Establishment and validation of an immune infiltration predictive model for ovarian cancer

BMC Medical Genomics

Research

Zhenxia Song¹  Xiaoning Liu¹  Jingwen Zhang¹  Yue Sun¹  Zhongmin Jiang² 

[1] Department of Obstetrics, Qingdao women and childeren’s hospital, #6 Tongfu Road, Shibei District, 266000, Qingdao, Shandong, P. R. China;Department of Pathology, Tian Jin Fifth’s Central Hospital, #41 Zhejiang Road, 300450, Binhai District, Tianjin, P. R. China;

关键词: TP53 mutation;    Ovarian cancer;    IPM;    Immune infiltration;   

DOI  :  10.1186/s12920-023-01657-x

 received in 2022-11-08, accepted in 2023-09-07,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundThe most prevalent mutation in ovarian cancer is the TP53 mutation, which impacts the development and prognosis of the disease. We looked at how the TP53 mutation associates the immunophenotype of ovarian cancer and the prognosis of the disease.MethodsWe investigated the state of TP53 mutations and expression profiles in culturally diverse groups and datasets and developed an immune infiltration predictive model relying on immune-associated genes differently expressed between TP53 WT and TP53 MUT ovarian cancer cases. We aimed to construct an immune infiltration predictive model (IPM) to enhance the prognosis of ovarian cancer and investigate the impact of the IPM on the immunological microenvironment.ResultsTP53 mutagenesis affected the expression of seventy-seven immune response-associated genes. An IPM was implemented and evaluated on ovarian cancer patients to distinguish individuals with low- and high-IPM subgroups of poor survival. For diagnostic and therapeutic use, a nomogram is thus created. According to pathway enrichment analysis, the pathways of the human immune response and immune function abnormalities were the most associated functions and pathways with the IPM genes. Furthermore, patients in the high-risk group showed low proportions of macrophages M1, activated NK cells, CD8+ T cells, and higher CTLA-4, PD-1, PD-L1, and TIM-3 than patients in the low-risk group.ConclusionsThe IPM model may identify high-risk patients and integrate other clinical parameters to predict their overall survival, suggesting it is a potential methodology for optimizing ovarian cancer prognosis.

【 授权许可】

CC BY   
© BioMed Central Ltd., part of Springer Nature 2023

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