Abstract
INTRODUCTION: Colorectal cancer (CRC) represents one of the most frequent human malignancies with its underlying pathogenesis still unclear. The prevalence of multi-omics in screening biomarkers associated with CRC has largely accelerated our understanding into the pathophysiology of CRC. The present work aimed to mine the Gene Expression Omnibus (GEO) datasets associated with CRC studies and identify potential targets correlated with CRC pathogenesis. METHODS: We screened the DEGs in GSE50760 and GSE104178 and performed functional Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Furthermore, the overlapped DEGs were subjected to functional GO enrichment and KEGG pathway enrichment analysis. The protein-protein interaction (PPI) network and miRNA-mRNA network were constructed based on the overlapped DEGs. The in vitro functional assays including qRT-PCR, caspase-3 and -9 activity assay, wound healing assay, CCK-8 assay and luciferase reporter assay were performed to determine the role of OTOP2/miR-3148 axis in regulating CRC cell progression. RESULTS: Fifty-three overlapped genes were screened over GSE50760 and GSE104178 and ten hub genes were identified by PPI network analysis. Expression levels of GCG, SST, NPY, GUCA2B, PYY, UCN3, GUCA2A, TMEM82 and BEST4 were not correlated with the overall survival of patients with CRC. However, the high expression of otopetrin 2 (OTOP2) in the CRC tissues was significantly correlated with better overall survival of patients with CRC. The expression of OTOP2 in CRC tissues was significantly lowever than that in normal ones. The in vitro functional assays demonstrated that OTOP2 silence reduced caspase-3/-9 activities, promoted cell migration, proliferation and epithelial-mesenchymal transition in HT29 and SW620 cells. Furthermore, miR-3148 could inversely regulate OTOP2 expression in CRC cell lines. CONCLUSION: Collectively, the work suggested the potential role of the OTOP2/miR-3148 axis in the pathophysiology of CRC by mining the GEO database.