Yang Yingmei, Li Zhaoyun, Zhong Qianyi, Zhao Lei, Wang Yichao, Chi Hongbo
Department of Clinical Laboratory Medicine, Taizhou Central Hospital (Taizhou University Hospital), Taizhou, China.
Department of Clinical Laboratory Medicine, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai, China.
Gland Surg. 2022 May;11(5):892-912. doi: 10.21037/gs-22-267.
Breast cancer (BRCA) is the leading cause of cancer mortality among women, and it is associated with many tumor suppressors and oncogenes. There is increasing evidence that transcription factors (TFs) play vital roles in human malignancies, but TFs-based biomarkers for BRCA prognosis were still rare and necessary. This study sought to develop and validate a prognostic model based on TFs for BRCA patients.
Differentially expressed TFs were screened from 1,109 BRCA and 113 non-tumor samples downloaded from The Cancer Genome Atlas (TCGA). Univariate Cox regression analysis was used to identify TFs associated with overall survival (OS) of BRCA, and multivariate Cox regression analysis was performed to establish the optimal risk model. The predictive value of the TF model was established using TCGA database and validated using a Gene Expression Omnibus (GEO) data set (GSE20685). A gene set enrichment analysis was conducted to identify the enriched signaling pathways in high-risk and low-risk BRCA patients. Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the TF target genes were also conducted separately.
A total of 394 differentially expressed TFs were screened. A 9-TF prognostic model, comprising PAX7, POU3F2, ZIC2, WT1, ALX4, FOXJ1, SPIB, LEF1 and NFE2, was constructed and validated. Compared to those in the low-risk group, patients in the high-risk group had worse clinical outcomes (P<0.001). The areas under the curve of the prognostic model for 5-year OS were 0.722 in the training cohort and 0.651 in the testing cohort. Additionally, the risk score was an independent prediction indicator for BRCA patients both in the training cohort (HR =1.757, P<0.001) and testing cohort (HR =1.401, P=0.001). It was associated with various cancer signaling pathways. Ultimately, 9 overlapping target genes were predicted by 3 prediction nomograms. The GO and KEGG enrichment analyses of these target genes suggested that the TFs in the model may regulate the activation of some classical tumor signaling pathways to control the progression of BRCA through these target genes.
Our study developed and validated a novel prognostic TF model that can effectively predict 5-year OS for BRCA patients.
乳腺癌（BRCA）是女性癌症死亡的主要原因，并且与许多肿瘤抑制基因和癌基因相关。越来越多的证据表明转录因子（TFs）在人类恶性肿瘤中起着至关重要的作用，但基于TFs的BRCA预后生物标志物仍然很少且很有必要。本研究旨在开发并验证一种基于TFs的BRCA患者预后模型。
从下载自癌症基因组图谱（TCGA）的1109例BRCA样本和113例非肿瘤样本中筛选差异表达的TFs。采用单因素Cox回归分析确定与BRCA总生存期（OS）相关的TFs，并进行多因素Cox回归分析以建立最佳风险模型。使用TCGA数据库确定TF模型的预测价值，并使用基因表达综合数据库（GEO）数据集（GSE20685）进行验证。进行基因集富集分析以确定高危和低危BRCA患者中富集的信号通路。还分别对TF靶基因进行了基因本体（GO）功能和京都基因与基因组百科全书（KEGG）通路富集分析。
共筛选出394个差异表达的TFs。构建并验证了一个由PAX7、POU3F2、ZIC2、WT1、ALX4、FOXJ1、SPIB、LEF1和NFE2组成的9-TF预后模型。与低危组患者相比，高危组患者的临床结局更差（P<0.001）。训练队列中5年OS预后模型的曲线下面积为0.722，测试队列中为0.651。此外，风险评分在训练队列（HR =1.757，P<0.001）和测试队列（HR =1.401，P=0.001）中均是BRCA患者的独立预测指标。它与多种癌症信号通路相关。最终，通过3个预测列线图预测出9个重叠的靶基因。对这些靶基因的GO和KEGG富集分析表明，模型中的TFs可能通过这些靶基因调节一些经典肿瘤信号通路的激活，从而控制BRCA的进展。
我们的研究开发并验证了一种新型的预后TF模型，该模型可以有效预测BRCA患者的5年OS。
