Shi Yan, Zhibin Han, Tianyu Wang, Aowen Wang, Feng Liu, Shengkun Yu, Lin Xu, Hong Shen, Li Liu, Zhiguo Lin, Meng Na
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摘要

背景:颞叶癫痫(TLE)和重度抑郁障碍(MDD)是影响全球个人的普遍而复杂的神经系统疾病。临床和流行病学研究表明,颞叶癫痫和重性抑郁症之间存在显著的合并症;然而,这种关系背后的共同分子机制仍不清楚。本研究旨在通过对基因表达谱的系统分析,探索与 TLE 和 MDD 相关的共同关键基因,阐明其潜在的分子病理机制,并评估这些基因在诊断和治疗方面的潜在应用:方法:从 GEO 数据库中获取 TLE 和 MDD 的脑组织基因表达数据。方法:研究人员从 GEO 数据库中获取了 TLE 和 MDD 的脑组织基因表达数据,并通过差异表达基因(DEGs)、加权基因共表达网络分析(WGCNA)、功能富集和蛋白质-蛋白质相互作用(PPI)网络构建来识别共有的基因模块。利用 LASSO 和随机森林(RF)机器学习模型选择诊断候选基因,并通过 ROC 曲线分析进行验证。免疫浸润分析探讨了关键基因的免疫参与,而单细胞测序则确认了不同细胞类型的基因表达。利用药物数据库确定了潜在的治疗药物:结果:共发现372个DEGs在TLE和MDD之间上调或下调,WGCNA揭示了9个共享基因模块。包括 HTR7 和 CDHR2 在内的 7 个中心基因显示出很强的 ROC 性能。免疫浸润分析揭示了与关键基因相关的免疫细胞群的变化,单细胞测序证实了这一点。奥帕他替尼被确定为针对这些基因的潜在治疗药物:本研究发现了TLE和MDD之间共同的基因表达谱,强调了与免疫通路相关的分子机制。免疫浸润分析和单细胞测序强调了免疫调节在这两种疾病中的重要性,而药物预测则突出了精准医疗的候选药物,为未来的研究和治疗策略奠定了基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Exploring the Immune-Related Molecular Mechanisms Underlying the Comorbidity of Temporal Lobe Epilepsy and Major Depressive Disorder through Integrated Data Set Analysis.

Background: Temporal lobe epilepsy (TLE) and major depressive disorder (MDD) are prevalent and complex neurological disorders that affect individuals globally. Clinical and epidemiological studies indicate a significant comorbidity between TLE and MDD; however, the shared molecular mechanisms underlying this relationship remain unclear. This study aims to explore the common key genes associated with TLE and MDD through a systematic analysis of gene expression profiles, elucidate their underlying molecular pathological mechanisms, and evaluate the potential applications of these genes in diagnostic and therapeutic contexts.

Methods: Brain tissue gene expression data for TLE and MDD were obtained from the GEO database. Differentially expressed genes (DEGs), weighted gene co-expression network analysis (WGCNA), functional enrichment, and protein-protein interaction (PPI) network construction were performed to identify shared gene modules. LASSO and random forest (RF) machine learning models were used to select diagnostic candidate genes, validated through ROC curve analysis. Immune infiltration analysis explored the immune involvement of key genes, while single-cell sequencing confirmed gene expression across cell types. Potential therapeutic drugs were identified using a drug database.

Results: A total of 372 DEGs were identified as either up- or down-regulated between TLE and MDD, with WGCNA revealing nine shared gene modules. Seven hub genes, including HTR7 and CDHR2, demonstrated strong ROC performance. Immune infiltration analysis revealed changes in immune cell populations linked to key genes, confirmed by single-cell sequencing. Upadacitinib was identified as a potential therapeutic drug targeting these genes.

Conclusion: This study identified shared gene expression profiles between TLE and MDD, emphasizing immune pathway-related molecular mechanisms. Immune infiltration analysis and single-cell sequencing underscored the significance of immune regulation in their comorbidity, while drug prediction highlights candidates for precision medicine, establishing a foundation for future research and therapeutic strategies.

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