Abstract
BACKGROUND: High-throughput data generation is developing in the cancer area and offers a better opportunity of understanding molecular pathways involved in the progression of tumors. Meta-analysis of gene expression based on data integration makes it possible to determine changes in gene expression with more accuracy. This approach and downstream analysis were utilized for colorectal cancer to identify promising biomarkers and drug targets. MATERIALS AND METHODS: First, a systematic search was performed in the Gene Expression Omnibus (GEO) database. Meta-analysis was used to obtain differentially expressed (DE) genes from the NetworkAnalyst database. Moreover, microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and transcription factors (TFs) enriched with DE genes were determined by the Enrichr database. The integrated DE genes-miRNAs-lncRNAs-TFs network was constructed and analyzed using Cytoscape software. Then, the downstream analyses of hub genes were performed. RESULTS: The primary candidate genes, HNF4A, FLI1, MITF, E2F4, SPI1, FOXA2, VDR, and SMAD3, which are overlapped nodes with the highest degree, betweenness, and closeness centrality parameters in the top 30 nodes were selected. Also, the hsa-miR-26b-5p, hsa-miR-16-5p, hsa-miR-124-3p, and hsa-miR-92a-3p were introduced as key non-coding RNAs with the highest degree and betweenness centralities. For the determination of the clinical significance of hub genes, the mutation, differential expression, and survival analysis were assayed. Also, the drugs related to candidate hub genes were determined. The functional analysis revealed pathways significantly related to cancer progression. CONCLUSION: Employing systems biology approaches with holistic insight can identify essential genes and their regulation as possibilities for further experimental testing.