Identification of NF-κB pathway-related biomarkers in myocardial ischemia-reperfusion injury: based on transcriptomics analysis and RT-qPCR validation

Abstract

Myocardial ischemia-reperfusion injury (MIRI) limits the success of reperfusion therapies. Identifying potential biomarkers within the nuclear factor kappa-B (NF-κB) pathway is critical for developing new treatments. Transcriptomic data from mouse MIRI models were combined with NF-κB pathway-related genes. Candidate genes were identified from the overlapping differentially expressed genes. Potential biomarkers were selected via protein-protein interaction network analysis and validated with independent datasets. We performed functional analysis, built transcription factor and competing endogenous RNA (ceRNA) networks, and conducted drug prediction and molecular docking. Reverse transcription quantitative polymerase chain reaction (RT-qPCR) validation was performed in a MIRI mouse model. Nine candidate genes were identified, with Nfkbia and Icam1 emerging as potential biomarkers. Functional analysis connected Nfkbia to mitochondrial metabolism and Icam1 to extracellular matrix processes/nuclear processes. A regulatory network involving mmu-miR-706 and seven lncRNAs was constructed. Drug prediction identified Tosyllysyl Chloromethyl Ketone (TLCK) as exhibiting favourable binding affinity for both targets. Experimental validation confirmed significant upregulation of Nfkbia and Icam1 in MIRI. This study established Nfkbia and Icam1 as key NF-κB-associated genes in MIRI and constructed a ceRNA network. These findings advance our understanding of MIRI mechanisms and support future therapy development. However, these findings were based on bioinformatics analysis and preliminary experimental validation, and required further functional experiments for confirmation.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-026-47878-9.

Introduction

Myocardial ischemia-reperfusion injury (MIRI) is a paradoxical situation in which the restoration of coronary blood flow to ischemic myocardium fails to recover its function and instead exacerbates tissue damage. MIRI occurs in various clinical settings, including percutaneous coronary intervention, thrombolysis, cardiac surgery, and the perioperative period^1,2. MIRI is a complex pathological process involving multiple mechanisms, such as oxidative stress triggered by an initial burst of reactive oxygen species (ROS)³, abnormal opening of the mitochondrial permeability transition pore (mPTP)⁴, ferroptosis⁵, inflammation cascades initiated by leukocyte infiltration⁶, and programmed cell death pathways including apoptosis, necrosis, and pyroptosis⁷. MIRI is closely associated with cardiac remodeling, functional impairment, and heart failure, significantly affecting patient prognosis. Although numerous drugs and interventional strategies have been shown to be effective in preclinical ischemia/reperfusion (I/R) models, these findings have not yet been successfully translated into clinical application^8,9. Increasing evidence reinforces that inflammation plays a crucial role in both ischemia-reperfusion injury and the subsequent repair process, making it an important therapeutic target^9,10. Given the pathophysiological complexity of MIRI and subsequent challenges in its clinical management, the discovery of relevant inflammatory biomarkers may contribute to improvements in therapeutic effects and patient outcomes.

The nuclear factor kappa-B (NF-κB) pathway is an important intracellular signaling network involved in the regulation of immune responses, inflammation, and cell survival. In the resting state, NF-κB dimers are sequestered in the cytoplasm by binding to inhibitory IκB proteins. Upon stimulation by pro-inflammatory cytokines or oxidative stress, the IκB kinase (IKK) complex becomes activated, leading to phosphorylation and subsequent degradation of IκB. This process releases NF-κB, enabling its translocation into the nucleus and thereby inducing the transcription of pro-inflammatory mediators (e.g., IL-6, TNF-α) and anti-apoptotic factors^11–14. Since the NF-κB pathway plays a central role in immune responses, inflammation, cell survival and proliferation, its abnormal activation is closely associated with the pathogenesis and progression of various inflammatory diseases, autoimmune disorders, and cancers^11,15. Previous studies have demonstrated that milk-derived exosomes carrying miR-146a (MEs-miR-146a), when conjugated with the ischemic myocardium-targeting peptide (IMTP), can alleviate MIRI. This effect is achieved by inhibiting the IRAK1/TRAF6 signaling axis and reducing the activity of the NF-κB pathway, thereby blocking the inflammatory cascades, improving cardiac function, and attenuating cardiomyocyte apoptosis and inflammation¹⁶. Although the critical role of the NF-κB pathway in MIRI has been well established, most existing studies have focused on the regulation of this pathway by individual genes or on validating the cardioprotective effects of drugs targeting this pathway^17–20. Such a research paradigm centered on single molecules or pharmaceutical agents is insufficient for systematically revealing the coordinated role of NF-κB pathway-related gene clusters throughout the MIRI process. Notably, in other disease contexts such as gastric cancer, researchers have employed bioinformatic approaches to systematically explore NF-κB pathway-related genes and investigate their relationship with immune infiltration²¹. However, in the field of MIRI, despite numerous transcriptomic studies identifying key genes involved in this process (e.g., STAT3)^22–24, integrative analyses treating the NF-κB pathway as a distinct functional module remain inadequate—particularly lacking systematic investigations aimed at precisely identifying key drivers underlying MIRI pathogenesis.

Therefore, this study focuses on NF-κB pathway-related genes (NF-κB-RGs) and employs bioinformatic approaches to identify differentially expressed NF-κB-RGs as candidate biomarkers in MIRI. Then, we determined the potential biomarkers through functional enrichment analysis, topological analysis of protein-protein interaction (PPI) network, and validating their consistent differential expression across independent datasets. To further elucidate the functional roles of the potential biomarkers, we performed Gene Set Enrichment Analysis (GSEA), constructed transcription factors (TFs)-mRNA interaction, gene-gene interaction (GGI), and competing endogenous RNA (ceRNA) regulatory networks. Additionally, drug prediction and molecular docking were conducted to explore potential therapeutic compounds targeting the potential biomarkers. Finally, the predicted expression pattern of the potential biomarkers was experimentally validated using an MIRI mouse model. By integrating bioinformatic analysis with experimental validation, this study aims to provide a theoretical and empirical basis for improving the diagnosis and treatment of MIRI.

Materials and methods

Data acquisition

The training set (GSE255933), the validation set (GSE281940) and microRNA (miRNA) dataset (GSE124176) were downloaded from Gene Expression Omnibus (GEO; https://www.ncbi.nlm.nih.gov/geo/) database. GSE255933 dataset (High-throughput sequencing, platform: GPL21626) consisted of 5 MIRI and 4 sham-operated (SHAM) mouse myocardial tissue samples. GSE281940 dataset (High-throughput sequencing, platform: GPL24247) contains 4 MIRI and 4 SHAM samples. GSE124176 dataset (Microarray, platform: GPL19117) was used to identify differentially expressed miRNAs (DE-miRNAs). After eliminating STAT3 knockout (STAT3_KO) and high-density lipoprotein (HDL)-treated subjects, this dataset comprised 3 MIRI and 3 SHAM samples. All three datasets were accessed on May 19, 2025. In addition, a total of 107 NF-κB-RGs were obtained from the Mus musculus NF-kappa B signaling pathway (pathway ID: mmu04064) in Kyoto Encyclopedia of Genes and Genomes (KEGG) database (accessed on May 22, 2025, https://www.kegg.jp/entry/mmu04064).

Identification of differentially expressed genes (DEGs)

DEGs between MIRI and SHAM groups within the training set GSE255933 were identified using the “DESeq2” R package (v 3.4.1)²⁵, setting a significance criteria as P < 0.05 and |log₂Fold Change (FC)| > 0.5. The DESeq2 package’s “results()” function was employed for differential expression analysis, which by default enables independent screening and automatically filters out low-expression genes to enhance statistical power. A volcano plot was generated by the “ggplot2” R package (v 3.4.1)²⁶ to visualize the gene expression patterns. Moreover, the “ComplexHeatmap” R package (v 2.14.0)²⁷ was used to create a heatmap, depicting the expression levels of DEGs in the MIRI and SHAM groups.

Candidate genes identification, functional enrichment, and PPI network construction

Candidate genes were defined as the intersection of the DEGs and the NF-κB-RGs, and visualized with a Venn diagram generated by the “ggvenn” R package (v 0.1.10) (https://CRAN.R-project.org/package=ggvenn). To explore the biological functions and signaling pathways related to these candidate genes, we used the “clusterProfiler” R package (v 4.2.2)²⁸ to conduct Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. The mouse gene annotations were provided by “org.Mm.eg.db”. We further determined the functionally enriched terms and pathways of these candidate genes, setting a significance threshold of P.adjust < 0.05. Moreover, we submitted the candidate genes to the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database, with the species specified as “Mus musculus”, for constructing a PPI network. A confidence score threshold of ≥ 0.4 was adopted to assess the interactions among the encoded proteins. Subsequently, the obtained network was imported into Cytoscape (v 3.9.1)²⁹ for visualization.

Identification of the potential biomarkers

In order to determine the potential biomarkers from candidate genes, we used CytoHubba, a plugin in Cytoscape, to perform further screening. Based on the established PPI network, we applied three topological analysis methods: Edge Percolated Component (EPC), Maximum Clique Centrality (MCC), and Clustering Coefficient. Then we ranked nodes and selected the top 5 genes from each method. By intersecting the shared genes across these three ranked lists. Differential expression of these genes between MIRI and SHAM groups was assessed using Wilcoxon rank-sum tests in both the training (GSE255933) and validation (GSE281940) datasets (P < 0.05). The results were visualized using box plots generated by the “ggplot2” R package (v 3.4.1)²⁶. Genes showing consistent expression trends along with significant differences in both datasets were determined as potential biomarkers.

Gene set enrichment analysis

To investigate the biological functions of the potential biomarkers and their associated signaling pathways, we analyzed all samples from the training set GSE255933. We calculated Spearman correlation coefficients between each biomarker and all other genes using the “psych” R package (v 2.2.9)³⁰ and ranked them in descending order. GSEA was subsequently performed using the “clusterProfiler” R package (v 4.2.2)²⁸ to characterize the functional enrichment profiles of each key biomarker, with the default background gene set from “org.Mm.eg.db”. Significance thresholds were set at P.adjust < 0.05 and |Normalized Enrichment Score (NES)| > 1. Finally, the top five pathways with the smallest P-values were visualized using the “enrichplot” R package (v 1.20.3) (https://yulab-smu.top/biomedical-knowledge-mining-book/).

Construction of TF-mRNA networks

TFs regulate the expression of target genes at the transcriptional level by attaching to their specific sequences. In order to explore the regulatory roles of TFs in MIRI, we screened for the TFs potentially targeting the potential biomarkers through the TRRUST database within NetworkAnalyst (https://www.networkanalyst.ca/) and assessed their regulatory impact. The TF-mRNA regulatory network was visualized by Cytoscape (v 3.9.1)²⁹.

Establishment of GGI networks

To further investigate the interactions and potential biological associations between the potential biomarkers and their functionally related genes, we uploaded the potential biomarkers to the GeneMANIA online database (http://genemania.org/), with the species specified as Mus musculus. We then employ this tool to create a comprehensive interaction network, targeting the visualization of complex relationships among genes, including physical associations, co-expression, and shared pathway participation.

ceRNA network construction

In order to investigate the regulatory functions of the potential biomarkers in the ceRNA network during MIRI, we initially predicted miRNAs targeting these biomarkers based on the TarBase v9.0 database of NetworkAnalyst (https://www.networkanalyst.ca/). Subsequently, differentially expressed miRNAs were identified from the GSE124176 dataset, setting a threshold of P < 0.05 and |log₂FC| > 0.5. The expression profile of these DE-miRNAs was presented in a volcano plot generated with the “ggplot2” R package (v 3.4.1)²⁶, highlighting the top 10 miRNAs with the most significant changes based on |log₂FC|. Moreover, a heatmap was created with the “ComplexHeatmap” R package (v 2.14.0)²⁷ to show the expression patterns of the top 10 DE-miRNAs (ranked by |log2FC|) in the MIRI and SHAM groups. The key miRNAs were obtained by overlapping the predicted miRNAs with DE-miRNAs. Using the DIANA-LncBase v3 database, we further predicted the interactions between these key miRNAs and long non-coding RNAs (lncRNAs). Finally, the miRNAs-biomarkers network and the biomarkers-miRNAs-lncRNAs network (or ceRNA network) were visualized with Cytoscape (v 3.9.1)²⁹.

Drug prediction and molecular docking analysis

The Drug Signatures Database (DsigDB; http://dsigdb.tanlab.org) was utilized to predict existing drugs or small organic molecules targeting the identified potential biomarkers. The obtained drug-biomarker interactions were then presented as a network diagram with Cytoscape (v 3.9.1)²⁹. To verify the binding affinity between the predicted drugs and biomarkers and increase the reliability of the interaction network, molecular docking was carried out for each biomarker-drug complex. The protein three-dimensional (3D) structures of the biomarkers were retrieved from the AlphaFold Database (https://alphafold.com/). Meanwhile, the 3D structures of the predicted drugs were sourced from PubChem (https://pubchem.ncbi.nlm.nih.gov/). Both the biomarker proteins and the drug molecule ligands were submitted to the CB-Dock2 online platform (https://cadd.labshare.cn/cb-dock2/index.php) for molecular docking and binding energy calculation. When the conformation of the ligand-receptor complex is stable, lower binding energy values imply a higher binding affinity. Generally, a binding energy value below − 5 kcal/mol is considered to indicate strong binding affinity.

Reverse transcription quantitative polymerase chain reaction (RT-qPCR)

RT-qPCR was conducted to validate the expression of biomarkers in MIRI mouse samples. All animal experiments were approved by the Animal Care and Use Committee of The Third Hospital of Hebei Medical University (Approval No. Z2025-036-1), and were reported in accordance with the ARRIVE guidelines 2.0. Ten male C57BL/6J mice, each 6 weeks old, were purchased from the Beijing SPF Biotechnology Co., Ltd [Laboratory Animal Production License No.: SCXK (Jing) 2024-0001]. They were randomly assigned to two groups: the MIRI group and the control group, with 5 mice in each group. The mice were anesthetized with isoflurane (an initial concentration of 3–5% for induction and maintained at 1.5-3%) and their hearts were exposed. Throughout the surgery, body temperature was maintained at 37 °C using a heating pad. In the MIRI group, the left anterior descending coronary artery was ligated with a nylon suture for 30 min to trigger myocardial ischemia; this step was omitted in the control group. Subsequently, the suture was removed to allow 3 h of reperfusion. Upon completion of the experiment, the mice were euthanized by an intraperitoneal injection of an overdose of pentobarbital sodium (150 mg/kg). Immediately after the cessation of breathing and the loss of pedal reflex, the hearts were rapidly harvested for RT-qPCR analysis. All experimental procedures were performed in strict accordance with the Guide for the Care and Use of Laboratory Animals published by the National Institutes of Health (NIH, USA), and every effort was made to minimize animal suffering. Total RNA was extracted from the 10 tissue samples with Trizol reagent (Ambion, USA) following the manufacturer’s guidelines. A NanoPhotometer N50 was used to measure the RNA concentration. Reverse transcription was performed using the SureScript First-Strand cDNA Synthesis Kit on an S1000TM Thermal Cycler (Bio-Rad, USA). The primer sequences are presented in Table 1. RT-qPCR was carried out using a CFX Connect Real-Time PCR System (Bio-Rad, USA) under these conditions: an initial denaturation at 95 °C for 1 min, followed by 40 cycles of denaturation at 95 °C for 20 s, annealing at 55 °C for 20 s, and extension at 72 °C for 30 s. The relative mRNA expression levels were computed using the 2^-ΔΔCT method. The RT-qPCR results were first exported to Microsoft Excel and then imported into GraphPad Prism 9 for statistical analysis and graph creation.

Table 1.

Primer sequences for RT-qPCR.

Genes	Primer Sequence
Nfkbia	F’-GAGGCCAGCGTCTGACATTA
	R’-CAGCCGAATCACCCCAGTAA
Icam1	F’-CCATCCATCCCAGAGAAGCC
	R’-CACTGAGTCTCCAAGCCCAG
Gapdh	F’-TGTGTCCGTCGTGGATCTGA
	R’-GAGTTGCTGTTGAAGTCGCA

Statistical analysis

All statistical analyses of bioinformatics data were carried out using R package (v4.2.2). For the bioinformatics analyses, comparison between MIRI and SHAM groups was performed using the non-parametric Wilcoxon rank-sum test, with a P-value < 0.05 considered statistically significant. The RT-qPCR data were expressed as mean ± standard deviation. The normality of the data distribution was verified using the Shapiro-Wilk test, and the homogeneity of variances was confirmed using Levene’s test. Comparison of RT-qPCR results between groups was applied using an unpaired two-tailed Student’s t-test on the condition that the data satisfied the assumptions of normality and homogeneity of variances. If these assumptions were not met, the non-parametric Wilcoxon rank-sum test was adopted. In every situation, a P-value of less than 0.05 was regarded as statistically significant.

Results

Identification and functional analysis of nine candidate genes

A total of 359 differentially expressed genes (DEGs) were identified in the training set GSE255933, with 257 upregulated genes and 102 downregulated genes in the MIRI group (P < 0.05, |log₂FC| > 0.5) (Fig. 1A-B). Intersection analysis between these DEGs and NF-κB-RGs yielded nine candidate genes (Fig. 1C). Functional enrichment analysis of these candidate genes identified 318 significantly enriched GO terms (P.adjust < 0.05) (Fig. 1D, Supplementary Table 1), including 299 biological process (BP) terms such as p38-associated processes including positive regulation of the JNK signaling pathway and symbiosis, and 19 molecular function (MF) terms such as death domain binding and cytokine activity. KEGG enrichment analysis revealed that these candidate genes were significantly enriched in 41 signaling pathways (P.adjust < 0.05), including small cell lung cancer, p53 signaling pathway, and NF-κB signaling pathway (Fig. 1E, Supplementary Table 2). The PPI network analysis further emphasized the important roles of these nine candidate genes in MIRI pathogenesis. Specifically, Nfkbia interacted with Icam1, Bcl2l1, Cxcl1, Cxcl2, and Gadd45b, while Icam1 interacted with Nfkbia, Bcl2l1, Cxcl1, and Cxcl2. These findings implied direct or indirect functional associations among these proteins, indicating their involvement in shared biological processes (Fig. 1F). Taken as a whole, these results provide novel insights into the pathogenesis of MIRI and lay a foundation for identifying novel diagnostic and therapeutic targets.

Fig. 1.

Identification and functional analysis of candidate genes in MIRI. (A) Volcano plot depicting the differentially expressed genes (DEGs) between the MIRI and SHAM groups (|log₂FC| > 0.5 and P < 0.05). Genes with no significant changes are represented by gray dots. (B) Heatmap depicting the expression patterns of DEGs. The top 10 genes with the highest upregulation and downregulation, sorted according to |log₂FC|, are presented. The color bar at the top designates the sample groups (blue stands for SHAM, red for MIRI). In the upper part, the expression distribution lines for each sample are shown. The colors in the heatmap signify the Z-score normalized gene expression levels (red indicates high expression, blue represents low expression). Rows are associated with genes, columns with samples, and the left dendrogram demonstrates gene clustering. (C) A Venn diagram was employed to show the intersection of DEGs and NF-κB-RGs. (D) GO enrichment bubble plot of the candidate genes (the top 5 terms for each category). The color of the point reflects the significance of enrichment, and the size of the point denotes the number of genes related to the term. (E) Bubble plot of KEGG pathway enrichment for the candidate genes (top 5 pathways). The color of the point reflects the significance of enrichment, while the size of the point represents the number of genes related to the pathway. This figure was generated from the KEGG pathway database (Kanehisa Laboratories, https://www.kegg.jp/). (F) The PPI network of the nine candidate genes. Nodes are colored according to their Degree value. A darker red color implies a stronger interaction.

Identification of two potential biomarkers and GSEA

To identify potential biomarkers from the nine candidate genes, we performed a topological analysis using the cytoHubba plugin in Cytoscape with three distinct algorithms: EPC, MCC, and Clustering Coefficient. The top five node genes identified by each method were intersected, resulting in three overlapping potential biomarkers: Cxcl1, Nfkbia, and Icam1 (Fig. 2A). Subsequently, analysis revealed that Nfkbia and Icam1 were significantly upregulated in the MIRI group compared with the SHAM controls (P < 0.05) in both the training set (GSE255933) and the validation set (GSE281940). As a result, Nfkbia and Icam1 were ultimately confirmed as potential biomarkers (Fig. 2B-E, Supplementary Table 3). GSEA results indicated that overexpression of Icam1 was significantly positively correlated with 552 pathways (P.adjust < 0.05, NES > 0) and negatively correlated with six pathways (P.adjust < 0.05, NES < 0). The positively enriched pathways primarily involved collagen-containing extracellular matrix, nuclear speckles, chromatin remodeling, RNA splicing, and histone binding (Fig. 2F, Supplementary Table 4). Conversely, high expression of Nfkbia was significantly positively correlated with 490 pathways (P.adjust < 0.05, NES > 0) and negatively correlated with 196 pathways (P.adjust < 0.05, NES < 0). The negatively enriched pathways predominantly involved mitochondrial protein complexes, mitochondrial matrix, generation of precursor metabolites and energy, oxidative energy derivation from organic compounds, and the inner mitochondrial membrane protein complex (Fig. 2G, Supplementary Table 5).

Fig. 2.

Identification of Two Potential Biomarkers and GSEA. (A) The top five genes ranked by the EPC, MCC, and clustering coefficient algorithms were intersected to obtain three overlapping biomarkers. A bar chart shows gene counts per intersection; pink bars indicate overlapping biomarkers. Dots and lines represent algorithms and their intersections. (B-E) Box plots display normalized expression levels of biomarkers in SHAM vs. MIRI groups for training (GSE255933) and validation (GSE281940) datasets. (F-G) Enrichment analysis results for Icam1 (F) and Nfkbia (G) (|NES| > 1, P < 0.05). The y-axis is Enrichment Score (ES); x-axis is the rank-ordered gene list. Positive ES indicates top enrichment; negative ES indicates bottom enrichment.

Molecular regulatory network analysis

To elucidate the transcriptional regulatory mechanism underlying MIRI, we predicted TFs targeting Nfkbia and Icam1. A total of 15 TFs were identified as potentially regulating Nfkbia, while four TFs were found to target Icam1 (Fig. 3A). Based on these findings, a gene-gene interaction (GGI) network was constructed to visualize functional relationships between these two potential biomarkers and their functionally interacting genes. The GGI network revealed that Nfkbia, Icam1, and their functionally related genes are involved in important biological processes, including the I-kappaB kinase/NF-kappaB signaling pathway, negative regulation of cytokine production, cellular response to nicotine, endothelial development, and negative regulation of DNA-binding transcription factor activity (Fig. 3B). To further investigate the post-transcriptional regulation of Nfkbia and Icam1, we identified 364 and 268 potential miRNAs that may target Nfkbia and Icam1, respectively. In GSE 124,176, seven DE-miRNAs were identified, including four upregulated and three downregulated miRNAs (Fig. 3C-D). By performing an intersection analysis between the predicted miRNAs and seven DE-miRNAs, a key miRNA named mmu-miR-706 was identified (Fig. 3E). Finally, based on the DIANA-LncBase v3 database, seven long non-coding RNAs (lncRNAs) that potentially target mmu-miR-706 were identified (Fig. 3F). Collectively, these findings provided crucial evidence for elucidating the post-transcriptional regulatory network of Nfkbia and Icam1 in MIRI. Additionally, we proposed a competing endogenous RNA (ceRNA) regulatory network model that elucidated how lncRNAs indirectly influence mRNA translation by competitively binding to miRNAs, thereby establishing a dynamic regulatory network system. These findings provided novel therapeutic targets and intervention strategies based on the competing endogenous RNA (ceRNA) regulatory network for the treatment of MIRI.

Fig. 3.

Molecular Regulatory Network Analysis. (A) Regulatory network of TF-mRNA. Biomarkers are represented by yellow nodes, while blue nodes stand for TFs. (B) Biomarker Network of GGI. The connecting lines are colored to differentiate various interaction types, while the colors of the nodes stand for different functional groups. (C) Volcano plot depicting MIRI-related differentially expressed (DE) miRNAs (GSE124176). (D) Heatmap depicting MIRI-related DE-miRNAs. (E) Regulatory Network of Biomarker-Key miRNAs. Biomarkers are denoted by yellow nodes, while miRNAs are represented by blue nodes. (F) Biomarker-Key ceRNA Regulatory Network. Biomarkers are represented by yellow nodes, the miRNA is represented by red, and lncRNAs are represented by blue nodes.

Drug prediction and molecular docking revealed tosyllysyl chloromethyl ketone (TLCK) as a potential therapeutic agent

To precisely screen potential therapeutic agents targeting MIRI, we individually conducted candidate drug screening for two targets, Nfkbia and Icam1, identifying 358 and 419 potential candidate drugs, respectively (Fig. 4A). Subsequently, molecular docking analysis was performed to further evaluate the binding affinity between these potential biomarkers and candidate drugs. The binding energies of the NFKBIA-TLCK complex and the ICAM1-TLCK complex were − 6.3 kcal/mol and − 5.1 kcal/mol, respectively, indicating high binding affinity and stability of both complexes (Fig. 4B and E; Table 2). These results revealed promising drug candidates that target Nfkbia and Icam1, thus providing an important foundation for individualized and precise treatment approaches to MIRI.

Fig. 4.

Drug Prediction and Molecular Docking. (A) The network of biomarker-targeted drug interactions. Yellow nodes stand for biomarkers, while blue nodes represent drugs. (B) Overall 3D structure rendering of the molecular docking between NFKBIA and TLCK. (C) A detailed perspective of the molecular docking interaction between NFKBIA and TLCK. The binding sites are presented in an enlarged view. Here, distinct protein chains are denoted by different colors. (D) Overall 3D structure rendering of the molecular docking between ICAM1 and TLCK. (E) A detailed perspective of the molecular docking interaction between ICAM1 and TLCK. The binding sites are presented in an enlarged view. Here, distinct protein chains are denoted by different colors.

Table 2.

Molecular docking results of potential biomarkers Nfkbia and Icam1 with TLCK.

Gene Symbol	Protein ID (AlphaFold)	Compound	Binding Free Energy (kcal/mol)
Nfkbia	AF-P13597	Tosyllysyl chloromethane	-6.3
Icam1	AF-Q9Z1E3	Tosyllysyl chloromethane	-5.1

Results of RT-qPCR

To validate the upregulation of the two potential biomarkers, RT-qPCR was performed on cardiac tissue from control and MIRI model mice. The results showed a significant increase in the mRNA expression of both Nfkbia and Icam1 in the MIRI group. The relative expression levels of Nfkbia were 1 ± 0.0696 in controls and increased to 1.1964 ± 0.0596 in the MIRI group (1.196-fold change, P = 0.0014). Icam1 expression was also significantly upregulated in the MIRI group, with relative expression levels of 1 ± 0.0811 in controls and 1.1269 ± 0.0903 in the MIRI group (1.127-fold change, P = 0.0475) (Fig. 5A-B; Table 3). Together, these results reflect that both Nfkbia and Icam1 may be potentially important regulators in MIRI progression.

Fig. 5.

Results of RT-qPCR. (A) The mRNA expression levels of Nfkbia in the control group and the MIRI group were detected (**P < 0.01). (B) The mRNA expression levels of Icam1 in both the control group and the MIRI group were examined (*P < 0.05).

Table 3.

Relative mRNA expression levels of Nfkbia and Icam1.

	Control	MIRI	P
Nfkbia	1 ± 0.0696	1.1964 ± 0.0596	0.0014
Icam1	1 ± 0.0811	1.1269 ± 0.0903	0.0475

Discussion

Notwithstanding advances in interventional cardiology, MIRI remains a significant clinical challenge, frequently leading to poor outcomes following reperfusion therapy³¹. The NF-κB pathway has been extensively validated as a key regulatory mechanism governing inflammatory responses and cell death during MIRI, making it a highly attractive therapeutic target³². By integrating bioinformatic analysis and experimental validation, we identified Nfkbia and Icam1 as critical NF-κB-RGs involved in MIRI. Analysis of multiple datasets revealed consistent upregulation of these two biomarkers in the MIRI group. Through enrichment analysis, construction of regulatory networks, drug prediction, and RT-qPCR validation, this study provided comprehensive evidence supporting their functional roles in the pathogenesis of MIRI and offered novel insights into the underlying processes.

Nfkbia and Icam1: Regulatory roles and pathological significance in MIRI

The Nfkbia gene is located at the 14q13.2 region of the human chromosome and encodes IκBα (NF-κB inhibitor alpha), the primary inhibitor of the NF-κB pathway³³. This pathway has been found to be involved in a variety of pathological processes, including inflammation, apoptosis, and oxidative stress, within a variety of pathological conditions like MIRI³⁴. Although direct studies on the role of Nfkbia in MIRI are currently lacking, this gene has been identified as an immune-related hub gene in hepatic ischemia-reperfusion injury, suggesting its regulatory role in the ischemia-reperfusion process of organs³⁵. Furthermore, there has been interest in studies investigating the roles of IκBα in pathways involved in MIRI. For example, in a mouse model of MIRI, berberine has been shown to upregulate IκBα levels by inhibiting IKK-β expression, thereby blocking the nuclear translocation of NF-κB p65 and effectively attenuating inflammation and apoptosis³⁶. This study suggested that upregulation of IκBα exerted a protective effect in MIRI. Similarly, electroacupuncture pretreatment at cardiac-specific acupoints led to an increased IκBα expression in the myocardium of MIRI rats. This was accompanied by the inhibition of NF-κB p65 and IKKβ, as well as a change in the pro-/anti-inflammatory balance³⁷. In this study, Nfkbia expression was significantly upregulated in both the training and validation sets within the MIRI groups. Although the elevated expression of this gene as an inhibitor of the NF-κB pathway appeared to be contrary to expectations, we interpreted it as a potential compensatory mechanism aimed at inhibiting the overactivated NF-κB signaling pathway under critical ischemia-reperfusion stress, which is consistent with the typical negative feedback regulatory profile of this pathway. Therefore, this elevated expression may reflect an endogenous attempt to suppress NF-κB-driven inflammatory and apoptotic signaling. However, this hypothesis required further validation through protein-level analysis and functional intervention studies to elucidate the dynamic role of Nfkbia in MIRI and its relationship with the activation state of the NF-κB pathway.

ICAM1 (Intercellular adhesion molecule 1, CD54) is a glycoprotein located on the cell surface and encoded by the Icam1 gene located on chromosome 19p13.2. This molecule is either constitutively expressed or induced on endothelial cells and immune cells. As a well-established key inflammatory mediator in MIRI, ICAM1 plays crucial roles in multiple cellular functions, such as cell adhesion, signal transduction, immune and inflammatory responses, as well as cellular processes like proliferation, migration, and apoptosis^38–41. In studies with MIRI rats, both Ischemic Preconditioning (IPC) and Limb Remote Ischemic Postconditioning (LRIPoC) were observed to reduce the levels of ICAM1 and other pro-inflammatory mediators in the serum and myocardium, resulting in smaller infarct sizes⁴². In addition, Buyang Huanwu Decoction (BYHWD) has been found to reduce infarct size and decrease the levels of the ICAM1 protein and pro-inflammatory cytokines in MIRI rats. This suggests that BYHWD may regulate tumor necrosis factor (TNF) and AK transforming 1 (AKT1) mediated inflammatory and apoptotic pathways by downregulating ICAM1⁴³. These findings were highly consistent with the previous studies. In this research, Icam1 was significantly overexpressed in the MIRI groups based on independent datasets, which was further validated by RT-qPCR. Icam1 is a crucial molecule mediating leukocyte-endothelial adhesion and promoting inflammatory cell infiltration. In MIRI, the upregulated Icam1 expression further enhances the recruitment and activation of inflammatory cells (e.g., neutrophils) in myocardial tissue, exacerbating microvascular dysfunction and secondary myocardial injury, thereby establishing a vicious cycle^44,45. Future studies should focus on elucidating the upstream regulatory mechanism of ICAM1 and evaluating its potential for clinical translation.

GSEA enrichment analysis revealed the distinct biological processes involving Icam1 and Nfkbia in MIRI

Functional enrichment analyses indicated that Icam1 and Nfkbia were involved in distinct pathological pathways. ICAM1 was most significantly enriched in the collagen-containing extracellular matrix pathway, whereas NFKBIA showed primary enrichment in the mitochondrial protein complex pathway. The results provide important clues to possible mechanisms by which these potential biomarkers may be involved in the pathogenesis of MIRI. However, it should be noted that these observations are based on bioinformatics predictions and reflect correlations rather than causality. Thus, further validation through functional experiments is required.

As an important supporting structure of tissue⁴⁶, extracellular matrix (ECM) dysfunction has a profound impact on a variety of diseases, including fibrotic diseases^47,48, aberrant vascular development⁴⁹, and the aging process⁵⁰. Although there is currently a lack of direct evidence that collagen-containing ECM is associated with MIRI, several studies have shown that ECM remodeling is a critical process in ischemia-reperfusion injury. For example, CLEC4E deficiency can reduce myocardial neutrophil infiltration and infarct size by upregulating transcripts involved in metabolism, antioxidant defense, and ECM remodeling⁵¹. Similarly, targeting Dectin-1, an upstream regulator of NF-κB, also modulates ECM-associated gene expression⁵². Notably, ischemia-reperfusion can enhance tissue stiffness via collagen glycation, activating mechanical stress-induced apoptotic pathways⁵³. These observations are consistent with our finding of upregulated Icam1 expression in MIRI, indicating that ICAM1 plays a pivotal mediating role in the pathogenesis of MIRI by integrating inflammatory signals and remodeling the mechanical microenvironment.

Nfkbia is primarily enriched in the mitochondrial protein complex pathway. Mitochondrial dynamics complexes include the fission mediator dynamin-related protein 1 (Drp1) and fusion regulators (Mfn1/2, OPA1). Beyond its canonical role as a cellular energy factory producing adenosine triphosphate (ATP), mitochondria are also involved in regulating ROS generation, calcium homeostasis, cell death (apoptosis and necrosis), and inflammatory signaling^54,55. Existing evidence suggests that balanced mitochondrial autophagy exerts cardioprotective effects by selectively eliminating damaged mitochondria, thus reducing excessive inflammation and maintaining cardiomyocyte integrity. Conversely, mitochondrial dysfunction leads to insufficient or excessive autophagy, thereby exacerbating MIRI^56–59. Isoliquiritigenin (ISL) has been shown to mitigate pathological mitochondrial fission and suppress ferroptosis by inhibiting Drp1 expression and activating the Nrf2/HO-1/SLC7A11/GPX4 pathway⁶⁰. These findings demonstrate that Drp1-mediated imbalance in mitochondrial dynamics is an important mechanism in MIRI, making inhibition of Drp1 an attractive therapeutic option^61–63. The enrichment of Nfkbia in mitochondrial protein complex pathways suggests its potential role as a therapeutic target in addressing the energy crisis by maintaining mitochondrial dynamics and metabolic homeostasis.

Integrated analysis predicted a potential ceRNA regulatory axis involving Nfkbia and Icam1 in MIRI

Although previous studies have confirmed that miR-107 regulates necroptosis in MIRI by targeting FADD⁶⁴, research on the ceRNA network associated with NF-κB pathway-related genes remains relatively limited. In this study, bioinformatics predictions revealed that Nfkbia and Icam1 are regulated by multiple upstream transcription factors. Among them, Icam1 may be involved in a ceRNA network mediated by mmu-miR-706. In this predicted ceRNA network, mmu-miR-706 directly targets Icam1 mRNA, while seven predicted long non-coding RNAs (lncRNAs) may act as molecular sponges. These lncRNAs sequester miR-706 by competing for its binding, thereby alleviating its repressive effect on Icam1, forming a functional ‘lncRNA–miR-706–Icam1’ axis. Notably, miR-706 has been shown to inhibit ferroptosis in cardiomyocytes during myocardial infarction by targeting Ptgs2⁶⁵, and it was identified as a miRNA significantly downregulated in white matter tissues following subarachnoid hemorrhage, where it alleviated neuroinflammation by suppressing the PKCα/MST1/NF-κB pathway⁶⁶. These findings in different disease models suggested that miR-706 may play a conserved protective role in ischemic injury. Contrary to these studies, the present work, based on bioinformatic predictions, proposed that miR-706 may participate in inflammatory regulation by targeting Icam1 in the setting of MIRI. We hypothesized that, during MIRI, ischemia-reperfusion stress may upregulate specific lncRNAs that act as miRNA sponges to act competitively by binding to mmu-miR-706. This process could potentially relieve the inhibitory effect of mmu-miR-706-mediated on Icam1, leading to increased expression of ICAM1, enhanced leukocyte adhesion and infiltration, and ultimately exacerbating inflammatory injury in myocardial tissue. It should be emphasized that this proposed regulatory network is currently based on the theoretical model of bioinformatics predictions, and its biological function and molecular mechanisms in the actual process of MIRI require further experimental validation. GGI analysis indicated that Nfkbia and Icam1, along with other functionally related genes, were involved in key biological processes such as the IκB kinase/NF-κB signaling pathway and negative regulation of cytokine production. As mentioned earlier, these processes were closely associated with the pathological mechanisms of MIRI.

The potential mechanisms of TLCK in MIRI

TLCK is an irreversible serine protease inhibitor that inhibits the activity of trypsin and serine-like proteases (e.g., thrombin) by covalently binding to the active sites of these proteases^67,68. Studies have shown that TLCK can inhibit abnormally elevated thrombin activity in the brain and block the excessive activation of the protease-activated receptor 1 (PAR1) signaling pathway⁶⁹. In MIRI, ischemia-reperfusion stress can activate the coagulation system, leading to increased thrombin levels in myocardial tissue. This, in turn, triggers inflammation, endothelial injury, and cell apoptosis via the PAR1 pathway⁷⁰. These findings suggested that TLCK may reduce PAR1-mediated inflammatory cascades and cellular damage by reducing thrombin activity in the myocardium. Additionally, TLCK can inhibit trypsin-like serine proteases, including thrombin, elastase, etc^71,72. In MIRI, these enzymes can activate inflammatory pathways such as NF-κB and degrade the extracellular matrix, thereby exacerbating inflammatory damage^73,74. Based on the molecular docking results obtained in this study, TLCK exhibited strong binding affinity to both NFKBIA and ICAM1. NFKBIA is a well-established negative regulator of the NF-κB pathway (64), while ICAM1 plays a key role in leukocyte adhesion to endothelial cells and the inflammatory response (65, 66). Therefore, TLCK may exert protective effects by stabilizing NFKBIA to suppress excessive activation of the NF-κB pathway and by interfering with ICAM1-related processes to modulate leukocyte infiltration. However, these hypothesized mechanisms were still based on molecular docking results and preliminary inferences in existing literature, and the functional correlation in cardiomyocytes or myocardial tissue has not been supported by direct experimental evidence, necessitating further validation through subsequent studies.

Limitations

Although this study preliminarily confirmed the upregulation of Nfkbia and Icam1 in MIRI through bioinformatics analysis and RT-qPCR validation, several limitations should be acknowledged. First, the validation was primarily focused on the mRNA level, lacking direct evidence from protein expression, NF-κB pathway activation status, or functional intervention experiments. Second, the analysis was based on a limited number of public datasets with relatively small sample sizes and without correction for multiple hypothesis testing, which may affect the robustness of the findings. In addition, the proposed ceRNA regulatory network and pathway enrichment analyses were based on database predictions, and the results merely reflected potential molecular associations. Finally, the protein structures used in molecular docking were predicted by AlphaFold predictions, and their binding conformations have not been experimentally validated. Therefore, the current data should be considered suggestive rather than conclusive and were insufficient to support any definitive conclusions about TLCK’s biological activity or therapeutic potential. To address these limitations, future studies will conduct multi-level experimental validations. On one hand, the regulatory roles of Nfkbia and Icam1, as well as the intervention effects of TLCK, will be systematically evaluated using western blot, immunohistochemistry, and gain- and loss-of-function experiments. Meanwhile, larger sample cohorts and more rigorous statistical analysis methods will be employed to enhance the reliability of the conclusions. On the other hand, luciferase reporter assays will be performed to validate the ceRNA regulatory axis, while surface plasmon resonance and other techniques will be utilized to verify the actual binding capacity between small molecules and target proteins. Functional experiments will further elucidate the molecular mechanisms underlying the relevant pathways, thereby establishing a more robust experimental foundation for targeted therapies in MIRI.

Conclusion

This study integrated bioinformatics analysis with RT-qPCR validation to identify Nfkbia and Icam1 as potential markers of the NF-κB pathway in MIRI. Both public datasets and our experimental results indicated that these two genes were upregulated in MIRI. To investigate the potential regulatory mechanisms, we constructed a preliminary transcription factor-associated network and, based on in silico predictions, proposed a ceRNA regulatory axis involving lncRNAs, mmu-miR-706, and Icam1, offering exploratory insights into the post-transcriptional regulation of these genes during MIRI. Moreover, drug prediction and molecular docking analyses suggested that TLCK may exhibit binding affinity toward NFKBIA and ICAM1; however, its specific role in cardiomyocytes and the associated anti-inflammatory mechanisms still need to be further verified by future functional studies. Collectively, these findings provide preliminary theoretical evidence for inflammatory responses and post-transcriptional regulatory networks in MIRI, laying a foundation for further research on targeted diagnostic and therapeutic strategies.

Supplementary Information

Below is the link to the electronic supplementary material.

Author contributions

Conceptualization and design: Wang Ting and Xiao Helong. Data curation: Hu Xiao and Wang Xiaoyu. Formal analysis and Visualization: Wang Chuanqiang and Yuan Zhe. Funding acquisition: Yang Yang, Xu Shaopeng, and Geng Xiaoyong. Investigation: Wang Xiaoyu, Hu Xiao, and Yang Yang. Methodology: Yang Yang and Geng Xiaoyong. Project administration: Yang Yang and Geng Xiaoyong. Resources: Yang Yang and Hu Xiao. Software: Wang Chuanqiang. Study supervision: Yang Yang, Xu Shaopeng, and Geng Xiaoyong. Validation: Wang Ting, Xiao Helong, and Wang Chuanqiang. Writing-original draft: Wang Ting and Xiao Helong. Manuscript editing and review: All authors. All authors contributed to the article and approved the submitted version.

Funding

This work was supported by the Government-Funded Program for Cultivating Outstanding Clinical Medicine Talents of Hebei Province (2025) (Grant Nos. ZF2025152).

Data availability

The transcriptomics datasets analyzed during the current study are publicly available in the Gene Expression Omnibus (GEO) repository: The training set (GSE255933) is available at: https://identifiers.org/geo: GSE255933; the validation set (GSE281940) is available at: https://identifiers.org/geo: GSE281940. The miRNA dataset (GSE124176) is available at: https://identifiers.org/geo: GSE124176. The NF-κB pathway-related genes were obtained from the KEGG database (pathway ID: mmu04064, https://www.kegg.jp/entry/mmu04064). The underlying data supporting the RT-qPCR results are available within the paper and its Supplementary Information (see Supplementary Table 6). All scripts and code used in this study may be obtained from the corresponding author upon reasonable request.

Declarations

Competing interests

The authors declare no competing interests.

Ethics approval

This study was reviewed and approved by the Animal Care and Use Committee of The Third Hospital of Hebei Medical University (Approval No. Z2025-036-1). All procedures were performed in accordance with the National Institutes of Health (NIH) Guide for the Care and Use of Laboratory Animals, and every effort was made to minimize animal suffering.