Zhai H J, Lin W, Tian T, Liu M
West China School of Basic Medical Sciences and Forensic Medicine, Sichuan University, Chengdu 610041, China.
Fa Yi Xue Za Zhi. 2020 Dec;36(6):755-761. doi: 10.12116/j.issn.1004-5619.2020.06.003.
Objective To screen serum biomarkers after skeletal muscle contusion in rats based on gas chromatography-mass spectrometry （GC-MS） metabolomics technology, and support vector machine （SVM） regression model was established to estimate skeletal muscle contusion time. Methods The 60 healthy SD rats were randomly divided into experimental group （n=50）, control group （n=5） and validation group （n=5）. The rats in the experimental group and the validation group were used to establish the model of skeletal muscle contusion through free fall method, the rats in experimental group were executed at 0 h, 2 h, 4 h, 8 h, 12 h, 24 h, 48 h, 96 h, 144 h and 240 h, respectively, and the rats in validation group were executed at 192 h, while the rats in the control group were executed after three days' regular feeding. The skeletal muscles were stained with hematoxylin-eosin （HE）. The serum metabolite spectrum was detected by GC-MS, and orthogonal partial least square-discriminant analysis （OPLS-DA） pattern recognition method was used to discriminate the data and select biomarkers. The SVM regression model was established to estimate the contusion time. Results The 31 biomarkers were initially screened by metabolomics method and 6 biomarkers were further selected. There was no regularity in the changes of the relative content of the 6 biomarkers with the contusion time and the SVM regression model can be successfully established according to the data of 6 biomarkers and the 31 biomarkers. Compared with the injury time ［（55.344±7.485） h］ estimated from the SVM regression model based on the data of 6 biomarkers, the injury time ［（195.781±1.629） h］ estimated from the SVM regression model based on the data of 31 biomarkers was closer to the actual value. Conclusion The SVM regression model based on metabolites data can be used for the contusion time estimation of skeletal muscles.
目的 基于气相色谱 - 质谱（GC-MS）代谢组学技术筛选大鼠骨骼肌挫伤后的血清生物标志物，并建立支持向量机（SVM）回归模型以估算骨骼肌挫伤时间。方法 将60只健康SD大鼠随机分为实验组（n = 50）、对照组（n = 5）和验证组（n = 5）。实验组和验证组大鼠采用自由落体法建立骨骼肌挫伤模型，实验组大鼠分别在0 h、2 h、4 h、8 h、12 h、24 h、48 h、96 h、144 h和240 h处死，验证组大鼠在192 h处死，对照组大鼠正常饲养3天后处死。骨骼肌进行苏木精 - 伊红（HE）染色。采用GC-MS检测血清代谢物谱，运用正交偏最小二乘法判别分析（OPLS-DA）模式识别方法对数据进行判别并筛选生物标志物。建立SVM回归模型估算挫伤时间。结果 代谢组学方法初步筛选出31个生物标志物，进一步筛选出6个生物标志物。6个生物标志物相对含量随挫伤时间变化无规律，根据6个生物标志物和31个生物标志物的数据均能成功建立SVM回归模型。基于6个生物标志物数据的SVM回归模型估算的损伤时间［（55.344±7.485） h］与基于31个生物标志物数据的SVM回归模型估算的损伤时间［（195.781±1.629） h］相比，后者更接近实际值。结论 基于代谢物数据的SVM回归模型可用于骨骼肌挫伤时间的估算。
