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Updated: Jul 20, 2025
Establishing Dual Resistance to EGFR-TKI and MET-TKI in Lung Adenocarcinoma Cells In Vitro with a 2-step Dose-escalation Procedure
Published on: August 11, 2017
Comprehensive analysis of resistance mechanisms to EGFR-TKIs and establishment and validation of prognostic model
Zhengzheng Yang1, Haiming Li1, Tongjing Dong1
1Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing, China.
Purpose:
Epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs) are the first-line therapy for patients with lung adenocarcinoma (LUAD) harboring activating EGFR mutations. However, the emergence of drug resistance to EGFR-TKIs remains a critical obstacle for successful treatment and is associated with poor patient outcomes. The overarching objective of this study is to apply bioinformatics tools to gain insights into the mechanisms underlying resistance to EGFR-TKIs and develop a robust predictive model.
Methods:
The genes associated with gefitinib resistance in the LUAD cell Gene Expression Omnibus (GEO) database were identified using gene chip expression data. Functional enrichment analysis, gene set enrichment analysis (GSEA), and immune infiltration analysis were performed to comprehensively explore the mechanism of gefitinib resistance. Furthermore, a GRRG_score was constructed by integrating genes related to LUAD prognosis from The Cancer Genome Atlas (TCGA) database with the screened Gefitinib Resistant Related differentially expressed genes (GRRDEGs) using the Least Absolute Shrinkage and Selection Operator (LASSO) and Cox regression analyses. Furthermore, we conducted an in-depth analysis of the tumor microenvironment (TME) features and their association with immune infiltration between different GRRG_score groups. A prognostic model for LUAD was developed based on the GRRG_score and validated. The HPA database was used to validate protein expression. The CTR-DB database was utilized to validate the results of drug therapy prediction based on the relevant genes.
Results:
A total of 110 differentially expression genes were identified. Pathway enrichment analysis of DEGs showed that the differentially expressed genes were mainly enriched in Mucin type O-glycan biosynthesis, Cytokine-cytokine receptor interaction, Sphingolipid metabolism. Gene set enrichment analysis showed that biological processes strongly correlated with gefitinib resistance were cell proliferation and immune-related pathways, EPITHELIAL_MESENCHYMAL_TRANSITION, APICAL_SURFACE, and APICAL_JUNCTION were highly expressed in the drug-resistant group; KRAS_SIGNALING_DN, HYPOXIA, and HEDGEHOG_SIGNALING were highly expressed in the drug-resistant group. The GRRG_score was constructed based on the expression levels of 13 genes, including HSPA2, ATP8B3, SPOCK1, EIF6, NUP62CL, BCAR3, PCSK9, NT5E, FLNC, KRT8, FSCN1, ANGPTL4, and ID1. We further screened and validated two key genes, namely, NUP62CL and KRT8, which exhibited predictive value for both prognosis and drug resistance.
Conclusions:
Our study identified several novel GRRDEGs and provided insight into the underlying mechanisms of gefitinib resistance in LUAD. Our results have implications for developing more effective treatment strategies and prognostic models for LUAD patients.
Insights
This study identifies novel genes and pathways driving resistance to epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs) in lung adenocarcinoma (LUAD). A new predictive model, the GRRG_score, incorporating NUP62CL and KRT8, shows promise for improving LUAD patient outcomes.
Area of Science:
- Oncology
- Bioinformatics
- Genomics
Background:
- Lung adenocarcinoma (LUAD) treatment relies on epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs) for patients with activating EGFR mutations.
- Drug resistance to EGFR-TKIs is a major challenge, leading to poor patient prognosis.
Purpose of the Study:
- To investigate the molecular mechanisms of EGFR-TKI resistance in LUAD using bioinformatics.
- To develop a predictive model for patient prognosis and drug resistance.
Main Methods:
- Utilized gene expression data from GEO and TCGA databases to identify gefitinib resistance-associated genes.
- Performed functional enrichment, gene set enrichment analysis (GSEA), and immune infiltration analysis.
- Constructed a prognostic risk score (GRRG_score) using LASSO and Cox regression, validated with HPA and CTR-DB databases.
Main Results:
- Identified 110 differentially expressed genes (DEGs) enriched in pathways like Mucin type O-glycan biosynthesis and Cytokine-cytokine receptor interaction.
- GSEA revealed cell proliferation, immune pathways, epithelial-mesenchymal transition, and signaling pathways like KRAS_SIGNALING_DN and HYPOXIA were associated with gefitinib resistance.
- Developed a 13-gene GRRG_score, with NUP62CL and KRT8 identified as key predictors of prognosis and drug resistance.
Conclusions:
- Identified novel gefitinib resistant related differentially expressed genes (GRRDEGs) and elucidated mechanisms of resistance in LUAD.
- The developed GRRG_score, particularly NUP62CL and KRT8, offers a potential tool for improving LUAD treatment strategies and prognostic assessment.
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