Dataset Information

Multi-Level Comparative Framework Based on Gene Pair-Wise Expression Across Three Insulin Target Tissues for Type 2 Diabetes.

ABSTRACT: Type 2 diabetes (T2D) is known as a disease caused by gene alterations characterized by insulin resistance, thus the insulin-responsive tissues are of great interest for T2D study. It's of great relevance to systematically investigate commonalities and specificities of T2D among those tissues. Here we establish a multi-level comparative framework across three insulin target tissues (white adipose, skeletal muscle, and liver) to provide a better understanding of T2D. Starting from the ranks of gene expression, we constructed the 'disease network' through detecting diverse interactions to provide a well-characterization for disease affected tissues. Then, we applied random walk with restart algorithm to the disease network to prioritize its nodes and edges according to their association with T2D. Finally, we identified a merged core module by combining the clustering coefficient and Jaccard index, which can provide elaborate and visible illumination of the common and specific features for different tissues at network level. Taken together, our network-, gene-, and module-level characterization across different tissues of T2D hold the promise to provide a broader and deeper understanding for T2D mechanism.

SUBMITTER: Sun S

PROVIDER: S-EPMC6443994 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Publications

Multi-Level Comparative Framework Based on Gene Pair-Wise Expression Across Three Insulin Target Tissues for Type 2 Diabetes.

Sun Shaoyan S Sun Fengnan F Wang Yong Y

Frontiers in genetics 20190326

Type 2 diabetes (T2D) is known as a disease caused by gene alterations characterized by insulin resistance, thus the insulin-responsive tissues are of great interest for T2D study. It's of great relevance to systematically investigate commonalities and specificities of T2D among those tissues. Here we establish a multi-level comparative framework across three insulin target tissues (white adipose, skeletal muscle, and liver) to provide a better understanding of T2D. Starting from the ranks of ge ...[more]

PMID: 30972105

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Project description:BACKGROUND: Precise classification of cancer types is critically important for early cancer diagnosis and treatment. Numerous efforts have been made to use gene expression profiles to improve precision of tumor classification. However, reliable cancer-related signals are generally lacking. METHOD: Using recent datasets on colon and prostate cancer, a data transformation procedure from single gene expression to pair-wise gene expression ratio is proposed. Making use of the internal consistency of each expression profiling dataset this transformation improves the signal to noise ratio of the dataset and uncovers new relevant cancer-related signals (features). The efficiency in using the transformed dataset to perform normal/tumor classification was investigated using feature partitioning with informative features (gene annotation) as discriminating axes (single gene expression or pair-wise gene expression ratio). Classification results were compared to the original datasets for up to 10-feature model classifiers. RESULTS: 82 and 262 genes that have high correlation to tissue phenotype were selected from the colon and prostate datasets respectively. Remarkably, data transformation of the highly noisy expression data successfully led to lower the coefficient of variation (CV) for the within-class samples as well as improved the correlation with tissue phenotypes. The transformed dataset exhibited lower CV when compared to that of single gene expression. In the colon cancer set, the minimum CV decreased from 45.3% to 16.5%. In prostate cancer, comparable CV was achieved with and without transformation. This improvement in CV, coupled with the improved correlation between the pair-wise gene expression ratio and tissue phenotypes, yielded higher classification efficiency, especially with the colon dataset - from 87.1% to 93.5%. Over 90% of the top ten discriminating axes in both datasets showed significant improvement after data transformation. The high classification efficiency achieved suggested that there exist some cancer-related signals in the form of pair-wise gene expression ratio. CONCLUSION: The results from this study indicated that: 1) in the case when the pair-wise expression ratio transformation achieves lower CV and higher correlation to tissue phenotypes, a better classification of tissue type will follow. 2) the comparable classification accuracy achieved after data transformation suggested that pair-wise gene expression ratio between some pairs of genes can identify reliable markers for cancer.

Dataset Information

Multi-Level Comparative Framework Based on Gene Pair-Wise Expression Across Three Insulin Target Tissues for Type 2 Diabetes.

Publications

Multi-Level Comparative Framework Based on Gene Pair-Wise Expression Across Three Insulin Target Tissues for Type 2 Diabetes.

Similar Datasets

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