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Data Mining Identifies CCN2 and THBS1 as Biomarker Candidates for Cardiac Hypertrophy
Högskolan i Skövde, Institutionen för biovetenskap. Högskolan i Skövde, Forskningsmiljön Systembiologi. 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
Högskolan i Skövde, Institutionen för biovetenskap. Högskolan i Skövde, Forskningsmiljön Systembiologi. (Translational Bioinformatics)ORCID-id: 0009-0002-5129-5374
Högskolan i Skövde, Institutionen för biovetenskap. Högskolan i Skövde, Forskningsmiljön Systembiologi. 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 (Engelska)Ingår i: Life, E-ISSN 2075-1729, Vol. 12, nr 5, artikel-id 726Artikel i tidskrift (Refereegranskat) 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.

Ort, förlag, år, upplaga, sidor
MDPI, 2022. Vol. 12, nr 5, artikel-id 726
Nyckelord [en]
biomarker, cardiac hypertrophy, disease model, endothelin-1, stem cells, transcriptomics
Nationell ämneskategori
Cell- och molekylärbiologi Medicinsk bioteknologi (med inriktning mot cellbiologi (inklusive stamcellsbiologi), molekylärbiologi, mikrobiologi, biokemi eller biofarmaci) Bioinformatik och beräkningsbiologi
Forskningsämne
Bioinformatik
Identifikatorer
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
Forskningsfinansiär
KK-stiftelsen, 20160294KK-stiftelsen, 20160330KK-stiftelsen, 20200014AstraZeneca
Anmärkning

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)

Tillgänglig från: 2022-06-02 Skapad: 2022-06-02 Senast uppdaterad: 2025-09-29Bibliografiskt granskad

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

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