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一种新型铁死亡相关基因特征可预测乳腺癌患者的总生存期。

A Novel Ferroptosis-Related Gene Signature Predicts Overall Survival of Breast Cancer Patients.

作者信息

Li Haifeng, Li Lu, Xue Cong, Huang Riqing, Hu Anqi, An Xin, Shi Yanxia

机构信息

Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou 510060, Guangdong, China.

State Key Laboratory of Oncology in South China, Guangzhou 510060 Guangdong, China.

出版信息

Biology (Basel). 2021 Feb 14;10(2):151. doi: 10.3390/biology10020151.

Abstract

Breast cancer is the second leading cause of death in women, thus a reliable prognostic model for overall survival (OS) in breast cancer is needed to improve treatment and care. Ferroptosis is an iron-dependent cell death. It is already known that siramesine and lapatinib could induce ferroptosis in breast cancer cells, and some ferroptosis-related genes were closely related with the outcomes of treatments regarding breast cancer. The relationship between these genes and the prognosis of OS remains unclear. The data of gene expression and related clinical information was downloaded from public databases. Based on the TCGA-BRCA cohort, an 8-gene prediction model was established with the least absolute shrinkage and selection operator (LASSO) cox regression, and this model was validated in patients from the METABRIC cohort. Based on the median risk score obtained from the 8-gene model, patients were stratified into high- or low-risk groups. Cox regression analyses identified that the risk score was an independent predictor for OS. The findings from CIBERSORT and ssGSEA presented noticeable differences in enrichment scores for immune cells and pathways between the abovementioned two risk groups. To sum up, this prediction model has potential to be widely applied in future clinical settings.

摘要

乳腺癌是女性第二大死因,因此需要一个可靠的乳腺癌总生存期(OS)预后模型来改善治疗和护理。铁死亡是一种铁依赖性细胞死亡。已知西拉米辛和拉帕替尼可诱导乳腺癌细胞发生铁死亡,且一些铁死亡相关基因与乳腺癌治疗结果密切相关。这些基因与OS预后之间的关系仍不清楚。从公共数据库下载基因表达数据和相关临床信息。基于TCGA-BRCA队列,采用最小绝对收缩和选择算子(LASSO)cox回归建立了一个8基因预测模型,并在METABRIC队列的患者中进行了验证。根据8基因模型获得的中位风险评分,将患者分为高风险组或低风险组。Cox回归分析确定风险评分是OS的独立预测因子。CIBERSORT和ssGSEA的结果显示,上述两个风险组在免疫细胞和通路的富集评分上存在显著差异。综上所述,该预测模型有望在未来临床中得到广泛应用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/59a5/7917807/d329e82fd90b/biology-10-00151-g0A1.jpg

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