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2型糖尿病血流样本中差异表达基因和通路的判别模型构建及比较分析

Developing discriminate model and comparative analysis of differentially expressed genes and pathways for bloodstream samples of diabetes mellitus type 2.

作者信息

Liu Chang, Lu Lili, Kong Quan, Li Yan, Wu Haihua, Yang William, Xu Shandan, Yang Xinyu, Song Xiaolei, Yang Jack Y, Yang Mary, Deng Youping

出版信息

BMC Bioinformatics. 2014;15 Suppl 17(Suppl 17):S5. doi: 10.1186/1471-2105-15-S17-S5. Epub 2014 Dec 16.

Abstract

BACKGROUND

Diabetes mellitus of type 2 (T2D), also known as noninsulin-dependent diabetes mellitus (NIDDM) or adult-onset diabetes, is a common disease. It is estimated that more than 300 million people worldwide suffer from T2D. In this study, we investigated the T2D, pre-diabetic and healthy human (no diabetes) bloodstream samples using genomic, genealogical, and phonemic information. We identified differentially expressed genes and pathways. The study has provided deeper insights into the development of T2D, and provided useful information for further effective prevention and treatment of the disease.

RESULTS

A total of 142 bloodstream samples were collected, including 47 healthy humans, 22 pre-diabetic and 73 T2D patients. Whole genome scale gene expression profiles were obtained using the Agilent Oligo chips that contain over 20,000 human genes. We identified 79 significantly differentially expressed genes that have fold change ≥ 2. We mapped those genes and pinpointed locations of those genes on human chromosomes. Amongst them, 3 genes were not mapped well on the human genome, but the rest of 76 differentially expressed genes were well mapped on the human genome. We found that most abundant differentially expressed genes are on chromosome one, which contains 9 of those genes, followed by chromosome two that contains 7 of the 76 differentially expressed genes. We performed gene ontology (GO) functional analysis of those 79 differentially expressed genes and found that genes involve in the regulation of cell proliferation were among most common pathways related to T2D. The expression of the 79 genes was combined with clinical information that includes age, sex, and race to construct an optimal discriminant model. The overall performance of the model reached 95.1% accuracy, with 91.5% accuracy on identifying healthy humans, 100% accuracy on pre-diabetic patients and 95.9% accuract on T2D patients. The higher performance on identifying pre-diabetic patients was resulted from more significant changes of gene expressions among this particular group of humans, which implicated that patients were having profound genetic changes towards disease development.

CONCLUSION

Differentially expressed genes were distributed across chromosomes, and are more abundant on chromosomes 1 and 2 than the rest of the human genome. We found that regulation of cell proliferation actually plays an important role in the T2D disease development. The predictive model developed in this study has utilized the 79 significant genes in combination with age, sex, and racial information to distinguish pre-diabetic, T2D, and healthy humans. The study not only has provided deeper understanding of the disease molecular mechanisms but also useful information for pathway analysis and effective drug target identification.

摘要

背景

2型糖尿病(T2D),也被称为非胰岛素依赖型糖尿病(NIDDM)或成人发病型糖尿病,是一种常见疾病。据估计,全球超过3亿人患有T2D。在本研究中,我们利用基因组、谱系和音素信息对T2D、糖尿病前期和健康人群(无糖尿病)的血液样本进行了研究。我们鉴定了差异表达基因和相关通路。该研究为T2D的发病机制提供了更深入的见解,并为该疾病的进一步有效预防和治疗提供了有用信息。

结果

共收集了142份血液样本,包括47名健康人、22名糖尿病前期患者和73名T2D患者。使用包含超过20000个人类基因的安捷伦寡核苷酸芯片获得了全基因组规模的基因表达谱。我们鉴定出79个显著差异表达基因,其折叠变化≥2。我们对这些基因进行了定位,并确定了它们在人类染色体上的位置。其中,有3个基因在人类基因组上定位不佳,但其余76个差异表达基因在人类基因组上定位良好。我们发现差异表达基因最丰富的是在1号染色体上,其中包含9个这样的基因,其次是2号染色体,包含76个差异表达基因中的7个。我们对这79个差异表达基因进行了基因本体(GO)功能分析,发现参与细胞增殖调控的基因是与T2D相关的最常见通路之一。将这79个基因的表达与包括年龄、性别和种族在内的临床信息相结合,构建了一个最佳判别模型。该模型的总体准确率达到95.1%,在识别健康人方面的准确率为91.5%,在识别糖尿病前期患者方面的准确率为100%,在识别T2D患者方面的准确率为95.9%。在识别糖尿病前期患者方面表现较高是由于这一特定人群中基因表达的变化更为显著,这表明患者在疾病发展过程中发生了深刻的基因变化。

结论

差异表达基因分布在各条染色体上,在1号和2号染色体上比人类基因组的其他部分更为丰富。我们发现细胞增殖调控实际上在T2D疾病发展中起着重要作用。本研究中开发的预测模型利用了79个显著基因以及年龄、性别和种族信息来区分糖尿病前期、T2D和健康人群。该研究不仅对疾病的分子机制提供了更深入的理解,也为通路分析和有效药物靶点识别提供了有用信息。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5aaf/4304197/7be5515c4495/1471-2105-15-S17-S5-1.jpg

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