Abstract
Background
Disulfidptosis, 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.
Methods
DRGs, 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.
Results
Single-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.
Conclusion
Multiplex 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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Data availability
The data used to construct and verify the prognosis model can be obtained from TCGA and GEO databases.
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Acknowledgements
We thank the Department of Clinical Biobank, Affiliated Hospital of Nantong University for providing pathological specimens and laboratory equipment for this study.
Funding
Grants from Research project on teaching reform of Nantong University (2020B43) and Postgraduate Research & Practice Innovation Program of Jiangsu Province (SJCX22_1631) provided funding for the current study.
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LX devised the study, wrote the report, and carried out the experiment. SW and DZ were in charge of image optimization. The data analysis was carried out by YW and JS. CW, HZ, and QW critically edited the text for key intellectual content. The final manuscript was reviewed and approved by all writers.
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Xu, L., Wang, S., Zhang, D. et al. 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. J Cancer Res Clin Oncol 149, 16511–16523 (2023). https://doi.org/10.1007/s00432-023-05378-7
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DOI: https://doi.org/10.1007/s00432-023-05378-7