| 吕鹏举,沈继红,郭 爽,蔡明霏,陈宇格.基于乒乓算法的复杂疾病标志物识别[J].,2018,(9):1780-1784 |
| 基于乒乓算法的复杂疾病标志物识别 |
| Identifying Biomarkers of Complex Diseases Based on Ping-Pong Algorithm |
| 投稿时间：2017-11-06 修订日期：2017-12-08 |
| DOI：10.13241/j.cnki.pmb.2018.09.039 |
| 中文关键词: 乒乓算法 生物标志物 复杂疾病 lncRNA-mRNA交互网络 |
| 英文关键词: Ping-Pong Algorithm Biomarker Complex diseases mRNA-lncRNA interaction network |
| 基金项目:国家自然科学基金面上项目（21371042） |
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| 中文摘要: |
| 摘要 目的：生物标志物是标识系统、器官、组织等改变或可能发生改变的生化指标，具有非常广泛的临床应用。本文希望从高通量数据出发，提出一种新的研究复杂疾病标志物的方法。方法：以"组学"数据为研究对象，利用乒乓算法构建lncRNA-mRNA交互网络，通过随机游走算法计算选出复杂疾病的生物标志物，并将其与t检验结果比较。结果：将本文方法运用于食管癌标志物的识别，得出与食管癌发生和发展过程相关的14个lncRNA（CCAT1、MEG3、Snhg1、MALAT1、HOTAIR、UCA1、PVT1、CASC9、LOC100130476、TUG1、BC200、POU6F2-AS2、TP73-AS1和ZEB1-AS1）和12个mRNA（SPARC、CMTM7、SphK1、NANOG、LOXL2、HMGCS2、FZD7、PTOV1、CADM1、CTHRC1、MGMT和RECK）。对比显示，识别出t检验未识别出的4个lncRNA（BC200、POU6F2-AS2、TP73-AS1和ZEB1-AS1）和3个mRNA（CADM1、SphK1和RECK）。结论：该方法能够更有效的预测复杂疾病相关的标志物。 |
| 英文摘要: |
| ABSTRACT Objective: Biomarkers are the biochemical indexes that indicate the changes or possible changes of systems, organs and tissues, which have very extensive clinical application. Based on the high-throughput data, it is very important to study the biomark- ers of complex diseases using the computer aided method. In this study, we proposed a novel approach to identify biomarkers of complex diseases. Methods: The biomarkers of complex diseases were identified referring to 'omics' data through constructing the lncRNA-mRNA interaction network based on Ping-Pong Algorithm. Then, a random walk algorithm was used to calculate the biomarkers of complex dis- eases and compare them with t-test results. Results: Using this method, lncRNAs（CCAT1, MEG3, Snhg1, MALAT1, HOTAIR, UCA1, PVT1, CASC9, LOC100130476, TUG1, BC200, POU6F2-AS2, TP73-AS1and ZEB1-AS1）and mRNAs（SPARC, CMTM7, SphK1, NANOG, LOXL2, HMGCS2, FZD7, PTOV1, CADM1, CTHRC1, MGMT and RECK）were identified as biomarkers of esophageal can- cer, which were related to the occurrence and development of esophageal cancer. Compared with the other identification method (t-test), four new lncRNAs（BC200, POU6F2-AS2, TP73-AS1 and ZEB1-AS1) and three new mRNAs (CADM1, SphK1and RECK)were identi- fied. Conclusion: This method was verified to be more effective to predict biomarkers related to the complex disease. |
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