Evidence map›Paper›PMID 42458519›Full record

ArticleRespiratory research2026

A USP15/KRT17-associated 13-gene prognostic cluster and functional validation in EGFR-mutant lung adenocarcinoma.

Jiamin Huang, Yi Huang, Qi Yuan, Aiwei Tang, Minzhe Su, Yuanyuan Li, Zhen Gan, Xiuxiu Yang, Junyi Song, Yaokai Ye and 7 more

Abstract readValidation Study
In one paragraph

Article in Respiratory research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

17 authors.

Jiamin Huang *Department of Microbiology, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510080, China.
Yi Huang *Department of Microbiology, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510080, China.
Qi Yuan *Central Laboratory, Integrated Chinese and Western Medicine Hospital, Southern Medical University, Guangzhou, 510315, China.
Aiwei TangCancer Institute, Southern Medical University, Guangzhou, 510515, China.
Minzhe SuDepartment of Microbiology, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510080, China.
Yuanyuan LiDepartment of Microbiology, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510080, China.
Zhen GanDepartment of Microbiology, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510080, China.
Xiuxiu YangDepartment of Microbiology, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510080, China.
Junyi SongCancer Institute, Southern Medical University, Guangzhou, 510515, China.
Yaokai YeDepartment of Thoracic Surgery, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510080, China.
Qing XieDepartment of Microbiology, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510080, China.
Liyun HuoDepartment of Vascular Surgery, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510080, China.
Runmin LiDepartment of Basic Medical Sciences, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510080, China.
Miaoling TangDepartment of Biochemistry, Guangzhou University of Chinese Medicine, Guangzhou, 510006, China.
Rong LianDepartment of Nephrology, Guangzhou First People's Hospital, The Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, 510180, China.
Junchao CaiDepartment of Immunology, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510080, China.
Tianyu TaoCancer Institute, Southern Medical University, Guangzhou, 510515, China. taoty@alumni.sysu.edu.cn.

Funding

National Key Research and Development Program of China 2023YFC3402100the China Postdoctoral Science Foundation 2025M782066the National Natural Science Foundation of China 82403091the National Natural Science Foundation of China 82573270
6 · The paper itself

Abstract

backgroundEGFR-mutant lung adenocarcinoma (LUAD) exhibits significant prognostic heterogeneity despite the efficacy of tyrosine kinase inhibitors. To uncover the transcriptomic markers of rapid disease progression, we integrated clinical and transcriptomic data to develop robust prognostic biomarkers for risk stratification.

methodsWe utilized ensemble machine learning algorithms, including Random Survival Forest and XGBoost, to analyze transcriptomic data from 293 patients with EGFR-mutant LUAD. Single-cell transcriptomic analysis combined with copy number variation (CNV) profiling was employed to assess the cellular distribution and tumor epithelial contribution of the identified prognostic signature. The functional effects of the candidate biomarkers, USP15 and KRT17, and their dynamic regulation by oncogenic EGFR signaling were experimentally validated in EGFR-mutant cell lines using siRNA knockdown, pharmacological blockade (osimertinib), and gain-of-function assays.

resultsThe optimized XGBoost model identified a 13-gene cluster with strong predictive performance (C-index = 0.863), which was independently validated in an external cohort (log-rank P = 0.0117; 1-year AUC = 0.698). Single-cell transcriptomic analysis demonstrated that the detectable 11-gene subset exhibited a mixed tumor-microenvironment distribution, including expression within CNV-defined malignant epithelial cells. Functional experiments further demonstrated that USP15 and KRT17 promote tumor cell proliferation, clonogenic growth, and migration. Moreover, the expression of both genes was responsive to downstream EGFR signaling. Collectively, these findings establish a robust 13-gene prognostic gene cluster with a mixed cellular origin and identify USP15- and KRT17-associated biology as a promising direction for future mechanistic investigation in EGFR-mutant LUAD.

conclusionsWe identify a 13-gene prognostic cluster associated with poor survival in EGFR-mutant LUAD. These findings position the EGFR-regulated USP15/KRT17 axis as a novel framework for patient risk stratification, with potential implications for guiding targeted therapeutic strategies.

Indexed as

Adenocarcinoma of LungBiomarkers, TumorKeratin-17Lung NeoplasmsMultigene FamilyMutationUbiquitin-Specific ProteasesCell Line, TumorErbB ReceptorsFemaleGene Expression Regulation, NeoplasticHumansMalePrognosisBiomarkers, TumorEGFR protein, humanErbB ReceptorsKeratin-17Ubiquitin-Specific ProteasesEGFR mutationLung adenocarcinomaMachine learningPrognostic biomarkerXGBoost

Identifiers

PMID42458519
PMCPMC13670687

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