Lung cancer is the predominant contributor to cancer-induced mortality, with non-small cell lung cancer (NSCLC) constituting the majority. However, the intricacies of tumorigenesis and progression in this context remain incompletely understood. Utilizing single-cell RNA sequencing data retrieved from the GEO database, we performed high-dimensional weighted gene co-expression network analysis to identify pivotal gene modules exhibiting the highest correlation with the clinical stages of NSCLC patients. Subsequently, we formulated a novel prognostic three-gene signature, subjecting it to thorough analysis using bulk RNA sequencing data obtained from the TCGA-LUAD dataset. A tumor-intrinsic prognostic signature, comprising SEC61G, NPTN, and ALDOA, emerged from our investigation. The risk score derived from this gene signature revealed significantly poorer overall survival among patients in the high-risk group. Patients in different risk groups exhibited distinct immune statuses, with those in the low-risk group likely to benefit more from immunotherapy. Furthermore, in vitro experiments demonstrated that SEC61G facilitates tumor proliferation and migration with the activation of the WNT/β-Catenin signaling pathway. Our study unveiled a novel tumor-intrinsic gene signature, shedding light on improved prognostication for NSCLC and facilitating risk-stratified management.
Zhang et al. (2026) studied this question.