Wang Qingzhong, Dwivedi Yogesh
Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, Birmingham, AL, USA.
Transl Psychiatry. 2025 Aug 11;15(1):275. doi: 10.1038/s41398-025-03497-y.
Major depressive disorder (MDD) is the most prevalent and severe form of mental illness and is significantly linked to suicide. At present, addressing the treatment and prevention of depression and suicide poses significant challenges, largely due to the remaining uncertainties surrounding their pathogenesis. Thus, there is an urgent need to find new molecular pathways, as well as effective biomarkers and drug targets, to provide effective diagnosis, prognosis, and treatments for depression and suicide. Recent advancements in high-throughput sequencing technology and whole-genome analysis have enabled the collection of extensive omics data from blood samples, human autopsy brain tissue, and various animal models. This data captures significant molecular-level changes, including alterations in gene transcripts, epigenomes, and proteins, effectively reflecting the biological state of the disease. This review provides a systematic overview of advancements in transcriptomics, non-coding RNA, and AI related to depression and suicide. It discusses new research approaches, such as spatial transcriptomics, addresses challenges connected to various research materials and methodologies, and proposes avenues for future studies.
重度抑郁症（MDD）是最普遍、最严重的精神疾病形式，与自杀密切相关。目前，应对抑郁症和自杀的治疗与预防面临重大挑战，这主要是由于其发病机制仍存在不确定性。因此，迫切需要找到新的分子途径以及有效的生物标志物和药物靶点，以提供针对抑郁症和自杀的有效诊断、预后和治疗方法。高通量测序技术和全基因组分析的最新进展使得能够从血液样本、人类尸检脑组织和各种动物模型中收集大量组学数据。这些数据捕捉到了显著的分子水平变化，包括基因转录本、表观基因组和蛋白质的改变，有效地反映了疾病的生物学状态。本综述系统概述了与抑郁症和自杀相关的转录组学、非编码RNA和人工智能的进展。它讨论了新的研究方法，如空间转录组学，解决了与各种研究材料和方法相关的挑战，并提出了未来研究的途径。
