MicroRNA expression aberration as potential peripheral blood biomarkers for schizophrenia - PubMed (original) (raw)
doi: 10.1371/journal.pone.0021635. Epub 2011 Jun 29.
Sung-Liang Yu, Ming H Hsieh, Chun-Houh Chen, Hsuan-Yu Chen, Chun-Chiang Wen, Yung-Hsiang Huang, Po-Chang Hsiao, Chuhsing Kate Hsiao, Chih-Min Liu, Pan-Chyr Yang, Hai-Gwo Hwu, Wei J Chen
Affiliations
- PMID: 21738743
- PMCID: PMC3126851
- DOI: 10.1371/journal.pone.0021635
MicroRNA expression aberration as potential peripheral blood biomarkers for schizophrenia
Chi-Yu Lai et al. PLoS One. 2011.
Abstract
Since brain tissue is not readily accessible, a new focus in search of biomarkers for schizophrenia is blood-based expression profiling of non-protein coding genes such as microRNAs (miRNAs), which regulate gene expression by inhibiting the translation of messenger RNAs. This study aimed to identify potential miRNA signature for schizophrenia by comparing genome-wide miRNA expression profiles in patients with schizophrenia vs. healthy controls. A genome-wide miRNA expression profiling was performed using a Taqman array of 365 human miRNAs in the mononuclear leukocytes of a learning set of 30 cases and 30 controls. The discriminating performance of potential biomarkers was validated in an independent testing set of 60 cases and 30 controls. The expression levels of the miRNA signature were then evaluated for their correlation with the patients' clinical symptoms, neurocognitive performances, and neurophysiological functions. A seven-miRNA signature (hsa-miR-34a, miR-449a, miR-564, miR-432, miR-548d, miR-572 and miR-652) was derived from a supervised classification with internal cross-validation, with an area under the curve (AUC) of receiver operating characteristics of 93%. The putative signature was then validated in the testing set, with an AUC of 85%. Among these miRNAs, miR-34a was differentially expressed between cases and controls in both the learning (P = 0.005) and the testing set (P = 0.002). These miRNAs were differentially correlated with patients' negative symptoms, neurocognitive performance scores, and event-related potentials. The results indicated that the mononuclear leukocyte-based miRNA profiling is a feasible way to identify biomarkers for schizophrenia, and the seven-miRNA signature warrants further investigation.
Conflict of interest statement
Competing Interests: The authors have declared that no competing interests exist.
Figures
Figure 1. miRNA expression profiling of 221 miRNAs in the learning set of 30 schizophrenia patients and 30 controls.
The heatmaps of individual miRNAs for each group of subjects were presented as the averaged rank-sum of the normalized threshold cycle number, with less numbers indicating higher expression levels. The eight differentially expressed miRNAs were marked by an asterisk.
Figure 2. A comparison of: (A) the TLDA-based miRNA expression levels between the schizophrenia patients (n = 30) and the controls (n = 30) for the seven miRNAs; and (B) quantitative RT-PCR-based miRNA expression levels between the schizophrenia patients (n = 60) and the controls (n = 30) for the same seven miRNAs.
The red, black, and green hues denote relatively high, intermediate, and low expression levels. To emphasize the rank-sum property of the Wilcoxon test, all the expression levels of miRNA were converted to relative expression ranks within each miRNA. Ranks for each miRNA were sorted separately for the control group and disease group before they were placed side-by-side for easier comparison between the two groups and across all putatively informative miRNAs.
Figure 3. The area under the receiver operative characteristics curve of the seven-miRNA signature in the learning (in blue color) and testing (in green color) sets, respectively, without (in solid line) or with (in dotted line) adjustment for age, gender, education, and tobacco smoking.
Figure 4. The relations in has-miR-34a miRNA expression levels between two platforms of miRNA quantification, the array-based TLDA vs. individual quantification using quantitative RT-PCR.
The differential expressions in hsa-miR-34a between the cases and controls in both the learning set (P = 0.005) and the testing set (P = 0.002).
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