Abstract
Background: The tumor microenvironment (TME) plays a crucial role in lung cancer development and outcome. The objective of this study was to construct a prognostic model using TME-related genes. Methods: The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) provided transcriptome profile and clinical characteristics. We examined and selected the key genes to obtain a predictive risk model for lung adenocarcinoma (LUAD) prognosis. We also assessed the efficiency of the model and the interaction of key genes with immune-related genes. Findings: After screening 760 TME-related genes, we established a risk model using ANGPTL4, FUT4, CDC25C, FLNC, KRT6A, NEIL3 , HS3ST2, and DAAM2 to independently predict LUAD prognosis using TCGA data. The ROC curve and C-index confirmed the usefulness of this risk model, and a nomogram that integrated this predictive risk model with age and TNM stages was more effective on prediction of LUAD prognosis. The risk model was further confirmed using the GEO data. Furthermore, the risk model of genes interacted with 11 types of immune cells and three immune checkpoint proteins (LAG3, PDL1 and TDO2) in LUAD. Interpretation: Our predictive risk model was effective on survival prediction and helpful for immune targeted therapy in LUAD patients. Funding Information: This study was supported in part by grants from the National Natural Science Foundation of China (#81703087, #81903185, and #81972916), the United Fund of the Second Hospital of Dalian Medical University and Dalian Institute of Chemical Physics, Chinese Academy of Sciences (#UF-ZD-202011), and Project of Education Department of Liaoning Province (#LZ2020009). Declaration of Interests: The authors declared that there is no conflict of interest in this work. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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