Identification of Hub Diagnostic Biomarkers and Candidate Therapeutic Drugs in Heart Failure

Knockdown of TGFB2 Attenuates Ischemic Heart Failure by Inhibiting Apoptosis

Heart failure (HF) is a clinical syndrome resulting from cardiac overload and injury. The molecular mechanisms underlying ischemic HF remain unclear. Using the GSE116250 and GSE203160 datasets, we screened for differentially expressed genes (DEGs) in ischemic HF, identifying 132 overlapping genes. Through the protein-protein interaction (PPI) network, we screened nine hub genes-SPP1, POSTN, CCN2, FGF7, OGN, BMP2, LUM, TGFB2, and BMP7-that may serve as diagnostic biomarkers for HF. FGF7 and BMP7 expression levels were reduced, while TGFB2, OGN, and CCN2 expression levels were elevated in rat models of left anterior descending coronary artery ligation. Notably, Cell Counting Kit-8 and flow cytometry showed that TGFB2 knockdown promoted viability and inhibited apoptosis in oxygen glucose deprivation-induced H9c2 cells. Western blot analysis further demonstrated that TGFB2 knockdown decreased cleaved Caspase-3/Caspase-3 and Bax protein levels while increasing Bcl-2 protein expression. These findings reveal that TGFB2 knockdown mitigates ischemic HF by suppressing apoptosis, offering novel insights into the fundamental molecular mechanisms underlying HF.

Combining Bulk and Single Cell RNA-Sequencing Data to Identify Hub Genes of Fibroblasts in Dilated Cardiomyopathy

Background

Dilated cardiomyopathy (DCM) is the second leading cause of heart failure, with intricate pathophysiological underpinnings. In order to shed fresh light on the mechanistic research of DCM, we combined bulk RNA-seq and single-cell RNA-seq (scRNA-seq) data to examine significant cells and genes implicated in the disease.

Methods

This analysis employed publicly accessible bulk RNA-seq and scRNA-seq DCM datasets. The scRNA-seq data underwent normalization, principal component, and t-distribution stochastic neighbor embedding analysis. Cell-to-cell communication networks and activity analysis were conducted using CellChat. Utilizing enrichment analysis, the marker genes' role in the active cells was evaluated. After screening by limma software and weighted gene co-expression network analysis, the differentially expressed genes (DEGs) served as hub genes. Furthermore, these hub genes were subjected to immunological studies, transcription factor expression, and gene set enrichment. Lastly, the expression of the four hub genes and their connection to DCM were verified using the rat models.

Results

Fibroblasts and monocytes were chosen as hub cells from among the eight identified cell clusters; their marker genes intersected with DEGs to yield six hub genes. In addition, the six hub genes and the essential module genes intersected to yield four essential genes (ASPN, SFRP4, LUM, and FRZB) that were connected to the Wnt signaling pathway and highly expressed in fibroblast. The four hub DEGs had an expression pattern in the DCM rat model experiment results that was in line with the findings of the bioinformatics study. Additionally, there was a strong correlation between decreased cardiac function and the up-regulation of ASPN, SFRP4, LUM, and FRZB.

Conclusion

Ultimately, bulk RNA-seq and scRNA-seq data identified fibroblasts and monocytes as the main cell types implicated in DCM. The highly expressed genes ASPN, FRZB, LUM, and SFRP4 in fibroblasts may aid in the mechanistic investigation of DCM.

Identification and exploration of aging-related subtypes and distinctive role of SERPINE1 in heart failure based on single-cell and bulk RNA sequencing data

Aging is a major risk factor for heart failure (HF) and is the leading cause of death worldwide. Currently, the nature of the relationship between aging and HF is not entirely clear. Herein, this study aimed to explore new diagnostic biomarkers, molecular typing and therapeutic strategies for HF by investigating the biological significance of aging-related genes in HF. A total of 157 differentially expressed genes (DEGs) were screened totally between HF and normal samples, and functional enrichment analysis of DEGs revealed the strong association of HF progression with aging, immune processes and metabolism. Six HF-specific aging-related genes were further identified, and a diagnostic model was developed and validated for good diagnostic efficacy. In addition, we collected blood samples from 10 normal controls and 10 HF patients for RT-qPCR analysis to verify the bioinformation. We also identified two aging-associated subtypes with distinctly different immune infiltration and metabolic microenvironment. Further single-cell sequencing analysis conducted in the study identified SERPINE1 as a key gene in HF. The distinctive role of SERPINE1 fibroblasts was revealed, including three main findings: (I) fibroblasts had a higher proportion and expression of SERPINE1 levels in HF; (II) the ligand-receptor pair MDK-LRP1 made the most contributions in high interactions with other cell types in SERPINE1 fibroblasts; and (III) SERPINE1 fibroblasts were associated with the interaction of extracellular matrix and receptor and may be regulated by the transcription factor EGR1. In conclusion, this study highlights the importance of aging-related genes in diagnosing HF and regulating immune infiltration. We also identified different HF subtypes and a potentially crucial gene, which may provide a better understanding of the molecular-level mechanisms of aging-related HF and aid in developing effective therapeutic strategies.

Identification of crucial genes related to heart failure based on GEO database

BACKGROUND

The molecular biological mechanisms underlying heart failure (HF) remain poorly understood. Therefore, it is imperative to use innovative approaches, such as high-throughput sequencing and artificial intelligence, to investigate the pathogenesis, diagnosis, and potential treatment of HF.

METHODS

First, we initially screened Two data sets (GSE3586 and GSE5406) from the GEO database containing HF and control samples from the GEO database to establish the Train group, and selected another dataset (GSE57345) to construct the Test group for verification. Next, we identified the genes with significantly different expression levels in patients with or without HF and performed functional and pathway enrichment analyses. HF-specific genes were identified, and an artificial neural network was constructed by Random Forest. The ROC curve was used to evaluate the accuracy and reliability of the constructed model in the Train and Test groups. Finally, immune cell infiltration was analyzed to determine the role of the inflammatory response and the immunological microenvironment in the pathogenesis of HF.

RESULTS

In the Train group, 153 significant differentially expressed genes (DEGs) associated with HF were found to be abnormal, including 81 down-regulated genes and 72 up-regulated genes. GO and KEGG enrichment analyses revealed that the down-regulated genes were primarily enriched in organic anion transport, neutrophil activation, and the PI3K-Akt signaling pathway. The upregulated genes were mainly enriched in neutrophil activation and the calcium signaling. DEGs were identified using Random Forest, and finally, 16 HF-specific genes were obtained. In the ROC validation and evaluation, the area under the curve (AUC) of the Train and Test groups were 0.996 and 0.863, respectively.

CONCLUSIONS

Our research revealed the potential functions and pathways implicated in the progression of HF, and designed an RNA diagnostic model for HF tissues using machine learning and artificial neural networks. Sensitivity, specificity, and stability were confirmed by ROC curves in the two different cohorts.

Highly Accessible Computational Prediction and In Vivo/In Vitro Experimental Validation: Novel Synthetic Phenyl Ketone Derivatives as Promising Agents against NAFLD via Modulating Oxidoreductase Activity

Nonalcoholic fatty liver disease (NAFLD) has reached epidemic proportions with no pharmacological treatment approved. Several highly accessible computational tools were employed to predict the activities of twelve novel compounds prior to actual chemical synthesis. We began our work by designing two or three hydroxyl groups appended to the phenyl ketone core, followed by prediction of drug-likeness and targets. Most predicted targets for each compound overlapped with NAFLD targets (≥80%). Enrichment analysis showed that these compounds might regulate oxidoreductase activity. Then, these compounds were synthesized and confirmed by IR, MS, ¹H, and ¹³C NMR. Their cell viability demonstrated that twelve compounds exhibited appreciable potencies against NAFLD (EC₅₀ values ≤ 13.5 μM). Furthermore, the most potent compound 5f effectively prevented NAFLD progression as evidenced by the change in histological features. 5f significantly reduced total cholesterol and triglyceride levels in vitro/in vivo, and the effects of 5f were significantly stronger than those of the control drug. The proteomic data showed that oxidoreductase activity was the most significantly enriched, and this finding was consistent with docking results. In summary, this validated presynthesis prediction approach was cost-saving and worthy of popularization. The novel synthetic phenyl ketone derivative 5f holds great therapeutic potential by modulating oxidoreductase activity to counter NAFLD.

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.

A systematic study of traditional Chinese medicine treating hepatitis B virus-related hepatocellular carcinoma based on target-driven reverse network pharmacology

Hepatocellular carcinoma (HCC) is a serious global health problem, and hepatitis B virus (HBV) infection remains the leading cause of HCC. It is standard care to administer antiviral treatment for HBV-related HCC patients with concurrent anti-cancer therapy. However, a drug with repressive effects on both HBV infection and HCC has not been discovered yet. In addition, drug resistance and side effects have made existing therapeutic regimens suboptimal. Traditional Chinese medicine (TCM) has multi-ingredient and multi-target advantages in dealing with multifactorial HBV infection and HCC. TCM has long been served as a valuable source and inspiration for discovering new drugs. In present study, a target-driven reverse network pharmacology was applied for the first time to systematically study the therapeutic potential of TCM in treating HBV-related HCC. Firstly, 47 shared targets between HBV and HCC were screened as HBV-related HCC targets. Next, starting from 47 targets, the relevant chemical components and herbs were matched. A network containing 47 targets, 913 chemical components and 469 herbs was established. Then, the validated results showed that almost 80% of the herbs listed in chronic hepatitis B guidelines and primary liver cancer guidelines were included in the 469 herbs. Furthermore, functional analysis was conducted to understand the biological processes and pathways regulated by these 47 targets. The docking results indicated that the top 50 chemical components bound well to targets. Finally, the frequency statistical analysis results showed the 469 herbs against HBV-related HCC were mainly warm in property, bitter in taste, and distributed to the liver meridians. Taken together, a small library of 913 chemical components and 469 herbs against HBV-related HCC were obtained with a target-driven approach, thus paving the way for the development of therapeutic modalities to treat HBV-related HCC.