The Immuno‐Metabolic Crosstalk in Lung Squamous Cell Carcinoma: Prognostic Insights and Therapeutic Clues

ABSTRACT Lung squamous cell carcinoma (LUSC) has a poor prognosis due to the lack of effective targeted therapies, and its incidence has increased dramatically in recent years, creating an urgent need for new prognostic markers. Given that tumor immune and metabolic heterogeneity can influence LUSC prognosis, this study aimed to construct a novel predictive model based on immune‐related and metabolism‐related genes for prognostic stratification in LUSC. Transcriptomic as well as clinical data of 502 and 43 LUSC cases were downloaded from The Cancer Genome Atlas Program (TCGA) and the Gene Expression Omnibus (GEO) databases. Core LUSC subtype genes were identified using nonnegative matrix factorization (NMF), and a prognostic risk model was subsequently constructed by applying machine learning, LASSO regression, and multivariate Cox regression. Based on this model, patients were stratified into low‐risk and high‐risk subgroups with distinct expression profiles and significant survival differences. Gene‐Set Enrichment Analysis of the marker genes revealed that immune pathways were active in the high‐risk group, whereas metabolic pathways were prominent in the low‐risk group. The two groups also differed in tumor mutation burden and response to clinical therapy. High expression levels of NRTN , CYP2C18 , TSLP , MIOX , and RORB and low expression levels of HBEGF , SERPIND1 , PTGIS , and LBP were correlated with high survival rates. Immunohistochemical validation in 42 patients confirmed the expression patterns of the identified genetic markers, which were stronger in tumor tissues than in adjacent normal tissues. In conclusion, six immune‐related and three metabolism‐related genes were identified as prognostic markers of LUSC, with their expression levels significantly associated with the survival rate. The resulting model demonstrates strong predictive power and is expected to help guide treatment strategy decisions.