Alopecia 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. We 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. Our 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.

中文翻译：

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基于系统模拟的元分析框架，用于预测脱发症的生理生物标志物

脱发或脱发是一种复杂的多基因和心理破坏性疾病，影响全球数以百万计的男女。由于脱发的基因注释和环境知识有限，因此需要对候选生物标志物进行鉴定的系统分析，以为脱发治疗提供潜在的治疗靶标。我们设计了一个交互式框架，以基于差异表达分析，系统生物学和功能蛋白质组学研究进行荟萃分析研究。我们分析了八个公开可用的微阵列数据集，并从p值<0.05的差异表达基因列表中找到了12种潜在的候选生物标记物，包括3种细胞外蛋白。经过表达分析和功能分析后，我们研究了蛋白质之间的相互作用，并观察到源蛋白质（包括WIF1，SPON1，LYZ，GPRC5B，PTPRE，ZFP36L2，HBB，PHF15，LMCD1，KRT35和VAV3）与目标蛋白质（包括APCDD1，WNT1，WNT3A，SHH，ESRI，TGFB1）的功能关联和APP。这些分子的途径分析揭示了它们在主要的生理反应中的作用，包括蛋白质代谢，信号转导，WNT，BMP，EDA，NOTCH和SHH途径。这些途径调节毛发生长，毛囊分化，色素沉着和形态发生。我们研究了β-连环蛋白，Nf-κB，细胞因子和视黄酸在头发生长发育中的调节作用。因此，这些重要蛋白质的差异表达将影响正常水平，并可能导致头发生长异常。我们的整合方法有助于确定生物标记的优先级，从而最终减轻实验研究的经济负担。在基因组数据中发现突变体，以增加对类似问题的新生物标志物的鉴定，也将是有价值的。