Identification of Key Prognosis-related microRNAs in Early- and Late- Stage Gynecological Cancers Based on TCGA Data

Abstract

Background: Gynecological cancers (GCs), mainly diagnosed in the late stages of the disease, remain the leading cause of global mortality in women. microRNAs (miRNAs) have been explored as diagnostic and prognostic biomarkers of cancer. Evaluating miRNA signatures to develop prognostic models could be useful in predicting high-risk patients with GC. Specifically, the identification of miRNAs associated with different stages of cancer can be beneficial in patients diagnosed with cancer. Objective: This study aimed to identify potential miRNA signatures for constructing optimal prognostic models in three major GCs using The Cancer Genome Atlas (TCGA) database Methods: Stage-specific Differentially Expressed microRNAs (DEmiRs) were identified and validated in public and in-house expression datasets. Moreover, various bioinformatics investigations were used to identify potential DEmiRs associated with the disease. All DEmiRs were analyzed using three penalized Cox regression models: lasso, adaptive lasso, and elastic net algorithms. The combined outcomes were evaluated using Best Subset Regression (BSR). Prognostic DEmiR models were evaluated using Kaplan–Meier plots to predict risk scores in patients. The biological pathways of the potential DEmiRs were identified using functional enrichment analysis. Results: A total of 65 DEmiRs were identified in the three cancer types; among them, 17 demonstrated dysregulated expression in public datasets of cervical cancer, and the expression profiles of 9 DEmiRs were changed in CCLE-OV cells, whereas those of 10 are dysregulated in CCLE-UCEC cells. Additionally, ten miRNA expression profiles were observed to be the same as DEmiRs in three OV cancer cell lines. Approximately 30 DEmiRs were experimentally validated in particular cancers. Furthermore, 23 DEmiRs were correlated with the overall survival of the patients. The combined analysis of the three penalized Cox models and BSR analysis predicted eight potential DEmiRs. A total of five models based on five DEmiRs (hsa-mir-526b, hsa-mir-508, and hsa-mir-204 in CESC and hsa-mir-137 and hsa-mir1251 in UESC samples) successfully differentiated high-risk and low-risk patients. Functional enrichment analysis revealed that these DEmiRs play crucial roles in GCs. Conclusion: We report potential DEmiR-based prognostic models to predict the high-risk patients with GC and demonstrate the roles of miRNA signatures in the early- and late-stage of GCs.