Table7_Identification of Pathway-Based Biomarkers with Crosstalk Analysis for Overall Survival Risk Prediction in Breast Cancer.XLS

Recently, many studies have investigated the role of gene-signature on the prognostic assessment of breast cancer (BC), however, the tumor heterogeneity and sequencing noise have limited the clinical usage of these models. Pathway-based approaches are more stable to the perturbation of certain gene expression. In this study, we constructed a prognostic classifier based on survival-related pathway crosstalk analysis. We estimated pathway’s deregulation scores (PDSs) for samples collected from public databases to select survival-related pathways. After pathway crosstalk analysis, we conducted K-means clustering analysis to cluster the patients into G1 and G2 subgroups. The survival outcome of the G2 subgroup was significantly worse than the G1 subgroup. Internal and external dataset exhibits high consistency with the training dataset. Significant differences were found between G2 and G1 subgroups on pathway activity, gene mutation, immune cell infiltration levels, and in particular immune cells/pathway’s activities were significantly negatively associated with BC patient’s outcomes. In conclusion, we established a novel classifier reflecting the overall survival risk of BC and successfully validated its clinical usage on multiple BC datasets, which could offer clinicians inspiration in formulating the clinical treatment plan.

近期，众多研究深入探讨了基因特征在乳腺癌（BC）预后评估中的作用，然而，肿瘤异质性和测序噪声限制了这些模型在临床上的应用。基于通路的方法对特定基因表达的扰动更为稳定。在本研究中，我们通过生存相关通路串扰分析构建了一个预后分类器。我们估计了来自公共数据库收集样本的通路失调评分（PDSs），以选择与生存相关的通路。在通路串扰分析之后，我们进行了K-means聚类分析，将患者分为G1和G2亚组。G2亚组的生存结果显著劣于G1亚组。内部和外部数据集与训练数据集表现出高度一致性。在通路活性、基因突变、免疫细胞浸润水平方面，G2和G1亚组之间存在着显著差异，特别是免疫细胞/通路的活动与乳腺癌患者的预后显著负相关。综上所述，我们建立了一个反映乳腺癌总体生存风险的全新分类器，并在多个乳腺癌数据集中成功验证了其临床应用的有效性，这为临床医生在制定治疗方案时提供了灵感。