EthicsGEO 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.AnimalsMale 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 stainingSkin 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).ELISAELISA 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 PCRRNA 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 identificationWe utilized the GEO database to obtain two datasets, namely GSE161683 for psoriasis and GSE66360 for AMI. The GSE161683 dataset provided gene expression profiles of LP and NP psoriasis skin samples from 9 patients, using microarray analysis. Similarly, the 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 analysisTo conduct pathway enrichment analysis, we utilized the DAVID database for KEGG pathway enrichment44 and GO enrichment45 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 networkTo 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 (https://string-db.org/). These networks were visualized using Cytoscape46. 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 genesTo 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 analysisTo investigate the immune microenvironment in psoriasis and AMI patients, we analyzed the immune cell infiltrations using the CIBERSORT algorithm47. 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 genesTo 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 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 drugsWe utilized the DSigDB database48 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 program49. 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 analysisWe 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.
