Dataset Information

The Correlation-Base-Selection Algorithm for Diagnostic Schizophrenia Based on Blood-Based Gene Expression Signatures.

ABSTRACT: Microarray analysis of gene expression is often used to diagnose different types of disease. Many studies report remarkable achievements in nervous system disease. Clinical diagnosis of schizophrenia (SCZ) still depends on doctors' experience, which is unreliable and needs to be more objective and quantified. To solve this problem, we collected whole blood gene expression data from four studies, including 152 individuals with schizophrenia (SCZ) and 138 normal controls in different regions. The correlation-based feature selection (CFS, one of the machine learning methods) algorithm was applied in this study, and 103 significantly differentially expressed genes between patients and controls, called "feature genes," were selected; then, a model for SCZ diagnosis was built. The samples were subdivided into 10 groups, and cross-validation showed that the model we constructed achieved nearly 100% classification accuracy. Mathematical evaluation of the datasets before and after data processing proved the effectiveness of our algorithm. Feature genes were enriched in Parkinson's disease, oxidative phosphorylation, and TGF-beta signaling pathways, which were previously reported to be associated with SCZ. These results suggest that the analysis of gene expression in whole blood by our model could be a useful tool for diagnosing SCZ.

SUBMITTER: Zhang H

PROVIDER: S-EPMC5322573 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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The Correlation-Base-Selection Algorithm for Diagnostic Schizophrenia Based on Blood-Based Gene Expression Signatures.

Zhang Hang H Xie Ziyang Z Yang Yuwen Y Zhao Yizhen Y Zhang Bao B Fang Jing J

BioMed research international 20170209

Microarray analysis of gene expression is often used to diagnose different types of disease. Many studies report remarkable achievements in nervous system disease. Clinical diagnosis of schizophrenia (SCZ) still depends on doctors' experience, which is unreliable and needs to be more objective and quantified. To solve this problem, we collected whole blood gene expression data from four studies, including 152 individuals with schizophrenia (SCZ) and 138 normal controls in different regions. The ...[more]

PMID: 28280741

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Project description:Early diagnosis of transthyretin (TTR) amyloid diseases remains challenging because of variable disease penetrance. Currently, patients must have an amyloid positive tissue biopsy to be eligible for disease modifying therapies. Early diagnosis is often difficult because the patient exhibits apparent symptoms of polyneuropathy or cardiomyopathy, but has a negative amyloid biopsy. Thus, there is a pressing need for more objective, quantitative diagnostics and biomarkers of TTR-aggregation-associated polyneuropathy and cardiomyopathy. This is especially true in the context of clinical trials demonstrating significant disease modifying effects, e.g. when the TTR tetramer stabilizer tafamidis was administered to familial amyloid polyneuropathy (FAP) patients early in the disease course. When asked if the findings of the tafamidis registration trial were âsufficiently robust to provide substantial evidence of efficacy for a surrogate endpoint that is reasonably likely to predict a clinical benefitâ the advisory committee said yes, but the FDA rejected the tetramer stabilization surrogate biomarker required for orphan tafamidis approvalâhence, acceptable biomarkers are badly needed. Herein, we explored whether peripheral blood cell mRNA expression profiles could differentiate symptomatic from asymptomatic V30M FAP patients, and if such a profile would normalize upon tafamidis treatment. We demonstrate that blood cell gene expression patterns reveal sex-independent as well as male and female specific inflammatory signatures in symptomatic FAP patients, but not in asymptomatic carriers, that normalize in FAP patients 6 months after tafamidis treatment. Thus these signatures have potential both as an early diagnostic and as a surrogate biomarker for measuring response to treatment in FAP patients. Study Subjects: Tafamidis-treated and untreated V30M FAP patients, asymptomatic V30M carriers, and healthy, age- and sex-matched controls without TTR mutations had whole blood drawn. RNA was extracted from a total of 309 peripheral blood samples collected in PAXgene tubes (Qiagen). RNA Extraction and Microarray Processing: RNA was extracted with the PAXgene RNA Whole Blood Kit. Each RNA sample (100ng) was amplified and labeled with the Ambion WT Express Kit and hybridized to Affymetrix HuGene 1.1 ST Arrays using the Affymetrix GeneTitan Multi-Channel (MC) platform. Normalized signals were generated using Robust Multichip Average (RMA) in Partek Genomics Suite software version 6.6. Probesets on the DNA microarrays with low expressed signals were determined using a kernel density plot, and signals <Log2 of 6 were excluded from analysis.

Project description:Schizophrenia is a disorder that is characterized by delusions, hallucinations, disorganized speech or behavior, and socio-occupational impairment. The duration of observation and variability in symptoms can make the accurate diagnosis difficult. Identification of biomarkers for schizophrenia (SCZ) can help in early diagnosis, ascertaining the diagnosis, and development of effective treatment strategies. Here we review peripheral blood-based gene expression studies for identification of gene expression biomarkers for SCZ. A literature search was carried out in PubMed and Web of Science databases for blood-based gene expression studies in SCZ. A list of differentially expressed genes (DEGs) was compiled and analyzed for overlap with genetic markers, differences based on drug status of the participants, functional enrichment, and for effect of antipsychotics. This literature survey identified 61 gene expression studies. Seventeen out of these studies were based on expression microarrays. A comparative analysis of the DEGs (n = 227) from microarray studies revealed differences between drug-naive and drug-treated SCZ participants. We found that of the 227 DEGs, 11 genes (ACOT7, AGO2, DISC1, LDB1, RUNX3, SIGIRR, SLC18A1, NRG1, CHRNB2, PRKAB2, and ZNF74) also showed genetic and epigenetic changes associated with SCZ. Functional enrichment analysis of the DEGs revealed dysregulation of proline and 4-hydroxyproline metabolism. Also, arginine and proline metabolism was the most functionally enriched pathway for SCZ in our analysis. Follow-up studies identified effect of antipsychotic treatment on peripheral blood gene expression. Of the 27 genes compiled from the follow-up studies AKT1, DISC1, HP, and EIF2D had no effect on their expression status as a result of antipsychotic treatment. Despite the differences in the nature of the study, ethnicity of the population, and the gene expression analysis method used, we identified several coherent observations. An overlap, though limited, of genetic, epigenetic and gene expression changes supports interplay of genetic and environmental factors in SCZ. The studies validate the use of blood as a surrogate tissue for biomarker analysis. We conclude that well-designed cohort studies across diverse populations, use of high-throughput sequencing technology, and use of artificial intelligence (AI) based computational analysis will significantly improve our understanding and diagnostic capabilities for this complex disorder.

Dataset Information

The Correlation-Base-Selection Algorithm for Diagnostic Schizophrenia Based on Blood-Based Gene Expression Signatures.

Publications

The Correlation-Base-Selection Algorithm for Diagnostic Schizophrenia Based on Blood-Based Gene Expression Signatures.

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