Identification of Prognostic Biomarkers for Stage III Non-Small Cell Lung Carcinoma in Female Nonsmokers Using Machine Learning
Huili Zheng, Qimin Zhang, Yiru Gong, Zheyan Liu, Shaohan Chen
Abstract
Lung cancer remains a leading cause of cancerrelated deaths globally, with non-small cell lung cancer (NSCLC) being the most common subtype. This study aimed to identify key biomarkers associated with stage III NSCLC in nonsmoking females using gene expression profiling from the GDS3837 dataset. Utilizing XGBoost, a machine learning algorithm, the analysis achieved a strong predictive performance with an AUC score of 0.835. The top biomarkers identified—CCAAT enhancer binding protein alpha (C/EBP $\alpha$), lactate dehydrogenase $\mathrm{A4}$ (LDHA), UNC-45 myosin chaperone B (UNC45B), checkpoint kinase 1 (CHK1), and hypoxia-inducible factor 1 subunit alpha (HIF1 $\alpha$)—have been validated in the literature as being significantly linked to lung cancer. These findings highlight the potential of these biomarkers for early diagnosis and personalized therapy, emphasizing the value of integrating machine learning with molecular profiling in cancer research.