The William Harvey Research Institute, Barts and The London School of Medicine, Queen Mary University of London, London, United Kingdom.
PLoS One. 2013 Oct 14;8(10):e76235. doi: 10.1371/journal.pone.0076235. eCollection 2013.
Improvement of patient quality of life is the ultimate goal of biomedical research, particularly when dealing with complex, chronic and debilitating conditions such as inflammatory bowel disease (IBD). This is largely dependent on receiving an accurate and rapid diagnose, an effective treatment and in the prediction and prevention of side effects and complications. The low sensitivity and specificity of current markers burden their general use in the clinical practice. New biomarkers with accurate predictive ability are needed to achieve a personalized approach that take the inter-individual differences into consideration.
We performed a high throughput approach using microarray gene expression profiling of colon pinch biopsies from IBD patients to identify predictive transcriptional signatures associated with intestinal inflammation, differential diagnosis (Crohn's disease or ulcerative colitis), response to glucocorticoids (resistance and dependence) or prognosis (need for surgery). Class prediction was performed with self-validating Prophet software package.
Transcriptional profiling divided patients in two subgroups that associated with degree of inflammation. Class predictors were identified with predictive accuracy ranging from 67 to 100%. The expression accuracy was confirmed by real time-PCR quantification. Functional analysis of the predictor genes showed that they play a role in immune responses to bacteria (PTN, OLFM4 and LILRA2), autophagy and endocytocis processes (ATG16L1, DNAJC6, VPS26B, RABGEF1, ITSN1 and TMEM127) and glucocorticoid receptor degradation (STS and MMD2).
We conclude that using analytical algorithms for class prediction discovery can be useful to uncover gene expression profiles and identify classifier genes with potential stratification utility of IBD patients, a major step towards personalized therapy.
提高患者的生活质量是生物医学研究的最终目标，尤其是在处理复杂、慢性和衰弱性疾病（如炎症性肠病）时。这在很大程度上取决于能否获得准确和快速的诊断、有效的治疗以及预测和预防副作用和并发症。当前标志物的低灵敏度和特异性限制了它们在临床实践中的广泛应用。需要具有准确预测能力的新型生物标志物来实现考虑个体间差异的个性化方法。
我们使用高通量方法对炎症性肠病患者的结肠钳夹活检进行了微阵列基因表达谱分析，以鉴定与肠道炎症、鉴别诊断（克罗恩病或溃疡性结肠炎）、对糖皮质激素的反应（耐药和依赖）或预后（需要手术）相关的预测转录特征。使用自验证 Prophet 软件包进行分类预测。
转录谱将患者分为与炎症程度相关的两个亚组。分类预测因子的识别具有 67%至 100%的预测准确性。通过实时 PCR 定量验证了表达的准确性。预测基因的功能分析表明，它们在细菌免疫反应（PTN、OLFM4 和 LILRA2）、自噬和内吞过程（ATG16L1、DNAJC6、VPS26B、RABGEF1、ITSN1 和 TMEM127）以及糖皮质激素受体降解（STS 和 MMD2）中发挥作用。
我们得出结论，使用分类预测发现的分析算法可以用于揭示基因表达谱，并鉴定具有潜在分层效用的分类器基因，这是迈向个体化治疗的重要一步。
