Lung Cancer Biomarker Identification from Differential Expression Analysis Using RNA-Seq Data for Multitargeted Drug Designing

Lung cancer poses a significant global health challenge, causing most cancer deaths. It therefore necessitates a detailed exploration into its molecular intricacies in search of potential treatment targets. The strategy is to delay disease progression and to practice early intervention to reduce the number of patients that ultimately develop lung cancer. Therefore, promising novel biomarkers for early diagnosis are urgently required. We performed an RNA sequencing analysis of lung cancer, using the SRA database (SRR119055), with 60 samples, including 30 control and 30 tumorous samples. A DEG analysis of tumorous and healthy subjects was performed using Bioconductor in R with its other packages to transform and normalize all transcriptomic data. We identified important genes from the lung cancer samples which include five upregulated genes, namely, COL11A1, CTPS1, SULF1, SPP1, and DIO2, and five downregulated genes, which are EN1, ARRDC3-AS1, VEGFD, TMEM100, and PSEN1, from the network and their associated genes. We also identified two hub genes, PRKACB and TAOK1, responsible for cell proliferation, differentiation, apoptosis, and cytoskeletal dynamics. Furthermore, we repurposed the FDA-approved drugs as multitargeted inhibitors against all upregulated genes to inhibit the function of most genes, followed by DFT and MD simulation to validate their effectiveness in lung cancer.