In this article, we make a theoretical and in silico study for uncovering and evaluating biomarkers in colon and rectal cancer (CRC) by the dynamic network biomarker (DNB) theory. We propose a strategy to employ the theoretical concept of UICC TNM classification in CRC. To reveal the critical transition of CRC, the DNB algorithm was implemented to analyze the genome-wide dynamic network through temporal gene expression data. The relationship between gene sets and clinical features was evaluated by weighted gene co-expression network analysis. The results show that MYC was significantly associated with tumor amplification, tumor immune cells, and survival times. The candidate tumor suppressor genes were ZBTB16, MAL, LIFR, and SLIT2. Protein–protein interaction (PPI) analysis shows that these candidate tumor suppressor genes were significant in immune cells. Data from the Human Protein Atlas showed that a high expression of these candidate tumor suppressor genes was associated with favorable prognosis in TNM stages I–IV. In conclusion, this work provides significant and novel information regarding the TNM stage, cause, and consequences of elevated MYC expression in CRC. MYC expression levels had significant negative correlations with tumor suppressor genes and immune cells.

在本文中，我们进行了理论上和 在计算机上动态网络生物标记（DNB）理论研究和发现结肠癌和直肠癌（CRC）中的生物标记。我们提出了一种在CRC中采用UICC TNM分类的理论概念的策略。为了揭示CRC的关键转变，实施了DNB算法，以通过时间基因表达数据分析全基因组动态网络。通过加权基因共表达网络分析评估基因组与临床特征之间的关系。结果表明，MYC与肿瘤扩增，肿瘤免疫细胞和存活时间显着相关。候选肿瘤抑制基因是ZBTB16，MAL，LIFR和SLIT2。蛋白质-蛋白质相互作用（PPI）分析表明，这些候选肿瘤抑制基因在免疫细胞中很重要。人类蛋白质图谱的数据表明，这些候选肿瘤抑制基因的高表达与TNM I–IV期的良好预后相关。总之，这项工作提供了有关TNM分期，CRC中MYC表达升高的原因和后果的重要且新颖的信息。MYC表达水平与肿瘤抑制基因和免疫细胞显着负相关。