Meta-analysis method for discovering reliable biomarkers by integrating statistical and biological approaches: An application to liver toxicity
| Autor: | Hyosil Kim, Hyeyoung Cho, Deokyeon Jo, Dokyun Na, Doheon Lee, So Youn Kim |
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| Jazyk: | angličtina |
| Předmět: |
0301 basic medicine
Biophysics Computational biology Biology Effect size Bioinformatics Sensitivity and Specificity Biochemistry 03 medical and health sciences 0302 clinical medicine Meta-Analysis as Topic Robustness (computer science) Protein Phosphatase 1 False positive paradox Data Mining Humans Computer Simulation Biomarker discovery Molecular Biology Models Statistical Gene Expression Profiling Intracellular Signaling Peptides and Proteins Nuclear Proteins Reproducibility of Results Cell Biology ZWINT Fold change Systems Integration Gene expression profiling Meta-analysis 030104 developmental biology Data Interpretation Statistical 030220 oncology & carcinogenesis Drug liver toxicity Biomarker (medicine) Chemical and Drug Induced Liver Injury Multidrug Resistance-Associated Proteins Biomarkers Software |
| Zdroj: | Biochemical and Biophysical Research Communications. (2):274-281 |
| ISSN: | 0006-291X |
| DOI: | 10.1016/j.bbrc.2016.01.082 |
| Popis: | Biomarkers that are identified from a single study often appear to be biologically irrelevant or false positives. Meta-analysis techniques allow integrating data from multiple studies that are related but independent in order to identify biomarkers across multiple conditions. However, existing biomarker meta-analysis methods tend to be sensitive to the dataset being analyzed. Here, we propose a meta-analysis method, iMeta, which integrates t-statistic and fold change ratio for improved robustness. For evaluation of predictive performance of the biomarkers identified by iMeta, we compare our method with other meta-analysis methods. As a result, iMeta outperforms the other methods in terms of sensitivity and specificity, and especially shows robustness to study variance increase; it consistently shows higher classification accuracy on diverse datasets, while the performance of the others is highly affected by the dataset being analyzed. Application of iMeta to 59 drug-induced liver injury studies identified three key biomarker genes: Zwint, Abcc3, and Ppp1r3b. Experimental evaluation using RT-PCR and qRT-PCR shows that their expressional changes in response to drug toxicity are concordant with the result of our method. iMeta is available at http://imeta.kaist.ac.kr/index.html. |
| Databáze: | OpenAIRE |
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