Abstract

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.

Highlights

  • Schizophrenia is a multifactorial disorder with 1.13 million incident cases globally

  • The effect of antipsychotics on peripheral gene expression appears to be well recognized as a quarter of the studies screened included drug-naive participants to identify potential diagnostic and clinical state biomarkers (Figure 2C)

  • The unprecedented insight offered by the differentially expressed genes (DEGs) into the complexity of schizophrenia and the ease of their application into clinics outweighs other proposed molecular biomarkers

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Summary

Introduction

Schizophrenia is a multifactorial disorder with 1.13 million incident cases globally. Schizophrenia has claimed 12.66 million disability-adjusted life years up to 2017 (He et al, 2020). Schizophrenia claims millions of lives globally every year, with cardiovascular diseases as a leading cause of death followed by suicide, and respiratory and cancerrelated disorders (Bushe et al, 2010). The current treatment for SCZ includes a combination of antipsychotics and behavioral therapy. The development of second generation antipsychotics has lead to effective treatment for both positive and negative symptoms of SCZ (Zhang et al, 2013; Mauri et al, 2014; Gründer et al, 2016; Chen and Nasrallah, 2019). The cure for SCZ still lies in the future; upon timely intervention of the available treatment, affected individuals can live a relatively symptomfree life

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