Ferroptosis-Related Genes Are Potential Therapeutic Targets and the Model of These Genes Influences Overall Survival of NSCLC Patients.
Ferroptosis-Related Genes Are Potential Therapeutic Targets and the Model of These Genes Influences Overall Survival of NSCLC Patients.
复制标题
铁蛋白沉积相关基因是潜在的治疗靶点，这些基因的模式影响NSCLC患者的总生存期。
DOI：
10.3390/cells11142207
复制
发表时间：
2022-07-15
期刊：
影响因子：
6
通讯作者：
Wang, Yaohe
中科院分区：
文献类型：
--
作者：
Zhang, Na;Wu, Yangyang;Wu, Yifan;Wang, Lihong;Chen, Jingfei;Wang, Xiaosa;Dunmall, Louisa S. Chard;Cheng, Zhenguo;Wang, Yaohe
背景资料：肺腺癌（LUAD）和肺鳞状细胞癌（LUSCC）是非小细胞肺癌（NSCLC）中最常见的两种亚型，在全球范围内具有高死亡率和不断上升的发病率。铁凋亡是一种由脂质过氧化、活性氧积累引起的程序性细胞死亡模式，并且依赖于铁。近年来，铁凋亡的发现为肿瘤的发生发展提供了新的视角，铁凋亡与肿瘤治疗的临床相关性也日益受到重视。然而，其在NSCLC中的作用仍有待探索。研究方法：1727例LUAD和LUSCC患者和73例对照个体的临床和分子数据来自基因表达综合数据库（GEO）和癌症基因组图谱（TCGA）数据库。采用单变量考克斯分析和一致性聚类分析对4个数据集的1727例肿瘤样本中57个铁蛋白沉积相关基因的基因表达谱、拷贝数变异和体细胞突变进行分析。每种模式的生物学特征被识别。通过结合单变量考克斯回归分析和随机森林算法，然后进行主成分分析（PCA），生成铁凋亡评分，并进一步研究其在LUAD和LUSCC中的预测和治疗价值。结果：57个铁凋亡相关基因在非小细胞肺癌组织中的表达与正常人相比有显著性差异。基于铁凋亡相关基因的无监督聚类，我们将所有患者分为三个铁凋亡表达模式组，这些组在铁凋亡相关基因表达模式、免疫细胞浸润水平、预后特征和富集途径方面存在差异。利用三种铁凋亡表达模式中的差异表达基因，建立了一组17个铁凋亡相关基因的预后模型，将队列中的所有患者聚类为低评分组和高评分组，其预后差异显著（p < 0.001）。高铁下垂评分与放疗阳性反应（p < 0.001）、高T分期（p < 0.001）、高N分期（p < 0.001）和高级别肿瘤（p < 0.001）特征显著相关。结论：17个铁中毒相关基因显示出将LUAD和LUSCC患者分为高风险组和低风险组的巨大潜力。有趣的是，LUAD患者的高铁凋亡评分与良好的预后相关，而LUSCC患者的类似高铁凋亡评分与不良预后相关。熟悉铁凋亡的机制及其对NSCLC治疗的意义，以及对OS和PFS的影响，可能为开发NSCLC新的治疗靶点提供指导和见解。
Background: Lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSCC) are two of the most common subtypes of non-small cell lung cancer (NSCLC), with high mortality rates and rising incidence worldwide. Ferroptosis is a mode of programmed cell death caused by lipid peroxidation, the accumulation of reactive oxygen species, and is dependent on iron. The recent discovery of ferroptosis has provided new insights into tumor development, and the clinical relevance of ferroptosis for tumor therapy is being increasingly appreciated. However, its role in NSCLC remains to be explored. Methods: The clinical and molecular data for 1727 LUAD and LUSCC patients and 73 control individuals were obtained from the Gene Expression Omnibus (GEO) database and the Cancer Genome Atlas (TCGA) database. Gene expression profiles, copy number variations and somatic mutations of 57 ferroptosis-related genes in 1727 tumor samples from the four datasets were used in a univariate Cox analysis and consensus clustering analysis. The biological signatures of each pattern were identified. A ferroptosis score was generated by combining the univariate Cox regression analysis and random forest algorithm followed by principal component analysis (PCA) and further investigated for its predictive and therapeutic value in LUAD and LUSCC. Results: The expression of 57 ferroptosis-related genes in NSCLC patients differed significantly from that of normal subjects. Based on unsupervised clustering of ferroptosis-related genes, we divided all patients into three ferroptosis expression pattern groups, which showed differences in ferroptosis-associated gene expression patterns, immune cell infiltration levels, prognostic characteristics and enriched pathways. Using the differentially expressed genes in the three ferroptosis expression patterns, a set of 17 ferroptosis-related gene prognostic models was established, which clustered all patients in the cohort into a low score group and a high score group, with marked differences in prognosis (p < 0.001). The high ferroptosis score was significantly associated with positive response to radiotherapy (p < 0.001), high T stage (p < 0.001), high N stage (p < 0.001) and high-grade tumor (p < 0.001) characteristics. Conclusions: The 17 ferroptosis-associated genes show great potential for stratifying LUAD and LUSCC patients into high and low risk groups. Interestingly, a high ferroptosis score in LUAD patients was associated with a good prognosis, whereas a similar high ferroptosis score in LUSCC patients was associated with a poor prognosis. Familiarity with the mechanisms underlying ferroptosis and its implications for the treatment of NSCLC, as well as its effect on OS and PFS, may provide guidance and insights in developing new therapeutic targets for NSCLC.
登录
查看更多内容
影响因子：
64.5
作者：
Dixon SJ;Lemberg KM;Lamprecht MR;Skouta R;Zaitsev EM;Gleason CE;Patel DN;Bauer AJ;Cantley AM;Yang WS;Morrison B 3rd;Stockwell BR
通讯作者：
Stockwell BR
影响因子：
3.7
作者：
Fan X;Ou Y;Liu H;Zhan L;Zhu X;Cheng M;Li Q;Yin D;Liao L
通讯作者：
Liao L
影响因子：
3.3
作者：
Wang W;Gao Z;Wang L;Li J;Yu J;Han S;Meng X
通讯作者：
Meng X
影响因子：
3.5
作者：
Pasipoularides A
通讯作者：
Pasipoularides A
影响因子：
29
作者：
Seiler, Alexander;Schneider, Manuela;Conrad, Marcus
通讯作者：
Conrad, Marcus