ArticleFrontiers in oncology2022
Identification of a tumor immune-inflammation signature predicting prognosis and immune status in breast cancer.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 4 citations in OpenAlex.
- Profiling the tumor immunological status in Japanese solid cancers using the tumor immune status scoring algorithm.Scientific reports · 2026Article
- Breast cancer immunotherapy: mechanisms of immune evasion, biomarkers, and emerging therapeutic strategies.Molecular cancer · 2026Review
- FREM1 serves as a novel therapeutic target in breast cancer through basement membrane-based prognostic modeling with integrated bioinformatics and experimental validation.Discover oncology · 2025Article
- Machine learning-based development of a cytotoxicity prediction model for NK cell therapy in cancers.Cellular oncology (Dordrecht, Netherlands) · 2025Article
- Eleven inflammation-related genes risk signature model predicts prognosis of patients with breast cancer.Translational cancer research · 2024Article
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Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Breast cancer has become the malignancy with the highest mortality rate in female patients worldwide. The limited efficacy of immunotherapy as a breast cancer treatment has fueled the development of research on the tumor immune microenvironment. Methods: In this study, data on breast cancer patients were collected from The Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) cohorts. Differential gene expression analysis, univariate Cox regression analysis, and least absolute shrinkage and selection operator (LASSO) Cox regression analysis were performed to select overall survival (OS)-related, tumor tissue highly expressed, and immune- and inflammation-related genes. A tumor immune-inflammation signature (TIIS) consisting of 18 genes was finally screened out in the LASSO Cox regression model. Model performance was assessed by time-dependent receiver operating characteristic (ROC) curves. In addition, the CIBERSORT algorithm and abundant expression of immune checkpoints were utilized to clarify the correlation between the risk signature and immune landscape in breast cancer. Furthermore, the association of IL27 with the immune signature was analyzed in pan-cancer and the effect of IL27 on the migration of breast cancer cells was investigated since the regression coefficient of IL27 was the highest. Results: A TIIS based on 18 genes was constructed Conclusion: The TIIS represents a promising prognostic tool for estimating OS in patients with breast cancer and is correlated with immune status.
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