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lnc RNA-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lnc RNA(CCAT1、MEG3、Snhg1、MALAT1、HOTAIR、UCA1、PVT1、CASC9、LOC100130476、TUG1、BC200、POU6F2-AS2、TP73-AS1和ZEB1-AS1)和12个mRNA(SPARC、CMTM7、Sph K1、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lnc RNA(BC200、POU6F2-AS2、TP73-AS1和ZEB1-AS1)和3个mRNA(CADM1、Sph K1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。
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 biomarkers 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 lnc RNA-mRNA interaction network based on Ping-Pong Algorithm. Then, a random walk algorithm was used to calculate the biomarkers of complex diseases and compare them with t-test results. Results： Using this method, lnc RNAs（CCAT1, MEG3, Snhg1, MALAT1, HOTAIR, UCA1,PVT1, CASC9, LOC100130476, TUG1, BC200, POU6 F2-AS2, TP73-AS1 and ZEB1-AS1）and mRNAs（SPARC, CMTM7, Sph K1,NANOG, LOXL2, HMGCS2, FZD7, PTOV1, CADM1, CTHRC1, MGMT and RECK）were identified as biomarkers of esophageal cancer, which were related to the occurrence and development of esophageal cancer. Compared with the other identification method（t-test）,four new lnc RNAs（BC200, POU6 F2-AS2, TP73-AS1 and ZEB1-AS1） and three new mRNAs（CADM1, Sph K1 and RECK）were identified. Conclusions： This method was verified to be more effective to predict biomarkers related to the complex disease.
作者
吕鹏举
沈继红
郭爽
蔡明霏
陈宇格
LV Peng-ju;SHEN Ji-hong;GUO Shuang;CAI Ming-fei;CHEN Yu-ge(Harbin Engineering University College of Automation, Harbin, Heilongjiang, 150001, China;Harbin Medical University Daqing Campus, Daqing, Heilongjiang, 163319, China)
出处
《现代生物医学进展》
CAS
2018年第9期1780-1784,共5页
Progress in Modern Biomedicine
基金
国家自然科学基金面上项目(21371042)
