基于microRNA表达谱初步构建PLS-DA体液识别模型

Tentatively Establishing PLS-DA Model for Identifying Forensicrelevant Body Fluids Based on Expression Profiling into microRNAs

针对外周血、唾液、精液、月经血、阴道分泌物这五种法医学常见的体液样本,采用二代测序(NGS)平台检测获得microRNA(miRNA)表达谱,使用偏最小二乘判别分析(PLS-DA)建立基于这五种体液的识别模型,并探讨PLS-DA在法医体液溯源中的应用价值。经优化小分子RNA文库制备过程,采用Ion Torrent S5 XL测序系统对前述法医学五种体液样本(每种10例)进行小RNA测序,以PLS-DA构建体液识别模型,评估不同数量miRNA标记组合下预测的准确性。本研究获得法医学常见五种体液样本的miRNA表达谱,外周血与月经血中表达量前10名的miRNAs有6个重叠;唾液和阴道分泌物中表达量前10名的miRNAs有4个重叠。基于全数据集、107个和11个miRNAs构建的体液来源识别模型的准确率分别为0.95、0.94、0.89。本研究通过NGS测序分析获得了五种体液样本的miRNA组(miRNome),利用PLS-DA初步构建了体液识别模型,对于应用miRNome进行体液识别的相关研究具有参考价值。

Targeting against small RNA(microRNA or miRNA)from five body fluids of forensic-relevant biological samples(venous blood,menstrual blood,saliva,semen and vaginal secretions),next generation sequencing(NGS)was adopted to sequence the expression profiles of miRNA from each of the above five body fluids.Partial least squares-discriminant analysis(PLS-DA)was operated to build the prediction model with the obtained data of expression profiles,successively having its applicability explored about tracing the source of forensic-relevant body fluids.Specifically,the small RNA library was optimized into its preparation process,then subjected to Ion Torrent S5 XL sequencing with the relevant above-mentioned five body fluids(10 samples each)collected from 20 unrelated healthy Han-ethnic individuals(10 ones of male and female,aging from 23 to 38 years old).The PLS-DA prediction model was constructed with the data from small RNAs of those five body fluids,and evaluated of its prediction accuracy for different subsets of combined miRNA markers.From the miRNA expression profiles and distribution characteristics of those five forensic-relevant body fluids,six were shown same among the top 10 miRNAs expressed in venous blood and the menstrual,and so were four in saliva and vaginal secretions.The prediction model achieved the respective accuracy of 0.95,0.94 and 0.89 for the miRNA markers(full,107 and 11 ones)to construct into their datasets.The miRNomes were here obtained from five forensic-relevant body fluids through NGS sequencing and other necessary processing.The resultant body fluid prediction model was tentatively established out of PLS-DA exploration,potential of value to provide a basis for miRNome to apply into forensic body fluid identification.