Constructing and identifying an eighteen-gene Tumor Microenvironment Prognostic Model for Non-Small Cell Lung Cancer

Abstract

Background The tumor microenvironment (TME) plays a crucial role in tumorigenesis and tumor progression. This study aimed to identify novel TME-related biomarkers and develop a prognostic model for patients with non-small-cell lung cancer (NSCLC). Methods After downloading and preprocessing data, we classified the molecular subtypes using the "NMF" R package. We performed survival analysis and quantified immune scores between clusters. A Cox proportional hazards model was then constructed, and its formula was produced. We assessed model performance and clinical utility. A prediction nomogram was also constructed and validated. Additionally, we explored the potential regulatory mechanisms of our TME gene signature using Gene Set Enrichment Analysis (GSEA). Results From data processing and univariate Cox regression, 57 TME-related prognostic genes were identified. Two clusters (C1 and C2) with significant differences were established. Immune scores, including those for cytotoxic lymphocytes, endothelial cells, monocytic cells, myeloid dendritic cells, neutrophils, and T cells, showed significant differences between the subtypes. Through univariate Cox analysis, lasso regression, and multivariate Cox regression analysis, an 18-gene TME-related prognostic model was developed. This model accurately predicted survival outcomes in subgroups with varying clinical features. Finally, a nomogram was constructed, and its predictive accuracy was validated. Conclusions We developed a prognostic model based on TME-related genes in NSCLC. Our 18-gene TME signature can effectively predict the prognosis of NSCLC with high accuracy.