Hostname: page-component-89b8bd64d-4ws75 Total loading time: 0 Render date: 2026-05-11T12:20:02.891Z Has data issue: false hasContentIssue false
  • English
  • Français

The genetic association between major depressive disorder and coronary heart disease

Published online by Cambridge University Press:  21 March 2025

Yue Shi Open the ORCID record for Yue Shi [Opens in a new window] ,
Feikang Xu ,
Yumei Wei ,
Duan Zeng ,
Shen He ,
Jingjing Huang  and
Huafang Li Open the ORCID record for Huafang Li [Opens in a new window]
Yue Shi
Affiliation:
Department of Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
Feikang Xu
Affiliation:
Schizophrenia Program, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
Yumei Wei
Affiliation:
Department of Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
Duan Zeng
Affiliation:
Department of Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
Shen He
Affiliation:
Department of Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
Jingjing Huang
Affiliation:
Department of Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
Huafang Li*
Affiliation:
Department of Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China Shanghai Clinical Research Center for Mental Health, Shanghai, China
*
Corresponding author: Huafang Li; Email: lihuafang@smhc.org.cn
Rights & Permissions [Opens in a new window]

Abstract

Major depressive disorder (MDD) and coronary heart disease (CHD) can both cause significant morbidity and mortality. The association of MDD and CHD has long been identified, but the mechanisms still require further investigation. Seven mRNA microarray datasets containing samples from patients with MDD and CHD were downloaded from Gene Expression Omnibus. Combined matrixes of MDD and CAD were constructed for subsequent analysis. Differentially expressed genes (DEGs) were identified. Functional enrichment analyses based on shared DEGs were conducted to identify pivotal pathways. A protein-protein network was also applied to further investigate the functional interaction. Results showed that 24 overlapping genes were identified. Enrichment analysis indicated that the shared genes are mainly associated with immune function and ribosome biogenesis. The functional interactions of shared genes were also demonstrated by PPI network analysis. In addition, three hub genes including MMP9, S100A8, and RETN were identified. Our results indicate that MDD and CHD have a genetic association. Genes relevant to immune function, especially IL-17 signalling pathway may be involved in the pathogenesis of MDD and CHD.

Keywords

major depressive disordercoronary heart diseasegenetics

Information

Type
Original Article
Information
Acta Neuropsychiatrica , Volume 37 , 2025 , e49
Check for updates
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2025. Published by Cambridge University Press on behalf of Scandinavian College of Neuropsychopharmacology

Significant Outcomes

  1. 1. Genetic association between major depressive disorder and coronary heart disease was reported in this study.

  2. 2. Three hub genes including MMP9, S100A8, and RETN were identified in the overlapping DEGs of MDD and CHD.

  3. 3. Immune function and ribosome biogenesis are pivotal pathways connecting MDD and CHD.

Limitations

  1. 1. This study lacks experimental validation.

  2. 2. Comparation of different methods of combined matrixes construction may help better data mining.

Introduction

Major depressive disorder (MDD) and coronary heart disease (CHD) are both important diseases leading to significant health burdens. MDD is now the number one single contributor to disability in the world according to the WHO’s 2017 Health Report (Benziger et al., Reference Benziger, Roth and Moran2016). More than 300 million people suffer from MDD, which is about 4.4 percent of the global population, and the incidence is twice as high in women as in men (Salk et al., Reference Salk, Hyde and Abramson2017, Parker and Brotchie, Reference Parker and Brotchie2010). MDD is also the leading cause of death by suicide (Hawton et al., Reference Hawton, Casanas, Haw and Saunders2013), accounting for as many as 800,000 people each year. Therefore, it’s crucial to further investigate the aetiology and pathogenesis of this debilitating mental disorder.

CHD, also known as coronary artery disease (CAD), is also among the leading causes of mortality, accounting for 7.4 million deaths worldwide (De Hert et al., Reference De Hert, Detraux and Vancampfort2018). Its characteristic lesion is the accumulation of fatty and/or a fibrous substance (Libby et al., Reference Libby, Buring, Badimon, Hansson, Deanfield, Bittencourt, Tokgozoglu and Lewis2019), also called atherosclerotic plaques, in the arteries of the heart, which can interrupt or block the heart’s coronary circulation and lead to tissue ischemia. CHD can cause angina pectoris, myocardial infarction, and chronic ischemia (Doenst et al., Reference Doenst, Haverich, Serruys, Bonow, Kappetein, Falk, Velazquez, Diegeler and Sigusch2019). In the case of severely stenotic CHD, myocardial infarction or chronic ischemia may cause heart failure and/or even death (Doenst et al., Reference Doenst, Haverich, Serruys, Bonow, Kappetein, Falk, Velazquez, Diegeler and Sigusch2019).

There is an association between MDD and CHD. For example, the symptom of chest tightness, palpitations, low energy, disturbance of sleep, and difficulty in carrying out daily routines can be observed in both patients with MDD and patients with heart diseases (Howard et al., Reference Howard, Adams, Clarke, Hafferty, Gibson, Shirali, Coleman, Hagenaars, Ward, Wigmore, Alloza, Shen, Barbu, Xu, Whalley, Marioni, Porteous, Davies, Deary, Hemani, Berger, Teismann, Rawal, Arolt, Baune, Dannlowski, Domschke, Tian, Hinds, Byrne, Ripke, Smith, Sullivan, Wray, Breen, Lewis and McIntosh2019, Malhi and Mann, Reference Malhi and Mann2018). The comorbidity of the two diseases is quite common, with a prevalence rate of MDD being about 10 to 31% in patients with CHD (Vaccarino et al., Reference Vaccarino, Badimon, Bremner, Cenko, Cubedo, Dorobantu, Duncker, Koller, Manfrini, Milicic, Padro, Pries, Quyyumi, Tousoulis, Trifunovic, Vasiljevic, De Wit and Bugiardini2020). This was far higher than the prevalence rate of 4.4% in the global population estimated by WHO. What’s more, several meta-analyses have shown that patients with baseline MDD symptoms were associated with increased risk (Harshfield et al., Reference Harshfield, Pennells, Schwartz, Willeit, Kaptoge, Bell, Shaffer, Bolton, Spackman, Wassertheil-Smoller, Kee, Amouyel, Shea, Kuller, Kauhanen, Van Zutphen, Blazer, Krumholz, Nietert, Kromhout, Laughlin, Berkman, Wallace, Simons, Dennison, Barr, Meyer, Wood, Danesh, Di Angelantonio and Davidson2020, Rajan et al., Reference Rajan, McKee, Rangarajan, Bangdiwala, Rosengren, Gupta, Kutty, Wielgosz, Lear, AlHabib, Co, Lopez-Jaramillo, Avezum, Seron, Oguz, Kruger, Diaz, Nafiza, Chifamba, Yeates, Kelishadi, Sharief, Szuba, Khatib, Rahman, Iqbal, Bo, Yibing, Wei and Yusuf2020, Correll et al., Reference Correll, Solmi, Veronese, Bortolato, Rosson, Santonastaso, Thapa-Chhetri, Fornaro, Gallicchio, Collantoni, Pigato, Favaro, Monaco, Kohler, Vancampfort, Ward, Gaughran, Carvalho and Stubbs2017), poor prognosis (Lichtman et al., Reference Lichtman, Froelicher, Blumenthal, Carney, Doering, Frasure-Smith, Freedland, Jaffe, Leifheit-Limson, Sheps, Vaccarino and Wulsin2014), and lower quality of life (O’Neil et al., Reference O’Neil, Stevenson, Williams, Mortimer, Oldenburg and Sanderson2013) for CHD. Such association leads to the development of psycho-cardiology, a field that investigates the role of psychosocial factors in the emergence, the course and the rehabilitation of cardiac diseases. However, the mechanisms underlying such a relationship remain unknown despite the increasing academic attention. One possible factor is that the changes in the autonomic nervous system and neurohormonal function. Studies have shown that there is a shift in the autonomic balance in depressed patients, manifested by the leaner relationship between heart rate variability (HRV) reduction and MDD severity (Kemp et al., Reference Kemp, Quintana, Gray, Felmingham, Brown and Gatt2010). And in patients with a history of CHD, increased sympathetic activity and reduced HRV (Stapelberg et al., Reference Stapelberg, Hamilton-Craig, Neumann, Shum and McConnell2012) are also commonly observed. Patients with MDD also show higher levels of plasma and urinary catecholamines (Carney and Freedland, Reference Carney and Freedland2017). Other potential biological mechanisms include platelet dysfunction (Carney and Freedland, Reference Carney and Freedland2017), endothelial dysfunction, such as plasminogen activator inhibitor-1 and fibrinogen (Hare et al., Reference Hare, Toukhsati, Johansson and Jaarsma2014), and abnormalities in the inflammatory response (Carney and Freedland, Reference Carney and Freedland2017, Wu et al., Reference Wu, Zhu, Chen, Duan, Luo, Yang and Yang2021). Behavioural mechanisms are also factors that potentially link MDD and CHD. Patients with comorbid CHD and MDD are prone to develop unhealthy behaviours and lifestyles, for example, smoking, sedentary lifestyle, poor adherence to medication, and poor dietary habits (Carney and Freedland, Reference Carney and Freedland2017). Newly raised possibilities are pointing towards gut microbiota and endocrine signalling. Although CHD and MDD are both polygenic and multifactorial disorders, the genetic association between these two diseases is less studied. Kenneth et al., found in 30,374 twins that the comorbidity in women arose primarily from genetic effects( + 0.16) (Kendler et al., Reference Kendler, Gardner, Fiske and Gatz2009), while genetic effects played a greater role in younger male patients. Gloria and her colleagues (Li et al., 2020) evaluated the bidirectional causal association using Mendelian randomisation and demonstrated that both MDD phenotypes were genetically correlated with cardiovascular diseases. Yunlong Lu et al. (Lu et al., Reference Lu, Wang, Georgakis, Lin and Zheng2021) later performed Mendelian randomisation estimates on 807553 individuals with MDD and 60,801 cases of CAD, and results showed that genetic liability to MDD is associated with an increased risk of CHD. However, whether there are overlapping related genes and pathways have not been further investigated.

In this study, we conducted a bioinformatics analysis to explore the genetic overlap of MDD and CAD. Differentially expressed genes (DEGs) identified in both disorders were collected to perform functional analyses. Weighted gene co-expression network analysis (WGCNA) was also performed in both MDD and CAD to identify key module genes.

Method

Data sources

This analysis was based on mRNA microarray datasets downloaded from the Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/), a database of the National Center for Biotechnology Information (NCBI). All relevant series publicly available up to June 2024 on GEO were searched. MDD datasets were searched using ‘MDD’, ‘depression’ or ‘major depressive disorder’. CAD datasets were searched using ‘CAD’, ‘coronary heart disease’ or ‘coronary artery disease’. Sample type is limited to peripheral blood and the sample size must be greater than 20. Only data series that both patients and healthy control are available in one single dataset were included. In order to reduce the interference of confounding factors, patient samples or data sets used as comorbidity studies other than MDD or CAD in the original study were excluded. In addition, CAD cases are defined as patients with ≥70% stenosis in >1 major vessel or ≥50% stenosis in >2 arteries, or patients with Duke CAD index (CADi) >23. To follow-up studies of treatment, only baseline information was retained. Finally, seven mRNA datasets were selected: MDD studies include GSE98793, GSE52790, GSE19738, and GSE201332. CAD studies include GSE10195, GSE12288, and GSE20680 (Table 1). Written informed consents were obtained from all participants in original studies, and the studies were approved by ethic committees. Trials of the datasets were approved by local institutional review boards, and informed consents were provided by all subjects according to original articles.

Table 1.

MDD and CAD expression profile data sets from GEO database

GEO, Gene Expression Omnibus; MDD, major depressive disorder; CAD, coronary artery disease.

Dataset preparation

Publicly available data were downloaded from GEO using the GEOquery R package. Probe ids were converted to official gene symbols using platform annotation files available. Gene expression information was obtained from series of matrix TXT files except GSE10195. The matrix provided by this dataset contains a large number of negative numbers. Therefore, raw data of GSE10195 were downloaded and background corrected to construct a normalised gene expression matrix. When all gene expression matrixes and clinical information (disease or healthy control) were ready, the four datasets from four MDD studies and the three datasets from three CAD studies were combined separately to obtain two combined matrixes. Batch effects (Sui et al., Reference Sui, Li, Fu, Zhao and Xing2023), which refer to potential differences in the data from different sources caused by non-biological factors, for example, different processing time or staff, were eliminated using function ComBat from sva R package (Figure. S1–S2). The code is as follows:

model=model.matrix(∼as.factor(Group))

combat_edata=ComBat(dat = as.matrix(dat),

batch = batch,

mod = model,

par.prior=TRUE,

ref.batch = 1)

Identification of differentially expressed genes

The limma package in R studio was used to process the significance analysis of DEGs between patients and healthy controls of MDD and CHD, separately. We employed the default Benjamini–Horchberg method to calculate adjusted p-values (Geng and Huang, Reference Geng and Huang2021; Hu et al., Reference Hu, Yu, Yuan, Hong, Wang, Zhang, Wang, Li, Yi and Fang2021; Long et al., Reference Long, Wang, Feng, Zhao, Liu, Ma, Yu, Li, Guo, Zhu, Teng and Zeng2021). In both combined datasets, genes with adjusted p value <0.05 and |log2 (foldchange)|> 0.1 as the cut-off criterion were considered as DEGs. Shared DEGs between MDD and CHD were selected for subsequent analysis.

Functional enrichment for shared DEGs

The Gene Ontology (GO) (Consortium, Reference Consortium2004) project provides a systematic description of biology, including consistent descriptors for gene products and standard classifications of sequences and sequence features, which has been widely used in annotation projects and biological analyses. Kyoto encyclopedia of genes and genomes (KEGG) (Kanehisa and Goto, Reference Kanehisa and Goto2000) is another knowledge base aimed to link individual genes with higher functional annotation and regulatory pathways. For a more extensive understanding of the DEGs, we performed GO (including biological process, molecular function, and cellular component) and KEGG enrichment analyses using clusterProfiler R package (Yu et al., Reference Yu, Wang, Han and He2012) and Metascape (Zhou et al., Reference Zhou, Zhou, Pache, Chang, Khodabakhshi, Tanaseichuk, Benner and Chanda2019). GO terms and KEGG pathways with P value <0.05 were considered statistically significant. Then we used the ggplot2 and GOplot R package to visualise the results.

PPI network construction and hub gene analyses

To further investigate the functional interaction network of DEG-encoding proteins in both MDD and CHD, Search Tool for the Retrieval Interacting Genes (Szklarczyk et al., Reference Szklarczyk, Gable, Nastou, Lyon, Kirsch, Pyysalo, Doncheva, Legeay, Fang, Bork, Jensen and von Mering2021) (STRING, https://string-db.org), a comprehensive database designed to integrate both physical interactions and functional associations between proteins, was applied to automatically predict and visualise the PPI network and clusters. The interaction score cut-off of 0.400 was set as a default option. Results were imported into Cytoscape (version 3.8.2) for hub gene analyses and visualisation. CytoHubba plugin and CytoNCA plugin were applied to identify the most connected genes as hub genes.

WGCNA module identification

WGCNA R package is used to reconstruct the co-expression network and yield relevant modules. We applied the algorithm of WGCNA to both combined matrixes. A minimum module size of 30 and a soft threshold of R2 >0.9 were selected. The Pearson’s correlation coefficients were computed to describe the relationship between the samples and within modules. The cut-off criteria of absolute eigengene-based connective (kME) were set to be over 0.8.

Results

Identification of DEGs

A total of 1830 DEGs (Figure 1A) were identified in the MDD group compared to the control group in the combined MDD matrix, including 244 up-regulated DEGs and 1586 down-regulated DEGs. In the combined CAD datasets, 137 DEGs (Figure 1B) were identified from CHD samples and control samples, with 84 up-regulated and 53 down-regulated. There was a total of 24 overlapping DEGs (Figure 2) between MDD and CHD.

Figure 1.

Volcano plot of combined matrix A. Volcano plot shows the differently expressed genes (DEGs) of combined major depressive disorder matrix. B. Volcano plot shows the DEGs of combined coronary artery disease matrix. MDD, major depressive disorder. CAD, coronary artery disease; FC, fold change.

Figure 2.

Venn diagram showing the number of overlapped genes.

Functional enrichment analysis for the overlapping DEGs

GO and KEGG enrichment analyses were performed for the 24 shared DEGs between MDD and CHD. Results from clusterProfiler showed that in biological process, shared DEGs were mainly associated with rhythmic process, regulation of peptidase activity, lymphocyte and leucocyte homoeostasis and apoptotic process, mitochondrial translation, and protein catabolic process. (Figure 3A). Cellular component analysis indicated that overlapping DEGs were mainly enriched in collagen-containing extracellular matrix, organellar ribosome, mitochondrial ribosome, peptidase inhibitor complex and alpha-beta T cell receptor complex (Figure 3A). The major molecular function identified for these genes were fatty acid binding, monocarboxylic acid binding, structural constituent of ribosome, voltage-gated channel activity, guanyl-nucleotide exchange factor activity, RAGE receptor binding, and T cell receptor binding (Figure 3A). Moreover, significantly enriched KEGG pathways of overlapping DEGs were IL-17 signalling pathway and ribosome (Figure 3B). Enrichment results of Metascape also showed that the major pathways were NABA matrisome associated, adaptive immune system, and leucocyte differentiation (Figure.S3).

Figure 3.

A. GO enrichment analysis base on overlapping genes of major depressive disorder (MDD) and coronary heart disease (CHD). B. KEGG pathway analysis based on overlapping genes between MDD and CHD. C. PPI network. D. Top three hub genes identified by Cytoscape. E. Correlation between modules and groups of CHD. F. Correlation between modules and groups of MDD.

PPI network analysis and hub gene analysis

Based on STRING database, a total of 11 proteins and 17 potential interactions were identified for 24 overlapping DEGs (Figure 3C), with PPI enrichment p-value <0.05 (p = 0.049). Results from STRING database were imported into Cytoscape for further analysis. According to results from CytoHubba plugin and CytoNCA, the top three hub genes of the highest degree of connectivity were MMP9, RETN, and S100A8, as shown in Figure 3D and Table.S1. Both MMP9 and S100A8 belong to the IL-17 signalling pathway (Figure.S4) while RETN belongs to cytokines and neuropeptides. MMP9 was up-regulated in both MDD and CAD (Figure.S5–S6). RETN was up-regulated in MDD but down-regulated in CAD, while S100A8 was down-regulated in MDD but up-regulated in CAD. In addition, the mRNA level of other key genes belonging to IL-17 signalling pathway also changed. For example, in the combined CAD matrix, the expression level of S100A9 increased. According to the combined MDD matrix, the expression level of MUC5 increased, while the expression level of COX2, NFKB1, APC5, A20, and Hsp90 decreased.

WGCNA analysis show more genes and pathways associated with MDD and CAD

To further investigate the relationship between genes and the two diseases, we applied WGCNA algorithm to both combined matrixes. Results showed that, in CAD, MEpink, MEpurple, MEtan, MEblack, and MEblue were significantly different (p < 0.05) (Figure 3E). The interesting modules in MDD occupied a larger number, MEblack, MEgreen, MEmagenta, ME purple, MEturquoise, MEpink, MEmidnightblue, MEsalmon, MEblue, MEyellow, MEbrown, MEtan, MElightcyan, and MEgrey can all be considered as related modules (Figure 3F). According to these interesting modules, 24 genes in CAD and 567 genes in MDD were identified as hub genes. We performed GO enrichment analysis to these genes and yield various pathways. After filtering pathways with gene counts more than two, 20 overlapped GO pathways shared between CAD and MDD were identified (Table.S2). These pathways were mainly associated with immune response, indicating that immune-related pathways were both relevant to the initiation of CAD and MDD.

Discussion

In this bioinformatic analysis of public database, we identified 24 DEGS shared by MDD and CHD. Enrichment analysis performed by clusterProfiler and Metascape indicated that the shared genes are mainly associated with immune function, ribosome biogenesis, regulation of peptidase activity, mitochondrial translation, protein catabolic process, and voltage-gated channel activity. The functional interactions of shared genes were also demonstrated by means of PPI network analysis. In addition, three hub genes (MMP9, RETN, and S100A8) were identified. Among these three hub genes, MMP9 and S100A8 both belongs to IL-17 signalling pathway while RETN also belongs to immune-related pathway. WGCNA algorithm was also applied and results showed that CAD and MDD shared 20 GO pathways related to immune response. Therefore, it could be summarised that MDD and CHD are genetically associated in the mechanism of immune function and ribosome biogenesis.

The results of GO biological process and KEGG suggest that the shared DEGs were enriched in lymphocyte and leucocyte homoeostasis and apoptotic process, alpha-beta T cell receptor complex, T cell receptor binding, IL-17 signalling pathway, adaptive immune system, and leucocyte differentiation. These findings are consistent with previous work reporting that immune systems and inflammation pathways are essential to the pathogenesis of both MDD and CHD. Many studies have shown that immune activation plays a pivotal role in MDD, demonstrated by T cell activation, and dysregulation (Maes, Reference Maes2011, Maes et al., Reference Maes, Song and Yirmiya2012, Osborne et al., Reference Osborne, Gilden, Kamperman, Hoogendijk, Spicer, Drexhage and Bergink2020) and increased level of inflammatory cytokines, including pro-inflammatory cytokines, acute phase protein (Maes et al., Reference Maes, Scharpe, Van Grootel, Uyttenbroeck, Cooreman, Cosyns and Suy1992), C-reactive protein (CRP), interleukin (IL-1β), interleukin-6 (IL-6), tumour necrosis factor alpha (TNF-α), and interferon gamma (IFN-γ) (Maes et al., Reference Maes, Scharpé, Meltzer, Okayli, Bosmans, D’Hondt, Vanden Bossche and Cosyns1994). In addition, analyses of postmortem brain samples from suicide victims with MDD also found increased expression of innate immune genes and proteins. A study comprising 153 patients and 153 controls observed elevated levels of memory T helper cells and Th17 cells in patients at high risk of suicide (Schiweck et al., Reference Schiweck, Valles-Colomer, Arolt, Müller, Raes, Wijkhuijs, Claes, Drexhage and Vrieze2020), suggesting the premature ageing of the immune system. Moreover, increased concentration of inflammatory biomarkers at baseline is associated with poor prognosis in depressed patients (Haroon et al., Reference Haroon, Daguanno, Woolwine, Goldsmith, Baer, Wommack, Felger and Miller2018), while randomised controlled trial showed that the administration of anti-inflammatory treatment had special efficacy in treatment-resistant patients with high baseline inflammatory biomarkers (Raison et al., Reference Raison, Rutherford, Woolwine, Shuo, Schettler, Drake, Haroon and Miller2013). Specifically, the increased activities of inflammation in patients with MDD may affect endothelial functions and facilitate plaque formation, leading to disorders in cardiovascular system, and ultimately, CAD. It is widely established that atherosclerosis is a chronic inflammatory disease, demonstrated by the inflammatory activity of cholesterol and other molecular mechanisms. Emerging evidence suggests that circulating levels of acute phase protein, CRP, and IL-6 predict future cardiovascular events (Stewart et al., Reference Stewart, Rand, Muldoon and Kamarck2009) and CRP has already been used widely as a biomarker for cardiovascular risk. On the other hand, the dysfunction of inflammation in CAD may have influence on hypothalamic–pituitary–adrenal axis and the depletion of neurotrophic factors, destroying biological processes which play neuroprotective role against depression (Makhija and Karunakaran, Reference Makhija and Karunakaran2013). Recently, a three-year follow-up study (Sforzini et al., Reference Sforzini, Pariante, Palacios, Tylee, Carvalho, Viganò and Nikkheslat2019) found that higher levels of baseline high-sensitivity C-reactive protein (hsCRP) in CHD patients predict future development of depression and supported the inflammatory connection between CHD and MDD.

Our findings are consistent with other work suggesting that the ribosome-related pathways may account for the aetiology of both MDD and CHD. The eukaryotic ribosome is a ribonucleoprotein complex built of small 40S and large 60S subunit. In mammals, the subunits are composed of 28S rRNA, 5.8S rRNA, 5S rRNA, and various ribosomal proteins (Ghosh and Shcherbik, Reference Ghosh and Shcherbik2020). Ribosomes translate the information contained in mRNAs into functional proteins and are critical organelles for protein synthesis. Moreover, ribosomes are essential to neuron development and the activation of protein synthesis conducted by ribosomes is indispensable to synaptic plasticity. TJ Younts et al. found that the presynaptic protein synthesis by ribosomes played a crucial role in maintaining the long-term plasticity of GABA (neurotransmitter gamma-aminobutyric acid) release (Younts et al., Reference Younts, Monday, Dudok, Klein, Jordan, Katona and Castillo2016). In MDD, experimental evidence indicates that ribosomal genes were upregulated in the hypothalamus (Smagin et al., Reference Smagin, Kovalenko, Galyamina, Bragin, Orlov and Kudryavtseva2016) but were downregulated in hippocampus (Smagin et al., Reference Smagin, Kovalenko, Galyamina, Bragin, Orlov and Kudryavtseva2016, Zhang et al., Reference Zhang, Zhang, Zhang, Zhong, Yao, Qu and Huang2019) in animal models, suggesting dysfunction of ribosomal gene expression is associated with the pathology of MDD. Hiroaki Hori et al. (Hori et al., Reference Hori, Nakamura, Yoshida, Teraishi, Sasayama, Ota, Hattori, Kim, Higuchi and Kunugi2018) found that increased expression levels of ribosomal genes were associated with stress vulnerability and were maximally upregulated in MDD patients. Furthermore, compared with healthy controls, the RNA sequencing-based genome-wide expression study of Darby and his colleagues (Darby et al., Reference Darby, Yolken and Sabunciyan2016) manifested the overexpression of ribosomal genes and the enrichment of ribosome pathway in the orbitofrontal cortex of MDD patients’ postmortem brains. However, the mechanisms underlying the association between ribosomal related pathways and MDD is unclear. In CHD, despite paucity of direct evidence of ribosome malfunction, Martinet and co-authors (Martinet et al., Reference Martinet, De Meyer, Herman and Kockx2004) found that compared with the good-quality RNA isolated from negative samples, eleven out of twenty total RNA extracted from atherosclerotic specimens showed extensive fragmentation of 18S and 28S rRNAs and modification with the oxidative stress marker 8-oxoguanosine (8-oxoG), implying the possibility that the degradation of oxidised ribosomes negatively affect the survival and proliferation of smooth muscle and endothelial cell (Ghosh and Shcherbik, Reference Ghosh and Shcherbik2020). Yang Cao et al. found that γ2-AMPK suppress pre-rRNA transcription and ribosome biogenesis during cardiac stress and protect against ischemia/reperfusion injury in mouse model (Cao et al., Reference Cao, Bojjireddy, Kim, Li, Zhai, Nagarajan, Sadoshima, Palmiter and Tian2017).

Interestingly, we also found that rhythmic process is associated with two diseases. The circadian rhythm abnormalities are well established in MDD patients. Symptoms in people with depression often show a diurnal pattern, presenting with mild symptoms in the morning lows and worsened in the evening. Such dysfunction often restores to normal after recovery. Circadian rhythm dysfunction also includes body temperature rhythms and hormone rhythms in the form of reduced amplitude in melatonin and disrupted cortisol secretion (Walker et al., Reference Walker, Walton, DeVries and Nelson2020). Meanwhile, agomelatine, a melatonergic antidepressant, appeared to be effective in treating major depression. In individuals with CAD, a lower 24-hour rest-activity rhythm amplitude is found (Moon et al., Reference Moon, Benson, Albashayreh, Perkhounkova and Burgess2023). Another study reviewed the role of cortisol, which is correlated with an increased risk of cardiovascular events. They conclude that regulating the circadian rhythm of cortisol may provide a potential platform for preventing cardiovascular events (Mohd et al., Reference Mohd Azmi, Juliana, Azmani, Mohd Effendy, Abu, Mohd Fahmi Teng and Das2021). Therefore, we can infer that depression and CHD may be linked through disturbances by hormone rhythms. Rhythm-regulating drugs may also help patients with CHD.

All three hub genes identified in this study have been reported to be associated with MDD and CAD. MMP9 is the most important zinc containing enzyme of MMP family (Hassanzadeh-Makoui et al.,2020). Several studies have demonstrated that MMP9 polymorphisms is associated with increased risk of CAD in different population (Morgan et al., Reference Morgan, Zhang, Tapper, Collins and Ye2003; Moradi et al., Reference Moradi, Fadaei, Ahmadi, Mohammad, Shahmohamadnejad, Tavakoli-Yaraki, Aghajani and Fallah2017; Hassanzadeh-Makoui et al., Reference Hassanzadeh-Makoui, Razi, Aslani, Imani and Tabaee2020). In MDD, MMP9 is reported to be increased in the development of stress model of depression (Alaiyed et al., Reference Alaiyed, McCann, Mahajan, Rajkowska, Stockmeier, Kellar, Wu and Conant2020; Bijata et al., Reference Bijata, Bączyńska, Müller, Bijata, Masternak, Krzystyniak, Szewczyk, Siwiec, Antoniuk, Roszkowska, Figiel, Magnowska, Olszyński, Wardak, Hogendorf, Ruszczycki, Gorinski, Labus, Stępień, Tarka, Bojarski, Tokarski, Filipkowski, Ponimaskin and Wlodarczyk2022). Furthermore, Lutgendorf et al. reported higher levels of MMP9 in patients with a higher number of negative life events over the last 6 months (Lutgendorf et al., Reference Lutgendorf, Lamkin, Jennings, Arevalo, Penedo, DeGeest, Langley, Lucci, Cole, Lubaroff and Sood2008). S100A8 has been identified as playing a significant role in all stages of development of heart disease in both human beings and mice through overexpression in various cardiovascular cell types (Averill et al., Reference Averill, Kerkhoff and Bornfeldt2012). The study by Gamboa-Sánchez et al. reported up-regulation of S100A8 in MDD patients than healthy controls but shows no significant difference between patients with and without antidepressant treatment (Gamboa-Sánchez et al., Reference Gamboa-Sánchez, Becerril-Villanueva, Alvarez-Herrera, Leyva-Mascareño, González-López, Estudillo, Fernández-Molina, Elizalde-Contreras, Ruiz-May, Segura-Cabrera, Jiménez-Genchi, Pavón, Zamudio and Pérez-Sánchez2023), laying groundwork for the investigation of S100A8 as potential biomarkers in MDD. However, this study only included 8 healthy volunteers and a total of 21 MDD patients. Larger sample sizes are warranted in future studies. Several studies have reported that RETN–420C > G and + 299 G > A genotypes might be associated with higher risk of CAD (Tang et al., Reference Tang, Wang, Yang, Zhou, Gu, Sun, Cong, Zhu and Wang2008; Hussain et al., Reference Hussain, Bibi and Javed2011) and higher expression level of RETN predicts higher rates of all-cause mortality (Chou et al., Reference Chou, Hsu, Juang, Chiang, Teng, Wu and Ko2022). There are few reports on the RETN gene in depression, and the only two articles (Zhang et al., Reference Zhang, Xu, Yuan and Shen2020; Sun et al., Reference Sun, Han, Bai, Zhong, Song and Sun2021) obtained the association between RETN and MDD through bioinformatics methods. In general, all these key genes are related to immune inflammation, suggesting that MDD and CHD are may share immune and inflammation-related pathogenesis, especially the differentiation and homoeostasis pathways of various immune cells, which explains the common comorbidity of CHD and depression.

The results of our study have several methodological challenges and limitations. First, our study lacks experimental validation. In particular, previous studies have only demonstrated that gene RETN is associated with depression from a bioinformatics perspective, lacking laboratory evidence. Future studies investigating RETN as a potential biomarker for depression may be feasible. Moreover, this study is based on cross-sectional data to analyse the relationship between CAD and MDD. Establishing a prospective cohort can be helpful to further explore the pathogenesis of the co-morbidity of the two diseases. Second, there is currently no standard method for combined matrixes construction. The sva R package used in this research is just one of them. Comparation of hub genes yielded by multiple methods may do favour in better data mining. Third, during WGCNA application, lack of demographic information limited our analysis of the relationship between genes and traits.

Conclusion

In conclusion, our study identified shared DEGs between MDD and CHD. These genes were mainly enriched in inflammation pathways and ribosome biogenesis. Both of these related pathways have been reported to play an important role in the pathogenesis of MDD and CHD. Further studies are expected to verify the effectiveness and accuracy of hub genes in identifying comorbidity of MDD and CAD.

Supplementary material

The supplementary material for this article can be found at https://doi.org/10.1017/neu.2024.40.

Funding statement

This study was supported from Shanghai clinical research centre for mental health [19MC1911100]; Collaborative Innovation Center for Translational Medicine at Shanghai Jiao Tong University School of Medicine; and Model-based Defining of Subtypes of Depression and Optimal Treatment: an Integrated Techniques Module in Multidimensional Omics for Peripheral Biomarkers [SHDC2020CR2053B]; The National Natural Science Foundation of China (82301712); Shanghai Sailing Program (23YF1438000); Medical Innovation Research Project of Shanghai Science and Technology Innovation Action Plan (21Y11905700).

Competing interests

None.

Ethical standards

The authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008.

References

Alaiyed, S, McCann, M, Mahajan, G, Rajkowska, G, Stockmeier, CA, Kellar, KJ, Wu, JY and Conant, K (2020) Venlafaxine stimulates an MMP-9-dependent increase in excitatory/Inhibitory balance in a stress model of depression. The Journal of neuroscience : the official journal of the Society for Neuroscience 40(22), 4418–4431.Google Scholar
Averill, MM, Kerkhoff, C and Bornfeldt, KE (2012) S100A8 and S100A9 in cardiovascular biology and disease. Arteriosclerosis, thrombosis, and vascular biology 32(2), 223–229.CrossRefGoogle ScholarPubMed
Benziger, CP, Roth, GA and Moran, AE (2016) The global burden of disease study and the preventable burden of NCD. Global Heart 11(4), 393–397.Google ScholarPubMed
Bijata, M, Bączyńska, E, Müller, FE, Bijata, K, Masternak, J, Krzystyniak, A, Szewczyk, B, Siwiec, M, Antoniuk, S, Roszkowska, M, Figiel, I, Magnowska, M, Olszyński, KH, Wardak, AD, Hogendorf, A, Ruszczycki, Błażej, Gorinski, N, Labus, J, Stępień, T, Tarka, S, Bojarski, AJ, Tokarski, K, Filipkowski, RK, Ponimaskin, E, Wlodarczyk, J (2022) Activation of the 5-HT7 receptor and MMP-9 signaling module in the hippocampal CA1 region is necessary for the development of depressive-like behavior. Cell reports 38(11), 110532.CrossRefGoogle ScholarPubMed
Cao, Y, Bojjireddy, N, Kim, M, Li, T, Zhai, P, Nagarajan, N, Sadoshima, J, Palmiter, RD and Tian, R (2017) Activation of γ2-AMPK suppresses ribosome biogenesis and protects against myocardial ischemia/reperfusion injury. Circulation Research 121(10), 1182–1191.CrossRefGoogle ScholarPubMed
Carney, RM and Freedland, KE (2017) Depression and coronary heart disease. Nature Reviews Cardiology 14(3), 145–155.CrossRefGoogle ScholarPubMed
Chou, HH, Hsu, LA, Juang, JJ, Chiang, FT, Teng, MS, Wu, S and Ko, YL (2022) Synergistic effects of weighted genetic risk scores and resistin and sST2 levels on the prognostication of long-term outcomes in patients with coronary artery disease. International journal of molecular sciences 23(8), 4292.CrossRefGoogle ScholarPubMed
Consortium, GO (2004) The Gene Ontology (GO) database and informatics resource. Nucleic Acids Research 32(90001), D258–D261.CrossRefGoogle Scholar
Correll, CU, Solmi, M, Veronese, N, Bortolato, B, Rosson, S, Santonastaso, P, Thapa-Chhetri, N, Fornaro, M, Gallicchio, D, Collantoni, E, Pigato, G, Favaro, A, Monaco, F, Kohler, C, Vancampfort, D, Ward, PB, Gaughran, F, Carvalho, AF and Stubbs, B (2017) Prevalence, incidence and mortality from cardiovascular disease in patients with pooled and specific severe mental illness: a large-scale meta-analysis of 3,211,768 patients and 113,383,368 controls. World Psychiatry 16(2), 163–180.CrossRefGoogle ScholarPubMed
Darby, M, Yolken, R and Sabunciyan, S (2016) Consistently altered expression of gene sets in postmortem brains of individuals with major psychiatric disorders. Translational Psychiatry 6(9), e890–e890.Google ScholarPubMed
De Hert, M, Detraux, J and Vancampfort, D (2018) The intriguing relationship between coronary heart disease and mental disorders. Dialogues in Clinical Neuroscience 20(1), 31–40.CrossRefGoogle ScholarPubMed
Doenst, T, Haverich, A, Serruys, P, Bonow, RO, Kappetein, P, Falk, V, Velazquez, E, Diegeler, A and Sigusch, H (2019) PCI and CABG for treating stable coronary artery disease: JACC review topic of the week. Journal of the American College of Cardiology 73(8), 964–976.CrossRefGoogle ScholarPubMed
Gamboa-Sánchez, C, Becerril-Villanueva, E, Alvarez-Herrera, S, Leyva-Mascareño, G, González-López, SL, Estudillo, E, Fernández-Molina, AE, Elizalde-Contreras, JM, Ruiz-May, E, Segura-Cabrera, A, Jiménez-Genchi, J, Pavón, L, Zamudio, SR and Pérez-Sánchez, G (2023) Upregulation of S100A8 in peripheral blood mononuclear cells from patients with depression treated with SSRIs: a pilot study. Proteome Science 21(1), 23.Google ScholarPubMed
Geng, R and Huang, X (2021) Identification of major depressive disorder disease-related genes and functional pathways based on system dynamic changes of network connectivity. BMC Medical Genomics 14(1), 55.CrossRefGoogle ScholarPubMed
Ghosh, A and Shcherbik, N (2020) Effects of oxidative stress on protein translation: implications for cardiovascular diseases. International Journal of Molecular Sciences 21(8), 2661.CrossRefGoogle ScholarPubMed
Hare, DL, Toukhsati, SR, Johansson, P and Jaarsma, T (2014) Depression and cardiovascular disease: a clinical review. European Heart Journal 35(21), 1365–1372.Google ScholarPubMed
Haroon, E, Daguanno, AW, Woolwine, BJ, Goldsmith, DR, Baer, WM, Wommack, EC, Felger, JC and Miller, AH (2018) Antidepressant treatment resistance is associated with increased inflammatory markers in patients with major depressive disorder. Psychoneuroendocrinology 95, 43–49.Google ScholarPubMed
Harshfield, EL, Pennells, L, Schwartz, JE, Willeit, P, Kaptoge, S, Bell, S, Shaffer, JA, Bolton, T, Spackman, S, Wassertheil-Smoller, S, Kee, F, Amouyel, P, Shea, SJ, Kuller, LH, Kauhanen, J, Van Zutphen, EM, Blazer, DG, Krumholz, H, Nietert, PJ, Kromhout, D, Laughlin, G, Berkman, L, Wallace, RB, Simons, LA, Dennison, EM, Barr, ELM, Meyer, HE, Wood, AM, Danesh, J, Di Angelantonio, E and Davidson, KW (2020) Association between depressive symptoms and incident cardiovascular diseases. JAMA 324(23), 2396.CrossRefGoogle ScholarPubMed
Hassanzadeh-Makoui, R, Razi, B, Aslani, S, Imani, D and Tabaee, SS (2020) The association between Matrix Metallo-proteinases-9 (MMP-9) gene family polymorphisms and risk of Coronary Artery Disease (CAD): a systematic review and meta-analysis. BMC Cardiovascular Disorders 20(1), 232.CrossRefGoogle Scholar
Hawton, K, Casanas, ICC, Haw, C and Saunders, K (2013) Risk factors for suicide in individuals with depression: a systematic review. Journal of Affective Disorders 147(1-3), 17–28.CrossRefGoogle ScholarPubMed
Hori, H, Nakamura, S, Yoshida, F, Teraishi, T, Sasayama, D, Ota, M, Hattori, K, Kim, Y, Higuchi, T and Kunugi, H (2018) Integrated profiling of phenotype and blood transcriptome for stress vulnerability and depression. Journal of Psychiatric Research 104, 202–210.CrossRefGoogle ScholarPubMed
Howard, DM, Adams, MJ, Clarke, T-K, Hafferty, JD, Gibson, J, Shirali, M, Coleman, JRI, Hagenaars, SP, Ward, J, Wigmore, EM, Alloza, C, Shen, X, Barbu, MC, Xu, EY, Whalley, HC, Marioni, RE, Porteous, DJ, Davies, G, Deary, IJ, Hemani, G, Berger, K, Teismann, H, Rawal, R, Arolt, V, Baune, BT, Dannlowski, U, Domschke, K, Tian, C, Hinds, DA, Byrne, EM, Ripke, S, Smith, DJ, Sullivan, PF, Wray, NR, Breen, G, Lewis, CM and McIntosh, AM (2019) Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions. Nature Neuroscience 22(3), 343–352.CrossRefGoogle ScholarPubMed
Hu, G, Yu, S, Yuan, C, Hong, W, Wang, Z, Zhang, R, Wang, D, Li, Z, Yi, Z and Fang, Y (2021) Gene expression signatures differentiating major depressive disorder from subsyndromal symptomatic depression. Aging (Albany NY) 13(9), 13124–13137.CrossRefGoogle ScholarPubMed
Hussain, S, Bibi, S and Javed, Q (2011) Heritability of genetic variants of resistin gene in patients with coronary artery disease: a family-based study. Clinical biochemistry 44(8-9), 618–622.CrossRefGoogle ScholarPubMed
Kanehisa, M and Goto, S (2000) KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids 28(1), 27–30.CrossRefGoogle ScholarPubMed
Kemp, AH, Quintana, DS, Gray, MA, Felmingham, KL, Brown, K and Gatt, JMJBP (2010) Impact of depression and antidepressant treatment on heart rate variability: a review and meta-analysis. Biological Psychiatry 67(11), 1067–1074.CrossRefGoogle ScholarPubMed
Kendler, KS, Gardner, CO, Fiske, A and Gatz, MJAOGP (2009) Major depression and coronary artery disease in the Swedish twin registry: phenotypic, genetic, and environmental sources of comorbidity. Archives of General Psychiatry 66(8), 857–863.CrossRefGoogle ScholarPubMed
Leday, GGR, Vértes, PE, Richardson, S, Greene, JR, Regan, T, Khan, S, Henderson, R, Freeman, TC, Pariante, CM, Harrison, NA, MRC Immunopsychiatry Consortium, Perry, VH, Drevets, WC, Wittenberg, GM and Bullmore, ET. (2018) Replicable and coupled changes in innate and adaptive immune gene expression in two case-control studies of blood microarrays in major depressive disorder. Biological psychiatry 83, 70–80.CrossRefGoogle ScholarPubMed
Li, GH, Cheung, CL, Chung, AK, Cheung, BM, Wong, IC, Fok, MLY, Au, PC and Sham, PC (2022) Evaluation of bi-directional causal association between depression and cardiovascular diseases: a Mendelian randomization study. Psychological medicine 52(9), 1765–1776. doi: 10.1017/S0033291720003566.CrossRefGoogle ScholarPubMed
Libby, P, Buring, JE, Badimon, L, Hansson, GK, Deanfield, J, Bittencourt, MS, Tokgozoglu, L and Lewis, EF (2019) Atherosclerosis. Nature Reviews Disease Primers 5(1), 56.Google ScholarPubMed
Lichtman, JH, Froelicher, ES, Blumenthal, JA, Carney, RM, Doering, LV, Frasure-Smith, N, Freedland, KE, Jaffe, AS, Leifheit-Limson, EC, Sheps, DS, Vaccarino, V, Wulsin, L and American Heart Association Statistics Committee of the Council on Epidemiology and Prevention and the Council on Cardiovascular and Stroke Nursing (2014) Depression as a risk factor for poor prognosis among patients with acute coronary syndrome: systematic review and recommendations: a scientific statement from the American heart association. Circulation 129(12), 1350–1369.CrossRefGoogle ScholarPubMed
Long, Q, Wang, R, Feng, M, Zhao, X, Liu, Y, Ma, X, Yu, L, Li, S, Guo, Z, Zhu, Y, Teng, Z and Zeng, Y (2021) Construction and analysis of a diagnostic model based on differential expression genes in patients with major depressive disorder. Front Psychiatry 12, 762683.CrossRefGoogle ScholarPubMed
Lu, Y, Wang, Z, Georgakis, MK, Lin, H and Zheng, L (2021) Genetic liability to depression and risk of coronary artery disease, myocardial infarction, and other cardiovascular outcomes. Journal of the American Heart Association 10(1), e017986.CrossRefGoogle ScholarPubMed
Lutgendorf, S K, Lamkin, D M, Jennings, N B, Arevalo, J M, Penedo, F, DeGeest, K, Langley, R R, Lucci, J A3rd, Cole, SW, Lubaroff, DM and Sood, AK (2008) Biobehavioral influences on matrix metalloproteinase expression in ovarian carcinoma. Clinical cancer research: an official journal of the American Association for Cancer Research 14(21), 6839–6846.CrossRefGoogle ScholarPubMed
Maes, M (2011) Depression is an inflammatory disease, but cell-mediated immune activation is the key component of depression. Progress in Neuro-Psychopharmacology and Biological Psychiatry 35(3), 664–675.CrossRefGoogle Scholar
Maes, M, Scharpe, S, Van Grootel, L, Uyttenbroeck, W, Cooreman, W, Cosyns, P and Suy, E (1992) Higher α1-antitrypsin, haptoglobin, ceruloplasmin and lower retinol binding protein plasma levels during depression: further evidence for the existence of an inflammatory response during that illness. Journal of Affective Disorders 24(3), 183–192.CrossRefGoogle Scholar
Maes, M, Scharpé, S, Meltzer, HY, Okayli, G, Bosmans, Eène, D’Hondt, P, Vanden Bossche, B and Cosyns, P (1994) Increased neopterin and interferon-gamma secretion and lower availability of L-tryptophan in major depression: further evidence for an immune response. Psychiatry research 54(2), 143–160.CrossRefGoogle ScholarPubMed
Maes, M, Song, C and Yirmiya, R (2012) Targeting IL-1 in depression. Expert Opinion on Therapeutic Targets 16(11), 1097–1112.CrossRefGoogle ScholarPubMed
Makhija, K and Karunakaran, S (2013) The role of inflammatory cytokines on the aetiopathogenesis of depression. Australian & New Zealand Journal of Psychiatry 47(9), 828–839.CrossRefGoogle ScholarPubMed
Malhi, GS and Mann, JJ (2018) Depression. The Lancet 392, 2299–2312.Google ScholarPubMed
Martinet, W, De Meyer, GRY, Herman, AG and Kockx, MM (2004) Reactive oxygen species induce RNA damage in human atherosclerosis. European Journal of Clinical Investigation 34(5), 323–327.CrossRefGoogle ScholarPubMed
Mohd Azmi, NAS, Juliana, N, Azmani, S, Mohd Effendy, N, Abu, IF, Mohd Fahmi Teng, NI and Das, S (2021) Cortisol on circadian rhythm and its effect on cardiovascular system. International journal of environmental research and public health 18(2), 676.Google ScholarPubMed
Moon, C, Benson, CJ, Albashayreh, A, Perkhounkova, Y and Burgess, HJ (2023) Sleep, circadian rhythm characteristics, and melatonin levels in later life adults with and without coronary artery disease. Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine 19(2), 283–292.Google ScholarPubMed
Moradi, N, Fadaei, R, Ahmadi, R, Mohammad, MH, Shahmohamadnejad, S, Tavakoli-Yaraki, M, Aghajani, H and Fallah, S (2017) Role of serum MMP-9 levels and vitamin D receptor polymorphisms in the susceptibility to coronary artery disease: An association study in Iranian population. Gene 628, 295–300.CrossRefGoogle ScholarPubMed
Morgan, AR, Zhang, B, Tapper, W, Collins, A and Ye, S (2003) Haplotypic analysis of the MMP-9 gene in relation to coronary artery disease. Journal of molecular medicine (Berlin, Germany) 81(5), 321–326.CrossRefGoogle ScholarPubMed
Osborne, LM, Gilden, J, Kamperman, AM, Hoogendijk, WJG, Spicer, J, Drexhage, HA and Bergink, V (2020) T-cell defects and postpartum depression. Brain Behavior and Immunity 87, 397–403.CrossRefGoogle ScholarPubMed
O’Neil, A, Stevenson, CE, Williams, ED, Mortimer, D, Oldenburg, B and Sanderson, K (2013) The health-related quality of life burden of co-morbid cardiovascular disease and major depressive disorder in Australia: findings from a population-based, cross-sectional study. Quality of Life Research 22(1), 37–44.CrossRefGoogle ScholarPubMed
Parker, G and Brotchie, H (2010) Gender differences in depression. International Review of Psychiatry 22(5), 429–436.CrossRefGoogle ScholarPubMed
Raison, CL, Rutherford, RE, Woolwine, BJ, Shuo, C, Schettler, P, Drake, DF, Haroon, E and Miller, AH (2013) A randomized controlled trial of the tumor necrosis factor antagonist infliximab for treatment-resistant depression: the role of baseline inflammatory biomarkers. JAMA Psychiatry 70(1), 31–41.CrossRefGoogle ScholarPubMed
Rajan, S, McKee, M, Rangarajan, S, Bangdiwala, S, Rosengren, A, Gupta, R, Kutty, VR, Wielgosz, A, Lear, S, AlHabib, KF, Co, HU, Lopez-Jaramillo, P, Avezum, A, Seron, P, Oguz, A, Kruger, Ié M, Diaz, R, Nafiza, M-N, Chifamba, J, Yeates, K, Kelishadi, R, Sharief, WM, Szuba, A, Khatib, R, Rahman, O, Iqbal, R, Bo, H, Yibing, Z, Wei, L, Yusuf, S and Prospective Urban Rural Epidemiology (PURE) Study Investigators (2020) Association of symptoms of depression with cardiovascular disease and mortality in low-, middle-, and high-income countries. JAMA Psychiatry 77(10), 1052–1063.CrossRefGoogle ScholarPubMed
Salk, RH, Hyde, JS and Abramson, LY (2017) Gender differences in depression in representative national samples: meta-analyses of diagnoses and symptoms. Psychological Bulletin 143(8), 783–822.CrossRefGoogle ScholarPubMed
Schiweck, C, Valles-Colomer, M, Arolt, V, Müller, N, Raes, J, Wijkhuijs, A, Claes, S, Drexhage, H and Vrieze, E (2020) Depression and suicidality: a link to premature T helper cell aging and increased Th17 cells. Brain, Behavior, and Immunity 87, 603–609.Google ScholarPubMed
Sforzini, L, Pariante, CM, Palacios, JE, Tylee, A, Carvalho, LA, Viganò, CA and Nikkheslat, N (2019) Inflammation associated with coronary heart disease predicts onset of depression in a three-year prospective follow-up: a preliminary study. Brain, Behavior, and Immunity 81, 659–664.CrossRefGoogle Scholar
Smagin, DA, Kovalenko, IL, Galyamina, AG, Bragin, AO, Orlov, YL and Kudryavtseva, NN (2016) Dysfunction in ribosomal gene expression in the hypothalamus and hippocampus following chronic social defeat stress in male mice as revealed by RNA-seq. Neural plasticity 2016, 3289187.CrossRefGoogle ScholarPubMed
Stapelberg, NJ, Hamilton-Craig, I, Neumann, DL, Shum, DHK and McConnell, H (2012) Mind and heart: heart rate variability in major depressive disorder and coronary heart disease-a review and recommendations. Australian & New Zealand Journal of Psychiatry 46(10), 946–957.CrossRefGoogle Scholar
Stewart, JC, Rand, KL, Muldoon, MF and Kamarck, TW (2009) A prospective evaluation of the directionality of the depression-inflammation relationship. Brain Behavior and Immunity 23(7), 936–944.CrossRefGoogle ScholarPubMed
Sui, Y, Li, S, Fu, XQ, Zhao, ZJ and Xing, S (2023) Bioinformatics analyses of combined databases identify shared differentially expressed genes in cancer and autoimmune disease. Journal of translational medicine 21(1), 109.CrossRefGoogle ScholarPubMed
Sun, C, Han, J, Bai, Y, Zhong, Z, Song, Y and Sun, Y (2021) Neuropeptides as the shared genetic crosstalks linking periodontitis and major depression disorder. Disease markers 2021, 3683189.CrossRefGoogle Scholar
Szklarczyk, D, Gable, AL, Nastou, KC, Lyon, D, Kirsch, R, Pyysalo, S, Doncheva, NT, Legeay, M, Fang, T, Bork, P, Jensen, LJ and von Mering, C (2021) The STRING database in 2021: customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Research 49(D1), D605–D612.Google ScholarPubMed
Tang, NP, Wang, LS, Yang, L, Zhou, B, Gu, HJ, Sun, QM, Cong, RH, Zhu, HJ and Wang, B (2008) A polymorphism in the resistin gene promoter and the risk of coronary artery disease in a Chinese population. Clinical endocrinology 68(1), 82–87.CrossRefGoogle ScholarPubMed
Vaccarino, V, Badimon, L, Bremner, JD, Cenko, E, Cubedo, J, Dorobantu, M, Duncker, DJ, Koller, A, Manfrini, O, Milicic, D, Padro, T, Pries, AR, Quyyumi, AA, Tousoulis, D, Trifunovic, D, Vasiljevic, Z, De Wit, C, Bugiardini, R and ESC Scientific Document Group Reviewers (2020) Depression and coronary heart disease: 2018 position paper of the ESC working group on coronary pathophysiology and microcirculation. European Heart Journal 41(17), 1687–1696.Google Scholar
Walker, WH 2nd, Walton, JC, DeVries, AC and Nelson, RJ (2020) Circadian rhythm disruption and mental health. Translational psychiatry 10(1), 28.CrossRefGoogle ScholarPubMed
Wu, Y, Zhu, B, Chen, Z, Duan, J, Luo, A, Yang, L and Yang, C (2021) New insights into the comorbidity of coronary heart disease and depression. Current Problems in Cardiology 46(3), 100413.CrossRefGoogle Scholar
Younts, TJ, Monday, HR, Dudok, B, Klein, ME, Jordan, BA, Katona, I and Castillo, PE (2016) Presynaptic protein synthesis is required for long-term plasticity of GABA release. Neuron 92(2), 479–492.Google ScholarPubMed
Yu, G, Wang, L-G, Han, Y and He, Q-Y (2012) clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS: A Journal of Integrative Biology 16(5), 284–287.CrossRefGoogle Scholar
Zhang, G, Xu, S, Yuan, Z and Shen, L (2020) Weighted gene coexpression network analysis identifies specific modules and hub genes related to major depression. Neuropsychiatric Disease and Treatment 16, 703–713.CrossRefGoogle ScholarPubMed
Zhang, J, Zhang, Z, Zhang, J, Zhong, Z, Yao, Z, Qu, S and Huang, Y (2019) iTRAQ-based protein profiling in CUMS rats provides insights into hippocampal ribosome lesion and Ras protein changes underlying synaptic plasticity in depression. Neural plasticity, 2019, 7492306.CrossRefGoogle ScholarPubMed
Zhou, Y, Zhou, B, Pache, L, Chang, M, Khodabakhshi, AH, Tanaseichuk, O, Benner, C and Chanda, SK (2019) Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nature Communications 10(1), 1523.Google ScholarPubMed
Figure 0

Table 1. MDD and CAD expression profile data sets from GEO database

Figure 1

Figure 1. Volcano plot of combined matrix A. Volcano plot shows the differently expressed genes (DEGs) of combined major depressive disorder matrix. B. Volcano plot shows the DEGs of combined coronary artery disease matrix. MDD, major depressive disorder. CAD, coronary artery disease; FC, fold change.

Figure 2

Figure 2. Venn diagram showing the number of overlapped genes.

Figure 3

Figure 3. A. GO enrichment analysis base on overlapping genes of major depressive disorder (MDD) and coronary heart disease (CHD). B. KEGG pathway analysis based on overlapping genes between MDD and CHD. C. PPI network. D. Top three hub genes identified by Cytoscape. E. Correlation between modules and groups of CHD. F. Correlation between modules and groups of MDD.

Supplementary material: File

Shi et al. supplementary material

Shi et al. supplementary material
Download Shi et al. supplementary material(File)
File 852.1 KB