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一种用于预测宫颈癌预后的新型铜死亡相关基因特征。

A novel cuproptosis-related gene signature for predicting prognosis in cervical cancer.

作者信息

Lei Lei, Tan Liao, Sui Long

机构信息

Cervical and Vaginal Precancerous Lesion Diagnosis and Treatment, Obstetrics and Gynecology Hospital of Fudan University, Shanghai, China.

Department of Cardiology, The Third Xiangya Hospital, Central South University, Changsha, China.

出版信息

Front Genet. 2022 Aug 25;13:957744. doi: 10.3389/fgene.2022.957744. eCollection 2022.

DOI:10.3389/fgene.2022.957744
PMID:36092887
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9453033/
Abstract

Cuproptosis, a form of copper-induced cell death, can be a promising therapeutic target for refractory cancers. Hence, we conducted this research to explore the association between cuproptosis and prognosis in cervical cancer (CC). For constructing a prognostic signature based on cuproptosis-related genes from TCGA database, the least absolute shrinkage and selection operator Cox regression was utilized. The GSE44001 cohort was utilized for validation. A total of nine cuproptosis-related genes showed distinct expression in CC and normal samples in TCGA-GTEx cohort. Two risk groups were identified based on a seven-gene signature. A significant decrease in overall survival was observed in the high-risk group ( < 0.001). The risk score (HR = 2.77, 95% CI = 1.58-4.86) was an autocephalous predictor with a better predictive ability than the clinical stage. Functional analysis indicated that immune activities were suppressed more in the high-risk group than in the low-risk group. A total of 11 candidate compounds targeting the signature were identified. A total of seven cuproptosis-related gene signatures were constructed to predict prognosis and propose a new therapeutic target for patients with CC.

摘要

铜死亡是一种铜诱导的细胞死亡形式,可能成为难治性癌症的一个有前景的治疗靶点。因此,我们开展了这项研究,以探索铜死亡与宫颈癌(CC)预后之间的关联。为了基于来自TCGA数据库的铜死亡相关基因构建一个预后特征,我们使用了最小绝对收缩和选择算子Cox回归。GSE44001队列用于验证。在TCGA-GTEx队列中,共有9个铜死亡相关基因在CC样本和正常样本中表现出不同的表达。基于一个七基因特征确定了两个风险组。在高风险组中观察到总生存期显著降低(<0.001)。风险评分(HR = 2.77,95%CI = 1.58 - 4.86)是一个独立的预测因子,其预测能力优于临床分期。功能分析表明,高风险组的免疫活性比低风险组受到更明显的抑制。共鉴定出11种针对该特征的候选化合物。构建了总共7个铜死亡相关基因特征以预测预后,并为CC患者提出了一个新的治疗靶点。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7a37/9453033/a5299bb0a5e4/fgene-13-957744-g011.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7a37/9453033/7caa61daff8b/fgene-13-957744-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7a37/9453033/b90a45c66450/fgene-13-957744-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7a37/9453033/a3a2f99f9ae0/fgene-13-957744-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7a37/9453033/5cd0accf954b/fgene-13-957744-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7a37/9453033/9384b62999dc/fgene-13-957744-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7a37/9453033/a2cfc42ed6bb/fgene-13-957744-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7a37/9453033/a5299bb0a5e4/fgene-13-957744-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7a37/9453033/f0fb04335638/fgene-13-957744-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7a37/9453033/10584192c43a/fgene-13-957744-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7a37/9453033/9693fd550f63/fgene-13-957744-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7a37/9453033/76307bade630/fgene-13-957744-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7a37/9453033/7caa61daff8b/fgene-13-957744-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7a37/9453033/b90a45c66450/fgene-13-957744-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7a37/9453033/a3a2f99f9ae0/fgene-13-957744-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7a37/9453033/5cd0accf954b/fgene-13-957744-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7a37/9453033/9384b62999dc/fgene-13-957744-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7a37/9453033/a2cfc42ed6bb/fgene-13-957744-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7a37/9453033/a5299bb0a5e4/fgene-13-957744-g011.jpg

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