Informatics in Medicine Unlocked (Jan 2021)
New potential anticancer drug-like compounds for squamous cell lung cancer using transcriptome network analysis
Abstract
Lung Squamous Cell Carcinoma (SQCC) is one of the deadliest non-small cell lung cancers that does not respond well to chemotherapy or radiation therapy. Targeted therapy can prevent SQCC progression by blocking messages sent to SQCC cells. Understanding the genetic causes of SQCC can help predict the most effective targeted therapeutic options by exploring novel candidate genes. In this respect, considering the importance of module or co-expressed genes, the RNA-seq data of SQCC from The Cancer Genome Atlas (TCGA) database was applied to create and analyze genetic modules of co-expressed genes using Weighted Gene Correlation Network Analysis (WGCNA). The results revealed that the genes were clustered into five modules. Among them, we have observed two modules with a high number of associated oncogenes for SQCC. Focusing on a subset of genes carries the advantage of removing challenges in large network analysis; ergo, from network analysis and module enrichment analysis, reported five important genes and nine pathways and biological processes associated with SQCC. The results of the co-expression network analysis were then applied to enable drug repurposing to candidate novel drug-like compounds for SQCC. This strategy can be different from other drug repurposing approaches since it employed co-expression network analysis results. Seven potential drug-like compounds associated with SQCC targeted therapy were then found. Though the study has a formal procedure flow, the introduction of a few drug-like compounds for SQCC not only presents a brighter vision for further follow-up studies but can also help scientists with positive biological evidence.
