The modularity and dynamicity of miRNA–mRNA interactions in high-grade serous ovarian carcinomas and the prognostic implication

Abstract

Ovarian carcinoma is the fifth-leading cause of cancer death among women in the United States. Major reasons for this persistent mortality include the poor understanding of the underlying biology and a lack of reliable biomarkers. Previous studies have shown that aberrantly expressed MicroRNAs (miRNAs) are involved in carcinogenesis and tumor progression by post-transcriptionally regulating gene expression. However, the interference of miRNAs in tumorigenesis is quite complicated and far from being fully understood. In this work, by an integrative analysis of mRNA expression, miRNA expression and clinical data published by The Cancer Genome Atlas (TCGA), we studied the modularity and dynamicity of miRNA-mRNA interactions and the prognostic implications in high-grade serous ovarian carcinomas. With the top transcriptional correlations (Bonferroni-adjusted p-value < 0.01) as inputs, we identified five miRNA-mRNA module pairs (MPs), each of which included one positive-connection (correlation) module and one negative-connection (correlation) module. The number of miRNAs or mRNAs in each module varied from 3 to 7 or from 2 to 873. Among the four major negative-connection modules, three fit well with the widely accepted miRNA-mediated post-transcriptional regulation theory. These modules were enriched with the genes relevant to cell cycle and immune response. Moreover, we proposed two novel algorithms to reveal the group or sample specific dynamic regulations between these two RNA classes. The obtained miRNA-mRNA dynamic network contains 3350 interactions captured across different cancer progression stages or tumor grades. We found that those dynamic interactions tended to concentrate on a few miRNAs (e.g. miRNA-936), and were more likely present on the miRNA-mRNA pairs outside the discovered modules. In addition, we also pinpointed a robust prognostic signature consisting of 56 modular protein-coding genes, whose co-expression patterns were predictive for the survival time of ovarian cancer patients in multiple independent cohorts.

Integrated microRNA and mRNA signatures associated with overall survival in epithelial ovarian cancer

“…One of the reasons of such discrepancy among various studies may be attributed to the source of miRNAs (whole blood, plasma, or serum) from EOC patients and/or the platform (qPCR or microarray) used for analysis [43]. Moreover, a substantial number of studies on miRNA/mRNA signatures in EOC have been developed based on analyzing tissue from patients with high grade serous OC from The Cancer Genome Atlas (TCGA) database [44][45][46][47][48][49][50][51][52]. However, the use of only one dataset for discovery may not reflect the heterogeneity of EOC [53].…”

Driver gene mutations based clustering of tumors: methods and applications

“…The prediction strength of a transcriptomic signature (i.e. the expression profiles of the PM-genes) for patient survival is tested by a clustering-based method and a Singular Value Decomposition (SVD) based procedure similar to that used in ( Zhang et al., 2016b ). In the former, tumor samples are firstly stratified into two or three groups based on their expression matrix of the PM-genes and then the survival curves of these groups are compared.…”

How hsa‐miR‐495 performed in the tumorigenesis of pancreatic adenocarcinoma by bioinformatics analysis

“…10,11 Because miRNAs are stable in plasma and serum, they are promising potential biomarkers and have been extensively studied in human cancer diagnosis, progression, and metastasis. 12 All those studies have proven that miRNAs and their regulatory pathways may provide new insights in cancer research. Recently, many studies focused on the application of miRNAs in the diagnosis of cancers and other diseases.…”

“…Importantly, miRNAs play critical roles in the regulation of normal physiological and pathophysiological processes . Because miRNAs are stable in plasma and serum, they are promising potential biomarkers and have been extensively studied in human cancer diagnosis, progression, and metastasis . All those studies have proven that miRNAs and their regulatory pathways may provide new insights in cancer research.…”