Computational Identification of Biomarker Signatures, Pathways and Regulators to Discover Therapeutic Target for Non-Small Cell Lung Cancer

Background Lung cancer is a critical health issue of human neoplasm in worldwide. Non-small cell lung cancer (NSCLC) is the most common lung cancer from malignant disease. This study is analyses to identify biomarkers for targeting systemic drugs based on systems biology in NSCLC. The aim of this study was to select the genes expressions and pathways to discover biomolecules at protein and RNA levels which could identify potential therapeutic targets. Methods Different statistical method: LIMMA, ANOVA, SAM and Kruskal Wallis (KW) were used to identify DEGs with significance from the transcriptome data which was obtained from the Gene Expression Omnibus (NCBI-GEO) dataset. By using Robust Multi-Array Average (RMA) expression measure DEGs were normalized and identified from the gene expression data set and it was applied in the “Affy†package of Bioconductor platform in R. Gene expression profiles were analyzed with genome-scale biomolecular networks (i,e., protein-protein interaction, DAVID, Kaplan-Meier Plot, molecular docking). Results Ten (10) hub proteins and four (4) transcription factors (TFs) were significant biomarkers as a potential drug target. Risk discrimination performance of the hub proteins- AURKB, CDK1, CDC20, MAD2L1, CCNB1, BUB1, CCNB2, AURKA, NDC80 and NUF2 were also evaluated. In the molecular docking simulation study, we are suggesting Lurbinectedin, Etopophos, Entrectinib, Imatinib, mesylate, and Irinotecanas candidate drugs that have high binding affinity scores with most of the key proteins. Among 10 hub proteins two were confirmed as novel and provided a prognostic model and suggested three candidate drugs. Conclusion Based on these molecular signatures and proposed drugs further experimental studies can continued. These findings not only demonstrate the diagnosis, but also provide prognostic markers and therapeutic targets for NSCLC.