DataSheet_1_A pyroptosis-related gene signature for prognostic and immunological evaluation in breast cancer.docx

PurposePyroptosis exerts an undesirable impact on the clinical outcome of breast cancer. Since any single gene is insufficient to be an appropriate marker for pyroptosis, our aim is to develop a pyroptosis-related gene (PRG) signature to predict the survival status and immunological landscape for breast cancer patients.MethodsThe information of breast cancer patients was retrieved from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. Quantitative real-time polymerase chain reaction (qRT-PCR) was performed to verify the gene expressions of this signature in breast cancer. Its prognostic value was evaluated by univariate Cox analysis, least absolute shrinkage and selection operator (LASSO) regression analysis, receiver operating characteristics (ROCs), univariate/multivariate analysis, and nomogram. Analyses of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were performed to explore its potential biological function in breast cancer. The potential correlation between this signature and tumor immunity was revealed based on single sample gene set enrichment analysis (ssGSEA), ESTIMATE and CIBERSORT algorithms.ResultsA PRG signature containing GSDMC, GZMB, IL18, and TP63 was created in a TCGA training cohort and validated in two validation GEO cohorts GSE58812 and GSE37751. Compared with a human mammary epithelial cell line MCF-10A, the expression levels of GSDMC, GZMB and IL18 were upregulated, while TP63 was found with lower expression level in breast cancer cells SK-BR-3, BT-549, MCF-7, and MDA-MB-231 using RT-qPCR assay. Based on univariate and multivariate Cox models, ROC curve, nomogram as well as calibration curve, it was revealed that this signature with high-risk score could independently predict poor clinical outcomes in breast cancer. Enrichment analyses demonstrated that the involved mechanism was tightly linked to immune-related processes. SsGSEA, ESTIMATE and CIBERSORT algorithms further pointed out that the established model might exert an impact on immune cell abundance, immune cell types and immune-checkpoint markers. Furthermore, individuals with breast cancer responded differently to these therapeutic agents based on this signature.ConclusionsOur data suggested that this PRG signature with high risk was tightly associated with impaired immune function, possibly resulting in an unfavorable outcome for breast cancer patients.

目的：细胞焦亡对乳腺癌的临床预后产生不利影响。鉴于单个基因不足以成为细胞焦亡的适宜标志物，本研究旨在开发一个与细胞焦亡相关的基因（PRG）特征，以预测乳腺癌患者的生存状态和免疫景观。方法：从癌症基因组图谱（TCGA）和基因表达综合数据库（GEO）中检索乳腺癌患者的相关信息。通过定量实时聚合酶链反应（qRT-PCR）验证该特征在乳腺癌中的基因表达。其预后价值通过单因素Cox分析、最小绝对收缩和选择算子（LASSO）回归分析、受试者工作特征（ROC）曲线、单因素/多因素分析和列线图进行评估。通过基因本体（GO）和京都基因与基因组百科全书（KEGG）分析，探索其在乳腺癌中的潜在生物学功能。基于单样本基因集富集分析（ssGSEA）、ESTIMATE和CIBERSORT算法，揭示了该特征与肿瘤免疫之间的潜在相关性。结果：在TCGA训练队列中创建了一个包含GSDMC、GZMB、IL18和TP63的PRG特征，并在两个验证GEO队列GSE58812和GSE37751中得到验证。使用RT-qPCR检测发现，与人类乳腺上皮细胞系MCF-10A相比，GSDMC、GZMB和IL18的表达水平上调，而TP63在乳腺癌细胞SK-BR-3、BT-549、MCF-7和MDA-MB-231中的表达水平较低。基于单因素和多因素Cox模型、ROC曲线、列线图以及校准曲线，揭示了该特征高风险评分可独立预测乳腺癌的不良临床结果。富集分析表明，涉及的机制与免疫相关过程密切相关。ssGSEA、ESTIMATE和CIBERSORT算法进一步指出，建立的模型可能对免疫细胞丰度、免疫细胞类型和免疫检查点标记产生影响。此外，基于此特征，乳腺癌患者对治疗剂的反应存在差异。结论：本研究数据表明，高风险的PRG特征与免疫功能受损密切相关，可能导致乳腺癌患者预后不佳。