Network Analysis-Based Approach for Exploring the Potential Diagnostic Biomarkers of Acute Myocardial Infarction

Identification of key genes associated with acute myocardial infarction using WGCNA and two-sample mendelian randomization study

OBJECTIVE

Acute myocardial infarction (AMI) is a severe condition with high morbidity and mortality rates. This study aimed to identify hub genes potentially associated with AMI and assess their clinical utility in predicting AMI occurrence.

METHODS

Gene microarray data were obtained from the Gene Expression Omnibus (GEO) database. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were conducted on samples from patients with AMI and control samples to identify modules significantly associated with AMI. GO and KEGG analyses were applied to investigate the potential functions of these hub genes. Lastly, the mendelian randomization (MR) method was applied to analyze the causal relationship between the hub gene TNF and AMI.

RESULTS

285 differentially expressed genes (DEGs) were identified through WCGNA and were clustered into 6 modules. The yellow module appeared most relevant to AMI. Further exploration through GO and KEGG pathway enrichment showed that key hub genes in the yellow module were linked to positive regulation of cytokine production, cytokine receptor binding, NF-kappa B signaling pathway, IL-17 signaling pathway, and TNF signaling pathway. The top 10 genes identified through Cytoscape software analysis were IL1B, TNF, TLR4, TLR2, FCGR3B, MMP9, CXCL8, TLR8, ICAM1, and JUK. Utilizing inverse variance weighting (IVW) analysis, we discovered a significant association between TNF and AMI risk, with an OR of 0.946 (95% CI = 0.911-0.984, p = 0.005).

CONCLUSIONS

The result of this study indicated that TNF, TLR2, TLR4, IL1B and FCGR3B may be potential biodiagnostic markers for AMI. TNF can inhibit inflammatory and oxidative stress responses in AMI, exerting a protective role in the heart.

Comprehensive bioinformatics analytics and in vivo validation reveal SLC31A1 as an emerging diagnostic biomarker for acute myocardial infarction

BACKGROUND

Globally, Acute Myocardial Infarction (AMI) is a common cause of heart failure (HF), which has been a leading cause of mortality resulting from non-communicable diseases. On the other hand, increasing evidence suggests that the role of energy production within the mitochondria strongly links to the development and progression of heart diseases, while Cuproptosis, a newly identified cell death mechanism, has not yet been comprehensively analyzed from the aspect of cardiovascular medicine.

MATERIALS AND METHODS

8 transcriptome profiles curated from the GEO database were integrated, from which a diagnostic model based on the Stacking algorithm was established. The efficacy of the model was evaluated in a multifaced manner (i.e., by Precision-Recall curve, Receiver Operative Characteristic curve, etc.). We also sequenced our animal models at the bulk RNA level and conducted qPCR and immunohistochemical staining, with which we further validated the expression of the key contributor gene to the model. Finally, we explored the immune implications of the key contributor gene.

RESULTS

A merged machine learning model containing 4 Cuproptosis-related genes (i.e., PDHB, CDKN2A, GLS, and SLC31A1) for robust AMI diagnosis was developed, in which SLC31A1 served as the key contributor. Through in vivo modeling, we validated the aberrant overexpression of SLC31A1 in AMI. Besides, further transcriptome analysis revealed that its high expression was correlated with significant potential immunological implications in the infiltration of many immune cell types, especially monocyte.

CONCLUSIONS

We constructed an AMI diagnostic model based on Cuproptosis-related genes and validated the key contributor gene in animal modeling. We also analyzed the effects on the immune system for its overexpression in AMI.

A Potential Role of NFIL3 in Atherosclerosis

Nuclear factor interleukin-3 (NFIL3), a proline- and acidic-residue-rich (PAR) bZIP transcription factor, is called the E4 binding protein 4 (E4BP4) as well, which is relevant to regulate the circadian rhythms and the viability of cells. More and more evidence has shown that NFIL3 is associated with different cardiovascular diseases. In recent years, it has been found that NFIL3 has significant functions in the progression of atherosclerosis (AS) via the regulation of inflammatory response, macrophage polarization, some immune cells and lipid metabolism. In this overview, we sum up the function of NFIL3 during the development of AS and offer meaningful views how to treat cardiovascular disease related to AS.

Integration of machine learning to identify diagnostic genes in leukocytes for acute myocardial infarction patients

BACKGROUND

Acute myocardial infarction (AMI) has two clinical characteristics: high missed diagnosis and dysfunction of leukocytes. Transcriptional RNA on leukocytes is closely related to the course evolution of AMI patients. We hypothesized that transcriptional RNA in leukocytes might provide potential diagnostic value for AMI. Integration machine learning (IML) was first used to explore AMI discrimination genes. The following clinical study was performed to validate the results.

METHODS

A total of four AMI microarrays (derived from the Gene Expression Omnibus) were included in bioanalysis (220 sample size). Then, the clinical validation was finished with 20 AMI and 20 stable coronary artery disease patients (SCAD). At a ratio of 5:2, GSE59867 was included in the training set, while GSE60993, GSE62646, and GSE48060 were included in the testing set. IML was explicitly proposed in this research, which is composed of six machine learning algorithms, including support vector machine (SVM), neural network (NN), random forest (RF), gradient boosting machine (GBM), decision trees (DT), and least absolute shrinkage and selection operator (LASSO). IML had two functions in this research: filtered optimized variables and predicted the categorized value. Finally, The RNA of the recruited patients was analyzed to verify the results of IML.

RESULTS

Thirty-nine differentially expressed genes (DEGs) were identified between controls and AMI individuals from the training sets. Among the thirty-nine DEGs, IML was used to process the predicted classification model and identify potential candidate genes with overall normalized weights > 1. Finally, two genes (AQP9 and SOCS3) show their diagnosis value with the area under the curve (AUC) > 0.9 in both the training and testing sets. The clinical study verified the significance of AQP9 and SOCS3. Notably, more stenotic coronary arteries or severe Killip classification indicated higher levels of these two genes, especially SOCS3. These two genes correlated with two immune cell types, monocytes and neutrophils.

CONCLUSION

AQP9 and SOCS3 in leukocytes may be conducive to identifying AMI patients with SCAD patients. AQP9 and SOCS3 are closely associated with monocytes and neutrophils, which might contribute to advancing AMI diagnosis and shed light on novel genetic markers. Multiple clinical characteristics, multicenter, and large-sample relevant trials are still needed to confirm its clinical value.

Machine learning-based integration develops biomarkers initial the crosstalk between inflammation and immune in acute myocardial infarction patients

Great strides have been made in past years toward revealing the pathogenesis of acute myocardial infarction (AMI). However, the prognosis did not meet satisfactory expectations. Considering the importance of early diagnosis in AMI, biomarkers with high sensitivity and accuracy are urgently needed. On the other hand, the prevalence of AMI worldwide has rapidly increased over the last few years, especially after the outbreak of COVID-19. Thus, in addition to the classical risk factors for AMI, such as overwork, agitation, overeating, cold irritation, constipation, smoking, and alcohol addiction, viral infections triggers have been considered. Immune cells play pivotal roles in the innate immunosurveillance of viral infections. So, immunotherapies might serve as a potential preventive or therapeutic approach, sparking new hope for patients with AMI. An era of artificial intelligence has led to the development of numerous machine learning algorithms. In this study, we integrated multiple machine learning algorithms for the identification of novel diagnostic biomarkers for AMI. Then, the possible association between critical genes and immune cell infiltration status was characterized for improving the diagnosis and treatment of AMI patients.

Construction of Novel Gene Signature-Based Predictive Model for the Diagnosis of Acute Myocardial Infarction by Combining Random Forest With Artificial Neural Network

Background

Acute myocardial infarction (AMI) is one of the most common causes of mortality around the world. Early diagnosis of AMI contributes to improving prognosis. In our study, we aimed to construct a novel predictive model for the diagnosis of AMI using an artificial neural network (ANN), and we verified its diagnostic value via constructing the receiver operating characteristic (ROC).

Methods

We downloaded three publicly available datasets (training sets GSE48060, GSE60993, and GSE66360) from Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) were identified between 87 AMI and 78 control samples. We applied the random forest (RF) and ANN algorithms to further identify novel gene signatures and construct a model to predict the possibility of AMI. Besides, the diagnostic value of our model was further validated in the validation sets GSE61144 (7 AMI patients and 10 controls), GSE34198 (49 AMI patients and 48 controls), and GSE97320 (3 AMI patients and 3 controls).

Results

A total of 71 DEGs were identified, of which 68 were upregulated and 3 were downregulated. Firstly, 11 key genes in 71 DEGs were screened with RF classifier for the classification of AMI and control samples. Then, we calculated the weight of each key gene using ANN. Furthermore, the diagnostic model was constructed and named neuralAMI, with significant predictive power (area under the curve [AUC] = 0.980). Finally, our model was validated with the independent datasets GSE61144 (AUC = 0.900), GSE34198 (AUC = 0.882), and GSE97320 (AUC = 1.00).

Conclusion

Machine learning was used to develop a reliable predictive model for the diagnosis of AMI. The results of our study provide potential gene biomarkers for early disease screening.

PRKAR1A and SDCBP Serve as Potential Predictors of Heart Failure Following Acute Myocardial Infarction

Background and Objectives

Early diagnosis of patients with acute myocardial infarction (AMI) who are at a high risk of heart failure (HF) progression remains controversial. This study aimed at identifying new predictive biomarkers of post-AMI HF and at revealing the pathogenesis of HF involving these marker genes.

Methods and Results

A transcriptomic dataset of whole blood cells from AMI patients with HF progression (post-AMI HF, n = 16) and without progression (post-AMI non-HF, n = 16) was analyzed using the weighted gene co-expression network analysis (WGCNA). The results indicated that one module consisting of 720 hub genes was significantly correlated with post-AMI HF. The hub genes were validated in another transcriptomic dataset of peripheral blood mononuclear cells (post-AMI HF, n = 9; post-AMI non-HF, n = 8). PRKAR1A, SDCBP, SPRED2, and VAMP3 were upregulated in the two datasets. Based on a single-cell RNA sequencing dataset of leukocytes from heart tissues of normal and infarcted mice, PRKAR1A was further verified to be upregulated in monocytes/macrophages on day 2, while SDCBP was highly expressed in neutrophils on day 2 and in monocytes/macrophages on day 3 after AMI. Cell-cell communication analysis via the "CellChat" package showed that, based on the interaction of ligand-receptor (L-R) pairs, there were increased autocrine/paracrine cross-talk networks of monocytes/macrophages and neutrophils in the acute stage of MI. Functional enrichment analysis of the abovementioned L-R genes together with PRKAR1A and SDCBP performed through the Metascape platform suggested that PRKAR1A and SDCBP were mainly involved in inflammation, apoptosis, and angiogenesis. The receiver operating characteristic (ROC) curve analysis demonstrated that PRKAR1A and SDCBP, as well as their combination, had a promising prognostic value in the identification of AMI patients who were at a high risk of HF progression.

Conclusion

This study identified that PRKAR1A and SDCBP may serve as novel biomarkers for the early diagnosis of post-AMI HF and also revealed their potentially regulatory mechanism during HF progression.

Systematic Bioinformatics Analysis Based on Public and Second-Generation Sequencing Transcriptome Data: A Study on the Diagnostic Value and Potential Mechanisms of Immune-Related Genes in Acute Myocardial Infarction

Acute myocardial infarction (AMI) is one of the most serious cardiovascular diseases worldwide. Advances in genomics have provided new ideas for the development of novel molecular biomarkers of potential clinical value for AMI.

Integrated Bioinformatics-Based Analysis of Hub Genes and the Mechanism of Immune Infiltration Associated With Acute Myocardial Infarction

Background

Acute myocardial infarction (AMI) is a fatal disease that causes high morbidity and mortality. It has been reported that AMI is associated with immune cell infiltration. Now, we aimed to identify the potential diagnostic biomarkers of AMI and uncover the immune cell infiltration profile of AMI.

Methods

From the Gene Expression Omnibus (GEO) data set, three data sets (GSE48060, GSE60993, and GSE66360) were downloaded. Differentially expressed genes (DEGs) from AMI and healthy control samples were screened. Furthermore, DEGs were performed via gene ontology (GO) functional and kyoto encyclopedia of genes and genome (KEGG) pathway analyses. The Gene set enrichment analysis (GSEA) was used to analyze GO terms and KEGG pathways. Utilizing the Search Tool for Retrieval of Interacting Genes/Proteins (STRING) database, a protein–protein interaction (PPI) network was constructed, and the hub genes were identified. Then, the receiver operating characteristic (ROC) curves were constructed to analyze the diagnostic value of hub genes. And, the diagnostic value of hub genes was further validated in an independent data set GSE61144. Finally, CIBERSORT was used to represent the compositional patterns of the 22 types of immune cell fractions in AMI.

Results

A total of 71 DEGs were identified. These DEGs were mainly enriched in immune response and immune-related pathways. Toll-like receptor 2 (TLR2), interleukin-1B (IL1B), leukocyte immunoglobulin-like receptor subfamily B2 (LILRB2), Fc fragment of IgE receptor Ig (FCER1G), formyl peptide receptor 1 (FPR1), and matrix metalloproteinase 9 (MMP9) were identified as diagnostic markers with the value of p < 0.05. Also, the immune cell infiltration analysis indicated that TLR2, IL1B, LILRB2, FCER1G, FPR1, and MMP9 were correlated with neutrophils, monocytes, resting natural killer (NK) cells, gamma delta T cells, and CD4 memory resting T cells. The fractions of monocytes and neutrophils were significantly higher in AMI tissues than in control tissues.

Conclusion

TLR2, IL1B, LILRB2, FCER1G, FPR1, and MMP9 are involved in the process of AMI, which can be used as molecular biomarkers for the screening and diagnosis of AMI. In addition, the immune system plays a vital role in the occurrence and progression of AMI.

Screening for Regulatory Network of miRNA–Inflammation, Oxidative Stress and Prognosis-Related mRNA in Acute Myocardial Infarction: An in silico and Validation Study

Background

Acute myocardial infarction (AMI), which commonly leads to heart failure, is among the leading causes of mortality worldwide. The aim of this study was to find potential regulatory network for miRNA-inflammation, oxidative stress and prognosis-related mRNA to uncover molecular mechanisms of AMI.

Methods

The expression profiles of miRNA and mRNA in the blood samples from AMI patients were downloaded from the Gene Expression Omnibus (GEO) dataset for differential expression analysis. Weighted gene co-expression network analysis (WGCNA) was used to further identify important mRNAs. The negatively regulatory network construction of miRNA–inflammation, oxidative stress and prognosis-related mRNAs was performed, followed by protein–protein interaction (PPI) and functional analysis of mRNAs.

Results

A total of three pairs of negatively regulatory network of miRNA–inflammation and prognosis-related mRNAs (hsa-miR-636/hsa-miR-491-3p/hsa-miR-188-5p/hsa-miR-188-3p-AQP9, hsa-miR-518a-3p-C5AR1 and hsa-miR-509-3-5p/hsa-miR-127-5p-PLAUR), two pairs of negatively regulatory network of miRNA–oxidative stress and prognosis-related mRNAs (hsa-miR-604-TLR4 and hsa-miR-139-5p-CXCL1) and three pairs of negatively regulatory network of miRNA-inflammation, oxidative stress and prognosis-related mRNA (hsa-miR-634/hsa-miR-591-TLR2, hsa-miR-938-NFKBIA and hsa-miR-520h/hsa-miR-450b-3p-ADM) were identified. In the KEGG analysis, some signaling pathways were identified, such as complement and coagulation cascades, pathogenic Escherichia coli infection, chemokine signaling pathway and cytokine–cytokine receptor interaction and Toll-like receptor signaling pathway.

Conclusion

Identified negatively regulatory network of miRNA-inflammation/oxidative stress and prognosis-related mRNA may be involved in the process of AMI. Those inflammation/oxidative stress and prognosis-related mRNAs may be diagnostic and prognostic biomarkers for AMI.

Identification of Biomarkers Related to Immune Cell Infiltration with Gene Coexpression Network in Myocardial Infarction

Background

There is evidence that the immune system plays a key critical role in the pathogenesis of myocardial infarction (MI). However, the exact mechanisms associated with immunity have not been systematically uncovered.

Methods

This study used the weighted gene coexpression network analysis (WGCNA) and the CIBERSORT algorithm to analyze the MI expression data from the Gene Expression Omnibus database and then identify the module associated with immune cell infiltration. In addition, we built the coexpression network and protein-protein interactions network analysis to identify the hub genes. Furthermore, the relationship between hub genes and NK cell resting was validated by using another dataset GSE123342. Finally, receiver operating characteristic (ROC) curve analyses were used to assess the diagnostic value of verified hub genes.

Results

Monocytes and neutrophils were markedly increased, and T cell CD8, T cell CD4 naive, T cell CD4 memory resting, and NK cell resting were significantly decreased in MI groups compared with stable coronary artery disease (CAD) groups. The WGCNA results showed that the pink model had the highest correlation with the NK cell resting infiltration level. We identified 11 hub genes whose expression correlated to the NK cell resting infiltration level, among which, 7 hub genes (NKG7, TBX21, PRF1, CD247, KLRD1, FASLG, and EOMES) were successfully validated in GSE123342. And these 7 genes had diagnostic value to distinguish MI and stable CAD.

Conclusions

NKG7, TBX21, PRF1, CD247, KLRD1, FASLG, and EOMES may be a diagnostic biomarker and therapeutic target associated with NK cell resting infiltration in MI.

Identification of key gene modules and pathways of human platelet transcriptome in acute myocardial infarction patients through co-expression network

Acute myocardial infarction (AMI) seriously threatens human life. In this study we aimed to systemically analyze the function of key gene modules in human platelets in AMI. We used weighted gene co-expression network analysis (WGCNA) to construct a co-expression module, and analyzed the relationship between potential modules and clinical characteristics based on platelet RNA-seq RPKM count reads of 16 ST-segment elevation myocardial infarction (STEMI) patients and 16 non-STEMI (NSTEMI) patients provided by the GEO database. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed with the DAVID tool. Hub genes were calculated by the Cytohubba package. A total of 3653 genes was selected to construct the co-expression modules. A significant correlation between BMI and the module with color of sky-blue in STEMI. In NSTEMI, there was a significant correlation between the sky blue module and CAD, the Salmon module and HT, and the Cyan module and HT. In STEMI, the Hub genes were mainly enriched in functions related to cell membrane signal transduction including Aqp1, Armcx1, Gsta4, Hist3h2a and Il17re. In NSTEMI, the Hub genes are related mainly to energy metabolism in the sky-blue module including Olr1, Nap1l3, Gfer, Dohh, Crispld1 and Ccdc8b; they are mainly related to extracellular space and calcium binding in the Cyan module, including Clec12b, Chd4, Asgr1, Armcx4, Chid1 and Alkbh7. The hub genes in the Salmon module include Ell3, Aldh1b1, Cavin4, Cabp4, Eif1ay and Dus3l. Our results provide a framework for co-expression gene modules in STEMI and NSTEMI patients, and identify key targets as biomarkers for patients with different subtypes of AMI.

Comparative analysis of hypothalamus transcriptome between laying hens with different egg-laying rates

Egg-laying performance is one of the most important economic traits in the poultry industry. Commercial layers can lay one egg almost every day during their peak-laying period. However, many Chinese indigenous chicken breeds show a relatively low egg-laying rate, even during their peak-laying period. To understand what makes the difference in egg production, we compared the hypothalamus transcriptome profiles of Lushi blue-shelled-egg chickens (LBS), a Chinese indigenous breed with low egg-laying rate and Rhode Island Red chickens (RIR), a commercial layer with relatively high egg-laying rate using RNA-seq. A total of 753 differentially expressed genes (DEGs) were obtained. Of these DEGs, 38 genes were enriched in 2 Gene Ontology (GO) terms, namely reproduction term and the reproductive process term, and 6 KEGG pathways, namely Wnt signaling pathway, Oocyte meiosis, GnRH signaling pathway, Thyroid hormone signaling pathway, Thyroid hormone synthesis and MAPK signaling pathway, which have been long known to be involved in egg production regulation. To further determine the core genes from the 38 DEGs, protein-protein interaction (PPI) network, co-expression network and transcriptional regulatory network analyses were carried out. After integrated analysis and experimental validation, 4 core genes including RAC1, MRE11A, MAP7 and SOX5 were identified as the potential core genes that are responsible for the laying-rate difference between the 2 breeds. These findings paved the way for future investigating the mechanism of egg-laying regulation and enriched the chicken reproductive regulation theory.

Identification of monocyte-associated genes as predictive biomarkers of heart failure after acute myocardial infarction

BACKGROUND

Acute myocardial infarction (AMI) is a major contributor of heart failure (HF). Peripheral blood mononuclear cells (PBMCs), mainly monocytes, are the essential initiators of AMI-induced HF. The powerful biomarkers for early identification of AMI patients at risk of HF remain elusive. We aimed to identify monocyte-related critical genes as predictive biomarkers for post-AMI HF.

METHODS

We performed weighted gene co-expression network analysis (WGCNA) on transcriptomics of PBMCs from AMI patients who developed HF or did not. Functional enrichment analysis of genes in significant modules was performed via Metascape. Then we obtained the single-cell RNA-sequencing data of recruited monocytes/macrophages from AMI and control mice using the Scanpy and screened 381 differentially expressed genes (DEGs) between the two groups. We validated the expression changes of the 25 genes in cardiac macrophages from AMI mice based on bulk RNA-sequencing data and PBMCs data mentioned above.

RESULTS

In our study, the results of WGCNA showed that two modules containing 827 hub genes were most significantly associated with post-AMI HF, which mainly participated in cell migration, inflammation, immunity, and apoptosis. There were 25 common genes between DEGs and hub genes, showing close relationship with inflammation and collagen metabolism. CUX1, CTSD and ADD3 exhibited consistent changes in three independent studies. Receiver operating characteristic curve analysis showed that each of the three genes had excellent performance in recognizing post-AMI HF patients.

CONCLUSION

Our findings provided a set of three monocyte-related biomarkers for the early prediction of HF development after AMI as well as potential therapeutic targets of post-AMI HF.

Weighted gene co-expression network analysis identified underlying hub genes and mechanisms in the occurrence and development of viral myocarditis

Background

Myocarditis is an inflammatory myocardial disease, which may lead to heart failure and sudden death. Despite extensive research into the pathogenesis of myocarditis, effective treatments for this condition remain elusive. This study aimed to explore the potential pathogenesis and hub genes for viral myocarditis.

Methods

A weighted gene co-expression network analysis (WGCNA) was performed based on the gene expression profiles derived from mouse models at different stages of viral myocarditis (GSE35182). Functional annotation was executed within the key modules. Potential hub genes were predicted based on the intramodular connectivity (IC). Finally, potential microRNAs that regulate gene expression were predicted by miRNet analysis.

Results

Three gene co-expression modules showed the strongest correlation with the acute or chronic disease stage. A significant positive correlation was detected between the acute disease stage and the turquoise module, the genes of which were mainly enriched in antiviral response and immune-inflammatory activation. Furthermore, a significant positive correlation and a negative correlation were identified between the chronic disease stage and the brown and yellow modules, respectively. These modules were mainly associated with the cytoskeleton, phosphorylation, cellular catabolic process, and autophagy. Subsequently, we predicted the underlying hub genes and microRNAs in the three modules.

Conclusions

This study revealed the main biological processes in different stages of viral myocarditis and predicted hub genes in both the acute and chronic disease stages. Our results may be helpful for developing new therapeutic targets for viral myocarditis in future research.

Distinct mRNA and long non-coding RNA expression profiles of decidual natural killer cells in patients with early missed abortion

Early non-chromosome-related missed abortion (MA) is commonly associated with an altered immunological environment during pregnancy. Human decidual natural killer (dNK) cells, the most abundant lymphocyte population within the first-trimester maternal-fetal interface, are vital maternal regulators of immune tolerance mediating successful embryo implantation and placentation. Previous studies have shown that dNK cells may play a role in MA. However, the gene expression status and specific altered manifestations of dNK cells in patients with early MA remain largely unknown. Here, we show that MA dNK cells have distinct mRNA and lncRNA expression profiles through RNA sequencing, with a total of 276 mRNAs and 67 lncRNAs being differentially expressed compared with controls. Protein-protein interaction analysis of differentially expressed mRNAs was performed to identify hub genes and key modules. An lncRNA-mRNA regulatory network characterized by the small-world property was constructed to reveal the regulation of mRNA transcription by differential hub lncRNAs. Functional annotation of differentially expressed mRNAs and lncRNAs was performed to disclose their potential roles in MA pathogenesis. Our data highlight several enriched biological processes (immune response, inflammatory response, cell adhesion, and extracellular matrix [ECM] organization) and signaling pathways (cytokine-cytokine receptor interaction, ECM-receptor interaction, Toll-like receptor signaling pathway, and phosphatidylinositol signaling system) that may influence MA. This study is the first to demonstrate the involvement of altered mRNA and lncRNA expression profiles in the dNK cell pathogenesis of early MA, facilitating a better understanding of the underlying molecular mechanisms and the development of novel MA therapeutic strategies targeting key mRNAs and lncRNAs.

A novel single-cell based method for breast cancer prognosis

Breast cancer prognosis is challenging due to the heterogeneity of the disease. Various computational methods using bulk RNA-seq data have been proposed for breast cancer prognosis. However, these methods suffer from limited performances or ambiguous biological relevance, as a result of the neglect of intra-tumor heterogeneity. Recently, single cell RNA-sequencing (scRNA-seq) has emerged for studying tumor heterogeneity at cellular levels. In this paper, we propose a novel method, scPrognosis, to improve breast cancer prognosis with scRNA-seq data. scPrognosis uses the scRNA-seq data of the biological process Epithelial-to-Mesenchymal Transition (EMT). It firstly infers the EMT pseudotime and a dynamic gene co-expression network, then uses an integrative model to select genes important in EMT based on their expression variation and differentiation in different stages of EMT, and their roles in the dynamic gene co-expression network. To validate and apply the selected signatures to breast cancer prognosis, we use them as the features to build a prediction model with bulk RNA-seq data. The experimental results show that scPrognosis outperforms other benchmark breast cancer prognosis methods that use bulk RNA-seq data. Moreover, the dynamic changes in the expression of the selected signature genes in EMT may provide clues to the link between EMT and clinical outcomes of breast cancer. scPrognosis will also be useful when applied to scRNA-seq datasets of different biological processes other than EMT.

Identifying the pattern of immune related cells and genes in the peripheral blood of ischemic stroke

Background

Ischemic stroke (IS) is the second leading cause of death worldwide which is a serious hazard to human health. Evidence suggests that the immune system plays a key role in the pathophysiology of IS. However, the precisely immune related mechanisms were still not been systematically understood.

Methods

In this study, we aim to identify the immune related modules and genes that might play vital role in the occurrence and development of IS by using the weighted gene co-expression network analysis (WGCNA). Meanwhile, we applied a kind of deconvolution algorithm to reveal the proportions of 22 subsets of immune cells in the blood samples.

Results

There were total 128 IS patients and 67 healthy control samples in the three Gene Expression Omnibus (GEO) datasets. Under the screening criteria, 1082 DEGs (894 up-regulated and 188 down-regulated) were chosen for further analysis. A total of 11 clinically significant modules were identified, from which immune-related hub modules and hub genes were further explored. Finally, 16 genes were selected as real hub genes for further validation analysis. Furthermore, these CIBERSORT results suggest that detailed analysis of the immune subtype distribution pattern has the potential to enhance clinical prediction and to identify candidates for immunotherapy. More specifically, we identified that neutrophil emerge as a promising target for IS therapies.

Conclusions

In the present study, we investigated the immune related gene expression modules, in which the SLAMF1, IL7R and NCF4 may be novel therapeutic targets to promote functional and histological recovery after ischemic stroke. Furthermore, these hub genes and neutrophils may become important biological targets in the drug screening and drug designing.

Critical Roles of ELVOL4 and IL-33 in the Progression of Obesity-Related Cardiomyopathy via Integrated Bioinformatics Analysis

The molecular mechanisms underlying obesity-related cardiomyopathy (ORCM) progression involve multiple signaling pathways, and the pharmacological treatment for ORCM is still limited. Thus, it is necessary to explore new targets and develop novel therapies. Microarray analysis for gene expression profiles using different bioinformatics tools has been an effective strategy for identifying novel targets for various diseases. In this study, we aimed to explore the potential genes related to ORCM using the integrated bioinformatics analysis. The GSE18897 (whole blood expression profiling of obese diet-sensitive, obese diet-resistant, and lean human subjects) and GSE47022 (regular weight C57BL/6 and diet-induced obese C57BL/6 mice) were used for bioinformatics analysis. Weighted gene co-expression network analysis (WGCNA) of GSE18897 was employed to investigate gene modules that were strongly correlated with clinical phenotypes. Gene Ontology (GO) functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed on the co-expression genes. The expression levels of the hub genes were validated in the clinical samples. Yellow co-expression module of WGCNA in GSE18897 was found to be significantly related to the caloric restriction treatment. In addition, GO functional enrichment analysis and KEGG pathway analysis were performed on the co-expression genes in yellow co-expression module, which showed an association with oxygen transport and the porphyrins pathway. Overlap analysis of yellow co-expression module genes from GSE18897 andGSE47022 revealed six upregulated genes, and further experimental validation results showed that elongation of very-long-chain fatty acids protein 4 (ELOVL4), matrix metalloproteinase-8 (MMP-8), and interleukin-33 (IL-33) were upregulated in the peripheral blood from patients with ORCM compared to that in the controls. The bioinformatics analysis revealed that ELOVL4 expression levels are positively correlated with that of IL-33. Collectively, using WGCNA in combination with integrated bioinformatics analysis, the hub genes of ELVOL4 and IL-33 might serve as potential biomarkers for diagnosis and/or therapeutic targets for ORCM. The detailed roles of ELVOL4 and IL-33 in the pathophysiology of ORCM still require further investigation.

Weighted gene correlation network analysis reveals novel biomarkers associated with mesenchymal stromal cell differentiation in early phase

Osteoporosis is a major public health problem that is associated with high morbidity and mortality, and its prevalence is increasing as the world’s population ages. Therefore, understanding the molecular basis of the disease is becoming a high priority. In this regard, studies have shown that an imbalance in adipogenic and osteogenic differentiation of bone marrow mesenchymal stem cells (MSCs) is associated with osteoporosis. In this study, we conducted a Weighted Gene Co-Expression Network Analysis to identify gene modules associated with the differentiation of bone marrow MSCs. Gene Ontology and Kyoto Encyclopedia of Genes and Genome enrichment analysis showed that the most significant module, the brown module, was enriched with genes involved in cell cycle regulation, which is in line with the initial results published using these data. In addition, the Cytoscape platform was used to identify important hub genes and lncRNAs correlated with the gene modules. Furthermore, differential gene expression analysis identified 157 and 40 genes that were upregulated and downregulated, respectively, after 3 h of MSCs differentiation. Interestingly, regulatory network analysis, and comparison of the differentially expressed genes with those in the brown module identified potential novel biomarker genes, including two transcription factors (ZNF740, FOS) and two hub genes (FOXQ1, SGK1), which were further validated for differential expression in another data set of differentiation of MSCs. Finally, Gene Set Enrichment Analysis suggested that the two most important candidate hub genes are involved in regulatory pathways, such as the JAK-STAT and RAS signaling pathways. In summary, we have revealed new molecular mechanisms of MSCs differentiation and identified novel genes that could be used as potential therapeutic targets for the treatment of osteoporosis.

Potentially Critical Roles of NDUFB5, TIMMDC1, and VDAC3 in the Progression of Septic Cardiomyopathy Through Integrated Bioinformatics Analysis

Septic cardiomyopathy (SC) is a rare and harmful cardiovascular disease with decreased left ventricular (LV) output and multiple organ failure, which poses a serious threat to human life. Despite the advances in SC, its diagnostic basis and treatment methods are limited, and the specific diagnostic biomarkers and its candidate regulatory targets have not yet been fully established. In this study, the GSE79962 gene expression profile was retrieved, with 20 patients with SC and 11 healthy donors as control. Weighted gene coexpression network analysis (WGCNA) was employed to investigate gene modules that were strongly correlated with clinical phenotypes. Blue module was found to be most significantly related to SC. Moreover, Gene Ontology (GO) functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed on the coexpression genes in blue module and showed that it was associated with metabolic pathways, oxidative phosphorylation, and cardiac muscle contraction. Furthermore, a total of 10 hub genes NDUFB5, TIMMDC1, VDAC3, COQ10A, MRPL16 (mitochondrial ribosomal protein L16), C3orf43, TMEM182, DLAT, NDUFA8, and PDHB (pyruvate dehydrogenase E1 beta subunit) in the blue module were identified at transcriptional level and further validated at translational level in myocardium of an lipopolysaccharide-induced septic cardiac dysfunction mouse model. Overall, the results of quantitative real-time polymerase chain reaction were consistent with most of the microarray analysis results. Intriguingly, we observed that the highest change was NDUFB5, TIMMDC1, and VDAC3. These identified and validated genes provided references that would advance the understanding of molecular mechanisms of SC. Taken together, using WGCNA, the hub genes NDUFB5, TIMMDC1, and VDAC3 might serve as potential biomarkers for diagnosis and/or therapeutic targets for precise treatment of SC in the future.

Weighted Gene Co-Expression Network Analysis Identifies Critical Genes in the Development of Heart Failure After Acute Myocardial Infarction

Background: The development of heart failure (HF) remains a common complication following an acute myocardial infarction (AMI), and is associated with substantial adverse outcomes. However, the specific predictive biomarkers and candidate therapeutic targets for post-infarction HF have not been fully established. We sought to perform a weighted gene co-expression network analysis (WGCNA) to identify key modules, hub genes, and possible regulatory targets involved in the development of HF following AMI.

Methods: Genes exhibiting the most (top 50%) variation in expression levels across samples in a GSE59867 dataset were imported to the WGCNA. Gene Ontology and pathway enrichment analyses were performed on genes identified in the key module by Metascape. Gene regulatory networks were constructed using the microarray probe reannotation and bioinformatics database. Hub genes were screened out from the key module and validated using other datasets.

Results: A total of 10,265 most varied genes and six modules were identified between AMI patients who developed HF within 6 months of follow-up and those who did not. Specifically, the blue module was found to be the most significantly related to the development of post-infarction HF. Functional enrichment analysis revealed that the blue module was primarily associated with the inflammatory response, immune system, and apoptosis. Seven transcriptional factors, including SPI1, ZBTB7A, IRF8, PPARG, P65, KLF4, and Fos, were identified as potential regulators of the expression of genes identified in the blue module. Further, non-coding RNAs, including miR-142-3p and LINC00537, were identified as having close interactions with genes from the blue module. A total of six hub genes (BCL3, HCK, PPIF, S100A9, SERPINA1, and TBC1D9B) were identified and validated for their predictive value in identifying future HFs.

Conclusions: By using the WGCNA, we provide new insights into the underlying molecular mechanism and molecular markers correlated with HF development following an AMI, which may serve to improve risk stratification, therapeutic decisions, and prognosis prediction in AMI patients.

Investigation of the underlying hub genes and mechanisms of reperfusion injury in patients undergoing coronary artery bypass graft surgery by integrated bioinformatic analyses

Background

Although coronary artery bypass graft (CABG) surgery is the main method to revascularize the occluded coronary vessels in coronary artery diseases, the full benefits of the operation are mitigated by ischemia-reperfusion (IR) injury. Although many studies have been devoted to reducing IR injury in animal models, the translation of this research into the clinical field has been disappointing. Our study aimed to explore the underlying hub genes and mechanisms of IR injury.

Methods

A weighted gene co-expression network analysis (WGCNA) was executed based on the expression profiles in patients undergoing CABG surgery (GSE29396). Functional annotation and protein-protein interaction (PPI) network construction were executed within the modules of interest. Potential hub genes were predicted, combining both intramodular connectivity (IC) and degrees. Meanwhile, potential transcription factors (TFs) and microRNAs (miRNAs) were predicted by corresponding bioinformatics tools.

Results

A total of 336 differentially expressed genes (DEGs) were identified. DEGs were mainly enriched in neutrophil activity and immune response. Within the modules of interest, 5 upregulated hub genes (IL-6, CXCL8, IL-1β, MYC, PTGS-2) and 6 downregulated hub genes (C3, TIMP1, VSIG4, SERPING1, CD163, and HP) were predicted. Predicted miRNAs (hsa-miR-333-5p, hsa-miR-26b-5p, hsa-miR-124-3p, hsa-miR-16-5p, hsa-miR-98-5p, hsa-miR-17-5p, hsa-miR-93-5p) and TF (STAT1) might have regulated gene expression in the most positively related module, while hsa-miR-333-5p and HSF-1 were predicted to regulate the genes within the most negatively related module.

Conclusions

Our study illustrates an overview of gene expression changes in human atrial samples from patients undergoing CABG surgery and might help translate future research into clinical work.

Network-based analysis reveals novel gene signatures in the peripheral blood of patients with sporadic nonsyndromic thoracic aortic aneurysm

Thoracic aortic aneurysm (TAA), a serious cardiovascular disease that causes morbidity and mortality worldwide. At present, few biomarkers can accurately diagnose the appearance of TAA before dissection or rupture. Our research has the intention to investigate the developing applicable biomarkers for TAA promising clinically diagnostic biomarkers or probable regulatory targets for TAA. In our research, we built correlation networks utilizing the expression profile of peripheral blood mononuclear cell obtained from a public microarray data set (GSE9106). Furthermore, we chose the turquoise module, which has the strongest significance with TAA and was further analyzed. Fourteen genes that overlapped with differentially expressed proteins in the medial aortic layer were obtained. Subsequently, we verified the results applying quantitative polymerase chain reaction (Q-PCR) to our clinical specimen. In general, the Q-PCR results coincide with the majority of the expression profile. Fascinatingly, a notable change occurred in CLU, DES, MYH10, and FBLN5. In summary, using weighted gene coexpression analysis, our study indicates that CLU, DES, MYH10, and FBLN5 were identified and validated to be related to TAA and might be candidate biomarkers or therapeutic targets for TAA.

Differentially Expressed Proteins in Primary Endothelial Cells Derived From Patients With Acute Myocardial Infarction

Endothelial dysfunction is one of the primary factors in the onset and progression of atherothrombosis resulting in acute myocardial infarction (AMI). However, the pathological and cellular mechanisms of endothelial dysfunction in AMI have not been systematically studied. Protein expression profiling in combination with a protein network analysis was used by the mass spectrometry-based label-free quantification approach. This identified and quantified 2246 proteins, of which 335 were differentially regulated in coronary arterial endothelial cells from patients with AMI compared with controls. The differentially regulated protein profiles reveal the alteration of (1) metabolism of RNA, (2) platelet activation, signaling, and aggregation, (3) neutrophil degranulation, (4) metabolism of amino acids and derivatives, (5) cellular responses to stress, and (6) response to elevated platelet cytosolic Ca²⁺ pathways. Increased production of oxidants and decreased production of antioxidant biomarkers as well as downregulation of proteins with antioxidant properties suggests a role for oxidative stress in mediating endothelial dysfunction during AMI. In conclusion, this is the first quantitative proteomics study to evaluate the cellular mechanisms of endothelial dysfunction in patients with AMI. A better understanding of the endothelial proteome and pathophysiology of AMI may lead to the identification of new drug targets.

A transcriptomic model to predict increase in fibrous cap thickness in response to high-dose statin treatment: Validation by serial intracoronary OCT imaging

Background

Fibrous cap thickness (FCT), best measured by intravascular optical coherence tomography (OCT), is the most important determinant of plaque rupture in the coronary arteries. Statin treatment increases FCT and thus reduces the likelihood of acute coronary events. However, substantial statin-related FCT increase occurs in only a subset of patients. Currently, there are no methods to predict which patients will benefit. We use transcriptomic data from a clinical trial of rosuvastatin to predict if a patient's FCT will increase in response to statin therapy.

Methods

FCT was measured using OCT in 69 patients at (1) baseline and (2) after 8–10 weeks of 40  mg rosuvastatin. Peripheral blood mononuclear cells were assayed via microarray. We constructed machine learning models with baseline gene expression data to predict change in FCT. Finally, we ascertained the biological functions of the most predictive transcriptomic markers.

Findings

Machine learning models were able to predict FCT responders using baseline gene expression with high fidelity (Classification AUC = 0.969 and 0.972). The first model (elastic net) using 73 genes had an accuracy of 92.8%, sensitivity of 94.1%, and specificity of 91.4%. The second model (KTSP) using 18 genes has an accuracy of 95.7%, sensitivity of 94.3%, and specificity of 97.1%. We found 58 enriched gene ontology terms, including many involved with immune cell function and cholesterol biometabolism.

Interpretation

In this pilot study, transcriptomic models could predict if FCT increased following 8–10 weeks of rosuvastatin. These findings may have significance for therapy selection and could supplement invasive imaging modalities.

Involvement of heat shock protein 40 in the wing dimorphism of the house cricket Acheta domesticus

Wing dimorphism is a common phenomenon in a wide range of insect taxa. In most insects, the two morphs are macropterous and micropterous, in extreme cases of the latter, wing shedding can occur. Wing dimorphism contributes significantly to the ecological success of many insect species. However, the molecular basis of wing dimorphism is not fully understood, especially for wing-shed. Here, differentially expressed genes over eight developmental stages of the house cricket Acheta domesticus, which undergoes wing-shed dimorphism, were studied. The results revealed a wing-shed peak during adult development in which many DEGs were highly upregulated and it's influenced by cricket population density. A weighted correlation network analysis (WGCNA) grouped 21,922 DEGs among 141,456 unigenes into 18 modules of different expression patterns. The module in which the gene expression pattern correlated with the wing-shed phenotypic data was selected for further analyses with STEM and Cytoscape, and three candidate genes (AdomHSP40: Heat shock protein 40, AdomCFDP: Craniofacial development protein, AdomDIS3L: DIS3 Like 3'-5' Exoribonuclease) were identified by gene network analysis as the DEGs most relevant to wing-shed occurrence. The RNA interference of these genes together with an insulin receptor and Nylanderia fulva virus showed that the silencing of AdomHSP40 significantly decreased wing-shed occurrence, whereas the silencing of other candidate genes did not, suggesting that AdomHSP40 plays a crucial role in the wing-shed of Acheta domesticus. These findings provide insights into the molecular mechanisms underlying wing dimorphism in the house crickets, which differ from those found in other insects such as the planthopper.

Identification of Crucial Genes and Pathways in Human Arrhythmogenic Right Ventricular Cardiomyopathy by Coexpression Analysis

As one common disease causing young people to die suddenly due to cardiac arrest, arrhythmogenic right ventricular cardiomyopathy (ARVC) is a disorder of heart muscle whose progression covers one complicated gene interaction network that influence the diagnosis and prognosis of it. In our research, differentially expressed genes (DEGs) were screened, and we established a weighted gene coexpression network analysis (WGCNA) and gene set net correlations analysis (GSNCA) for identifying crucial genes as well as pathways related to ARVC pathogenic mechanism (n = 12). In the research, the results demonstrated that there were 619 DEGs in total between non-failing donor myocardial samples and ARVC tissues (FDR < 0.05). WGCNA analysis identified the two gene modules (brown and turquoise) as being most significantly associated with ARVC state. Then the ARVC-related four key biological pathways (cytokine–cytokine receptor interaction, chemokine signaling pathway, neuroactive ligand receptor interaction, and JAK-STAT signaling pathway) and four hub genes (CXCL2, TNFRSF11B, LIFR, and C5AR1) in ARVC samples were further identified by GSNCA method. Finally, we used t-test and receiver operating characteristic (ROC) curves for validating hub genes, results showed significant differences in t-test and their AUC areas all greater than 0.8. Together, these results revealed that the new four hub genes as well as key pathways that might be involved into ARVC diagnosis. Even though further experimental validation is required for the implication by association, our findings demonstrate that the computational methods based on systems biology might complement the traditional gene-wide approaches, as such, might offer a new insight in therapeutic intervention within rare diseases of people like ARVC.

Integrated bioinformatic analysis reveals YWHAB as a novel diagnostic biomarker for idiopathic pulmonary arterial hypertension

Idiopathic pulmonary arterial hypertension (IPAH) is a severe cardiovascular disease that is a serious threat to human life. However, the specific diagnostic biomarkers have not been fully clarified and candidate regulatory targets for IPAH have not been identified. The aim of this study was to explore the potential diagnostic biomarkers and possible regulatory targets of IPAH. We performed a weighted gene coexpression network analysis and calculated module-trait correlations based on a public microarray data set (GSE703) and six modules were found to be related to IPAH. Two modules which have the strongest correlation with IPAH were further analyzed and the top 10 hub genes in the two modules were identified. Furthermore, we validated the data by quantitative real-time polymerase chain reaction (qRT-PCR) in an independent sample set originated from our study center. Overall, the qRT-PCR results were consistent with most of the results of the microarray analysis. Intriguingly, the highest change was found for YWHAB, a gene encodes a protein belonging to the 14-3-3 family of proteins, members of which mediate signal transduction by binding to phosphoserine-containing proteins. Thus, YWHAB was subsequently selected for validation. In congruent with the gene expression analysis, plasma 14-3-3β concentrations were significantly increased in patients with IPAH compared with healthy controls, and 14-3-3β expression was also positively correlated with mean pulmonary artery pressure ( R ²  = 0.8783; p < 0.001). Taken together, using weighted gene coexpression analysis, YWHAB was identified and validated in association with IPAH progression, which might serve as a biomarker and/or therapeutic target for IPAH.

Identification of differentially expressed genes, lncRNAs and miRNAs which are associated with tumor malignant phenotypes in hepatoblastoma patients

Hepatoblastoma (HB) is one of the most common hepatic malignancies in the pediatric population. HB are composed of a variety of tumors, which derived from different origins and had varying clinical outcomes. However, the unclear underlying mechanisms of HB limited exploring novel biomarkers and effective therapeutic targets. We searched microarray datasets on Gene Expression Omnibus (GEO) database and selected GSE75271 and GSE75283 datasets for comprehensive analysis. Weighted gene correlation network analysis (WGCNA) was employed to identify genes which were associated with tumor malignant phenotypes, including HB subtypes, Cairo classification and tumor stage. Coexpression analysis of identified genes was also performed and lncRNA-miRNA-mRNA network was finally conducted. Our results showed that a total of 22 lncRNAs, 13 miRNAs and 66 mRNAs were identified to be associated with tumor malignant phenotypes. Mechanistically, these molecules might promote the malignant phenotypes via regulating metabolic pathways. Among of them, 6 miRNAs (hsa-miR-106b, hsa-miR-130b, hsa-miR-19a, hsa-miR-19b, hsa-miR-20a and hsa-miR-301a), 8 lncRNAs (NR_102317, XR_245338, XR_428373, XR_924945, XR_929728, XR_931611, XR_935074 and XR_946696), and 6 mRNAs (EGFR, GAREM, INSIG1, KRT81, SAR1B and SDC1) were selected to conduct a lncRNA-miRNA-mRNA network. Taken together, our findings provide evidence for exploring molecular mechanisms of HB. Those identified malignant phenotype-associated molecules might be potential biomarkers and anti-cancer therapeutic targets in future.

Identification of two novel biomarkers of rectal carcinoma progression and prognosis via co-expression network analysis

mRNA expression profiles provide important insights on a diversity of biological processes involved in rectal carcinoma (RC). Our aim was to comprehensively map complex interactions between the mRNA expression patterns and the clinical traits of RC. We employed the integrated analysis of five microarray datasets and The Cancer Genome Atlas rectal adenocarcinoma database to identify 2118 consensual differentially expressed genes (DEGs) in RC and adjacent normal tissue samples, and then applied weighted gene co-expression network analysis to parse DEGs and eight clinical traits in 66 eligible RC samples. A total of 16 co-expressed gene modules were identified. The green-yellow and salmon modules were most appropriate to the pathological stage (R = 0.36) and the overall survival (HR =13.534, P = 0.014), respectively. A diagnostic model of the five pathological stage hub genes (SCG3, SYP, CDK5R2, AP3B2, and RUNDC3A) provided a powerful classification accuracy between localized RC and non-localized RC. We also found increased Secretogranin III (SCG3) expression with higher pathological stage and poorer prognosis in the test and validation set. The increased Homer scaffolding protein 2 (HOMER2) expression with the favorable survival prediction efficiency significantly correlated with the markedly reduced overall survival of RC patients and the higher pathological stage during the test and validation set. Our findings indicate that the SCG3 and HOMER2 mRNA levels should be further evaluated as predictors of pathological stage and survival in patients with RC.

Weighted Gene Co-expression Network Analysis Identifies FKBP11 as a Key Regulator in Acute Aortic Dissection through a NF-kB Dependent Pathway

Acute aortic dissection (AAD) is a life-threatening disease. Despite the higher risk of mortality, currently there are no effective therapies that can ameliorate AAD development or progression. Identification of meaningful clusters of co-expressed genes or representative biomarkers for AAD may help to identify new pathomechanisms and foster development of new therapies. To this end, we performed a weighted gene co-expression network analysis (WGCNA) and calculated module-trait correlations based on a public microarray dataset (GSE 52093) and discovered 9 modules were found to be related to AAD. The module which has the strongest positive correlation with AAD was further analyzed and the top 10 hub genes SLC20A1, GINS2, CNN1, FAM198B, MAD2L2, UBE2T, FKBP11, SLMAP, CCDC34, and GALK1 were identified. Furthermore, we validated the data by qRT-PCR in an independent sample set originated from our study center. Overall, the qRT-PCR results were consistent with the results of the microarray analysis. Intriguingly, the highest change was found for FKBP11, a protein belongs to the FKBP family of peptidyl-prolyl cis/trans isomerases, which catalyze the folding of proline-containing polypeptides. In congruent with the gene expression analysis, FKBP11 expression was induced in cultured endothelial cells by angiotensin II treatment and endothelium of the dissected aorta. More importantly we show that FKBP11 provokes inflammation in endothelial cells by interacting with NF-kB p65 subunit, resulting in pro-inflammatory cytokines production. Accordingly, siRNA mediated knockdown of FKBP11 in cultured endothelial cells suppressed angiotensin II induced monocyte transmigration through the endothelial monolayer. Based on these data, we hypothesize that pro-inflammatory cytokines elicited by FKBP11 overexpression in the endothelium under AAD condition could facilitate transendothelial migration of the circulating monocytes into the aorta, where they differentiate into active macrophages and secrete MMPs and other extracellular matrix (ECM) degrading proteins, contributing to sustained inflammation and AAD. Taken together, our data identify important role of FKBP11 which can serve as biomarker and/or therapeutic target for AAD.