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Updated: Jul 20, 2026
A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds
Published on: April 6, 2016
A USP15/KRT17-associated 13-gene prognostic cluster and functional validation in EGFR-mutant lung adenocarcinoma
Jiamin Huang1,2, Yi Huang1, Qi Yuan3
1Department of Microbiology, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510080, China.
Background:
EGFR-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.
Methods:
We 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.
Results:
The 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.
Conclusions:
We 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.
