ArticleJournal of cancer research and clinical oncology2024
Identification and validation of Golgi apparatus-related signature for predicting prognosis and immunotherapy response in breast cancer.
Article in Journal of cancer research and clinical oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Development and validation of an immune-related molecular subtyping model based on a four-gene prognostic biomarker signature and multi-omics integrative analysis in Luminal B breast cancer.Breast cancer research : BCR · 2026Article
- Applying integrated transcriptome and single-cell sequencing analysis to develop a prognostic signature based on M2-like tumor-associated macrophages for breast cancer.Discover oncology · 2025Article
- A Golgi apparatus‑related subtype and risk signature predicts prognosis and evaluates immunotherapy response in gastric cancer.Discover oncology · 2025Article
- Identification of Molecular Subtypes and Prognostic Features for Triple-Negative Breast Cancer Based on Golgi Apparatus-Related Gene Signature.Oncology research · 2025Article
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5 authors.
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Abstract
backgroundThe Golgi apparatus plays a pivotal role in various aspects of cancer. This study aims to investigate the predictive value of Golgi apparatus-related genes (GARGs) in breast cancer prognosis and immunotherapy response evaluation.
methodsTranscriptional and clinical data from the TCGA-BRCA cohort and GSE96058 cohort were utilized to construct and validate a prognostic model for breast cancer using Cox regression analysis. Differences in immune landscape, somatic mutations, gene expression, drug sensitivity, and immunotherapy response between different risk groups were assessed. A prognostic nomogram for breast cancer was further developed and evaluated. qPCR and single-cell sequencing analyses were performed to validate the expression of GARGs.
resultsA total of 394 GARGs significantly associated with breast cancer prognosis were identified, leading to the construction of a prognostic risk feature comprising 10 GARGs. This feature effectively stratified breast cancer patients into high-risk and low-risk groups, with the high-risk group exhibiting significantly worse prognosis. Meanwhile, significant differences in clinicopathological features, immune infiltration, drug sensitivity, and immunotherapy response were observed between the high- and low-risk groups. The constructed nomogram incorporating these factors showed superior performance in prognostic assessment for breast cancer patients. Ultimately, the utilization of qPCR and single-cell sequencing techniques substantiated the disparate expression patterns of these prognostic genes in breast cancer.
conclusionOur findings demonstrate that a prognostic risk feature derived from GARGs holds promising application potential for predicting prognosis and evaluating immunotherapy response in breast cancer patients.
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