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Immune Cell Infiltration Landscape of Ovarian Cancer to Identify Prognosis and Immunotherapy-Related Genes to Aid Immunotherapy.

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机构: [1]Department of Obstetrics and Gynecology, Shenzhen University General Hospital, Shenzhen, China, [2]Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University Health Science Center, Shenzhen, China, [3]Shenzhen Key Laboratory, Shenzhen University General Hospital, Shenzhen, China, [4]Harbin Institute of Technology, Harbin, China, [5]Guangzhou University of Chinese Medicine, Guangzhou, China, [6]ZhuJiang Hospital of Southern Medical University, Guangzhou, China, [7]The Precise Medicine Center, Department of Basic Medical College, Shenyang Medical College, Shenyang, China, [8]Clinical Medical Academy, Shenzhen University, Shenzhen, China
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关键词: ovarian cancer immune cell infiltration tumor mutation burden immunotherapy prognosis

摘要:
Ovarian cancer (OC) is the second leading cause of death in gynecological cancer. Multiple study have shown that the efficacy of tumor immunotherapy is related to tumor immune cell infiltration (ICI). However, so far, the Immune infiltration landscape of tumor microenvironment (TME) in OC has not been elucidated. In this study, We organized the transcriptome data of OC in the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, evaluated the patient's TME information, and constructed the ICI scores to predict the clinical benefits of patients undergoing immunotherapy. Immune-related genes were further used to construct the prognostic model. After clustering analysis of ICI genes, we found that patients in ICI gene cluster C had the best prognosis, and their tumor microenvironment had the highest proportion of macrophage M1 and T cell follicular helper cells. This result was consistent with that of multivariate cox (multi-cox) analysis. The prognostic model constructed by immune-related genes had good predictive performance. By estimating Tumor mutation burden (TMB), we also found that there were multiple genes with statistically different mutation frequencies in the high and low ICI score groups. The model based on the ICI score may help to screen out patients who would benefit from immunotherapy. The immune-related genes screened may be used as biomarkers and therapeutic targets.Copyright © 2021 Li, Liang, Zhao, Jin, Shi, Xie, Wang and Wu.

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出版当年[2020]版
大类 | 2 区 生物
小类 | 2 区 发育生物学 3 区 细胞生物学
最新[2025]版:
大类 | 2 区 生物学
小类 | 2 区 发育生物学 3 区 细胞生物学
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出版当年[2019]版:
Q1 DEVELOPMENTAL BIOLOGY Q2 CELL BIOLOGY
最新[2023]版:
Q1 DEVELOPMENTAL BIOLOGY Q2 CELL BIOLOGY

影响因子: 最新[2023版] 最新五年平均 出版当年[2019版] 出版当年五年平均 出版前一年[2018版] 出版后一年[2020版]

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第一作者机构: [1]Department of Obstetrics and Gynecology, Shenzhen University General Hospital, Shenzhen, China, [2]Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University Health Science Center, Shenzhen, China, [3]Shenzhen Key Laboratory, Shenzhen University General Hospital, Shenzhen, China,
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通讯机构: [1]Department of Obstetrics and Gynecology, Shenzhen University General Hospital, Shenzhen, China, [2]Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University Health Science Center, Shenzhen, China, [3]Shenzhen Key Laboratory, Shenzhen University General Hospital, Shenzhen, China, [8]Clinical Medical Academy, Shenzhen University, Shenzhen, China
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