Dataset Information

Development and validation of m6A regulators' prognostic significance for endometrial cancer.

ABSTRACT:

Background

Endometrial cancer (EC) is the sixth most common cancer in women globally. It has been found that the expression levels of m6A regulators can be potentially used for prognostic stratification in some cancers, but the role of m6A regulators in EC prognosis remains unclear.

Methods

The data of 584 EC samples were downloaded from The Cancer Genome Atlas and the mRNA expression profiles of 20 m6A regulators were analyzed, followed by functional enrichment analysis, immune infiltration analysis, and least absolute shrinkage and selection operator method-COX regression analysis.

Results

The mRNA expression levels of 20 m6A regulators were significantly different between cancer samples across different grades. The 548 EC samples could be clearly divided into 2 clusters. Kaplan-Meier survival analysis proved that these two groups had highly different overall survival probabilities. Besides, the univariate regression analysis further reserved eight genes related to overall survival from the 20 m6A regulators. We established a prognostic signature including two genes, that is, IGF2BP1 and YTHDF3, that showed a strong ability for stratifying prognostically different EC patients. We identified 3239 differentially expressed genes between the high- and low-risk groups, involving in multiple biological processes and signaling pathways. Meanwhile, 6 differentially infiltrated immune cell types between the high- and low-risk groups could effectively distinguish the high- and low-risk EC groups. The expressions of immune checkpoints were different between high- and low-risk EC patients.

Conclusion

We first report the prognostic role of m6A regulators in EC, which should contribute to a better understanding of the underlying mechanisms of EC pathogenesis and progression.

SUBMITTER: Pang X

PROVIDER: S-EPMC8257909 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Uterine corpus endometrial carcinoma (UCEC) is the most common type of gynecologic malignancy worldwide. Despite advances in the treatments of UCEC, its incidence and mortality rates are still increasing. N6-methyladenosine (m6A) is the most common form of RNA modification and has attracted increasing interest in cancer pathogenesis and progression. Thus, we aimed to identify the landscape of m6A regulators and build a prognostic gene signature in UCEC. In this study, we first analyzed copy number variations (CNVs), single nucleotide variations (SNVs) and gene expression profiles as well as matched clinical information of UCEC patients from The Cancer Genome Atlas (TCGA) database. Next, we determined that CNVs in m6A regulatory genes had a significant negative impact on patient survival. The mRNA expression levels of a total of 16 m6A regulators were significantly correlated with different CNV patterns. Using univariate Cox regression analysis, IGF2BP1, KIAA1429, IGF2BP3, YTHDF3, and IGF2BP2 were found to be closely associated with UCEC patient survival outcomes. Based on the least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression models, we built a 3-gene (IGF2BP3, KIAA1429 and IGF2BP1) signature of m6A regulators with prognostic value in UCEC that could effectively predict patient prognosis (log-rank test p-value < 0.0001). In addition, risk scores were significantly different between patients stratified by tumor stage, SNV, and CNV. Multivariate Cox regression analysis suggested that risk score might be an independent prognostic indicator for the overall survival of patients with UCEC (p-value < 0.05). Gene enrichment analysis indicated that high IGF2BP1 gene expression is associated with cytoplasmic stress granules. KIAA1429 gene expression is associated with cellular nucleic acid metabolism. The expression of the IGF2BP3 gene is associated with RNA binding processes. In conclusion, we determined that genetic alterations in m6A regulatory genes could be effective and reliable biomarkers for UCEC prognosis prediction.