Abstract
MicroRNAs (miRNAs) are small non-coding RNAs regulating the expression of target genes, and they are involved in cancer initiation and progression. Even though many cancer-related miRNAs were identified, their functional impact may vary, depending on their effects on the regulation of other miRNAs and genes. In this study, we propose a novel method for the prioritization of candidate cancer-related miRNAs that may affect the expression of other miRNAs and genes across the entire biological network. For this, we propose three important features: the average expression of a miRNA in multiple cancer samples, the average of the absolute correlation values between the expression of a miRNA and expression of all genes, and the number of predicted miRNA target genes. These three features were integrated using order statistics. By applying the proposed approach to four cancer types, glioblastoma, ovarian cancer, prostate cancer, and breast cancer, we prioritized candidate cancer-related miRNAs and determined their functional roles in cancer-related pathways. The proposed approach can be used to identify miRNAs that play crucial roles in driving cancer development, and the elucidation of novel potential therapeutic targets for cancer treatment.
Highlights
Several algorithms predict miRNA target genes based on the sequence complementary between these genes and miRNAs in the seed regions, and the predicted gene-miRNA interactions can be accessed through databases such as microCosm[3], Pictar[4], and TargetScans[5]
We propose a novel approach based on order statistics that prioritizes miRNAs whose expression changes significantly affect cancer development
We demonstrated that our order statistics-based method outperformed the average ranking ratio approach
Summary
Several algorithms predict miRNA target genes based on the sequence complementary between these genes and miRNAs in the seed regions, and the predicted gene-miRNA interactions can be accessed through databases such as microCosm[3], Pictar[4], and TargetScans[5]. These approaches may uncover novel miRNA-disease relationships, they are highly dependent on the previously obtained knowledge, while predicting miRNAs with unknown relationships to any disease is difficult. Zhang et al.[14] identified seven differentially expressed miRNAs (DE miRNAs), with their expression significantly associated with the survival time in hepatocellular carcinoma They associated these seven signature miRNAs with several clinical parameters, such as tumor stage, tumor status, and gender, and found independent prognostic parameters based on univariate and multivariate analysis. Computational approaches incorporating negative correlations between gene and miRNA expression levels have accelerated the identification of cancer-related miRNAs. MiRNA-gene pairs have been predicted based on various models, including linear regression, lasso regression, and Bayesian model, and these models have been applied to several cancer datasets[17]. The recent availability of paired miRNA and gene expression levels in multiple cancer datasets found in The Cancer Genome Atlas (TCGA)[18,19,20,21] allowed simultaneous analysis of miRNA and gene expression in multiple cancer types[22]
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