Expression characteristics of lipid metabolism-related genes and correlative immune infiltration landscape in acute myocardial infarction

Lipid metabolism is an important part of the heart's energy supply. The expression pattern and molecular mechanism of lipid metabolism-related genes (LMRGs) in acute myocardial infarction (AMI) are still unclear, and the link between lipid metabolism and immunity is far from being elucidated. In this study, 23 Common differentially expressed LMRGs were discovered in the AMI-related mRNA microarray datasets GSE61144 and GSE60993. These genes were mainly related to “leukotriene production involved in inflammatory response”, “lipoxygenase pathway”, “metabolic pathways”, and “regulation of lipolysis in adipocytes” pathways. 12 LMRGs (ACSL1, ADCY4, ALOX5, ALOX5AP, CCL5, CEBPB, CEBPD, CREB5, GAB2, PISD, RARRES3, and ZNF467) were significantly differentially expressed in the validation dataset GSE62646 with their AUC > 0.7 except for ALOX5AP (AUC = 0.699). Immune infiltration analysis and Pearson correlation analysis explored the immune characteristics of AMI, as well as the relationship between these identified LMRGs and immune response. Lastly, the up-regulation of ACSL1, ALOX5AP, CEBPB, and GAB2 was confirmed in the mouse AMI model. Taken together, LMRGs ACSL1, ALOX5AP, CEBPB, and GAB2 are significantly upregulated in AMI patients' blood, peripheral blood of AMI mice, myocardial tissue of AMI mice, and therefore might be new potential biomarkers for AMI.


PPI
Protein-protein interaction ROC Receiver operating characteristic Acute myocardial infarction (AMI) is known for its high disability and mortality, which seriously endangers human health 1 .AMI is the result of acute interruption of myocardial blood flow followed by myocardial ischemic hypoxic necrosis 2 .Generally speaking, the traditional risk factors include age, smoking, hypertension, obesity, diabetes, family history, etc. 3 .Early opening of the infarcted vessel and restoration of myocardial blood perfusion, namely drug thrombolysis or percutaneous coronary intervention (PCI) surgery, are the main strategies to reduce the size of myocardial infarction, reduce mortality and improve the prognosis 4,5 .Clinically, ECG and high-sensitive cardiac troponin I are widely used as the diagnostic indicators of AMI.However, about one-third of patients still do not receive reperfusion therapy as early as possible due to the belated diagnosis 6 .Therefore, searching for potential biomarkers with high sensitivity and specificity at the beginning of AMI is of great value for the individualized diagnosis and treatment of AMI patients.
Lipid metabolism is closely related to AMI.Under physiological conditions, 50-70 percent of the heart's energy support comes from fatty acid β-oxidation and the rest comes from the metabolism of glucose, lactate, ketone bodies, and amino acids 7 .During AMI, the blood supply to the heart is insufficient, oxygen and nutrients are not available, and the metabolic pattern of the heart is correspondingly altered 8,9 .There have been some researches on gene regulation or molecular therapy related to lipid metabolism in AMI.Higher lipid availability has been reported to promote ischemia-induced cardiac dysfunction and reduce myocardial mitochondrial efficiency 10 .PEDF (Cytochrome c-550 PedF) promotes TG (Triglycerides) degradation of cardiomyocytes through ATGL (Patatin like phospholipase domain containing 2), reduces infarct size, and protects cardiac function 11 .APOE (Apolipoprotein E) deficiency leads to the formation of excess neutrophil extracellular trap and aggravates myocardial injury in mice model of myocardial infarction 12 .Omega-3 polyunsaturated fatty acid supplementation reduced the levels of apolipoprotein B, triglycerides, lipoprotein (a), and exerted a protective effect on AMI by affecting the systemic eicosanoid metabolic status 13 .These studies indicate that lipid metabolism is associated with the formation and development of AMI, and that intervention targeting this process may be an effective therapeutic strategy.
The expression pattern and molecular mechanism of lipid metabolism-related genes (LMRGs) in AMI remain unclear, and their relationship with immunity is far from clarified.This study used bioinformatics methods to screen and verify differentially expressed LMRGs based on AMI-related datasets.ROC analysis was performed to evaluate the diagnostic value of these genes.Immune infiltration analysis and Pearson correlation analysis were performed to explore the relationship between these genes and immunity.Finally, the expression of the identified LMRGs was verified in the mouse AMI model.Figure 1 shows the workflow of the specific analysis.Above all, this study will explore the mechanism of lipid metabolism involved in AMI and provide new directions for the clinical individualized diagnosis and treatment of AMI.

Expression pattern and molecular mechanism of Common differentially expressed LMRGs in AMI
The expression patterns of common differentially expressed LMRGs were compared based on their original gene expression data in GSE61144 and GSE60993.We used the online drawing tool of the Sangerbox website to draw the violin plots.As shown in Fig. 3A,B, the expression trend of these LMRGs was completely consistent in these two datasets, and their expression was up-regulated except for CCL5 and RARRES3.The correlation heatmap demonstrated the correlation of the expression of these identified genes (Fig. 3C,D).The Pearson correlation analysis showed that MTMR3 and OSBPL2 had a strong positive correlation (GSE61144, correlation coefficient (CRC) = 0.93; GSE60993, CRC = 0.93), GAB2 and MTMR3 had a strong positive correlation (GSE61144, CRC = 0.96; GSE60993, CRC = 0.92), ALOX5 and CEBPD had a strong positive correlation (GSE61144, CRC = 0.94; GSE60993, CRC = 0.87), but ALOX5 and RARRES3 had a strong negative correlation (GSE61144, CRC = − 0.9; GSE60993, CRC = − 0.9).Detailed Pearson correlation analysis of genes with strong correlations (CRC > 0.9) was presented in Supplementary File 2.
GO annotation and KEGG pathway enrichment analysis of these LMRGs were performed to explore the biological processes or signaling pathways involved in AMI.GO annotation results showed that these genes are mainly enriched in "phosphatidylinositol biosynthetic process", "lipid homeostasis", "leukotriene production involved in inflammatory response", "lipoxygenase pathway", "cytosol", "membrane", and "protein serine/threonine phosphatase activity" pathways (Fig. 3E).For KEGG pathway enrichment analysis, the mainly enriched pathways are "metabolic pathways", "regulation of lipolysis in adipocytes", "insulin resistance", "TNF signaling pathway", "autophagy-animal", "cGMP-PKG signaling pathway", "cAMP signaling pathway", and "ovarian steroidogenesis" (Fig. 3F).PPI analysis of the proteins expressed by these LMRGs was performed by the String database.Finally, a PPI network with 22 nodes and 55 edges was constructed (Fig. 3G).In this network diagram, orange-red nodes represent high-expressed genes, blue nodes represent low-expressed gene genes, and the depth of color represents the degree of gene expression change in GSE61144.

Validation of common differentially expressed LMRGs in external dataset GSE62646
Based on the original gene expression data in GSE62646 (log2 transformed data), the expression of common differentially expressed LMRGs was evaluated.Compared with the control group (CON-admission), 12 LMRGs were significantly differentially expressed in patients with AMI (Fig. 4A-L).They were ACSL1, ADCY4, ALOX5,

Characteristics of immune infiltration in patients with AMI
Immune infiltration analysis based on GSE62646 was performed to identify immune regulation in AMI.The results showed that AMI patients had a higher level of memory B cells, regulatory T cells (Tregs), Monocytes, M0 macrophages, and a lower level of resting NK cells, M2 macrophages (Fig. 6A).The Monocytes and Neutrophils of the AMI patients were significantly decreased after reperfusion therapy (Fig. 6A).In addition, Native B cells and M2 macrophages increased significantly, memory B cells, Plasma cells, and Monocytes decreased significantly 6 months after patient discharge (Fig. 6A).The Pearson correlation analysis was performed to explore the correlation between identified LMRGs and immune cells (Fig. 6B).The results showed that ACSL1 (CRC = 0.81), ALOX5 (CRC = 0.76), CEBPD (CRC = 0.75), and CREB5 (CRC = 0.80) had a strong positive correlation with Monocytes (Fig. 6C-F).

RT-qPCR verification of identified LMRGs in mouse AMI model
To further validate the identified LMRGs, the mouse AMI model was generated (Fig. 7A).HE staining showed a disordered arrangement of myocardial cells and increased inflammatory cell infiltration in the infarcted myocardium (Fig. 7B).Tunel staining showed a significant increase in cell apoptosis during infarction (Fig. 7C).
To verify the expression trend of the identified biomarkers (Acsl1, Adcy4, Alox5, Alox5ap, Ccl5, Cebpb, Cebpd, Creb5, Gab2, Pisd, and Znf467), RT-qPCR was performed on peripheral blood from mice with AMI (Fig. 7D).RT-qPCR results showed that 6 genes were significantly differentially expressed.They were Acsl1, Alox5, Alox5ap, Cebpb, Gab2 and Znf467.Consistent with the analysis results, these genes were significantly upregulated in the  blood of mice with AMI.To explore whether the identified genes are differentially expressed and play certain functions in cardiac tissue, we further performed RT-qPCR on the cardiac tissue of mice (Fig. 7E).The results showed that 7 genes were significantly differentially expressed.They were Acsl1, Alox5ap, Ccl5, Cebpb, Cebpd, Creb5, and Gab2.Notably, these genes were all significantly upregulated in myocardial tissue, while the trend of Acsl1, Alox5ap, Cebpb, and Gab2 was consistent with the blood test results.
It should be especially mentioned that after careful searching of NCBI (https:// www.ncbi.nlm.nih.gov/ gene/?term= RARRE S3) database and Biomart (https:// bioco nduct or.org/ packa ges/ relea se/ bioc/ html/ bioma Rt. html) database, no homologous gene of RARRES3 gene in the species of Mus musculus was found, so it was not tested.

Discussion
AMI is one of the leading causes of cardiovascular death worldwide 14 .Since dead cardiomyocytes are unable to regenerate and repair, early reperfusion treatment has become the main measure to reduce myocardial infarction size, reduce mortality, and improve the prognosis of these patients 15 .Therefore, it is helpful and urgent to find biomarkers with high sensitivity and specificity for the early diagnosis of AMI to improve the clinical outcome of patients.Energy metabolism and inflammatory reactions play important roles in the formation and development of AMI, and lipid metabolism plays a vital part in the energy metabolism of myocardial cells 16 .Our study is based on the studies of lipid metabolism and its link with immunity in the field of AMI.
This study identified 23 common differentially expressed LMRGs in the existing AMI-related public datasets GSE61144 and GSE60993 in the GEO database.These genes had the same expression trends in both datasets.The results of enrichment analysis indicated that these genes were mainly associated with "lipid homeostasis", "leukotriene production involved in inflammatory response", "lipoxygenase pathway", "metabolic pathways", "regulation of lipolysis in adipocytes", "insulin resistance", "TNF signaling pathway" pathways.The String database was used for PPI analysis and a network diagram was constructed.It has been reported that adrenergic overdrive and adipose tissue lipolysis during AMI induce cardiac AMPK-FGF21 feed-forward loop that may provide cardiac protection against ischemic injury 17 .The lipoxygenase pathway and its products 5-oxo-6,8,11,14-eicosatetraenoic acid (5-oxo-ETE) and leukotrienes induce cardiomyocyte injury in the mice model of ischemic myocardial injury and hypoxia/glucose deprivation 18 .The role of insulin resistance and the TNF signaling pathway in AMI has also been investigated [19][20][21] .These results suggest that the biological processes or signaling pathways predicted in this study are consistent with the existing findings.The mechanism of the identified LMRGs in the corresponding signaling pathways still needs to be further studied.
The external dataset GSE62646 further confirmed the differential expression of these 12 common differentially expressed LMRGs: ACSL1, ADCY4, ALOX5, ALOX5AP, CCL5, CEBPB, CEBPD, CREB5, GAB2, PISD, RARRES3, and ZNF467.Except for ALOX5AP (AUC = 0.6990), the AUC of these 12 genes is all over 0.7.A clinical study involving 75 AMI patients and 70 healthy controls suggests that ACSL1 is highly expressed in the peripheral blood of AMI patients and may serve as a molecular marker for assessing the risk of AMI 22 .The diagnostic value of CCL5 and CEBPB in AMI has also been identified in other studies 23,24 .These studies suggest that the genes identified in our study may serve as potential biomarkers to provide early warning signals of AMI.
The results of immune infiltration analysis showed that AMI patients had a higher level of memory B cells, regulatory T cells (Tregs), Monocytes, M0 macrophages, and a lower level of resting NK cells, M2 macrophages.These results are consistent with the previously reported results of AMI-related immune infiltration analysis 25,26 .The association of these identified immune cells with AMI has been partially investigated.One study has shown that CD4 + FoxP3 + CD73 + regulatory T cells promote cardiac healing after myocardial infarction 27 , and another www.nature.com/scientificreports/study has shown that M0 macrophages upregulate and participate in immune regulation in AMI 25 .Exosomes derived from regulatory T cells could ameliorate AMI by promoting M2 polarization of macrophages 28 .The Pearson correlation analysis suggested a positive correlation between ACSL1, ALOX5, CEBPD, CREB5 and Monocytes.It has been shown that LPS could induce ALOX5 promoter activation and promote 5-lipoxygenase expression in human monocytes 29 .It has also been found that CEBPD is highly expressed in CD14(+) monocytes from patients with primary Sjogren's syndrome, and is involved in the TNF-α signaling pathway through NF-κB 30 .Whether these identified immune cells or genes are involved in AMI via a similar mechanism requires further investigation.
In addition, the expression of Acsl1, Alox5, Alox5ap, Cebpb, Gab2, and Znf467 was confirmed in the mouse AMI model.ACSL1 is a key rate-limiting enzyme in the process of lipid metabolism that catalyzes the conversion of long-chain fatty acids to their active form acyl-CoAs for cellular lipid synthesis 31 .ACSL1 overexpression in hepatocytes has been reported to increase intracellular triglyceride levels by reducing fatty acid β-oxidation through the PPARγ pathway 32 .The decreased expression of ACSL1 can alleviate hypoxia-induced injury of AC16 cardiomyocytes 33 .ALOX5 belongs to a class of non-heme iron dioxygenases involved in the catalysis of leukotriene biosynthesis.Targeted inhibition of ALOX5 can protect the heart from remodeling and heart failure stimulated by hypertension by disturbing the LLPS of Runx2 in cardiomyocytes 34 .Pharmacological inhibition of ALOX12, a member of the same gene family, ameliorates myocardial ischemia-reperfusion injury in a variety of animals 35 .ALOX5AP acts by activating ALOX5.CEBPB is an important transcription factor that regulates the expression of genes related to immune and inflammatory responses, and it also plays an important role in lipogenesis, gluconeogenesis, liver regeneration, and hematopoiesis 36 .GAB2 acts downstream of a variety of membrane receptors, such as cytokines, growth factor receptors, antigens, and hormones, and regulates a variety of signaling pathways.A recent study has shown that the Gab2-MALT1 axis regulates thrombotic inflammation and is associated with deep vein thrombosis 37 .ZNF467 is a transcription factor that promotes adipocyte differentiation and inhibits osteoblast differentiation in the bone marrow 38 .CEBPB, GAB2, and ZNF467 have not been studied in the field of AMI, which represents a new research direction.
To explore whether the identified genes are differentially expressed and play certain functions in cardiac tissue, we further performed RT-qPCR on the cardiac tissue of mice.Finally, the expression of Acsl1, Alox5ap, Ccl5, Cebpb, Cebpd, Creb5, and Gab2 was confirmed.Taken together, we found that ACSL1, ALOX5AP, CEBPB, and GAB2 are significantly upregulated in AMI patients' blood, the peripheral blood of AMI mice, and the myocardial tissue of AMI mice.These genes may be used as potential biomarkers of AMI in blood, and may also affect the pathophysiological process of AMI by regulating lipid metabolism in cardiac myocytes.
This study is based on database mining and preliminary experiments, which also have some limitations.Although the results of multiple datasets were combined to enhance the strength of the results, the total sample size was still limited.Enrichment analysis and PPI analysis have explored the potential mechanisms of the identified LMRGs, but further functional experiments are still lacking.It is necessary to further explore the expression patterns of the identified genes in large human populations and conduct functional studies in vivo or in vitro to explore the exact mechanisms of these genes.

Data acquisition and collation
Data mining of transcriptome sequencing data collected in online databases and finding target genes of interest is an important method for life science research.The AMI-related mRNA microarray datasets GSE61144, GSE60993, and GSE62646 were obtained from the Gene Expression Omnibus (GEO) database (https:// www.ncbi.nlm.nih.gov/).These datasets all contain the following features: (1) From patients with AMI; (2) The sample source was human peripheral blood; (3) Provide complete raw mRNA profiling data.GSE61144 was obtained from the GPL6106 platform Sentrix Human-6 v2 Expression BeadChip.GSE60993 was obtained from the GPL6884 platform Illumina HumanWG-6 v3.0 expression beadchip 39 .GSE62646 was obtained from the GPL6244 platform [HuGene-1_0-st] Affymetrix Human Gene 1.0 ST Array [transcript (gene) version] 40 .The sample source for GSE61144 and GSE60993 was whole peripheral blood from human AMI patients and controls.The samples in GSE62646 were obtained from peripheral blood mononuclear cells of human AMI patients and controls.GSE61144 included 10 healthy controls and 7 AMI patients.GSE60993 included 7 healthy controls and 7 AMI patients.GSE62646 included 14 healthy controls and 28 AMI patients.Blood samples of AMI patients in GSE62646 were collected from patients on admission (1st day of MI), at discharge (4-6 days after MI), and at follow-up (6 months after MI).Supplementary File 3 presents the details of the included datasets and patients.The original gene expression data for each sample in these datasets were downloaded, and then further analysis was performed using GSE61144 and GSE60993 as the analysis datasets and GSE62646 as the external validation dataset.
The data preprocessing method is as follows: (1) Download the original gene expression matrix file; (2) Probe names were converted to gene names using the annotated list of datasets in the GEO database; (3) Quality control: Unqualified microarray data were excluded, such as genes with very low expression; (4) The log2 transformed was used to normalize the data.

Identification of DEGs and differentially expressed LMRGs in AMI
Differential expression analysis of datasets GSE61144 and GSE60993 was performed by the "limma package" of R software (version 4.0.1) to screen differentially expressed genes (DEGs) between AMI and healthy controls.Benjamini-Hochberg method was used to correct adj.P for potential false positive results.The threshold for DEGs was set as adj.P value < 0.05 and |logFC|≥ 0.8 (FC stands for Fold change, it refers to the ratio of the mean relative gene expression of the AMI group to the control group).Previous studies related to bioinformatics www.nature.com/scientificreports/analysis of lipid metabolism were retrieved and integrated to obtain LMRGs [41][42][43][44] .1454 LMRGs were finally sorted out as candidate genes for subsequent analysis (Supplementary File 4).Differentially expressed LMRGs in datasets GSE61144 and GSE60993 were identified by the online Venn diagram website (http:// bioin forma tics.psb.ugent.be /webtools/Venn/).LMRGs that were differentially expressed in both datasets were considered as common differentially expressed LMRGs.

Pathway enrichment analysis
Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis 45 were performed on the common differentially expressed LMRGs by the "cluster Profiler package" of R software (version 4.0.1) to explore the biological pathways and molecular mechanisms associated with these genes.GO annotation consists of biological process (BP) analysis, cellular component (CC) analysis, and molecular function (MF) analysis.A threshold of p < 0.05 was used to screen the relevant pathways.Based on the analysis results, the Sangerbox online website (version 3.0, http:// sange rbox.com/ home.html) was used to visualize and draw the relevant lollipop and chord diagrams 46 .

Protein-protein interaction analysis
The String (https:// cn.string-db.org) online database was used to carry out the protein-protein interaction (PPI) analysis to identify the interactions between the proteins expressed by the common differentially expressed LMRGs.The default parameters of the String database were used for analysis and the Cytoscape software was used to visualize the analysis results and draw the network diagram.

Validation of common differentially expressed LMRGs in external datasets GSE62646
Dataset GSE62646 contains gene expression data on admission (1st day of MI), at discharge (4-6 days after MI), and at follow-up (6 months after MI) in AMI patients.Based on the original gene expression data in this dataset, the expression differences and dynamic changes of common differentially expressed LMRGs were further verified.Independent sample T-test was used for comparison between the two groups, and P < 0.05 was considered statistically significant.

ROC curve analysis
Based on the original gene expression data of the AMI-admission group and control group in GSE62646 (log2 transformed data), the diagnostic value of the common differentially expressed LMRGs for AMI was assessed by receiver operating characteristic (ROC) curve analysis through GraphPad Prism software (version: 9.0).The area under the curve (AUC) ≥ 7.0 and P < 0.05 were used as screening criteria for potential biomarkers in this analysis.We then sorted the gene expression data in GSE62646 from small to large, and took the value ranked in 1/3 (high-expressed genes) as the diagnostic threshold of disease (nCON: nAMI = 1:2).For low-expressed genes, the value is sequenced in 2/3 digits.The sensitivity, specificity, and accuracy of each gene were calculated based on the diagnosis and actual results.

Immune infiltration analysis
To further explore the association between LMRGs and immune response, the samples in GSE62646 were analyzed for immune infiltration by the CIBERSORT algorithm 47 .The composition and scores of 22 immune cell subpopulations in AMI and control groups were calculated by gene expression profiling.We performed log2 transformations on the original gene expression data and used the Pearson correlation analysis method to analyze the correlation between gene expression levels and immune cell scores.The "ggplot2 package" of R software was performed to visualize the analysis results.

Establishment of the mouse AMI model
Animal facilities and experimental protocols were performed in accordance with the Care and Use of Laboratory Animals of the National Institute of Health (8th edition, 2011).C57BL/6 wild-type mice (SPF, male, 8-week-old) were purchased from the Shanghai Animal Laboratory Center.The mice were randomly divided into the sham operation group and the AMI group.The AMI model was established by ligation of the left anterior descending artery according to the previous published literature [48][49][50] .The brief method is as follows: after one week of adaptive rearing, mice were anesthetized with intraperitoneal injection of pentobarbital (70 mg/kg); After confirming no response to foot compression, endotracheal intubation was performed, the chest cavity was opened, and left anterior descending branch was ligated with 8.0 silk thread.To examine gene expression changes in the acute phase of AMI, heart tissue and orbital venous blood were obtained from mice at 24 h after AMI.This study was ethically reviewed and approved by the Animal Experiment Center of Wuhan University (Approval No. ZN2023201).All the experiments were conducted following the ARRIVE (Animal Research: Reporting of In Vivo Experiments) guidelines.

Hematoxylin-eosin (HE) staining and TUNEL assay
To assess cardiac morphological features, freshly isolated hearts were fixed in 4% paraformaldehyde, embedded in paraffin, transverse sections (4-5 μm) were made and stained with HE.Images of stained cardiac tissue were obtained using a Leica DMI3000B microscope.To assess apoptosis, sections of paraffin-embedded myocardial tissue were stained using the TUNEL Kit (Roche Applied Science, Upper Bavaria, Germany).The results were observed by an inverted fluorescence microscope (Olympus, Tokyo, Japan), and the apoptotic cells were stained brown-yellow.With Gapdh as the internal reference gene, the method of 2 −ΔΔCt was used to compare the expression changes of LMRGs between the mouse AMI group and the Sham group 51,52 .Supplementary File 5 presents the details of the primers used for the RT-qPCR reactions.

Figure 1 .
Figure 1.The workflow of the specific analysis.

Figure 2 .
Figure 2. Identification of DEGs and differentially expressed LMRGs in AMI.(A) The volcano plot of GSE61144.(B) The volcano plot of GSE60993.The top three up-regulated or down-regulated gene names are shown in panel A,B.The horizontal lines represent the filtering thresholds for P-values.The vertical lines represent the screening threshold for the fold changes.(C) Venn diagram of DEGs in GSE61144, GSE60993, and candidate LMRGs.(D) Heatmap of common differentially expressed LMRGs in GSE61144.(E) Heatmap of common differentially expressed LMRGs in GSE60993.

Figure 3 .
Figure 3. Expression pattern and molecular mechanism of Common differentially expressed LMRGs in AMI.(A) Expression pattern of common differentially expressed LMRGs in GSE61144 (n CON:n AMI = 10:7).(B) Expression pattern of common differentially expressed LMRGs in GSE60993 (n CON:n AMI = 7:7).(C) Correlation heatmap of common differentially expressed LMRGs in GSE61144.(D) Correlation heatmap of common differentially expressed LMRGs in GSE60993.(E) Lollipop plots of GO annotation results.(F) Circle plot of KEGG pathway enrichment analysis.The color blocks in the left half correspond to the gene names, the color blocks in the right half correspond to the signal pathways, the inner circle in the right half represents the P-value, and the thickness of the line represents the number of genes in the signal pathway.(G) The PPI Network of common differentially expressed LMRGs.For panels (A,B) **P < 0.01; ***P < 0.001; ****P < 0.0001.

Table 1 .
The sensitivity, specificity, and accuracy of Common differentially expressed LMRGs for AMI.