ArticleFrontiers in oncology2023
A novel risk score model based on gamma-aminobutyric acid signature predicts the survival prognosis of patients with breast cancer.
Article in Frontiers in oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Salivary Metabolic Characteristics and Response to Neoadjuvant Systemic Therapy in Breast Cancer.International journal of molecular sciences · 2026Article
- The neuroimmune axis in breast cancer: from mechanistic insights to clinical applications.BMC medicine · 2026Review
- Distinctive GABA A receptor subunit expression modulates cell specific EMT and functional responses in glioblastoma breast and ovarian cancer.Scientific reports · 2025Article
- JASMINE: A powerful representation learning method for enhanced analysis of incomplete multi-omics data.bioRxiv : the preprint server for biology · 2025Article
- The double role of GABAergic system in systemic tumors: an updated review.Frontiers in oncology · 2025Review
- Endoplasmic reticulum stress in breast cancer: a predictive model for prognosis and therapy selection.Frontiers in immunology · 2024Article
- Identification of Novel Prognostic Biomarkers That are Associated with Immune Microenvironment Based on GABA-Related Molecular Subtypes in Gastric Cancer.Pharmacogenomics and personalized medicine · 2023Article
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6 authors.
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Abstract
Background: Gamma-aminobutyric acid (GABA) participates in the migration, differentiation, and proliferation of tumor cells. However, the GABA-related risk signature has never been investigated. Hence, we aimed to develop a reliable gene signature based on GABA pathways-related genes (GRGs) to predict the survival prognosis of breast cancer patients. Methods: GABA-related gene sets were acquired from the MSigDB database, while mRNA gene expression profiles and corresponding clinical data of breast cancer patients were downloaded from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Univariate Cox regression analysis was used to identify prognostic-associated GRGs. Subsequently, LASSO analysis was applied to establish a risk score model. We also constructed a clinical nomogram to perform the survival evaluation. Besides, ESTIMATE and ssGSEA algorithms were used to assess the immune cell infiltration among the risk score subgroups. Results: A GRGs-related risk score model was constructed in the TCGA cohort, and validated in the GSE21653 cohort. The risk score was significantly related to the overall survival of breast cancer patients, which could predict the survival prognosis of breast cancer patients independently of other clinical features. Breast cancer patients in the low-risk score group exhibited higher immune cell infiltration levels. Conclusion: A novel prognostic model containing five GRGs could accurately predict the survival prognosis and immune infiltration of breast cancer patients. Our findings provided a novel insight into investigating the immunoregulation roles of GRGs.
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