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卵巢癌中CD8 T细胞相关生物标志物的鉴定

Identification of CD8 T Cell Related Biomarkers in Ovarian Cancer.

作者信息

Li Ling, Chen Dian, Luo Xiaolin, Wang Zhengkun, Yu Hanjie, Gao Weicheng, Zhong Weiqiang

机构信息

Department of Anesthesiology, Affiliated Foshan Maternity & Child Healthcare Hospital, Southern Medical University (Foshan Maternity & Child Healthcare Hospital), Foshan, China.

Division of Respiratory and Critical Care Medicine, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.

出版信息

Front Genet. 2022 May 27;13:860161. doi: 10.3389/fgene.2022.860161. eCollection 2022.

Abstract

Immunotherapy is a promising strategy for ovarian cancer (OC), and this study aims to identify biomarkers related to CD8 T cell infiltration to further discover the potential therapeutic target. Three datasets with OC transcriptomic data were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Two immunotherapy treated cohorts were obtained from the Single Cell Portal and Mariathasan's study. The infiltration fraction of immune cells was quantified using three different algorithms, Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT), and microenvironment cell populations counter (MCPcounter), and single-sample GSEA (ssGSEA). Weighted gene co-expression network analysis (WGCNA) was applied to identify the co-expression modules and related genes. The nonnegative matrix factorization (NMF) method was proposed for sample classification. The mutation analysis was conducted using the "maftools" R package. Key molecular markers with implications for prognosis were screened by univariate COX regression analysis and K-M survival analysis, which were further determined by the receiver operating characteristic (ROC) curve. A total of 313 candidate CD8 T cell-related genes were identified by taking the intersection from the TCGA-OV and GSE140082 cohorts. The NMF clustering analysis suggested that patients in the TCGA-OV cohort were divided into two clusters and the Cluster 1 group showed a worse prognosis. In contrast, Cluster 2 had higher amounts of immune cell infiltration, elevated ssGSEA scores in immunotherapy, and a higher mutation burden. CSMD3, MACF1, PDE4DIP, and OBSCN were more frequently mutated in Cluster 1, while SYNE2 was more frequently mutated in Cluster 2. CD38 and CXCL13 were identified by univariate COX regression analysis and K-M survival analysis in the TCGA-OV cohort, which were further externally validated in GSE140082 and GSE32062. Of note, patients with lower CXCL13 expression showed a worse prognosis and the CR/PR group had a higher expression of CXCL13 in two immunotherapy treated cohorts. OC patients with different CD8 T cell infiltration had distinct clinical prognoses. CXCL13 might be a potential therapeutic target for the treatment of OC.

摘要

免疫疗法是治疗卵巢癌(OC)的一种很有前景的策略,本研究旨在识别与CD8 T细胞浸润相关的生物标志物,以进一步发现潜在的治疗靶点。从癌症基因组图谱(TCGA)和基因表达综合数据库(GEO)下载了三个包含OC转录组数据的数据集。从单细胞门户和马里亚塔桑的研究中获得了两个接受免疫治疗的队列。使用三种不同算法对免疫细胞的浸润分数进行量化,即通过估计RNA转录本相对子集进行细胞类型鉴定(CIBERSORT)、微环境细胞群体计数器(MCPcounter)和单样本基因集富集分析(ssGSEA)。应用加权基因共表达网络分析(WGCNA)来识别共表达模块和相关基因。提出了非负矩阵分解(NMF)方法用于样本分类。使用“maftools”R包进行突变分析。通过单变量COX回归分析和K-M生存分析筛选出对预后有影响的关键分子标志物,并通过受试者工作特征(ROC)曲线进一步确定。通过取TCGA-OV和GSE140082队列的交集,共鉴定出313个与CD8 T细胞相关的候选基因。NMF聚类分析表明,TCGA-OV队列中的患者被分为两个簇,第1簇组的预后较差。相比之下,第2簇具有更高的免疫细胞浸润量、免疫治疗中升高的ssGSEA分数和更高的突变负担。CSMD3、MACF1、PDE4DIP和OBSCN在第1簇中更频繁发生突变,而SYNE2在第2簇中更频繁发生突变。在TCGA-OV队列中通过单变量COX回归分析和K-M生存分析鉴定出CD38和CXCL13,并在GSE140082和GSE32062中进行了进一步的外部验证。值得注意的是,CXCL13表达较低的患者预后较差,并且在两个接受免疫治疗的队列中,CR/PR组的CXCL13表达较高。不同CD8 T细胞浸润的OC患者具有不同的临床预后。CXCL13可能是治疗OC的潜在治疗靶点。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0dab/9196910/cbacd255fdbb/fgene-13-860161-g001.jpg

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