Computational approaches for discovering significant microRNAs, microRNA-mRNA regulatory pathways, and therapeutic protein targets in endometrial cancer

Abstract

Endometrial cancer (EC) is a urogenital cancer affecting millions of post-menopausal women, globally. This study aims to identify key miRNAs, target genes, and drug targets associated with EC metastasis. The global miRNA and mRNA expression datasets of endometrial tissue biopsies (24 tumors +3 healthy tissues for mRNA and 18 tumor +4 healthy tissues for miRNAs), were extensively analyzed by mapping of DEGs, DEMi, biological pathway enrichment, miRNA-mRNA networking, drug target identification, and survival curve output for differentially expressed genes. Our results reveal the dysregulated expression of 26 miRNAs and their 66 target genes involved in focal adhesions, p53 signaling pathway, ECM-receptor interaction, Hedgehog signaling pathway, fat digestion and absorption, glioma as well as retinol metabolism involved in cell growth, migration, and proliferation of endometrial cancer cells. The subsequent miRNA-mRNA network and expression status analysis have narrowed down to 2 hub miRNAs (hsa-mir-200a, hsa-mir-429) and 6 hub genes (PTCH1, FOSB, PDGFRA, CCND2, ABL1, ALDH1A1). Further investigations with different systems biology methods have prioritized ALDH1A1, ABL1 and CCND2 as potential genes involved in endometrial cancer metastasis owing to their high mutation load and expression status. Interestingly, overexpression of PTCH1, ABL1 and FOSB genes are reported to be associated with a low survival rate among cancer patients. The upregulated hsa-mir-200a-b is associated with the decreased expression of the PTCH1, CCND2, PDGFRA, FOSB and ABL1 genes in endometrial cancer tissue while hsa-mir-429 is correlated with the decreased expression of the ALDH1A1 gene, besides some antibodies, PROTACs and inhibitory molecules. In conclusion, this study identified key miRNAs (hsa-mir-200a, hsa-mir-429) and target genes ALDH1A1, ABL1 and CCND2 as potential biomarkers for metastatic endometrial cancers from large-scale gene expression data using systems biology approaches.