He Zhixian, Zhou Zuoyuan, Wang Feiran, Gai Ling, Huang Yeqing, Zhong Xiang, Li Jing, Zuo Ling, Zhang Nannan, Ni Sujie
Department of General Surgery, Affiliated Hospital of Nantong University, Nantong, China.
Department of Oncology, Affiliated Hospital of Nantong University, Nantong, China.
Ann Transl Med. 2022 Aug;10(15):833. doi: 10.21037/atm-22-3736.
Ferroptosis is a kind of programmed cell death that is characterized by iron dependence. It differs from apoptosis, necrosis, autophagy, pyroptosis, and other types of cell death. Some studies have found that most of the genes involved in the regulation of ferroptosis or act as markers of ferroptosis are related to the poor prognosis of cancer patients.
This study evaluated the expression, mutation, and copy number variation (CNV) of 60 previously reported ferroptosis genes in breast cancer samples from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Unsupervised clustering of breast cancer samples with ferroptosis genes was performed, followed by enrichment analysis with Gene Set Variation Analysis (GSVA), mutation display, and correlation analysis of clinical characteristics. Based on the analysis of differences among groups, the ferroptosis-related genes were identified, and the consistent clustering of breast cancer samples was performed. The characteristic genes were screened by stochastic forest algorithm and COX analysis, and a ferroptosis score (ferr.score) model was constructed to evaluate the prognosis of breast cancer patients.
Copy number amplification and deletion of ferroptosis genes are common in breast cancer. Breast cancer patients grouped by ferroptosis gene clusters showed significant differences in survival, immune cell infiltration, and enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathways. The ferroptosis-related differential genes were identified by comparison among clustering groups of ferroptosis gene. Characteristic genes were screened from these ferroptosis-related differential genes to construct the ferr.score model. The scoring model could accurately distinguish and predict the survival prognosis and immunotherapy efficacy in breast cancer patients.
Ferroptosis plays an important role in the occurrence and development of tumors. According to the ferr.score model, the breast cancer samples can be divided into two groups with significantly different prognoses. These results provide novel insights and ideas for immunotherapy in breast cancer patients.
铁死亡是一种以铁依赖性为特征的程序性细胞死亡。它不同于凋亡、坏死、自噬、焦亡及其他类型的细胞死亡。一些研究发现，大多数参与铁死亡调控或作为铁死亡标志物的基因与癌症患者的不良预后相关。
本研究评估了来自癌症基因组图谱（TCGA）和基因表达综合数据库（GEO）的乳腺癌样本中60个先前报道的铁死亡基因的表达、突变及拷贝数变异（CNV）情况。对乳腺癌样本与铁死亡基因进行无监督聚类，随后采用基因集变异分析（GSVA）进行富集分析、突变展示及临床特征的相关性分析。基于组间差异分析，鉴定铁死亡相关基因，并对乳腺癌样本进行一致性聚类。通过随机森林算法和COX分析筛选特征基因，构建铁死亡评分（ferr.score）模型以评估乳腺癌患者的预后。
铁死亡基因的拷贝数扩增和缺失在乳腺癌中很常见。按铁死亡基因簇分组的乳腺癌患者在生存、免疫细胞浸润及京都基因与基因组百科全书（KEGG）信号通路富集方面存在显著差异。通过比较铁死亡基因的聚类组间差异，鉴定出铁死亡相关差异基因。从这些铁死亡相关差异基因中筛选特征基因构建ferr.score模型。该评分模型能够准确区分和预测乳腺癌患者的生存预后及免疫治疗疗效。
铁死亡在肿瘤的发生发展中起重要作用。根据ferr.score模型，可将乳腺癌样本分为预后显著不同的两组。这些结果为乳腺癌患者的免疫治疗提供了新的见解和思路。
