Bioinformatics Analysis and Identification of Genes and Pathways in Ischemic Cardiomyopathy

Identification of Cbx6 as a potential biomarker in renal ischemia/reperfusion injury

BACKGROUND

Renal ischemia/reperfusion injury (RIRI) is an inevitable consequence of kidney transplantation and has a negative impact on both short-term and long-term graft survival. The identification of key markers in RIRI to improve the prognosis of patients would be highly advantageous.

METHODS

Gene expression profile data of GSE27274 were obtained from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were analyzed using the Limma package. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment of DEGs were performed. Support vector machine-recursive feature elimination and least absolute shrinkage and selection operator regression modeling were both performed to identify potential biomarkers. The GSE148420 dataset, quantitative reverse transcriptase-PCR, and western blotting results of kidney tissue samples were used to validate the bioinformatic analysis. Lastly, exploring differences between different groups through gene set enrichment analysis and using DsigDB database to identify potential therapeutic drugs targeting hub genes.

RESULTS

A total of 160 upregulated and 180 downregulated DEGs were identified. Functional enrichment analysis identified significant enrichment in processes involving peroxisomes. As a subunit of Polycomb Repressive Complex 1(PRC1), chromobox 6(Cbx6) was identified as a potential biomarker with an area under the receiver operating characteristic curve of 0.875 (95% confidence interval 0.624-1.000) in the validation cohort, and it was highly expressed in the RIRI group (p < 0.05). In the high expression group Cbx6 was more enriched in the toll-like receptor signaling pathway. We predicted 15 potential drugs targeting hub genes of RIRI.

CONCLUSIONS

We identified Cbx6 as a potential biomarker for RIRI and 15 potential drugs for the treatment of RIRI, which might shed a light on the treatment of RIRI.

Identification of Co-diagnostic Genes for Heart Failure and Hepatocellular Carcinoma Through WGCNA and Machine Learning Algorithms

This research delves into the intricate relationship between hepatocellular carcinoma (HCC) and heart failure (HF) by exploring shared genetic characteristics and molecular processes. Employing advanced methodologies such as differential analysis, weighted correlation network analysis (WGCNA), and algorithms like Random Forest (RF), Least Absolute Shrinkage Selection (LASSO), and XGBoost, we meticulously identified modular differential genes (DEGs) associated with both HF and HCC. Gene Set Variation Analysis (GSVA) and single sample gene set enrichment analysis (ssGSEA) were employed to unveil underlying biological mechanisms. The study revealed 88 core genes shared between HF and HCC, indicating a common mechanism. Enrichment analysis emphasized the roles of immune responses and inflammation in both diseases. Leveraging XGBoost, we crafted a robust multigene diagnostic model (including FCN3, MAP2K1, AP3M2, CDH19) with an area under the curve (AUC) > 0.9, showcasing exceptional predictive accuracy. GSVA and ssGSEA analyses unveiled the involvement of immune cells and metabolic pathways in the pathogenesis of HF and HCC. This research uncovers a pivotal interplay between HF and HCC, highlighting shared pathways and key genes, offering promising insights for future clinical treatments and experimental research endeavors.

Identifying OGN as a Biomarker Covering Multiple Pathogenic Pathways for Diagnosing Heart Failure: From Machine Learning to Mechanism Interpretation

BACKGROUND

The pathophysiologic heterogeneity of heart failure (HF) necessitates a more detailed identification of diagnostic biomarkers that can reflect its diverse pathogenic pathways.

METHODS

We conducted weighted gene and multiscale embedded gene co-expression network analysis on differentially expressed genes obtained from HF and non-HF specimens. We employed a machine learning integration framework and protein-protein interaction network to identify diagnostic biomarkers. Additionally, we integrated gene set variation analysis, gene set enrichment analysis (GSEA), and transcription factor (TF)-target analysis to unravel the biomarker-dominant pathways. Leveraging single-sample GSEA and molecular docking, we predicted immune cells and therapeutic drugs related to biomarkers. Quantitative polymerase chain reaction validated the expressions of biomarkers in the plasma of HF patients. A two-sample Mendelian randomization analysis was implemented to investigate the causal impact of biomarkers on HF.

RESULTS

We first identified COL14A1, OGN, MFAP4, and SFRP4 as candidate biomarkers with robust diagnostic performance. We revealed that regulating biomarkers in HF pathogenesis involves TFs (BNC2, MEOX2) and pathways (cell adhesion molecules, chemokine signaling pathway, cytokine-cytokine receptor interaction, oxidative phosphorylation). Moreover, we observed the elevated infiltration of effector memory CD4+ T cells in HF, which was highly related to biomarkers and could impact immune pathways. Captopril, aldosterone antagonist, cyclopenthiazide, estradiol, tolazoline, and genistein were predicted as therapeutic drugs alleviating HF via interactions with biomarkers. In vitro study confirmed the up-regulation of OGN as a plasma biomarker of HF. Mendelian randomization analysis suggested that genetic predisposition toward higher plasma OGN promoted the risk of HF.

CONCLUSIONS

We propose OGN as a diagnostic biomarker for HF, which may advance our understanding of the diagnosis and pathogenesis of HF.

Current Insights and Future Directions in the Treatment of Heart Failure with Preserved Ejection Fraction

Heart failure is a clinical syndrome associated with poor quality of life, substantial healthcare resource utilization, and premature mortality, in large part related to high rates of hospitalizations. The clinical manifestations of heart failure are similar regardless of the ejection fraction. Unlike heart failure with reduced ejection fraction, there are few therapeutic options for treating heart failure with preserved ejection fraction. Molecular therapies that have shown reduced mortality and morbidity in heart failure with reduced ejection have not been proven to be effective for patients with heart failure and preserved ejection fraction. The study of pathophysiological processes involved in the production of heart failure with preserved ejection fraction is the basis for identifying new therapeutic means. In this narrative review, we intend to synthesize the existing therapeutic means, but also those under research (metabolic and microRNA therapy) for the treatment of heart failure with preserved ejection fraction.

Identifying immune cell infiltration and diagnostic biomarkers in heart failure and osteoarthritis by bioinformatics analysis

Heart failure (HF) and osteoarthritis (OA) are medical conditions that can significantly impact daily activities. Evidence has shown that HF and OA may share some pathogenic mechanisms. However, the underlying genomic mechanisms remain unclear. This study aimed to explore the underlying molecular mechanism and identify diagnostic biomarkers for HF and OA. With the cutoff criteria of fold change (FC) > 1.3 and P < .05, 920, 1500, 2195, and 2164 differentially expressed genes (DEGs) were identified in GSE57338, GSE116250, GSE114007, and GSE169077, respectively. After making the intersection of DEGs, we obtained 90 upregulated DEGs and 51 downregulated DEGs in HF datasets and 115 upregulated DEGs and 75 downregulated DEGs in OA datasets. Afterward, we conducted genome ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses, protein-protein interaction (PPI) networks, and hub genes screening based on DEGs. Then, 4 common DEGs (fibroblast activation protein alpha [FAP], secreted frizzled-related protein 4 (SFRP4), Thy-1 cell surface antigen (THY1), matrix remodeling associated 5 [MXRA5]) between HF and OA were screened and validated in GSE5406 and GSE113825 datasets, based on which we established the support vector machine (SVM) models. The combined area under the receiver operating characteristic curve (AUC) of THY1, FAP, SFRP4, and MXRA5 in the HF training and test sets reached 0.949 and 0.928. While in the OA training set and test set, the combined AUC of THY1, FAP, SFRP4, and MXRA5 reached 1 and 1, respectively. The analysis of immune cells in HF revealed high levels of dendritic cell (DC), B cells, natural killer T cell (NKT), Type 1 regulatory T cell (Tr1), cytotoxic T cell (Tc), exhausted T cell (Tex), and mucosal-associated invariant T cell (MAIT), while displaying lower levels of monocytes, macrophages, NK, CD4 + T, gamma delta T (γδ T), T helper type 1 (Th1), T helper type 2 (Th2), and effector memory T cell (Tem). Moreover, the 4 common DEGs were positively correlated with DCs and B cells and negatively correlated with γδ T. In OA patients, the abundance of monocyte, macrophage, CD4 + naïve, and natural T regulatory cell (nTreg) was higher, while the infiltration of CD8 + T, γδ T, CD8 + naïve, and MAIT was lower. The expression of THY1 and FAP was significantly correlated with macrophage, CD8 + T, nTreg, and CD8 + naïve. SFRP4 was correlated with monocyte, CD8 + T, γδ T, CD4 + naïve, nTreg, CD8 + naïve and MAIT. MXRA5 was correlated with macrophage, CD8 + T, nTreg and CD8 + naïve. FAP, THY1, MXRA5, and SFRP4 may be diagnostic biomarkers for both HF and OA, and their correlation with immune cell infiltrations suggests shared immune pathogenesis.

Exploring Potential Biomarkers and Molecular Mechanisms of Ischemic Cardiomyopathy and COVID-19 Comorbidity Based on Bioinformatics and Systems Biology

Cardiovascular complications combined with COVID-19 (SARS-CoV-2) lead to a poor prognosis in patients. The common pathogenesis of ischemic cardiomyopathy (ICM) and COVID-19 is still unclear. Here, we explored potential molecular mechanisms and biomarkers for ICM and COVID-19. Common differentially expressed genes (DEGs) of ICM (GSE5406) and COVID-19 (GSE164805) were identified using GEO2R. We performed enrichment and protein–protein interaction analyses and screened key genes. To confirm the diagnostic performance for these hub genes, we used external datasets (GSE116250 and GSE211979) and plotted ROC curves. Transcription factor and microRNA regulatory networks were constructed for the validated hub genes. Finally, drug prediction and molecular docking validation were performed using cMAP. We identified 81 common DEGs, many of which were enriched in terms of their relation to angiogenesis. Three DEGs were identified as key hub genes (HSP90AA1, HSPA9, and SRSF1) in the protein–protein interaction analysis. These hub genes had high diagnostic performance in the four datasets (AUC > 0.7). Mir-16-5p and KLF9 transcription factor co-regulated these hub genes. The drugs vindesine and ON-01910 showed good binding performance to the hub genes. We identified HSP90AA1, HSPA9, and SRSF1 as markers for the co-pathogenesis of ICM and COVID-19, and showed that co-pathogenesis of ICM and COVID-19 may be related to angiogenesis. Vindesine and ON-01910 were predicted as potential therapeutic agents. Our findings will contribute to a deeper understanding of the comorbidity of ICM with COVID-19.

Relationship between asporin and extracellular matrix behavior: A literature review

Asporin (ASPN), as a member of the small leucine-rich repeat proteoglycan family, is a type of protein that is found in the extracellular matrix. Collagen deposition or transformation is involved in a variety of pathological processes. ASPN is identified in cancerous tissue, pathological cardiac tissue, articular cartilage, keloid, and fibrotic lung tissue, and it has a role in the development of cancer, cardiovascular, bone and joint, keloid, and pulmonary fibrosis by interfering with collagen metabolism. This review article summarizes the data on ASPN expressions in mouse and human and highlights that overexpress of ASPN might play a role in a variety of diseases. Although our knowledge of ASPN is currently limited, these instances may help us better understand how it interacts with diseases.

Construction and analysis of heart failure diagnosis model based on random forest and artificial neural network

Heart failure is a global health problem and the number of sufferers is increasing as the population grows and ages. Existing diagnostic techniques for heart failure have various limitations in the clinical setting and there is a need to develop a new diagnostic model to complement the existing diagnostic methods. In recent years, with the development and improvement of gene sequencing technology, more genes associated with heart failure have been identified. We screened for differentially expressed genes in heart failure using available gene expression data from the Gene Expression Omnibus database and identified 6 important genes by a random forest classifier (ASPN, MXRA5, LUM, GLUL, CNN1, and SERPINA3). And we have successfully constructed a new heart failure diagnostic model using an artificial neural network and validated its diagnostic efficacy in a public dataset. We calculated heart failure-related differentially expressed genes and obtained 24 candidate genes by random forest classification, and selected the top 6 genes as important genes for subsequent analysis. The prediction weights of the genes of interest were determined by the neural network model and the model scores were evaluated in 2 independent sample datasets (GSE16499 and GSE57338 datasets). Since the weights of RNA-seq predictions for constructing neural network models were theoretically more suitable for disease classification of RNA-seq data, the GSE57338 dataset had the best performance in the validation results. The diagnostic model derived from our study can be of clinical value in determining the likelihood of HF occurring through cardiac biopsy. In the meantime, we need to further investigate the accuracy of the diagnostic model based on the results of our study.

Identification of potential biomarkers of inflammation-related genes for ischemic cardiomyopathy

Objective

Inflammation plays an important role in the pathophysiology of ischemic cardiomyopathy (ICM). We aimed to identify potential biomarkers of inflammation-related genes for ICM and build a model based on the potential biomarkers for the diagnosis of ICM.

Materials and methods

The microarray datasets and RNA-Sequencing datasets of human ICM were downloaded from the Gene Expression Omnibus database. We integrated 8 microarray datasets via the SVA package to screen the differentially expressed genes (DEGs) between ICM and non-failing control samples, then the differentially expressed inflammation-related genes (DEIRGs) were identified. The least absolute shrinkage and selection operator, support vector machine recursive feature elimination, and random forest were utilized to screen the potential diagnostic biomarkers from the DEIRGs. The potential biomarkers were validated in the RNA-Sequencing datasets and the functional experiment of the ICM rat, respectively. A nomogram was established based on the potential biomarkers and evaluated via the area under the receiver operating characteristic curve (AUC), calibration curve, decision curve analysis (DCA), and Clinical impact curve (CIC).

Results

64 DEGs and 19 DEIRGs were identified, respectively. 5 potential biomarkers (SERPINA3, FCN3, PTN, CD163, and SCUBE2) were ultimately selected. The validation results showed that each of these five potential biomarkers showed good discriminant power for ICM, and their expression trends were consistent with the bioinformatics results. The results of AUC, calibration curve, DCA, and CIC showed that the nomogram demonstrated good performance, calibration, and clinical utility.

Conclusion

SERPINA3, FCN3, PTN, CD163, and SCUBE2 were identified as potential biomarkers associated with the inflammatory response to ICM. The proposed nomogram could potentially provide clinicians with a helpful tool to the diagnosis and treatment of ICM from an inflammatory perspective.

Reveal the Mechanisms of Yi-Fei-Jian-Pi-Tang on Covid-19 through Network Pharmacology Approach

Objectives

The Traditional Chinese Medicine (TCM) formula Yi-Fei-Jian-Pi-Tang (YFJPT) has been demonstrated effective against Corona Virus Disease 2019 (Covid-19). The aim of this article is to make a thorough inquiry about its active constituent as well as mechanisms against Covid-19 via TCM network pharmacology.

Methods

All the ingredients of YFJPT are obtained from the pharmacology database of the TCM system. The genes which are associated with the targets are obtained by utilizing UniProt. The herb-target network is built up by utilizing Cytoscape. The target protein-protein interaction network is built by utilizing the STRING database and Cytoscape. The critical targets of YFJPT are explored by Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG).

Results

The outcomes show that YFJPT might has 33 therapeutic targets on Covid-19, namely, interleukin 2 (IL2), heme oxygenase 1 (HMOX1), interleukin 4 (IL4), interferon gamma (FNG), α nuclear factor of kappa light polypeptide gene enhancer in Bcells inhibitor, alpha (NFKBIA), nuclear factor-k-gene binding (NFKB), nitric oxide synthase 3 (NOS3), intercellular adhesion molecule 1 (ICAM1), hypoxia inducible factor 1 subunit alpha (HIF1A), mitogen-activated protein kinase 3 (MAPK3), epidermal growth factor receptor (EGFR), interleukin 10 (IL10), jun proto-oncogene (JUN), C-C motif chemokine ligand 2 (CCL2), C-X-C motif chemokine ligand 8 (CXCL8), tumor protein p53 (TP53), interleukin 1 beta (IL1B), AKT serine/threonine kinase 1 (AKT1), tumor necrosis factor (TNF), interleukin 6 (IL6), erb-b2 receptor tyrosine kinase 2 (ERBB2), RELA proto-oncogene (RELA), NF-κB subunit, caspase 8 (CASP8), peroxisome proliferator activated receptor alpha (PPARA), TIMP metallopeptidase inhibitor 1 (TIMP1), transforming growth factor beta 1 (TGFB1), interleukin 1 alpha (IL1A), signal transducer and activator of transcription 1 (STAT1), mitogen-activated protein kinase 8 (MAPK8), myeloperoxidase (MPO), matrix metallopeptidase 3 (MMP3), matrix metallopeptidase 1 (MMP1), and NFE2 like bZIP transcription factor 2 (NFE2L2). The gene enrichment analysis prompts that YFJPT most likely contributes to patients related to Covid-19 by regulating the pathways of cancers.

Conclusions

That will lay a foundation for the clinical rational application and further experimental research of YFJPT.

Single-cell transcriptomics identifies Col1a1 and Col1a2 as hub genes in obesity-induced cardiac fibrosis

Obesity is a risk factor for cardiovascular disease, leading to ventricular dysfunction and cardiac fibrosis, in which non-cardiomyocytes (nonCMs) play an important role. Early detection and treatment of heart illness may help to limit its progression. We screened for key markers of obesity-induced cardiac fibrosis using single-cell transcriptomics techniques. To begin, an obese mouse model was constructed using a high-fat diet. From a pathogenic perspective, pathological alterations in the obesity-induced heart were found. Differentially expressed genes (DEGs) were identified and functional enrichment analysis was performed. Then, to look for hub genes, key modules of DEGs were built. Finally, the cellular location of the hub genes was investigated. In mice, a high-fat diet raised body weight, messed up myocardial shape, and increased cardiac collagen content. NonCMs transcriptome data revealed 15 different cell types, including fibroblasts, immunological cells, and endothelial cells. There were a total of 33 DEGs found, with 22 up-regulated genes and 11 down-regulated genes. DEGs have a high connection with collagen and extracellular matrix (ECM), according to functional enrichment analysis. Col1a1 and Col1a2 scored well in module analysis and hub gene screening, and were chosen as hub genes. Col1a1 and Col1a2 were shown to be mostly expressed by fibroblasts after localization study. As a result, we believe Col1a1 and Col1a2 may be important markers of obesity-induced cardiac fibrosis, in which fibroblasts play a critical role.

Integrated Strategies of Diverse Feature Selection Methods Identify Aging-Based Reliable Gene Signatures for Ischemic Cardiomyopathy

Objective: Ischemic cardiomyopathy (ICM) is a major cardiovascular state associated with prominently increased morbidity and mortality. Our purpose was to detect reliable gene signatures for ICM through integrated feature selection strategies.

Methods: Transcriptome profiles of ICM were curated from the GEO project. Classification models, including least absolute shrinkage and selection operator (LASSO), support vector machine (SVM), and random forest, were adopted for identifying candidate ICM-specific genes for ICM. Immune cell infiltrates were estimated using the CIBERSORT method. Expressions of candidate genes were verified in ICM and healthy myocardial tissues via Western blotting. JC-1 staining, flow cytometry, and TUNEL staining were presented in hypoxia/reoxygenation (H/R)-stimulated H9C2 cells with TRMT5 deficiency.

Results: Following the integration of three feature selection methods, we identified seven candidate ICM-specific genes including ASPN, TRMT5, LUM, FCN3, CNN1, PCNT, and HOPX. ROC curves confirmed the excellent diagnostic efficacy of this combination of previous candidate genes in ICM. Most of them presented prominent interactions with immune cell infiltrates. Their deregulations were confirmed in ICM than healthy myocardial tissues. TRMT5 expressions were remarkedly upregulated in H/R-stimulated H9C2 cells. TRMT5 deficiency enhanced mitochondrial membrane potential and reduced apoptosis in H/R-exposed H9C2 cells.

Conclusion: Collectively, our findings identified reliable gene signatures through combination strategies of diverse feature selection methods, which facilitated the early detection of ICM and revealed the underlying mechanisms.