Wang Yanyan, Xie Chengkai, Su Yuan
Department of Respiratory and Critical Care Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Key Laboratory of Pulmonary Diseases of Ministry of Health, School of Basic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
J Thorac Dis. 2023 Mar 31;15(3):1335-1352. doi: 10.21037/jtd-23-149.
Lung cancer is a highly aggressive disease and the leading cause of cancer-related deaths. Lung adenocarcinoma (LUAD) is the most common histological subtype of lung cancer. As a type of programmed cell death, anoikis serves a key role in tumor metastasis. However, as few studies have focused on anoikis and prognostic indicators in LUAD, in this study, we constructed an anoikis-related risk model to explore how anoikis could influence the tumor microenvironment (TME), clinical treatment, and prognosis in LUAD patients; we aimed to provide new insight for future research.
Using patient data from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA), we utilized the 'limma' package to select differentially expressed genes (DEGs) associated with anoikis and then they were divided into 2 clusters with consensus clustering. Risk models were constructed with least absolute shrinkage and selection operator (LASSO) Cox regression (LCR). Kaplan-Meier (KM) analysis and receiver operating characteristic (ROC) curves were performed to assess the independent risk factors for different clinical characteristics, including age, sex, disease stage, grade, and their associated risk scores. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA) were performed to explore the biological pathways in our model. The effectiveness of clinical treatment was detected according to tumor immune dysfunction and exclusion (TIDE), The Cancer Immunome Atlas (TCIA), and IMvigor210.
Our model was found to divide LUAD patients into high- and low-risk groups well, in which high risk groups had poor overall survival (OS), indicating that risk score could be an independent risk factor to predict the prognosis of LUAD patients. Interestingly, we found that anoikis could not only influence the extracellular organization but also play great roles in immune infiltration and immunotherapy, which might provide a new insight for future research.
The risk model constructed in this study can benefit to predict patient survival. Our results provided new potential treatment strategies.
肺癌是一种侵袭性很强的疾病，也是癌症相关死亡的主要原因。肺腺癌（LUAD）是肺癌最常见的组织学亚型。作为一种程序性细胞死亡，失巢凋亡在肿瘤转移中起关键作用。然而，由于很少有研究关注LUAD中的失巢凋亡和预后指标，在本研究中，我们构建了一个与失巢凋亡相关的风险模型，以探讨失巢凋亡如何影响LUAD患者的肿瘤微环境（TME）、临床治疗和预后；我们旨在为未来的研究提供新的见解。
利用来自基因表达综合数据库（GEO）和癌症基因组图谱（TCGA）的患者数据，我们使用“limma”软件包选择与失巢凋亡相关的差异表达基因（DEG），然后通过一致性聚类将它们分为2个簇。使用最小绝对收缩和选择算子（LASSO）Cox回归（LCR）构建风险模型。进行Kaplan-Meier（KM）分析和受试者工作特征（ROC）曲线分析，以评估不同临床特征（包括年龄、性别、疾病分期、分级）的独立危险因素及其相关风险评分。进行基因本体（GO）、京都基因与基因组百科全书（KEGG）和基因集富集分析（GSEA），以探索我们模型中的生物学途径。根据肿瘤免疫功能障碍和排除（TIDE）、癌症免疫图谱（TCIA）和IMvigor210检测临床治疗的有效性。
我们的模型能够很好地将LUAD患者分为高风险组和低风险组，其中高风险组的总生存期（OS）较差，这表明风险评分可能是预测LUAD患者预后的独立危险因素。有趣的是，我们发现失巢凋亡不仅可以影响细胞外组织，还在免疫浸润和免疫治疗中发挥重要作用，这可能为未来的研究提供新的见解。
本研究构建风险模型有助于预测患者生存。我们的结果提供了新的潜在治疗策略。
