College of Computer Science and Technology, Jilin University, Changchun, China; Key Laboratory of Symbol Computation and Knowledge Engineer of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, China.
College of Computer Science and Technology, Jilin University, Changchun, China; Key Laboratory of Symbol Computation and Knowledge Engineer of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, China.
Comput Biol Chem. 2019 Dec;83:107106. doi: 10.1016/j.compbiolchem.2019.107106. Epub 2019 Sep 3.
Colorectal cancer is one of the top leading causes of cancer mortality worldwide, especially in China. However, most of the current treatments are invasive and can only be applied to very few cancers. The earlier a malignant tumor is diagnosed, the higher the patient's survival rate. In this study, we proposed a computational framework to identify highly-reliable and easierly-detectable biomarkers capable of secreting into blood, urine and saliva by integrating transcriptomics and proteomics data at the system biology level. First, a large number of transcriptome data were processed to identify candidate biomarkers for colorectal cancer. Second, three classified models are constructed to predict biomarkers for colorectal cancer capable of secreting into blood, urine and saliva, which are effective disease diagnosis media to facilitate clinical screening. Then biological functions and molecular mechanisms of the candidate biomarkers of colorectal cancer are inferred utilizing multi-source biological knowledge and literature mining. Furthermore, the classification power of different combinations of candidate biomarkers is verified by machine learning models. In addition, the targeted drugs of the predicted biomarkers are further analyzed to provide assistance for clinical treatment of colorectal cancer. In this paper, our proposed computational model not only provides the effective candidate biomarkers ESM1, CTHRC1, AZGP1 for colorectal cancer capable of secreting into blood, urine and saliva, but also helps to understand the molecular mechanism of colorectal cancer. This computational framework can span the huge gap between transcriptome and proteomics, which can easily be applied to the biomarker research for other types of tumor.
结直肠癌是全球癌症死亡的主要原因之一，尤其在中国。然而，目前大多数治疗方法都是有创的，而且只能应用于极少数癌症。恶性肿瘤越早诊断，患者的生存率越高。在本研究中，我们提出了一个计算框架，通过整合系统生物学水平的转录组学和蛋白质组学数据，来识别能够分泌到血液、尿液和唾液中的高度可靠和易于检测的生物标志物。首先，我们处理了大量的转录组数据，以鉴定结直肠癌的候选生物标志物。其次，构建了三个分类模型来预测能够分泌到血液、尿液和唾液中的结直肠癌生物标志物，这些生物标志物是有效的疾病诊断介质，有助于临床筛查。然后，我们利用多源生物知识和文献挖掘来推断结直肠癌候选生物标志物的生物学功能和分子机制。此外，我们还通过机器学习模型验证了不同候选生物标志物组合的分类能力。此外，我们还进一步分析了预测生物标志物的靶向药物，为结直肠癌的临床治疗提供帮助。在本文中，我们提出的计算模型不仅提供了能够分泌到血液、尿液和唾液中的结直肠癌的有效候选生物标志物 ESM1、CTHRC1 和 AZGP1，还有助于理解结直肠癌的分子机制。该计算框架可以跨越转录组学和蛋白质组学之间的巨大差距，并且易于应用于其他类型肿瘤的生物标志物研究。
