Transcriptional insights into pathogenesis of cutaneous systemic sclerosis using pathway driven meta-analysis assisted by machine learning methods.
Computational Biology
Fibrosis
/ genetics
Humans
Interleukin-6
/ genetics
Machine Learning
Phospholipase C gamma
/ genetics
Potassium Channels, Inwardly Rectifying
/ genetics
Receptor, Fibroblast Growth Factor, Type 1
/ genetics
Scleroderma, Systemic
/ genetics
Signal Transduction
/ genetics
Skin
/ metabolism
Toll-Like Receptor 7
/ genetics
Transcription, Genetic
Transcriptome
/ genetics
Journal
PloS one
ISSN: 1932-6203
Titre abrégé: PLoS One
Pays: United States
ID NLM: 101285081
Informations de publication
Date de publication:
2020
2020
Historique:
received:
27
07
2020
accepted:
10
11
2020
entrez:
30
11
2020
pubmed:
1
12
2020
medline:
8
1
2021
Statut:
epublish
Résumé
Pathophysiology of systemic sclerosis (SSc, Scleroderma), an autoimmune rheumatic disease, comprises of mechanisms that drive vasculopathy, inflammation and fibrosis. Understanding of the disease and associated clinical heterogeneity has advanced considerably in the past decade, highlighting the necessity of more specific targeted therapy. While many of the recent trials in SSc failed to meet the primary end points that predominantly relied on changes in modified Rodnan skin scores (MRSS), sub-group analysis, especially those focused on the basal skin transcriptomic data have provided insights into patient subsets that respond to therapies. These findings suggest that deeper understanding of the molecular changes in pathways is very important to define disease drivers in various patient subgroups. In view of these challenges, we performed meta-analysis on 9 public available SSc microarray studies using a novel pathway pivoted approach combining consensus clustering and machine learning assisted feature selection. Selected pathway modules were further explored through cluster specific topological network analysis in search of novel therapeutic concepts. In addition, we went beyond previously described SSc class divisions of 3 clusters (e.g. inflammation, fibro-proliferative, normal-like) and expanded into a much finer stratification in order to profile SSc patients more accurately. Our analysis unveiled an important 80 pathway signatures that differentiated SSc patients into 8 unique subtypes. The 5 pathway modules derived from such signature successfully defined the 8 SSc subsets and were validated by in-silico cellular deconvolution analysis. Myeloid cells and fibroblasts involvement in different clusters were confirmed and linked to corresponding pathway activities. Collectively, our findings revealed more complex disease subtypes in SSc; Key gene mediators such as IL6, FGFR1, TLR7, PLCG2, IRK2 identified by network analysis underscored the scientific rationale for exploring additional targets in treatment of SSc.
Identifiants
pubmed: 33253326
doi: 10.1371/journal.pone.0242863
pii: PONE-D-20-22496
pmc: PMC7703909
doi:
Substances chimiques
IL6 protein, human
0
Interleukin-6
0
Potassium Channels, Inwardly Rectifying
0
TLR7 protein, human
0
Toll-Like Receptor 7
0
inward rectifier potassium channel 2
0
FGFR1 protein, human
EC 2.7.10.1
Receptor, Fibroblast Growth Factor, Type 1
EC 2.7.10.1
PLCG2 protein, human
EC 3.1.4.3
Phospholipase C gamma
EC 3.1.4.3
Types de publication
Journal Article
Meta-Analysis
Langues
eng
Sous-ensembles de citation
IM
Pagination
e0242863Subventions
Organisme : NCATS NIH HHS
ID : KL2 TR003168
Pays : United States
Déclaration de conflit d'intérêts
The authors of the manuscript are employed by the commercial pharmaceutical company Boehringer Ingelheim Pharmaceuticals Inc. This commercial affiliation does not alter our adherence to PLOS ONE policies on sharing data and materials. There are no patents, products in development or marketed products to declare.
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