Research article
Analysis of multi-factor regulated network and different clusters in hypertrophic obstructive cardiomyopathy
Jian Zhuang
Guangdong Cardiovascular Institute
Corresponding Author
ORCiD: https://orcid.org/0000-0003-0142-4238
License:
This work is licensed under a CC BY 4.0 License. Read Full License
We herein explored the regulatory network between lncRNA, miRNAs, mRNAs, and TFs in the HOCM and divided HOCM samples into different clusters to identify key genes and regulatory factors. The four gene sets from GEO, GENE and OMIM databases were integrated and WGCNA network was used to obtain two modules and 32 core genes. Through online database, miRNA-lncRNA, miRNA-mRNA, and TF-mRNA interaction pairs were obtained to screen the regulatory factors, and finally a multi-factor regulatory network was obtained. The first 7 regulatory factors were obtained as the core regulatory factors. The unsupervised clustering method was used to divide HOCM samples into 4 clusters. As a result, four genes including COMP, FMOD, AEBP1 and SULF1 showed significant expression in different clusters. Finally, we got 4 genes are considered as important biomarkers for different progressive stages or prognosis of HOCM. These might assist in discovering molecular mechanisms of HOCM in the future.
Keywords
different clusters, hypertrophic obstructive cardiomyopathy, multi-factor regulatory network, weighted gene co-expression network
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This is a list of supplementary files associated with this preprint. Click to download.
- SupplementaryFigure1.tif
Supplementary Figure 1: Volcano plots showing the significance of the differential gene expression.
- SupplementaryFigure2.tif
Supplementary Figure 2: The scatter plot of gene significance versus module membership for turquoise module and blue module.
- SupplementaryFigure3.tif
Supplementary Figure 3: The BP analysis of the targeted genes regulated by core regulators.
- SupplementaryTable1.xlsx
Supplementary Table 1: The list of differential expressed genes in GSE36961 and GSE89714 datasets.
- SupplementaryTable2.xlsx
Supplementary Table 2: HCM-related genes from OMIM and NCBI database.
- SupplementaryTable3.xlsx
Supplementary Table 3: The BP and KEGG analysis of candidate genes.
- SupplementaryTable4.xlsx
Supplementary Table 4: Candidate genes and gene expression values for WGCNA.
- SupplementaryTable5.xlsx
Supplementary Table 5: The BP analysis of genes in the turquiose module and the blue module.
- SupplementaryTable6.xlsx
Supplementary Table 6: The turquoise and blue module network analysis.
- SupplementaryTable7.xlsx
Supplementary Table 7: The miRNA-lncRNA, miRNA-mRNA and TF-mRNA pairs from the starBase database and the TRRUST database.
- SupplementaryTable8.xlsx
Supplementary Table 8: Multi-factor regulatroy network with 175 interaction pairs and BP terms of target genes of core regulators.
- SupplementaryTable9.xlsx
Supplementary Table 9: Cluster of samples and core genes expression value in four clusters.
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Research article
Analysis of multi-factor regulated network and different clusters in hypertrophic obstructive cardiomyopathy
Jian Zhuang
Guangdong Cardiovascular Institute
Corresponding Author
ORCiD: https://orcid.org/0000-0003-0142-4238
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
History
CURRENT STATUS: Posted
Version 2
Posted 01 Mar, 2021
No community comments so far
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
We herein explored the regulatory network between lncRNA, miRNAs, mRNAs, and TFs in the HOCM and divided HOCM samples into different clusters to identify key genes and regulatory factors. The four gene sets from GEO, GENE and OMIM databases were integrated and WGCNA network was used to obtain two modules and 32 core genes. Through online database, miRNA-lncRNA, miRNA-mRNA, and TF-mRNA interaction pairs were obtained to screen the regulatory factors, and finally a multi-factor regulatory network was obtained. The first 7 regulatory factors were obtained as the core regulatory factors. The unsupervised clustering method was used to divide HOCM samples into 4 clusters. As a result, four genes including COMP, FMOD, AEBP1 and SULF1 showed significant expression in different clusters. Finally, we got 4 genes are considered as important biomarkers for different progressive stages or prognosis of HOCM. These might assist in discovering molecular mechanisms of HOCM in the future.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5