法医学杂志

法医学杂志 ›› 2020, Vol. 36 ›› Issue (6): 755-761.DOI: 10.12116/j.issn.1004-5619.2020.06.003

• 论 著 • 上一篇    下一篇

大鼠骨骼肌挫伤后血清生物标志物的时序性变化

翟豪杰，林伟，田甜，刘敏   

  1. 四川大学华西基础医学与法医学院，四川 成都 610041
  • 收稿日期:2019-08-21 发布日期:2020-12-25 出版日期:2020-12-28
  • 通讯作者: 刘敏，男，教授，主要从事法医病理学教学；E-mail：min8liu@hotmail.com
  • 作者简介:翟豪杰（1995—），男，硕士研究生，主要从事法医病理学研究；E-mail：ZhaiHJ5331@163.com

Sequential Changes of Serum Biomarkers after Skeletal Muscle Contusion in Rats

ZHAI Hao-jie, LIN Wei, TIAN Tian, LIU Min   

  1. West China School of Basic Medical Sciences and Forensic Medicine, Sichuan University, Chengdu 610041, China
  • Received:2019-08-21 Online:2020-12-25 Published:2020-12-28

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摘要： 目的 基于气相色谱-质谱法（gas chromatography-mass spectrometry，GC-MS）代谢组学技术筛选大 鼠骨骼肌挫伤后血清生物标志物，建立支持向量机（support vector machine，SVM）回归模型推断骨骼肌挫伤 时间。 方法 60只健康SD大鼠随机分为实验组（50只）、对照组（5只）和验证组（5只）。实验组及验证组 大鼠采取自由落体方法建立骨骼肌损伤模型，实验组大鼠分别于伤后0、2、4、8、12、24、48、96、144、240 h处 死，验证组大鼠于伤后192 h处死，对照组大鼠常规饲养3 d后处死。取骨骼肌行常规苏木精-伊红（hema? toxylin-eosin，HE）染色，利用GC-MS获得血清代谢物谱图，应用正交偏最小二乘判别分析（orthogonal partial least square-discriminant analysis，OPLS-DA）模式识别方法对数据进行判别分析并筛选生物标志物，建立 推断损伤时间的SVM回归模型。 结果 代谢组学方法初步筛选出31种生物标志物，在此基础上进一步选 出6种生物标志物，但该6种生物标志物的相对含量随损伤时间变化的规律欠佳。根据6种生物标志物和 31种生物标志物数据均能成功建立SVM回归模型，且以31种生物标志物数据建立SVM回归模型推断的损伤 时间［（195.781±1.629）h］比以6种生物标志物数据建立SVM回归模型推断的损伤时间［（55.344±7.485）h］ 更加接近实际值。 结论 以代谢产物数据建立SVM回归模型有望用于骨骼肌挫伤时间推断。

关键词: 法医病理学, 代谢组学, 创伤和损伤, 骨骼肌, 生物标志物, 血清, 大鼠

Abstract: 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.

Key words: forensic pathology, metabolomics, wounds and injuries, skeletal muscle, biomarkers, serum, rats

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