A tumor microenvironment gene set-Based prognostic signature for non-small-cell lung cancer

Background: The tumor microenvironment (TME) is involved in the development and progression of lung carcinomas. A deeper understanding of TME landscape would offer insight into prognostic biomarkers and potential therapeutic targets investigation. To this end, we aimed to identify the TME components of lung cancer and develop a prognostic signature to predict overall survival (OS). Methods: Expression data was retrieved from The Cancer Genome Atlas (TCGA) database and differentially expressed TME-related genes were calculated between tumor and normal tissues. Then nonnegative matrix factorization (NMF) clustering was used to identify two distinct subtypes. Results: Our analysis yielded a gene panel consisting of seven TME-related genes as candidate signature set. With this panel, our model showed that the high-risk group experienced a shorter survival time. This model was further validated by an independent cohort with data from Gene Expression Omnibus (GEO) database (GSE50081 and GSE13213). Additionally, we integrated the clinical factors and risk score to construct a nomogram for predicting prognosis. Our data suggested less immune cells infiltration but more fibroblasts were found in tumor tissues derived from patients at high-risk and those patients exhibited a worse immunotherapy response. Conclusion: The signature set proposed in this work could be an effective model for estimating OS in lung cancer patients. Hopefully analysis of the TME could have the potential to provide novel diagnostic, prognostic and therapeutic opportunities.