miRNA-mRNA regulatory network analysis of tumor-associated MSCs and cancer-associated fibroblasts (CAFs) in lung cancer microenvironment.

Abstract

e20521 Background: Cancer is a complex disease involving not only cancer cells, but also the surrounding microenvironment, which includes various cells, such as cancer-associated fibroblasts (CAFs) and mesenchymal stem cells (MSCs). These cells are essential for tumorigenesis, progression, and metastasis by interacting with cancer cells through a variety of mechanisms, such as the modulation of gene expression through microRNAs (miRNAs) and messenger RNAs (mRNAs). Identifying miRNA-mRNA network in CAFs and MSCs can show novel regulatory mechanisms for developing more effective therapeutic strategies targeting both cancer cells and their supportive microenvironment. Methods: Transcriptomics (GSE104636) and miRNA expression (GSE113805) datasets downloaded from Gene Expression Omnibus database. Differentially expressed genes (DEGs) and differential expressed miRNAs (DEMs) were identified using TAC and R software. The STRING database and Cytoscape software were employed to identify and analyze hub genes. The related target genes of DEMs were predicted using miRWalk database. Then, the target genes of DEM were overlapped with DEGs to obtain the relationships between DEMs and DEGs. MATLAB software was used to determine the gene-miRNA interactions, and the miRNA-mRNA network was visualized using Cytoscape software. Enrichr Database was used to conduct KEGG pathway enrichment analyses in order to investigate the underlying functions of all DEGs, genes in the network, and modules. Results: In this study, 1496 DEGS were identified between paired tumor- and normal tissue-associated MSCs. Utilizing the String database and cytoscape software, we identified 150 hub proteins from the microarray data through the PPI network analysis. Also, in this analysis, 110 miRNAs were detected as differentially expressed. 478 Gene target of these mirs determined by MirWalk database. The potential interactions between hub genes and DEMs were determined by analyzing DEM and hub gene data using MATLAB software. Subsequently, we constructed the miRNA-mRNA network by Cytoscape and identified several miRNAs (hsa-miR-1193; hsa-miR-1197; hsa-miR-300; hsa-miR-543; hsa-miR-544a and hsa-miR-665), genes (such as FBXO32, LSM5, MYCBP, NEMP1, FOSL2), and pathways (such as FoxO signaling pathway, PI3K-Akt signaling pathway and p53 signaling pathway). Conclusions: This investigation identified substantial modifications in the expression of genes and miRNAs in fibroblasts and mesenchymal stem cells associated with lung cancer. Our findings provide valuable insights into tumor-stromal interactions, the discovery of hub genes and important miRNAs in lung cancer.