Comprehensive analysis of pyroptotic gene prognostic signatures associated with tumor immune microenvironment and genomic mutation in breast cancer

Abstract

BackgroundBreast cancer is becoming a tumor with the highest morbidity rate, and inflammation-induced cell death namely pyroptosis reportedly plays dual roles in cancers. However, the specific mechanism between pyroptosis and the clinical prognosis of breast cancer patients is indistinct. Hence, novel pyroptosis-related biomarkers and their contributing factors deserve further exploration to predict the prognosis in breast cancer.MethodsPearson’s correlation analysis, and univariate and multivariate Cox regression analysis were utilized to obtain six optimal pyroptosis-related gene prognostic signatures (Pyro-GPS). The risk score of each breast cancer patient was calculated. Next, a Pyro-GPS risk model was constructed and verified in TCGA cohort (n=1,089) and GSE20711 cohort (n=88). Then analyses of immune microenvironment, genomic variation, functional enrichment, drug response and clinicopathologic feature stratification associated with the risk score of Pyro-GPS were performed. Subsequently, a nomogram based on the risk score and several significant clinicopathologic features was established. Ultimately, we further investigated the role of CCL5 in the biological behavior of MDA-MB-231 cell line.ResultsThe low-risk breast cancer patients have better survival outcomes than the high-risk patients. The low-risk patients also show higher immune cell infiltration levels and immune-oncology target expression levels. There is no significant difference in genetic variation between the two risk groups, but the frequency of gene mutations varies. Functional enrichment analysis shows that the low-risk patients are prominently correlated with immune-related pathways, whereas the high-risk patients are enriched in cell cycle, ubiquitination, mismatch repair, homologous recombination and biosynthesis-related pathways. Pyro-GPS is positively correlated with the IC50 of anti-tumor drugs. Furthermore, comprehensive analyses based on risk score and clinicopathological features were performed to predict the prognosis of breast cancer patients. Additionally, in vitro experiments confirmed that breast cancer cells with high expression of CCL5 had weaker proliferation, invasion and metastasis abilities as well as stronger apoptosis and cell cycle arrest abilities.ConclusionsThe risk score of Pyro-GPS can serve as a promising hallmark to predict the prognosis of BRCA patients. Risk score evaluation may assist to acquire relevant information of tumor immune microenvironment, genomic mutation status, functional pathways and drug sensitivity, and thus provide more effective personalized strategies.

Comprehensive analysis of nicotinamide metabolism-related signature for predicting prognosis and immunotherapy response in breast cancer

“…For example, the breast cancer 21-gene assay (Oncotype DX) is used to predict the recurrence risk and chemotherapy benefits in patients with hormone receptor-positive, HER2 receptor negative and lymph node-negative subtypes ( 59 ). Additionally, although some molecular risk models have not been used in clinical management, they show great potential for the precise prediction of BC, such as pyroptosis-associated and TP53 mutation-related models ( 60 , 61 ). However, the significance of the NAM metabolism in BC remains unclear.…”

A fine-tuned machine learning model to predict survivals of breast cancer patients based on gamma-delta T cell markers

“…TYMS has been identi ed as a predictor of poor prognosis in patients with metastatic colorectal cancer undergoing chemotherapy (28). Similarly, elevated CCL5 expression in breast cancer cells is associated with reduced proliferation, invasion, and metastasis capabilities (29). However, these studies share limitations, including small sample sizes, reliance on linear models, and a lack of well-designed external validation.…”

Establishment of a N1-methyladenosine-related risk signature for breast carcinoma by bioinformatics analysis and experimental validation

“…The differences in subgroups of clinicopathological characteristics [age (> 60 and ≤ 60), T (T1, T2, T3, T4), M (M0, M1), N (N0, N1, N2, N3), sex (male, female), subtype (BRCA_LumA, BRCA_LumB, BRCA_Normal, BRCA_Basal, BRCA_Her2), and Vital (Alive, Dead)] between the low- and high-risk groups were examined by the Chi-square test. In addition, the rank-sum test was further utilized to compare differences in RiskScores expression among the different subgroups of clinicopathological characteristics [ 38 ]. Considering the vital significance of the intrinsic subtype of BRCA in prognosis, risk stratification analysis was further conducted to investigate the utilization of the risk model in patients with different intrinsic subtypes of BRCA [ 39 ].…”