Liu Xiaohua, Su Lili, Li Jingcong, Ou Guoping
State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou, China.
School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, China.
Front Genet. 2021 Oct 21;12:689715. doi: 10.3389/fgene.2021.689715. eCollection 2021.
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.
最近，许多研究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的临床应用。基于通路的方法对某些基因表达的扰动更稳定。在本研究中，我们基于生存相关通路串扰分析构建了一个预后分类器。我们估计了从公共数据库收集的样本的通路失调分数（PDS），以选择生存相关通路。经过通路串扰分析后，我们进行了K均值聚类分析，将患者分为G1和G2亚组。G2亚组的生存结果明显比G1亚组差。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亚组之间存在显著差异，特别是免疫细胞/通路的活性与BC患者的预后显著负相关。总之，我们建立了一个反映BC总体生存风险的新型分类器，并在多个BC数据集上成功验证了其临床应用，这可为临床医生制定临床治疗方案提供启示。
