法医学杂志

法医学杂志 ›› 2020, Vol. 36 ›› Issue (5): 660-665，671.DOI: 10.12116/j.issn.1004-5619.2020.05.010

• 论 著 • 上一篇    下一篇

蛋白芯片检测技术结合多维统计推断死亡时间

李文晋1 ，李健1 ，卢晓军1，2 ，姜垚如1 ，王靓1 ，靳茜茜1 ，王英元1 ，孙俊红1   

  1. 1. 山西医科大学法医学院，山西 太原 030001；2. 包头市公安局刑事侦查支队，内蒙古 包头 014030
  • 收稿日期:2020-05-06 发布日期:2020-10-25 出版日期:2020-10-28
  • 通讯作者: 孙俊红，男，博士，教授，主要从事损伤病理学和猝死病理学研究；E-mail：junhong.sun@sxmu.edu.cn
  • 作者简介:李文晋（1995—），女，硕士研究生，主要从事法医病理学研究；E-mail：lwj20181001@163.com
  • 基金资助:
    国家重点研发计划资助项目（2018YFC0807204）

Estimating Postmortem Interval by Protein Chip Detection Technology Combined with Multidimensional Statistics

LI Wen-jin1 , LI Jian1 , LU Xiao-jun1,2 , JIANG Yao-ru1 , WANG Liang1 , JIN Qian-qian1 , WANG Ying-yuan1 , SUN Jun-hong1   

  1. 1. School of Forensic Medicine, Shanxi Medical University, Taiyuan 030001, China; 2. Criminal Investigation Detachment, Baotou Public Security Bureau, Baotou 014030, Inner Mongolia Autonomous Region, China
  • Received:2020-05-06 Online:2020-10-25 Published:2020-10-28

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摘要： 目的 利用2100生物分析仪结合蛋白芯片获取死后大鼠肝组织蛋白质表达谱，推测其与死亡时间的关系。 方法 大鼠腹腔麻醉致死后置于 16 ℃环境中，提取死后 14 个时间点肝组织中的水溶性蛋白质，使用蛋白芯片获取相对分子质量在14 000~230 000的蛋白质表达谱数据，并利用主成分分析（principal component analysis，PCA）、偏最小二乘-判别分析（partial least squares-discriminant analysis，PLS-DA）、 Fisher判别对数据进行分析。 结果 根据蛋白质表达谱的变化和PLS-DA结果将死亡时间分为A组（0 d）、 B组（1~9 d）、C组（12~30 d）。经Fisher判别，模型的训练集及测试集的预测准确率均为100.0%，训练集的内 部交叉验证准确率为100.0%。通过建立B、C组各时间点的Fisher判别模型缩小推断死亡时间窗口，B组中 训练集及测试集的预测准确率均为100.0%，训练集的内部交叉验证准确率为100.0%；C组中训练集及测试 集的预测准确率分别为95.2%、78.6%，训练集的内部交叉验证准确率为88.1%。 结论 蛋白芯片检测技术 可以快捷简便地获取大鼠死后不同时间点肝组织相对分子质量在14 000~230 000的水溶性蛋白质表达谱，建立PLS-DA及Fisher判别模型对死亡时间进行分类预测判断，有望为死亡时间推断提供新的思路和方法。

关键词: 法医病理学, 芯片分析技术, 蛋白质类, 死亡时间, 肝, 大鼠

Abstract: Objective To obtain the protein expression profile of rat liver tissue after death by the 2100 bioanalyzer combined with protein chip, and infer the relationship between protein expression profile and postmortem interval. Methods Rats were killed by abdominal anesthesia and placed at 16 ℃. Water-soluble proteins in liver tissues were extracted at 14 time points after death. The expression profile data of proteins with relative molecular weight of 14 000- 230 000 were obtained using protein chip, and principal component analysis （PCA）, partial least squares-discriminant analysis （PLS-DA） and Fisher discriminant were used to analyze the data. Results According to the changes of protein expression profile, the postmortem interval was divided into group A（0 d）, group B （1-9 d）, group C （12-30 d） according to the result of PLS- DA. The prediction accuracy of the training set and test set of the model were all 100.0%, and the internal cross-validation of the training set was 100.0% according to Fisher discriminant. The Fisher discriminant model at each time point of group B and C was established to narrow the time window of postmortem interval estimation. The prediction accuracy of the training set and test set were all 100.0%, and the internal cross-validation accuracy of the training set was 100.0% in group B. The prediction accuracy of the training set and test set were respectively 95.2% and 78.6% in group C, and the internal cross-validation of the training set was 88.1%. Conclusion Protein chip detection technology can quickly and easily obtain the expression profile of water-soluble proteins of rat liver tissue with a relative molecular weight of 14 000-230 000 at different time points after death. PLS-DA and Fisher discriminant models are established to classify and predict the postmortem interval, in order to provide new ideas and methods for postmortem interval estimation.

Key words: forensic pathology, microchip analytical procedures, proteins, postmortem interval, liver, rats

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