摘要
目的：肺腺癌（LC）是非小细胞肺癌的主要类型，5年生存率仅为14.6%。肿瘤血管生成是导致LC进展的主要因素。本研究旨在探讨血管生成相关基因（ARG）在LC发生和诊断中的作用。
方法：从TCGA和GEO数据库下载LC患者的临床和转录组数据，并将其分为训练队列和验证队列。结合分子特征数据库的ARGs，进行聚类分析以确定新的聚类子群。进行富集分析以阐明亚群差异的潜在机制。MCPCounter、CIBERSORT和xCell分析用于确定肿瘤免疫微环境（TIM）和已鉴定亚组的免疫状态。采用Lasso算法和多变量Cox回归分析构建预后风险模型，并结合LC患者的临床信息验证风险模型的有效性。
结果：我们根据LC生存预后基因和ARGs确定了2个可以显著预测差异生存的聚类亚组。其中，簇2显示出更好的预后，并与高免疫评分、高丰度的免疫浸润细胞和相对较高的免疫状态有关。富集分析显示，两个亚组之间的DEG主要富集在血管生成和免疫相关途径中。结合临床特征，较高的风险评分与疾病进展的LC恶化呈正相关，预测生存率低。验证队列GSE68465证实了风险模型的有效性。
结论：ARGs的异常表达与LC患者的TIM密切相关。我们构建的ARG风险模型可用于准确预测LC的生存预后。
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.

https://www.researchsquare.com/article/rs-2568517/v1