Differentially expressed genes in systemic sclerosis: Towards predictive medicine with new molecular tools for clinicians

Keret, Shiri; Rimar, Doron; Lansiaux, Pauline; Feldman, Erik; Lescoat, Alain; Milman, Neta; Farge, Dominique

doi:10.1016/j.autrev.2023.103314

Cited by 7 publications

(2 citation statements)

References 66 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…SSc is a multifaceted disease that exhibits heterogeneity and clinical variability among patients, often complicating diagnosis and decisions regarding treatment. Gene expression profiling using microarray technology integrated with several bioinformatics techniques can provide information about the expression of thousands of genes in the human genome and identify pivot genes related to the pathology, as mentioned in a similar report by Keret et al [ 12 ]. This approach has been successfully used in cancer and infectious disease research [ 13 , 14 ] and recently also in systemic autoimmune disorders [ 15 , 16 ].…”

Section: Global Gene-expression Profiling In Sscmentioning

confidence: 99%

Skin Gene Expression Profiles in Systemic Sclerosis: From Clinical Stratification to Precision Medicine

Benfaremo

Agarbati

Mozzicafreddo

et al. 2023

IJMS

View full text Add to dashboard Cite

Systemic sclerosis, also known as scleroderma or SSc, is a condition characterized by significant heterogeneity in clinical presentation, disease progression, and response to treatment. Consequently, the design of clinical trials to successfully identify effective therapeutic interventions poses a major challenge. Recent advancements in skin molecular profiling technologies and stratification techniques have enabled the identification of patient subgroups that may be relevant for personalized treatment approaches. This narrative review aims at providing an overview of the current status of skin gene expression analysis using computational biology approaches and highlights the benefits of stratifying patients upon their skin gene signatures. Such stratification has the potential to lead toward a precision medicine approach in the management of SSc.

show abstract

Section: Global Gene-expression Profiling In Sscmentioning

confidence: 99%

Skin Gene Expression Profiles in Systemic Sclerosis: From Clinical Stratification to Precision Medicine

Benfaremo

Agarbati

Mozzicafreddo

et al. 2023

IJMS

View full text Add to dashboard Cite

show abstract

“…Recent investigations have utilized machine learning techniques to identify novel biomarkers for various diseases including SSc. However, the application of machine learning methods to construct diagnostic prediction models specific to SSc is currently rarely reported ( 26 , 27 ). Furthermore, in the context of SSc, the immune microenvironment undergoes abnormal changes in immune cells, inflammatory cytokines, autoantibodies and extracellular matrix components, ultimately fostering fibrosis and organ damage ( 28 ).…”

Section: Introductionmentioning

confidence: 99%

Development and validation of a new diagnostic prediction model of ENHO and NOX4 for early diagnosis of systemic sclerosis

Zheng,

Wu,

Chen

et al. 2024

Front. Immunol.

View full text Add to dashboard Cite

ObjectiveSystemic sclerosis (SSc) is a chronic autoimmune disease characterized by fibrosis. The challenge of early diagnosis, along with the lack of effective treatments for fibrosis, contribute to poor therapeutic outcomes and high mortality of SSc. Therefore, there is an urgent need to identify suitable biomarkers for early diagnosis of SSc.MethodsThree skin gene expression datasets of SSc patients and healthy controls were downloaded from Gene Expression Omnibus (GEO) database (GSE130955, GSE58095, and GSE181549). GSE130955 (48 early diffuse cutaneous SSc and 33 controls) were utilized to screen differentially expressed genes (DEGs) between SSc and normal skin samples. Least absolute shrinkage and selection operator (LASSO) regression and support vector machine recursive feature elimination (SVM-RFE) were performed to identify diagnostic genes and construct a diagnostic prediction model. The results were further validated in GSE58095 (61 SSc and 36 controls) and GSE181549 (113 SSc and 44 controls) datasets. Receiver operating characteristic (ROC) curves were applied for assessing the level of diagnostic ability. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was used to verify the diagnostic genes in skin tissues of out cohort (10 SSc and 5 controls). Immune infiltration analysis were performed using CIBERSORT algorithm.ResultsA total of 200 DEGs were identified between SSc and normal skin samples. Functional enrichment analysis revealed that these DEGs may be involved in the pathogenesis of SSc, such as extracellular matrix remodeling, cell-cell interactions, and metabolism. Subsequently, two critical genes (ENHO and NOX4) were identified by LASSO and SVM-RFE. ENHO was found down-regulated while NOX4 was up-regulated in skin of SSc patients and their expression levels were validated by above three datasets and our cohort. Notably, these differential expressions were more pronounced in patients with diffuse cutaneous SSc than in those with limited cutaneous SSc. Next, we developed a novel diagnostic model for SSc using ENHO and NOX4, which demonstrated strong predictive power in above three cohorts and in our own cohort. Furthermore, immune infiltration analysis revealed dysregulated levels of various immune cell subtypes within early SSc skin specimens, and a negative correlation was observed between the levels of ENHO and Macrophages M1 and M2, while a positive correlation was observed between the levels of NOX4 and Macrophages M1 and M2.ConclusionThis study identified ENHO and NOX4 as novel biomarkers that can be serve as a diagnostic prediction model for early detection of SSc and play a potential role in the pathogenesis of the disease.

show abstract

External and internal exposome as triggers of biological signalling in systemic sclerosis – A narrative synthesis

Janssen,

Lemaire,

Sanchez-Calero

et al. 2025

Journal of Autoimmunity

View full text Add to dashboard Cite

Differentially expressed genes in systemic sclerosis: Towards predictive medicine with new molecular tools for clinicians

Cited by 7 publications

References 66 publications

Skin Gene Expression Profiles in Systemic Sclerosis: From Clinical Stratification to Precision Medicine

Skin Gene Expression Profiles in Systemic Sclerosis: From Clinical Stratification to Precision Medicine

Development and validation of a new diagnostic prediction model of ENHO and NOX4 for early diagnosis of systemic sclerosis

External and internal exposome as triggers of biological signalling in systemic sclerosis – A narrative synthesis

Contact Info

Product

Resources

About