ArticleFunctional & integrative genomics2024
Integration of osimertinib-targeted EGFR gene-associated differential gene expression in constructing a prognostic model for lung adenocarcinoma.
Article in Functional & integrative genomics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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.
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Who cites it
5 citing papers in PubMed.
- Article
- Chaperonin in health and disease.Molecular biomedicine · 2026Review
- The role of BLZF1 in lung adenocarcinoma and its value as a diagnostic and prognostic biomarker.Scientific reports · 2026Article
- Integrative machine learning of hypoxia and centrosome-related gene signatures enables prognostic stratification and therapeutic insights in lung adenocarcinoma.Translational cancer research · 2026Article
- [A Case of Endometrial Metastasis in Lung Adenocarcinoma after EGFR-TKIs Treatment Failure and Literature Review].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 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
6 authors.
Funding
Abstract
Lung adenocarcinoma (LUAD) is one of the deadliest cancers. Epidermal growth factor receptor-tyrosine kinase inhibitor (EGFR-TKI)-targeted therapy is an important approach for treating LUAD. However, the development of acquired resistance poses a serious clinical challenge. Our objective was to explore the differentially expressed genes (DEGs) associated with EGFR and detect biomarkers for diagnosing and treating osimertinib resistance in LUAD patients. LUAD datasets were downloaded from public databases. Differential expression analysis was performed to screen DEGs, and prognostic modules were constructed by Cox regression. Enrichment analysis, gene regulatory network analysis and immune microenvironment analysis were employed to explore the underlying mechanisms in LUAD. Finally, the expression of prognosis module genes (PMGs) was validated in 8 LUAD tissue specimens and 5 cell lines by qRT-PCR. In total, 13 differential module genes (BIRC3, CCT6A, CPLX2, GLCCI1, GSTA1, HLA-DQB2, ID1, KCTD12, MUC15, NOTUM, NT5E, TCIM, and TM4SF4) were screened for the construction of a prognostic module. Notably, CCT6A and KCTD12 demonstrated excellent accuracy in the diagnosis of LUAD. Immune dysregulation and BIRC3, HLA-DQB2, KCTD12, and NT5E expression were significantly associated with invasive immune cells in LUAD patients. The expression level of CCT6A was highest in PC9-OR and H1975-OR cells, while the expression level of KCTD12 was highest in paracancerous tissue and HBE cells. The constructed prognostic model showed promise in predicting the survival of LUAD patients. Notably, KCTD12 and CCT6A might be candidate biomarkers for improving diagnostic performance and guiding individualized therapy for EGFR-TKI-resistant LUAD patients.
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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.