Yang Xiahui, Liang Minchao, Yu Zhiqi, Fan Jiaquan
Department of Respiratory and Critical Care Medicine, The Second Affiliated Hospital of Guangzhou Medical University, PCODE:440100, Guangzhou, China.
Department of Oncology, HaploX Biotechnology, 8 / F, Aotexin Power Building, No.1, Songpingshan Road, High Tech North District, Nanshan District, Shenzhen 110000, Guangdong Province, China.
J Oncol. 2022 Jul 30;2022:6373226. doi: 10.1155/2022/6373226. eCollection 2022.
Hypoxia is a typical microenvironmental feature of most solid tumors, affecting a variety of physiological processes. We developed a hypoxia-related prognostic risk score (HPRS) model to reveal tumor microenvironment (TME) and predict prognosis of lung adenocarcinoma (LUAD).
LUAD sample expression data were from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Weighted gene co-expression network analysis (WGCNA) and least absolute shrinkage and selection operator (LASSO) Cox regression identified hypoxia-related genes (HRGs) to create HPRS. The prognostic value, genetic mutation and TME, and therapeutic response of distinct HPRS groups were analyzed. Univariate and multivariate Cox regression analysis identified independent factors associated with the prognosis of LUAD. A decision tree based on HPRS and clinicopathological variables was established using the classification system based on decision tree algorithm. A nomogram was constructed with important clinical features and HPRS by the RMS package.
A HPRS model with five HRGs was developed and verified in two separate cohorts of GEO. HPRS model divided patients with LUAD into two groups. High HPRS was related to high probability of genetic alterations. HPRS could predict the prognosis, TME, and sensitivity to immunotherapy/chemotherapy of LUAD. The decision tree defined four risk subgroups with significant OS differences. Nomogram with integrated HPRS and clinical features had acceptable accuracy in predicting LUAD prognosis.
A HPRS model was developed to evaluate prognosis, genetic alterations, TME, and response to immunotherapy, which may provide theoretical reference for the study of molecular mechanism of hypoxia in LUAD.
缺氧是大多数实体瘤典型的微环境特征，影响多种生理过程。我们开发了一种缺氧相关的预后风险评分（HPRS）模型，以揭示肿瘤微环境（TME）并预测肺腺癌（LUAD）的预后。
LUAD样本表达数据来自癌症基因组图谱（TCGA）和基因表达综合数据库（GEO）。加权基因共表达网络分析（WGCNA）和最小绝对收缩和选择算子（LASSO）Cox回归确定缺氧相关基因（HRGs）以创建HPRS。分析了不同HPRS组的预后价值、基因突变和TME以及治疗反应。单因素和多因素Cox回归分析确定了与LUAD预后相关的独立因素。使用基于决策树算法的分类系统，基于HPRS和临床病理变量建立决策树。通过RMS包构建具有重要临床特征和HPRS的列线图。
开发了一个包含五个HRGs的HPRS模型，并在GEO的两个独立队列中进行了验证。HPRS模型将LUAD患者分为两组。高HPRS与高基因改变概率相关。HPRS可以预测LUAD的预后、TME以及对免疫治疗/化疗的敏感性。决策树定义了四个总生存期有显著差异的风险亚组。整合了HPRS和临床特征的列线图在预测LUAD预后方面具有可接受的准确性。
开发了一个HPRS模型来评估预后、基因改变、TME和免疫治疗反应，这可能为LUAD缺氧分子机制的研究提供理论参考。
