Prognostic And Immune Implications Of A Novel Pyroptosis-Related Five-Gene Signature In Breast Cancer

Background: Breast cancer is the most common cancer in women worldwide and is a serious threat to women’s health.Patients with advanced breast cancer have a poor prognosis due to a high degree of heterogeneity and potential drug resistance.Traditional pathological risk indicators of tumor recurrence and metastasis are inadequate to assess the risk of breast cancer recurrence and metastasis.Pyroptosis not only helps to prevent infection but also plays a key role in tumorigenesis,metastasis and drug resistance.Since cancer tends to evade apoptosis,the induction of tumor pyroptosis may be a very promising therapeutic strategy.Furthermore,the possible correlation between pyroptosis-related genes and the immune microenvironment of breast cancer remains to be investigated.The aim of this study is to construct a prognostic risk model for breast cancer based on differentially expressed genes associated with pyroptosis,which can be used to predict long-term outcomes and provide accurate treatment options for breast cancer patients.METHODS: Breast cancer patients data were obtained from the TCGA and GEO databases.In the TCGA database,38 pyroptosis-related genes showed significant differences between malignant and non-malignant breast tissues.Based on these differentially expressed genes,2 pyroptosis-related clusters were constructed by consensus clustering analysis,with significantly disparate immune cell infiltration characteristics between the clusters.Based on the differentially expressed genes between the 2 clusters,key prognostic genes were screened using Univariate Cox and LASSO regression analysis.ROC analysis,survival analysis,external cohort validation,Cox regression analysis,clinical correlation analysis and immune cell infiltration signature analysis were used to validate the reliability and function of the risk model.The tools estimation of stromal and immune cells in malignant tumours using expression data(ESTIMATE),cell type identification by estimating relative subsets Of RNA transcripts(CIBERSORT),and single-sample gene set enrichment analysis(ss GSEA)were used to investigate the BC tumor microenvironment(TME).Functional enrichment analysis demonstrated a strong relationship between this risk model and immunity.RESULTS: In the tumor microenvironment,2 pyroptosis-associated clusters exhibit different clinicopathological features,survival outcomes and immune cell infiltration characteristics.Here,we constructed a 5-gene prognostic risk model based on differentially expressed genes associated with pyroptosis by combining transcriptomic and prognostic survival data through machine learning approaches.Breast cancer patients were classified into two risk groups based on estimated median risk scores.The prognostic risk model showed significantly longer survival and more extensive immune cell infiltration in the low-risk group.Clinical correlation analysis found that the high-risk group was significantly associated with the severity of disease in breast cancer patients.Multivariate Cox regression analysis showed that the risk score was an independent predictor of prognosis in breast cancer patients.The risk score was combined with traditional clinical prognostic factors to construct a nomogram that better predicted prognosis for breast cancer patients.The calibration curves confirmed good agreement between the nomogram predictions and actual observations.Correlation analysis between risk scores and immunotherapy genes suggests that our risk profile can predict the effect of immune-precision therapy.An external validation set could also produce similar results.CONCLUSIONS: Our study provides new insights into understanding gene expression imbalances in breast cancer,highlighting five differentially expressed genes associated with pyroptosis that are prognostically relevant for patients and which can have a significant impact on the immune microenvironment of breast cancer.The prognostic risk model helps to identify patients at different risks and suggests new therapeutic strategies in the precise treatment of breast cancer.