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抑郁症相关的先天免疫基因与泛癌基因分析及验证

Depression-related innate immune genes and pan-cancer gene analysis and validation.

作者信息

Yang Yakun, Han Wei, Zhang Xiaoyu, Yuan Hao, Wang Ran, Yang Jia, An Cuixia, Huang Dongyang

机构信息

Department of Pharmacology, The Key Laboratory of Neural and Vascular Biology, The Key Laboratory of New Drug Pharmacology and Toxicology, Ministry of Education, Collaborative Innovation Center of Hebei Province for Mechanism, Diagnosis and Treatment of Neuropsychiatric Diseases, Hebei Medical University, Shijiazhuang, Hebei, China.

Sharing Service Platform for Large-Scale Scientific Research Instruments and Equipment, Hebei Medical University, Shijiazhuang, Hebei, China.

出版信息

Front Genet. 2025 Jan 10;15:1521238. doi: 10.3389/fgene.2024.1521238. eCollection 2024.

Abstract

BACKGROUND

Depression, a prevalent chronic mental disorder, presents complexities and treatment challenges that drive researchers to seek new, precise therapeutic targets. Additionally, the potential connection between depression and cancer has garnered significant attention.

METHODS

This study analyzed depression-related gene expression data from the GEO database. Using data normalization, differential expression analysis, WGCNA, and machine learning, we identified core genes strongly associated with depression. These genes were validated in depression patients through q-PCR and examined for expression patterns and potential roles across various cancers.

RESULTS

We identified six core genes (GRB10, TDRD9, BCL7A, GPR18, KLRG1, and THEM4) significantly associated with depression and cancer. In depression, GRB10 and TDRD9, involved in cell growth and stress responses, exhibited elevated expression, while BCL7A, GPR18, KLRG1, and THEM4, linked to immune regulation and apoptosis, showed reduced expression, suggesting dysregulated cellular signaling and impaired immune function. In cancer, these genes displayed altered expression patterns across tumor types, influencing tumor progression, prognosis, and immune microenvironment modulation. Shared molecular pathways, such as immune dysregulation and apoptosis, highlight their potential as biomarkers and therapeutic targets for both depression and cancer.

CONCLUSION

This study integrates bioinformatics and machine learning to uncover key molecular pathways and targets for depression, introducing innovative therapeutic prospects that may enhance precision treatment for depression. Furthermore, by revealing shared mechanisms between depression and cancer, we have identified six core genes with significant functional roles in immune regulation, apoptosis, and cellular signaling. These findings not only deepen our understanding of the molecular overlap between these conditions but also lay the groundwork for developing dual-targeted therapeutic strategies. This study uniquely contributes to bridging mental health and oncology research, offering new insights and hope for improving patient outcomes in both fields.

摘要

背景

抑郁症是一种常见的慢性精神障碍,存在复杂性和治疗挑战,促使研究人员寻找新的、精确的治疗靶点。此外,抑郁症与癌症之间的潜在联系已引起广泛关注。

方法

本研究分析了来自基因表达综合数据库(GEO数据库)的与抑郁症相关的基因表达数据。通过数据归一化、差异表达分析、加权基因共表达网络分析(WGCNA)和机器学习,我们确定了与抑郁症密切相关的核心基因。这些基因在抑郁症患者中通过定量聚合酶链反应(q-PCR)进行验证,并检测其在各种癌症中的表达模式和潜在作用。

结果

我们确定了六个与抑郁症和癌症显著相关的核心基因(GRB10、TDRD9、BCL7A、GPR18、KLRG1和THEM4)。在抑郁症中,参与细胞生长和应激反应的GRB10和TDRD9表达升高,而与免疫调节和细胞凋亡相关的BCL7A、GPR18、KLRG1和THEM4表达降低,提示细胞信号失调和免疫功能受损。在癌症中,这些基因在不同肿瘤类型中表现出改变的表达模式,影响肿瘤进展、预后和免疫微环境调节。免疫失调和细胞凋亡等共享分子途径突出了它们作为抑郁症和癌症生物标志物及治疗靶点的潜力。

结论

本研究整合生物信息学和机器学习,揭示抑郁症的关键分子途径和靶点,引入创新的治疗前景,可能提高抑郁症的精准治疗。此外,通过揭示抑郁症和癌症之间的共同机制,我们确定了六个在免疫调节、细胞凋亡和细胞信号传导中具有重要功能作用的核心基因。这些发现不仅加深了我们对这些疾病之间分子重叠的理解,也为开发双靶点治疗策略奠定了基础。本研究独特地为连接心理健康和肿瘤学研究做出了贡献,为改善这两个领域的患者预后提供了新的见解和希望。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c5fd/11757255/4db6a1f9a70e/fgene-15-1521238-g001.jpg

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