Biological heterogeneity in idiopathic pulmonary arterial hypertension identified through unsupervised transcriptomic profiling of whole blood
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Published:2021-12
Issue:1
Volume:12
Page:
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ISSN:2041-1723
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Container-title:Nature Communications
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language:en
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Short-container-title:Nat Commun
Author:
Kariotis SokratisORCID, Jammeh Emmanuel, Swietlik Emilia M., Pickworth Josephine A.ORCID, Rhodes Christopher J.ORCID, Otero Pablo, Wharton JohnORCID, Iremonger JamesORCID, Dunning Mark J.ORCID, Pandya Divya, Mascarenhas Thomas S., Errington NiamhORCID, Thompson A. A. RogerORCID, Romanoski Casey E., Rischard Franz, Garcia Joe G. N., Yuan Jason X.-J., An Tae-Hwi Schwantes, Desai Ankit A., Coghlan Gerry, Lordan Jim, Corris Paul A., Howard Luke S.ORCID, Condliffe Robin, Kiely David G., Church Colin, Pepke-Zaba Joanna, Toshner Mark, Wort Stephen, Gräf StefanORCID, Morrell Nicholas W., Wilkins Martin R.ORCID, Lawrie AllanORCID, Wang DennisORCID, Bleda Marta, Hadinnapola Charaka, Haimel Matthias, Auckland Kate, Tilly Tobias, Martin Jennifer M., Yates Katherine, Treacy Carmen M., Day Margaret, Greenhalgh Alan, Shipley Debbie, Peacock Andrew J., Irvine Val, Kennedy Fiona, Moledina Shahin, MacDonald Lynsay, Tamvaki Eleni, Barnes Anabelle, Cookson Victoria, Chentouf Latifa, Ali Souad, Othman Shokri, Ranganathan Lavanya, Gibbs J. Simon R., DaCosta Rosa, Pinguel Joy, Dormand Natalie, Parker Alice, Stokes Della, Ghedia Dipa, Tan Yvonne, Ngcozana Tanaka, Wanjiku Ivy, Polwarth Gary, Mackenzie Ross Rob V., Suntharalingam Jay, Grover Mark, Kirby Ali, Grove Ali, White Katie, Seatter Annette, Creaser-Myers Amanda, Walker Sara, Roney Stephen, Elliot Charles A., Charalampopoulos Athanasios, Sabroe Ian, Hameed Abdul, Armstrong Iain, Hamilton Neil, Rothman Alex M. K., Swift Andrew J., Wild James M., Soubrier Florent, Eyries Mélanie, Humbert Marc, Montani David, Girerd Barbara, Scelsi Laura, Ghio Stefano, Gall Henning, Ghofrani Ardi, Bogaard Harm J., Vonk Noordegraaf Anton, Houweling Arjan C., Veld Anna Huis in’t, Schotte Gwen,
Abstract
AbstractIdiopathic pulmonary arterial hypertension (IPAH) is a rare but fatal disease diagnosed by right heart catheterisation and the exclusion of other forms of pulmonary arterial hypertension, producing a heterogeneous population with varied treatment response. Here we show unsupervised machine learning identification of three major patient subgroups that account for 92% of the cohort, each with unique whole blood transcriptomic and clinical feature signatures. These subgroups are associated with poor, moderate, and good prognosis. The poor prognosis subgroup is associated with upregulation of the ALAS2 and downregulation of several immunoglobulin genes, while the good prognosis subgroup is defined by upregulation of the bone morphogenetic protein signalling regulator NOG, and the C/C variant of HLA-DPA1/DPB1 (independently associated with survival). These findings independently validated provide evidence for the existence of 3 major subgroups (endophenotypes) within the IPAH classification, could improve risk stratification and provide molecular insights into the pathogenesis of IPAH.
Publisher
Springer Science and Business Media LLC
Subject
General Physics and Astronomy,General Biochemistry, Genetics and Molecular Biology,General Chemistry
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