The construction and validation of integrated immune and metabolic gene signatures for clinical prognostic model of lung squamous cell carcinoma

Background: Lung squamous cell carcinoma (LUSC) has a poor prognosis due to the lack of effective targeted therapies, and its incidence has dramatically increased in recent years. Therefore, new prognostic markers are urgently needed. Since tumour immune and metabolic heterogeneity can influence LUSC prognosis, systematic combinatorial analysis of immune-related and metabolism-related genomic patterns may identify such markers. Thus, this study aimed to construct a novel predictive model based on immune-related and metabolism-related genes for prognostic stratification in LUSC. Methods: 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). A risk model based on prognostic LUSC genes was constructed using machine learning, LASSO regression, and multivariate Cox regression. Subsequently, we defined low-risk and high-risk expression profiles comprising these markers and revealed survival differences. Gene-Set Enrichment Analysis of these marker genes revealed the active pathways in the high-risk group versus the low-risk group. Diverse clinical treatment strategies for both risk groups were also examined. Immunohistochemical validation involving 42 patients demonstrated the expression patterns of the identified genetic markers. Results: The constructed risk model for nine LUSC genes effectively stratified patients into low-risk and high-risk subgroups with different survival rates, tumour 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. The high-risk group was strongly associated with immune pathways, and the low-risk group was strongly associated with metabolism pathways. The expression of model markers was stronger in tumours than in adjacent normal tissues. Conclusions: 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 prognostic model constructed using these markers has a strong predictive power. Accordingly, the findings are expected to guide decisions on treatment strategies.