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. 2016 Dec 13;6(12):e981.
doi: 10.1038/tp.2016.148.

Insights Into Psychosis Risk From Leukocyte microRNA Expression

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Free PMC article

Insights Into Psychosis Risk From Leukocyte microRNA Expression

C D Jeffries et al. Transl Psychiatry. .
Free PMC article

Abstract

Dysregulation of immune system functions has been implicated in schizophrenia, suggesting that immune cells may be involved in the development of the disorder. With the goal of a biomarker assay for psychosis risk, we performed small RNA sequencing on RNA isolated from circulating immune cells. We compared baseline microRNA (miRNA) expression for persons who were unaffected (n=27) or who, over a subsequent 2-year period, were at clinical high risk but did not progress to psychosis (n=37), or were at high risk and did progress to psychosis (n=30). A greedy algorithm process led to selection of five miRNAs that when summed with +1 weights distinguished progressed from nonprogressed subjects with an area under the receiver operating characteristic curve of 0.86. Of the five, miR-941 is human-specific with incompletely understood functions, but the other four are prominent in multiple immune system pathways. Three of those four are downregulated in progressed vs. nonprogressed subjects (with weight -1 in a classifier function that increases with risk); all three have also been independently reported as downregulated in monocytes from schizophrenia patients vs. unaffected subjects. Importantly, these findings passed stringent randomization tests that minimized the risk of conclusions arising by chance. Regarding miRNA-miRNA correlations over the three groups, progressed subjects were found to have much weaker miRNA orchestration than nonprogressed or unaffected subjects. If independently verified, the leukocytic miRNA biomarker assay might improve accuracy of psychosis high-risk assessments and eventually help rationalize preventative intervention decisions.

Figures

Figure 1
Figure 1
Histogram of one AUC from true data vs. 1000 AUCs of classifiers built by the same greedy algorithm applied to pseudo data (NP and P labels randomly permuted). Fitted with a beta distribution, the AUC from real data indicates a P-value of 0.012. Since 17 random AUCs of 1000 by chance exceed the true AUC, an algebraic method gives alternative P-value=0.018. Thus, the performance of the Greedy Algorithm limited to selection of at most six markers and applied to the full data set is unlikely to be chance. AUC, area under the curve of receiver operating characteristic.
Figure 2
Figure 2
(a) The Greedy Algorithm was applied to 1000 selections of random 80% subsets of nonprogressed subjects and random 80% subsets of progressed subjects. Each time up to six markers could be chosen. The seven most frequently chosen markers are shown with their selection rates. The solid bars indicate the five markers that were also selected in the first six markers chosen by the Greedy Algorithm for the full data set (Figure 1), yielding as a sum of z-scores the classifier function in sum (equation (1)). This function applied to the full data yields AUC=0.86. (b) ROC of the five-miRNA classifier function (equation (1)). Dotted lines are 95% confidence levels, and the dashed line is hypothetical performance of a random classifier. AUC, area under the curve; miRNA, microRNA; ROC, receiving operating characteristics.
Figure 3
Figure 3
Graphs from miRNA–miRNA correlations. Edges represent strong correlations. Looped regions are common subgraphs. Networks shown are strongly correlated miRNAs among unaffected controls. miRNA, microRNA.
Figure 4
Figure 4
Graphs from miRNA–miRNA correlation with the same criteria as in Figure 3 but for nonprogressed subjects. This graph is similar to that in Figure 3. miRNA, microRNA.
Figure 5
Figure 5
Graphs from miRNA–miRNA correlations with the same criteria as in Figures 3 and 4 but for progressed subjects. Evidently much organisation of miRNA networks is lost in subjects who eventually progressed to psychosis. miRNA, microRNA.

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