Liu Yihao, Zhao Wenhao, Zhao Zexia, Duan Zhixuan, Huang Hua, Ding Chen, Hou Sensen, Liu Minghui, Zhang Hongbing, Li Yongwen, Wang Min, Meng Wenjun, Chen Jun, Zhang Haoling, Zhao Honglin
Department of Lung Cancer Surgery, Tianjin Medical University General Hospital Tianjin 300052, The People's Republic of China.
Tianjin Key Laboratory of Lung Cancer Metastasis and Tumor Microenvironment, Tianjin Lung Cancer Institute, Tianjin Medical University General Hospital Tianjin 300052, The People's Republic of China.
Am J Cancer Res. 2025 Aug 25;15(8):3762-3780. doi: 10.62347/GQVA7530. eCollection 2025.
Hypoxia plays a crucial role in the pathogenesis of various cancers, especially lung adenocarcinoma (LUAD), by altering cancer metabolism to promote escape mechanisms. Anoikis, a specialized form of programmed cell death, is evaded by LUAD cells during tumor progression and metastasis through upregulation of anti-apoptotic proteins. Investigating the impact of hypoxia-anoikis-related genes on prognosis and therapy prediction in LUAD is essential. Gene expression and clinical data from 489 LUAD patients and 49 normal tissues in The Cancer Genome Atlas (TCGA) dataset were used as the training set, while GSE72094, GSE31210, and GSE30219 datasets were used for validation. Weighted Gene Co-Expression Network Analysis (WGCNA) identified genes associated with hypoxia and anoikis. Machine learning models were evaluated using the C-index. Kaplan-Meier survival analysis, immune cell infiltration, tumor mutational burden (TMB), and sensitivity to therapy were assessed based on risk scores. A total of 21 hypoxia-anoikis-related prognostic genes were identified. The Random Survival Forest (RSF) model had the highest C-index. High-risk patients had significantly lower survival rates. Immune analysis showed higher immune infiltration in the low-risk group, with lower immune escape potential in these patients. Risk scores were correlated with sensitivity to targeted therapy and chemotherapy. MCF2 was identified as a key prognostic gene, and its knockdown inhibited LUAD cell proliferation and metastasis. These 21 genes offer insights into LUAD prognosis and therapy response, guiding personalized treatment strategies for LUAD patients.
缺氧通过改变癌症代谢以促进逃逸机制，在各种癌症尤其是肺腺癌（LUAD）的发病机制中起着关键作用。失巢凋亡是程序性细胞死亡的一种特殊形式，在肿瘤进展和转移过程中，LUAD细胞通过上调抗凋亡蛋白来逃避失巢凋亡。研究缺氧-失巢凋亡相关基因对LUAD预后和治疗预测的影响至关重要。来自癌症基因组图谱（TCGA）数据集的489例LUAD患者和49个正常组织的基因表达及临床数据用作训练集，而GSE72094、GSE31210和GSE30219数据集用于验证。加权基因共表达网络分析（WGCNA）确定了与缺氧和失巢凋亡相关的基因。使用C指数评估机器学习模型。基于风险评分评估Kaplan-Meier生存分析、免疫细胞浸润、肿瘤突变负担（TMB）和对治疗的敏感性。共鉴定出21个缺氧-失巢凋亡相关的预后基因。随机生存森林（RSF）模型的C指数最高。高危患者的生存率显著较低。免疫分析显示低风险组的免疫浸润较高，这些患者的免疫逃逸潜力较低。风险评分与对靶向治疗和化疗的敏感性相关。MCF2被确定为关键的预后基因，其敲低可抑制LUAD细胞的增殖和转移。这21个基因有助于深入了解LUAD的预后和治疗反应，为LUAD患者指导个性化治疗策略。
