Huang Cong, You Zhipeng, He Yijie, Li Jiran, Liu Yang, Peng Chunyan, Liu Zhixiong, Liu Xingan, Sun Jiahang
Department of Neurosurgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, China.
Department of Neurology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China.
Front Neurosci. 2023 Mar 30;17:1145805. doi: 10.3389/fnins.2023.1145805. eCollection 2023.
Temporal lobe epilepsy (TLE) is a common chronic episodic illness of the nervous system. However, the precise mechanisms of dysfunction and diagnostic biomarkers in the acute phase of TLE are uncertain and hard to diagnose. Thus, we intended to qualify potential biomarkers in the acute phase of TLE for clinical diagnostics and therapeutic purposes.
An intra-hippocampal injection of kainic acid was used to induce an epileptic model in mice. First, with a TMT/iTRAQ quantitative labeling proteomics approach, we screened for differentially expressed proteins (DEPs) in the acute phase of TLE. Then, differentially expressed genes (DEGs) in the acute phase of TLE were identified by linear modeling on microarray data (limma) and weighted gene co-expression network analysis (WGCNA) using the publicly available microarray dataset GSE88992. Co-expressed genes (proteins) in the acute phase of TLE were identified by overlap analysis of DEPs and DEGs. The least absolute shrinkage and selection operator (LASSO) regression and support vector machine recursive feature elimination (SVM-RFE) algorithms were used to screen Hub genes in the acute phase of TLE, and logistic regression algorithms were applied to develop a novel diagnostic model for the acute phase of TLE, and the sensitivity of the diagnostic model was validated using receiver operating characteristic (ROC) curves.
We screened a total of 10 co-expressed genes (proteins) from TLE-associated DEGs and DEPs utilizing proteomic and transcriptome analysis. LASSO and SVM-RFE algorithms for machine learning were applied to identify three Hub genes: Ctla2a, Hapln2, and Pecam1. A logistic regression algorithm was applied to establish and validate a novel diagnostic model for the acute phase of TLE based on three Hub genes in the publicly accessible datasets GSE88992, GSE49030, and GSE79129.
Our study establishes a reliable model for screening and diagnosing the acute phase of TLE that provides a theoretical basis for adding diagnostic biomarkers for TLE acute phase genes.
颞叶癫痫（TLE）是一种常见的慢性发作性神经系统疾病。然而，TLE急性期功能障碍的确切机制和诊断生物标志物尚不确定且难以诊断。因此，我们旨在确定TLE急性期的潜在生物标志物，用于临床诊断和治疗。
采用海马内注射 kainic 酸诱导小鼠癫痫模型。首先，利用TMT/iTRAQ定量标记蛋白质组学方法，筛选TLE急性期差异表达蛋白（DEP）。然后，使用公开可用的微阵列数据集GSE88992，通过微阵列数据的线性建模（limma）和加权基因共表达网络分析（WGCNA），鉴定TLE急性期差异表达基因（DEG）。通过DEP和DEG的重叠分析，鉴定TLE急性期共表达基因（蛋白质）。使用最小绝对收缩和选择算子（LASSO）回归和支持向量机递归特征消除（SVM-RFE）算法筛选TLE急性期的Hub基因，并应用逻辑回归算法建立TLE急性期的新型诊断模型，使用受试者工作特征（ROC）曲线验证诊断模型的敏感性。
利用蛋白质组学和转录组分析，我们从TLE相关的DEG和DEP中总共筛选出10个共表达基因（蛋白质）。应用机器学习的LASSO和SVM-RFE算法鉴定出三个Hub基因：Ctla2a、Hapln2和Pecam1。应用逻辑回归算法，基于公开可用数据集GSE88992、GSE49030和GSE79129中的三个Hub基因，建立并验证了TLE急性期的新型诊断模型。
我们的研究建立了一个可靠的TLE急性期筛选和诊断模型，为添加TLE急性期基因的诊断生物标志物提供了理论基础。
Zotero 插件
