A novel disulfidptosis-associated expression pattern in breast cancer based on machine learning

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.

Prognostic and therapeutic model based on disulfidptosis-related genes for patients with clear cell renal cell carcinoma

“…Disulfidptosis is closely related to tumor progression, and studies have found that disulfidptosis plays an important role in disease progression and prognosis in bladder, lung, breast, and cervical cancer [ [38] , [39] , [40] , [41] , [42] ]. Studies have confirmed that SLC7A11, SLC3A2, RPN1, NCKAP1, NDUFS1, NDUFA11, OXSM, NUBPL, and LRPPRC greatly contribute to the process of disulfidptosis, and interfering with these genes can effectively inhibit the progression of disulfidptosis.…”

Exosomes: a promising avenue for cancer diagnosis beyond treatment

“…Wang et al employed the Least Absolute Shrinkage and Selection Operator (LASSO) regression algorithm to construct a prognostic model based on differentially expressed genes (DEGs). The model’s predictive accuracy and sensitivity were validated through prognostic analysis and receiver operating characteristic curve analysis ( Wang Z et al, 2023 ). These algorithms can differentiate between cancerous and non-cancerous samples, classify cancer subtypes, and predict treatment outcomes.…”

Disulfidptosis decoded: a journey through cell death mysteries, regulatory networks, disease paradigms and future directions

“…Wang and colleagues also discovered three disulfidptosis-related genes, DNUFS1, LRPPRC, and SLC7A11, with differential expression and prognostic values. These findings offer important insights into predicting the prognosis of breast cancer patients [ 35 ].…”

“…Lung Cancer: G6PD [32]; OGFRP1 [33] Reproductive System Breast Cancer: CD80, CD276 [34]; DNUFS1, LRPPRC, SLC7A11 [35] Neurodeenerative diseases MYH9, IQGAP1, ACTN4, DSTN, ACTB, MYL6, GYS1 [45] Metabolic diseases TRXR1, NFATC1 [46]…”