A novel disulfidptosis-associated expression pattern in breast cancer based on machine learning
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Updated: Jul 23, 2025
Optimization of a Multiplex RNA-based Expression Assay Using Breast Cancer Archival Material
Published on: August 1, 2018
Zhitang Wang1, Xianqiang Du1, Weibin Lian1
1Department of Breast, The First Hospital of Quanzhou Affiliated to Fujian Medical University, Quanzhou, China.
Abstract:
Background: Breast cancer (BC), the leading cause of cancer-related deaths among women, remains a serious threat to human health worldwide. The biological function and prognostic value of disulfidptosis as a novel strategy for BC treatment via induction of cell death remain unknown. Methods: Gene mutations and copy number variations (CNVs) in 10 disulfidptosis genes were evaluated. Differential expression, prognostic, and univariate Cox analyses were then performed for 10 genes, and BC-specific disulfidptosis-related genes (DRGs) were screened. Unsupervised consensus clustering was used to identify different expression clusters. In addition, we screened the differentially expressed genes (DEGs) among different expression clusters and identified hub genes. Moreover, the expression level of DEGs was detected by RT-qPCR in cellular level. Finally, we used the least absolute shrinkage and selection operator (LASSO) regression algorithm to establish a prognostic feature based on DEGs, and verified the accuracy and sensitivity of its prediction through prognostic analysis and subject operating characteristic curve analysis. The correlation of the signature with the tumor immune microenvironment and tumor stemness was analyzed. Results: Disulfidptosis genes showed significant CNVs. Two clusters were identified based on three DRGs (DNUFS1, LRPPRC, SLC7A11). Cluster A was found to be associated with better survival outcomes(p < 0.05) and higher levels of immune cell infiltration(p < 0.05). A prognostic signature of four disulfidptosis-related DEGs (KIF21A, APOD, ALOX15B, ELOVL2) was developed by LASSO regression analysis. The signature showed a good prediction ability. In addition, the prognostic signature in this study were strongly related to the tumor microenvironment (TME), tumor immune cell infiltration, tumor mutation burden (TMB), tumor stemness, and drug sensitivity. Conclusion: The prognostic signature we constructed based on disulfidptosis-DEGs is a good predictor of prognosis in patients with BC. This prognostic signature is closely related to TME, and its potential correlation provides clues for further studies.
Insights
This study identifies a novel prognostic signature for breast cancer (BC) based on disulfidptosis-related genes. This signature accurately predicts patient outcomes and correlates with the tumor microenvironment, offering new therapeutic insights.
Area of Science:
- Oncology
- Molecular Biology
- Genetics
Background:
- Breast cancer (BC) is a leading cause of cancer-related deaths globally.
- The role of disulfidptosis, a novel cell death pathway, in BC prognosis and treatment remains unexplored.
Purpose of the Study:
- To investigate the biological function and prognostic value of disulfidptosis in breast cancer.
- To develop a prognostic signature for BC based on disulfidptosis-related genes.
Main Methods:
- Evaluated gene mutations and CNVs in disulfidptosis genes.
- Performed differential expression, prognostic, and Cox analyses to identify BC-specific DRGs.
- Utilized unsupervised clustering and LASSO regression to develop a prognostic signature.
- Analyzed the signature's correlation with TME, immune infiltration, stemness, and drug sensitivity.
Main Results:
- Identified two distinct clusters based on three DRGs (DNUFS1, LRPPRC, SLC7A11), with Cluster A showing better survival and higher immune infiltration.
- Developed a four-gene prognostic signature (KIF21A, APOD, ALOX15B, ELOVL2) with good predictive ability.
- The signature demonstrated strong correlations with TME, immune cell infiltration, TMB, stemness, and drug sensitivity.
Conclusions:
- A prognostic signature based on disulfidptosis-DEGs effectively predicts BC patient prognosis.
- This signature is closely linked to the tumor microenvironment, suggesting potential therapeutic strategies.
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