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Evaluating normalization approaches for the better identification of aberrant microRNAs associated with
Jing Shen1, Qiao Wang1, Irina Gurvich1
1Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University Medical Center, New York, NY 10032, USA.
Aim:
Dysregulated microRNAs (miRNAs) have been identified in hepatocellular carcinoma (HCC), but only a small proportion have been confirmed. An appropriate normalizer is crucial to determining the accuracy and reliability of data from miRNA studies.
Methods:
Different normalization strategies were used to validate genome-wide miRNA profiles in HCC tumor and non-tumor tissues, and to determine the consistency and discrepancy of data on dysregulated miRNAs.
Results:
Two sets of stable miRNAs (miR-30c/miR-30b and miR-30c/miR-126) were identified in HCC tissues by geNorm and NormFinder tools, respectively. The mean of global miRNAs also showed good stability for ranking the top 1-2 miRNAs, but the stabilities of the manufacturer-recommended ncRNAs controls were poor. Four panels of miRNAs were significantly associated with HCC by separately using various normalizers, and 14 miRNAs were consistently identified by three normalization strategies. Although fewer miRNAs (17-26) were dysregulated in HCC using the global mean or the 2 stable miRNAs as normalizers, perfect clustering of tissues was also obtained with only 1 to 2 misclassifications, suggesting the efficiency of the miRNA panels. Using global mean as the normalizer, the authors identified 7 miRNAs, including 2 novel (miR-324-5p and miR-550) significantly upregulated in HCC that were omitted when using 3 endogenous controls as the normalizer.
Conclusion:
An optimal normalization strategy to identify biologically important miRNAs in HCC tissue studies of miRNA may be the combination of global mean and 2 stable miRNAs. Selection of appropriate normalization strategies to adjust miRNAs levels is particularly important for epidemiological studies dealing with large data sets and covering multiple experimental batches.