Identification of biomarkers correlated with hypertrophic cardiomyopathy with co‐expression analysis

[Pathogenesis and potential diagnostic biomarkers of atrial fibrillation in Chinese population: a study based on bioinfor-matics]

OBJECTIVES

To explore the pathogenesis and potential biomarkers of atrial fibrillation based on bioinformatics.

METHODS

Differentially expressed genes and module genes related to atrial fibrillation were obtained from GSE41177 and GSE79768 datasets (Chinese-origin tissue samples) through differential expression analysis and weighted gene co-expression network analysis. Candidate hub genes were obtained by taking intersections, and hub genes were obtained after gender stratification. Subsequently, functional enrichment analysis and immune infiltration analysis were performed. Four machine learning models were constructed based on the hub genes, and the optimal model was selected to construct a prediction nomogram. The prediction ability of the nomogram was verified using calibration curves and decision curves. Finally, potential therapeutic drugs for atrial fibrillation were screened from the DGIdb database.

RESULTS

A total of 67 differentially expressed genes and 65 module genes related to atrial fibrillation were identified. Functional enrichment analysis indicated that the pathogenesis of atrial fibrillation was closely related to inflammatory response, immune response, and immune and infectious diseases. Four common hub genes (TYROBP, FCER1G, EVI2B and SOD2), and two genes specifically expressed in male (PILRA and SLC35G3) and female (HLA-DRA and GATP) patients with atrial fibrillation were obtained after gender-segregated screening. The extreme gradient boosting model had satisfactory diagnostic efficiency, and the nomogram constructed based on the hub genes, male significant variables (PILRA, SLC35G3 and SOD2), and female significant variables (FCER1G, SOD2 and TYROBP) had satisfactory predictive ability. Immune infiltration analysis demonstrated a disturbed immune infiltration microenvironment in atrial fibrillation with a higher abundance of plasma cells, neutrophils, and γδT cells, with a higher abundance of neutrophils in males and resting mast cells in females. Two potential drugs for the treatment of atrial fibrillation, valproic acid and methotrexate, were obtained by database and literature screening.

CONCLUSIONS

The pathogenesis of atrial fibrillation is closely related to inflammation and immune response, and the microenvironment of immune cell infiltration of cardiomyocytes in the atrial tissue of patients with atrial fibrillation is disordered. TYROBP, FCER1G, EVI2B and SOD2 serve as potential diagnostic biomarkers of atrial fibrillation; PILRA and SLC35G3 serve as potential specific diagnostic biomarkers of atrial fibrillation in the male population, which can effectively predict the risk of atrial fibrillation development and are also potential targets for the treatment of atrial fibrillation.

Construction of LncRNA-mediated CeRNA network for investigating the immune pathogenesis of myocardial infarction

BACKGROUND

Myocardial infarction (MI) is a cardiovascular disease that seriously threatens human health. However, an immune-related competitive endogenous RNA (ceRNA) network has not been reported in MI.

METHODS

The GSE66360, GSE19339, GSE97320, GSE61741, and GSE168281 datasets were acquired from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) and differentially expressed miRNAs (DEmiRNAs) from MI patients and healthy controls were screened and an immune-related ceRNA network was constructed. Furthermore, the key long noncoding RNAs(lncRNAs) highly related to the immune mechanism of MI were identified utilizing the random walk with restart algorithm. Finally, the expression of the hub genes was further verified in the GSE66360, GSE19339, and GSE97320 datasets, and quantitativereal-time polymerase chain reaction (qRT-PCR) was performed for the MI patients and healthy controls.

RESULTS

A total of 184 differentially expressed immune-related genes(DE-IRGs) and 432 DE-miRNAs were obtained, and an immune-related ceRNA network comprising 1421 lncRNAs, 61 DE-miRNAs, and 139 DE-IRGs was constructed. According to the order of stress, betweenness, and closeness, NEAT1, KCNQ1OT1, and XIST were identified as key lncRNAs. Moreover, random walk with restart analysis also suggested that NEAT1, KCNQ1OT1, and XIST are key lncRNAs. Subsequently, a ceRNA network of 10 hub genes and 3 lncRNAs was constructed. Finally, we found that the expression of FCER1G and TYROBP significantly differed between MI patients and control individuals in the GSE66360, GSE19339, and GSE97320 datasets. qRT-PCR revealed that the expression of NEAT1, KCNQ1OT1, XIST, FCER1G, and TYROBP was significantly elevated in MI tissue samples compared to healthy control tissue samples.

CONCLUSION

NEAT1, KCNQ1OT1, XIST, FCER1G, and TYROBP are involved in MI and can be used as molecular biomarkers for the screening and diagnosis of MI. Furthermore, the immune system plays an essential role in the onset and progression of MI.

Rare disease gene association discovery from burden analysis of the 100,000 Genomes Project data

To discover rare disease-gene associations, we developed a gene burden analytical framework and applied it to rare, protein-coding variants from whole genome sequencing of 35,008 cases with rare diseases and their family members recruited to the 100,000 Genomes Project (100KGP). Following in silico triaging of the results, 88 novel associations were identified including 38 with existing experimental evidence. We have published the confirmation of one of these associations, hereditary ataxia with UCHL1 , and independent confirmatory evidence has recently been published for four more. We highlight a further seven compelling associations: hypertrophic cardiomyopathy with DYSF and SLC4A3 where both genes show high/specific heart expression and existing associations to skeletal dystrophies or short QT syndrome respectively; monogenic diabetes with UNC13A with a known role in the regulation of β cells and a mouse model with impaired glucose tolerance; epilepsy with KCNQ1 where a mouse model shows seizures and the existing long QT syndrome association may be linked; early onset Parkinson's disease with RYR1 with existing links to tremor pathophysiology and a mouse model with neurological phenotypes; anterior segment ocular abnormalities associated with POMK showing expression in corneal cells and with a zebrafish model with developmental ocular abnormalities; and cystic kidney disease with COL4A3 showing high renal expression and prior evidence for a digenic or modifying role in renal disease. Confirmation of all 88 associations would lead to potential diagnoses in 456 molecularly undiagnosed cases within the 100KGP, as well as other rare disease patients worldwide, highlighting the clinical impact of a large-scale statistical approach to rare disease gene discovery.

Identification of key modules and driving genes in nonalcoholic fatty liver disease by weighted gene co-expression network analysis

BACKGROUND

Nonalcoholic fatty liver disease (NAFLD) is characterized by excessive liver fat deposition, and progresses to liver cirrhosis, and even hepatocellular carcinoma. However, the invasive diagnosis of NAFLD with histopathological evaluation remains risky. This study investigated potential genes correlated with NAFLD, which may serve as diagnostic biomarkers and even potential treatment targets.

METHODS

The weighted gene co-expression network analysis (WGCNA) was constructed based on dataset E-MEXP-3291. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to evaluate the function of genes.

RESULTS

Blue module was positively correlated, and turquoise module negatively correlated with the severity of NAFLD. Furthermore, 8 driving genes (ANXA9, FBXO2, ORAI3, NAGS, C/EBPα, CRYAA, GOLM1, TRIM14) were identified from the overlap of genes in blue module and GSE89632. And another 8 driving genes were identified from the overlap of turquoise module and GSE89632. Among these driving genes, C/EBPα (CCAAT/enhancer binding protein α) was the most notable. By validating the expression of C/EBPα in the liver of NAFLD mice using immunohistochemistry, we discovered a significant upregulation of C/EBPα protein in NAFLD.

CONCLUSION

we identified two modules and 16 driving genes associated with the progression of NAFLD, and confirmed the protein expression of C/EBPα, which had been paid little attention to in the context of NAFLD, in the present study. Our study will advance the understanding of NAFLD. Moreover, these driving genes may serve as biomarkers and therapeutic targets of NAFLD.

Spleen tyrosine kinase (SYK) signals are implicated in cardio-cerebrovascular diseases

Post-translational modifications regulate numerous biochemical reactions and functions through covalent attachment to proteins. Phosphorylation, acetylation and ubiquitination account for over 90% of all reported post-translational modifications. As one of the tyrosine protein kinases, spleen tyrosine kinase (SYK) plays crucial roles in many pathophysiological processes and affects the pathogenesis and progression of various diseases. SYK is expressed in tissues outside the hematopoietic system, especially the heart, and is involved in the progression of various cardio-cerebrovascular diseases, such as atherosclerosis, heart failure, diabetic cardiomyopathy, stroke and others. Knowledge on the role of SYK in the progress of cardio-cerebrovascular diseases is accumulating, and many related mechanisms have been discovered and validated. This review summarizes the role of SYK in the progression of various cardio-cerebrovascular diseases, and aims to provide a theoretical basis for future experimental and clinical research targeting SYK as a therapeutic option for these diseases.

Characteristics of immune clusters and cell abundance in patients with different subtypes of nonparoxysmal atrial fibrillation

Atrial fibrillation (AF) is the most common sustained arrhythmia in clinical practice. Inflammation plays an important role in the initiation and perpetuation of AF. The present study was conducted to characterize immune clusters in nonparoxysmal AF and to distinguish immune subtypes of nonparoxysmal AF. Immune-related algorithms (CIBERSORT, ESTIMATE, and ssGSEA) were used to evaluate the immune cluster characterization and cell abundance, and multivariable logistics analysis was performed to determine the most relevant immune cells. We identified differentially expressed genes (DEGs) and used consensus clustering analysis to identify nonparoxysmal AF subtypes. Weighted gene coexpression network analysis (WGCNA) was used for finding highly correlated gene sets and attach to external sample traits. And it was conducted twice to identify the immune- and subtype- related modules. Finally, Metascape was used to compare the biological functions of the two nonparoxysmal AF subtypes we obtained. CytoHubba was used to identify the hub genes of these two subtypes. Based on the results of bioinformatics analysis, regulatory T cells, resting NK cells, active mast cells and neutrophils were considered to be closely related to nonparoxysmal AF. The brown module was identified as the most relevant module to the above immune cells by WGCNA. We identified two major nonparoxysmal AF subtypes by consensus clustering analysis and their enriched biological functions by Metascape. The hub genes are TYROBP, PTPRC, ITGB2, SPI1, PLEK, and CSF1R in permanent AF and JAM3, S100P, ARPC5, TRIM34, and GREB1L in persistent AF. This study revealed two major nonparoxysmal AF subtypes and eleven hub genes, which provide potential therapeutic targets for anti-inflammatory treatments of nonparoxysmal AF.

Coexpression network analysis of human candida infection reveals key modules and hub genes responsible for host-pathogen interactions

Invasive fungal infections are a significant reason for morbidity and mortality among organ transplant recipients. Therefore, it is critical to investigate the host and candida niches to understand the epidemiology of fungal infections in transplantation. Candida albicans is an opportunistic fungal pathogen that causes fatal invasive mucosal infections, particularly in solid organ transplant patients. Therefore, identifying and characterizing these genes would play a vital role in understanding the complex regulation of host-pathogen interactions. Using 32 RNA-sequencing samples of human cells infected with C. albicans, we developed WGCNA coexpression networks and performed DESeq2 differential gene expression analysis to identify the genes that positively correlate with human candida infection. Using hierarchical clustering, we identified 5 distinct modules. We studied the inter- and intramodular gene network properties in the context of sample status traits and identified the highly enriched genes in the correlated modules. We identified 52 genes that were common in the most significant WGCNA turquoise module and differentially expressed genes in human endothelial cells (HUVEC) infection vs. control samples. As a validation step, we identified the differentially expressed genes from the independent Candida-infected human oral keratinocytes (OKF6) samples and validated 30 of the 52 common genes. We then performed the functional enrichment analysis using KEGG and GO. Finally, we performed protein-protein interaction (PPI) analysis using STRING and CytoHubba from 30 validated genes. We identified 8 hub genes (JUN, ATF3, VEGFA, SLC2A1, HK2, PTGS2, PFKFB3, and KLF6) that were enriched in response to hypoxia, angiogenesis, vasculogenesis, hypoxia-induced signaling, cancer, diabetes, and transplant-related disease pathways. The discovery of genes and functional pathways related to the immune system and gene coexpression and differential gene expression analyses may serve as novel diagnostic markers and potential therapeutic targets.

Identification of key genes for hypertrophic cardiomyopathy using integrated network analysis of differential lncRNA and gene expression

Objective

Hypertrophic cardiomyopathy (HCM) is a complex heterogeneous heart disease. Recent reports found that long non-coding RNAs (lncRNAs) play an important role in the progression of cardiovascular diseases. The present study aimed to identify the novel lncRNAs and messenger RNAs (mRNAs) and determine the key pathways involved in HCM.

Methods

The lncRNA and mRNA sequencing datasets of GSE68316 and GSE130036 were downloaded from the Gene Expression Omnibus (GEO) database. An integrated co-expression network analysis was conducted to identify differentially expressed lncRNAs and differentially expressed mRNAs in patients with HCM. Then, gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were explored to identify the biological functions and signaling pathways of the co-expression network. Protein–protein interaction (PPI) and hub gene networks were constructed by using Cytoscape software. Plasma samples of patients with HCM and the GSE89714 dataset were used to validate the bioinformatics results.

Results

A total of 1,426 differentially expressed long non-coding RNAs (lncRNAs) and 1,715 differentially expressed mRNAs were obtained from GSE68316, of which 965 lncRNAs and 896 mRNAs were upregulated and 461 lncRNAs and 819 mRNAs were downregulated. A total of 469 differentially expressed lncRNAs and 2,407 differentially expressed mRNAs were screened from GSE130036, of which 183 lncRNAs and 1,283 mRNAs were upregulated and 286 lncRNAs and 1,124 mRNAs were downregulated. A co-expression network was constructed and contained 30 differentially expressed lncRNAs and 63 differentially expressed mRNAs, which were primarily involved in ‘G-protein beta/gamma-subunit complex binding,' ‘polyubiquitin modification-dependent protein binding,' ‘Apelin signaling pathway,' and ‘Wnt signaling pathway.' The 10 hub genes in the upregulated network [G Protein Subunit Alpha I2 (GNAI2), G Protein Subunit Alpha I1 (GNAI1), G Protein Subunit Alpha I3 (GNAI3), G Protein Subunit Gamma 2 (GNG2), G Protein Subunit Beta 1 (GNB1), G Protein Subunit Gamma 13 (GNG13), G Protein Subunit Gamma Transducin 1 (GNGT1), G Protein Subunit Gamma 12 (GNG12), AKT Serine/Threonine Kinase 1 (AKT1) and GNAS Complex Locus (GNAS)] and the 10 hub genes in the downregulated network [Nucleotide-Binding Oligomerization Domain Containing Protein 2 (NOD2), Receptor-Interacting Serine/Threonine Kinase 2 (RIPK2), Nucleotide-Binding Oligomerization Domain Containing Protein 1 (NOD1), Mitochondrial Antiviral Signaling Protein (MAVS), Autophagy Related 16-Like 1 (ATG16L1), Interferon Induced With Helicase C Domain 1 (IFIH1), Autophagy Related 5 (ATG5), TANK-Binding Kinase 1 (TBK1), Caspase Recruitment Domain Family Member 9 (CARD9), and von Willebrand factor (VWF)] were screened using cytoHubba. The expression of LA16c-312E8.2 and RP5-1160K1.3 in the plasma of patients with HCM was elevated, and the expression of the MIR22 host gene (MIR22HG) was decreased, which was consistent with our analysis, while the expression of LINC00324 and Small Nucleolar RNA Host Gene 12 (SNHG12) was not significantly different between the two groups. Verification analyses performed on GSE89714 showed the upregulated mRNAs of Chloride Voltage-Gated Channel 7 (CLCN7), N-Acetylglucosamine-1-Phosphate Transferase Subunit Gamma (GNPTG), Unk Like Zinc Finger (UNKL), Adenosine Monophosphate Deaminase 2 (AMPD2), GNAI3, WD Repeat Domain 81 (WDR81), and Serpin Family F Member 1 (SERPINF1) and downregulated mRNAs of TATA-Box Binding Protein Associated Factor 12 (TAF12) co-expressed with five crucial lncRNAs. Moreover, GNAI2, GNAI3, GNG12, and vWF were upregulated and GNAS was downregulated in the top 10 hub genes of upregulated and downregulated PPI networks.

Conclusion

These findings from integrative biological analysis of lncRNA-mRNA co-expression networks explored the key genes and pathways and provide new insights into the understanding of the mechanism and discovering new therapeutic targets for HCM. Three differentially expressed pivotal lncRNAs (LA16c-312E8.2, RP5-1160K1.3, and MIR22HG) in the co-expression network may serve as biomarkers and intervention targets for the diagnosis and treatment of HCM.

Weighted Gene Co-expression Network Analysis Identifies Specific Modules and Hub Genes Related to Subacute Ruminal Acidosis

Weighted gene co-expression network analysis (WGCNA) was used to understand the pathogenesis of subacute ruminal acidosis (SARA) and identify potential genes related to the disease. Microarray data from dataset GSE143765, which included 22 cows with and nine cows without SARA, were downloaded from the NCBI Gene Expression Omnibus (GEO). Results of WGCNA identified highly correlated modules of sample genes, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses allowed further biological insights into SARA-related modules. The protein-protein interaction (PPI) network, modules from the PPI network, and cistron annotation enrichment of modules were also analyzed. A total of 14,590 DEGs were used for the WGCNA. Construction of a protein-protein network identified DCXR, MMP15, and MMP17 as hub genes. Functional annotation showed that DCXR mainly exhibited L-xylulose reductase (NADP+) activity, glucose metabolic process, xylulose metabolic process, and carbonyl reductase (NADPH) activity, which are involved in the pentose and glucuronate interconversion pathways. MMP15 and MMP17 mainly have a collagen catabolic process. Overall, the results of this study aid the clarification of the biological and metabolic processes associated with SARA at the molecular level. The data highlight potential mechanisms for the future development of intervention strategies to reduce or alleviate the risk of SARA.

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.

An Robust Rank Aggregation and Least Absolute Shrinkage and Selection Operator Analysis of Novel Gene Signatures in Dilated Cardiomyopathy

Objective: Dilated cardiomyopathy (DCM) is a heart disease with high mortality characterized by progressive cardiac dilation and myocardial contractility reduction. The molecular signature of dilated cardiomyopathy remains to be defined. Hence, seeking potential biomarkers and therapeutic of DCM is urgent and necessary.

Methods: In this study, we utilized the Robust Rank Aggregation (RRA) method to integrate four eligible DCM microarray datasets from the GEO and identified a set of significant differentially expressed genes (DEGs) between dilated cardiomyopathy and non-heart failure. Moreover, LASSO analysis was carried out to clarify the diagnostic and DCM clinical features of these genes and identify dilated cardiomyopathy derived diagnostic signatures (DCMDDS).

Results: A total of 117 DEGs were identified across the four microarrays. Furthermore, GO analysis demonstrated that these DEGs were mainly enriched in the regulation of inflammatory response, the humoral immune response, the regulation of blood pressure and collagen–containing extracellular matrix. In addition, KEGG analysis revealed that DEGs were mainly enriched in diverse infected signaling pathways. Moreover, Gene set enrichment analysis revealed that immune and inflammatory biological processes such as adaptive immune response, cellular response to interferon and cardiac muscle contraction, dilated cardiomyopathy are significantly enriched in DCM. Moreover, Least absolute shrinkage and selection operator (LASSO) analyses of the 18 DCM-related genes developed a 7-gene signature predictive of DCM. This signature included ANKRD1, COL1A1, MYH6, PERELP, PRKACA, CDKN1A, and OMD. Interestingly, five of these seven genes have a correlation with left ventricular ejection fraction (LVEF) in DCM patients.

Conclusion: Our present study demonstrated that the signatures could be robust tools for predicting DCM in clinical practice. And may also be potential treatment targets for clinical implication in the future.

Comprehensive analysis of miRNA-mRNA regulatory network and potential drugs in chronic chagasic cardiomyopathy across human and mouse

Background

Chronic chagasic cardiomyopathy (CCC) is the leading cause of heart failure in Latin America and often causes severe inflammation and fibrosis in the heart. Studies on myocardial function and its molecular mechanisms in patients with Chronic chagasic cardiomyopathy are very limited. In order to understand the development and progression of Chronic chagasic cardiomyopathy and find targets for its diagnosis and treatment, the field needs to better understand the exact molecular mechanisms involved in these processes.

Methods

The mRNA microarray datasets GSE84796 (human) and GSE24088 (mouse) were obtained from the Gene Expression Omnibus (GEO) database. Homologous genes between the two species were identified using the online database mining tool Biomart, followed by differential expression analysis, gene enrichment analysis and protein–protein interaction (PPI) network construction. Cytohubba plug-in of Cytoscape software was used to identify Hub gene, and miRNet was used to construct the corresponding miRNA–mRNA regulatory network. miRNA-related databases: miRDB, Targetscan and miRWalk were used to further evaluate miRNAs in the miRNA–mRNA network. Furthermore, Comparative Toxicogenomics Database (CTD) and L1000 Platform were used to identify hub gene-related drugs.

Results

A total of 86 homologous genes were significantly differentially expressed in the two datasets, including 73 genes with high expression and 13 genes with low expression. These differentially expressed genes were mainly enriched in the terms of innate immune response, signal transduction, protein binding, Natural killer cell mediated cytotoxicity, Tuberculosis, Chemokine signaling pathway, Chagas disease and PI3K−Akt signaling pathway. The top 10 hub genes LAPTM5, LCP1, HCLS1, CORO1A, CD48, TYROBP, RAC2, ARHGDIB, FERMT3 and NCF4 were identified from the PPI network. A total of 122 miRNAs were identified to target these hub genes and 30 of them regulated two or more hub genes at the same time. miRDB, Targetscan and miRWalk were further analyzed and screened out hsa-miR-34c-5p, hsa-miR-34a-5p and hsa-miR-16-5p as miRNAs regulating these hub genes. Finally, Progesterone, Flutamide, Nimesulide, Methotrexate and Temozolomide were identified to target these hub genes and might be targeted therapies for Chronic chagasic cardiomyopathy.

Conclusions

In this study, the potential genes associated with Chronic chagasic cardiomyopathy are identified and a miRNA–mRNA regulatory network is constructed. This study explores the molecular mechanisms of Chronic chagasic cardiomyopathy and provides important clues for finding new therapeutic targets.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12920-021-01134-3.

Identification and verification of promising diagnostic biomarkers in patients with hypertrophic cardiomyopathy associate with immune cell infiltration characteristics

AIMS

To explore immune cell infiltration characteristics of, and hub genes associated with, hypertrophic cardiomyopathy (HCM).

MATERIALS AND METHODS

The GSE130036 dataset was downloaded and the differentially expressed genes (DEGs) were identified. The DEGs were analyzed via the CIBERSORT algorithm to understand the composition of 22 immune cell types between the HCM and normal myocardial tissue specimens. Weighted gene co-expression network analysis (WGCNA) was performed to segregate the DEGs into several modules and explore correlation between the key modules and specific immune cells enriched in the myocardial tissues of HCM patients. The biofunctional and disease enrichment of the genes among the modules was explored, and hub genes serving as potential biomarkers of HCM were identified. These genes were validated by GSE36961 dataset, and the discrimination ability was assessed by receiver operating characteristic curve analysis.

KEY FINDINGS

CIBERSORT analysis showed that neutrophils and B-cells (naive and memory B-cells) were highly abundant in HCM samples, while macrophages (M0, M1, M2) were highly abundant in normal samples. WGCNA analysis of the DEGs yielded seven modules, and the gray and yellow modules were strongly associated with neutrophils and B-cells, and with macrophages, respectively. Yellow module genes were mainly functional in immune and inflammation processes. Gray module genes were mainly functional in the transportation of intercellular substances. SLITRK4 and CD163 showed a notably high area under the curve values in both datasets and may serve as potential biomarkers for HCM.

SIGNIFICANCE

SLITRK4 and CD163 may be promising Diagnostic Biomarkers of Hypertrophic Cardiomyopathy.

Identification and Verification of Feature Immune-Related Genes in Patients With Hypertrophic Cardiomyopathy Based on Bioinformatics Analyses

Objective: To identify feature immune-related genes (IRGs) in patients with hypertrophic cardiomyopathy (HCM) and verify their ability to diagnose HCM.

Methods: The GSE160997 dataset on cardiac tissue from 18 HCM patients and 5 controls was downloaded from the Gene Expression Omnibus database. A false discovery rate <0.05 and |log2 fold change| >1 were the filters applied to identify the differentially expressed genes (DEGs). The differentially expressed IRGs were the intersection results between the DEGs and an IRG dataset from the IMMPORT database. The protein-protein interaction network of differentially expressed IRGs was constructed, and the top 20 hub genes with the most adjacent nodes in the network were selected. The least absolute shrinkage and selection operator regression algorithm and a random forest algorithm were used to identify the feature IRGs as biomarkers that were then verified against GSE36961.

Results: A total of 1079 DEGs were identified in GSE160997. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses indicated that immune-related mechanisms play an important role in the pathogenesis of HCM. A total of 121 differentially expressed IRGs were identified, and 5 feature IRGs were selected, 4 of which were confirmed as potential biomarkers of HCM by external verification with excellent discrimination ability. A diagnosis model of HCM based on the four feature IRGs was developed and visualized as a nomogram with a C-index of 0.925 (95% confidence interval 0.869–0.981).

Conclusion: Our study identified four feature IRGs as biomarkers for the diagnosis of HCM, offering an innovative perspective of the underlying immune-related pathological molecular mechanisms.

Detecting imperative genes and infiltrating immune cells in chronic Chagas cardiomyopathy by bioinformatics analysis

Chronic Chagas cardiomyopathy (CCC) is an acquired inflammatory cardiomyopathy triggered by the protozoan Trypanosoma cruzi infection. Although microvascular and neurogenic dysfunction and inflammation with persistent parasite presence in the heart may play a major pathogenetic role, little is known about the overall picture of gene co-expression regulating CCC. In this study, we aimed to explore the key biological pathways, hub genes and the landscope of infiltrating immune cells associated with inflammation in chronic Chagas cardiomyopathy. A weighted gene co-expression network analysis (WGCNA) was conducted based on the gene expression profiles from patients with and without chronic Chagas cardiomyopathy (GSE84796). Twelve coexpression modules were identified from the top 25% variant genes. Among them, the turquoise and black module were significantly positively correlated with CCC, which were highly enriched in Th1 and Th2 cell differentiation, the Cytokine-cytokine receptor interaction,NF-kappa B signaling pathway and T cell receptor signaling pathway. In addition, four genes (TBX21, ZAP70,IL2RB and CD69) were selected as candidate hub genes. Gene expression for hub genes were higher in CCC tissues compared to tissues from healthy controls. Additionally, gene set enrichment analysis (GSEA) analysis showed that high expressions of these hub genes were significantly correlated with interferon α response and interferon γ response. The microarray dataset GSE41089 further confirmed that although CD69 was not detected, the expression of TBX21, IL2RB and ZAP70 was also significantly up-regulated in the CCC mice compared to controls. We further studied the immune cells infiltration in CCC patients with CIBERSORT. The fraction of Mast cells activated,T cells CD8 and T cells gamma delta were significantly increased in CCC patients compared with control. Our research provides a more effective understanding of the pathogenesis of CCC and could help in future strategies for new diagnostic and therapeutic approaches for CCC patients.

The Immune and Inflammatory Basis of Acquired Pediatric Cardiac Disease

Children with acquired heart disease face significant health challenges, including a lifetime of strict medical management, multiple cardiac surgeries, and a high mortality risk. Though the presentation of these conditions is diverse, a unifying factor is the role of immune and inflammatory responses in their development and/or progression. For example, infectious agents have been linked to pediatric cardiovascular disease, leading to a large health burden that disproportionately affects low-income areas. Other implicated mechanisms include antibody targeting of cardiac proteins, infection of cardiac cells, and inflammation-mediated damage to cardiac structures. These changes can alter blood flow patterns, change extracellular matrix composition, and induce cardiac remodeling. Therefore, understanding the relationship between the immune system and cardiovascular disease can inform targeted diagnostic and treatment approaches. In this review, we discuss the current understanding of pediatric immune-associated cardiac diseases, challenges in the field, and areas of research with potential for clinical benefit.

Exploration and validation of hub genes and pathways in the progression of hypoplastic left heart syndrome via weighted gene co-expression network analysis

Background

Despite significant progress in surgical treatment of hypoplastic left heart syndrome (HLHS), its mortality and morbidity are still high. Little is known about the molecular abnormalities of the syndrome. In this study, we aimed to probe into hub genes and key pathways in the progression of the syndrome.

Methods

Differentially expressed genes (DEGs) were identified in left ventricle (LV) or right ventricle (RV) tissues between HLHS and controls using the GSE77798 dataset. Then, weighted gene co-expression network analysis (WGCNA) was performed and key modules were constructed for HLHS. Based on the genes in the key modules, protein–protein interaction networks were conducted, and hub genes and key pathways were screened. Finally, the GSE23959 dataset was used to validate hub genes between HLHS and controls.

Results

We identified 88 and 41 DEGs in LV and RV tissues between HLHS and controls, respectively. DEGs in LV tissues of HLHS were distinctly involved in heart development, apoptotic signaling pathway and ECM receptor interaction. DEGs in RV tissues of HLHS were mainly enriched in BMP signaling pathway, regulation of cell development and regulation of blood pressure. A total of 16 co-expression network were constructed. Among them, black module (r = 0.79 and p value = 2e−04) and pink module (r = 0.84 and p value = 4e−05) had the most significant correlation with HLHS, indicating that the two modules could be the most relevant for HLHS progression. We identified five hub genes in the black module (including Fbn1, Itga8, Itga11, Itgb5 and Thbs2), and five hub genes (including Cblb, Ccl2, Edn1, Itgb3 and Map2k1) in the pink module for HLHS. Their abnormal expression was verified in the GSE23959 dataset.

Conclusions

Our findings revealed hub genes and key pathways for HLHS through WGCNA, which could play key roles in the molecular mechanism of HLHS.

FCER1G and PTGS2 Serve as Potential Diagnostic Biomarkers of Acute Myocardial Infarction Based on Integrated Bioinformatics Analyses

This study aimed to explore the potential diagnostic biomarkers and mechanisms underlying acute myocardial infarction (AMI). We downloaded four datasets (GSE19339, GSE48060, GSE66360, and GSE97320) from the Gene Expression Omnibus database and combined them as an integrated dataset. A total of 153 differentially expressed genes (DEGs) were analyzed by the linear models for microarray analysis (LIMMA) package. Weighted gene co-expression network analysis was used to screen for the significant gene modules. The intersection of DEGs and genes in the most significant module was termed "common genes" (CGs). CGs were mainly enriched in "inflammatory response," "neutrophil chemotaxis," and "IL-17 signaling pathway" through functional enrichment analyses. Subsequently, 15 genes were identified as the hub genes in the protein-protein interaction network. The Fc fragment of IgE receptor Ig (FCER1G) and prostaglandin-endoperoxide synthase 2 (PTGS2) showed significantly increased expression in AMI patients and mice at the 12-h time point in our experiments. The receiver operating characteristic (ROC) curve was used to evaluate the diagnostic value of FCER1G and PTGS2. The area under ROC curve of FCER1G and PTGS2 was 77.6% and 80.7%, respectively. Moreover, the micro (mi)RNA-messenger (m)RNA network was also visualized; the results showed that miRNA-143, miRNA-144, and miRNA-26 could target PTGS2 in AMI progression.

A novel ferroptosis related gene signature is associated with prognosis in patients with ovarian serous cystadenocarcinoma

Ovarian cancer (OV) is a common type of carcinoma in females. Many studies have reported that ferroptosis is associated with the prognosis of OV patients. However, the mechanism by which this occurs is not well understood. We utilized Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) to identify ferroptosis-related genes in OV. In the present study, we applied Cox regression analysis to select hub genes and used the least absolute shrinkage and selection operator to construct a prognosis prediction model with mRNA expression profiles and clinical data from TCGA. A series of analyses for this signature was performed in TCGA. We then verified the identified signature using International Cancer Genome Consortium (ICGC) data. After a series of analyses, we identified six hub genes (DNAJB6, RB1, VIMP/ SELENOS, STEAP3, BACH1, and ALOX12) that were then used to construct a model using a training data set. The model was then tested using a validation data set and was found to have high sensitivity and specificity. The identified ferroptosis-related hub genes might play a critical role in the mechanism of OV development. The gene signature we identified may be useful for future clinical applications.

Cardiac Fibroblasts Play Pathogenic Roles in Idiopathic Restrictive Cardiomyopathy

BACKGROUND

Restrictive cardiomyopathy (RCM) is characterized by impaired ventricular relaxation. Although several mutations were reported in some patients, no mutations were identified in cardiomyocyte expressing genes of other patients, indicating that pathological mechanisms underlying RCM could not be determined by cardiomyocytes only. Cardiac fibroblasts (CFs) are a major cell population in the heart; however, the pathological roles of CFs in cardiomyopathy are not fully understood.

METHODS AND RESULTS

This study established 4 primary culture lines of CFs from RCM patients and analyzed their cellular physiology, the effects on the contraction and relaxation ability of healthy cardiomyocytes under co-culture with CFs, and RNA sequencing. Three of four patients hadTNNI3mutations. There were no significant alterations in cell proliferation, apoptosis, migration, activation, and attachment. However, when CFs from RCM patients were co-cultured with healthy cardiomyocytes, the relaxation velocity of cardiomyocytes was significantly impaired both under direct and indirect co-culture conditions. RNA sequencing revealed that gene expression profiles of CFs in RCM were clearly distinct from healthy CFs. The differential expression gene analysis identified that several extracellular matrix components and cytokine expressions were dysregulated in CFs from RCM patients.

CONCLUSIONS

The comprehensive gene expression patterns were altered in RCM-derived CFs, which deteriorated the relaxation ability of cardiomyocytes. The specific changes in extracellular matrix composition and cytokine secretion from CFs might affect pathological behavior of cardiomyocytes in RCM.

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.

Cardiomyopathies in China: A 2018-2019 state-of-the-art review

Cardiomyopathies are diseases of the cardiac muscle and are often characterized by ventricular dilation, hypertrophy, and cardiac arrhythmia. Patients with cardiomyopathies often experience sudden death and cardiac failure and require cardiac transplantation during the course of disease progression. Early diagnosis, differential diagnosis, and genetic consultation depend on imaging techniques, genetic testing, and new emerging diagnostic tools such as serum biomarkers. The molecular genetics of cardiomyopathies has been widely studied recently. The discovery of mechanisms underlying heterogeneity and overlapping of the phenotypes of cardiomyopathies has revealed the existence of disease modifiers, and this has led to the emergence of novel disease-modifying therapy. This 2018-2019 state-of-the-art review outlines the pathogenesis, diagnosis, and treatment of cardiomyopathies in China.

Weighted gene co-expression network analysis revealed key biomarkers associated with the diagnosis of hypertrophic cardiomyopathy

Objective

To reveal the molecular mechanism underlying the pathogenesis of HCM and find new effective therapeutic strategies using a systematic biological approach.

Methods

The WGCNA algorithm was applied to building the co-expression network of HCM samples. A sample cluster analysis was performed using the hclust tool and a co-expression module was constructed. The WGCNA algorithm was used to study the interactive connection between co-expression modules and draw a heat map to show the strength of interactions between modules. The genetic information of the respective modules was mapped to the associated GO terms and KEGG pathways, and the Hub Genes with the highest connectivity in each module were identified. The Wilcoxon test was used to verify the expression level of hub genes between HCM and normal samples, and the “pROC” R package was used to verify the possibility of hub genes as biomarkers. Finally, the potential functions of hub genes were analyzed by GSEA software.

Results

Seven co-expression modules were constructed using sample clustering analysis. GO and KEGG enrichment analysis judged that the turquoise module is an important module. The hub genes of each module are RPL35A for module Black, FH for module Blue, PREI3 for module Brown, CREB1 for module Green, LOC641848 for module Pink, MYH7 for module Turquoise and MYL6 for module Yellow. The results of the differential expression analysis indicate that MYH7 and FH are considered true hub genes. In addition, the ROC curves revealed their high diagnostic value as biomarkers for HCM. Finally, in the results of the GSEA analysis, MYH7 and FH highly expressed genes were enriched with the “proteasome” and a “PPAR signaling pathway,” respectively.

Conclusions

The MYH7 and FH genes may be the true hub genes of HCM. Their respective enriched pathways, namely the “proteasome” and the “PPAR signaling pathway,” may play an important role in the development of HCM.

Identification of KIF18B as a Hub Candidate Gene in the Metastasis of Clear Cell Renal Cell Carcinoma by Weighted Gene Co-expression Network Analysis

Background

Clear cell renal cell carcinoma (ccRCC) is a common type of fatal malignancy in the urinary system. As the therapeutic strategies of ccRCC are severely limited at present, the prognosis of patients with metastatic carcinoma is usually not promising. Revealing the pathogenesis and identifying hub candidate genes for prognosis prediction and precise treatment are urgently needed in metastatic ccRCC.

Methods

In the present study, we conducted a series of bioinformatics studies with the gene expression profiles of ccRCC samples from Gene Expression Omnibus (GEO) and the cancer genome atlas (TCGA) database for identifying and validating the hub gene of metastatic ccRCC. We constructed a co-expression network, divided genes into co-expression modules, and identified ccRCC-related modules by weighted gene co-expression network analysis (WGCNA) with data from GEO. Then, we investigated the functions of genes in the ccRCC-related modules by enrichment analyses and built a sub-network accordingly. A hub candidate gene of the metastatic ccRCC was identified by maximal clique centrality (MCC) method. We validate the hub gene by differentially expressed gene analysis, overall survival analysis, and correlation analysis with clinical traits with the external dataset (TCGA). Finally, we explored the function of the hub gene by correlation analysis with targets of precise therapies and single-gene gene set enrichment analysis.

Results

We conducted WGCNA with the expression profiles of GSE73731 from GEO and divided all genes into 8 meaningful co-expression modules. One module is proved to be positively correlated with pathological stage and tumor grade of ccRCC. Genes in the ccRCC-related module were mainly enriched in functions of mitotic cell division and several proverbial tumor related signal pathways. We then identified KIF18B as a hub gene of the metastasis of ccRCC. Validating analyses in external dataset observed the up-regulation of KIF18B in ccRCC and its correlation with worse outcomes. Further analyses found that the expression of KIF18B is related to that of targets of precise therapies.

Conclusion

Our study proposed KIF18B as a hub candidate gene of ccRCC for the first time. Our conclusion may provide a brand-new clue for prognosis evaluating and precise treatment for ccRCC in the future.

A module of multifactor-mediated dysfunction guides the molecular typing of coronary heart disease

Background

Coronary atherosclerotic heart disease (CHD) is the most common cardiovascular disease and has become a leading cause of death globally. Various molecular typing methods are available for the diagnosis and treatment of tumors. However, molecular typing results are not routinely used for CHD.

Methods and Results

Aiming to uncover the underlying molecular features of different types of CHD, we screened the differentially expressed genes (DEGs) associated with CHD based on the Gene Expression Omnibus (GEO) data and expanded those with the NCBI‐gene and OMIM databases to finally obtain 2021 DEGs. The weighted gene co‐expression analysis (WGCNA) was performed on the candidate genes, and six distinctive WGCNA modules were identified, two of which were associated with CHD. Moreover, DEGs were mined as key genes for co‐expression based on the module network relationship. Furthermore, the differentially expressed miRNAs in CHD and interactions in the database were mined in the GEO data set to build a multifactor regulatory network of key genes for co‐expression. Based on the network, the CHD samples were further classified into five clusters and we defined FTH1, HCAR3, RGS2, S100A9, and TYROBP as the top genes of the five subgroups. Finally, the mRNA levels of FTH1, S100A9, and TYROBP were found to be significantly increased, while the expression of HCAR3 was decreased in the blood of CHD patients. We did not detect measurable levels of RGS2.

Conclusion

The screened core clusters of genes may be a target for the diagnosis and treatment of CHD as a molecular typing module.

Identification of SPP1 as an Extracellular Matrix Signature for Metastatic Castration-Resistant Prostate Cancer

Resistance to androgen deprivation therapy (ADT) is the main challenge for advanced fatal prostate cancer (PCa), which can gradually develop into metastatic castration-resistant prostate cancer (mCRPC). However, the pathologic mechanisms of mCRPC are still far from clear. Given the high incidence and mortality related to mCRPC, understanding the causes and pathogenesis of this condition as well as identifying potential biomarkers are of great importance. In the research reported here, we integrated several gene expression profiles from hormone sensitive prostate cancer (HSPC) and mCRPC datasets to identify differentially expressed genes (DEGs), key biological pathways, and cellular components. We found that extracellular matrix (ECM) genes were significantly enriched, and further filtered them using Pearson correlation analysis and stepwise regression to find ECM signatures to differentiate between the HSPC and mCRPC phenotypes. Six ECM signatures were input into K-nearest neighbor, logistic regression, naive Bayes, and random forest classifiers models. Random forest algorithm with the six-gene prognostic signatures showed best performance, which had high sensitivity and specificity for HSPC and mCRPC classification and further the six ECM signatures were validated in organoid models. Among the six ECM genes, SPP1 was identified as the key hub signature for PCa metastasis and drug resistance development; we found that both protein and mRNA expression levels of SPP1 were remarkably up-regulated in mCRPC compared with HSPC in organoid models and could regulate the androgen receptor signaling pathway. Therefore, SPP1 is a potential novel biomarker and therapeutic target for mCRPC. Further understanding of the role of SPP1 in mCRPC development may help to explore effectively therapeutic approaches for the prevention and intervention of drug resistance and metastasis.

Cardiac remodeling in response to embryonic crude oil exposure involves unconventional NKX family members and innate immunity genes

Cardiac remodeling results from both physiological and pathological stimuli. Compared to mammals, fish hearts show a broader array of remodeling changes in response to environmental influences, providing exceptional models for dissecting the molecular and cellular bases of cardiac remodeling. We recently characterized a form of pathological remodeling in juvenile pink salmon (Oncorhynchus gorbuscha) in response to crude oil exposure during embryonic cardiogenesis. In the absence of overt pathology (cardiomyocyte death or inflammatory infiltrate), cardiac ventricles in exposed fish showed altered shape, reduced thickness of compact myocardium, and hypertrophic changes in spongy, trabeculated myocardium. Here we used RNA sequencing to characterize molecular pathways underlying these defects. In juvenile ventricular cardiomyocytes, antecedent embryonic oil exposure led to dose-dependent up-regulation of genes involved in innate immunity and two NKX homeobox transcription factors not previously associated with cardiomyocytes, nkx2.3 and nkx3.3. Absent from mammalian genomes, the latter is largely uncharacterized. In zebrafish embryos nkx3.3 demonstrated a potent effect on cardiac morphogenesis, equivalent to nkx2.5, the primary transcription factor associated with ventricular cardiomyocyte identity. The role of nkx3.3 in heart growth is potentially linked to the unique regenerative capacity of fish and amphibians. Moreover, these findings support a cardiomyocyte-intrinsic role for innate immune response genes in pathological hypertrophy. This study demonstrates how an expanding mechanistic understanding of environmental pollution impacts – i.e., the chemical perturbation of biological systems – can ultimately yield new insights into fundamental biological processes.