A new nucleosomic-based model to identify and diagnose SSc-ILD.
Journal
Clinical epigenetics
ISSN: 1868-7083
Titre abrégé: Clin Epigenetics
Pays: Germany
ID NLM: 101516977
Informations de publication
Date de publication:
17 08 2020
17 08 2020
Historique:
received:
21
04
2020
accepted:
30
07
2020
entrez:
19
8
2020
pubmed:
19
8
2020
medline:
27
10
2021
Statut:
epublish
Résumé
Systemic sclerosis (SSc) is a rare connective tissue disease associated with rapid evolving interstitial lung disease (SSc-ILD), driving its mortality. Specific biomarkers associated with the evolution of the lung disease are highly needed. We aimed to identify specific biomarkers of SSc-ILD to predict the evolution of the disease. Nucleosomes are stable DNA/protein complexes that are shed into the blood stream making them ideal candidates for biomarkers. We studied circulating cell-free nucleosomes (cf-nucleosomes) in SSc patients, 31 with ILD (SSc-ILD) and 67 without ILD. We analyzed plasma levels for cf-nucleosomes and investigated whether global circulating nucleosome levels in association with or without other biomarkers of interest for systemic sclerosis or lung fibrosis (e.g., serum growth factors: IGFBP-1 and the MMP enzyme: MMP-9), could be suitable potential biomarkers for the correct identification of SSc-ILD disease. We found that H3.1 nucleosome levels were significantly higher in patients with SSc-ILD compared SSc patients without ILD (p < 0.05) and levels of MMP-9 were significantly increased in patients with SSc-ILD compared to SSc patients without ILD (p < 0.05). Conversely, IGFBP-1 was significantly reduced in patients with SSc-ILD compared to SSc without ILD (p < 0.001). The combination of cf-nucleosomes H3.1 coupled to MMP-9 and IGFBP-1 increased the sensitivity for the differential detection of SSc-ILD. High levels of accuracy were reached with this combined model: its performances are strong with 68.4% of positive predictive value and 77.2% of negative predictive value for 90% of specificity. With our model, we identified a significant negative correlation with FVC % pred (r = -0.22) and TLC % pred (r = -0.31). The value of our model at T1 (baseline) has a predictive power over the Rodnan score at T2 (after 6-18 months), showed by a significant linear regression with R In our study, we identified a new blood-based model with nucleosomic biomarker in order to diagnose SSc-ILD in a SSc cohort. This model is correlated with TLC and FVC at baseline and predictive of the skin evolution and the DLCO. Further longitudinal exploration studies should be performed in order to evaluate the potential of such diagnostic and predictive model.
Sections du résumé
BACKGROUND
Systemic sclerosis (SSc) is a rare connective tissue disease associated with rapid evolving interstitial lung disease (SSc-ILD), driving its mortality. Specific biomarkers associated with the evolution of the lung disease are highly needed. We aimed to identify specific biomarkers of SSc-ILD to predict the evolution of the disease. Nucleosomes are stable DNA/protein complexes that are shed into the blood stream making them ideal candidates for biomarkers.
METHODS
We studied circulating cell-free nucleosomes (cf-nucleosomes) in SSc patients, 31 with ILD (SSc-ILD) and 67 without ILD. We analyzed plasma levels for cf-nucleosomes and investigated whether global circulating nucleosome levels in association with or without other biomarkers of interest for systemic sclerosis or lung fibrosis (e.g., serum growth factors: IGFBP-1 and the MMP enzyme: MMP-9), could be suitable potential biomarkers for the correct identification of SSc-ILD disease.
RESULTS
We found that H3.1 nucleosome levels were significantly higher in patients with SSc-ILD compared SSc patients without ILD (p < 0.05) and levels of MMP-9 were significantly increased in patients with SSc-ILD compared to SSc patients without ILD (p < 0.05). Conversely, IGFBP-1 was significantly reduced in patients with SSc-ILD compared to SSc without ILD (p < 0.001). The combination of cf-nucleosomes H3.1 coupled to MMP-9 and IGFBP-1 increased the sensitivity for the differential detection of SSc-ILD. High levels of accuracy were reached with this combined model: its performances are strong with 68.4% of positive predictive value and 77.2% of negative predictive value for 90% of specificity. With our model, we identified a significant negative correlation with FVC % pred (r = -0.22) and TLC % pred (r = -0.31). The value of our model at T1 (baseline) has a predictive power over the Rodnan score at T2 (after 6-18 months), showed by a significant linear regression with R
CONCLUSION
In our study, we identified a new blood-based model with nucleosomic biomarker in order to diagnose SSc-ILD in a SSc cohort. This model is correlated with TLC and FVC at baseline and predictive of the skin evolution and the DLCO. Further longitudinal exploration studies should be performed in order to evaluate the potential of such diagnostic and predictive model.
Identifiants
pubmed: 32807242
doi: 10.1186/s13148-020-00915-4
pii: 10.1186/s13148-020-00915-4
pmc: PMC7430109
doi:
Substances chimiques
Biomarkers
0
Insulin-Like Growth Factor Binding Protein 1
0
Nucleosomes
0
Matrix Metalloproteinase 9
EC 3.4.24.35
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
124Références
J Rheumatol. 2008 Dec;35(12):2363-71
pubmed: 19004037
Ann N Y Acad Sci. 2008 Aug;1137:180-9
pubmed: 18837945
Arthritis Res Ther. 2005;7(1):R71-9
pubmed: 15642145
J Autoimmun. 2017 Sep;83:73-94
pubmed: 28526340
J Scleroderma Relat Disord. 2017 Jan-Apr;2(1):11-18
pubmed: 28516167
Gen Comp Endocrinol. 2005 May 15;142(1-2):44-52
pubmed: 15862547
N Engl J Med. 2012 May 24;366(21):1968-77
pubmed: 22607134
Eur Respir Rev. 2013 Sep 1;22(129):236-43
pubmed: 23997050
Chest. 2020 Aug;158(2):646-659
pubmed: 32268131
Arthritis Res Ther. 2018 Aug 29;20(1):187
pubmed: 30157947
Am J Respir Crit Care Med. 2017 Jun 15;195(12):1640-1650
pubmed: 28085486
Respir Med. 2007 Oct;101(10):2199-206
pubmed: 17643278
Anticancer Res. 2014 May;34(5):2357-62
pubmed: 24778043
Eur Respir J. 2018 Aug 2;52(2):
pubmed: 29946005
Clin Epigenetics. 2015 Oct 07;7:106
pubmed: 26451166
Epigenetics. 2012 Sep;7(9):987-93
pubmed: 22894907
Rev Med Liege. 2019 Jan;74(1):47-53
pubmed: 30680974
Cytokine. 2017 Nov;99:1-8
pubmed: 28668545
Int J Cancer. 2001 Mar 20;95(2):114-20
pubmed: 11241322
BMC Pulm Med. 2016 May 23;16(1):86
pubmed: 27215343
J Clin Med. 2019 Aug 29;8(9):
pubmed: 31470655
Front Immunol. 2015 Jun 08;6:272
pubmed: 26106387
Lung. 2017 Jun;195(3):273-280
pubmed: 28353114
Front Endocrinol (Lausanne). 2018 Aug 30;9:499
pubmed: 30214426
PLoS One. 2017 Feb 8;12(2):e0171344
pubmed: 28178340
Clin Sci (Lond). 1994 Feb;86(2):141-8
pubmed: 8143424
N Engl J Med. 2019 Jun 27;380(26):2518-2528
pubmed: 31112379
Arthritis Rheumatol. 2014 Feb;66(2):397-406
pubmed: 24504812
Ann Rheum Dis. 2016 Oct;75(10):1858-65
pubmed: 26567180
Clin Epigenetics. 2017 Aug 15;9:84
pubmed: 28824731
Ann Rheum Dis. 2007 Jul;66(7):940-4
pubmed: 17329309
Rheumatol Int. 2018 Mar;38(3):363-374
pubmed: 29322341
Rheumatology (Oxford). 2019 Sep 1;58(9):1534-1546
pubmed: 31292645
Ann Rheum Dis. 2013 Nov;72(11):1747-55
pubmed: 24092682
RMD Open. 2018 Oct 18;4(Suppl 1):e000782
pubmed: 30402270
Biochem Cell Biol. 2015 Apr;93(2):159-70
pubmed: 25659821
Front Immunol. 2018 Oct 16;9:2390
pubmed: 30386340