Identification of key differentially expressed MicroRNAs in cancer patients through pan-cancer analysis. (1st December 2018)

• Record Type: Journal Article
• Authors: Hu, Yu; Dingerdissen, Hayley; Gupta, Samir; Kahsay, Robel; Shanker, Vijay; Wan, Quan; Yan, Cheng; Mazumder, Raja
• Is Part Of: Computers in biology and medicine. Volume 103(2018)
• Page Start: 183
• Page End: 197
• Publication Date: 2018-12-01
• Subjects: miRNA -- Differential expression analysis -- Pan-cancer -- miRNA target -- Functional analysis; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2018.10.021
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: microRNAs (miRNAs) functioning in gene silencing have been associated with cancer progression. However, common abnormal miRNA expression patterns and their potential roles in cancer have not yet been evaluated. To account for individual differences between patients, we retrieved miRNA sequencing data for 575 patients with both tumor and adjacent non-tumorous tissues from 14 cancer types from The Cancer Genome Atlas (TCGA). We then performed differential expression analysis using DESeq2 and edgeR. Results showed that cancer types can be grouped based on the distribution of miRNAs with different expression patterns between tumor and non-tumor samples. We found 81 significantly differentially expressed miRNAs (SDEmiRNAs) in a single cancer. We also found 21 key SDEmiRNAs (nine over-expressed and 12 under-expressed) associated with at least eight cancers each and enriched in more than 60% of patients per cancer, including four newly identified SDEmiRNAs (hsa-mir-4746, hsa-mir-3648, hsa-mir-3687, and hsa-mir-1269a). The downstream effects of these 21 SDEmiRNAs on cellular function were evaluated through enrichment and pathway analysis of 7186 protein-coding gene targets mined from literature reports of differential expression of miRNAs in cancer. This analysis enables identification of SDEmiRNA functional similarity in cell proliferation control across a wide range of cancers, and assembly of common regulatory networks over cancer-related pathways. These findings were validated by construction of a regulatory network in the PI3K pathway. This study provides evidence for the value of further analysis of SDEmiRNAs as potential biomarkers and therapeutic targets for cancer diagnosis and treatment. Highlights: A large-scale investigation of miRNAs between tumor and adjacent non-tumorous samples. A novel perspective to group cancer types based on the expression profiles of miRNAs. Identification of common potential biomarker miRNAs across eight or more cancer types. A method to build common regulatory networks of key miRNAs across multiple cancers.