Machine learning-driven gene expression profiling for lung cancer stage determination
Year: 2025
Type: article
Abstract: Background Lung cancer remains a leading cause of cancer-related mortality, with accurate staging essential for guiding treatment. Advances in next-generation sequencing (NGS) and machine learning (ML) enable more precise classification, improving on traditional imaging-based methods. Objective This retrospective study applies XGBoost with cross-validation (CV) to classify early vs. late-stage lung cancer using RNA-Seq data from 993 patients in The Cancer Genome Atlas (TCGA) cohort. Methods Gene selection was conducted using the Wilcoxon rank-sum test on training data, and the XGBoost model was optimized via cross-validation. Model performance was assessed using the Area Under the Curve (AUC), with sensitivity-specificity analysis across classification thresholds. Results The XGBoost model achieved a test AUC of 0.6534, identifying 40 key genes that optimize predictive accuracy while minimizing overfitting. Thresholds of 0.3 and 0.4 were optimal, balancing sensitivity and specificity for clinical application . Conclusions Integrating RNA-Seq data with machine learning improves lung cancer staging accuracy. Future research should focus on dataset expansion, model benchmarking, and multi-omics integration to enhance clinical applicability.
Source: Cancer Biomarkers
Authors: Yinbo Wang, Kai Fu
Institutions: University of Michigan, University of California, Los Angeles
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: hybrid
OpenAlex ID: W4414161850