ARTICLE
https://doi.org/10.1038/s42003-019-0324-7 OPEN Risk prediction models for dementia constructed by supervised principal component analysis using miRNA expression data
Daichi Shigemizu 1,2,3,4, Shintaro Akiyama1, Yuya Asanomi1, Keith A. Boroevich 3, Alok Sharma 3,4,5,6,
1234567890():,; Tatsuhiko Tsunoda 2,3,4, Kana Matsukuma7, Makiko Ichikawa7, Hiroko Sudo7, Satoko Takizawa7, Takashi Sakurai8,9, Kouichi Ozaki1,3, Takahiro Ochiya10,11 & Shumpei Niida1
Alzheimer’s disease (AD) is the most common subtype of dementia, followed by Vascular Dementia (VaD), and Dementia with Lewy Bodies (DLB). Recently, microRNAs (miRNAs) have received a lot of attention as the novel biomarkers for dementia. Here, using serum miRNA expression of 1,601 Japanese individuals, we investigated potential miRNA bio- markers and constructed risk prediction models, based on a supervised principal component analysis (PCA) logistic regression method, according to the subtype of dementia. The final risk prediction model achieved a high accuracy of 0.873 on a validation cohort in AD, when using 78 miRNAs: Accuracy = 0.836 with 86 miRNAs in VaD; Accuracy = 0.825 with 110 miRNAs in DLB. To our knowledge, this is the first report applying miRNA-based risk prediction models to a dementia prospective cohort. Our study demonstrates our models to be effective in prospective disease risk prediction, and with further improvement may contribute to practical clinical use in dementia.
1 Medical Genome Center, National Center for Geriatrics and Gerontology, Obu, Aichi 474-8511, Japan. 2 Department of Medical Science Mathematics, Medical Research Institute, Tokyo Medical and Dental University (TMDU), Tokyo 113-8510, Japan. 3 RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa 230-0045, Japan. 4 CREST, JST, Tokyo 102-8666, Japan. 5 School of Engineering & Physics, University of the South Pacific, Suva, Fiji. 6 Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, QLD 4111, Australia. 7 Toray Industries, Inc., Kamakura, Kanagawa 248-0036, Japan. 8 The Center for Comprehensive Care and Research on Memory Disorders, National Center for Geriatrics and Gerontology, Obu, Aichi 474-8511, Japan. 9 Department of Cognitive and Behavioral Science, Nagoya University Graduate School of Medicine, Nagoya, Aichi 466-8550, Japan. 10 Division of Molecular and Cellular Medicine, Fundamental Innovative Oncology Core Center, National Cancer Center Research Institute, Tokyo 104-0045, Japan. 11 Institute of Medical Science, Tokyo Medical University, Tokyo 160-8402, Japan. Correspondence and requests for materials should be addressed to D.S. (email: [email protected])
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ith an increasingly aging global human population, the and the principal component scores (PC scores), we then con- Wnumber of people with dementia is rapidly increasing, structed risk prediction models based on a supervised principal and is estimated to reach 75 million by 2030 and component analysis (PCA) logistic regression method. Finally, we 135 million by 2050, worldwide1. Since dementia is a clinical determined the optimal miRNA and PC score set though cross- syndrome that leads to difficulties in daily activities involving validation. This final risk prediction model, constructed based on memory, language and behavior, this rapid increase raises a the entire discovery cohort, was evaluated with an independent substantial burden for medical care and public health systems2. validation cohort by the area under the receiver operating char- On the other hand, there is no current cure for this disease, and acteristic curve (AUC). We further evaluated the predictive ability the available treatments are only able to postpone the progres- of our model using a prospective cohort. Our findings indicate sion3. Therefore, identification of new biomarkers for earlier that the prediction models using serum miRNA expression data diagnosis and therapeutic intervention of the disease is promptly may be useful as biomarkers for dementia and contribute to the required4. development of future therapeutic measurement for this common The diagnosis of dementia is generally based on the patients’ but serious disorder. cognitive function5. Alzheimer’s disease (AD) is the most com- mon subtype of dementia, followed by vascular dementia (VaD), Results and dementia with Lewy bodies (DLB)1. While recent studies Japanese samples. We divided 1601 Japanese individuals (1021 have showed that three proteins in the cerebrospinal fluid (CSF): AD cases, 91 VaD cases, 169 DLB cases, 32 mild cognitive amyloid-beta 1–42 (Aβ ), total tau (T-tau) and phosphorylated 142 impairment (MCI), and 288 NC) into a discovery cohort of 786 tau 181 (P-tau ), could be effective in characterizing AD6,7,itis 181 individuals (511 AD cases, 46 VaD cases, 85 DLB cases and 144 still challenging to use these CSF molecules as biomarkers in NC) and a validation cohort of 783 individuals (510 AD cases, 45 general physical examination for early diagnosis and therapeutic VaD cases, 84 DLB cases, and 144 NC) (see Materials and intervention due to the highly invasive collection process. In methods). The separation was performed to result in a similar addition, new imaging-based techniques, including positron distribution in the age between the discovery and validation emission tomography scans for detection of amyloid-beta cohorts for each disease (Table 1). deposition or tau tracers, and the volumetric magnetic reso- nance imaging with determination of hippocampal or medial temporal lobe atrophy, are not suitable for initial screening due to Construction of risk prediction models. Our risk prediction the high cost performance8–10. It has also been reported that models were constructed based on a supervised PCA logistic microRNAs play a key role in the control of glial cell development regression method. All approaches that we considered were car- in the central nervous system11. Therefore, the present study is ried out on datasets of the p pre-selected miRNAs (p ≤ 2562). The evaluated on the hypothesis that neurite and synapse destruction, selection of miRNAs was carried out based on the z-value in the associated with pathologic of dementia and other neurodegen- logistic regression. Nine-tenths of entire training set was used for erative diseases, can be detected in vitro by quantitative analysis the calculation of the z-values and to fit the model for each cross- of brain-enriched cell-free microRNA in the human blood5. validation step. The adjusted model was evaluated using the MicroRNAs (miRNAs) are approximately 22-nucleotide small remaining one-tenth of the training set. This process was repeated non-coding RNAs, which have been shown to regulate gene 10 times (10-fold cross-validation). The cutoff value T of the z- expression by binding to complementary regions of messenger values was then raised from 0.1 to 5.0 at an interval 0.1. The transcripts. The alteration of some miRNAs expression has number of top PC scores used, m, was set from 1 to 10 (Fig. 1). recently been found in neurons of patients with AD and other On the basis of the average AUC, we investigated all combina- neurodegenerative diseases12–14, and hence miRNAs are expected tions of the T and m, and in AD, the combination of (T, m) = to be useful as easily accessible and non-invasive biomarkers15. (4.5, 10) achieved the highest AUC of 0.877 in the discovery Here, we performed a comprehensive miRNA expression cohort. In VaD, a (T, m) = (4.0, 10) achieved an AUC = 0.923, analysis using 1601 serum samples, composed of dementia and in DLB, a (T, m) = (3.4, 9) achieved an AUC = 0.885 (Fig. 2). patients and individuals with cognitive normal function (referred Final risk prediction models were constructed based on the to as normal controls (NC)), in order to investigate new bio- optimal T and m detected in each disease using the entire training markers for earlier diagnosis and therapeutic intervention and to set (discovery cohort). The adjusted models were then evaluated construct risk prediction models using the biomarkers. We on the validation cohort, which was completely independent from applied 10-fold cross-validation to a discovery cohort of 1092 the discovery cohort. As a result, 78 miRNAs out of 2562 were individuals, separated from a validation cohort of 1089 indivi- employed for the final model construction in AD, which achieved duals. We performed a two-step procedure similar to those used an AUC of 0.874 in the validation cohort (Fig. 3a). Of the 78, two for risk prediction in several previous disease studies16–19.We miRNAs (MIMAT0004947 and MIMAT0022726) were AD- first selected effective miRNA biomarker candidates in the logistic specific miRNAs reported in previous studies20. The remaining regression risk prediction models. Using the pre-selected miRNAs previously reported miRNAs did not show significantly better
Table 1 Average age, sex and APOE information in the discovery and validation cohorts
Discovery cohort Validation cohort Phenotype #Sample Age Sex (Male) APOEa #Sample Age Sex (Male) APOEa AD 511 79.2 0.29 0.53 510 79.2 0.31 0.47 VaD 46 79.0 0.63 0.33 45 79.1 0.56 0.18 DLB 85 79.5 0.45 0.34 84 79.5 0.36 0.30 NC 144 71.7 0.49 0.22 144 71.8 0.56 0.15
APOE apolipoprotein E, AD Alzheimer’s disease, VaD vascular dementia, DLB dementia with Lewy bodies, NC normal control a APOE shows the average of the number of APOE ε4 allele genotype
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miRNA2 miRNA3 1. For each miRNA miRNA3 |z-value| > T
miRNA1
miRNA1
4. Evaluation of T and m 2. PCA using cross-validation
1 PC1 t 0 3. m PC scores
–1
0 1 x logit(Pi ) = 1PC1 + ··· + mPCm, where PC = l x + ··· + l x i 1 1 n n PC2
Fig. 1 Workflow of our risk prediction model construction with supervised principal component analysis (PCA) logistic regression method. We calculated z-value in the logistic regression method for each microRNA (miRNA). The cutoff value of the z-value, T and the number of miRNAs, n, was pre-selected (1). The PCA was performed using the pre-selected miRNAs (2). The risk prediction models were constructed based on the combination of the miRNAs and m PC scores (3). This optimal parameter set (T, m) was determined in the discovery cohort using 10-fold cross-validation (4) outcome in logistic regression in the selection of miRNAs (Sup- were shared between VaD and DLB (32 miRNAs) and among plementary Data 1). A maximum average sensitivity and speci- all three (31 miRNAs) (Fig. 4a). AD possessed the most disease- ficity of the ROC curve was achieved at a sensitivity of 0.933 and specific miRNAs compared to the other diseases (AD; 29/78 = specificity of 0.660 in AD. The accuracy showed 0.873 when the 0.371, DLB; 34/110 = 0.309, VaD; 18/86 = 0.209) (Fig. 4a). prognostic index was 0.281 (Table 2). In a similar way, 86 miR- Overall, miRNAs regulate the expression of thousands of NAs and 110 miRNAs were employed for our final model con- protein-coding gene targets (mRNAs) at both post-transcriptional struction in VaD (Fig. 3b) and DLB (Fig. 3c), which achieved and translational levels21–23. To determine the biological AUCs of 0.867 and 0.870 in the validation cohort, respectively. A significance of our findings (miRNAs), we examined microRNA maximum average sensitivity and specificity of the ROC curve Target Prediction and Functional Study Database (miRDB24), was achieved at a sensitivity of 0.733 and specificity of 0.868 in which can predict miRNA functional target genes. The 78 VaD and at a sensitivity of 0.762 and specificity of 0.861 in DLB. miRNAs in AD were predicted to target 1755 genes. In the similar The accuracies in VaD and DLB were 0.836 and 0.825 when the way, the 86 miRNAs in VaD and 110 miRNAs in DLB were prognostic index was −0.761 and 0.0392, respectively (Table 2). predicted to target 2017 and 2521 genes, respectively. Compared We also constructed risk prediction models based on the with miRNAs, a large number of mRNAs were shared among logistic regression method using only clinical information (age, three diseases (miRNAs: 31/162 = 0.191, mRNAs: 960/3370 = sex and apolipoprotein E (APOE)) for the entire training data set. 0.285) (Fig. 4a, b). The adjusted models were then evaluated on the validation cohort. The AUCs achieved were 0.857 in AD, 0.813 in VaD and 0.827 in DLB. Our finding’s miRNAs contributed to an increase Functional modules using co-expression network analysis. of AUCs for the risk prediction models. We further compared our Since we detected several candidate gene targets in the three two-step method with a one-step penalized regression method, diseases, we next attempted to elucidate functional modules from LASSO (least absolute shrinkage and selection operator). We the candidates. We focused on the occurrence of hub genes, constructed risk prediction models based on the LASSO method which have relationships with many genes, through large-scale gene co-expression network analysis. The gene co-expression using all miRNAs in the entire training data set. The adjusted 25 models were then evaluated on the validation cohort. The AUCs information was gathered from the COXPRESdb database (see Materials and methods). Gene co-expression network visualiza- achieved were 0.898 in AD, 0.821 in VaD and 0.892 in DLB. For 26 AD and DLB, the one-step penalized regression method showed tion was performed using Cytoscape software . Three hub genes, similar AUCs to our two-step method, but the penalized method which co-expressed with >25 genes, were detected in the func- showed a lower AUC in VaD than our method (LASSO = 0.821, tional modules (EXOC5, DDX3X and YTHDF3, Fig. 5). EXOC5 our method = 0.867). was associated with AD and VaD, and the remaining two genes were common among the three diseases. These three genes were also verified to express in brain tissue through the Genotype- Effective miRNAs and the functional gene annotations. The Tissue Expression (GTEx) project27,28. number of miRNAs used for final risk prediction models were 78, 86 and 110 in AD, VaD and DLB, respectively (Supplemen- tary Data 2). We next examined the common and disease-specific Validation in a prospective cohort. We measured miRNA miRNAs among these three diseases. A large number of miRNAs expression for 32 MCI subjects, which were obtained from the
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a AD |Z-value| AUC 0.3 0.5 0.7 0.9 1.1 1.3 1.5 1.6 1.7 1.8 1.9 2 2.1 2.2 2.3 2.4 2.5 2.6 2.8 2.9 3 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 4 4.1 4.2 4.3 4.4 4.5 4.6 4.7 4.8 4.9 0.1 0.2 0.4 0.6 0.8 1 1.2 1.4 2.7 0.88 PC1 0.87 PC2 0.87 PC3 0.87 PC4 0.86 PC5 0.86 PC6 0.86 PC7
#PC score 0.85 PC8 0.85 PC9 0.84 PC10 0.84 b VaD 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 4 0.92 PC1 0.91 PC2 0.9 PC3 0.88 PC4 0.87 PC5 0.86 PC6 0.84 PC7 #PC score 0.83 PC8 0.81 PC9 0.8 PC10 0.79 c DLB 0.3 0.5 0.7 0.8 1 1.8 2 2.3 2.8 3 3.1 3.3 3.7 3.8 4 0.1 0.2 0.4 0.6 0.9 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.9 2.1 2.2 2.4 2.5 2.6 2.7 2.9 3.2 3.4 3.5 3.6 3.9 0.88 PC1 0.88 PC2 0.87 PC3 0.86 PC4 0.85 PC5 0.84 PC6 0.83 PC7 #PC score 0.82 PC8 0.82 PC9 0.81 PC10 0.8
Fig. 2 Risk prediction models using 10-fold cross-validation on a discovery cohort. The x and y axes show the cutoff value of the z-value in the logistic regression (T) and the number of top principal component (PC) scores used (m) for the prediction models, respectively. In AD (a), a combination of (T, m) = (4.5, 10) achieved the highest AUC of 0.877 in the discovery cohort. In VaD (b), a (T, m) = (4.0, 10) achieved an AUC = 0.923, and in DLB (c), a (T, m) = (3.4, 9) achieved an AUC = 0.885. AUC area under the curve, AD Alzheimer’s disease, VaD vascular dementia, DLB dementia with Lewy bodies
abcAD VaD DLB 1.0 1.0 1.0
0.8 0.8 0.8
0.6 0.6 0.6
0.4 0.4 0.4 True positive rate True positive rate 0.2 0.2 0.2
0.0 0.0 0.0
0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 False positive rate False positive rate False positive rate
Fig. 3 The ROC curves of our risk prediction models in a validation cohort. Final risk prediction models were constructed based on the supervised principal component analysis (PCA) logistic regression method in each disease using the complete discovery cohort. The adjusted models were then evaluated on the validation cohort. The final model construction in AD achieved an AUC of 0.874 in the validation cohort (a): AUC = 0.867 in VaD (b); AUC = 0.870 in DLB (c). Sensitivity and specificity were maximized at a sensitivity of 0.933 and specificity of 0.660 in AD (a): a sensitivity of 0.733 and specificity of 0.868 in VaD (b); and a sensitivity of 0.762 and specificity of 0.861 in DLB (c). ROC receiver operating characteristic, AUC area under the curve, AD Alzheimer’s disease, VaD vascular dementia, DLB dementia with Lewy bodies prospective data, of which 10 subjects converted to AD after at greater than 0.281 predicted the subject would convert to AD least 6 months. We evaluated if our risk prediction model in AD (Table 2). As a result, all of the 10 converted subjects were cor- could predict the converted subjects after 6 months. Prognosis rectly predicted by our model (sensitivity = 1.0). Furthermore, indices (PIs) assigned to each subject were calculated by applying all 4 subjects predicted not to covert to AD did not actually 78 miRNA expression values to our prediction model. A PI score convert to AD (negative predictive value = 1.0). The remaining
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on the validation cohort18,40. The difference of the AUCs is due Table 2 Accuracy estimation in three diseases using the fi validation cohort to over tting of the model construction criterion. However, our risk models showed only small differences between discovery and validation cohorts in the AUCs (AD = 0.877 and 0.874, Disease PI cutoff Accuracy Sensitivity Specificity VaD = 0.923 and 0.867, and DLB = 0.885 and 0.870). These AD 0.281 0.873 0.933 0.660 results imply the miRNAs used in our models were efficient VaD −0.761 0.836 0.733 0.868 DLB 0.0392 0.825 0.762 0.861 to classify disease samples and non-disease samples, although additional replication studies are necessary in future work. We PI prognostic index, AD Alzheimer’s disease, VaD vascular dementia, DLB dementia with Lewy also constructed the risk prediction models using a larger max- bodies imum value of PC scores (m = 50). In AD and DLB, the AUCs of the final models were slightly increased in a validation cohort in the combination of (T, m) = (3.6, 41) in AD and that ab of (T, m) = (3.2, 12) in DLB, compared with those in m = 10 =+ =+ VaD VaD (AD 0.007, DLB 0.002, Supplementary Table 1), but that 18 304 AD 122 AD in VaD was considerably decreased in the combination of 5 (T, m) = (3.8, 13) (VaD = −0.015, Supplementary Table 1). For 29 423 all, a larger number of miRNAs were required for the final model = = 32 31 631 960 construction: m 50 (AD, VaD, DLB) (171, 134, 143) miR- NAs, m = 10 (AD, VaD, DLB) = (78, 86, 110) miRNAs (Sup- 13 250 plementary Table 1). When considering prediction models with a low number of biomarkers, our approaches would be efficient 680 34 DLB DLB for optimal risk prediction models. We further compared our final models using pre-selected miRNAs to those using all miR- NAs. Our models using pre-selected miRNAs had superior AUCs Fig. 4 Effective microRNAs (miRNAs) and genes used in risk prediction to those using all miRNAs in all three diseases for both m = 10 fi model. a The number of miRNAs used for nal risk prediction models were and m = 50 (Supplementary Table 2). Investigations using larger 78, 86 and 110 in AD, VaD and DLB, respectively. The pie-chart showed the sample sizes will lead to further improvement in the performance fi common and disease-speci c miRNAs among these three diseases. b Using of risk prediction models. microRNA Target Prediction and Functional Study Database (miRDB), The annotation of gene targets for miRNAs is critical for which can predict miRNA functional target genes, the 78 miRNAs in AD functional characterization of our findings. We used miRDB24 for were predicted to target 1755 genes. The 86 miRNAs in VaD and 110 these functional gene annotation from miRNAs. A large number miRNAs in DLB were predicted to target 2017 and 2521 genes, respectively. of genes associated with the dementia was detected. We further fi The pie-chart showed the common and disease-speci c target genes elucidated three functionally important modules (i.e. hub genes, ’ among these three diseases. AD Alzheimer s disease, VaD vascular EXOC5, DDX3X and YTHDF3) through large-scale gene co- dementia, DLB dementia with Lewy bodies expression network analysis. These three genes were verified to express in brain tissue through the GTEx project27,28. Jun et al.41 18 subjects were predicted to convert to AD, but had not yet have reported that a single-nucleotide polymorphism (SNP) converted (specificity = 0.18) (Table 3). For further validation in the EXOC5 showed evidence for association with AD. DDX3X, of this discordance, we may have to follow-up with the subjects the DEAD (Asp-Glu-Ala-Asp) box helicase 3, X-linked, belongs in the future and use the additional comprehensive information, to ATP-dependent RNA helicase, the activation of which is including genetic data and/or whole transcriptome data, for fur- – associated with cancer in many tissues, including brain42 44. ther improvement for practical clinical use in dementia. Previous studies have reported that DDX3X expression level is positively correlated with poor survival outcome in human Discussion glioma45. Also, several studies have reported that YTHDF New biomarkers for early diagnosis and intervention have been protein could be associated with accumulation of m6A-modified examined in many diseases29–31. The role of serum miRNAs has transcripts46, and this m6A mRNA modification is critical for recently been reviewed with emphasis on their impact on the glioblastoma stem cell self-renewal and tumorigenesis47. Fur- etiopathogenesis of sporadic AD32 and cancers21–23,33,34. For thermore, recent transcriptomic meta-analyses revealed that AD example, dysregulated serum miRNAs, such as the down- and glioblastoma patients had similar expression patterns in a regulation of miR-137, miR-181c, miR-9, and miR-29a/b in the number of genes48. These observations support the existence blood of AD patients, have been identified35–38. It has also been of molecular substrates that could partially account for direct reported that expressional differences between AD and other co-morbidity relationships49–51. These results suggest that the dementia types were observed for some miRNAs39. However, three hub genes detected could not only play a key role in due to the small sample sizes in these previous reports, com- pathogenesis of dementia, but also contribute to discovery of prehensive miRNA expression analysis had not been performed novel drug targets. in AD or the other subtypes of dementia. Therefore, in this study The diagnosis of dementia is not always consistent with brain we investigated biomarkers with respect to each subtype of pathological changes52,53. Also, elderly dementia patients often dementia, using serum miRNAs and a larger sample size. have concomitant cerebrovascular disease pathologies as well as We first detected optimal parameters for risk prediction other concomitant neurodegenerative disease pathologies54.We models using cross-validation of a discovery cohort. The final proposed a methodology that finds the best risk prediction models were then constructed with the optimal parameters using model for each disease rather than a general model that could be the complete discovery cohort. The adjusted models were finally applied to any data set. Our proposed models might be able to evaluated on an independent validation cohort using AUC as differentiate these complex neurological disorders. However, the discriminative accuracy of these risk prediction models. In further refinement of this methodology will be required before its general, these risk prediction models on cross-validation of the practical use in healthcare. One way may be to consider genetic discovery cohort achieved higher AUCs than the adjust models variations, such as SNPs and insertions and deletions (indels)
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LNPK PIKFYVE DNAJB4 TOR1AIP2 HNRNPA2B1 HERC3
RASGEF1B
NRAS
FAM126B
DNASE2
KMT2A CASK WDR47
TAOK1 SMG1
UBE2J1 DDX3X SRSF5 TSC22D2
FKBP1A
C5orf24 ARID4A
PSME3
DESI2
KLHL24 CCNYL1 RMND5A
SFPQ GOLT1B BTAF1
FBXO28
PHIP
CORO1C TRRAP
FKBP14 CISD2
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TUBG1
SLC31A1 AP2B1
RNF38 MYCBP2
SPCS3
ITM2B
PAN3 TNPO1
PRDM10 OPA1
JUND SELENOI TRIM23
MARCH6 PATL1 UBQLN1 ATXN7
CSNK1G3
SLC30A7 UBE4A
CALU
EDC3 MAPK1IP1L
TMEM33
SENP7 BTRC
APH1A
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ZBTB41 USP42 NFYA KPNA4 ELMOD2 OGFOD1 STRN
SEL1L SP4 FLT1
MIGA1
SSR3 ZCCHC8 ZFC3H1 ING3 ACTR1A MIER3
GMFB SPAG9 CDC73
GPBP1 IRF2BP2 NR2C2
FAM199X
NUCKS1
NAA16
CHD1
SYNJ1 RBM39
GNA13 TM9SF2
SOS2
DCUN1D1 RABL3 OGT ZNF12 SEPHS1 CSNK2A1
SRSF6 TPP2
PHAX
FNDC3A INTS6 YTHDF3
ELAVL1 ARIH2
KPNA1 BIRC6 CDK17 BRWD1 KAT6B SYNGAP1 TACC1 GTF2I
EEA1 MAPK14
RERE CLIC4 ARGLU1 RBM26
AMMECR1 POGZ ABHD17B TNRC6A CERS6 ZBTB21
RALGAPB
EPM2AIP1
UBE4B BZW1
BLZF1 RNF6 SPN SLC39A9
AP1S1 CEP120 POLR2E RSRC2
SAR1A PGGT1B
SRSF11
TBC1D15 SOGA1
SPTY2D1 PDE4C UBR3 LIN7C
ATF2
EPG5 PPM1B PAXBP1 RHOB MYSM1 ZMYM5 STX16 EXOC5 FBXW11 CPSF6 RAB28 UTRN
SAR1B
UACA
UBA6 KHDC4 DLGAP4 LUC7L3 PRR11
C5orf22 REEP3
ARL8B
CHUK
CDK13 PDAP1
AFF4 TMEM50B PANK3
TMEM167A ZC3H7B
UBQLN4 C20orf24
USP47 PTPN11
CNIH4 SLMAP TNKS2
ZNF644
PGF
CTDSPL2 PRPF40A TASP1
ESF1 SCAF11
ATF7IP
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RAB6A EZH1 CNIH1 PUM2
ENSA DOCK5 SP3 IMPAD1 ARF6
BTG2 SS18
TRAM1 CBFB BBX SLF2
NONO
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EIF5 TBL1XR1 ZRANB2
SETX DMC1 KBTBD2
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TMEM245 API5 JMJD6 UFM1 MAP3K2
WAPL
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STAG1 SCYL2 RBMX
GABPA ZNF440 STXBP3
EDEM3 ABRAXAS2 EBLN2 LARP4
IL13RA1 RNF111 USP34 FBXL3 CREBZF DUSP1
OSGIN2 NAA50 ACTR2 GSPT1 USP24
MAPK1 SLC25A46
TMEM30A RBM15 UBR5
OPHN1
CCPG1 RAPGEF6
SEC24B AGPS
CELF1 GPATCH2L
TOR1AIP1 MBNL1 TM9SF3 CTNNB1
TARDBP SREK1 MTMR6 VPS35 THUMPD1
FOS KLF4 CAPRIN1 ZFAND6
ZMAT3
ZNF207 NUDT4 ATL2 MPZL1
HNRNPA3
SLC4A7
TMED2 KLF6 BTBD7 C2orf49 AEBP2 PRPF4B ATP6V1A SNX13
NPAT
CHD6
SRSF1 MAPRE1
MSANTD2 PCDHGA8
CPD PTP4A1 RREB1 SORT1
FAR1 NAA25 ARFGEF1 PHTF2
MBTD1
FOPNL RAB14
UBE2B SRSF2 ZMYND11
ATF3 SH3BGRL CREB1 ABHD13 SRP19 SLAIN2 PRKRA
PAPOLA
ZXDC JOSD1
NR4A2 PDS5B
YWHAZ CUL3
ATP6AP2
NAMPT
PAFAH1B1 AZIN1 UBE2D3
NAA15
ADAM10 SCOC ANKRD28
COMMD10 DDIT3 PPM1A
GGNBP2 ITPKB
DLG1 FBXO33
PCNP PPTC7
MAFF B3GNT2 SELENOT HIPK3 UBE2K RAB18
ZBTB44
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PLXDC1 DENND1B SRPK2 XPO1 ZNF202 CMTR2 STK4 TRPM7 TRA2A
EML4 CUL5
RAB21 BECN1 REST
PDE7A CGGBP1 CAB39
MAP3K8 KIF5B PTPN12
TFCP2 ZNF148 ARID2 FOSL2
MFN1
GXYLT1 SMNDC1 REEP5 PFKFB3 ACTR10
ARFIP1 CREM
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DUSP2 HNRNPF
CTBS
PPHLN1 AP5M1 CNOT8 DAZAP2 RHOA KLHL15 BRCC3 GDAP2
CSNK1A1
STAG2
RNF7 RANBP9
STAM PAPD4 GPBP1L1
ACSL3
PNN
NAA30
PCDHGA10 PLK3 BHLHE40 SYNCRIP
SNX1
TAB2
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SPOPL
USP38 OAZ1 DPP8
RLIM PSME4
PPP1CA
TMEM161B ANKRD13A
FMR1 HNRNPC CBLL1
MAPK8 RB1 PPP2R5E
HAUS6
ATF1 RSBN1L SENP6
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ATF7 TRUB1 TMEM106B ARPC5 RFX7
CUL4B GIGYF2
BTG1
VIRMA
CRK
FGFR1OP2
SUMO2 NSF
ACTG1
TRA2B NCK1 TMEM260 DSTN
RAP1A
PCNX1
CDKN1A
SKP1 ZZZ3
HNRNPA0 VEGFA
SRSF3 NAB1 MARCH5
RALGAPA2
PCDHGC3
VAPA CCDC91
SHPRH MED13L ABI2
STAU1
AP4E1 KMT5B FAM76B
SUB1
TIA1 BNIP2
PAIP2
ZNF706 GSK3B PCDHGA3 PPP6R3
NT5C2 FXR1 RAP1B PPP1R15B
ZNF430
BRD4
TIMM17A
ZNF117
SOS1 NF1 TFDP2
CCDC126 IRF2
BIRC2 PGK1 PRKCI
ZNF107 ETF1
SERF2 CLASP2
RABEP1 MYO9A
RNF138 VAPB PNRC1
ZKSCAN8 SEC11A TIPRL ZNF138
QTRT2 RALYL ASXL1 DCP2
PPIP5K2
EIF2S1 TMCC1
VDAC1 CELF3 NUTF2
YBX1
PIGA
ADO
KATNBL1
ZNF468
PPP2CA
HSPD1 PCDHGA1 CAPZA1 MYNN
TFAM
SMIM7
ZKSCAN1
ZNF254 VGLL4
MAP2K1
GPI
UBE3A
RIT1 COPS8 PRKAA1 TBC1D23 KCMF1 ZNF765 LDHA
ZNF518A HSP90B1 CACYBP SCAI MAP2K4 SERINC3
ZMYM4
DIP2B
CCNC
RALGPS2 YWHAQ NFYB
UBE2D2
KRIT1 MITD1
CUL4A
TMEM183A HSPA9 ZNF267 SEC63
SLC35B3 WDR7 PPIF PI4K2B OXR1
RP2 LAMTOR3
ATP11C
COPS2 M6PR
CAAP1
MFSD6
NRBF2
VPS4B
THAP1
XRCC5
TIAL1
UBE2G1 PDCD6IP
DPM1 TCAIM
FGFR2
CLIP3 PURA
BTBD1 UTP15 DOCK7 VPS13A PCMTD1
GRSF1
CNBP
PHF21A DPY19L4
MAPK6 PGRMC2 TMEM151B PFDN4
RNF141 SLC35A5
VPS36 PHF6 RAP2C
CLSPN DTX3L
ZDHHC21 MAP1A
TRAPPC8
STX17 EIF3J HDAC2
SMARCAD1
CSDE1
SMAD2
DCAF10
RNF146 KRCC1
MCM10 SCO1
CHEK1
ABCE1
AGGF1
CREBL2
C18orf25
FEN1
EIF2S2 ANKRD46 RHEB
MBLAC2
NIP7
CDC6 PPP4R1 GINS3 FAM220A
ARMC1
TMEM151A
PARG ARAP2
KIAA1468 DUSP11 NDC1
C6orf89
SEZ6L2
FKTN
RFX3
LRRC57 PIAS2
SEH1L TAP1
MOB1B METAP1 CACUL1
SUN1
IDH3A
POU2F2
ZNF189
DHX33 UBE2W TNKS FAM149B1 PCMT1
LTBR
ZNF22 NACC1
CD3EAP
PEX3
STX7 TMEM248 GOLPH3 AKAP11AP3M1 GSK3A
WDR43
TRIM8 WDR3
PAK1IP1
BBOF1
CSTF3 ZNF507 ACSL4
NUDCD1
BBS10 VWA8
RANBP1 NHLRC3
CPEB3
FAM84A
STK3
CNOT7
ORC3 CLCN3
MED28
ELAVL3 ANAPC10
LTBP3
GSE1
KCNC2
FLT4 RALA
ARSK
MAK16
ERCC4
PTPRA
SLC6A2 JAG1
ADARB1 ASB1 ENY2
DLG4 DPY19L3 WBP1L
LRCH1
TFRC
SEMA6D DCBLD2
MEX3D
ADAMTS10
LANCL2 TPD52L3 SLC39A8
RASL10B ZNF302 VPS37A
ADPRHL2 MLLT1
PDE4A
FAM91A1
ZNF322 PCYT2
SNRPF
LSM6
MEX3A RTN4IP1
CDH15 MGAT5B SLC35E2B
ACAA2
ORC4 PSMB2 ERI1 BTF3L4
CDK4 ZFP62
ZNF439
UBE2J2
PTMS
ORAI2 ARHGAP23 KCTD9
RPRD1B EFR3A
SPTBN4 SIRT5 ZNF763
COQ3
VSX1
PNKD CNPY2
COX14
PSMC4
PDSS2
MRPL51
MTCH2
PRMT1 COQ5 EIF4E2
COA3
DYNLL1
PHF20L1
Fig. 5 Gene co-expression network analysis. Node size corresponds to the number of connected edges. The gene name is displayed for nodes with >25 edges. Node color corresponds to which diseases the gene is associated: AD (orange), VaD (blue), DLB (green), AD and VaD (pink), AD and DLB (yellow), VaD and DLB (purple), and all three (red). AD Alzheimer’s disease, VaD vascular dementia, DLB dementia with Lewy bodies
Table 3 Validation using the prospective cohort Clinical samples. All 1601 serum subjects and the associated clinical data were distributed from the NCGG Biobank, which collects human biomaterials and data for geriatrics research. Of them, 1021 subjects were patients with AD: Prospective cohort 91 patients with VaD, 169 patients with DLB, 32 patients with MCI and 288 subjects were normal controls with normal cognitive function (NC). NCs Conversion MCI to AD MCI to MCI Total who had subjective cognitive complaints, but normal cognition on the neu- Prediction MCI to AD 10 18 28 ropsychological assessment, were categorized as normal controls. The AD and MCI to MCI 0 4 4 MCI subjects were diagnosed with a probable or possible AD based on the criteria ’ 55,56 Total 10 22 32 of the National Institute on Aging Alzheimer s Association workgroups . We used the probable ADs as AD subjects in this study. The VaD and DLB MCI mild cognitive impairment, AD Alzheimer’s disease subjects were diagnosed based on the criteria of report of the NINDS-AIREN International Workshop57 and fourth report of the DLB Consortium58, respec- tively. The diagnosis of all subjects was conducted based on medical history, physical examination and diagnostic tests, neurological examination, neu- and gene expressions. The development of next-generation ropsychological tests and brain imaging with magnetic resonance imaging or sequencing technology has facilitated comprehensive analysis of computerized tomography by experts including neurologists, psychiatrists, geria- these genetic and expression data. There is no doubt that these tricians or a neurosurgeon, all experts in dementia who are familiar with its additional data would contribute to further improvement of our diagnostic criteria. Comprehensive neuropsychological tests included Mini-Mental State Examination (MMSE), Alzheimer’s Disease Assessment Scale Cognitive risk prediction models. Component Japanese version, Logical Memory I and II from the Wechsler Memory Scale–Revised, frontal assessment battery, Raven’s colored progressive matrices and Geriatric Depression Scale59. If necessary, dopamine transporter imaging and Materials and methods metaiodobenzylguanidine myocardial scintigraphy were performed for the diag- Ethics statements. This study was approved by the ethics committee of the nosis of DLB. Pathological tests and biomarkers in cerebrospinal fluid tests were National Center for Geriatrics and Gerontology (NCGG). The design and per- not used for the diagnosis of dementia. For all of the subjects, the status of the ε formance of current study involving human subjects were clearly described in a APOE 4 allele genotype (the major genetic risk factor with AD) and the MMSE research protocol. All participants were voluntary and completed informed consent score were obtained. All subjects were >60 years in age. All NC subjects had a in writing before registering to NCGG Biobank. MMSE score of >23.
6 COMMUNICATIONS BIOLOGY | (2019) 2:77 | https://doi.org/10.1038/s42003-019-0324-7 | www.nature.com/commsbio COMMUNICATIONS BIOLOGY | https://doi.org/10.1038/s42003-019-0324-7 ARTICLE miRNA expression. Serum samples were isolated from whole blood following the Code availability. We used open source program languages R (version 3.4.1), Ruby standard operating procedure of NCGG Biobank. In brief, blood samples tubes (version 2.4.0) and Python (version 3.5.1) to analyze data and create plots. Code is were gently inverted a few times, put in an upright-position for at least 30 min to available upon request from the corresponding authors. clot, and then centrifuged for 15 min at 3500 rpm at 4 °C. After centrifugation, serum was transferred to storage tubes containing 500 μl per tube and immediately Reporting summary. Further information on experimental design is available in stored in −80 °C freezers. Total RNA was extracted from a 300 μl serum sample the Nature Research Reporting Summary linked to this article. using a 3D‐Gene RNA extraction reagent from a liquid sample kit (Toray Indus- tries, Inc.), as previously described34. Comprehensive miRNA expression analysis was performed using a 3D‐Gene miRNA Labeling kit and a 3D‐Gene Human Data availability miRNA Oligo Chip (Toray Industries, Inc.), which was designed to detect 2562 All microarray data (2562 miRNAs) of this study are publicly available through the Gene miRNA sequences registered in miRBase release 21 (http://www.mirbase.org/). Expression Omnibus (GEO) database at the National Center for Biotechnology Infor- The normalization of miRNA expression was performed by the following steps. mation (NCBI) and accessible through GEO series accession number GSE120584. Mean and standard deviation (SD) were calculated using a set of pre-selected Datasets generated during the current study are available from the corresponding author negative control signals (background signals), the top and bottom 5% of which on reasonable request. were removed. Signal values greater than mean + 2 SD of the background signals were replaced using log2(signal–mean) and labeled effective signals. The remaining signal values were replaced by the minimum of the effective signals–0.1. Received: 21 May 2018 Accepted: 24 January 2019 Undetected signal values were replaced by the average signal of each miRNA signal. To normalize the signals across different microarrays, a set of pre-selected internal control miRNAs (miR-149-3p, miR-2861 and miR-4463), which had been stably detected in more than 500 serum samples, was used. Each miRNA signal value was standardized with the ratio of the average signal of the three internal control References miRNA signals34. 1. 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