{"title":"Construction of a prognostic survival model with tumor immune-related genes for breast cancer.","authors":"Shuai Guo, Liang Guo, Jiangyun Li, Jianguo Li, Qiqi Zhang, Jing Zhang, Stergios Boussios, Masakazu Toi","doi":"10.21037/tcr-24-2137","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Numerous studies have demonstrated that immune cell infiltration is a significant predictor in the prognosis of those with breast cancer. This study aimed to develop a prognostic model for undifferentiated breast cancer using immune-related markers.</p><p><strong>Methods: </strong>Differentially expressed genes (DEGs) and prognostic factors were identified from The Cancer Genome Atlas (TCGA) database. Cancer immune-associated genes were filtered using the GeneCards database. Least absolute shrinkage and selection operator (LASSO) and Cox proportional hazards regression were employed to select prognostic indicators. The single-sample gene set enrichment analysis (ssGSEA) algorithm and the CIBERSORT algorithm were used to analyze the correlation of prognostic indicators with immune cells in breast cancer.</p><p><strong>Results: </strong>We identified six tumor immune-related genes, including zic family member 2 (<i>ZIC2</i>), solute carrier family 7 member 5 (<i>SLC7A5</i>), forkhead box J1 (<i>FOXJ1</i>), C-X-C motif chemokine ligand 9 (<i>CXCL9</i>), tumor necrosis factor receptor superfamily member 18 (<i>TNFRSF18</i>), and serine protease 2 (<i>PRSS2</i>), for the development of a prognostic model for patients with breast cancer. Notably, the results of the correlation analysis indicated that <i>CXCL9</i> was associated with antitumor immune cells, including CD8<sup>+</sup> T cells, cytotoxic cells, M1 macrophages, and activated memory CD4 T cells, and with the enrichment of natural killer (NK) CD56dim cells. Furthermore, <i>CXCL9</i> exhibited a significant negative association with the tumor-promoting M2 macrophage phenotype.</p><p><strong>Conclusions: </strong>Our study established a six-gene model for predicting breast cancer prognosis. Furthermore, we unexpectedly discovered that <i>CXCL9</i> is integral to immune infiltration in breast cancer and may serve as a critical biomarker for evaluating immune response and therapeutic efficacy in breast cancer treatment.</p>","PeriodicalId":23216,"journal":{"name":"Translational cancer research","volume":"13 12","pages":"6919-6935"},"PeriodicalIF":1.5000,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11730693/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Translational cancer research","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.21037/tcr-24-2137","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/12/27 0:00:00","PubModel":"Epub","JCR":"Q4","JCRName":"ONCOLOGY","Score":null,"Total":0}
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Abstract
Background: Numerous studies have demonstrated that immune cell infiltration is a significant predictor in the prognosis of those with breast cancer. This study aimed to develop a prognostic model for undifferentiated breast cancer using immune-related markers.
Methods: Differentially expressed genes (DEGs) and prognostic factors were identified from The Cancer Genome Atlas (TCGA) database. Cancer immune-associated genes were filtered using the GeneCards database. Least absolute shrinkage and selection operator (LASSO) and Cox proportional hazards regression were employed to select prognostic indicators. The single-sample gene set enrichment analysis (ssGSEA) algorithm and the CIBERSORT algorithm were used to analyze the correlation of prognostic indicators with immune cells in breast cancer.
Results: We identified six tumor immune-related genes, including zic family member 2 (ZIC2), solute carrier family 7 member 5 (SLC7A5), forkhead box J1 (FOXJ1), C-X-C motif chemokine ligand 9 (CXCL9), tumor necrosis factor receptor superfamily member 18 (TNFRSF18), and serine protease 2 (PRSS2), for the development of a prognostic model for patients with breast cancer. Notably, the results of the correlation analysis indicated that CXCL9 was associated with antitumor immune cells, including CD8+ T cells, cytotoxic cells, M1 macrophages, and activated memory CD4 T cells, and with the enrichment of natural killer (NK) CD56dim cells. Furthermore, CXCL9 exhibited a significant negative association with the tumor-promoting M2 macrophage phenotype.
Conclusions: Our study established a six-gene model for predicting breast cancer prognosis. Furthermore, we unexpectedly discovered that CXCL9 is integral to immune infiltration in breast cancer and may serve as a critical biomarker for evaluating immune response and therapeutic efficacy in breast cancer treatment.
期刊介绍：
Translational Cancer Research (Transl Cancer Res TCR; Print ISSN: 2218-676X; Online ISSN 2219-6803; http://tcr.amegroups.com/) is an Open Access, peer-reviewed journal, indexed in Science Citation Index Expanded (SCIE). TCR publishes laboratory studies of novel therapeutic interventions as well as clinical trials which evaluate new treatment paradigms for cancer; results of novel research investigations which bridge the laboratory and clinical settings including risk assessment, cellular and molecular characterization, prevention, detection, diagnosis and treatment of human cancers with the overall goal of improving the clinical care of cancer patients. The focus of TCR is original, peer-reviewed, science-based research that successfully advances clinical medicine toward the goal of improving patients'' quality of life. The editors and an international advisory group of scientists and clinician-scientists as well as other experts will hold TCR articles to the high-quality standards. We accept Original Articles as well as Review Articles, Editorials and Brief Articles.
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