Machine-Learning Algorithm-Based Prediction of Diagnostic Gene Biomarkers Related to Immune Infiltration in Patients With Chronic Obstructive Pulmonary Disease

Causal Role of Immune Cells in Chronic Obstructive Pulmonary Disease: A Two-Sample Mendelian Randomization Study

Accumulating evidence has highlighted the importance of immune cells in the pathogenesis of chronic obstructive pulmonary disease (COPD). However, the understanding of the causal association between immunity and COPD remains incomplete due to the existence of confounding variables. In this study, we employed a two-sample Mendelian randomization (MR) analysis, utilizing the genome-wide association study database, to investigate the causal association between 731 immune-cell signatures and the susceptibility to COPD from a host genetics perspective. To validate the consistency of our findings, we utilized MR analysis results of lung function data to assess directional concordance. Furthermore, we employed MR-Egger intercept tests, Cochrane's Q test, MR-PRESSO global test, and "leave-one-out" sensitivity analyses to evaluate the presence of horizontal pleiotropy, heterogeneity, and stability, respectively. Inverse variance weighting results showed that seven immune phenotypes were associated with the risk of COPD. Analyses of heterogeneity and pleiotropy analysis confirmed the reliability of MR results. These results highlight the interactions between the immune system and the lungs. Further investigations into their mechanisms are necessary and will contribute to inform targeted prevention strategies for COPD.

Validation of mitochondrial biomarkers and immune dynamics in polycystic ovary syndrome

PROBLEM

Polycystic ovary syndrome (PCOS), a prevalent endocrine-metabolic disorder, presents considerable therapeutic challenges due to its complex and elusive pathophysiology.

METHOD OF STUDY

We employed three machine learning algorithms to identify potential biomarkers within a training dataset, comprising GSE138518, GSE155489, and GSE193123. The diagnostic accuracy of these biomarkers was rigorously evaluated using a validation dataset using area under the curve (AUC) metrics. Further validation in clinical samples was conducted using PCR and immunofluorescence techniques. Additionally, we investigate the complex interplay among immune cells in PCOS using CIBERSORT to uncover the relationships between the identified biomarkers and various immune cell types.

RESULTS

Our analysis identified ACSS2, LPIN1, and NR4A1 as key mitochondria-related biomarkers associated with PCOS. A notable difference was observed in the immune microenvironment between PCOS patients and healthy controls. In particular, LPIN1 exhibited a positive correlation with resting mast cells, whereas NR4A1 demonstrated a negative correlation with monocytes in PCOS patients.

CONCLUSION

ACSS2, LPIN1, and NR4A1 emerge as PCOS-related diagnostic biomarkers and potential intervention targets, opening new avenues for the diagnosis and management of PCOS.

The crucial prognostic signaling pathways of pancreatic ductal adenocarcinoma were identified by single-cell and bulk RNA sequencing data

Pancreatic ductal adenocarcinoma (PDAC) is a malignant tumor with poor prognosis and high mortality. Although a large number of studies have explored its potential prognostic markers using traditional RNA sequencing (RNA-Seq) data, they have not achieved good prediction effect. In order to explore the possible prognostic signaling pathways leading to the difference in prognosis, we identified differentially expressed genes from one scRNA-seq cohort and four GEO cohorts, respectively. Then Cox and Lasso regression analysis showed that 12 genes were independent prognostic factors for PDAC. AUC and calibration curve analysis showed that the prognostic model had good discrimination and calibration. Compared with the low-risk group, the high-risk group had a higher proportion of gene mutations than the low-risk group. Immune infiltration analysis revealed differences in macrophages and monocytes between the two groups. Prognosis related genes were mainly distributed in fibroblasts, macrophages and type 2 ducts. The results of cell communication analysis showed that there was a strong communication between cancer-associated fibroblasts (CAF) and type 2 ductal cells, and collagen formation was the main interaction pathway.

Predicting feature genes correlated with immune infiltration in patients with abdominal aortic aneurysm based on machine learning algorithms

Abdominal aortic aneurysm (AAA) is a condition characterized by a pathological and progressive dilatation of the infrarenal abdominal aorta. The exploration of AAA feature genes is crucial for enhancing the prognosis of AAA patients. Microarray datasets of AAA were downloaded from the Gene Expression Omnibus database. A total of 43 upregulated differentially expressed genes (DEGs) and 32 downregulated DEGs were obtained. Function, pathway, disease, and gene set enrichment analyses were performed, in which enrichments were related to inflammation and immune response. AHR, APLNR, ITGA10 and NR2F6 were defined as feature genes via machine learning algorithms and a validation cohort, which indicated high diagnostic abilities by the receiver operating characteristic curves. The cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) method was used to quantify the proportions of immune infiltration in samples of AAA and normal tissues. We have predicted AHR, APLNR, ITGA10 and NR2F6 as feature genes of AAA. CD8 + T cells and M2 macrophages correlated with these genes may be involved in the development of AAA, which have the potential to be developed as risk predictors and immune interventions.

Prediction Model for Unfavorable Outcome in Spontaneous Intracerebral Hemorrhage Based on Machine Learning

OBJECTIVE

The spontaneous intracerebral hemorrhage (ICH) remains a significant cause of mortality and morbidity throughout the world. The purpose of this retrospective study is to develop multiple models for predicting ICH outcomes using machine learning (ML).

METHODS

Between January 2014 and October 2021, we included ICH patients identified by computed tomography or magnetic resonance imaging and treated with surgery. At the 6-month check-up, outcomes were assessed using the modified Rankin Scale. In this study, four ML models, including Support Vector Machine (SVM), Decision Tree C5.0, Artificial Neural Network, Logistic Regression were used to build ICH prediction models. In order to evaluate the reliability and the ML models, we calculated the area under the receiver operating characteristic curve (AUC), specificity, sensitivity, accuracy, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR).

RESULTS

We identified 71 patients who had favorable outcomes and 156 who had unfavorable outcomes. The results showed that the SVM model achieved the best comprehensive prediction efficiency. For the SVM model, the AUC, accuracy, specificity, sensitivity, PLR, NLR, and DOR were 0.91, 0.92, 0.92, 0.93, 11.63, 0.076, and 153.03, respectively. For the SVM model, we found the importance value of time to operating room (TOR) was higher significantly than other variables.

CONCLUSION

The analysis of clinical reliability showed that the SVM model achieved the best comprehensive prediction efficiency and the importance value of TOR was higher significantly than other variables.

Early Diagnosis of High-Risk Chronic Obstructive Pulmonary Disease Based on Quantitative High-Resolution Computed Tomography Measurements

Purpose

Quantitative computed tomography (QCT) techniques, focusing on airway anatomy and emphysema, may help to detect early structural changes of COPD disease. This retrospective study aims to identify high-risk COPD participants by using QCT measurements.

Patients and Methods

We enrolled 140 participants from the Second Affiliated Hospital of Shenyang Medical College who completed inspiratory high-resolution CT scans, pulmonary function tests (PFTs), and clinical characteristics recorded. They were diagnosed Non-COPD by PFT value of FEV1/FVC >70% and divided into two groups according percentage predicted FEV1 (FEV1%), low-risk COPD group: FEV1% ≥ 95%, high-risk group: 80% < FEV1% < 95%. The QCT measurements were analyzed by the Student's t-test (or Mann-Whitney U-test) method. Then, feature candidates were identified using the LASSO method. Meanwhile, the correlation between QCT measurements and PFTs was assessed by the Spearman rank correlation test. Furthermore, support vector machine (SVM) was performed to identify high-risk COPD participants. The performance of the models was evaluated in terms of accuracy (ACC), sensitivity (SEN), specificity (SPE), F1-score, and area under the ROC curve (AUC), with p <0.05 considered statistically significant.

Results

The SVM based on QCT measurements achieved good performance in identifying high-risk COPD patients with 85.71% of ACC, 88.34% of SEN, 84.00% of SPE, 83.33% of F1-score, and 0.93 of AUC. Further, QCT measurements integration of clinical data improved the performance with an ACC of 90.48%. The emphysema index (%LAA-950) of left lower lung was negatively correlated with PFTs (P < 0.001). The airway anatomy indexes of lumen diameter (LD) were correlated with PFTs.

Conclusion

QCT measurements combined with clinical information could provide an effective tool for an early diagnosis of high-risk COPD. The QCT indexes can be used to assess the pulmonary function status of high-risk COPD.

A Macrophage-Related Gene Signature for Identifying COPD Based on Bioinformatics and ex vivo Experiments

Background

This study aims to investigate the association between immune cells and the development of COPD, while providing a new method for the diagnosis of COPD according to the changes in immune microenvironment.

Methods

In this study, the "CIBERSORT" algorithm was used to estimate the tissue infiltration of 22 types of immune cells in GSE20257 and GSE10006. The "limma" package was used for differentially expressed analysis. The key modules associated with vital immune cells were identified using WGCNA. GO and KEGG enrichment analysis revealed the biological functions of the candidate genes. Ultimately, a novel diagnostic prediction model was constructed via machine learning methods and multivariate logistic regression analysis based on GSE20257. Furthermore, we examined the stability of the model on one internal test set (GSE10006), three external test sets (GSE8545, GSE57148 and GSE76925), one single-cell transcriptome dataset (GSE167295), macrophages (THP-M cells) and lung tissue from COPD patients.

Results

M0 macrophages (AUC > 0.7 in GSE20257 and GSE10006) were considered as the most important immune cells through exploring the immune microenvironment landscapes in COPD patients and healthy controls. The differentially expressed genes from GSE20257 and GSE10006 were divided into six and five modules via WGCNA, respectively. The green module in GSE20257 (cor = 0.41, P < 0.001) and the brown module in GSE10006 (cor = 0.67, P < 0.001) were highly correlated with M0 macrophages and were selected as key modules. Forty-one intersected genes obtained from two modules were primarily involved in regulation of cytokine production, regulation of innate immune response, specific granule, phagosome, lysosome, ferroptosis, and other biological processes. On the basis of the candidate genetic markers further characterized via the "Boruta" and "LASSO" algorithm for COPD, a diagnostic model comprising CLEC5A, FTL and SLC2A3 was constructed, which could accurately distinguish COPD patients from healthy controls in multiple datasets. GSE20257 as the training set has an AUC of 0.916. The AUCs of the internal test set and three external test sets were 0.873, 0.932, 0.675 and 0.688, respectively. Single-cell sequencing analysis suggested that CLEC5A, FTL and SLC2A3 were expressed in macrophages from COPD patients. The expressions of CLEC5A, FTL and SLC2A3 were up-regulated in THP-M cells and lung tissue from COPD patients.

Conclusion

According to the variations of immune microenvironment in COPD patients, we constructed and validated a novel macrophage M0-associated diagnostic model with satisfactory predictive value. CLEC5A, FTL and SLC2A3 are expected to be promising targets of immunotherapy in COPD.

Combining single-cell RNA sequencing data and transcriptomic data to unravel potential mechanisms and signature genes of the progression of idiopathic pulmonary fibrosis to lung adenocarcinoma and predict therapeutic agents

Patients with idiopathic pulmonary fibrosis (IPF) have a significantly higher prevalence of lung adenocarcinoma (LUAD) than normal subjects, although the underlying association is unclear. The raw data involved were obtained from the Gene Expression Omnibus (GEO) database. Differential expression analysis and weighted gene co-expression network analysis were used to screen for differentially expressed genes (DEGs) and modular signature genes (MSGs). Genes intersecting DEGs and MSGs were considered hub genes for IPF and LUAD. Machine learning algorithms were applied to capture epithelial cell-derived signature genes (EDSGs) shared. External cohort data were exploited to validate the robustness of EDSGs. Immunohistochemical staining and K-M plots were used to denote the prognostic value of EDSGs in LUAD. Based on EDSGs, we constructed a TF-gene-miRNA regulatory network. Molecular docking can validate the strength of action between candidate drugs and EDSGs. Epithelial cells, 650 DEGs, and 1773 MSGs were shared by IPF and LUAD. As for 379 hub genes, we performed pathway and functional enrichment analysis. By analyzing sc-RNA seq data, we identified 1234 marker genes of IPF epithelial cell-derived and 1481 of LUAD. And these genes shared 8 items with 379 hub genes. Through the machine learning algorithms, we further fished TRIM2, S100A14, CYP4B1, LMO7, and SFN. The ROC curves emphasized the significance of EDSGs in predicting the onset of LUAD and IPF. The TF-gene-miRNA network revealed regulatory relationships behind EDSGs. Finally, we predicted appropriate therapeutic agents. Our study preliminarily identified potential mechanisms between IPF and LUAD, which will inform subsequent studies.

Bioinformatics analysis and machine learning approach applied to the identification of novel key genes involved in non-alcoholic fatty liver disease

Non-alcoholic fatty liver disease (NAFLD) comprises a range of chronic liver diseases that result from the accumulation of excess triglycerides in the liver, and which, in its early phases, is categorized NAFLD, or hepato-steatosis with pure fatty liver. The mortality rate of non-alcoholic steatohepatitis (NASH) is more than NAFLD; therefore, diagnosing the disease in its early stages may decrease liver damage and increase the survival rate. In the current study, we screened the gene expression data of NAFLD patients and control samples from the public dataset GEO to detect DEGs. Then, the correlation betweenbetween the top selected DEGs and clinical data was evaluated. In the present study, two GEO datasets (GSE48452, GSE126848) were downloaded. The dysregulated expressed genes (DEGs) were identified by machine learning methods (Penalize regression models). Then, the shared DEGs between the two training datasets were validated using validation datasets. ROC-curve analysis was used to identify diagnostic markers. R software analyzed the interactions between DEGs, clinical data, and fatty liver. Ten novel genes, including ABCF1, SART3, APC5, NONO, KAT7, ZPR1, RABGAP1, SLC7A8, SPAG9, and KAT6A were found to have a differential expression between NAFLD and healthy individuals. Based on validation results and ROC analysis, NR4A2 and IGFBP1b were identified as diagnostic markers. These key genes may be predictive markers for the development of fatty liver. It is recommended that these key genes are assessed further as possible predictive markers during the development of fatty liver.

Screening of potential biomarkers for polycystic ovary syndrome and identification of expression and immune characteristics

BACKGROUND

Polycystic ovary syndrome (PCOS) seriously affects the fertility and health of women of childbearing age. We look forward to finding potential biomarkers for PCOS that can aid clinical diagnosis.

METHODS

We acquired PCOS and normal granulosa cell (GC) expression profiles from the Gene Expression Omnibus (GEO) database. After data preprocessing, differentially expressed genes (DEGs) were screened by limma package, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis and Gene Set Enrichment Analysis (GSEA) were performed. Recursive feature elimination (RFE) algorithm and the least absolute shrinkage and selection operator (LASSO) Cox regression analysis were used to acquire feature genes as potential biomarkers. Time-dependent receiver operator characteristic curve (ROC curve) and Confusion matrix were used to verify the classification performance of biomarkers. Then, the expression characteristics of biomarkers in PCOS and normal cells were analyzed, and the insulin resistance (IR) score of samples was computed by ssGSEA. Immune characterization of biomarkers was evaluated using MCP counter and single sample gene set enrichment analysis (ssGSEA). Finally, the correlation between biomarkers and the scores of each pathway was assessed.

RESULTS

We acquired 93 DEGs, and the enrichment results indicated that most of DEGs in PCOS group were significantly enriched in immune-related biological pathways. Further screening results indicated that JDP2 and HMOX1 were potential biomarkers. The area under ROC curve (AUC) value and Confusion matrix of the two biomarkers were ideal when separated and combined. In the combination, the training set AUC = 0.929 and the test set AUC = 0.917 indicated good diagnostic performance of the two biomarkers. Both biomarkers were highly expressed in the PCOS group, and both biomarkers, which should be suppressed in the preovulation phase, were elevated in PCOS tissues. The IR score of PCOS group was higher, and the expression of JDP2 and HMOX1 showed a significant positive correlation with IR score. Most immune cell scores and immune infiltration results were significantly higher in PCOS. Comprehensive analysis indicated that the two biomarkers had strong correlation with immune-related pathways.

CONCLUSION

We acquired two potential biomarkers, JDP2 and HMOX1. We found that they were highly expressed in the PCOS and had a strong positive correlation with immune-related pathways.

MMP14 is a diagnostic gene of intrahepatic cholangiocarcinoma associated with immune cell infiltration

BACKGROUND

Intrahepatic cholangiocarcinoma (ICC) is a malignant tumor of the hepatobiliary system with concealed onset, strong invasiveness and poor prognosis.

AIM

To explore the disease characteristic genes that may be helpful in the diagnosis of ICC and affect immune cell infiltration.

METHODS

We downloaded two ICC-related human gene expression profiles from GEO database as the training group (GSE26566 and GSE32958 datasets) for difference analysis, and performed enrichment analysis on differential genes. The least absolute shrinkage and selection operator (LASSO), support vector machine-recursive feature elimination (SVM-RFE) and random forest (RF), three machine learning algorithms, were used to screen the characteristic genes. Double verification was carried out on GSE107943 and The Cancer Genome Atlas, two verification groups. Receiver operating characteristic curve and area under the curve (AUC) were used to evaluate the diagnostic efficacy of genes for ICC. CIBERSORT and ssGSEA algorithms were used to evaluate the effect of characteristic genes on immune infiltration pattern. Human Protein Atlas (HPA) was used to analyze the protein expression level of the target gene.

RESULTS

A total of 1091 differential genes were obtained in the training group. Enrichment analysis showed that the above genes were mainly enriched in small molecular catabolism, complement and coagulation cascade, bile secretion and other functions and pathways. Twenty-five characteristic genes were screened by LASSO regression, 19 by SVM-RFE algorithm, and 30 by RF algorithm. Three algorithms were used in combination to determine the characteristic gene of ICC: MMP14. The verification group confirmed that the genes had a high diagnostic accuracy (AUC values of the training group and the verification group were 0.960, 0.999, and 0.977, respectively). Comprehensive analysis of immune infiltration showed that MMP14 could affect the infiltration of monocytes, activated memory CD4 T cells, resting memory CD4 T cells, and other immune cells, and was closely related to the expression of CD200, cytotoxic T-lymphocyte-associated antigen 4, CD14, CD44, and other immune checkpoints. The results of immunohistochemistry in HPA database showed was indeed overexpressed in ICC.

CONCLUSION

MMP14 can be used as a disease characteristic gene of ICC, and may regulate the distribution of immune-infiltrating cells in the ICC tumor microenvironment, which provides a new method for the determination of ICC diagnostic markers and screening of therapeutic targets.

Transcriptome-wide identification of altered RNA m6A profiles in cardiac tissue of rats with LPS-induced myocardial injury

Purpose

Myocardial injury is a common complication in patients with endotoxaemia/sepsis, especially in children. Moreover, it develops through an unclear pathophysiological mechanism, and effective therapies are lacking. Recently, RNA modification, particularly N ⁶-methyladenosine (m⁶A) modification, has been found to be involved in various physiological processes and to play important roles in many diseases. However, the role of m⁶A modification in endotoxaemia/sepsis-induced myocardial injury is still in its infancy. Therefore, we attempted to construct the m⁶A modification map of myocardial injury in a rat model treated by lipopolysaccharide (LPS) and explore the role of m⁶A modification in LPS-induced myocardial injury.

Method

Myocardial injury adolescent rat model was constructed by intraperitoneal injection of LPS. m⁶A RNA Methylation Quantification Kit was used to detect overall level of m⁶A modification in rat cardiac tissue. m⁶A-specific methylated RNA immunoprecipitation followed by high-throughput sequencing (MeRIP-seq) and RNA sequencing (RNA-seq) were conducted to identify the altered m⁶A-modified genes and differentially expressed genes in cardiac tissue of rats treated by LPS and control rats (6 versus. 6). Bioinformatics was used to analyze the functions of differentially m⁶A modified genes, differentially expressed genes, and genes with both differential m⁶A modification and differential expression. qPCR was used to detect expression of m⁶A modification related enzymes.

Result

We found that the overall level of m⁶A modification in cardiac tissue of the LPS group was up-regulated compared with that of the control group. MeRIP-seq and RNA-seq results showed that genes with differential m⁶A modification, genes with differential expression and genes with both differential m⁶A modification and differential expression were closely associated with inflammatory responses and apoptosis. In addition, we found that m⁶A-related enzymes (Mettl16, Rbm15, Fto, Ythdc2 and Hnrnpg) were differentially expressed in the LPS group versus. the control group.

Conclusion

m⁶A modification is involved in the pathogenesis process of LPS-induced myocardial injury, possibly through the regulation of inflammatory response and apoptosis-related pathways. These results provide valuable information regarding the potential pathogenic mechanisms underlying LPS-induced myocardial injury.

Machine learning-based prediction of candidate gene biomarkers correlated with immune infiltration in patients with idiopathic pulmonary fibrosis

Objective

This study aimed to identify candidate gene biomarkers associated with immune infiltration in idiopathic pulmonary fibrosis (IPF) based on machine learning algorithms.

Methods

Microarray datasets of IPF were extracted from the Gene Expression Omnibus (GEO) database to screen for differentially expressed genes (DEGs). The DEGs were subjected to enrichment analysis, and two machine learning algorithms were used to identify candidate genes associated with IPF. These genes were verified in a validation cohort from the GEO database. Receiver operating characteristic (ROC) curves were plotted to assess the predictive value of the IPF-associated genes. The cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) algorithm was used to evaluate the proportion of immune cells in IPF and normal tissues. Additionally, the correlation between the expression of IPF-associated genes and the infiltration levels of immune cells was examined.

Results

A total of 302 upregulated and 192 downregulated genes were identified. Functional annotation, pathway enrichment, Disease Ontology and gene set enrichment analyses revealed that the DEGs were related to the extracellular matrix and immune responses. COL3A1, CDH3, CEBPD, and GPIHBP1 were identified as candidate biomarkers using machine learning algorithms, and their predictive value was verified in a validation cohort. Additionally, ROC analysis revealed that the four genes had high predictive accuracy. The infiltration levels of plasma cells, M0 macrophages and resting dendritic cells were higher and those of resting natural killer (NK) cells, M1 macrophages and eosinophils were lower in the lung tissues of patients with IPF than in those of healthy individuals. The expression of the abovementioned genes was correlated with the infiltration levels of plasma cells, M0 macrophages and eosinophils.

Conclusion

COL3A1, CDH3, CEBPD, and GPIHBP1 are candidate biomarkers of IPF. Plasma cells, M0 macrophages and eosinophils may be involved in the development of IPF and may serve as immunotherapeutic targets in IPF.

Identification of hub biomarkers of myocardial infarction by single-cell sequencing, bioinformatics, and machine learning

Background

Myocardial infarction (MI) is one of the first cardiovascular diseases endangering human health. Inflammatory response plays a significant role in the pathophysiological process of MI. Messenger RNA (mRNA) has been proven to play a key role in cardiovascular diseases. Single-cell sequencing (SCS) technology is a new technology for high-throughput sequencing analysis of genome, transcriptome, and epigenome at the single-cell level, and it also plays an important role in the diagnosis and treatment of cardiovascular diseases. Machine learning algorithms have a wide scope of utilization in biomedicine and have demonstrated superior efficiency in clinical trials. However, few studies integrate these three methods to investigate the role of mRNA in MI. The aim of this study was to screen the expression of mRNA, investigate the function of mRNA, and provide an underlying scientific basis for the diagnosis of MI.

Methods

In total, four RNA microarray datasets of MI, namely, GSE66360, GSE97320, GSE60993, and GSE48060, were downloaded from the Gene Expression Omnibus database. The function analysis was carried out by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Disease Ontology (DO) enrichment analysis. At the same time, inflammation-related genes (IRGs) were acquired from the GeneCards database. Then, 52 co-DEGs were acquired from differentially expressed genes (DEGs) in differential analysis, IRGs, and genes from SCS, and they were used to construct a protein-protein interaction (PPI) network. Two machine learning algorithms, namely, (1) least absolute shrinkage and selection operator and (2) support vector machine recursive feature elimination, were used to filter the co-DEGs. Gene set enrichment analysis (GSEA) was performed to screen the hub-modulating signaling pathways associated with the hub genes. The results were validated in GSE97320, GSE60993, and GSE48060 datasets. The CIBERSORT algorithm was used to analyze 22 infiltrating immune cells in the MI and healthy control (CON) groups and to analyze the correlation between these immune cells. The Pymol software was used for molecular docking of hub DEGs and for potential treatment of MI drugs acquired from the COREMINE.

Results

A total of 126 DEGs were in the MI and CON groups. After screening two machine learning algorithms and key co-DEGs from a PPI network, two hub DEGs (i.e., IL1B and TLR2) were obtained. The diagnostic efficiency of IL1B, TLR2, and IL1B + TLR2 showed good discrimination in the four cohorts. GSEA showed that KEGG enriched by DEGs were mainly related to inflammation-mediated signaling pathways, and GO biological processes enriched by DEGs were linked to biological effects of various inflammatory cells. Immune analysis indicated that IL1B and TLR2 were correlated with various immune cells. Dan shen, san qi, feng mi, yuan can e, can sha, san qi ye, san qi hua, and cha shu gen were identified as the potential traditional Chinese medicine (TCM) for the treatment of MI. 7-hydroxyflavone (HF) had stable combinations with IL1B and TLR2, respectively.

Conclusion

This study identified two hub DEGs (IL1B and TLR2) and illustrated its potential role in the diagnosis of MI to enhance our knowledge of the underlying molecular mechanism. Infiltrating immune cells played an important role in MI. TCM, especially HF, was a potential drug for the treatment of MI.