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Diagnostic biomarkers and immune infiltration profiles common to COVID-19, acute myocardial infarction and acute ischaemic stroke using bioinformatics methods and machine learning

BMC Neurology volume 25, Article number: 201 (2025) Cite this article

Abstract

Background

COVID-19 is a disease that affects people globally. Beyond affecting the respiratory system, COVID-19 patients are at an elevated risk for both venous and arterial thrombosis. This heightened risk contributes to an increased probability of acute complications, including acute myocardial infarction (AMI) and acute ischemic stroke (AIS). Given the unclear relationship between COVID-19, AMI, and AIS, it is crucial to gain a deeper understanding of their associations and potential molecular mechanisms. This study aims to utilize bioinformatics to analyze gene expression data, identify potential therapeutic targets and biomarkers, and explore the role of immune cells in the disease.

Methods

This study employed three Gene Expression Omnibus (GEO) datasets for analysis, which included data on COVID-19, AMI and AIS. We performed enrichment analysis on the co-DEGs for these three diseases to clarify gene pathways and functions, and also examined the relationship between co-DEGs and immune infiltration. Machine learning techniques and protein–protein interaction networks (PPI) were used to identify hub genes within the co-DEGs. Finally, we employed a dual validation strategy integrating independent GEO datasets and in vitro experiments with human blood samples to comprehensively assess the reliability of our experimental findings.

Results

We identified 88 co-DEGs associated with COVID-19, AMI and AIS. Enrichment analysis results indicated that co-DEGs were significantly enriched in immune inflammatory responses related to leukocytes and neutrophils. Immune infiltration analysis revealed significant differences in immune cell populations between the disease group and the normal group. Finally, genes selected through machine learning methods included: CLEC4E, S100A12, and IL1R2. Based on the PPI network, the top ten most influential DEGs were identified as MMP9, TLR2, TLR4, ITGAM, S100A12, FCGR1A, CD163, FCER1G, FPR2, and CLEC4D. The integration of the protein–protein interaction (PPI) network with machine learning techniques facilitated the identification of S100A12 as a potential common biomarker for early diagnosis and a therapeutic target for all three diseases. Ultimately, validation of S100A12 showed that it was consistent with our experimental results, confirming its reliability as a biomarker. Moreover, it demonstrated good diagnostic performance for the three diseases.

Conclusion

We employed bioinformatics methods and machine learning to investigate common diagnostic biomarkers and immune infiltration characteristics of COVID-19, AMI and AIS. Functional and pathway analyses indicated that the co-DEGs were primarily enriched in immune inflammatory responses related to leukocytes and neutrophils. Through two machine learning approaches and the PPI network, and subsequent validation and evaluation, we identified S100A12 as a potential common therapeutic target and biomarker related to immune response that may influence these three diseases.

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Introduction

The outbreak of the COVID-19 virus in 2019 was a global event that affected nearly all of humanity. Due to the lack of specific symptomatic treatments, it prompted the global medical community to delve deeply into its pathophysiological mechanisms. However, the virus continues to spread globally, driven by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), commonly known as COVID-19. Clinically, it primarily presents as lung infections and various respiratory symptoms. In severe instances, the condition can escalate to acute respiratory distress syndrome (ARDS) [1].

In COVID-19 patients, in addition to respiratory system impairment, researchers have also observed severe complications related to the cardiovascular and cerebrovascular systems [2, 3]. COVID-19 patients face an elevated risk of venous and arterial thrombosis, which in turn raises the likelihood of acute complications, including acute myocardial infarction (AMI) and acute ischemic stroke (AIS) [4, 5]. Many studies have indicated an increased incidence of AMI and AIS in the COVID-19 population, particularly among the elderly, who exhibit elevated levels of D-dimer and abnormal fibrinogen levels. This group also has a higher mortality rate compared to other populations [2, 6]. The damage to the cardiovascular and cerebrovascular systems is associated with coagulation abnormalities leading to thrombosis. The mechanisms may involve inflammation-induced endothelial injury, the release of cytokines, activation of tissue factor (TF), complement system activation, and the presence of antiphospholipid antibodies [7]. This coagulation abnormality in the COVID-19 population, particularly among the elderly, is prone to result in the occurrence of acute diseases, especially those associated with poor prognosis [2, 3, 8].

Given the ambiguous connections between COVID-19, acute myocardial infarction (AMI), and acute ischemic stroke (AIS), it is essential to explore their relationships and the underlying molecular mechanisms more thoroughly. Although numerous studies have explored the relationship between COVID-19 and coagulation-related disorders, the existing literature is largely confined to analyses of single diseases or associations between two diseases, lacking a comprehensive exploration of the common mechanisms underlying all three conditions. This study aims to utilize bioinformatics to analyze gene expression data, exploring the common gene expression changes among these diseases and identifying potential therapeutic targets and biomarkers. Through this research, we hope to provide scientific evidence for the treatment and prevention strategies of COVID-19 and its related complications, thereby improving clinical outcomes for patients and reducing disease risk.

This study integrated three Gene Expression Omnibus (GEO) datasets for analysis: GSE171110, GSE66360, and GSE58294. Initially, we employed bioinformatics methods to identify differentially expressed genes (DEGs) in each dataset and to pinpoint the common DEGs (co-DEGs) among these three diseases. Subsequently, we conducted enrichment analysis on the co-DEGs to clarify gene pathways and functions, and examined the relationship between co-DEGs and immune infiltration. Finally, machine learning techniques and protein–protein interaction networks (PPI) were used to identify hub genes within the co-DEGs. Our research provides new perspectives for the early diagnosis and treatment of the three diseases. Compared to existing studies, this work not only fills the gap in research on common mechanisms across multiple diseases but also lays a theoretical foundation for developing combined therapeutic strategies targeting COVID-19, AMI, and AIS.

Materials and methods

Collection of the datasets

To analyze the potential relationships and therapeutic targets among COVID-19, AMI, and AIS, we utilized three GEO databases to obtain microarray and RNA-seq datasets. The GEO dataset for COVID-19 is GSE171110 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE171110),which includes transcriptomic profiles from 44 COVID-19 samples and 10 healthy control samples. GSE171110 is based on the Illumina HiSeq 2500 (Homo sapiens) (GPL16791) platform for RNA sequencing analysis. The GEO dataset for AMI is GSE66360 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE66360),comprising trans-criptomic profiles from 49 AMI patients and 50 healthy control samples, based on the Affymetrix Human Genome U133 Plus 2.0 Array (GPL570) platform. Similarly, the dataset for AIS is GSE58294 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE58294), which includes gene expression profiles from 69 patients with cardioembolic stroke (CES) and 23 healthy individuals. Table 1 provides the essential information for the three datasets. We also selected three additional datasets as validation datasets. The COVID-19 validation dataset is GSE152418, which includes transcriptomic profiles of 17 COVID-19 samples and 17 healthy control samples. The AMI validation dataset is GSE48060, which includes transcriptomic profiles of 31 AMI samples and 21 healthy control samples. Similarly, the AIS dataset is GSE16561, which includes gene expression profiles of 39 AIS patients and 24 healthy controls.

Table 1 The three datasets and their characteristics and analysed measurements in this study
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Identification of the DEGs and co-DEGs for COVID-19, AMI and AIS.

Based on the research design, the samples were divided into three groups: COVID-19, AMI, and AIS. The R package limma was used to perform differential expression analysis for each group, with a threshold for differentially expressed genes (DEGs) set at log2(Fold Change) > ± 1.5 and p value < 0.05. DEGs were classified into upregulated and downregulated genes according to the criteria of log2(Fold Change) > 1.5 and log2(Fold Change) < −1.5, respectively [9].

To identify co-DEGs among the three groups, we intersected the upregulated and downregulated DEGs from each group. A Venn diagram was then generated to visualize the overlapping DEGs. The R package ggplot2 was used to create volcano plots, while the VennDiagram package was utilized to generate the Venn diagram [10, 11].

GO and KEGG enrichment analysis

To determine the potential functions and pathways of the co-DEGs, we performed GO and KEGG analyses and predictions [12, 13]. GO can predict the possible functions of genes, and after screening the co-DEGs, we classified them according to three criteria: Biological Process (BP), Molecular Function (MF), and Cellular Component (CC). The KEGG database includes pathways that represent molecular interactions, reactions, and relationships, allowing enrichment analysis to explain the functional profiles of co-DEGs from the perspective of influencing specific biological pathways. We employed the R package clusterProfiler for GO and KEGG pathway enrichment of the co-DEGs [14].

Immune infiltration analysis

To assess the composition and abundance of 22 immune cell types in the gene expression data, we utilized the CIBERSORT method, which employs linear support vector regression to deconvolute the transcriptomic expression matrix [15]. This approach explored whether the abundance of relevant immune cells increased or decreased during the progression of the diseases compared to normal individuals, aiding in further investigations into the relationships among COVID-19, AMI, and AIS.

Identification of hub genes

To investigate early biomarkers and potential therapeutic targets for COVID-19, AMI, and AIS, we conducted random forest and XGBoost analyses [16,17,18]. Random forest is an ensemble algorithm among various machine learning methods, based on multiple decision trees [19]. It predicts the final outcome by aggregating the results of each decision tree, resulting in high accuracy and reliability. In this study, we implemented the Random Forest algorithm using the rfPermute package. We set the number of decision trees (ntree) to 1000 to enhance the model's stability and calculated feature importance based on accuracy decrease and Gini impurity by setting importance = TRUE. To evaluate the statistical significance of feature importance, we performed 1000 permutation tests (nrep = 1000). To address sample imbalance, we employed stratified sampling during the training process, ensuring proportional representation of each class in the training set. On the other hand, XGBoost is a predictive algorithm that integrates small predictive models generated at each step into a larger predictive model. For the XGBoost algorithm, we utilized the XGBoost package in R with key parameters tailored for multi-class classification. We chose a tree-based model (booster ='gbtree') and limited the maximum depth of each tree to 6 (max_depth = 6) to prevent overfitting. The learning rate (eta) was set to 0.5 to balance convergence speed and accuracy. Additionally, to further identify hub genes, we examined the known and predicted interactions between proteins in the STRING database (version 12.0, https://cn.string-db.org) and used this information to construct a PPI network involving the co-DEGs. After exploring the protein interaction relationships, we visualized the network using Cytoscape software (version 3.10.3, https://cytoscape.org) and selected the top 10 nodes as hub genes [20].

Verification and Evaluation of Hub Gene

After preprocessing the data from the three validation datasets, we used the ggboxplot and ggpubr packages in R to create box plots, visually illustrating the expression differences of S100 A12 between the disease groups and the control groups [21, 22]. To evaluate the diagnostic performance of S100 A12 as a biomarker for the three diseases, we performed ROC curve analysis using the pROC package in R and calculated the area under the curve (AUC) along with its 95% confidence interval (CI) [23].

Human Blood Sample Collection

Blood samples from 5 patients each with COVID-19, AMI, and AIS hospitalized at the Cardiovascular and Cerebrovascular Hospital of the General Hospital of Ningxia Medical University in March 2025 were collected. The samples were stored at −80 °C prior to extraction. This study was approved by the Medical Research Ethics Review Committee of the General Hospital of Ningxia Medical University (Approval No.: KYLL-2025–0941). The basic information of the patients is provided in Table 2.

Table 2 Basic information on patients used for QRT-PCR validation
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QRT-PCR

Total RNA extraction was performed using TRIzol reagent, followed by quantification and purity assessment of the extracted RNA. Subsequently, RNA was reverse-transcribed into cDNA according to the manufacturer's protocol. Using cDNA as the template, QRT-PCR experiments were conducted to monitor the expression levels of target genes through real-time fluorescence signals. Data analysis was performed using the 2-△△CT method. The primer sequences used in the experiments are detailed in Table 3.

Table 3 Primer sequences for QRT-PCR
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Statistical analysis

In this study, we conducted statistical analyses using R. Independent sample t-tests were used to determine statistical significance between groups. We performed all bioinformatics analyses with R packages of R software. Statistics were considered significant when P < 0.05, whereas P > 0.05 was not considered significant [24].

Result

Identification of differentially expressed genes

According to our criteria for screening DEGs (log2 Fold Change > 1.5 and adjusted p value < 0.05), the COVID-19 dataset GSE171110 identified 6,588 DEGs, including 3,090 upregulated genes and 3,498 downregulated genes. In GSE66360, 1,632 DEGs were identified (833 upregulated and 799 downregulated), while GSE58294 identified 824 DEGs (754 upregulated and 70 downregulated). Figure 1 shows volcano plots for COVID-19, AMI, and AIS, with red and blue points representing upregulated and downregulated genes, respectively (Fig. 1A–C). We identified 88 common DEGs among GSE171110, GSE66360, and GSE58294 (Fig. 1D). The results of the differential expression analysis suggest that there are certain shared mechanisms and interconnections among COVID-19, AMI, and AIS.

Fig.1
figure 1

Volcano plots exhibit DEGs of (A)COVID-19, (B)AMI and (C)AIS (CES). Red for up-regulated and green for down-regulated. (D)The Venn diagram depicts the co-DEGs among GSE171110(COVID-19), GSE66360(AMI) and GSE58294(AIS). The results showed that they had a total of 88 co-DEGs

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GO and KEGG enrichment analysis

GO analysis includes Biological Process (BP), Cellular Component (CC) and Molecular Function (MF), with the GO database selected as the annotation source. Figure 2A displays the top 5 GO terms for BP, CC and MF. Table 4 presents the top 10 terms in each category for BP, CC and MF. The DEGs were significantly enriched in immune inflammatory responses related to leukocytes (BP), granules and granule membranes (CC), and receptors (MF). These enrichments are linked to immune therapy-related functions.

Fig.2
figure 2

(A)The bar graphs of the ontological analysis of the co-DEGs among COVID-19, AMI and AIS(CES). BP, biological progress; CC, cellular component; MF, molecular function. (B)Bubble graphs indicate the results for KEGG analysis based on the co-DEGs among COVID-19, AMI and AIS

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Table 4 Ontological analysis of co-DEGs among COVID-19, AMI and AIS (CES)
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KEGG pathway analysis identified the top 10 pathways, including tuberculosis, transcriptional dysregulation in cancer, hematopoietic cell lineage, amoebiasis, formation of neutrophil extracellular traps, leishmaniasis, phagosome, hepatitis B, legionellosis, and acute myeloid leukemia. The bubble chart in Fig. 2B lists the potentially enriched pathways for the co-DEGs. For a more detailed illustration, the pathway enrichment analysis is presented in Table 5.

Table 5 Pathway enrichment analysis of co-DEGs among COVID-19, AMI and AIS (CES)
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Immune infiltration

Applying the genetic data from the three groups of COVID-19, AMI and AIS, the correlation between 22 types of immune cells and the disease group and the normal group was calculated by the CIBERSORT algorithm, respectively. Figure 3A shows a box-and-line plot comparing immune cell expression between the COVID-19 (yellow) and HC (blue) groups. Certain immune cells, such as Dendritic.cells.resting, Neutrophils, T.cells.CD4.memory.resting, Macrophages.M0, and B.cells.memory, showed significantly different infiltration proportions in the COVID-19 group compared to the HC group. These cells may play key roles in COVID-19 pathology. Figure 3B is a stacked bar graph to show the composition of immune cells in HC group and disease group. Different colours represent different types of immune cells. Observing the significant difference in the composition of immune cells in HC and COVID-19 groups, certain immune cells may play a more prominent role in the COVID-19 immune response. Figure 4 presents an immune heatmap matrix illustrating the correlations between various types of immune cells. In this matrix, red signifies a positive correlation, with darker shades indicating stronger correlations. Conversely, blue represents negative correlations, where darker hues indicate a more substantial negative relationship. White indicates a weak or no significant correlation. From the graph, it can be concluded that there are significant positive correlations between certain immune cells, such as Mast cells activated and Neutrophils, T cells follicular helper and NK cells activated. There is a significant negative correlation between certain immune cells, for example, Dendritic cells esting and Neutrophils, Plasma cells and T cells CD4 memory resting show a significant negative correlation. The synergistic and antagonistic roles of these cells in the immune response together participate in disease-specific immunoregulatory processes. Similarly, Fig. 5A shows significantly different and highly statistically significant proportions of T.cells.CD4.memory.resting, Neutrophils and T.cells.gamma.delta as compared to the HC group.Fig. 5B shows a significantly different composition of immune cells in the HC and AMI groups. Figure 6 shows a significant positive correlation between B cells naive and T cells CD8, Macrophages M2 and Neutrophils. There is a significant negative correlation between Neutrophils and T cells gamma delta. Figure 7A shows significantly different proportions of B.cells.naive, T.cells.CD8, and Neutrophils compared to the HC group. Figure 7B shows significantly different immune cell compositions in HC and AIS groups. Immune cell composition was significantly different.Fig. 8 showed a significant positive correlation between Macrophages M2 and Plasma cells, T cells CD8 and T cells regulatory Tregs. There was a significant negative correlation between T cells CD8 and Neutrophils.

Fig.3
figure 3

(A) Boxplots were used to show the proportional distribution of different types of immune cells in HC group and COVID-19 group. The results showed that 13/22 immune cells were significantly different between the COVID-19 and HC groups. (B) Stacked bar graphs were used to demonstrate the immune cell composition of HC group and COVID-19 group. The results showed a large difference in immune cell levels between the two groups. P values were showed as:*, p < 0.05;**, p < 0.01;***, p < 0.001, ****, p < 0.0001

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Fig.4
figure 4

The heatmap matrix is used to demonstrate the correlation between different types of immune cells. The colors and significance markers reflect the strength and significance of the correlation between the cells. Red: positive correlation, the darker the color, the stronger the correlation. Blue: negative correlation, the darker the color, the stronger the negative correlation. White: weak or no significant correlation. P values were showed as: *, p < 0.05; **, p < 0.01; ***, p < 0.001

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Fig.5
figure 5

(A) Boxplots were used to show the proportional distribution of different types of immune cells in HC group and AMI group. The results showed that 9/22 immune cells were significantly different between the AMI and HC groups. (B) Stacked bar graphs were used to demonstrate the immune cell composition of HC group and AMI group. The results showed a large difference in immune cell levels between the two groups. P values were showed as:*, p < 0.05;**, p < 0.01;***, p < 0.001, ****, p < 0.0001

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Fig.6
figure 6

The heatmap matrix is used to demonstrate the correlation between different types of immune cells. The colors and significance markers reflect the strength and significance of the correlation between the cells. Red: positive correlation, the darker the color, the stronger the correlation. Blue: negative correlation, the darker the color, the stronger the negative correlation. White: weak or no significant correlation. P values were showed as: *, p < 0.05; **, p < 0.01; ***, p < 0.001

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Fig.7
figure 7

(A) Boxplots were used to show the proportional distribution of different types of immune cells in HC group and AIS(CES) group. The results showed that 7/22 immune cells were significantly different between the AIS and HC groups. (B) Stacked bar graphs were used to demonstrate the immune cell composition of HC group and AIS group. The results showed a difference in immune cell levels between the two groups. P values were showed as:*, p < 0.05;**p < 0.01;***, p < 0.001, ****, p < 0.0001

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Fig.8
figure 8

The heatmap matrix is used to demonstrate the correlation between different types of immune cells. The colors and significance markers reflect the strength and significance of the correlation between the cells. Red: positive correlation, the darker the color, the stronger the correlation. Blue: negative correlation, the darker the color, the stronger the negative correlation. White: weak or no significant correlation. P values were showed as: *, p < 0.05; **, p < 0.01; ***, p < 0.001

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Identification of hub genes

Figure 9 illustrates the PPI network analysis results, highlighting the top 10 most influential DEGs. These genes are MMP9, TLR2, TLR4, ITGAM, S100 A12, FCGR1 A, CD163, FCER1G, FPR2, and CLEC4D. We employed two machine learning methods to further identify common early diagnostic biomarkers and potential therapeutic targets for COVID-19, AMI, and AIS. These methods included random forest and XGBoost analyses (Figs .10, 11, 12).

Fig. 9
figure 9

PPI network and hub genes. Proteins are represented as nodes and functional relationships by edges. The top 10 most influential genes were MMP9, TLR2, TLR4, ITGAM, S100 A12, FCGR1 A, CD163, FCER1G, FPR2, and CLEC4D

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Fig. 10
figure 10

Feature importance analysis using two machine learning methods: XGBoost and random forest model. A) corresponds to COVID-19, B) to AMI and C) to AIS. These plots show the rankings of feature importance derived from the XGBoost model (Plot A) and the Random Forest model (Plot B). In plot A, feature importance is assessed by gain (Gain), which indicates the contribution of each gene to the predictive power of the model. In plot B, importance is measured by mean accuracy decrease, which indicates the importance of each gene to the model's prediction accuracy

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Fig. 11
figure 11

Feature importance analysis using two machine learning methods: XGBoost and random forest model. A) corresponds to COVID-19, B) to AMI and C) to AIS. These plots show the rankings of feature importance derived from the XGBoost model (Plot A) and the Random Forest model (Plot B). In plot A, feature importance is assessed by gain (Gain), which indicates the contribution of each gene to the predictive power of the model. In plot B, importance is measured by mean accuracy decrease, which indicates the importance of each gene to the model's prediction accuracy

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Fig. 12
figure 12

Feature importance analysis using two machine learning methods: XGBoost and random forest model. A) corresponds to COVID-19, B) to AMI and C) to AIS. These plots show the rankings of feature importance derived from the XGBoost model (Plot A) and the Random Forest model (Plot B). In plot A, feature importance is assessed by gain (Gain), which indicates the contribution of each gene to the predictive power of the model. In plot B, importance is measured by mean accuracy decrease, which indicates the importance of each gene to the model's prediction accuracy

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Figure 13 illustrates that in the GSE171110 dataset, random forest selected 74 genes while XGBoost identified 13 genes. In the GSE66360 dataset (Fig. 11), random forest selected 82 genes, with XGBoost identifying 37 genes. For the GSE58294 dataset (Fig. 12), random forest selected 80 genes and XGBoost identified 12 genes. By employing a cross-method approach, we derived the co-DEGs: CLEC4E, S100 A12, and IL1R2.

Fig.13
figure 13

The Venn diagram is used to show the overlap of important genes obtained from different datasets when feature screening is performed using XGBoost and Random Forest models. The different ellipses in the figure represent the set of feature genes for different combinations of datasets and algorithms, and the overlap in the central region indicates the common genes identified in all combinations. By screening the genes using two separate machine learning methods on each disease dataset, the final co-DEGs for them were CLEC4E, S100 A12, IL1R2

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These hub genes have the potential to act as biomarkers and targets for novel therapeutic strategies in disease research. Ultimately, integrating the PPI network and machine learning methods, we identified S100 A12 as a likely common early diagnostic biomarker and potential therapeutic target for these three diseases.

Verification and Evaluation of Hub Gene

As shown in Fig. 14A, the expression of S100 A12 in the COVID-19 patient group was significantly higher than in the healthy control group, with a statistically significant difference (P < 0.05). As shown in Fig. 14B, the expression of S100 A12 in the AMI patient group was significantly higher than in the healthy control group, with a highly statistically significant difference (P < 0.001). As shown in Fig. 14C, the expression of S100 A12 in the AIS patient group was significantly higher than in the healthy control group, with a highly statistically significant difference compared to the healthy control group (P < 0.0001).

Fig.14
figure 14

Expression of S100 A12 in the validation dataset. A The expression of S100 A12 was increased and significant in the COVID-19 group relative to the HC group. B The expression of S100 A12 was increased and significant in the AMI group relative to the HC group. C The expression of S100 A12 was increased and meaningful in the AIS group relative to the HC group. Orange indicates the disease group and blue indicates the normal group, P values are shown as *, P < 0.05; **, P < 0.01; ***, P < 0.001

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The results of evaluating the diagnostic performance of S100 A12 are as follows: As shown in Fig. 15A, the area under the curve (AUC) for S100 A12 in diagnosing COVID-19 was 0.775(95% CI: 0.608–0.942), indicating moderate diagnostic ability. As shown in Fig. 15B, the AUC for S100 A12 in diagnosing AMI was 0.782(95% CI: 0.653–0.911),indicating good diagnostic ability. As shown in Fig. 15C, the AUC for S100 A12 in diagnosing AIS was 0.812(95% CI: 0.702–0.922),indicating strong diagnostic ability.

Fig.15
figure 15

ROC curves for S100 A12 in the validation dataset. (A) ROC curve of S100 A12 in the COVID-19 dataset. (B) ROC curve of S100 A12 in the AMI dataset. (C) ROC curve of S100 A12 in the AIS dataset. The horizontal coordinate is the rate of false positives, expressed as 1-specificity, and the vertical coordinate is the rate of true positives, expressed as sensitivity

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In conclusion, S100 A12 was significantly up-regulated in patients with COVID-19, AMI and AIS, consistent with the experimental results.ROC curve analyses showed that S100 A12 had moderate to good diagnostic efficacy in discriminating between diseased patients and healthy controls, with the best performance in AIS (AUC = 0.812).

QRT-PCR

To further validate the expression characteristics of S100 A12 in different disease states, we used QRT-PCR to measure the expression levels of S100 A12 in patients with COVID-19, AMI, and AIS, as well as in healthy individuals. As shown in Fig. 16, compared to the healthy control group, S100 A12 exhibited an up-regulated expression pattern in all three disease groups. The results are consistent with experimental findings, further confirming the reliability of S100 A12 as a potential biomarker.

Fig.16
figure 16

QRT-PCR was performed to validate the expression of S100 A12 in patients with COVID-19, AMI, and AIS. S100 A12 expression was relatively low in healthy individuals, while significant up-regulation was observed in patients. Orange indicates the disease group and blue indicates the normal group, P values are shown as *, P < 0.05; **, P < 0.01; ***, P < 0.001

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Discussion

Strictly speaking, COVID-19 is not a single disease but a clinical syndrome. Studies have shown that because of its association with immunity and inflammation, COVID-19 infection causes more than just pneumonia, especially in elderly patients with underlying diseases such as hypertension and diabetes, and is also associated with thrombosis in the organism, which can lead to dreadful diseases such as AMI and AIS. Consequently, it is crucial to investigate new targets and biomarkers for the early diagnosis of thrombotic diseases associated with COVID-19.

Under normal circumstances, the immune system plays a protective role for the body. However, in cases of severe or prolonged inflammatory stimuli, this protective effect can become detrimental, leading to negative outcomes such as a hypercoagulable state in the blood [25, 26]. Numerous reports have indicated a correlation between COVID-19 infection and coagulopathy, including thrombus formation. Early in the infection, certain laboratory markers can reflect changes in the level of inflammation, such as elevated levels of D-dimer and fibrinogen. The underlying causes of coagulopathy may involve factors such as hypoxia, direct viral infection, and the induction of tissue factor production by pro-inflammatory cytokines, including IL-1, IL-6, and TNF. These cytokines activate the extrinsic coagulation pathway [27,28,29]. It has been shown that the inflammatory response also attenuates certain anticoagulant mechanisms, such as the antithrombin and proteinase C pathways [30, 31]. In addition, novel coronaviruses can also cause direct damage to vascular endothelial cells, thereby reducing their antithrombotic activity [32, 33]. It has been shown that endothelial dysfunction and endothelial cell damage in COVID-19 patients can cause increased vascular endothelial permeability and increased endothelial cell concentration in the systemic circulation, leading to higher levels of soluble intercellular adhesion molecules, which further exacerbate the hypercoagulable state of the blood, and that the massive release of tissue factor (TF) and neutrophils promotes the formation of immune thrombi [34,35,36]. More specifically, COVID-19 is a multisystem disease involving endothelial cells from multiple systems. And the more severe the neocoronary pneumonia infection, the higher the levels of markers of endothelial damage, such as von willebrand factor (VWF), intercellular adhesion molecules including intercellular cell adhesion molecule-1(ICAM-1) and vascular cell adhesion molecule-1(VCAM-1), which can promote thrombosis [25, 37, 38]. Studies have shown a significant increase in the incidence of cardiovascular disease in patients with COVID-19, particularly thrombosis-related diseases such as AMI, AIS and pulmonary embolism, with rates in critically ill patients tens of times higher than in patients with mild infections [3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40].

Research has demonstrated a seven-fold increase in the incidence of myocardial infarction within one week of COVID-19 infection, and a long-term cardiovascular risk after infection, with stroke being the most common manifestation of cerebrovascular disease [41,42,43]. AMI and AIS are the same as thrombotic diseases, and they are very closely related to each other. After an AMI, certain factors promote thrombus formation, such as post-infarction myocardial ischaemia, dyskinesia and hypercoagulability of the blood. These clots do not only stay in the heart and cause cardiac complications, but also reach the body's centre through the circulatory system and cause cerebral embolism, leading to AIS. Studies have shown that CES account for a large proportion of all ischaemic strokes, and that the risk of AIS increases dramatically within a short period of time after myocardial infarction [44, 45]. Because of the decline in cardiac ejection volume, left ventricular dilatation and ventricular remodelling occur in long-term conditions and also increase the risk of cardiogenic stroke [46]. The interaction between AMI and CES suggests that heart disease negatively affects cerebral perfusion, thereby increasing the risk and severity of stroke. Neocoronavirus also crosses the blood–brain barrier and directly attacks brain endothelial cells, inducing hypercoagulability of blood in the brain and promoting thrombosis [7]. Therefore, clinical practice should emphasise early diagnosis, as well as monitoring and management of patients with new crown pneumonia who may develop AMI and AIS. Currently, there are relatively few studies on the correlation between COVID-19, AMI and AIS. We applied bioinformatics methods to explore these three diseases using three GEO datasets, GSE171110, GSE66360, and GSE58294, and found co-DEGs among them. To investigate how the genes affect disease onset and progression at the molecular level, we used GO and KEGG enrichment analyses to make predictions about the possible functions and pathways of these genes. Enrichment analysis showed that co-DEGs were significantly enriched in leukocytes, neutrophils and other related immune-inflammatory responses. The immune system plays a crucial role in the progression of diseases, and neutrophils are intrinsic immune cells, which are activated during infections and play an active role in the body's immunity, but their over-activation is harmful to the human body, as it can induce excessive immune responses and lead to microvascular and even macrovascular thrombosis, which can lead to inadequate perfusion of organs and tissues, and is one of the main causes of AMI and AIS, and excessive immune responses can directly damage the cardio-cerebral vascular system [47]. It has been shown that COVID-19 infection can increase the ability of neutrophils to produce neutrophil extracellular traps (NETs), and NETs have been shown to be a biomarker of the severity of COVID-19 infection [48, 49]. Activated neutrophils can secrete NETs, which consists of a meshwork of DNA, histones and other substances to further kill pathogens. However, its formation may lead to increased inflammation in the organism and promote coagulation, vascular occlusion, and thrombosis [50, 51]. NETs can be degraded by circulating DNA enzymes, and the products of degradation, such as cell-free DNA (cfDNA) and histones, can activate intrinsic pathways of coagulation, which can further contribute to the onset of disease [52,53,54]. In addition, COVID-19 leads to an increase in circulating NETs and attacks the body's endothelial cells, also causing VCAM-1 and ICAM-1 present in endothelial cells to be released, further promoting blood hypercoagulability [55, 56]. Studies have found that in patients who recovered from COVID-19, the neutrophil-to-lymphocyte ratio (NLR) at the time of admission was significantly lower compared to those whose condition worsened. Therefore, NLR can serve as a biomarker for the early prognosis of COVID-19 patients [57]. In COVID-19 patients, lymphocyte reduction is commonly observed, including both CD4 + T cells and CD8 + T cells [58]. It is worth noting that γδ T cells, a specialized subset of lymphocytes, also exhibit a reduction in number due to COVID-19 infection [59]. The reduction in γδ T cells may be related to excessive activation and cellular exhaustion. Certain cytokines, such as IL-10, IL-6, and TNF-α, show a negative correlation with T cell count, suggesting that these cytokines may also play a role in the process of T cell reduction [60, 61]. In addition, changes in spleen function are also involved in the process of reduced B cell and T cell lymphocyte counts [62]. It is noteworthy that mast cells may become a therapeutic target for alleviating the characteristic airway inflammation during the hyperacute phase of COVID-19 [63]. Activated neutrophils and monocytes interact with platelets and the coagulation cascade, leading to intravascular thrombosis in small and large blood vessels [64].

Fibronectin is a substance that plays a crucial role in the recognition and activation of the lectin pathway (LP), one of the complement agglutination pathways, and of which neutrophils are the main source. It is found predominantly in cells, with low levels in the circulation. There are three types of fibronectin, of which ficolin-1 may be involved in part of the body's innate immunity, meaning that it contributes to the body's immune defence against infection [65]. R. Zangari et al. demonstrated that ficolin-1 is heavily depleted within a short period of time after AIS and can be used as a prognostic predictive marker in patients with AIS [66, 67]. In addition, ficolin-3 has also been reported to be associated with AIS and subarachnoid haemorrhage [68, 69].

The relationship between COVID-19 and the immune mechanisms involved in the pathogenesis of cardiovascular disease is intricate and multifaceted. The results of our enrichment analysis of COVID-19, AMI and AIS co-DEGs showed that co-DEGs were mainly associated with neutrophils, leukocytes and immune responses, indicating that the three diseases mentioned above are extremely related to inflammatory immune responses. Therefore, we later performed immune infiltration analysis, which revealed significant differences in the content of immune cells—including B cells, T cells, NK cells, neutrophils, eosinophils, dendritic cells, and macrophages—between the three diseases and normal samples. After an organism is infected with a virus, immune cells such as neutrophils, eosinophils and NK cells react first and can directly kill the pathogen; this immune response is known as non-specific immunity. In addition, specific immunity will also play a role sometime after the infection occurs, which relies on the antigen presentation of dendritic cells, macrophages and B cells, thus activating the specific immune response of T cells and B cells [70, 71].。The pathological features of COVID-19 are primarily characterized by significant infiltration of macrophages and neutrophils in the lungs, which subsequently leads to ARDS, including typical manifestations such as alveolar epithelial cell injury, hyaline membrane formation, and interstitial fibrosis [72, 73]. The highly activated pulmonary macrophages, derived from infiltrating inflammatory monocytes, promote the massive release of pro-inflammatory cytokines (such as IL-6 and TNF-α) and the abnormal recruitment of cytotoxic effector cells (such as neutrophils and CD8 + T cells), ultimately leading to further exacerbation of local tissue damage [74]. Lymphocytes play a critical role in antiviral immunity. CD8 + cytotoxic T cells (CTL) directly mediate viral clearance through the release of effector molecules such as perforin, granzymes, and IFN-γ. Additionally, CD4 + helper T cells can synergistically enhance the ability of T cells and B cells to combat viral infections. However, under conditions of chronic viral infection or persistent antigen exposure, T cells may enter an exhausted state, thereby weakening the effectiveness of antiviral immunity [75,76,77,78]. As previously mentioned, in COVID-19 patients, NETs released by activated neutrophils are one of the key mechanisms mediating coagulation dysfunction and the development of thrombotic diseases. Additionally, the inflammatory microenvironment induces the expression of tissue factor by endothelial cells, macrophages, and neutrophils, significantly enhancing the activation of the coagulation cascade in the lungs. The formation of microthrombi may also lead to dysfunction in other organs [64, 79]. The disruption of the blood–brain barrier caused by AIS creates conditions for the migration of immune cells into the brain. White blood cells and other immune cells, including monocytes/macrophages, neutrophils, and lymphocytes from the bloodstream, migrate to the brain and induce inflammatory responses [80,81,82]. As the earliest subpopulation of leukocytes to infiltrate ischaemic brain tissue, neutrophils persist in the cerebral microvasculature and further exacerbate blood–brain barrier disruption by releasing agents such as protein hydrolases [83]. As the earliest infiltrating T cell subset in ischemic brain tissue, CTL can release effector molecules such as perforin and granzymes after cell–cell interactions and antigen-dependent activation, leading to neuronal death and exacerbating brain tissue damage [84, 85].

The immune cell response following AMI exhibits a distinct temporal pattern: within 6 to 24 h after infarction, neutrophils are the first to infiltrate the infarcted area, followed by a significant accumulation of pro-inflammatory monocytes and macrophages between 48 to 72 h, collectively mediating myocardial ischemia/reperfusion injury (IRI)-related cardiomyocyte death and tissue damage. Subsequently, the process enters an anti-inflammatory repair phase (days 4–7), which is primarily dominated by anti-inflammatory monocytes/macrophages. These cells regulate the inflammatory response afterAMI by suppressing, clearing, and limiting the initial pro-inflammatory cells [86, 87]. Interestingly, researchers have found through animal experiments that mice lacking dendritic cells exhibit worse recovery after AMI, suggesting that dendritic cells may play a protective role in the inflammatory response following AMI [88].

We constructed a PPI network and identified the top 10 DEGs common to COVID-19, AMI, and AIS as the most influential genes, and these pivotal genes include MMP9, TLR2, TLR4, ITGAM, S100 A12, FCGR1 A, CD163, FCER1G, FPR2, and CLEC4D.Random Forest and XGBoost analyses of these two machine learning methods can further identify early diagnostic biomarkers and potential therapeutic targets common to these three diseases. These DEGs are: CLEC4E, S100 A12, IL1R2. The final gene derived from the combined PPI network and machine learning approach is S100 A12, which may be the common early diagnostic biomarker and potential therapeutic target for these three diseases. The C-type lectin domain family 4 member E (CLEC4E) holds significant importance in the field of sterile inflammation. It is typically expressed by leukocytes and activated through inflammatory responses. Denise Veltman and colleagues demonstrated, through complementary experimental methods in pig, mouse, and human samples, the substantial potential of CLEC4E as a biomarker for the severity of AMI [89]. In addition, studies have shown that CLEC4E, along with other genes, can serve as biomarkers for AIS [90]. IL1R2 is a member of the interleukin-1 receptor family (ILRs). It is released in a soluble form (sIL1R2) and exerts a negative regulatory effect on the IL-1 system, primarily participating in the regulation of local inflammation [91]. According to relevant reports, IL1R2 is highly expressed in AIS patients and is closely associated with the severity of coronary artery disease. Additionally, IL1R2 serves as a biomarker for AMI, and plays a role in the development of pulmonary fibrosis in severe COVID-19 patients [92,93,94,95].

S100 A12, identified as a biomarker for the three diseases through various methods, is a calcium-binding protein expressed by neutrophils and monocyte macrophages and belongs to the S100 protein subfamily of myeloid-associated proteins [96, 97]. It plays a crucial role in the body's defense against inflammation and in maintaining immune homeostasis. During the pathological process of diseases, its expression significantly increases, making it a potential biomarker for disease diagnosis and early intervention. Numerous studies have reported that S100 A12 promotes the secretion of pro-inflammatory cytokines and is associated with the development of many diseases, such as coronary artery disease, ARDS, and cerebrovascular disease [96, 98,99,100,101,102,103]. In addition, it has been demonstrated that S100 A12 is a hub gene immunologically related to AMI and AIS [103, 104]。Interestingly, S100 A12 was found to be associated with the severity of COVID-19 [105]. It has been reported that the receptor for advanced glycation end products (RAGE) acts as a specific receptor for the S100 A12 protein, primarily located on the surface of endothelial cells, monocytes, and macrophages. S100 A12 can bind to these cell surface receptors and activate them. This interaction promotes the expression of ICAM-1 and VCAM-1 on endothelial cells [98, 106]. This may be related to the widespread endothelial cell damage across multiple systems caused by COVID-19, which further exacerbates the hypercoagulable state in the body, leading to a sharp increase in the incidence of thrombotic-related diseases, such as AMI and AIS. The mechanism by which S100 A12 contributes to the development of thrombotic diseases may involve multiple aspects. Firstly, S100 A12 binds with high affinity to receptor for RAGE, activating the NF-κB signaling pathway, which in turn upregulates the expression levels of related cytokines. Additionally, S100 A12 may participate in the pathological process of thrombosis through its chemotactic activity. Researchers constructed a transgenic mouse model with smooth muscle cell-specific expression of S100 A12 and found that this model exhibited significantly increased calcification in the coronary arteries and aorta, along with reduced stability of arterial plaques [97, 106, 107]. Patricia Mester et al. found in their study of serum from moderate and severe COVID-19 patients that the severity of COVID-19 was positively correlated with the degree of neutrophil dysfunction. The S100 A12 levels in severe COVID-19 patients were higher than in moderate patients, but the increase in neutrophil count was less pronounced than that of S100 A12, indicating that neutrophil function was impaired in severe COVID-19 patients. This study proposed S100 A12 as a marker for the severity of COVID-19 [108]. Furthermore, the lung tissue is rich in RAGE, and elevated levels of S100 A12 in COVID-19 patients bind to RAGE in the lungs, leading to the activation of pulmonary endothelial cells, leukocyte extravasation, and neutrophil accumulation, thereby contributing to the occurrence of acute lung injury and acute respiratory distress syndrome [100, 106].

Although the content and results of our study have largely achieved what was originally envisaged, there are still some shortcomings. Firstly, our research data is based on public databases, and there is a lack of validation of the results. Second, the results based on the sequencing of blood samples were not directly sequenced in the organs where the disease occurs, such as the lungs and the heart. Finally, this study only used genomics-related methods, without comprehensive multi-omics analysis, and the results are slightly less credible. However, based on a large number of studies, our results are basically consistent with theirs. To further enhance the clinical value of the research, future studies should focus on conducting multicenter, large-sample clinical validation of the diagnostic value of S100 A12, evaluating its diagnostic efficacy across different diseases and stages. Simultaneously, the development of quantitative detection kits, along with the establishment of a rapid detection platform, is essential to meet the timeliness requirements of clinical testing. Early diagnosis and timely clinical interventions based on S100 A12 levels can thereby improve patient outcomes. Additionally, S100 A12 could serve as a routine screening indicator for high-risk populations and be used in combination with other biomarkers to enhance screening specificity. Through these strategies, S100 A12 is expected to become a bridge connecting basic research and clinical practice, providing innovative solutions for the precise diagnosis and treatment of related diseases.

Conclusion

We investigated the diagnostic biomarkers and immune infiltration characteristics common to COVID-19, AMI and AIS using bioinformatics methods and machine learning. Functional and pathway analyses showed that co-DEGs were mainly enriched in immune-inflammatory responses such as leukocytes and neutrophils. Using two machine learning methods and PPI networks, we obtain the common immune-related therapeutic target and biomarker that may ultimately affect these three diseases: S100 A12. In addition, we employed a dual-validation approach, including independent dataset validation and laboratory validation, and found that it was significantly upregulated in all three diseases, consistent with the experimental results. The ROC curve analysis revealed that S100 A12 demonstrated moderate to good diagnostic performance in distinguishing disease patients from healthy controls, with the most prominent performance observed in AIS (AUC = 0.812). Our study suggests that S100 A12, as a potential biomarker, may offer new insights for the early diagnosis of COVID-19, AMI, and AIS. However, this study has some limitations, such as the small sample size of the datasets, lack of laboratory validation, and failure to account for other potential comorbidities in patients. Therefore, future studies should further validate the diagnostic performance of S100 A12 in different populations and explore its clinical application potential.

Data availability

No datasets were generated or analysed during the current study.

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Acknowledgements

The authors would like to thank the GEO databases for providing data for the study and those authors who uploaded valuable.

Funding

This study was supported by the General Hospital of Ningxia Medical University [2023] No. 394 2023 New Entry Masters Talent Training Project.

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Author notes

  1. Ya-Nan Ma and Si-Rong Ma contributed equally to this work and share first authorship.

Authors and Affiliations

  1. Department of Geriatrics and Specialty Medicine, General Hospital of Ningxia Medical University, Yinchuan, Ningxia, China

    Ya-Nan Ma, Li Yang, Juan Wu, Ya-Rong Wang, Li-Jia Bao, Li Ma & Qing-Qiu Wu

  2. School of Basic Medical Sciences, Ningxia Medical University, Yinchuan, Ningxia, China

    Si-Rong Ma

  3. Institute of Medical Sciences, General Hospital of Ningxia Medical University, Yinchuan, Ningxia, China

    Zhen-Hai Wang

  4. Diagnosis and Treatment Engineering Technology Research Center of Nervous System Diseases of Ningxia Hui Autonomous Region, Yinchuan, Ningxia, China

    Zhen-Hai Wang

  5. Neurology Center, Ningxia Medical University General Hospital, Yinchuan, Ningxia, China

    Zhen-Hai Wang

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Contributions

Y-NM and S-RM drafted the manuscript. LY and JW searched the literature to identify eligible bioinformatics methods. Y-RW，L-JB and LM analyzed the data. Z-HW and Q-QW finally reviewed the first draft. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Qing-Qiu Wu or Zhen-Hai Wang.

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Ethics approval and consent to participate

Data retrieved from the GEO database were uploaded in accordance with the guidelines established by the GEO Ethics, Law and Policy Group, so ethical review and informed consent were not required. The research involving human subjects has been approved by the Medical Research Ethics Review Committee of the General Hospital of Ningxia Medical University (Approval No.: KYLL-2025–0941). The study was conducted in compliance with local laws and institutional requirements, and informed consent was obtained from all participants.

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The authors declare no competing interests.

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Ma, YN., Ma, SR., Yang, L. et al. Diagnostic biomarkers and immune infiltration profiles common to COVID-19, acute myocardial infarction and acute ischaemic stroke using bioinformatics methods and machine learning. BMC Neurol 25, 201 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12883-025-04212-6

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BMC Neurology

ISSN: 1471-2377

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