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Data Mining Identifies CCN2 and THBS1 as Biomarker Candidates for Cardiac Hypertrophy
University of Skövde, School of Bioscience. University of Skövde, Systems Biology Research Environment. Department of Molecular and Clinical Medicine, Institute of Medicine, The Sahlgrenska Academy at University of Gothenburg, Sweden. (Translational Bioinformatics)ORCID iD: 0000-0002-5134-4749
University of Skövde, School of Bioscience. University of Skövde, Systems Biology Research Environment. (Translational Bioinformatics)
University of Skövde, School of Bioscience. University of Skövde, Systems Biology Research Environment. Bioscience, Research and Early Development, Cardiovascular, Renal and Metabolism (CVRM), BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden. (Translational Bioinformatics)
Department of Molecular and Clinical Medicine, Institute of Medicine, The Sahlgrenska Academy at University of Gothenburg, Sweden ; Department of Cardiothoracic Surgery, Sahlgrenska University Hospital, Gothenburg, Sweden.
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2022 (English)In: Life, E-ISSN 2075-1729, Vol. 12, no 5, article id 726Article in journal (Refereed) Published
Abstract [en]

Cardiac hypertrophy is a condition that may contribute to the development of heart failure. In this study, we compare the gene-expression patterns of our in vitro stem-cell-based cardiac hypertrophy model with the gene expression of biopsies collected from hypertrophic human hearts. Twenty-five differentially expressed genes (DEGs) from both groups were identified and the expression of selected corresponding secreted proteins were validated using ELISA and Western blot. Several biomarkers, including CCN2, THBS1, NPPA, and NPPB, were identified, which showed significant overexpressions in the hypertrophic samples in both the cardiac biopsies and in the endothelin-1-treated cells, both at gene and protein levels. The protein-interaction network analysis revealed CCN2 as a central node among the 25 overlapping DEGs, suggesting that this gene might play an important role in the development of cardiac hypertrophy. GO-enrichment analysis of the 25 DEGs revealed many biological processes associated with cardiac function and the development of cardiac hypertrophy. In conclusion, we identified important similarities between ET-1-stimulated human-stem-cell-derived cardiomyocytes and human hypertrophic cardiac tissue. Novel putative cardiac hypertrophy biomarkers were identified and validated on the protein level, lending support for further investigations to assess their potential for future clinical applications. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

Place, publisher, year, edition, pages
MDPI, 2022. Vol. 12, no 5, article id 726
Keywords [en]
biomarker, cardiac hypertrophy, disease model, endothelin-1, stem cells, transcriptomics
National Category
Cell and Molecular Biology Medical Biotechnology (with a focus on Cell Biology (including Stem Cell Biology), Molecular Biology, Microbiology, Biochemistry or Biopharmacy) Bioinformatics and Systems Biology
Research subject
Bioinformatics
Identifiers
URN: urn:nbn:se:his:diva-21200DOI: 10.3390/life12050726ISI: 000802500000001PubMedID: 35629393Scopus ID: 2-s2.0-85130327246OAI: oai:DiVA.org:his-21200DiVA, id: diva2:1663675
Funder
Knowledge Foundation, 20160294Knowledge Foundation, 20160330Knowledge Foundation, 20200014AstraZeneca
Note

CC BY 4.0

© 2022 by the authors. Licensee MDPI, Basel, Switzerland.

This research was funded by the Systems Biology Research Centre at the University of Skövde under grants from the Knowledge Foundation (20160294, 20160330, 20200014), Takara Bio Europe, Gothenburg, Sweden, and AstraZeneca R&D, Gothenburg.

Data Availability Statement: This study is based on two trancriptomics datasets, which are available for download at ArrayExpress (https://www.ebi.ac.uk/arrayexpress/, accessed on 4 April 2022) accession numbers: E-MTAB-11030 and E-MEXP-2296.

Acknowledgments :The graphical abstract was created with BioRender software. The networks and functional analyses were generated through the use of IPA (Qiagen Inc., https://www.qiagenbioinformatics.com/products/ingenuity-pathway-analysis, accessed on 1 March 2022)

Available from: 2022-06-02 Created: 2022-06-02 Last updated: 2022-07-12Bibliographically approved

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Johansson, MarkusTangruksa, BenyapaHeydarkhan-Hagvall, SepidehSartipy, PeterSynnergren, Jane

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