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Integrated bioinformatic analysis of gene expression profiling data to identify combinatorial biomarkers in inflammatory skin disease.

ABSTRACT: Selection of appropriate biomarker to identify inflammatory skin diseases is complicated by the involvement of thousands of differentially expressed genes (DEGs) across multiple cell types and organs. This study aimed to identify combinatorial biomarkers in inflammatory skin diseases. From one gene expression microarray profiling dataset, we performed bioinformatic analyses on dataset from lesional skin biopsies of patients with inflammatory skin diseases (atopic dermatitis [AD], contact eczema [KE], lichen planus [Li], psoriasis vulgaris [Pso]) and healthy controls to identify the involved pathways, predict upstream regulators, and potential measurable extracellular biomarkers. Overall, 434, 629, 581, and 738 DEGs were mapped in AD, KE, Li, and Pso, respectively; 238 identified DEGs were shared among four different inflammatory skin diseases. Bioinformatic analysis on four inflammatory skin diseases showed significant activation of pathways with known pathogenic relevance. Common upstream regulators, with upregulated predicted activity, identified were CNR1 and BMP4. We found the following common serum biomarkers: ACR, APOE, ASIP, CRISP1, DKK1, IL12B, IL9, MANF, MDK, NRTN, PCSK5, and VEGFC. Considerable differences of gene expression changes, involved pathways, upstream regulators, and biomarkers were found in different inflammatory skin diseases. Integrated bioinformatic analysis identified 12 potential common biomarkers of inflammatory skin diseases requiring further evaluation.

SUBMITTER: Bang H

PROVIDER: S-EPMC8989986 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature

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Integrated bioinformatic analysis of gene expression profiling data to identify combinatorial biomarkers in inflammatory skin disease.

Bang Heejin H Kim Ja Eun JE Lee Hyun Su HS Park Sang Man SM Park Dong-Joon DJ Lee Eun Jung EJ

Scientific reports 20220407 1

Selection of appropriate biomarker to identify inflammatory skin diseases is complicated by the involvement of thousands of differentially expressed genes (DEGs) across multiple cell types and organs. This study aimed to identify combinatorial biomarkers in inflammatory skin diseases. From one gene expression microarray profiling dataset, we performed bioinformatic analyses on dataset from lesional skin biopsies of patients with inflammatory skin diseases (atopic dermatitis [AD], contact eczema ...[more]

PMID: 35393522

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Project description:BackgroundUnbiased studies using different genome-wide methods have identified a great number of candidate biomarkers for diagnosis and treatment response in pediatric ulcerative colitis (UC). However, clinical translation has been proven difficult. Here, we hypothesized that one reason could be differences between inflammatory responses in an inflamed gut and in peripheral blood cells.MethodsWe performed meta-analysis of gene expression microarray data from intestinal biopsies and whole blood cells (WBC) from pediatric patients with UC and healthy controls in order to identify overlapping pathways, predicted upstream regulators, and potential biomarkers.ResultsAnalyses of profiling datasets from colonic biopsies showed good agreement between different studies regarding pathways and predicted upstream regulators. The most activated predicted upstream regulators included TNF, which is known to have a key pathogenic and therapeutic role in pediatric UC. Despite this, the expression levels of TNF were increased in neither colonic biopsies nor WBC. A potential explanation was increased expression of TNFR2, one of the membrane-bound receptors of TNF in the inflamed colon. Further analyses showed a similar pattern of complex relations between the expression levels of the regulators and their receptors. We also found limited overlap between pathways and predicted upstream regulators in colonic biopsies and WBC. An extended search including all differentially expressed genes that overlapped between colonic biopsies and WBC only resulted in identification of three potential biomarkers involved in the regulation of intestinal inflammation. However, two had been previously proposed in adult inflammatory bowel diseases (IBD), namely, MMP9 and PROK2.ConclusionsOur findings indicate that biomarker identification in pediatric UC is complicated by the involvement of multiple pathways, each of which includes many different types of genes in the blood or inflamed intestine. Therefore, further studies for identification of combinatorial biomarkers are warranted. Our study may provide candidate biomarkers for such studies.

Dataset Information

Integrated bioinformatic analysis of gene expression profiling data to identify combinatorial biomarkers in inflammatory skin disease.

Publications

Integrated bioinformatic analysis of gene expression profiling data to identify combinatorial biomarkers in inflammatory skin disease.

Similar Datasets

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