ArticleJournal of cancer research and clinical oncology2023
Comprehensive analysis of resistance mechanisms to EGFR-TKIs and establishment and validation of prognostic model.
Article in Journal of cancer research and clinical oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed, 3 citations in OpenAlex.
- Comprehensive analysis of KRT18 as a novel prognostic biomarker and potential target in lung adenocarcinoma.Clinical and experimental medicine · 2025Article
- PCSK9 Manipulates Lipid Metabolism and the Immune Microenvironment in Cancer.OncoTargets and therapy · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeEpidermal 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.
methodsThe 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.
resultsA 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.
conclusionsOur 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.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.