A bioinformatics exploration of lung adenocarcinoma identifies hub genes with prognostic significance: from data to discovery.

Abstract

Background: Lung adenocarcinoma (LUAD) is one of the main forms of carcinomas that contribute towards cancer-related mortality and morbidity. Identification of hub genes through various in silico approaches can lead to the successful prognosis of LUAD and may serve in reducing mortalities rising from it respectively.

Method: This research employs an integrated bioinformatics approach to uncover the molecular intricacies of LUAD. Utilizing the Gene Expression Omnibus (GEO) dataset, we identified GSE19188, GSE18842, GSE31210, and GSE19804 specific datasets from 423 LC tissues and 190 healthy tissues (controls). Differential gene expression analysis using GEO2R and Venn diagrams led to the identification of 851 differentially expressed genes (DEGs), comprising 240 overexpressed and 611 under-expressed genes. To elucidate their roles in LUAD etiology, we conducted protein-protein interaction (PPI) analysis utilizing Cytoscape and Cytohubba software's, revealing densely interconnected gene clusters with potential prognostic significance. Additionally, gene ontology (GO) enrichment and Kyoto Encyclopaedia of Genes and Genomes (KEGG) analyses were able to shed light on the involvement of these DEGs in processes such as cell cycle modulation and apoptosis, which are crucial in LUAD pathogenesis. Moreover, validation of the hub gene expression and their association with overall survival was performed using the University of Alberta Cancer Research Network (UALCAN) and Human Protein Atlas (HPA) databases, supporting our findings.

Results: The identified DEGs, including cyclin-dependent kinase-1 (CDK1), cyclin B2 (CCNB2), cell division cycle 20 (CDC20), BUB1 mitotic checkpoint serine/threonine kinase B (BUB1B), cyclin A2 (CCNA2), discs-large associated protein 5 (DLGAP5), abnormal spindle microtubule assembly (ASPM), arrestin beta 1 (ARRB1), and caveolin-1 (CAV1), may serve as potential biomarkers for LUAD pathogenesis and should be explored further.

Conclusion: The present bioinformatics analysis enhances our understanding of molecular mechanisms contributing to LUAD and suggests that the hub genes identified could be promising targets for accurate diagnosis and novel therapeutic strategies in LUAD. Further investigations are necessary to validate and translate these findings into real-world clinical applications, paving the way for more effective treatments and improved outcomes in LUAD patients.