An integrated bioinformatics approach to early diagnosis, prognosis and therapeutics of non-small-cell lung cancer.

Abstract

Non-small-cell lung cancer (NSCLC) is one of the most deadly tumors characterized by poor survival rates. Advances in therapeutics and precise identification of biomarkers can potentially reduce the mortality rate. Thus, this study aimed to identify a set of common and stable gene biomarkers through integrated bioinformatics approaches that might be effective for NSCLC early diagnosis, prognosis, and therapies. Four gene expression profiles (GSE19804, GSE19188, GSE10072, and GSE32863) downloaded from the Gene Expression Omnibus database to identify common differential expressed genes (DEGs). A total of 213 overlapping DEGs (oDEGs) between NSCLC and healthy samples were identified by using statistical LIMMA method. Then 6 common top-ranked key genes (KGs) (CENPF, CAV1, ASPM, CCNB2, PRC1, and KIAA0101) were selected by using four network-measurer methods in the protein- protein interaction network. The GO functional and KEGG pathway enrichment analysis were performed to reveal some significant functions and pathways associated with NSCLC progression. Transcriptional and post-transcriptional factors of KGs were identified through the regulatory interaction network. The prognostic power and expression level of KGs were validated by using the independent data through the Kaplan-Meier and Box plots, respectively. Finally, 4 KGs-guided repositioning candidate drugs (ZSTK474, GSK2126458, Masitinib, and Trametinib) were proposed. The stability of three top-ranked drug-target interactions (CAV1 vs. ZSTK474, CAV1 vs. GSK2126458, and ASPM vs. Trametinib) were investigated by computing their binding free energies for 140 ns MD-simulation based on MM-PBSA approach. Therefore, the findings of this computational study may be useful for early prognosis, diagnosis and therapies of NSCLC.