biological processes in various types of cancer by directly or indirectly interfering with gene expression by inter-communication through the ceRNA mechanism –33. ThusargetScan. Of the argetScan, and lowargetScan. OverexpargetScan. In CRC,argetScan. ExpressMACC1 axis in COAD should be highly expressed. Analysis using starBase v2.0 software predicted 113 lncRNAs that could be associated with the hsa-miR-642a-5p/MACC1 axis. Expression and correlation analyses identified ZFAS1 as the most likely upstream lncRNA. ZFAS1 lncRNA is a major protein regulator involved in various human cancers, including COAD. ZFAS1 has been shown to promote the proliferation, migration, and invasion of esophageal squamous cell carcinoma (ESCC) cells, and to inhibit their apoptosis by upregulating STAT3 and downregulating miR-124 [MACC1 axis constitutes the potential regulatory pathway that promotes the progression of COAD.Based on the ceRNA hypothesis , the lev miR-124 . ZFAS1 w miR-124 . The onc miR-124 . Further miR-124 . Taken tMACC1 upregulation increases the infiltration of various immune cells into COADs, including NK cells, macrophages, and neutrophils. Moreover, MACC1 expression showed significant positive correlations with biomarkers of NK cells, macrophages, neutrophils, and dendritic cells. These results suggest that MACC1 is associated with immune cell invasion into COAD. However, the present study has several limitations. The results of this study were based on bioinformatics analysis from various databases but were not validated experimentally. Confirmation of these findings requires in vivo and in vitro experiments.Immune cell infiltration into tumors has been shown to affect the effectiveness of chemotherapy, radiotherapy, and immunotherapy, as well as the prognosis of cancer patients –47. The MACC1 is overexpressed in several types of human cancers and that MACC1 overexpression correlates with poor prognosis and increased tumor immune cell infiltration in patients with COAD. The present study also identified the ZFAS1/hsa-miR-642a-5p axis (MACC1 in COAD. These results suggest that MACC1 participates in tumor immunity and progression of COAD through the ZFAS1/hsa-miR-642a-5p/MACC1 pathway.In conclusion, this study we have demonstrated that -5p axis  as the m