Integrated Transcriptome Analysis Reveals Novel Molecular Signatures for Schizophrenia Characterization

Area of Science:

• Neuroscience
• Genetics
• Psychiatry

Background:

• Schizophrenia (SCZ) is a complex psychiatric disorder with challenging molecular characterization.
• Identifying key genes involved in SCZ pathogenesis is crucial for advancing diagnostic and therapeutic strategies.

Purpose of the Study:

• To identify and evaluate disease-responsive essential genes (DREGs) for enhanced molecular characterization of SCZ.
• To develop and validate a machine-learning model based on DREGs for SCZ classification.

Main Methods:

• Analysis of RNA-sequencing data from large patient cohorts (PsychENCODE) and peripheral blood transcriptomes.
• Utilized three algorithms for differential gene expression analysis and SVM-based recursive feature elimination to identify DREGs.
• Examined biological relevance via network analysis, pathway enrichment, polygenic scoring, and validated in SCZ animal models.

Main Results:

• Identified 184 DREGs forming a network implicated in synaptic plasticity, inflammation, neuronal development, and neurotransmission.
• DREGs showed distinct expression patterns in SCZ-related brain regions and animal models, with genetic contributions comparable to polygenic risk scores.
• A DREGs-based SVM model achieved high performance (AUC 85% for SCZ characterization, 79% for specificity).

Conclusions:

• The identified DREGs offer novel insights into the molecular mechanisms underlying SCZ.
• DREGs hold significant potential for improving the molecular characterization and diagnostic accuracy of schizophrenia.