STAT4 and COL1A2 are potential diagnostic biomarkers and therapeutic targets for heart failure comorbided with depression

Huang, Kai; Zhang, Xinying; Duan, Jiahao; Wang, Ruting; Wu, Zifeng; Yang, Chun; Yang, Ling

doi:10.1016/j.brainresbull.2022.03.014

Cited by 22 publications

(14 citation statements)

References 33 publications

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“…The DEGs were mapped to the STRING database (http://strin gdb.org) to assess protein-protein interaction (PPI) within HCC tissue (21). The PPI network was constructed using Cytoscape software (version 3.6.0).…”

Section: Protein-protein Interaction Network Constructionmentioning

confidence: 99%

Single-Cell Transcriptomic Analysis Reveals Macrophage–Tumor Crosstalk in Hepatocellular Carcinoma

Liu

Zhang

Ju³

et al. 2022

Front. Immunol.

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As one of the most malignant cancer, hepatocellular carcinoma (HCC) has a complex ecosystem featured by high heterogeneity. Cell crosstalk is demonstrated to be critical for HCC development. However, the cell communication orchestration in HCC remains largely unknown. Here, by analyzing the single-cell transcriptomes of the primary tumor tissues (n = 10) and tumor-adjacent tissues (n = 8) derived from 10 patients with HCC, we found that the proportions of plasmacytoid dendritic cells (pDCs) and natural killer (NK) cells were reduced and that the proportion of macrophages was increased in the immune component of the primary tumor, compared with those in the tumor-adjacent tissue. Furthermore, we found widespread communication between macrophage populations and other cell types, and this communication was remarkably strengthened in the primary tumor, especially with HCC malignant cells. In addition, the SPP1–CD44 axis was identified as a unique interaction between macrophages and HCC malignant cells. Our comprehensive portrait of cell communication patterns over the HCC ecosystem reveals further insights into immune infiltration.

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Section: Protein-protein Interaction Network Constructionmentioning

confidence: 99%

Single-Cell Transcriptomic Analysis Reveals Macrophage–Tumor Crosstalk in Hepatocellular Carcinoma

Liu

Zhang

Ju³

et al. 2022

Front. Immunol.

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“…In terms of COL1α2 in heart research, a bioinformatics analysis revealed that COL1α2 underlies the comorbidity mechanisms of HF and depression. [49] Single-cell sequencing results using an obese mouse model suggest that COL1α2 and COL1α1 may be important markers of obesity-induced cardiac brosis [50]. Another investigation con rmed the association between COL1α2 and cardiac brosis [51] .…”

Section: Discussionmentioning

confidence: 98%

Role of DNA Methylation in Ischemic-Hypoxic Endothelial Cells and its Clinical Implications in AMI Patients

Tang,

Wang,

Wang

et al. 2023

Preprint

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The obstructed coronary artery undergoes a series of pathological changes due to ischemia and hypoxic shocks during acute myocardial infarction (AMI). However, the altered DNA methylation levels in endothelial cells under these conditions and their implication for the etiopathology of AMI have not been investigated in detail. This study aimed to explore the relationship between DNA methylation and pathologically altered gene expression profile in human umbilical vein endothelial cells (HUVECs) subjected to oxygen-glucose deprivation (OGD), and its clinical implications in AMI Patients. The Illumina Infinium MethylationEPIC BeadChip assay was used to explore the genome-wide DNA methylation profile using the Novaseq6000 platform for mRNA sequencing in 3 pairs of HUVEC-OGD and control samples. GO and KEGG pathway enrichment analyses, as well as correlation, causal inference test (CIT), and protein-protein interaction (PPI) analyses identified 22 hub genes that were validated by MethylTarget sequencing as well as qRT-PCR. ELISA was used to detect four target molecules associated with the progression of AMI. A total of 2524 differentially expressed genes (DEGs) and 22148 differentially methylated positions corresponding to 6642 differentially methylated genes were screened (|Δβ|>0.1 and detection p<0.05). After GO, KEGG, correlation, CIT, and PPI analyses, 441 genes were filtered. qRT-PCR confirmed the overexpression of VEGFA, CCL2, THBS1, SQSTM1, BCL2L11, and TIMP3 genes, and downregulation of MYC, CD44, BDNF, GNAQ, RUNX1, ETS1, NGFR, MME, SEMA6A, GNAI1, IFIT1, and MEIS1. DNA fragments BDNF_1_ (r=0.931, p<0.0001) and SQSTM1_2_NEW (r=0.758, p=0.0043) were positively correlated with the expressions of corresponding genes, and MYC_1_ (r=-0.8245, p=0.001) was negatively correlated. Furthermore, ELISA confirmed TNFSF10 and BDNF were elevated in the peripheral blood of AMI patients (p=0.0284 and p=0.0142, respectively). Combined sequencing from in-vitro cellular assays with clinical samples, focusing on the importance of endothelial cells in AMI, our study identified promising OGD-specific genes, which provided a solid basis for screening fundamental diagnostic and prognostic biomarkers of coronary endothelial cell injury of AMI. Moreover, it furnished the first evidence that during ischemia and hypoxia, the expression of BNDF was regulated by DNA methylation in endothelial cells and elevated in peripheral blood.

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“…[6,8] Bioinformatics analysis provides useful information and assistance to identify the role of relevant genes, proteins, and signaling pathways in the development of disease. [17,18] In this study, 36 co-expressed DEGs were identified in 2 datasets (GSE1869 and GSE9128). GO enrichment and KEGG pathway analyses were performed, and PPI networks were constructed to further analyze the molecular processes involved in NICM progression.…”

Section: Discussionmentioning

confidence: 99%

Bioinformatics analysis and identification of potential key genes and pathways in the pathogenesis of nonischemic cardiomyopathy

Jia,

Zhang,

et al. 2024

Medicine

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Nonischemic cardiomyopathy (NICM) is a major cause of advanced heart failure, and the morbidity and mortality associated with NICM are serious medical problems. However, the etiology of NICM is complex and the related mechanisms involved in its pathogenesis remain unclear. The microarray datasets GSE1869 and GSE9128 retrieved from the Gene Expression Omnibus database were used to identify differentially expressed genes (DEGs) between NICM and normal samples. The co-expressed genes were identified using Venn diagrams. Kyoto Encyclopedia of Genes and Genomes pathway analyses and gene ontology enrichment were used to clarify biological functions and signaling pathways. Analysis of protein–protein interaction networks using Search Tool for the Retrieval of Interacting Genes/Proteins online to define the hub genes associated with NICM pathogenesis. A total of 297 DEGs were identified from GSE1869, 261 of which were upregulated genes and 36 were downregulated genes. A total of 360 DEGs were identified from GSE9128, 243 of which were upregulated genes and 117 were downregulated genes. In the 2 datasets, the screening identified 36 co-expressed DEGs. Kyoto Encyclopedia of Genes and Genomes pathway and gene ontology analysis showed that DEGs were mainly enriched in pantothenate and CoA biosynthesis, beta-alanine metabolism, kinetochore, G-protein beta/gamma-subunit complex, and other related pathways. The PPI network analysis revealed that DUSP6, EGR1, ZEB2, and XPO1 are the 4 hub genes of interest in the 2 datasets. Bioinformatics analysis of hub genes and key signaling pathways is an effective way to elucidate the mechanisms involved in the development of NICM. The results will facilitate further studies on the pathogenesis and therapeutic targets of NICM.

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STAT4 and COL1A2 are potential diagnostic biomarkers and therapeutic targets for heart failure comorbided with depression

Cited by 22 publications

References 33 publications

Single-Cell Transcriptomic Analysis Reveals Macrophage–Tumor Crosstalk in Hepatocellular Carcinoma

Single-Cell Transcriptomic Analysis Reveals Macrophage–Tumor Crosstalk in Hepatocellular Carcinoma

Role of DNA Methylation in Ischemic-Hypoxic Endothelial Cells and its Clinical Implications in AMI Patients

Bioinformatics analysis and identification of potential key genes and pathways in the pathogenesis of nonischemic cardiomyopathy

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