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心肌梗死与抑郁症之间共享分子机制及枢纽基因的研究

Investigation of the shared molecular mechanisms and hub genes between myocardial infarction and depression.

作者信息

Wang Mengxi, Cheng Liying, Gao Ziwei, Li Jianghong, Ding Yuhan, Shi Ruijie, Xiang Qian, Chen Xiaohu

机构信息

Department of Cardiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China.

Department of Cardiology, Jiangsu Province Hospital of Chinese Medicine, Nanjing, China.

出版信息

Front Cardiovasc Med. 2023 Jul 21;10:1203168. doi: 10.3389/fcvm.2023.1203168. eCollection 2023.

DOI:10.3389/fcvm.2023.1203168
PMID:37547246
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10401437/
Abstract

BACKGROUND

The pathogenesis of myocardial infarction complicating depression is still not fully understood. Bioinformatics is an effective method to study the shared pathogenesis of multiple diseases and has important application value in myocardial infarction complicating depression.

METHODS

The differentially expressed genes (DEGs) between control group and myocardial infarction group (M-DEGs), control group and depression group (D-DEGs) were identified in the training set. M-DEGs and D-DEGs were intersected to obtain DEGs shared by the two diseases (S-DEGs). The GO, KEGG, GSEA and correlation analysis were conducted to analyze the function of DEGs. The biological function differences of myocardial infarction and depression were analyzed by GSVA and immune cell infiltration analysis. Four machine learning methods, nomogram, ROC analysis, calibration curve and decision curve were conducted to identify hub S-DEGs and predict depression risk. The unsupervised cluster analysis was constructed to identify myocardial infarction molecular subtype clusters based on hub S-DEGs. Finally, the value of these genes was verified in the validation set, and blood samples were collected for RT-qPCR experiments to further verify the changes in expression levels of these genes in myocardial infarction and depression.

RESULTS

A total of 803 M-DEGs, 214 D-DEGs, 13 S-DEGs and 6 hub S-DEGs (CD24, CSTA, EXTL3, RPS7, SLC25A5 and ZMAT3) were obtained in the training set and they were all involved in immune inflammatory response. The GSVA and immune cell infiltration analysis results also suggested that immune inflammation may be the shared pathogenesis of myocardial infarction and depression. The diagnostic models based on 6 hub S-DEGs found that these genes showed satisfactory combined diagnostic performance for depression. Then, two molecular subtypes clusters of myocardial infarction were identified, many differences in immune inflammation related-biological functions were found between them, and the hub S-DEGs had satisfactory molecular subtypes identification performance. Finally, the analysis results of the validation set further confirmed the value of these hub genes, and the RT-qPCR results of blood samples further confirmed the expression levels of these hub genes in myocardial infarction and depression.

CONCLUSION

Immune inflammation may be the shared pathogenesis of myocardial infarction and depression. Meanwhile, hub S-DEGs may be potential biomarkers for the diagnosis and molecular subtype identification of myocardial infarction and depression.

摘要

背景

心肌梗死并发抑郁症的发病机制仍未完全明确。生物信息学是研究多种疾病共同发病机制的有效方法，在心肌梗死并发抑郁症方面具有重要应用价值。

方法

在训练集中鉴定对照组与心肌梗死组之间的差异表达基因（M-DEGs）、对照组与抑郁症组之间的差异表达基因（D-DEGs）。将M-DEGs和D-DEGs进行交集运算，以获得两种疾病共有的差异表达基因（S-DEGs）。进行基因本体（GO）、京都基因与基因组百科全书（KEGG）、基因集富集分析（GSEA）和相关性分析，以分析差异表达基因的功能。通过基因集变异分析（GSVA）和免疫细胞浸润分析，分析心肌梗死和抑郁症的生物学功能差异。采用四种机器学习方法，即列线图、ROC分析、校准曲线和决策曲线，以鉴定核心S-DEGs并预测抑郁症风险。构建无监督聚类分析，以基于核心S-DEGs鉴定心肌梗死分子亚型聚类。最后，在验证集中验证这些基因的价值，并采集血样进行逆转录定量聚合酶链反应（RT-qPCR）实验，以进一步验证这些基因在心肌梗死和抑郁症中的表达水平变化。

结果

在训练集中共获得803个M-DEGs、214个D-DEGs、13个S-DEGs和6个核心S-DEGs（CD24、CSTA、EXTL3、RPS7、SLC25A5和ZMAT3），它们均参与免疫炎症反应。GSVA和免疫细胞浸润分析结果也提示，免疫炎症可能是心肌梗死和抑郁症的共同发病机制。基于6个核心S-DEGs的诊断模型发现，这些基因对抑郁症具有令人满意的联合诊断性能。然后，鉴定出心肌梗死的两种分子亚型聚类，发现它们在免疫炎症相关生物学功能方面存在许多差异，且核心S-DEGs具有令人满意的分子亚型鉴定性能。最后，验证集的分析结果进一步证实了这些核心基因的价值，血样的RT-qPCR结果进一步证实了这些核心基因在心肌梗死和抑郁症中的表达水平。

结论

免疫炎症可能是心肌梗死和抑郁症的共同发病机制。同时，核心S-DEGs可能是心肌梗死和抑郁症诊断及分子亚型鉴定的潜在生物标志物。

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