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基于文献的方法用于多维疾病中基因特征的策展。

A literature-based approach for curating gene signatures in multifaceted diseases.

机构信息

Research Department, Sidra Medicine, Doha, Qatar.

出版信息

J Transl Med. 2020 Jul 10;18(1):279. doi: 10.1186/s12967-020-02408-7.

Abstract

BACKGROUND AND AIMS

The task of identifying a representative and yet manageable target gene list for assessing the pathogenesis of complicated and multifaceted diseases is challenging. Using Inflammatory Bowel Disease (IBD) as an example, we conceived a bioinformatic approach to identify novel genes associated with the various disease subtypes, in combination with known clinical control genes.

METHODS

From the available literature, we used Acumenta Literature Lab (LitLab), network analyses, and LitLab Gene Retriever to assemble a gene pool that has a high likelihood of representing immunity-related subtype-specific signatures of IBD.

RESULTS

We generated six relevant gene lists and 21 intersections that contain genes with unique literature associations to Crohn's Disease (n = 60), Ulcerative Colitis (n = 17), and unclassified (n = 45) subtypes of IBD. From this gene pool, we then filtered and constructed, using network analysis, a final list of 142 genes that are the most representative of the disease and its subtypes.

CONCLUSIONS

In this paper, we present the bioinformatic construction of a gene panel that putatively contains subtype signatures of IBD, a multifactorial disease. These gene signatures will be tested as biomarkers to classify patients with IBD, which has been a clinically challenging task. Such approach to diagnose and monitor complicated disease pathogenesis is a stepping-stone towards personalized care.

摘要

背景与目的

确定具有代表性且易于管理的靶基因列表,以评估复杂和多方面疾病的发病机制是一项具有挑战性的任务。以炎症性肠病(IBD)为例,我们构思了一种生物信息学方法,以识别与各种疾病亚型相关的新基因,同时结合已知的临床对照基因。

方法

我们从现有文献中使用 Acumenta Literature Lab(LitLab)、网络分析和 LitLab Gene Retriever 来组合一个基因库,该基因库极有可能代表与 IBD 的免疫相关亚型特异性特征相关的基因。

结果

我们生成了六个相关的基因列表和 21 个交集,其中包含与克罗恩病(n=60)、溃疡性结肠炎(n=17)和未分类(n=45)IBD 亚型具有独特文献关联的基因。然后,我们从这个基因库中筛选并构建了一个使用网络分析的最终包含 142 个基因的列表,这些基因最能代表疾病及其亚型。

结论

在本文中,我们提出了一种生物信息学构建的基因面板,该面板可能包含 IBD 的亚型特征,这是一种多因素疾病。这些基因特征将作为分类 IBD 患者的生物标志物进行测试,这是一项具有临床挑战性的任务。这种诊断和监测复杂疾病发病机制的方法是迈向个性化护理的一步。

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