Front Cell Dev Biol|中山医院范虹团队揭示肿瘤多组学及相关模型与铁死亡的关系

2022年8月4日 0条评论 371次阅读 0人点赞

最新细胞功能及机制文献分享

铁死亡是一种新发现的调节性细胞死亡（RCD）方式，其特征是铁依赖性脂质过氧化和随后的膜氧化损伤，这与多种类型的癌症有关。具有高/低铁死亡评分的癌细胞系之间的多组学差异仍有待阐明。2021年12月6日，上海复旦大学中山医院胸外科范虹团队在Frontiers in Cell and Developmental Biology上发表了题为“Multi-Omics Analysis of Cancer Cell Lines with High/Low Ferroptosis Scores and Development of a Ferroptosis-Related Model for Multiple Cancer Types”的研究论文。在本研究中，团队系统分析了高/低铁死亡评分癌细胞系之间的多组学差异，并针对多种癌症类型开发了铁死亡相关模型，提高了对铁死亡在癌症中的作用的理解，并为恶性肿瘤的治疗提供新的见解。

长期以来，一直认为细胞凋亡（经典RCD）是唯一适合开发抗肿瘤疗法的RCD形式，而近年来的研究显示，与细胞凋亡不同的是铁死亡有其独特的分子机制，其中，外源性途径由细胞膜转运蛋白如胱氨酸/谷氨酸转运蛋白的阻塞或铁转运蛋白血清转铁蛋白和乳转铁蛋白的激活启动；内源性途径由抑制细胞内抗氧化系触发。与正常细胞相比，癌细胞（尤其是癌症干细胞）的生长强烈依赖于铁，铁死亡提供了单独或与其他常规疗法结合杀死肿瘤细胞的新策略。鉴于铁死亡在癌症治疗中的潜力，阐明受该途径影响的多组学差异势在必行。

在本研究中，团队系统分析了依赖图谱（DEPMAP）中癌细胞系之间的多组学差异，并通过基因集变异分析（GSVA）生成高/低铁死亡评分。与来自癌症基因组图谱（TCGA）的大量RNA-seq数据不同，DEPMAP中的基因表达数据仅来自癌细胞，可以更好地反映癌症患者的肿瘤特征。因此，团队基于癌细胞系的基因表达数据建立了一个最小绝对收缩和选择算子（LASSO）-Logistic模型，并依据基因组变异分析产生的铁死亡评分中位数将癌症患者分为高分（HS）组和低分（LS）组。最后，在TCGA中的膀胱癌（BLCA）、宫颈癌（CESC）、食管癌（ESCA）、头颈癌（HNSC）、肺腺癌（LUAD）和肺鳞状细胞癌（LUSC）患者中验证了该模型。

在HS和LS分组完成后，团队发现奥沙利铂等66种药物的IC50（p < 0.001）有显著差异，其中HS组有65人较高。两组之间有851个基因存在差异性沉默。此外，团队还检测到在差异表达基因、miRNA和代谢物中，两组之间存在多个差异项。最后，团队发现在HS组中NFE2L2的突变率较高，以及12个表达水平较高的铁死亡相关基因。

《Front Cell Dev Biol|中山医院范虹团队揭示肿瘤多组学及相关模型与铁死亡的关系》

图 A 本研究的整体设计；B 本研究癌细胞系组织来源；C 癌细胞系GSVA热图；D-F HS和LS组之间药剂IC50差异。

期刊及DOI号

Front Cell Dev Biol. 2021 Dec 06.

doi: 10.3389/fcell.2021.794475

题目

Multi-Omics Analysis of Cancer Cell Lines with High/Low Ferroptosis Scores and Development of a Ferroptosis-Related Model for Multiple Cancer Types

摘要

背景：Ferroptosis is a newly identified regulated cell death characterized by iron-dependent lipid peroxidation and subsequent membrane oxidative damage, which has been implicated in multiple types of cancers. The multi-omics differences between cancer cell lines with high/low ferroptosis scores remain to be elucidated.

方法和材料：We used RNA-seq gene expression, gene mutation, miRNA expression, metabolites, copy number variation, and drug sensitivity data of cancer cell lines from DEPMAP to detect multi-omics differences associated with ferroptosis. Based on the gene expression data of cancer cell lines, we performed LASSO-Logistic regression analysis to build a ferroptosis-related model. Lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), esophageal cancer (ESCA), bladder cancer (BLCA), cervical cancer (CESC), and head and neck cancer (HNSC) patients from the TCGA database were used as validation cohorts to test the efficacy of this model.

结果：After stratifying the cancer cell lines into high score (HS) and low score (LS) groups according to the median of ferroptosis scores generated by gene set variation analysis, we found that IC50 of 66 agents such as oxaliplatin (p<0.001) were significantly different, among which 65 were higher in the HS group. 851 genes such as KEAP1 and NRAS were differentially muted between the two groups. Differentially expressed genes, miRNAs and metabolites were also detected—multiple items such as IL17F (logFC=6.58, p<0.001) differed between the two groups. Unlike the TCGA data generated by bulk RNA-seq, the gene expression data in DEPMAP are from pure cancer cells, so it could better reflect the traits of tumors in cancer patients. Thus, we built a 15-signature model (AUC=0.878) based on the gene expression data of cancer cell lines. The validation cohorts demonstrated a higher mutational rate of NFE2L2 and higher expression levels of 12 ferroptosis-related genes in HS groups.

结论：This article systemically analyzed multi-omics differences between cancer cell lines with high/low ferroptosis scores and a ferroptosis-related model was developed for multiple cancer types. Our findings could improve our understanding of the role of ferroptosis in cancer and provide new insight into treatment for malignant tumors.