Identification of Key Genes and Pathways in Gefitinib-Resistant Lung Adenocarcinoma using Bioinformatics Analysis

Area of Science:

• Oncology
• Molecular Biology
• Genomics

Background:

• Gefitinib resistance poses a significant challenge in treating non-small cell lung cancer (NSCLC).
• Understanding the molecular mechanisms driving gefitinib resistance is crucial for developing effective therapeutic strategies.
• Identifying novel biomarkers for predicting treatment response is essential for personalized medicine in NSCLC.

Purpose of the Study:

• To identify differentially expressed genes (DEGs) associated with gefitinib resistance in NSCLC.
• To elucidate the functional pathways and key genes involved in gefitinib resistance.
• To investigate the role of CCL20 in gefitinib resistance and its potential as a prognostic biomarker in lung adenocarcinoma (LUAD).

Main Methods:

• Differential gene expression analysis of the GSE122005 dataset comparing gefitinib-sensitive and resistant NSCLC samples.
• Gene Ontology (GO) and KEGG pathway analyses using DAVID to interpret DEGs.
• Protein-protein interaction (PPI) network construction to identify hub genes.
• Gene Set Enrichment Analysis (GSEA) to explore the functional roles of CCL20 in LUAD.

Main Results:

• DEGs were primarily associated with inflammatory responses, cell chemotaxis, and PI3K signaling pathways.
• Ten hub genes were identified through PPI network analysis.
• CCL20 was identified as a significant hub gene affecting the prognosis of LUAD patients.
• GSEA revealed enrichment of cytokine-related signaling pathways in the high CCL20 expression group.

Conclusions:

• Alterations in inflammatory and cytokine-related signaling pathways are implicated in gefitinib resistance in NSCLC.
• The gene CCL20 may play a role in promoting gefitinib resistance.
• CCL20 shows potential as a novel biomarker for predicting gefitinib resistance in NSCLC patients.