This study aims to investigate the role of PRGs in MIRI using bioinformatics approaches. By analyzing myocardial ischemia-reperfusion datasets from Gene Expression Omnibus (GEO), this study further identifies PRGs associated with ischemia-reperfusion injury and applies various machine learning methods to select key genes. The results of this study are expected to provide new insights into the role of PANoptosis in MIRI and reveal potential biomarkers and therapeutic targets, offering novel strategies for the diagnosis and treatment of MIRI.
This study utilized the keyword "ischemia-reperfusion" to search for gene expression profile datasets related to MIRI in the GEO database. The included datasets had to meet the following criteria: (i) containing genome-wide mRNA microarray data; (ii) including both myocardial ischemia-reperfusion and control group samples of myocardial tissue; and (iii) having a sufficiently large sample size to support statistical analysis. Ultimately, the datasets GSE108940 and GSE160516 were selected for further analysis, and probes were annotated using the corresponding annotation files. As the data in the GEO database is publicly available, no additional ethical approval was required for this study. PRGs were sourced from the GENCARD database and, in combination with prior studies, a total of 277 PRGs were selected (supplementary Table 1). Differentially expressed PRGs (DPRGs) were identified using the "sva" and "limma" packages in R software (version 4.2.3; R Foundation for Statistical Computing, Vienna, Austria; https://www.r-project.org/), with a threshold of P < 0.05. Heatmaps and correlation plots were generated using the "pheatmap" and "corrplot" packages to visualize the results.
GO and KEGG functional enrichment analyses for DPRGs were performed using the "clusterProfiler" package in R software. GO analysis primarily includes three components: Molecular Function (MF), Cellular Component (CC), and Biological Process (BP).
GSEA was conducted using the "clusterProfiler" package in R to calculate the normalized enrichment scores and assess the correlation between DPRGs and specific pathways. The p-value adjusted for the gene set less than 0.05 was considered significant.
A univariate logistic regression model was fitted and significance testing was performed to assess the correlation between the expression levels of DPRGs and the PANoptosis status. Based on the significance testing results and odds ratios (OR), DPRGs significantly associated with PANoptosis and with a substantial impact were selected for further analysis.
DPRGs were further screened using three machine learning methods: Support Vector Machine Recursive Feature Elimination (SVM-RFE), Least Absolute Shrinkage and Selection Operator (LASSO) regression, and random forest. The final set of key PRGs (KPRGs) was obtained by taking the intersection of genes identified through these three machine learning approaches.
ROC curve analysis was performed using the "pROC" package in R software to further evaluate the stability and diagnostic sensitivity of KPRGs for ischemia-reperfusion. An area under the curve (AUC) greater than 0.7 indicates a good diagnostic performance.
The study utilized the CIBERSORT method to analyze mRNA expression profiles and characterize the cellular composition of the left atrium and normal tissues, including 22 immune cell types (7 T cell subsets, naïve and memory B cells, plasma cells, NK cells, and myeloid subgroups) based on 547 genes. The P-value of < 0.05 was considered statistically significant.
Potential drugs targeting KPRGs were predicted using the DsigDB database. Subsequently, molecular docking analysis was performed using CB-Dock2 (version 2.0; http://cadd.labshare.cn/cb-dock2/) to examine the binding modes and affinity between the candidate drugs and their targets. The three-dimensional structures of the targets were obtained from the RCSB Protein Data Bank, and the drug structures were sourced from the PubChem database.
The AC16 human cardiomyocyte cell line used in this study was obtained from the Shanghai Institute of Cell Biology, Chinese Academy of Sciences. To simulate MIRI in vitro, an OGD/R model was established. Briefly, AC16 cells were cultured in glucose-free DMEM medium and incubated under hypoxic conditions (5% CO₂, 95% N₂) at 37 °C for 6 h to mimic ischemia. Subsequently, the medium was replaced with high-glucose DMEM, and cells were returned to normoxic conditions (95% air, 5% CO₂) at 37 °C for 24 h to simulate reperfusion. This model replicates key pathophysiological features of MIRI, including hypoxia-induced stress and reperfusion-associated injury.
To investigate the functional role of IL1R1 in PANoptosis under ischemic-like conditions, this study employed siRNA-mediated silencing of IL1R1 in an OGD/R-induced cellular model to assess its impact on PANoptotic activity. IL1R1 expression was silenced in AC16 cells using specific small interfering RNA (siRNA) (MedChemExpress, HY-RS06720), and changes in phosphorylated P65 (p-P65) levels were subsequently examined. This approach aimed to determine whether IL1R1 knockdown modulates PANoptosis pathway activation.
The male C57BL/6 mice (8-10 weeks old) used in this experiment were purchased from Jiangsu Jucui Yaokang Biotechnology Co., Ltd., China. After adaptive feeding, the mice were randomly assigned to the control group (12 mice) and the ischemia-reperfusion group (12 mice). The mice were anesthetized with isoflurane and ventilated, followed by ligation of the left anterior descending (LAD) coronary artery for 30 min, and reperfusion for 24 h to induce MIRI. Sham-operated mice underwent the same procedure without LAD ligation. After reperfusion, the mice were euthanized, and heart tissues were harvested for analysis. All procedures were approved by the Institutional Animal Care and Use Committee of Jiangsu Haohan Biotechnology Co., Ltd. (Approval No. HJSW-25061501).
AC16 cells were seeded at a density of 1 × 10 cells per well in 96-well plates. After 24 h of incubation, the old medium was replaced with a medium containing 10% CCK-8 reagent, and cells were incubated at 37 °C for 4 h. The absorbance at 450 nm was measured using a microplate reader.
Mitochondrial membrane potential was assessed using the JC-1 dye kit (Solarbio, Cat: M8650) to evaluate mitochondrial function. The JC-1 dye was prepared into a working solution and evenly applied to AC16 cells. After incubation, cells were washed with buffer, and fluorescence images were captured under a fluorescence microscope. Strong red fluorescence indicates a high mitochondrial membrane potential and normal mitochondrial function, while increased green fluorescence suggests a decrease in mitochondrial membrane potential and mitochondrial dysfunction.
Total cellular proteins were extracted and quantified. Equal amounts of protein samples were separated by SDS-PAGE and transferred onto PVDF membranes. The membranes were incubated with the following primary antibodies: p-P65 (Proteintech, 82335-1-RR, 1:5000), P65 (Proteintech, 10745-1-AP, 1:3000), IL-1R1 (Proteintech, 27348-1-AP, 1:2000), and NLRP3 (Proteintech, 30109-1-AP, 1:2000). GAPDH (Proteintech, 60004-1-Ig, 1:50000) was used as a loading control. Protein bands were visualized using enhanced chemiluminescence and quantified with ImageJ software (version 1.53t; National Institutes of Health, USA; https://imagej.nih.gov/ij/).
TTC (Solarbio, Cat: G3005) and Masson staining were used to evaluate myocardial injury and fibrosis following ischemia-reperfusion. Mice were euthanized 24 h after reperfusion, and hearts were rapidly excised, frozen for 30 min, and sectioned into 1-2 mm slices. The slices were incubated in 2% TTC solution at 37 °C for 15 min in the dark. Viable myocardium stained red, while infarcted areas appeared white. Additional paraffin-embedded heart sections were stained using Masson's trichrome staining, where myocardial tissue appeared red and collagen fibers were stained blue.
Data processing and statistical analysis were performed using GraphPad Prism software (version 9.0; GraphPad Software, San Diego, CA, USA; https://www.graphpad.com). Differences between two groups were analyzed using unpaired t-tests, with P < 0.05 considered statistically significant.