ArticleJournal of oncology2021
Construction of a Macrophage Infiltration Regulatory Network and Related Prognostic Model of High-Grade Serous Ovarian Cancer.
Article in Journal of oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 9 citations in OpenAlex.
- Identification of integrated stress response-related prognostic genes in high-grade serous ovarian cancer using Mendelian randomization, single-cell RNA sequencing, and bulk RNA sequencing.Frontiers in oncology · 2026Article
- Unveiling histotype-specific biomarkers in ovarian carcinoma using proteomics.Molecular therapy. Oncology · 2025Article
- Artificial intelligence: illuminating the depths of the tumor microenvironment.Journal of translational medicine · 2024Review
- Immune cell landscapes are associated with high-grade serous ovarian cancer survival.Scientific reports · 2024Article
- CT-based radiomics predicts CD38 expression and indirectly reflects clinical prognosis in epithelial ovarian cancer.Heliyon · 2024Article
- Construction of an Immunophenoscore-Related Signature for Evaluating Prognosis and Immunotherapy Sensitivity in Ovarian Cancer.ACS omega · 2023Article
- Identification and Verification of Necroptosis-Related Gene Signature With Prognosis and Tumor Immune Microenvironment in Ovarian Cancer.Frontiers in immunology · 2022Article
- Applications of machine learning in tumor-associated macrophages.Frontiers in immunology · 2022Review
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundHigh-grade serous ovarian cancer (HGSOC) carries the highest mortality in the gynecological cancers; however, therapeutic outcomes have not significantly improved in recent decades. Macrophages play an essential role in the occurrence and development of ovarian cancer, so the mechanisms of macrophage infiltration should be elucidated.
methodWe downloaded transcriptome data of ovarian cancers from the Gene Expression Omnibus and The Cancer Genome Atlas. After rigorous screening, 1566 HGSOC were used for data analysis. CIBERSORT was used to estimate the level of macrophage infiltration and WGCNA was used to identify macrophage-related modules. We constructed a macrophage-related prognostic model using machine learning LASSO algorithm and verified it using multiple HGSOC cohorts.
resultsIn the GPL570-OV cohort, high infiltration level of M1 macrophages was associated with a good outcome, while high infiltration level of M2 macrophages was associated with poor outcomes. We used WGCNA to select genes correlated with macrophage infiltration. These genes were used to construct protein-protein interaction maps of macrophage infiltration. IFL44L, RSAD2, IFIT3, MX1, IFIH1, IFI44, and ISG15 were the hub genes in the network. We then constructed a macrophage-related prognostic model composed of CD38, ACE2, BATF2, HLA-DOB, and WARS. The model had the ability to predict the overall survival rate of HGSOC patients in GPL570-OV, GPL6480-OV, TCGA-OV, GSE50088, and GSE26712. In exploring the immune microenvironment, we found that CD4 memory T cells and activated mast cells showed that the degree of infiltration was higher in the high-risk group, while M1 macrophages were the opposite, and HLA molecules were overexpressed in the high-risk group.
conclusionWe constructed a macrophage infiltration-related protein interaction network that provides a basis for studying macrophages in HGSOC. Our macrophage-related prognostic model is robust and widely applicable. It predicts overall survival in HGSOC patients and may improve HGSOC treatment.
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Registered trials
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