Identification of SLED1 as a Potential Predictive Biomarker and Therapeutic Target of Post-Infarct Heart Failure by Bioinformatics Analyses

Uncovering potential molecular markers and pathological mechanisms of Parkinson's disease and myocardial infarction based on bioinformatics analysis

BACKGROUND

The direct association between Parkinson's disease (PD) and Myocardial infarction (MI) has been the subject of relatively limited research.

OBJECTIVE

The purpose of this study was to identify the genes most associated with PD and MI to explore their common pathogenesis.

METHODS

The gene expression profiles of PD and MI were downloaded from GEO database. Differential expression analysis was performed to identify the common differential expression genes (DEGs) of PD and MI, followed by functional annotation. Subsequently, protein-protein interaction network were constructed, and hub DEGs were identified based on CytoHubba plugin and LASSO regression analysis. To explore the potential molecular mechanism of hub DEGs, GSEA analysis, immune correlation analysis, drug prediction and molecular docking were performed, and transcription factors (TF) and lncRNA-miRNA-mRNA (ceRNA) regulatory networks were constructed.

RESULTS

A total of 48 DEGs with the same expression trend were identified in the MI vs. normal control (NC) and PD vs. NC groups. Functional annotation results showed that the common DEGs were significantly enriched in immune and inflammation-related pathways. RPS4Y1 and UTY were the most relevant hub DEGs for PD and MI, and may be involved in the HALLMARK_MYC_TARGETS_V1 and HALLMARK_PROTEIN_SECRETION pathways. TP63 was a common TF of RPS4Y1 and UTY. The PVT1/KCNQ1OT1-hsa-miR-31-5p-RPS4Y1 and KCNQ1OT1-hsa-let-7a-5p/hsa-miR-19b-3p-UTY axes may play an important role in regulating PD and MI. CYCLOHEXIMIDE and ATALAREN may be potential drugs for the treatment of PD and MI comorbidity. In addition, PD and MI exhibit different patterns of immune cell infiltration and immune function status, which may be related to the specific pathological processes of the disease.

CONCLUSIONS

This study revealed for the first time that RPS4Y1 and UTY may be common biomarkers of PD and MI and may be potential therapeutic targets. This study provides new perspective on the common molecular mechanisms between PD and MI.

Combining WGCNA and machine learning to identify mechanisms and biomarkers of ischemic heart failure development after acute myocardial infarction

Background

Ischemic heart failure (IHF) is a serious complication after acute myocardial infarction (AMI). Understanding the mechanism of IHF after AMI will help us conduct early diagnosis and treatment.

Methods

We obtained the AMI dataset GSE66360 and the IHF dataset GSE57338 from the GEO database, and screened overlapping genes common to both diseases through WGCNA analysis. Subsequently, we performed GO and KEGG enrichment analysis on overlapping genes to elucidate the common mechanism of AMI and IHF. Machine learning algorithms are also used to identify key biomarkers. Finally, we performed immune cell infiltration analysis on the dataset to further evaluate immune cell changes in AMI and IHF.

Results

We obtained 74 overlapping genes of AMI and IHF through WGCNA analysis, and the enrichment analysis results mainly focused on immune and inflammation-related mechanisms. Through the three machine learning algorithms of LASSO, RF and SVM-RFE, we finally obtained the four Hub genes of IL1B, TIMP2, IFIT3, and P2RY2, and verified them in the IHF dataset GSE116250, and the diagnostic model AUC = 0.907. The results of immune infiltration analysis showed that 8 types of immune cells were significantly different in AMI samples, and 6 types of immune cells were significantly different in IHF samples.

Conclusion

We explored the mechanism of IHF after AMI by WGCNA, enrichment analysis, and immune infiltration analysis. Four potential diagnostic candidate genes and therapeutic targets were identified by machine learning algorithms. This provides a new idea for the pathogenesis, diagnosis, and treatment of IHF after AMI.

Inhibition of Jumonji demethylases reprograms severe dilated cardiomyopathy and prolongs survival

Hypertrophic/dilated cardiomyopathy, often a prequel to heart failure, is accompanied by maladaptive transcriptional changes that contribute to arrythmias and contractile misfunction. Transgenic mice constitutively expressing high levels of calcineurin are known to develop extreme heart hypertrophy, which progresses to dilated cardiomyopathy, and to die several weeks after birth. Here, we characterized aberrant transcriptional and epigenetic pathways in this mouse model and established a pharmacological approach to treat established cardiomyopathy. We found that H3K4me3 (trimethyl histone 3 lysine 4) and H3K9me3 (trimethyl histone 3 lysine 9) Jumonji histone demethylases are markedly increased at the protein level and show enhanced enzymatic activity in diseased hearts. These epigenetic regulators continued to increase with time, further affecting cardiac gene expression. Our findings parallel the lower H3K4me3 and H3K9me3 levels seen in human patients. Inhibition of Jumonji demethylase activities in vivo results in lower histone demethylase enzymatic function in the heart and higher histone methylation levels and leads to partial reduction of heart size, reversal of maladaptive transcriptional programs, improved heart function, and prolonged survival. At the molecular level, target genes of transcription factor myocyte enhancer factor 2 are specifically regulated in response to pharmacological or genetic inhibition of Jumonji demethylases. Similar transcriptional reversal of disease-associated genes is seen in a second disease model based on cardiac mechanical overload. Our findings validate pharmacological inhibitors of Jumonji demethylases as potential therapeutics for the treatment of cardiomyopathies across disease models and provide evidence of the reversal of maladaptive transcriptional reprogramming leading to partial restoration of cardiac function. In addition, this study defines pathways of therapeutic resistance upregulated with disease progression.

Identification of Hub Genes Associated with Abnormal Endothelial Function in Early Coronary Atherosclerosis

Abnormal coronary endothelial function is an important step in the development of atherosclerosis. Coronary atherosclerosis is one of the main causes of death worldwide. We constructed a co-expression network to identify hub genes associated with abnormal coronary endothelial function in early coronary atherosclerosis. In brief, we used the GSE132651 dataset from the gene expression omnibus database. The top 5000 genes with greatest variances were used for weighted gene co-expression network analysis, and the module most strongly correlated with abnormal coronary endothelial function was chosen as key module. Functional enrichment analysis was performed for genes in the key module, a protein-protein interaction network was constructed to find hub genes, and gene set enrichment analysis (GSEA) was also performed. Genes were classified into 7 modules, with the midnightblue module being the one that was most related to abnormal coronary endothelial function and containing genes enriched in DNA replication, cell cycle, nucleotide excision repair, and Human T-cell leukemia virus 1 infection. We identified nine hub genes (HOXC5, PRND, PADI3, RC3H1, DAPP1, SIT1, DRICH1, GPRIN2, and RHO), which differently expressed in abnormal and normal coronary endothelial function samples. GSEA suggested that samples associated with abnormal coronary endothelial function and highly expressed hub genes were linked with immune, coagulation, hypoxia, and angiogenesis processes. These hub genes, their expression pattern, and pathways may be involved in the development of abnormal coronary endothelial function and promotion of early coronary atherosclerosis.