Bioinformatics Analysis of Key Genes Associated with Resistance to the Combination of Bevacizumab and Pemetrexed Chemotherapy in Non-small Cell Lung Cancer.

Abstract

Objective: This study aimed to identify key genes linked to resistance to a combination treatment regimen of bevacizumab and pemetrexed in non-small cell lung cancer (NSCLC) through bioinformatics analysis and analysis of their associated pathways.

Methods: Expression data from the Gene Expression Omnibus (GEO) database (GSE154286) were analyzed. The differentially expressed genes (DEGs) between tissues sensitive and resistant to combined bevacizumab and pemetrexed treatment in NSCLC were identified. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment was investigated, and protein-protein interaction (PPI) networks, as well as transcription factor (TFs)- DEGs-miRNA networks, were created using the STRING tool. Key genes were identified with the help of the MCODE plugin. Additionally, gene set enrichment analysis (GSEA) was utilized to identify pathways linked to the key genes. A retrospective analysis was conducted on clinical data from 80 NSCLC patients. Patients were categorized into drug-resistant and non-resistant groups based on RECIST1.1 criteria. The expression of the key gene TNFSF4 was analyzed using quantitative real-time PCR (qRT-PCR).

Results: In the GSE154286 dataset, 35 downregulated DEGs were discovered. KEGG pathway enrichment analysis revealed that these DEGs were primarily associated with immunity and inflammation-related pathways. The PPI network construction highlighted a significant module and led to the identification of 8 candidate genes: TNFRSF18, TNFSF4, LGALS9, FAS, LAG3, CD86, CD80, and FOXP3. The TFs-DEGs-miRNA network analysis pinpointed TNFSF4 as a key gene, potentially regulated by 7 transcription factors and interacting with 9 miRNAs. GSEA analysis suggested that TNFSF4 may influence NSCLC's pathological processes through involvement in pathways involved in chemokine, JAK/STAT, NOD-like receptor, T cell receptor, toll-like receptor, and PPAR signaling. qRT-PCR detection displayed significantly lower expression of TNFSF4 in the peripheral blood of the patients in the resistant group relative to the non-resistant group (p < 0.0001). Logistic regression analysis showed that low TNFSF4 levels were independently linked to a raised risk of resistance to bevacizumab combined with pemetrexed therapy in lung adenocarcinoma patients.

Conclusion: The identification of key genes, such as TNFSF4, and resistance-related signaling pathways through bioinformatics analysis offers valuable insights into potential mechanisms of chemotherapy resistance in NSCLC when treated with the combination of bevacizumab and pemetrexed. These findings provide a theoretical foundation for advancing clinical research on diagnosis and treatment.