Department of Breast Disease, Henan Breast Cancer Center, Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China.
The First Department of Breast Cancer, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin, China.
Cancer Med. 2023 Feb;12(3):3758-3772. doi: 10.1002/cam4.5071. Epub 2022 Jul 26.
Following the implementation of breast screening programs, the occurrence of ductal carcinoma in situ (DCIS) as an early type of neoplasia has increased. Although the prognosis is promising, 20%-50% of DCIS patients will progress to invasive ductal carcinoma (IDC) if not treated. It is essential to look for promising biomarkers for predicting DCIS prognosis. The Gene Expression Omnibus (GEO) database was used to explore the expression of genes that differed between DCIS and normal tissue in this investigation. Enrichment analysis was performed to characterize the biological role and intrinsic process pathway. The Cancer Genome Atlas Breast Cancer Dataset was used to categorize the hub genes, and the results were confirmed using the Cytoscape plugin CytoHubba and MCODE. The prognostic ability of the core gene signature was determined through time-dependent receiver operating characteristic (ROC), Kaplan-Meier survival curve, Oncomine databases, and UALCAN databases. In addition, the prognostic value of core genes was verified in proliferation assays. We identified 217 common differentially expressed genes (DEGs) in the present study, with 101 upregulated and 138 downregulated genes. The top genes were obtained from the PPI network (protein-protein interaction). A unique six-gene signature (containing GAPDH, CDH2, BIRC5, NEK2, IDH2, and MELK) was developed for DCIS prognostic prediction. Centered on the Cancer Genome Atlas (TCGA) cohort, the ROC curve showed strong results in prognosis prediction. The six core gene signatures is often overexpressed in DCIS, with a weak prognosis. Furthermore, when breast cancer cells are transfected with small interfering RNAs, downregulation of core gene expression substantially inhibits cell proliferation, revealing a high potential for employing core genes in DCIS prognosis. In conclusion, the current investigation verified the six core genes signatures for prospective DCIS biomarkers, which may aid clinical decision-making for individual care.
在实施乳房筛查计划后,作为一种早期肿瘤类型的导管原位癌(DCIS)的发生率有所增加。尽管预后良好,但如果不治疗,20%-50%的 DCIS 患者会进展为浸润性导管癌(IDC)。寻找有前途的生物标志物来预测 DCIS 预后至关重要。本研究使用基因表达综合数据库(GEO)来探索 DCIS 与正常组织之间差异表达的基因。通过富集分析来描述生物学作用和内在过程途径。使用癌症基因组图谱乳腺癌数据集对枢纽基因进行分类,并使用 Cytoscape 插件 CytoHubba 和 MCODE 进行验证。通过时间依赖性接收器操作特征(ROC)、Kaplan-Meier 生存曲线、Oncomine 数据库和 UALCAN 数据库来确定核心基因特征的预后能力。此外,通过增殖实验验证了核心基因的预后价值。我们在研究中确定了 217 个常见的差异表达基因(DEGs),其中 101 个上调,138 个下调。通过蛋白质-蛋白质相互作用(PPI)网络得到了排名最高的基因。开发了一个独特的六基因签名(包含 GAPDH、CDH2、BIRC5、NEK2、IDH2 和 MELK)用于 DCIS 预后预测。以癌症基因组图谱(TCGA)队列为中心,ROC 曲线在预后预测方面表现出了较强的效果。六个核心基因标志在 DCIS 中常过表达,预后不良。此外,当乳腺癌细胞用小干扰 RNA 转染时,核心基因表达的下调会显著抑制细胞增殖,这表明核心基因在 DCIS 预后中有很高的应用潜力。总之,本研究验证了六个核心基因标志作为有前途的 DCIS 生物标志物,可能有助于为个体化治疗做出临床决策。