Development and validation of a 16-gene T-cell- related prognostic model in non-small cell lung cancer - Anbing Zhang; Anbing Zhang; Huang Ting; Huang Ting; Jun Ma; Jun Ma; Xiuqiong Xia; Xiaoli Lao; Xiaoli Lao; Siqi Li; Jianping Liang; Jianping Liang (Frontiers in Immunology)
BackgroundNon-small cell lung cancer (NSCLC) exhibits variable T-cell responses, influencing prognosis and outcomes.MethodsWe analyzed 1,027 NSCLC and 108 non-cancerous samples from TCGA using ssGSEA, WGCNA, and differential expression analysis to identify T-cell-related subtypes. A prognostic model was constructed using LASSO Cox regression and externally validated with GEO datasets (GSE50081, GSE31210, GSE30219). Immune cell infiltration and drug sensitivity were assessed. Gene expression alterations were validated in NSCLC tissues using qRT-PCR.ResultsA 16-gene prognostic model (LATS2, LDHA, CKAP4, COBL, DSG2, MAPK4, AKAP12, HLF, CD69, BAIAP2L2, FSTL3, CXCL13, PTX3, SMO, KREMEN2, HOXC10) was established based on their strong association with T-cell activity and NSCLC prognosis. The model effectively stratified patients into high- and low-risk groups with significant survival differences, demonstrating strong predictive performance (AUCs of 0.68, 0.72, and 0.69 for 1-, 3-, and 5-year survival in the training cohort). External validation confirmed its robustness. A nomogram combining risk scores and clinical factors improved survival prediction (AUCs>0.6). High-risk patients responded better to AZD5991-1720, an MCL1 inhibitor, while low-risk patients showed improved responses to IGF1R-3801-1738, an IGF1R inhibitor, suggesting that risk stratification may help optimize treatment selection based on tumor-specific vulnerabilities. qRT-PCR validation confirmed the differential expression of model genes in NSCLC tissues, consistent with TCGA data.ConclusionWe identified a 16-gene T-cell-related prognostic model for NSCLC, which stratifies patients by risk and predicts treatment response, aiding personalized therapy decisions. However, prospective validation is needed to confirm its clinical applicability. Potential limitations such as sample size and generalizability should be considered.