Blood-based dynamic genomic signature for obsessive-compulsive disorder.

Area of Science:

• Genomics
• Psychiatry
• Molecular Biology

Background:

• Current psychiatric diagnoses rely solely on clinical symptoms, lacking objective biological markers.
• Obsessive-compulsive disorder (OCD), schizophrenia, and major depressive disorder (MDD) often present with overlapping symptoms and can be comorbid.
• A biological, blood-based diagnostic tool for differentiating these disorders is highly needed.

Purpose of the Study:

• To identify a specific, blood-based genomic signature for diagnosing obsessive-compulsive disorder (OCD).
• To develop a predictive model capable of differentiating OCD from healthy controls, major depressive disorder (MDD), and schizophrenia.
• To assess the clinical utility of a dynamic genomic signature for psychiatric disorder diagnosis.

Main Methods:

• Blood gene expression analysis was performed on 77 OCD patients, 39 MDD patients, 40 schizophrenia patients, and 67 healthy controls.
• A logistic regression algorithm was used to construct a predictive model for OCD based on gene expression.
• The predictive model's accuracy was validated using a null set with randomly reassigned disease/control status.

Main Results:

• A six-gene panel (COPS7A, FKBP1A, FIBP, TP73-AS1, SDF4, GOLGA8A) successfully discriminated OCD patients from healthy controls, MDD, and schizophrenia.
• The predictive model achieved high performance metrics: 0.938 AUC, 86% accuracy, 88% sensitivity, and 85% specificity.
• The identified gene signature was robust, as confirmed by testing against a null set.

Conclusions:

• A blood transcriptomic signature can reliably distinguish obsessive-compulsive disorder (OCD) from major depressive disorder (MDD) and schizophrenia.
• This study demonstrates the feasibility of using dynamic, blood-based genomic signatures for psychiatric disorder diagnosis.
• The findings suggest a potential new tool to aid clinicians in diagnosing OCD and making treatment decisions.