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Identification of m6A-Related Biomarkers Associated with Prognosis of Colorectal Cancer.

ABSTRACT: BACKGROUND Colorectal cancer (CRC) is the second most deadly cancer in the world according to GLOBOCAN 2020 data. Accumulating evidence suggests that RNA methylation modification is also misregulated in human cancers and may be a potential ideal target for cancer treatment. MATERIAL AND METHODS m6A-related differentially expressed genes (DEGs) were identified from colon adenocarcinoma and rectum adenocarcinoma esophageal carcinoma patients with different pathological stages. The protein-protein interaction (PPI) network construction, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of DEGs were conducted. Cox regression analysis was applied to the screening of m6A-related DEGs significantly associated with the overall survival (OS), and those selected genes were used for LASSO regression analysis to construct prognostic signature and calculate patients' risk scores. RESULTS We identified 673 m6A-related DEGs from CRC patients in different pathologic stages, and 146 of them were associated with OS. CTNNB1, TRIM37, RAB7A, CASC5/KNL1, CENPE, CCNB1, UBE2H, HSPA8, KIF1A, and FBXW4 were hub genes of the PPI network. Nine m6A-related genes were screened out to build the prognostic risk model. TNM stage, vascular invasion, and the risk score were independently related to the OS of CRC patients. CONCLUSIONS Nine candidate m6A-related mRNA biomarkers (LRRC17, NFKB1, NOS2, PCDHB2, RAB7A, RPS6KA1, RRNAD1, TLE6, and UBE2H) were found to be closely related to the clinicopathology and prognosis of colorectal cancer, indicating that they could be potential prognostic biomarkers for patients with colorectal cancer.

SUBMITTER: Zhang Z

PROVIDER: S-EPMC8364289 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Genome-Wide Identification of m6A-Associated Single-Nucleotide Polymorphisms in Colorectal Cancer.

Project description:BackgroundN6-methyladenosine (m6A)-associated single-nucleotide polymorphisms (SNPs) play important roles in cancers, with previous research suggesting potential associations between m6A-SNPs and cancer. However, the relationship between the genetic determinants of m6A modification and colorectal cancer (CRC) remains unclear.MethodsAn integrative method combining raw data and summary statistics of genome-wide association studies with expression quantitative trait loci (eQTL) and differential expression data was applied to screen potential candidate CRC-associated m6A-SNPs.ResultsA total of 402 m6A-SNPs were identified as being associated with CRC (p < 0.001), with 98 showing eQTL signals. In particular, three genes were found to harbor CRC-associated m6A-SNPs: rs178184 in NOVA1, rs35782901 in HTR4, and rs60571683 in SLCO1B3. These genes were differentially expressed in at least one publicly available dataset (p < 0.05), with NOVA1 (p = 3.41×10-11) and HTR4 (p = 5.56×10-7) being significantly downregulated in CRC (dataset: GSE89076), and SLCO1B3 was significantly overexpressed (datasets: GSE32323 [p = 3.27×10-5], GSE21510 [p = 1.09×10-6], and GSE89076 [p = 7.63×10-6]).ConclusionThis study identified three m6A-SNPs (rs178184, rs35782901, and rs60571683) that may be associated with CRC. However, the lack of analysis of primary CRC samples in order to further elucidate the underlying pathogenesis is a major limitation of this study. Future investigations are needed to validate these CRC-associated m6A-SNPs and explore the m6A-mediated pathogenic mechanism in CRC.

| S-EPMC8297552 | biostudies-literature

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| S-EPMC8739709 | biostudies-literature

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| S-EPMC8753275 | biostudies-literature

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Identification and Validation of m6A-Related lncRNA Signature as Potential Predictive Biomarkers in Breast Cancer.

Project description:The metastasis and poor prognosis are still regarded as the main challenge in the clinical treatment of breast cancer (BC). Both N6-methyladenosine (m6A) modification and lncRNAs play vital roles in the carcinogenesis and evolvement of BC. Considering the unknown association of m6A and lncRNAs in BC, this study therefore aims to discern m6A-related lncRNAs and explore their prognostic value in BC patients. Firstly, a total of 6 m6A-related lncRNAs were screened from TCGA database and accordingly constructed a prognostic-predicting model. The BC patients were then divided into high-risk and low-risk groups dependent on the median cutoff of risk score based on this model. Then, the predictive value of this model was validated by the analyses of cox regression, Kaplan-Meier curve, ROC curve, and the biological differences in the two groups were validated by PCA, KEGG, GSEA, immune status as well as in vitro assay. Finally, we accordingly constructed a risk prognostic model based on the 6 identified m6A-related lncRNAs, including Z68871.1, AL122010.1, OTUD6B-AS1, AC090948.3, AL138724.1, EGOT. Interestingly, the BC patients were divided into the low-risk and high-risk groups with different prognoses according to the risk score. Notably, the risk score of the model was an excellent independent prognostic factor. In the clinical sample validation, m6A regulatory proteins were differentially expressed in patients with different risks, and the markers of tumor-associated macrophages and m6A regulators were co-localized in high-risk BC tissues. This well-validated risk assessment tool based on the repertoire of these m6A-related genes and m6A-related lncRNAs, is of highly prognosis-predicting ability, and might provide a supplemental screening method for precisely judging BC prognosis.

| S-EPMC8555664 | biostudies-literature

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Project description:Colorectal cancer (CRC) represents one of the most common malignancies with high morbidity worldwide. RNA methylation (m6A) has been considered to tremendously contribute to cancer initiation and progression since its first discovery. In this study, we comprehensively analyzed associations between mRNA expressions of m6A regulators and CRC tumor samples' epidemiologic information from the Cancer Genome Atlas (TCGA). Multivariate Cox proportional hazard model was applied to screening of m6A regulators whose mRNA expressions were significantly associated with CRC tumor samples' overall survival (OS) probability and those significant regulators were used for LASSO regression analysis to construct CRC prognosis prediction signature. As a result, two regulators i.e., YTHDC2 and ALKBH5 were picked out in multivariate analysis. CRC prognosis signature was constructed based on those two regulators through which CRC tumor samples with favorable and inferior prognosis could definitely be distinguished independent of potential confounding factors. This study should be helpful for identifying prognostic different CRC patients and guiding therapeutic method selection.

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Project description:BACKGROUND Colorectal cancer (CRC) is a common malignant tumor with high incidence and mortality worldwide. The aim of this study was to evaluate the association between differentially expressed genes (DEGs), which may function as biomarkers for CRC prognosis and therapies, and the clinical outcome in patients with CRC. MATERIAL AND METHODS A total of 116 normal mucous tissue and 930 CRC tissue datasets were downloaded from the Gene Expression Omnibus database (GEO) and The Cancer Genome Atlas (TCGA). After screening DEGs based on limma package in R. Gene Ontology (GO) and KEGG enrichment analysis as well as the protein-protein interaction (PPI) networks were performed to predict the function of these DEGs. Meanwhile, Cox proportional hazards regression was used to build a prognostic model of these DEGs. Then, Kaplan-Meier risk analysis was used to test the model in TCGA datasets and validation datasets. RESULTS In the present study, 300 DEGs with 100 upregulated genes and 200 downregulated genes were identified. The PPI networks including 162 DEGs and 256 nodes were constructed and 2 modules with high degree were selected. Moreover, 5 genes (MMP1, ACSL6, SMPD1, PPARGC1A, and HEPACAM2) were identified using the Cox proportional hazards stepwise regression. Kaplan-Meier risk curve in the TCGA and validation cohorts showed that high-risk group had significantly poor overall survival than the low-risk group. CONCLUSIONS Our study provided insights into the mechanisms of CRC formation and found 5 prognostic genes, which could potentially inform further studies and clinical therapies.

| S-EPMC6065283 | biostudies-other

Project description:Background: The incidence of skin cutaneous melanoma (SKCM) has risen more rapidly than any other solid tumor in the past few decades. The median survival for metastatic melanoma is only six to nine months and the 5°years survival rate of patients with conventional therapy is less than 5%. Our aim was to reveal the potential molecular mechanism in m6A modification of lncRNA and provide candidate prognostic biomarkers for metastatic SKCM. Methods: lncRNAs expression level was obtained by re-annotation in TCGA and CCLE datasets. m6A-related lncRNAs were selected though correlation analysis. Univariate cox regression analysis was used to screen out independent prognostic factors. LASSO Cox regression was performed to construct an m6A-related lncRNA model (m6A-LncM). Univariate survival analysis and ROC curve were used to assess the prognostic efficacy of this model and candidate lncRNAs. Enrichment analysis was used to explore the candidate genes' functions. Results: We obtained 1,086 common m6A-related lncRNAs after Pearson correlation analysis in both two datasets. 130 out of the 1,086 lncRNAs are independent prognostic factors. 24 crucial lncRNAs were filtered after LASSO Cox regression analysis. All the m6A-LncM and the 24 lncRNAs were related to overall survival. Stratified survival analysis of m6A-LncM showed that the model retains its prognostic efficacy in recurrence, radiation therapy and other subgroups. Enrichment analysis also found that these lncRNAs were immune associated. Conclusion: Here, we obtained 24 crucial lncRNAs that may be potential biomarkers to predict survival of metastatic SKCM and may provide a new insight to improve the prognosis of it.

| S-EPMC8131514 | biostudies-literature

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