bmm-2022-0071 Table S1

Aims: 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. Materials & methods: 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. Results: All 37 genes reliably classified CRC samples and ETV4, CLDN1 and CA2 together were top-ranked by DDR (accuracy: 89%; F1 score: 0.89). Conclusion: Biological and statistical information were combined to produce a better set of CRC detection biomarkers.研究目的：为应对结直肠癌（colorectal cancer, CRC）发病率与死亡率的上升态势，学界已从差异表达基因（differentially expressed genes, DEGs）中筛选出可实现癌症客观检测的生物标志物。然而差异表达基因数量繁多，需引入额外参数以筛选出更可靠的生物标志物。本研究针对结直肠癌差异表达基因进行筛选，得到优先级排序的生物标志物列表。材料与方法：将两个独立数据集（COAD-READ [样本量n=698]与GSE50760 [样本量n=36]）分别输入近期发表的数据驱动参考方法。基于上皮间质转化（epithelial–mesenchymal transition）富集分析结果（卡方统计量：919.05；P值=2.2e-16）对所得结果进行筛选，最终得到37个潜在结直肠癌生物标志物。结果：该37个基因均可对结直肠癌样本实现可靠分类，其中ETV4、CLDN1与CA2三者联合经数据驱动参考（Data-Driven Reference, DDR）分析（准确率：89%；F1分数：0.89）位列前茅。结论：本研究整合生物学与统计学信息，构建得到性能更优的结直肠癌检测生物标志物集。