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2000
Volume 20, Issue 3
  • ISSN: 1574-8928
  • E-ISSN: 2212-3970

Abstract

Objectives

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 factors (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 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: , and . The TFs-DEGs-miRNA network analysis pinpointed as a key gene, potentially regulated by 7 TFs and interacting with 9 miRNAs. GSEA analysis suggested that 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 in the peripheral blood of the patients in the resistant group relative to the non-resistant group ( < 0.0001). Logistic regression analysis showed that low 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 , 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.

© 2025 Bentham Science Publishers
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2025-02-12
2025-09-21

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/content/journals/pra/10.2174/0115748928376905250210055408
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Bioinformatics Analysis of Key Genes Associated with Resistance to the Combination of Bevacizumab and Pemetrexed Chemotherapy in Non-small Cell Lung Cancer
Recent Patents on Anti-Cancer Drug Discovery 20, 385 (2025); https://doi.org/10.2174/0115748928376905250210055408
/content/journals/pra/10.2174/0115748928376905250210055408
/content/journals/pra/10.2174/0115748928376905250210055408
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  • Article Type:     Research Article
Keyword(s): bevacizumab; bioinformatics; Non-small cell lung cancer; pemetrexed; resistance; TNFSF4

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