Abstract

   Psoriasis, a chronic inflammatory skin disorder, is associated with
   comorbidities such as acute myocardial infarction (AMI). However, the
   molecular mechanisms connecting these conditions are unclear. In this
   study, we conducted bioinformatics analyses using gene expression
   datasets to identify differentially expressed genes and hub genes
   associated with both psoriasis and AMI. Our findings emphasize the
   involvement of immune-related pathways in the pathogenesis of both
   conditions. Furthermore, we investigated the expression levels of hub
   genes in AMI patients and myocardial infarction (MI) mice. ELISA
   measurements revealed significantly higher levels of CXCL8, IL1B,
   S100A9, and S100A12 in the serum of AMI patients compared to normal
   individuals. Immunohistochemical staining of heart tissue from MI mice
   showed a progressive increase in the expression of CXCL8 and IL-1B as
   MI advanced, while S100A9 exhibited high expression at day 3 post-MI.
   mRNA expression analysis validated these findings. Additionally, we
   explored the skin lesions of psoriasis patients and found significantly
   higher expression of CXCL8, IL-1B, S100A9, and S100A12 in the affected
   skin areas compared to unaffected regions. These results highlight the
   consistent upregulation of hub genes in both AMI and psoriasis
   patients, as well as in myocardial infarction mice, underscoring their
   potential as reliable markers for disease diagnosis. Moreover,
   molecular docking simulations revealed potential interactions between
   simvastatin and key target proteins, suggesting a potential therapeutic
   avenue. Overall, our study uncovers shared molecular signatures and
   potential therapeutic targets, providing a foundation for future
   investigations targeting common pathways in psoriasis and AMI.

   Keyword: Psoriasis, Acute myocardial infarction, Differentially
   expressed genes, Hub-genes

   Subject terms: Computational biology and bioinformatics, Biomarkers,
   Cardiology, Molecular medicine

Introduction

   Psoriasis is a chronic inflammatory skin disorder that affects millions
   of people worldwide and has a significant impact on patients' quality
   of life^[40]1,[41]2. It is characterized by the presence of red, scaly
   plaques on the skin, often accompanied by itching and pain^[42]3. In
   addition to its effects on the skin, psoriasis is now recognized as a
   systemic disease associated with various comorbidities, including
   cardiovascular diseases such as acute myocardial infarction
   (AMI)^[43]4,[44]5.

   AMI, commonly known as a heart attack, is a serious and
   life-threatening condition that occurs when blood flow to the heart
   muscle is blocked, leading to tissue damage and potential heart
   failure^[45]6. It is a major cause of morbidity and mortality
   worldwide^[46]7. Numerous epidemiological investigations have
   consistently shown that psoriasis significantly increases the risk of
   developing AMI, resulting in higher morbidity and mortality rates in
   affected individuals^[47]8,[48]9. This correlation cannot be solely
   attributed to traditional cardiovascular risk factors, as the increased
   risk persists even after accounting for confounding variables. These
   observations strongly suggest the involvement of shared underlying
   mechanisms linking psoriasis and AMI.

   Despite the established association, the precise molecular mechanisms
   connecting these two conditions remain elusive. Unraveling the shared
   pathogenic networks between psoriasis and AMI is crucial to gain a
   comprehensive understanding of the complex interplay between
   inflammation, immune dysregulation, and cardiovascular complications.
   By identifying these shared pathways, we can unlock valuable insights
   into disease development and potentially develop targeted therapies and
   preventive strategies for both psoriasis and AMI. To address this
   research gap, we propose employing sophisticated bioinformatics
   analyses to elucidate the shared pathogenic networks between psoriasis
   and AMI. Bioinformatics techniques have proven instrumental in
   integrating and analyzing large-scale omics data, facilitating the
   identification of key molecular players and pathways involved in
   disease progression. By leveraging transcriptomic, proteomic, and
   genomic datasets from cohorts encompassing both psoriasis and AMI
   patients, we aim to identify common differentially expressed genes,
   proteins, and genetic variants that contribute to the pathogenesis of
   both conditions. Furthermore, network-based analysis methodologies will
   be employed to construct robust molecular interaction networks that
   integrate the identified differentially expressed molecules. These
   networks will allow us to explore the intricate interconnectedness and
   functional relationships between genes, proteins, and pathways
   implicated in both psoriasis and AMI. Additionally, functional
   enrichment analysis will be conducted to determine the biological
   processes, molecular functions, and cellular components enriched within
   the shared pathogenic networks. The specific process was shown in
   Fig. [49]1.

Figure 1.

   [50]Figure 1
   [51]Open in a new tab

   Flow chart of the analysis program in this study.

   The findings from this study hold immense promise for shedding light on
   the molecular mechanisms underpinning the association between psoriasis
   and AMI. By unveiling the shared pathogenic networks, we can identify
   potential therapeutic targets that simultaneously address both
   conditions, paving the way for the development of innovative treatment
   strategies. Targeting common molecular pathways holds tremendous
   potential to improve patient outcomes and reduce the burden of
   comorbidities in individuals suffering from psoriasis and AMI.
   Moreover, this research carries substantial significance for
   personalized medicine approaches. By deciphering the molecular
   intricacies connecting psoriasis and AMI, we can refine risk
   stratification models and tailor interventions based on individualized
   patient profiles. This approach would enable more precise and effective
   management of both psoriasis-related skin symptoms and the heightened
   risk of AMI, ultimately improving patients' overall well-being and
   prognosis.

Results

Identification of common differentially expressed genes (DEGs) in patients
with psoriasis and AMI

   In the [52]GSE161683 dataset, a total of 194 genes showed
   down-regulation, 18,312 genes displayed no significant change (stable),
   and 287 genes exhibited up-regulation (Fig. [53]2A). The heat maps
   depict the expression patterns of the top 20 DEGs with the most
   significant changes (Fig. [54]2C). Similarly, in the [55]GSE66360
   dataset, 82 genes showed down-regulation, 20,428 genes displayed no
   significant change (stable), and 314 genes exhibited up-regulation
   (Fig. [56]2B). The heat maps illustrate the expression patterns of the
   top 20 DEGs with the most significant changes (Fig. [57]2D).

Figure 2.

   [58]Figure 2
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   DEG identification. (A) Volcano plot illustrating the DEGs in
   [60]GSE161683 dataset, with red indicating up-regulated genes and blue
   indicating downregulated genes. (B) Volcano plot illustrating the DEGs
   in [61]GSE66360 dataset, with red indicating up-regulated genes and
   blue indicating downregulated genes. (C) Heatmaps of the top 20 DEGs
   showing the most significant changes in [62]GSE161683 dataset. (D)
   Heatmaps of the top 20 DEGs showing the most significant changes in
   [63]GSE66360 dataset.

Functional enrichment analysis of DEGs

   We performed functional enrichment analysis on the DEGs identified in
   [64]GSE161683 and [65]GSE66360 datasets. We found 37 overlapping genes
   for further investigation (Fig. [66]3A). In order to gain deeper
   insights into these DEGs, we conducted Kyoto Encyclopedia of Genes and
   Genomes (KEGG) pathway analysis to identify potential biological
   pathways involved. The analysis revealed significant enrichment of
   genes in the IL-17 signaling pathway and NOD-like receptor signaling
   pathway (Fig. [67]3B). Furthermore, gene ontology biological process
   (GO-BP) enrichment analysis showed that these proteins were primarily
   associated with response to external biotic stimulus, response to
   bacterium, and neutrophil chemotaxis (Fig. [68]3C). In terms of gene
   ontology cellular component (GO-CC), the majority of these proteins
   were found to be enriched in secretory granules, secretory vesicles,
   and specific granular cells (Fig. [69]3D). Lastly, gene ontology
   molecular function (GO-MF) enrichment analysis indicated that the 37
   overlapping DEGs were related to RAGE receptor binding, Toll-like
   receptor binding, and signaling receptor binding (Fig. [70]3E).

Figure 3.

   [71]Figure 3
   [72]Open in a new tab

   Functional enrichment analysis of DEGs. (A) Venn diagram depicting the
   overlapping DEGs between the two GEO datasets. (B) KEGG pathway
   analysis of the 37 overlapping DEGs. (C) GO-BP enrichment analysis of
   the 37 overlapping DEGs. (D) GO-CC enrichment analysis of the 37
   overlapping DEGs. (E) GO-MF enrichment analysis of the 37 overlapping
   DEGs.

Analysis of protein–protein interaction (PPI) networks and transcription
factor (TF) target regulating networks

   The PPI network of the 37 overlapping genes was constructed using the
   STRING database and visualized with Cytoscape software (Fig. [73]4A).
   Additionally, to gain deeper insights into the regulatory TFs and
   selected DEGs, we employed the iRegulon plugin to predict the TFs for
   the DEGs. The predicted network consisted of 33 DEGs and 6 TFs (FOXC1,
   GATA2, YY1, GATA3, STAT3, FOXL1). The results revealed that FOXC1 was
   predicted to have 21 DEG targets, GATA2 had 16 DEG targets, YY1 was
   associated with 12 DEGs, GATA3 was associated with 11 DEG targets,
   STAT3 had 11 DEG targets, and FOXL1 had 10 DEGs (Fig. [74]4B).

Figure 4.

   Figure 4
   [75]Open in a new tab

   Construction of PPI and TF-DEG regulation network. (A) PPI network of
   DEGs constructed using string and Cytoscape. (B) TF-DEGs network
   consisting of 33 DEGs and 6 TFs with an NES > 4.

Identification of hub genes using various ranking methods

   Using Cytoscape v3.10.0, the query protein was visualized as a PPI
   network, and hub genes were identified using the cytoHubba tool, which
   provided a list of the top-ranked proteins. Five ranking methods were
   employed to determine the hub genes in CytoHubba: betweenness ranking,
   closeness ranking, edge percolated component (EPC) ranking, matthews
   correlation coefficient (MCC) ranking, and maximal neighborhood
   component (MNC) ranking. The top ten hub genes selected by each method
   were shown (Fig. [76]5A-E). By intersecting the results from these five
   approaches, SLPI, S100A9, IL1B, CYBB, CXCL8, S100A12, and CXCL1 were
   identified as the common hub genes involved in interactions
   (Fig. [77]5F).

Figure 5.

   [78]Figure 5
   [79]Open in a new tab

   Identification of hub genes within the overlapping DEGs from the two
   GEO datasets. (A–E) Hub gene identification using different ranking
   methods: betweenness ranking, closeness ranking, edge percolated
   component (EPC) ranking, matthews correlation coefficient (MCC)
   ranking, and maximal neighborhood component (MNC) ranking. (F) Venn
   diagram illustrating the overlap of hub genes identified by different
   ranking methods.

Association between psoriasis, AMI, and immune infiltration

   To explore the immunological microenvironment in psoriasis and AMI, we
   assessed the transcript abundance of various immune cell subtypes.
   Specifically, we evaluated the infiltration of immune cells in lesional
   (LP) and non-lesional (NP) skin of psoriasis patients. We found that
   the LP group had higher proportions of monocytes, M1 and M2
   macrophages, activated dendritic cells, and CD4 memory resting T cells
   compared to the NP group (Fig. [80]6A). To further investigate the role
   of SLPI, S100A9, IL1B, CYBB, CXCL8, S100A12, and CXCL1 genes in the
   immune microenvironment of psoriasis, we examined inflammatory cell
   infiltration. Our analysis revealed significant associations of these
   genes with M1 macrophages and activated dendritic cells (Fig. [81]6B).
   Similarly, in the [82]GSE66360 database, we illustrated the relative
   abundance of distinct immune cell subsets in the context of AMI
   (Fig. [83]6C). Compared to the Control group, the AMI group showed
   higher proportions of monocytes, activated dendritic cells, activated
   mast cells, eosinophils, and neutrophils. We also performed correlation
   analysis between immune cell infiltration and the expression levels of
   SLPI, S100A9, IL1B, CYBB, CXCL8, S100A12, and CXCL1 in the AMI cohort
   (Fig. [84]6D). These genes were significantly associated with
   neutrophils in the context of AMI. Notably, IL1B exhibited a
   significant correlation with activated mast cells.

Figure 6.

   [85]Figure 6
   [86]Open in a new tab

   Association of immune infiltration with psoriasis and AMI. (A) Relative
   abundance of distinct immune cell subsets in [87]GSE161683 database.
   (B) Correlation between immune cell infiltration and the expression of
   SLPI, S100A9, IL1B, CYBB, CXCL8, S100A12, and CXCL1 in the Psoriasis
   cohort. (C) Relative abundance of distinct immune cell subsets in
   [88]GSE66360 database. (D) Correlation between immune cell infiltration
   and the expression of SLPI, S100A9, IL1B, CYBB, CXCL8, S100A12, and
   CXCL1 in the AMI cohort.

Expression levels and diagnostic values of hub genes

   In order to investigate the consistency of gene expression between the
   two diseases, we analyzed the expression of the seven differentially
   expressed genes in the [89]GSE161638 and [90]GSE66360 datasets. We
   observed a significant upregulation of these genes in the diseased
   tissues of psoriasis patients (Fig. [91]7A). Similarly, we found a
   consistent expression pattern in plasma circulating endothelial cells
   of patients with AMI (Fig. [92]7B). To evaluate the diagnostic
   potential of the hub genes for AMI, we developed a predictive model
   using the [93]GSE66360 dataset. The predictive performance of the model
   was determined by the receiver operating characteristic (ROC) curve and
   the corresponding area under the curve (AUC) values (Fig. [94]7C). The
   ROC curve assesses the model's ability to distinguish between AMI cases
   and controls, while the AUC represents the overall accuracy. Among the
   hub genes, all except SLPI and CXCL1 exhibited AUC values greater than
   0.800. Notably, S100A12 showed the highest diagnostic value with an AUC
   of 0.8931, followed by IL1B with an AUC of 0.8804.

Figure 7.

   [95]Figure 7
   [96]Open in a new tab

   Expression levels and diagnostic values of hub genes. (A) Expression of
   hub genes in CSE161683 dataset. (B) Expression of hub genes in
   [97]CSE66360 dataset. (C) ROC curve and AUC statistics of the
   predictive model for AMI in [98]GSE66360 dataset.

Molecular docking diagrams of simvastatin with target proteins

   To identify potential therapeutic options for psoriasis and AMI, we
   screened drugs targeting CXCL8, IL1B, S100A9, and S100A12 using the
   DsigDB database. Our findings revealed that simvastatin may be a
   suitable therapeutic agent for CXCL8, IL1B, and S100A9 (Supplementary
   Table 1). Although simvastatin was not specifically reported to
   interact with S100A12 in the experimentally validated database, our
   results demonstrated that simvastatin exhibited similar binding
   affinity towards S100A12 as it did towards CXCL8, IL1B, and S100A9. The
   molecular docking results are presented (Fig. [99]8A-D). These findings
   suggest that simvastatin could be considered as a potential therapeutic
   option, warranting further investigation into its mechanisms of action
   and therapeutic effects in the context of psoriasis and AMI.

Figure 8.

   [100]Figure 8
   [101]Open in a new tab

   Three-dimensional molecular docking diagrams of simvastatin with target
   proteins: (A) CXCL8, (B) IL1B, (C) S100A9, and (D) S100A12.

Hub genes are highly expressed in AMI and psoriasis patients as well as in
myocardial infarction (MI) mice

   In order to investigate the expression of hub genes in AMI patients, we
   conducted ELISA to measure the levels of CXCL8, IL1B, S100A9, and
   S100A12 in the serum of both normal individuals and AMI patients
   (Fig. [102]9A-D). The results clearly demonstrated that the expression
   of these four genes was significantly higher in patients with AMI. To
   further examine the impact of MI, we induced MI mice and collected
   heart tissue samples at 1, 3, and 7 days post-MI. Initially, we
   performed hematoxylin and eosin (HE) staining on the mouse heart tissue
   to observe the area of infarction as well as the surrounding region.
   Subsequently, we conducted immunohistochemical staining for CXCL8,
   IL-1B, and S100A9 in the edge area of the infarction. The findings
   revealed a progressive increase in the expression of CXCL8 and IL-1B as
   MI advanced, while S100A9 exhibited high expression in the heart
   tissues of MI mice at day 3 (Fig. [103]9E-H). Additionally, mRNA
   expression analysis of the four genes in the heart tissue of MI-induced
   mice confirmed the consistency with the immunohistochemical results
   (F[104]ig. [105]9I).

Figure 9.

   [106]Figure 9
   [107]Open in a new tab

   Expression of hub genes in serum of AMI patients and border zone of
   cardiac infarction in MI mice. ELISA was used to detect the expression
   of CXCL8 (A), IL-1B (B), S100A9 (C) and S100A12 (D) in serum of Normal
   and AMI patients. (E) The sham group and myocardial infarction 1 d, 3
   d, 7 d heart tissue in mice of HE staining and CXCL8, IL-1B and S100A9
   immunohistochemical staining and quantitative (F–H). (I) The mRNA
   expression of Il-8, Il-1b and S100a9 in cardiac tissue of MI mice on
   day 1, 3, 7 and sham group were detected.

   In addition to MI, we also investigated the skin lesions of patients
   with psoriasis. Immunohistochemical staining of CXCL8, IL-1B, S100A9,
   and S100A12 in the skin lesion areas revealed significantly higher
   expression compared to unaffected skin regions (Fig. [108]10A-E).

Figure 10.

   [109]Figure 10
   [110]Open in a new tab

   High expression of disease hub genes in psoriasis skin. (A)
   Immunohistochemical staining and quantitative (B-E) detection of the
   normal skin of patients with psoriasis skin disease in CXCL8, IL-1B,
   S100A9 and S100A12 expression.

Discussion

   In this study, we aimed to identify common DEGs in patients with
   psoriasis and AMI. Psoriasis is a chronic inflammatory skin disease
   characterized by immune dysregulation, while AMI is a cardiovascular
   disease associated with atherosclerosis and inflammation. Understanding
   the shared molecular mechanisms between these two diseases can provide
   valuable insights into their pathogenesis and potentially uncover novel
   therapeutic targets.

   Our analysis of two independent datasets ([111]GSE161683 and
   [112]GSE66360) revealed a set of DEGs that showed significant changes
   in expression levels. Through functional enrichment analysis, we gained
   further insights into the biological pathways and processes involved in
   both psoriasis and AMI. The DEGs identified in our study were
   significantly enriched in the IL-17 signaling pathway and NOD-like
   receptor signaling pathway. These findings are consistent with previous
   studies that have implicated the IL-17 pathway in the pathogenesis of
   psoriasis^[113]10,[114]11 and the NOD-like receptor signaling pathway
   in inflammatory responses and atherosclerosis^[115]12,[116]13. The
   IL-17 signaling pathway plays a crucial role in the recruitment and
   activation of immune cells, leading to the production of
   pro-inflammatory cytokines and the initiation of tissue
   inflammation^[117]14,[118]15. Activation of NOD-like receptors, on the
   other hand, triggers innate immune responses and the release of
   pro-inflammatory mediators^[119]16. The enrichment of DEGs in these
   pathways suggests that immune dysregulation and inflammation contribute
   to both psoriasis and AMI.

   GO-BP analysis revealed that the DEGs were mainly associated with
   response to external biotic stimulus, response to bacterium, and
   neutrophil chemotaxis. This is in line with previous studies
   demonstrating the involvement of neutrophils in the pathogenesis of
   psoriasis^[120]17,[121]18 and their role in the progression of
   atherosclerosis and AMI^[122]19,[123]20. Neutrophils are key players in
   the inflammatory response, and their excessive activation can lead to
   tissue damage and disease exacerbation^[124]21. The enrichment of DEGs
   related to neutrophil chemotaxis suggests their potential contribution
   to the recruitment and activation of neutrophils in both psoriasis and
   AMI.

   GO-CC analysis showed that the majority of the DEGs were enriched in
   secretory granules and vesicles. This finding is supported by previous
   studies demonstrating the involvement of secretory granules in the
   release of pro-inflammatory mediators, such as cytokines and
   chemokines, which play critical roles in the pathogenesis of
   psoriasis^[125]22 and AMI^[126]23. Secretory granules serve as storage
   sites for these bioactive molecules and their release contributes to
   local inflammation and immune cell recruitment^[127]24. The enrichment
   of DEGs in secretory granules highlights their potential role in
   regulating the secretion of pro-inflammatory mediators in both
   diseases.

   To gain insights into the regulatory networks and TFs controlling the
   DEGs, we performed PPI network analysis and TF target prediction. The
   PPI network revealed potential interactions among the DEGs, suggesting
   coordinated regulation of gene expression in both diseases. The
   identification of hub genes using various ranking methods allowed us to
   prioritize the most important genes within the network. Among the hub
   genes, SLPI, S100A9, IL1B, CYBB, CXCL8, S100A12, and CXCL1 were
   consistently identified as common interacting hub genes. SLPI
   (Secretory Leukocyte Protease Inhibitor): SLPI is an anti-inflammatory
   protein that inhibits proteases and modulates immune responses^[128]25.
   SLPI is involved in regulating neutrophil chemotaxis and can contribute
   to tissue damage when excessively activated^[129]26. S100A9, also known
   as calgranulin B, is a calcium-binding protein expressed in myeloid
   cells, including neutrophils and monocytes^[130]27. It acts as an
   alarmin and plays a role in the recruitment and activation of immune
   cells^[131]28. IL1B (Interleukin-1 Beta): IL1B is a pro-inflammatory
   cytokine involved in the regulation of immune responses^[132]29. It is
   produced primarily by activated macrophages and plays a crucial role in
   the pathogenesis of various inflammatory diseases, including psoriasis
   and AMI^[133]30. CYBB (Cytochrome b-245 Beta Chain): CYBB encodes a
   subunit of NADPH oxidase, which is responsible for the production of
   reactive oxygen species (ROS) in immune cells, particularly
   neutrophils^[134]31. CXCL8 (C-X-C Motif Chemokine Ligand 8): CXCL8,
   also known as interleukin-8 (IL-8), is a chemokine that attracts
   neutrophils to sites of inflammation. It plays a critical role in the
   recruitment and activation of neutrophils, contributing to the
   inflammatory response^[135]32. S100A12: S100A12, also known as
   calgranulin C, is another member of the S100 protein family. It is
   predominantly expressed in neutrophils and monocytes and functions as
   an alarmin in inflammation^[136]33. S100A12 has been implicated in
   various inflammatory conditions, including psoriasis and cardiovascular
   diseases^[137]34. CXCL1 (C-X-C Motif Chemokine Ligand 1): CXCL1, also
   known as growth-regulated oncogene-alpha (GRO-α), is a chemokine
   involved in the recruitment of neutrophils and monocytes to inflamed
   tissues^[138]35. These genes have been implicated in inflammation,
   immune responses, and disease pathogenesis, supporting their potential
   roles as key regulators in both psoriasis and AMI.

   We observed distinct patterns of immune cell infiltration in the
   lesional and non-lesional skin of psoriasis patients, as well as in the
   context of AMI. Psoriasis is characterized by immune cell infiltration,
   primarily driven by activated T cells and dendritic cells^[139]36. Our
   findings support these observations and demonstrate an increased
   proportion of monocytes, M1 macrophages, M2 macrophages, activated
   dendritic cells, and CD4 memory resting T cells in the lesional skin of
   psoriasis patients. These immune cell subsets play critical roles in
   the pathogenesis of psoriasis through the production of inflammatory
   cytokines and the activation of adaptive immune responses. Similarly,
   AMI is associated with immune cell infiltration in the affected cardiac
   tissue^[140]37. We observed an increased proportion of monocytes,
   activated dendritic cells, activated mast cells, eosinophils, and
   neutrophils in the context of AMI. These immune cell subsets contribute
   to the inflammatory response and tissue damage following AMI. The
   correlation analysis between the hub genes and immune cell infiltration
   highlighted their potential roles in modulating specific immune cell
   types. For example, SLPI, S100A9, IL1B, CYBB, CXCL8, S100A12, and CXCL1
   were found to be highly associated with M1 macrophages, activated
   dendritic cells, and neutrophils. These findings suggest that the hub
   genes may contribute to the recruitment and activation of these immune
   cell subsets, further promoting inflammation in both psoriasis and AMI.

   To assess the diagnostic potential of the hub genes, we evaluated their
   expression levels in the diseased tissues of psoriasis patients and
   plasma circulating endothelial cells of AMI patients. Consistent
   upregulation of these hub genes was observed in both diseases,
   indicating their potential as reliable markers for diagnosing psoriasis
   and AMI. Furthermore, the diagnostic model developed using the hub
   genes exhibited promising performance, with high AUC values for most of
   the hub genes. Notably, S100A12 showed the highest diagnostic value,
   followed by IL1B. These findings suggest that the hub genes could serve
   as potential diagnostic markers for distinguishing psoriasis and AMI
   cases from controls. This is consistent with previous studies reporting
   the diagnostic value of these genes in psoriasis^[141]38,[142]39 and
   AMI^[143]40,[144]41.

   To explore potential therapeutic options, we selected four proteins
   with an AUC > 0.800 for further analysis. We predicted potential
   therapeutic agents that could target these proteins and discovered that
   simvastatin could be a promising treatment option for both psoriasis
   and AMI. Simvastatin is a widely used statin medication known for its
   lipid-lowering effects^[145]42. Beyond its lipid-lowering properties,
   simvastatin has demonstrated anti-inflammatory and immunomodulatory
   activities^[146]43. Through molecular docking analysis, we identified
   potential binding interactions between simvastatin and CXCL8, IL1B,
   S100A9, and S100A12. These findings suggest that simvastatin may have
   therapeutic effects in mitigating the inflammatory processes associated
   with both psoriasis and AMI.

   While our study provides valuable insights into the shared molecular
   mechanisms between psoriasis and AMI, there are certain limitations
   that need to be acknowledged. Firstly, our findings are based on the
   analysis of publicly available gene expression datasets. Although we
   utilized two independent datasets to increase the robustness of our
   results, the heterogeneity of these datasets and potential batch
   effects may introduce bias and affect the generalizability of our
   findings. Secondly, the sample sizes in these datasets were relatively
   small. Although these datasets provided valuable insights into the
   molecular mechanisms underlying psoriasis and AMI, the limited sample
   sizes may introduce statistical uncertainties and reduce the power of
   our analyses. Hence, replication studies with larger cohorts are
   necessary to enhance the robustness and generalizability of our
   findings. Finally, although the potential therapeutic effects of
   simvastatin on target proteins have been investigated through molecular
   docking analysis, further in vitro and in vivo studies are necessary to
   validate these interactions and evaluate the effectiveness and safety
   of simvastatin as a potential treatment for psoriasis and AMI.

   In conclusion, our study identified common DEGs, functional pathways,
   and regulatory networks associated with both psoriasis and AMI. The
   overlapping genes enriched in immune-related pathways and their
   association with immune cell infiltration highlight the importance of
   immune dysregulation in the pathogenesis of these diseases. Moreover,
   the hub genes identified in this study provide valuable insights into
   the central players and potential diagnostic markers for psoriasis and
   AMI. Further investigation of these DEGs and hub genes may contribute
   to a better understanding of the shared molecular mechanisms underlying
   psoriasis and AMI, ultimately leading to the development of novel
   therapeutic strategies for managing these diseases.

Methods

Ethics

   GEO is public database. The patients listed in the database have
   ethical clearance. Users can submit pertinent articles and get
   pertinent data for free. Sera were obtained from normal subjects
   (n = 30) as well as patients admitted to hospital with a diagnosis of
   AMI (n = 30), aged between 40 and 60. Skin tissue from patients with
   psoriasis was obtained from patients diagnosed with psoriasis, and
   normal tissue was obtained from portions of undiseased tissue from the
   same patient. All samples were collected following The Code of Ethics
   of the World Medical Association and with the informed consent of the
   patients/donors.

Animals

   Male C57BL/6 mice aged 8 weeks were purchased from Shanghai Model
   Organisms Center (shanghai, China), and raised at the specific
   pathogen-free (SPF) laboratory animal facility. Mice were maintained on
   a 12-h light/dark cycle at 25 °C and provided free access to commercial
   rodent chow (sterilized by Cobalt-60) and tap water (high-temperature
   sterilization) before initiation of the experiments. Randomized
   grouping was used and the same group of mice were co-housed with less
   than 5 animals per cage. All animal experiments were complied with
   Directive 2010/63/EU, Commission Implementing Decision (EU) 2020/569,
   Recommendation 2007/526/EC and 1991 International Guidelines for
   Ethical Review of Epidemiological Studies.

   Left anterior descending coronary artery was ligated to create the MI
   mice model. Under sterile conditions, left anterior descending coronary
   artery was tied by a 7–0 silk suture. Sham-operated (Sham) mice were
   treated with the same surgery without tying the left anterior
   descending coronary artery.

Immunohistochemical staining

   Skin tissues obtained from human psoriasis patients and heart tissues
   collected from mice with myocardial infarction were fixed in 4%
   paraformaldehyde for 48 h. Subsequently, the tissues were dehydrated,
   embedded in paraffin, and sectioned to prepare histological samples for
   observation. Immunohistochemical staining was performed on the tissue
   sections using an immunohistochemical kit (Maxim Biology, China,
   KIT-9730). Antibody Application: Anti-CXCL8 antibody (Proteintech,
   27095-1-AP, dilution ratio: 1:100); Anti-IL-1B antibody (Affinity,
   DF6251, dilution ratio: 1:100); Anti-S100A9 antibody (ABclonal, A22131,
   dilution ratio: 1:100); Anti-S100A12 antibody (ABclonal, A12499,
   dilution ratio: 1:100).

ELISA

   ELISA was employed to detect the levels of CXCL8, IL1B, S100A9, and
   S100A12 in serum samples obtained from acute myocardial infarction
   (AMI) patients (n = 20) and AMI mice (n = 6). The ELISA kits used in
   this study were procured from Animaluni (China) under the product codes
   LV10281, LV10309, LV10651, LV10435, LV30300, LV30328, and LV30461.

RNA isolation and real-time PCR

   RNA extraction was performed using TRIzol reagent (Invitrogen). Using a
   PrimeScriptTM RT reagent Kit (Takara, Beijing, China) with gDNA Eraser,
   1 g of total RNA was reverse-transcribed into cDNA. On a CFX96 TouchTM
   Real-Time PCR Detection System (Hercules, CA, USA), quantitative
   real-time PCR was conducted using TB Green® Fast quantitative
   polymerase chain reaction (qPCR) Mix (Takara) and specific primers
   (Ribobio).

Data source and DEG identification

   We utilized the GEO database to obtain two datasets, namely
   [147]GSE161683 for psoriasis and [148]GSE66360 for AMI. The
   [149]GSE161683 dataset provided gene expression profiles of LP and NP
   psoriasis skin samples from 9 patients, using microarray analysis.
   Similarly, the [150]GSE66360 dataset included information on
   circulating endothelial cells from patients with AMI (n = 49) as well
   as healthy control subjects (n = 50). Both male and female patients
   were included in these datasets. To analyze the differential gene
   expression, we performed standardization and differential analysis
   using R Project. Specifically, we employed LIMMA and DESeq2 packages
   for data standardization and differential analysis. Volcano plots and
   heatmaps were generated using R to visualize the significantly DEGs.
   The statistical significance cutoff for DEGs was set at |log2(FC)|> 1
   and p < 0.05.

KEGG and GO enrichment analysis

   To conduct pathway enrichment analysis, we utilized the DAVID database
   for KEGG pathway enrichment^[151]44 and GO enrichment^[152]45 analysis
   of the 37 DEGs. We selected the top 11 pathways for KEGG studies and
   the top 20 for GO studies based on their biological relevance. To
   identify significantly enriched pathways, a false discovery rate (FDR)
   value of less than 0.05 was applied as the cutoff criterion.

Construction of PPI and TF-DEG regulation network

   To integrate biomolecular interaction networks, high-throughput
   expression data, and other molecular states, we obtained
   protein–protein and functional interaction networks for the 37 combined
   hub genes from the STRING database ([153]https://string-db.org/). These
   networks were visualized using Cytoscape^[154]46. To identify potential
   interactions between DEGs and TFs, we utilized the iRegulon plugin in
   Cytoscape. This plugin helped identify TFs that potentially target the
   DEGs. Enriched motifs in iRegulon were ranked based on direct targets
   using position weight matrix 50. The TF-DEG crosstalk pairs were
   obtained from databases such as TRANSFAC, JASPAR, and others. Finally,
   the TF-DEG regulatory network was visualized using Cytoscape.

Identification of hub genes

   To explore the hub gene network, we utilized the CytoHubb Plugin in
   Cytoscape. This plugin provides various techniques for identifying
   critical nodes within biological networks and their interactions with
   other genes. In our study, we employed multiple algorithms (including
   betweenness, closeness, EPC, MCC, and MNC ranking methods) to identify
   the top 10 hub genes. The resulting network was colored to reflect the
   relevance of the hub nodes; red indicating the highest score and yellow
   indicating the lowest. By performing a Venn diagram analysis on the top
   10 hub genes from each ranking method, we identified SLPI, S100A9,
   IL1B, CYBB, CXCL8, S100A12, and CXCL1 as the common interacting hub
   genes.

Immune cell infiltration and correlation analysis

   To investigate the immune microenvironment in psoriasis and AMI
   patients, we analyzed the immune cell infiltrations using the CIBERSORT
   algorithm^[155]47. This algorithm allowed us to determine the
   proportions of different immune cell types present. We depicted the
   expression levels of 22 immune infiltrating cells using box plots. To
   visualize the relationship between different immune cell types and
   SLPI, S100A9, IL1B, CYBB, CXCL8, S100A12, and CXCL1, we used the
   ggplot2 package and performed Spearman correlation analysis.

Identification of diagnostic genes

   To determine the predictive values of the hub genes, we conducted ROC
   curve analysis and calculated the corresponding AUCs using the pROC
   package in R software. Diagnostic genes were selected from
   [156]GSE66360 dataset based on the criterion of having an AUC greater
   than 0.800. To observe the dynamic changes in the diagnostic genes in
   AMI patients, we analyzed the ROC curves of these diagnostic genes in
   both AMI and control patients.

Prediction of potential therapeutic drugs

   We utilized the DSigDB database^[157]48 to predict potential
   therapeutic drugs targeting the hub genes. The chemical structure of
   simvastatin was obtained from a reliable source and converted into a
   suitable format compatible with the molecular docking software. The
   ligand was then prepared by adding hydrogen atoms and optimizing its
   geometry. Crystal structures of the target proteins (CXCL8, IL1B,
   S100A9, and S100A12) were retrieved from the PDB database. The protein
   structures were prepared by removing any existing water molecules,
   heteroatoms, and other ligands. Molecular docking simulations were
   performed using the AutoDock Vina program^[158]49. The results of the
   molecular docking simulations were visualized using PyMOL software,
   generating three-dimensional docking diagrams that depict the predicted
   binding modes of simvastatin within the binding sites of each target
   protein (CXCL8, IL1B, S100A9, and S100A12).

Statistical analysis

   We utilized R 4.2.0 software and GraphPad Prism (version 8.0.1) for
   data analysis in this research. Data are presented as the mean ± SD,
   and comparisons between groups were performed using an unpaired
   Student's t test (LP vs NP, AMI vs Control). ROC curves were used to
   evaluate the AUC and the predictive abilities of the models. A p-value
   less than 0.05 was considered statistically significant.

Supplementary Information

   [159]Supplementary Information.^ (36.2KB, xlsx)

Acknowledgements