The Expression of Angiogenesis Genes is Related to Immune Microenvironment and Prognosis of Lung Adenocarcinoma

Author:

Wang Xue1,Liu Xijian1,Wang Lu1,Li Jiuwei1,Li Ling1,Li Yaxing1,Huang Hailiang1,Han Tao1

Affiliation:

1. Shandong University of Traditional Chinese Medicine

Abstract

Abstract Objective: Lung adenocarcinoma (LC), the main type of non-small cell lung cancer, has a 5-year survival rate of only 14.6%. Tumor angiogenesis is the primary factor leading to the progression of LC. This study aimed to discuss the role of angiogenesis-related genes(ARGs) in the development and diagnosis of LC. Methods: Clinical and transcriptomic data of LC patients were downloaded from TCGA and GEO databases and divided into training cohorts and validation cohorts. Combined with the ARGs of the Molecular Signatures Database, cluster analysis was performed to identify new clusrer subgroups. Enrichment analyses were performed to elucidate the underlying mechanisms of subpopulation differences. MCPCounter, CIBERSORT and xCell analysis was used to determine the tumor immune microenvironment (TIM) and the immune status of identified subgroups. Lasso algorithm and multivariate Cox regression analysis were used to construct the prognostic risk model, and combined with the clinical information of patients with LC to verify the effectiveness of the risk model. Results: We identified 2 cluster subgroups that could significantly predict differential survival based on LC survival prognostic genes and ARGs. Among them, cluster 2 showed a better prognosis and was associated with a high immune score, a high abundance of immune infiltrating cells, and a relatively high immune status. Enrichment analysis revealed that DEGs between the two subgroups were mainly enriched in angiogenesis and immune related pathways. Combined with clinical features, higher risk scores were positively associated with LC worsening of disease progression, predicting poor survival. The validation cohort GSE68465 corroborates the validity of the risk model. Conclusion: The abnormal expression of ARGs is closely related to the TIM of LC patients. The ARG risk model we constructed can be used to accurately predict the survival prognosis of LC.

Publisher

Research Square Platform LLC

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