In Silico Analysis of MicroRNA Expression Data in Liver Cancer

Nourhan Abu-Shahba; Elsayed Hegazy; Faiz M Khan; Mahmoud Elhefnawi

doi:10.1177/11769351231171743

In Silico Analysis of MicroRNA Expression Data in Liver Cancer

Cancer Inform. 2023 May 10:22:11769351231171743. doi: 10.1177/11769351231171743. eCollection 2023.

Authors

Nourhan Abu-Shahba^{1

2}, Elsayed Hegazy³, Faiz M Khan⁴, Mahmoud Elhefnawi⁵

Affiliations

¹ Department of Medical Molecular Genetics, Human Genetics and Genome Research Institute, National Research Centre, Cairo, Egypt.
² Stem Cell Research Group, Medical Research Center of Excellence, National Research Centre, Cairo, Egypt.
³ School of Information Technology and Computer Science, Nile University, Giza, Egypt.
⁴ Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany.
⁵ Biomedical Informatics and Chemoinformatics Group, Informatics and Systems Department, National Research Centre, Cairo, Egypt.

Abstract

Abnormal miRNA expression has been evidenced to be directly linked to HCC initiation and progression. This study was designed to detect possible prognostic, diagnostic, and/or therapeutic miRNAs for HCC using computational analysis of miRNAs expression. Methods: miRNA expression datasets meta-analysis was performed using the YM500v2 server to compare miRNA expression in normal and cancerous liver tissues. The most significant differentially regulated miRNAs in our study undergone target gene analysis using the mirWalk tool to obtain their validated and predicted targets. The combinatorial target prediction tool; miRror Suite was used to obtain the commonly regulated target genes. Functional enrichment analysis was performed on the resulting targets using the DAVID tool. A network was constructed based on interactions among microRNAs, their targets, and transcription factors. Hub nodes and gatekeepers were identified using network topological analysis. Further, we performed patient data survival analysis based on low and high expression of identified hubs and gatekeeper nodes, patients were stratified into low and high survival probability groups. Results: Using the meta-analysis option in the YM500v2 server, 34 miRNAs were found to be significantly differentially regulated (P-value ⩽ .05); 5 miRNAs were down-regulated while 29 were up-regulated. The validated and predicted target genes for each miRNA, as well as the combinatorially predicted targets, were obtained. DAVID enrichment analysis resulted in several important cellular functions that are directly related to the main cancer hallmarks. Among these functions are focal adhesion, cell cycle, PI3K-Akt signaling, insulin signaling, Ras and MAPK signaling pathways. Several hub genes and gatekeepers were found that could serve as potential drug targets for hepatocellular carcinoma. POU2F1 and PPARA showed a significant difference between low and high survival probabilities (P-value ⩽ .05) in HCC patients. Our study sheds light on important biomarker miRNAs for hepatocellular carcinoma along with their target genes and their regulated functions.

Keywords: MirNome analysis; hepatocellular carcinoma; miRNA TF target network; miRNA expression analysis; miRNA target enrichment analysis; miRNA target prediction.