Kim Dong Hyeok, Lee Kyung Eun
Department of Clinical Laboratory Science, College of Health Sciences, Catholic University of Pusan, Busan 46252, Korea.
Clinical Trial Specialist Program for In Vitro Diagnostics, Brain Busan 21 Plus Program, The Graduate School, Catholic University of Pusan, Busan 46252, Korea.
J Pers Med. 2022 Oct 21;12(10):1753. doi: 10.3390/jpm12101753.
Background: Research on the discovery of tumor biomarkers based on big data analysis is actively being conducted. This study aimed to secure foundational data for identifying new biomarkers of breast cancer via breast cancer datasets in The Cancer Genome Atlas (TCGA). Methods: The mRNA profiles of 526 breast cancer and 60 adjacent non-cancerous breast tissues collected from TCGA datasets were analyzed via MultiExperiment Viewer and GraphPad Prism. Diagnostic performance was analyzed by identifying the pathological grades of the selected differentially expressed (DE) mRNAs and the expression patterns of molecular subtypes. Results: Via DE mRNA profile analysis, we selected 14 mRNAs with downregulated expression (HADH, CPN2, ADAM33, TDRD10, SNF1LK2, HBA2, KCNIP2, EPB42, PYGM, CEP68, ING3, EMCN, SYF2, and DTWD1) and six mRNAs with upregulated expression (ZNF8, TOMM40, EVPL, EPN3, AP1M2, and SPINT2) in breast cancer tissues compared to that in non-cancerous tissues (p < 0.001). Conclusions: In total, 20 DE mRNAs had an area under cover of 0.9 or higher, demonstrating excellent diagnostic performance in breast cancer. Therefore, the results of this study will provide foundational data for planning preliminary studies to identify new tumor biomarkers.
基于大数据分析发现肿瘤生物标志物的研究正在积极开展。本研究旨在通过癌症基因组图谱（TCGA）中的乳腺癌数据集获取识别乳腺癌新生物标志物的基础数据。方法：通过多实验查看器和GraphPad Prism软件对从TCGA数据集中收集的526例乳腺癌组织和60例相邻非癌乳腺组织的mRNA谱进行分析。通过确定所选差异表达（DE）mRNA的病理分级和分子亚型的表达模式来分析诊断性能。结果：通过DE mRNA谱分析，我们在乳腺癌组织中筛选出14种表达下调的mRNA（HADH、CPN2、ADAM33、TDRD10、SNF1LK2、HBA2、KCNIP2、EPB42、PYGM、CEP68、ING3、EMCN、SYF2和DTWD1）以及6种表达上调的mRNA（ZNF8、TOMM40、EVPL、EPN3、AP1M2和SPINT2），与非癌组织相比差异有统计学意义（p < 0.001）。结论：共有20种DE mRNA的曲线下面积为0.9或更高，在乳腺癌诊断中表现出优异的性能。因此，本研究结果将为规划识别新肿瘤生物标志物的初步研究提供基础数据。
