Limited cutaneous systemic sclerosis skin demonstrates distinct molecular subsets separated by a cardiovascular development gene expression signature

Derrett-Smith, Emma; Martyanov, Viktor; Chighizola, Cecilia Beatrice; Moinzadeh, Pia; Campochiaro, Corrado; Khan, Korsa; Wood, Tammara A.; Meroni, Pier Luigi; Abraham, David; Ong, Voon H; Lafyatis, Robert; Whitfield, Michael L.; Denton, Christopher P.

doi:10.1186/s13075-017-1360-7

Cited by 13 publications

(9 citation statements)

References 27 publications

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“…In our study, the GO term analysis results indicated that the downregulated DEGs were mainly enriched in response to wounding, regulation of cell proliferation, blood vessel morphogenesis, cytokine activity, and extracellular matrix, and the upregulated DEGs were significantly enriched in multicellular organismal metabolic process, cell division, cell cycle phase, metalloendopeptidase activity, and spindle. Response to wounding means a change in the state or activity of a cell or a stimulus indicating damage to the organism, which is closely related to the occurrence and development of various diseases 18-20. Dysfunction of the regulation of cell proliferation, blood vessel morphogenesis, cell division and cell cycle are critical causes of tumorigenesis and cancer progression 21-23.…”

Section: Discussionmentioning

confidence: 99%

Identification of Key Genes and Pathways in Female Lung Cancer Patients Who Never Smoked by a Bioinformatics Analysis

Shi¹,

Li²,

Yang³

et al. 2019

J. Cancer

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Smoking is considered the major risk factor for lung cancer, but only a small portion of female lung adenocarcinoma patients are associated with smoking. Thus, identifying crucial genes and pathways related to nonsmoking female lung cancer patients is of great importance. Gene expression profiles were downloaded from the Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA) databases. The R software packages were applied to screen the differentially expressed genes (DEGs). GO term enrichment and KEGG pathway analyses were carried out using DAVID tools. The protein-protein interaction (PPI) network was constructed by Cytoscape software. In total, 487 downregulated and 199 upregulated DEGs were identified. The down-regulated DEGs were mainly enriched for behavior and response to wounding, and the upregulated DEGs were significantly enriched for multicellular organismal metabolic process and cell division. The KEGG pathway analysis revealed that the downregulated DEGs were significantly enriched for cell adhesion molecules and neuroactive ligand-receptor interaction, while the upregulated DEGs were mainly enriched for cell cycle and the p53 signaling pathway. The top 10 hub genes and top 3 gene interaction modules were selected from the PPI network. Of the ten hub genes, a high expression of five genes was related to a poor OS in female lung cancer patients who never smoked, including IL6, CXCR2, FPR2, PPBP and HBA1. However, a low expression of GNG11, LRRK2, CDH5, CAV1 and SELE was associated with a worse OS for the female lung cancer patients who never smoked. In conclusion, our study provides novel insight for a better understanding of the pathogenesis of nonsmoking female lung cancer, and these identified DEGs may serve as biomarkers for diagnostics and treatment.

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Section: Discussionmentioning

confidence: 99%

Identification of Key Genes and Pathways in Female Lung Cancer Patients Who Never Smoked by a Bioinformatics Analysis

Shi¹,

Li²,

Yang³

et al. 2019

J. Cancer

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“…The 28 genes and promoters identified in this study (Table 2) were compared to results from published genome-wide DNA methylation [13] and gene expression studies [39][40][41][42][43][44][45][46][47][48][49] in cultured dermal fibroblasts or skin biopsies. Of our top genes and promoters, two CpGs in distal-less homeobox 5 (DLX5) were reported as hypermethylated in skin fibroblasts from dcSSc patients [13], which is consistent with our results.…”

Section: Comparison Of Dna Methylation With Previous Reports In Dermal Tissuesmentioning

confidence: 99%

Differential DNA Methylation Landscape in Skin Fibroblasts from African Americans with Systemic Sclerosis

Frost

Silveira

Hazard

et al. 2021

Genes

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The etiology and reasons underlying the ethnic disparities in systemic sclerosis (SSc) remain unknown. African Americans are disproportionally affected by SSc and yet are underrepresented in research. The aim of this study was to comprehensively investigate the association of DNA methylation levels with SSc in dermal fibroblasts from patients of African ancestry. Reduced representation bisulfite sequencing (RRBS) was performed on primary dermal fibroblasts from 15 SSc patients and 15 controls of African ancestry, and over 3.8 million CpG sites were tested for differential methylation patterns between cases and controls. The dermal fibroblasts from African American patients exhibited widespread reduced DNA methylation. Differentially methylated CpG sites were most enriched in introns and intergenic regions while depleted in 5′ UTR, promoters, and CpG islands. Seventeen genes and eleven promoters showed significant differential methylation, mostly in non-coding RNA genes and pseudogenes. Gene set enrichment analysis (GSEA) and gene ontology (GO) analyses revealed an enrichment of pathways related to interferon signaling and mesenchymal differentiation. The hypomethylation of DLX5 and TMEM140 was accompanied by these genes’ overexpression in patients but underexpression for lncRNA MGC12916. These data show that differential methylation occurs in dermal fibroblasts from African American patients with SSc and identifies novel coding and non-coding genes.

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“…Gene expression profiling of the whole transcriptome is increasingly being used to explore disease-related genes and enable disease classification and clinical prediction (5,6). Derrett-Smith et al (7) conducted gene expression profiling analysis of unaffected skin obtained from patients with localized cutaneous systemic sclerosis (lcSSc). The results showed that the differentially expressed genes (DEGs) are related to cardiovascular system and mainly enriched in fibrotic signaling pathways.…”

Section: Introductionmentioning

confidence: 99%

Screening and identification of biomarkers for systemic sclerosis via microarray technology

Meng

Duan

et al. 2019

Int J Mol Med

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Systemic sclerosis (SSc) is a complex autoimmune disease. The pathogenesis of SSc is currently unclear, although like other rheumatic diseases its pathogenesis is complicated. However, the ongoing development of bioinformatics technology has enabled new approaches to research this disease using microarray technology to screen and identify differentially expressed genes (DEGs) in the skin of patients with SSc compared with individuals with healthy skin. Publicly available data were downloaded from the Gene Expression Omnibus (GEO) database and intra-group data repeatability tests were conducted using Pearson's correlation test and principal component analysis. DEGs were identified using an online tool, GEO2R. Functional annotation of DEGs was performed using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Finally, the construction and analysis of the protein-protein interaction (PPI) network and identification and analysis of hub genes was carried out. A total of 106 DEGs were detected by the screening of SSc and healthy skin samples. A total of 10 genes [interleukin-6, bone morphogenetic protein 4, calumenin (CALU), clusterin, cysteine rich angiogenic inducer 61, serine protease 23, secretogranin II, suppressor of cytokine signaling 3, Toll-like receptor 4 (TLR4), tenascin C] were identified as hub genes with degrees ≥10, and which could sensitively and specifically predict SSc based on receiver operator characteristic curve analysis. GO and KEGG analysis showed that variations in hub genes were mainly enriched in positive regulation of nitric oxide biosynthetic processes, negative regulation of apoptotic processes, extracellular regions, extracellular spaces, cytokine activity, chemo-attractant activity, and the phosphoinositide 3 kinase-protein kinase B signaling pathway. In summary, bioinformatics techniques proved useful for the screening and identification of biomarkers of disease. A total of 106 DEGs and 10 hub genes were linked to SSc, in particular the TLR4 and CALU genes.

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Limited cutaneous systemic sclerosis skin demonstrates distinct molecular subsets separated by a cardiovascular development gene expression signature

Cited by 13 publications

References 27 publications

Identification of Key Genes and Pathways in Female Lung Cancer Patients Who Never Smoked by a Bioinformatics Analysis

Identification of Key Genes and Pathways in Female Lung Cancer Patients Who Never Smoked by a Bioinformatics Analysis

Differential DNA Methylation Landscape in Skin Fibroblasts from African Americans with Systemic Sclerosis

Screening and identification of biomarkers for systemic sclerosis via microarray technology

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