Mohammadi Majd Tahereh, Kalantari Shiva, Raeisi Shahraki Hadi, Nafar Mohsen, Almasi Afshin, Samavat Shiva, Parvin Mahmoud, Hashemian Amirhossein
Department of Biostatistics and Epidemiology, Kermanshah University of Medical Sciences, School of Public Health, Kermanshah, Iran.
Chronic Kidney Disease Research Center, Labbafinejad Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Iran Biomed J. 2018 Nov;22(6):374-84. doi: 10.29252/.22.6.374. Epub 2018 Mar 10.
IgA nephropathy (IgAN) is the most common primary glomerulonephritis diagnosed based on renal biopsy. Mesangial IgA deposits along with the proliferation of mesangial cells are the histologic hallmark of IgAN. Non-invasive diagnostic tools may help to prompt diagnosis and therapy. The discovery of potential and reliable urinary biomarkers for diagnosis of IgAN depends on applying robust and suitable models. Applying two multivariate modeling methods on a urine proteomic dataset were obtained from IgAN patients, and comparison of the results of these methods were the purpose of this study.
Two models were constructed for urinary protein profiles of 13 patients and 8 healthy individuals, based on sparse linear discriminant analysis (SLDA) and elastic net (EN) regression methods. A panel of selected biomarkers with the best coefficients were proposed and further analyzed for biological relevance using functional annotation and pathway analysis.
Transferrin, α1-antitrypsin, and albumin fragments were the most important up-regulated biomarkers, while fibulin-5, YIP1 family member 3, prasoposin, and osteopontin were the most important down-regulated biomarkers. Pathway analysis revealed that complement and coagulation cascades and extracellular matrix-receptor interaction pathways impaired in the pathogenesis of IgAN.
SLDA and EN had an equal importance for diagnosis of IgAN and were useful methods for exploring and processing proteomic data. In addition, the suggested biomarkers are reliable candidates for further validation to non-invasive diagnose of IgAN based on urine examination.
IgA 肾病（IgAN）是基于肾活检诊断的最常见的原发性肾小球肾炎。系膜 IgA 沉积以及系膜细胞增殖是 IgA 肾病的组织学特征。非侵入性诊断工具可能有助于促进诊断和治疗。发现用于诊断 IgA 肾病的潜在且可靠的尿液生物标志物取决于应用强大且合适的模型。本研究旨在对从 IgA 肾病患者获得的尿液蛋白质组数据集应用两种多变量建模方法，并比较这些方法的结果。
基于稀疏线性判别分析（SLDA）和弹性网（EN）回归方法，为 13 例患者和 8 名健康个体的尿液蛋白质谱构建了两个模型。提出了一组具有最佳系数的选定生物标志物，并使用功能注释和通路分析进一步分析其生物学相关性。
转铁蛋白、α1 - 抗胰蛋白酶和白蛋白片段是最重要的上调生物标志物，而纤连蛋白 - 5、YIP1 家族成员 3、脯氨酰寡肽酶和骨桥蛋白是最重要的下调生物标志物。通路分析表明补体和凝血级联以及细胞外基质 - 受体相互作用通路在 IgA 肾病的发病机制中受损。
SLDA 和 EN 在 IgA 肾病的诊断中具有同等重要性，是探索和处理蛋白质组数据的有用方法。此外，所建议的生物标志物是基于尿液检查对 IgA 肾病进行非侵入性诊断进一步验证的可靠候选物。
