School of Interdisciplinary Informatics, College of Information Science & Technology, University of Nebraska at Omaha, 1110 S. 67th Street, Omaha, NE 68182, USA.
Department of Biochemistry & Molecular Biology, University of Nebraska Medical Center, 42nd & Emile Streets, Omaha, NE 68198, USA.
Biomark Med. 2022 Aug;16(12):889-901. doi: 10.2217/bmm-2022-0071. Epub 2022 Jul 27.
To combat increases in colorectal cancer (CRC) incidence and mortality, biomarkers among differentially expressed genes (DEGs) have been identified to objectively detect cancer. However, DEGs are numerous, and additional parameters may identify more reliable biomarkers. Here, CRC DEGs were filtered into a prioritized list of biomarkers. Two independent datasets (COAD-READ [n = 698] and GSE50760 [n = 36]) were input alternatively to the recently published data-driven reference method. Results were filtered based on epithelial-mesenchymal transition enrichment (χ-square statistic: 919.05; p = 2.2e-16) to produce 37 potential CRC biomarkers. All 37 genes reliably classified CRC samples and , and together were top-ranked by DDR (accuracy: 89%; F1 score: 0.89). Biological and statistical information were combined to produce a better set of CRC detection biomarkers.
为了降低结直肠癌（CRC）的发病率和死亡率，人们已经确定了差异表达基因（DEGs）中的生物标志物，以客观地检测癌症。然而，DEGs 数量众多，额外的参数可能可以识别出更可靠的生物标志物。在这里，CRC 的 DEGs 被筛选到一个有优先级的生物标志物列表中。两个独立的数据集（COAD-READ[n=698]和 GSE50760[n=36]）被交替输入到最近发表的基于数据驱动的参考方法中。结果基于上皮-间充质转化（epithelial-mesenchymal transition，EMT）富集进行过滤（卡方检验：919.05；p=2.2e-16），以产生 37 个潜在的 CRC 生物标志物。所有 37 个基因都可以可靠地区分 CRC 样本，并且通过 DDR（准确率：89%；F1 得分为 0.89）， 和 一起被排在首位。生物和统计信息被结合起来，以产生一组更好的 CRC 检测生物标志物。
