Identification and Validation of a Novel Prognostic Model of Inflammation-related Gene Signature of Lung Adenocarcinoma

Abstract

Previous literatures have suggested the importance of inflammatory response during lung adenocarcinoma (LUAD) development. This study aimed at exploring the inflammation-related genes and developing a prognostic signature for predicting the prognosis of LUAD. Survival‑associated inflammation-related genes were identified by univariate Cox regression analysis in the dataset of The Cancer Genome Atlas (TCGA). The least absolute shrinkage and selection operator (LASSO) penalized COX regression model was used to derive a risk signature and divide samples into high-, medium- and low- risk group. Univariate and multivariate analyses suggested that the level of risk group was an independent prognostic factor of survival. Time-dependent receiver operating characteristic (ROC) curve indicated the AUC of 1-, 3- and 5- years of the risk signature was 0.715, 0.719, 0.699 respectively. A prognostic nomogram was constructed by integrating risk group and clinical features. The independent dataset GSE30129 of Gene Expression Omnibus (GEO) was used for verification. Furthermore, we performed Gene set enrichment analysis(GSEA) and discussed the differences in tumor mutation, tumor microenvironment among risk groups. Single Sample Gene Set Enrichment Analysis (ssGSEA) and CIBERSORT results suggested the status of immune cell infiltration was highly associated with risk groups. In a cohort of LUAD from The Cancer Immunome Atlas(TCIA) that predicted immune responses to CTLA-4 and PD-1/PD-L1 inhibitors, immunotherapy performed better in the low-risk group. Partial targeted drugs and chemotherapy drugs for lung cancer had higher drug sensitivity in the high-risk group. The level of risk group can provide some reference significance for the selection of immunotherapy and drug regimen for LUAD patients.