Proteomic Biomarkers Prognostic for Diabetic Wound Healing

项目总结/摘要 伤口愈合受损是糖尿病中令人担忧的问题，归因于超过750，000例糖尿病患者的发展。 糖尿病足溃疡（DFU）和70,000下肢截肢每年在美国。DFU是一个 具有可变临床病程的异质性疾病，并且存在识别受试者的迫切且未满足的需求 伤口愈合受损的风险更高我们对循环的初步合作研究 蛋白质组指出了DFU过程的感兴趣的候选生物标志物蛋白质预后。在我们的试点中，研究 我们已经鉴定出与预期的伤口愈合结果相关的蛋白质， 如粘附分子、羧肽酶和炎性蛋白等。途径网络 接种这些蛋白质的分析显示，β连环蛋白和细胞Myc（c-Myc）是最相关的 结有趣的是，Wnt/β catenin通路和转录因子c-Myc广泛地参与了 在DFU中发挥作用，包括糖尿病足联盟（DFC）研究者的工作。DFU课程是 异质性和多因素，因此我们假设多生物标志物组将提供最佳 精确化。蛋白质水平反映的生物过程往往是相互关联的，因此我们假设， 通过采用并行的生物统计和机器学习方法，将为我们提供构建鲁棒签名的工具。 该提案有三个目标：目标1（R61阶段）将专注于精炼候选蛋白质样本， 糖尿病受试者的预期伤口愈合。我们将进行半目标测量我们的 DFC队列受试者中的候选蛋白（在我们的初步研究中鉴定）， 病程。我们将采用由机器学习方法支持的生物统计工具，足以 关联数据以最终确定关键蛋白质样本。在目标2（R61阶段），我们将制定一个重点突出， 蛋白质组学生物标志物特征包括10-12种蛋白质的靶向定量组。为此， 我们将进行广泛的分析验证，随后我们将建立一个重点和定量的多， 生物标志物组。最后，在目标3（R33阶段）将开始确定我们的预后评估的临床效用。 DFU课程的生物标志物签名。为此，我们将在实验室中进行靶向生物标志物测量。 DFC队列。我们将采用严格的生物统计学指标来评估生物标志物签名的性能 （综合辨别能力、Akake标准等）。该项目的进展将确定和 最初确定生物标志物特征对于预期糖尿病伤口愈合的预后值 当然了这些努力将通过提供客观的证据来帮助改进具有感兴趣的表型的目标群体。 可量化的指标，可以提高2/3期研究的临床试验入组标准。

PROJECT SUMMARY / ABSTRACT Impaired wound healing is an alarming problem in diabetes, attributed to the development of over 750,000 diabetic foot ulcerations (DFU) and 70,000 lower extremity amputations per year in the USA. DFU is a heterogeneous disease with a variable clinical course and there is an urgent, unmet need to identify subjects who are at higher risk of an impaired wound healing. Our preliminary collaborative study of the circulating proteome points to the interesting candidate biomarker proteins prognostic of the DFU course. In our pilot, study we have identified proteins associated with the prospective wound healing outcome belonging to such classes as adhesion molecules, carboxypeptidases, and inflammatory proteins among others. Pathway network analyses seeded with these proteins revealed β catenin and cellular Myc (c-Myc) among the most connected nodes. Interestingly, Wnt/β catenin pathway and transcription factor, c-Myc have been extensively implicated to play a role in the DFU including work of the Diabetic Foot Consortium (DFC) investigators. The DFU course is heterogeneous and multi-factorial, thus we hypothesize that multi-biomarker panel will offer the most optimal prognostication. Biological processes reflected by protein levels are often connected, thus we hypothesize that by employing parallal biostatistical and machine learning approach will offer us tools to build a robust signature. This proposal has the three aims: Aim 1 (R61 phase) will focus on refining candidate protein exemplars for a prospective wound healing among subjects with diabetes. We will perform semi-targeted measurements of our candidate proteins (identified in our preliminary study) in subjects of the DFC cohort followed for a 3-month disease course. We will employ biostatistical tools supported by machine learning methods, adequate for correlated data to finally determine key protein exemplars. In Aim 2, (R61 phase), we will develop a focused, proteomics biomarker signature comprising of a targeted quantitative panel of 10-12 proteins. For that purpose, we will perform an extensive analytical validation and subsequently we will build a focused and quantitative multi- biomarker panel. Finally, in Aim 3 (R33 phase) will initiate determination of the clinical utility of our prognostic biomarker signature for the DFU course. To this end, we will perform targeted biomarker measurements in the DFC cohort. We will employ rigorous biostatistical metrics to evaluate the biomarker signature’s performance (integrated discrimination ability, Akake criterion among others). Advancements in this project will identify and initially determine a value of the biomarker signature prognostic for the prospective diabetic wound healing course. These efforts will aid in refining a target population with a phenotype of interest by providing objective quantifiable metrics that can enhance clinical trial enrollment criteria for Phase 2/3 studies.