Dataset Information

Construction of a risk prediction model using m6A RNA methylation regulators in prostate cancer: comprehensive bioinformatic analysis and histological validation.

ABSTRACT:

Background

Epigenetic reprogramming reportedly has a crucial role in prostate cancer (PCa) progression. RNA modification is a hot topic in epigenetics, and N6-methyladenosine (m6A) accounts for approximately 60% of RNA chemical modifications. The aim of this study was to evaluate the m6A modification patterns in PCa patients and construct a risk prediction model using m6A RNA regulators.

Materials and methods

Analyses were based on the levels of 25 m6A regulators in The Cancer Genome Atlas (TCGA). Differentially expressed gene (DEG) and survival analyses were performed according to TCGA-PRAD clinicopathologic and follow-up information. To detect the influences of m6A regulators and their DEGs, consensus clustering analysis was performed, and tumor mutational burden (TMB) estimation and tumor microenvironment (TME) cell infiltration were assessed. mRNA levels of representative genes were verified using clinical PCa data.

Results

Diverse expression patterns of m6A regulators between tumor and normal (TN) tissues were detected regarding Gleason score (GS), pathological T stage (pT), TP53 mutation, and survival comparisons, with HNRNPA2B1 and IGFBP3 being intersecting genes. HNRNPA2B1 was upregulated in advanced stages (GS > 7, pT3, HR > 1, and TP53 mutation), as verified using clinical PCa tissue. Three distinct m6A modification patterns were identified through consensus clustering analysis, but no significant difference was found among these groups in recurrence-free survival (RFS) analysis. Six DEGs of m6A clusters (m6Aclusters) were screened through univariate Cox regression analysis. MMAB and PAIAP2 were intersecting genes for the five clinical factors. MMAB, which was upregulated in PCa compared with TN, was verified using clinical PCa samples. Three distinct subgroups were established according to the 6 DEGs. Cluster A involved the most advanced stages and had the poorest RFS. The m6A score (m6Ascore) was calculated based on the 6 genes, and the low m6Ascore group showed poor RFS with a negative association with infiltration for 16 of 23 immune-related cells.

Conclusion

We screened DEGs of m6Aclusters and identified 6 genes (BAIAP2, TEX264, MMAB, JAGN1, TIMM8AP1, and IMP3), with which we constructed a highly predictive model with prognostic value by dividing TCGA-PRAD into three distinct subgroups and performing m6Ascore analysis. This study helps to elucidate the integral effects of m6A modification patterns on PCa progression.

SUBMITTER: Quan Y

PROVIDER: S-EPMC8772220 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Intervertebral disc degeneration (IVDD) is one of the most prevalent causes of chronic low back pain. The role of m6A methylation modification in disc degeneration (IVDD) remains unclear. We investigated immune-related m6A methylation regulators as IVDD biomarkers through comprehensive analysis and experimental validation of m6A methylation regulators in disc degeneration. The training dataset was downloaded from the GEO database and analysed for differentially expressed m6A methylation regulators and immunological features, the differentially regulators were subsequently validated by a rat IVDD model and RT-qPCR. Further screening of key m6A methylation regulators based on machine learning and LASSO regression analysis. Thereafter, a predictive model based on key m6A methylation regulators was constructed for training sets, which was validated by validation set. IVDD patients were then clustered based on the expression of key m6A regulators, and the expression of key m6A regulators and immune infiltrates between clusters was investigated to determine immune markers in IVDD. Finally, we investigated the potential role of the immune marker in IVDD through enrichment analysis, protein-to-protein network analysis, and molecular prediction. By analysising of the training set, we revealed significant differences in gene expression of five methylation regulators including RBM15, YTHDC1, YTHDF3, HNRNPA2B1 and ALKBH5, while finding characteristic immune infiltration of differentially expressed genes, the result was validated by PCR. We then screen the differential m6A regulators in the training set and identified RBM15 and YTHDC1 as key m6A regulators. We then used RBM15 and YTHDC1 to construct a predictive model for IVDD and successfully validated it in the training set. Next, we clustered IVDD patients based on the expression of RBM15 and YTHDC1 and explored the immune infiltration characteristics between clusters as well as the expression of RBM15 and YTHDC1 in the clusters. YTHDC1 was finally identified as an immune biomarker for IVDD. We finally found that YTHDC1 may influence the immune microenvironment of IVDD through ABL1 and TXK. In summary, our results suggest that YTHDC1 is a potential biomarker for the development of IVDD and may provide new insights for the precise prevention and treatment of IVDD.

Project description:BackgroundN6-methyladenosine (m6A) is found in almost all nuclear RNAs of eukaryotes, playing an important and diverse role in many biological processes. Nonetheless, the roles of m6A regulators in abdominal aortic aneurysm (AAA) unknown. Therefore, there is a pressing need to identify m6A RNA methylation regulators in the diagnosis of AAA, determination of individualized risk, discovery of therapeutic targets, and improve understanding of pathogenesis.MethodsThe GSE98278 dataset were obtained from the Gene Expression Omnibus (GEO) database to perform differential analysis of m6A-related regulators between elective stable abdominal aortic aneurysms (eAAA) and abdominal aortic aneurysm ruptured (rAAA). The random forest model was used to screen candidate m6A regulators to predict the risk of rAAA. The single sample gene set enrichment analysis (ssGSEA) method was then used to evaluate the abundance of 23 immune cells in AAA. The m6A RNA Methylation Quantification Kit was used to measure the total m6A levels of AAA and normal abdominal aorta. The terminal deoxynucleotidyl transferase mediated dUTP Nick-End Labeling (TUNEL) apoptosis assay kit was used to detect the human aortic smooth muscle cells (HASMCs) apoptotic after RBM15 knockdown. Mechanically, RBM15 knockdown was found to reduce the expression of CASP3 in an m6A-dependent manner by Western blotting, RNA immunoprecipitation (RIP), and methylated RNA immunoprecipitation-quantitative polymerase chain reaction (MeRIP-RT-PCR).ResultsRBM15, WTAP, ALKBH5, and IGFBP3 were highly expressed in rAAA. In contrast, RBM15B showed opposite results (P<0.05). The high m6A level in the rAAA compared with eAAA and normal abdominal aorta (P<0.05). The random forest model was used to screen 5 candidate m6A regulators to predict the risk of rAAA. Expression of the 5 m6A methylation regulators was validated in AAA samples (P<0.05). RBM15 knockdown inhibited the apoptosis of HASMCs. RBM15 knockdown reduced the expression of CASP3 in an m6A-dependent manner. A strong correlation between the five m6A methylation regulators and immune cell infiltration was identified.ConclusionsIn summary, m6A regulators play nonnegligible roles in the occurrence of rAAA. Our investigation of m6A patterns may be able to guide future immunotherapy strategies for AAA.