Constructing and identifying an eighteen-gene tumor microenvironment prognostic model for non-small cell lung cancer

• Published in: World journal of surgical oncology 2024-11, Vol.22 (1), p.319-14
• Main Authors: Li, Zaishan, Meng, Zhenzhen, Xiao, Lin, Du, Jiahui, Jiang, Dazhi, Liu, Baoling
• Format: Article
• Language: English
• Subjects: Aged; Analysis; Anopheles; B cells; Biological markers; Biomarkers, Tumor - genetics; Cancer; Carcinogenesis; Carcinoma, Non-Small-Cell Lung - genetics; Carcinoma, Non-Small-Cell Lung - mortality; Carcinoma, Non-Small-Cell Lung - pathology; Care and treatment; Female; Follow-Up Studies; Gene expression; Gene Expression Profiling; Gene Expression Regulation, Neoplastic; Genes; Genetic aspects; Genomes; Health aspects; Humans; Lung cancer, Non-small cell; Lung cancer, Small cell; Lung Neoplasms - genetics; Lung Neoplasms - mortality; Lung Neoplasms - pathology; Male; Middle Aged; Nomograms; Nomography (Mathematics); Non-small cell lung cancer; Prognosis; Prognostic model; ROC Curve; Survival Rate; TCGA data; Tumor Microenvironment; Tumors
• ISSN: 1477-7819; 1477-7819
• DOI: 10.1186/s12957-024-03588-y

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). After downloading and preprocessing data from The Cancer Genome Atlas (TCGA) data portal and Gene Expression Omnibus (GEO) datasets, 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). From data processing and univariate Cox regression analysis, 57 TME-related prognostic genes were identified, and two significantly distinct clusters were established. Using Cox regression and Lasso regression, an 18-gene TME-related prognostic model was developed. Patients were stratified into high- and low-risk groups based on the risk score, with survival analysis showing that the low-risk group had significantly better outcomes than the high-risk group (P