Dataset Information

Distinct molecular subtypes of systemic sclerosis and gene signature with diagnostic capability.

ABSTRACT:

Background

As Systemic Sclerosis (SSc) is a connective tissue ailment that impacts various bodily systems. The study aims to clarify the molecular subtypes of SSc, with the ultimate objective of establishing a diagnostic model that can inform clinical treatment decisions.

Methods

Five microarray datasets of SSc were retrieved from the GEO database. To eliminate batch effects, the combat algorithm was applied. Immune cell infiltration was evaluated using the xCell algorithm. The ConsensusClusterPlus algorithm was utilized to identify SSc subtypes. Limma was used to determine differential expression genes (DEGs). GSEA was used to determine pathway enrichment. A support vector machine (SVM), Random Forest(RF), Boruta and LASSO algorithm have been used to select the feature gene. Diagnostic models were developed using SVM, RF, and Logistic Regression (LR). A ROC curve was used to evaluate the performance of the model. The compound-gene relationship was obtained from the Comparative Toxicogenomics Database (CTD).

Results

The identification of three immune subtypes in SSc samples was based on the expression profiles of immune cells. The utilization of 19 key intersectional DEGs among subtypes facilitated the classification of SSc patients into three robust subtypes (gene_ClusterA-C). Gene_ClusterA exhibited significant enrichment of B cells, while gene_ClusterC showed significant enrichment of monocytes. Moderate activation of various immune cells was observed in gene_ClusterB. We identified 8 feature genes. The SVM model demonstrating superior diagnostic performance. Furthermore, correlation analysis revealed a robust association between the feature genes and immune cells. Eight pertinent compounds, namely methotrexate, resveratrol, paclitaxel, trichloroethylene, formaldehyde, silicon dioxide, benzene, and tetrachloroethylene, were identified from the CTD.

Conclusion

The present study has effectively devised an innovative molecular subtyping methodology for patients with SSc and a diagnostic model based on machine learning to aid in clinical treatment. The study has identified potential molecular targets for therapy, thereby offering novel perspectives for the treatment and investigation of SSc.

SUBMITTER: Wang Q

PROVIDER: S-EPMC10577296 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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Publications

Distinct molecular subtypes of systemic sclerosis and gene signature with diagnostic capability.

Wang Qi Q Li Chen-Long CL Wu Li L Hu Jing-Yi JY Yu Qi Q Zhang Sheng-Xiao SX He Pei-Feng PF

Frontiers in immunology 20231002

<h4>Background</h4>As Systemic Sclerosis (SSc) is a connective tissue ailment that impacts various bodily systems. The study aims to clarify the molecular subtypes of SSc, with the ultimate objective of establishing a diagnostic model that can inform clinical treatment decisions.<h4>Methods</h4>Five microarray datasets of SSc were retrieved from the GEO database. To eliminate batch effects, the combat algorithm was applied. Immune cell infiltration was evaluated using the xCell algorithm. The Co ...[more]

PMID: 37849750

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Project description:BackgroundSystemic sclerosis (SSc; scleroderma) is an uncommon autoimmune rheumatic disease characterised by autoimmunity, vasculopathy and fibrosis. Gene expression profiling distinguishes scleroderma from normal skin, and can detect different subsets of disease, with potential to identify prognostic biomarkers of organ involvement or response to therapy. We have performed gene expression profiling in skin samples from patients with limited cutaneous SSc (lcSSc).MethodsTotal RNA was extracted from clinically uninvolved skin biopsies of 15 patients with lcSSc and 8 healthy controls (HC). Gene expression profiling was performed on a DNA oligonucleotide microarray chip. Differentially expressed genes (DEG) were identified using significance analysis of microarrays (SAM). Functional enrichment analysis of gene signatures was done via g:Profiler.ResultsThere were 218 DEG between lcSSc and HC samples (false discovery rate <10%): 181/218 DEG were upregulated in lcSSc samples. Hierarchical clustering of DEG suggested the presence of two separate groups of lcSSc samples: "limited 1" and "limited 2". The limited-1 group (13 samples, 10 unique patients) showed upregulation of genes involved in cell adhesion, cardiovascular system (CVS) development, extracellular matrix and immune and inflammatory response. The CVS development signature was of particular interest as its genes showed very strong enrichment in response to wounding, response to transforming growth factor (TGF)-β and kinase cascade. Neither limited-2 samples (six samples, five unique patients) nor HC samples showed functional enrichment. There were no significant differences in demographic or clinical parameters between these two groups. These results were confirmed using a second independent cohort.ConclusionsOur study suggests the presence of molecular subsets in lcSSc based on gene expression profiling of biopsies from uninvolved skin. This may reflect important differences in pathogenesis within these patient groups. We identify differential expression of a subset of genes that relate to CVS and are enriched in fibrotic signalling. This may shed light on mechanisms of vascular disease in SSc. The enrichment in profibrotic profile suggests that dysregulated gene expression may contribute to vasculopathy and fibrosis in different disease subsets.

Project description:BackgroundRhinitis is a complex condition characterized by various subtypes, including allergic rhinitis (AR), which involves inflammatory reactions. The objective of this research was to identify crucial genes associated with inflammatory response that are relevant for the treatment and diagnosis of AR.MethodsWe acquired the AR-related expression datasets (GSE75011 and GSE50223) from the Gene Expression Omnibus (GEO) database. In GSE75011, we compared the gene expression profiles between the HC and AR groups and identified differentially expressed genes (DEGs). By intersecting these DEGs with inflammatory response-related genes (IRGGs), resulting in the identification of differentially expressed inflammatory response-related genes (DIRRGs). Afterwards, we utilized the protein-protein interaction (PPI) network, machine learning algorithms, namely least absolute shrinkage and selection operator (LASSO) regression and random forest, to identify the signature markers. We employed a nomogram to evaluate the diagnostic effectiveness of the method, which has been confirmed through validation using GSE50223. qRT-PCR was used to confirm the expression of diagnostic genes in clinical samples. In addition, a consensus clustering method was employed to categorize patients with AR. Subsequently, extensive investigation was conducted to explore the discrepancies in gene expression, enriched functions and pathways, as well as potential therapeutic drugs among these distinct subtypes.ResultsA total of 22 DIRRGs were acquired, which participated in pathways including chemokine and TNF signaling pathway. Additionally, machine learning algorithms identified NFKBIA, HIF1A, MYC, and CCRL2 as signature genes associated with AR's inflammatory response, indicating their potential as AR biomarkers. The nomogram based on feature genes could offer clinical benefits to AR patients. We discovered two molecular subtypes, C1 and C2, and observed that the C2 subtype exhibited activation of immune- and inflammation-related pathways.ConclusionsNFKBIA, HIF1A, MYC, and CCRL2 are the key genes involved in the inflammatory response and have the strongest association with the advancement of disease in AR. The proposed molecular subgroups could provide fresh insights for personalized treatment of AR.

Dataset Information

Distinct molecular subtypes of systemic sclerosis and gene signature with diagnostic capability.

Background

Methods

Results

Conclusion

Publications

Distinct molecular subtypes of systemic sclerosis and gene signature with diagnostic capability.

Similar Datasets

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