ArticleJournal of cancer research and clinical oncology2023
Machine learning- and WGCNA-mediated double analysis based on genes associated with disulfidptosis, cuproptosis and ferroptosis for the construction and validation of the prognostic model for breast cancer.
Article in Journal of cancer research and clinical oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed, 14 citations in OpenAlex.
- Disulfidptosis: molecular mechanisms and therapeutic targets.Signal transduction and targeted therapy · 2026Review
- Epigenetic modification of cuproptosis by non-coding RNAs in cancer drug resistance.Molecular cancer · 2025Review
- Integrated machine learning identifies disulfidptosis-related and ferroptosis-related genes to evaluate survival prognosis and treatment efficacy in kidney renal clear cell carcinoma.Biochemistry and biophysics reports · 2025Article
- Integrated multi-omics analysis identifies SELENOP and PKMYT1 as immune-metabolic hub genes in breast cancer.Biochemistry and biophysics reports · 2025Article
- Advances in novel cell death mechanisms in breast cancer: intersecting perspectives on ferroptosis, cuproptosis, disulfidptosis, and pyroptosis.Molecular cancer · 2025Review
- Targeting cuproptosis for cancer therapy: Focus on the anti-tumor immune system.Cancer pathogenesis and therapy · 2025Review
- Harnessing ferroptosis for precision oncology: challenges and prospects.BMC biology · 2025Review
- Development and validation of a prognostic model based on disulfidptosis-related ferroptosis genes:Translational cancer research · 2025Article
- Construction and Validation of a T Cell Exhaustion-Related Prognostic Signature in Cholangiocarcinoma.International journal of genomics · 2025Article
- Cross-talk between cuproptosis and ferroptosis to identify immune landscape in cervical cancer for mRNA vaccines development.European journal of medical research · 2024Article
- Machine learning-driven prognostic analysis of cuproptosis and disulfidptosis-related lncRNAs in clear cell renal cell carcinoma: a step towards precision oncology.European journal of medical research · 2024Article
- Elucidating the evolving role of cuproptosis in breast cancer progression.International journal of biological sciences · 2024Review
- Comprehensive identification of a disulfidptosis-associated long non-coding RNA signature to predict the prognosis and treatment options in ovarian cancer.Frontiers in endocrinology · 2024Article
- Targeting the initiator to activate both ferroptosis and cuproptosis for breast cancer treatment: progress and possibility for clinical application.Frontiers in pharmacology · 2024Review
- Impact of disulfidptosis-associated clusters on breast cancer survival rates and guiding personalized treatment.Frontiers in endocrinology · 2023Article
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundDisulfidptosis, a recently discovered cellular death mechanism, has not been extensively studied in relation to breast cancer (BC). Specifically, no previous research has integrated disulfidptosis-related genes (DRGs), cuproptosis-related genes (CRGs), and ferroptosis-related genes (FRGs) to construct a prognostic signature for BC.
methodsDRGs, CRGs and FRGs with prognostic potential were identified through Cox regression analysis. A predictive model was constructed by intersecting the core genes obtained from unsupervised cluster analysis and weighted correlation network analysis (WGCNA). Differences in chemotherapy drug sensitivity, immune checkpoint levels were analyzed according to different risk score groups. The expression of the core disulfidptosis gene, SLC7A11, was analyzed using immunofluorescence.
resultsSingle-cell RNA sequencing analysis revealed differential expression of DRGs in the BC tumor microenvironment. We developed a prognostic model, consisting of six genes, based on machine learning which included unsupervised cluster analysis and Lasso-Cox analysis. An internal training set and a validation set, both derived from the Cancer Genome Atlas-Breast Cancer (TCGA-BRCA) database, GSE20685 and GSE42568 as external validation sets all verified the model's validity. The low-risk group exhibited increased sensitivity to paclitaxel. Additionally, the high-risk group demonstrated significantly higher expression of tumor mutation burden and microsatellite instability compared to the low-risk group. A nomogram confirmed that the risk score can be an independent risk factor for BC. Notably, our findings highlighted the impact of SLC7A11 on the BC tumor microenvironment. Immunofluorescence analysis revealed significantly higher expression of SLC7A11 in BC tissues compared to paracancerous tissues.
conclusionMultiplex analysis based on DRGs, CRGs and FRGs correlated strongly with BC, providing new insights for developing clinical prognostic tools and designing immunotherapy regimens for BC patients.
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