Prediction of overall survival based upon a new ferroptosis-related gene signature in patients with clear cell renal cell carcinoma.
Prediction of overall survival based upon a new ferroptosis-related gene signature in patients with clear cell renal cell carcinoma.
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基于透明细胞肾细胞癌患者新的铁死亡相关基因特征的总生存率预测
DOI：
10.1186/s12957-022-02555-9
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发表时间：
2022-04-14
影响因子：
3.2
通讯作者：
Wang, Hua
中科院分区：
文献类型：
--
作者：
Sun, Zhuolun;Li, Tengcheng;Xiao, Chutian;Zou, Shaozhong;Zhang, Mingxiao;Zhang, Qiwei;Wang, Zhenqing;Zhan, Hailun;Wang, Hua
透明细胞肾细胞癌（ccRCC）是最常见和最致命的肾细胞癌（RCC）组织学亚型。铁下垂是一种新发现的程序性细胞死亡，在肿瘤的发生发展中起着重要作用。本研究的目的是分析嗜铁相关基因（FRG）的表达谱，构建预测ccRCC患者预后的多基因标记。从The Cancer Genome Atlas （TCGA）下载ccRCC患者的rna测序数据和临床病理数据。使用“limma”包识别ccRCC和正常组织之间差异表达的FRGs，并进行GO和KEGG富集分析以阐明差异表达的FRGs的生物学功能和途径。采用一致聚类法研究FRGs表达与临床表型之间的关系。采用单变量和最小绝对收缩和选择算子（LASSO） Cox回归分析筛选与预后相关的基因，构建最优特征。然后，结合临床特征和预后特征，建立预测个体生存概率的nomogram。共鉴定出19个差异表达的FRGs。共识聚类鉴定出两组预后显著的ccRCC患者。功能分析显示代谢相关通路丰富，尤其是脂质代谢。构建了7个基因的凋亡相关预后标记，将TCGA训练队列分为高危组和低危组，高危组预后明显较差。该特征被确定为ccRCC的独立预后指标。这些发现在测试队列、整个队列和国际癌症基因组联盟（ICGC）队列中得到了验证。我们进一步证明，基于特征的风险评分与ccRCC进展高度相关。进一步的分层生存分析显示，高危组的总生存率（OS）明显低于低危组。此外，我们构建了一个对ccRCC患者的OS有较强预测能力的nomogram。我们构建了凋亡相关的预后特征，为临床医生指导临床决策和结局研究提供可靠的预后评估工具。在线版本包含补充材料，可在10.1186/s12957-022-02555-9获得。
Clear cell renal cell carcinoma (ccRCC) is the most common and lethal renal cell carcinoma (RCC) histological subtype. Ferroptosis is a newly discovered programmed cell death and serves an essential role in tumor occurrence and development. The purpose of this study is to analyze ferroptosis-related gene (FRG) expression profiles and to construct a multi-gene signature for predicting the prognosis of ccRCC patients. RNA-sequencing data and clinicopathological data of ccRCC patients were downloaded from The Cancer Genome Atlas (TCGA). Differentially expressed FRGs between ccRCC and normal tissues were identified using ‘limma’ package in R. GO and KEGG enrichment analyses were conducted to elucidate the biological functions and pathways of differentially expressed FRGs. Consensus clustering was used to investigate the relationship between the expression of FRGs and clinical phenotypes. Univariate and the least absolute shrinkage and selection operator (LASSO) Cox regression analysis were used to screen genes related to prognosis and construct the optimal signature. Then, a nomogram was established to predict individual survival probability by combining clinical features and prognostic signature. A total of 19 differentially expressed FRGs were identified. Consensus clustering identified two clusters of ccRCC patients with distinguished prognostic. Functional analysis revealed that metabolism-related pathways were enriched, especially lipid metabolism. A 7-gene ferroptosis-related prognostic signature was constructed to stratify the TCGA training cohort into high- and low-risk groups where the prognosis was significantly worse in the high-risk group. The signature was identified as an independent prognostic indicator for ccRCC. These findings were validated in the testing cohort, the entire cohort, and the International Cancer Genome Consortium (ICGC) cohort. We further demonstrated that the signature-based risk score was highly associated with the ccRCC progression. Further stratified survival analysis showed that the high-risk group had a significantly lower overall survival (OS) rate than those in the low-risk group. Moreover, we constructed a nomogram that had a strong ability to forecast the OS of the ccRCC patients. We constructed a ferroptosis-related prognostic signature, which might provide a reliable prognosis assessment tool for the clinician to guide clinical decision-making and outcomes research. The online version contains supplementary material available at 10.1186/s12957-022-02555-9.
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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：
5.6
作者：
Lu B;Chen XB;Ying MD;He QJ;Cao J;Yang B
通讯作者：
Yang B
铁死亡相关基因特征预测神经胶质瘤细胞死亡和神经胶质瘤患者进展
影响因子：
5.5
作者：
Liu, Han-jie;Hu, Hui-min;Jiang, Tao
通讯作者：
Jiang, Tao
影响因子：
64.8
作者：
Jiang, Le;Kon, Ning;Li, Tongyuan;Wang, Shang-Jui;Su, Tao;Hibshoosh, Hanina;Baer, Richard;Gu, Wei
通讯作者：
Gu, Wei
DOI：
10.1073/pnas.0910258107
发表时间：
2010-08-24
影响因子：
11.1
作者：
Clendening, James W.;Pandyra, Aleks;Penn, Linda Z.
通讯作者：
Penn, Linda Z.