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基因表达亚型分析揭示免疫改变:用于卵巢浆液性囊腺癌预后评估的TCGA数据库

Gene Expression Subtyping Reveals Immune alterations:TCGA Database for Prognosis in Ovarian Serous Cystadenocarcinoma.

作者信息

Feng Chunxia, Xu Yan, Liu Yuanyuan, Zhu Lixia, Wang Le, Cui Xixi, Lu Jingjing, Zhang Yan, Zhou Lina, Chen Minbin, Zhang Zhiqin, Li Ping

机构信息

Department of Radiotherapy and Oncology, The Second Affiliated Hospital of Soochow University, Suzhou, China.

Department of Radiotherapy and Oncology, Affiliated Kunshan Hospital of Jiangsu University, Kunshan, China.

出版信息

Front Mol Biosci. 2021 Sep 24;8:619027. doi: 10.3389/fmolb.2021.619027. eCollection 2021.

Abstract

Serous ovarian cancer is the most common and primary death type in ovarian cancer. In recent studies, tumor microenvironment and tumor immune infiltration significantly affect the prognosis of ovarian cancer. This study analyzed the four gene expression types of ovarian cancer in TCGA database to extract differentially expressed genes and verify the prognostic significance. Meanwhile, functional enrichment and protein interaction network analysis exposed that these genes were related to immune response and immune infiltration. Subsequently, we proved these prognostic genes in an independent data set from the GEO database. Finally, multivariate cox regression analysis revealed the prognostic significance of TAP1 and CXCL13. The genetic alteration and interaction network of these two genes were shown. Then, we established a nomogram model related to the two genes and clinical risk factors. This model performed well in Calibration plot and Decision Curve Analysis. In conclusion, we have obtained a list of genes related to the immune microenvironment with a better prognosis for serous ovarian cancer, and based on this, we have tried to establish a clinical prognosis model.

摘要

浆液性卵巢癌是卵巢癌中最常见的主要死亡类型。在最近的研究中,肿瘤微环境和肿瘤免疫浸润显著影响卵巢癌的预后。本研究分析了TCGA数据库中卵巢癌的四种基因表达类型,以提取差异表达基因并验证其预后意义。同时,功能富集和蛋白质相互作用网络分析表明,这些基因与免疫反应和免疫浸润有关。随后,我们在来自GEO数据库的独立数据集中验证了这些预后基因。最后,多变量cox回归分析揭示了TAP1和CXCL13的预后意义。展示了这两个基因的基因改变和相互作用网络。然后,我们建立了一个与这两个基因和临床危险因素相关的列线图模型。该模型在校准图和决策曲线分析中表现良好。总之,我们获得了一份与浆液性卵巢癌预后较好相关的免疫微环境相关基因列表,并在此基础上尝试建立了一个临床预后模型。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a7a0/8497788/8fff4ccf06c2/fmolb-08-619027-g001.jpg

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