Identification of three subtypes of ovarian cancer and construction of prognostic models based on immune-related genes

被引:1
|
作者
Gao, Wen [1 ]
Yuan, Hui [3 ]
Yin, Sheng [4 ]
Deng, Renfang [5 ]
Ji, Zhaodong [2 ]
机构
[1] Chinese Acad Sci, Zhejiang Canc Hosp, Hangzhou Inst Med HIM, Dept Gynecol Oncol, Hangzhou 310022, Zhejiang, Peoples R China
[2] Fudan Univ, Huashan Hosp, Dept Lab Med, Shanghai 200040, Peoples R China
[3] Dian Diagnost Grp Co Ltd, Key Lab Digital Technol Med Diagnost Zhejiang Prov, Hangzhou City 310022, Zhejiang, Peoples R China
[4] Fudan Univ, Zhongshan Hosp, Dept Obstet & Gynecol, Shanghai 200032, Peoples R China
[5] Second Hosp Zhuzhou City, Dept Oncol, Zhuzhou 412000, Peoples R China
关键词
Ovarian cancer; Immunotherapy; Immune subtype; Vaccine-related genes; TUMOR MUTATION BURDEN; REGULATORY T-CELLS; HOMOLOGOUS RECOMBINATION; FOLLOW-UP; IMMUNOTHERAPY; EXPRESSION; BIOMARKER; FEATURES; REPAIR; HE-4;
D O I
10.1186/s13048-024-01526-w
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
BackgroundImmunotherapy has revolutionized the treatment of ovarian cancer (OC), but different immune microenvironments often constrain the efficacy of immunotherapeutic interventions. Therefore, there is an imperative to delineate novel immune subtypes for development of efficacious immunotherapeutic strategies.MethodsThe immune subtypes of OC were identified by consensus cluster analysis. The differences in clinical features, genetic mutations, mRNA stemness (mRNAsi) and immune microenvironments were analyzed among subtypes. Subsequently, prognostic risk models were constructed based on differentially expressed genes (DEGs) of the immune subtypes using weighted correlation network analysis.ResultsOC patients were classified into three immune subtypes with distinct survival rates and clinical features. Different subtypes exhibited varying tumor mutation burdens, homologous recombination deficiencies, and mRNAsi levels. Significant differences were observed among immune subtypes in terms of immune checkpoint expression and immunogenic cell death. Prognostic risk models were validated as independent prognostic factors demonstrated great predictive performance for survival of OC patients.ConclusionIn this study, three distinct immune subtypes were identified based on gene sets related to vaccine response, with the C2 subtype exhibiting significantly worse prognosis. While no statistically significant differences in tumor mutation burden (TMB) were observed across the three subtypes, the homologous recombination deficiency (HRD) score and mRNA stemness index (mRNAsi) were notably elevated in the C2 group compared to the others. Immune infiltration analysis indicated that the C2 subtype may have an increased presence of regulatory T (Treg) cells, potentially contributing to a more favorable response to combination therapies involving PARP inhibitors and immunotherapy. These findings offer a precision medicine approach for tailoring immunotherapy in ovarian cancer patients. Moreover, the C3 subtype demonstrated significantly lower expression levels of immune checkpoint genes, a pattern validated by independent datasets, and associated with a better prognosis. Further investigation revealed that the immune-related gene FCRL5 correlates with ovarian cancer prognosis, with in vitro experiments showing that it influences the proliferation and migration of the ovarian cancer cell line SKOV3.
引用
收藏
页数:14
相关论文
共 50 条
  • [41] Construction of a prognostic model for lung squamous cell carcinoma based on immune-related genes
    Pu, Jiangtao
    Teng, Zhangyu
    Yang, Wenxing
    Zhu, Peiquan
    Zhang, Tao
    Zhang, Dengguo
    Wang, Biao
    Hu, Zhi
    Song, Qi
    CARCINOGENESIS, 2023, 44 (02) : 143 - 152
  • [42] Screening and identification of immune-related genes for immunotherapy and prognostic assessment in colorectal cancer patients
    Wang, Shuwei
    Cheng, Liang
    Jing, Fa
    Li, Gan
    BMC MEDICAL GENOMICS, 2022, 15 (01)
  • [43] Systematic Construction and Validation of a Prognostic Model for Hepatocellular Carcinoma Based on Immune-Related Genes
    Yu, Jiahao
    Ma, Shuoyi
    Tian, Siyuan
    Zhang, Miao
    Ding, Xiaopeng
    Liu, Yansheng
    Yang, Fangfang
    Hu, Yinan
    Xuan, Guoyun
    Zhou, Xinmin
    Wang, Jingbo
    Han, Ying
    FRONTIERS IN CELL AND DEVELOPMENTAL BIOLOGY, 2021, 9
  • [44] Screening and identification of immune-related genes for immunotherapy and prognostic assessment in colorectal cancer patients
    Shuwei Wang
    Liang Cheng
    Fa Jing
    Gan Li
    BMC Medical Genomics, 15
  • [45] Identification and validation of novel lung adenocarcinoma subtypes and construction of prognostic models: based on cuprotosis-related genes
    Wang, Guangyao
    Wang, Anqiao
    Wang, Li
    Xu, Guanglan
    Hong, Xiaohua
    Fang, Fang
    BMC PULMONARY MEDICINE, 2023, 23 (01)
  • [46] Identification and validation of novel lung adenocarcinoma subtypes and construction of prognostic models: based on cuprotosis-related genes
    Guangyao Wang
    Anqiao Wang
    Li Wang
    Guanglan Xu
    Xiaohua Hong
    Fang Fang
    BMC Pulmonary Medicine, 23
  • [47] Identification of three subtypes of thyroid cancer based on IFN-γ-related genes to reveal their prognostic characteristics
    Huang, Fang
    Sui, Qian
    Li, Ke
    LANGENBECKS ARCHIVES OF SURGERY, 2025, 410 (01)
  • [48] Construction of a five-gene prognostic model based on immune-related genes for the prediction of survival in pancreatic cancer
    Liu, Bo
    Fu, Tingting
    He, Ping
    Du, Chengyou
    Xu, Ke
    BIOSCIENCE REPORTS, 2021, 41 (07)
  • [49] Construction of a prognostic model based on nine immune-related genes and identification of small molecule drugs for hepatocellular carcinoma (HCC)
    Zhang, Jiaxin
    Chen, Guang
    Zhang, Jiaying
    Zhang, Peng
    Ye, Yong'an
    AMERICAN JOURNAL OF TRANSLATIONAL RESEARCH, 2020, 12 (09): : 5108 - 5130
  • [50] Construction of a prognostic signature of RFC5 immune-related genes in patients with cervical cancer
    Chen, Huaqiu
    Xie, Huanyu
    Zhang, Yuanyuan
    Wang, Guangming
    CANCER BIOMARKERS, 2023, 37 (04) : 261 - 277