A systematic simulation-based meta-analytical framework for prediction of physiological biomarkers in alopecia

Syed Aun Muhammad,Jake Y Chen,Amjad Ali,Nighat Fatima,Rehan Zafar Paracha

doi:10.1186/s40709-019-0094-x

Abstract

BackgroundAlopecia or hair loss is a complex polygenetic and psychologically devastating disease affecting millions of men and women globally. Since the gene annotation and environmental knowledge is limited for alopecia, a systematic analysis for the identification of candidate biomarkers is required that could provide potential therapeutic targets for hair loss therapy.ResultsWe designed an interactive framework to perform a meta-analytical study based on differential expression analysis, systems biology, and functional proteomic investigations. We analyzed eight publicly available microarray datasets and found 12 potential candidate biomarkers including three extracellular proteins from the list of differentially expressed genes with a p-value < 0.05. After expression profiling and functional analysis, we studied protein–protein interactions and observed functional associations of source proteins including WIF1, SPON1, LYZ, GPRC5B, PTPRE, ZFP36L2, HBB, PHF15, LMCD1, KRT35 and VAV3 with target proteins including APCDD1, WNT1, WNT3A, SHH, ESRI, TGFB1, and APP. Pathway analysis of these molecules revealed their role in major physiological reactions including protein metabolism, signal transduction, WNT, BMP, EDA, NOTCH and SHH pathways. These pathways regulate hair growth, hair follicle differentiation, pigmentation, and morphogenesis. We studied the regulatory role of β-catenin, Nf-kappa B, cytokines and retinoic acid in the development of hair growth. Therefore, the differential expression of these significant proteins would affect the normal level and could cause aberrations in hair growth.ConclusionOur integrative approach helps to prioritize the biomarkers that ultimately lessen the economic burden of experimental studies. It will also be valuable to discover mutants in genomic data in order to increase the identification of new biomarkers for similar problems.

Highlights

Alopecia or hair loss is a complex polygenetic and psychologically devastating disease affecting millions of men and women globally
The gene expression profiling of the epidermal and dermal keratinocytes is responsible for the activation of adipogenic factors and the levels of chemical-markers [58] including WIF1, GPRC5B, PTPRE, and LIM and cysteine-rich domains 1 (LMCD1)
Results cDNA datasets and normalization We accessed eight Gene Expression Omnibus (GEO) datasets related to alopecia, hair loss, and scalp hair follicles cases

Summary

Introduction

Alopecia or hair loss is a complex polygenetic and psychologically devastating disease affecting millions of men and women globally. Alopecia or baldness is characterized by either patchy or complete loss of scalp hair [1] It affects millions of men and women of all ethnic backgrounds and can be psychologically shocking. Biomarkers are quantifiable traits that can be used to analyze normal as well as pathological processes [6] These candidate molecules can be used for prediction of relapse, screening and to examine the response of the treatment of hair loss. The gene expression profiling of the epidermal and dermal keratinocytes is responsible for the activation of adipogenic factors and the levels of chemical-markers [58] including WIF1, GPRC5B, PTPRE, and LMCD1 These biological molecules are known to regulate hair growth cycle. Recent progress in genomics and proteomics analysis enabled the identification of many proteins and the discovery of new biomarkers

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Discussion

Conclusion