Gene expression profiling of monocytes in recent-onset schizophrenia

Transcriptomics reveals preterm birth risk: identification and validation of key genes in monocytes

BACKGROUND

Preterm birth (PTB) is a leading cause of neonatal mortality and long-term disability worldwide. However, the molecular mechanisms underlying PTB remain incompletely understood, and the etiology of many PTB cases is still largely unexplained. Due to their close association with PTB, monocytes serve as an ideal matrix for identifying peripheral biomarkers predictive of preterm birth risk.

OBJECTIVE

This study aims to identify and validate biomarkers that could predict PTB, improving clinical diagnostic accuracy and enhancing preventive measures against PTB.

METHODS

This study conducted a comprehensive transcriptomic analysis of monocytes obtained from PTB patients (gestational age = 28-36 weeks) and age-matched healthy controls (HC, gestational age = 37+ 1-41+ 4 weeks). Blood samples were collected within 30 min of hospital admission and prior to labor initiation to ensure consistency. We further validated the findings after screening for potential biomarkers using quantitative real-time PCR (qPCR). While the sample size was relatively small, this study provides foundational evidence supporting the role of CXCL3 and IL-6 as biomarkers for PTB, laying a framework for future prospective research.

RESULTS

We identified 295 significantly differentially expressed genes compared to the control group, and Weighted Gene Co-expression Network Analysis (WGCNA) further revealed genes significantly associated with PTB. These genes are involved in immune pathways such as rheumatoid arthritis, influenza A, and the MAPK signaling pathway. Machine learning analysis and qPCR validation identified two essential genes-CXCL3 and IL-6. Based on these two genes, the diagnostic model achieved an AUC value of 1 in the discovery cohort, distinguishing PTB patients from healthy controls.

CONCLUSION

The immune responses observed in peripheral blood mononuclear cells (PBMCs) may be closely related to the mechanisms underlying PTB. Monocyte-derived genes CXCL3 and IL-6 are promising biomarkers for predicting PTB risk, offering new diagnostic tools for clinical practice. These findings have the potential to enhance PTB prevention and management strategies.

Combined Predictors for the Diagnostic Transition from Acute and Transient Psychotic Disorder to Schizophrenia: A Retrospective Study

Purpose

Acute and transient psychotic disorder (ATPD), a psychosis frequently diagnosed, can potentially evolve into chronic conditions like schizophrenia (SCZ) and other mental disorders. This study aimed to develop a predictive model based on clinical data to forecast the transition from ATPD to SCZ and to identify the predictive factors.

Methods

According to the diagnostic criteria issued by the International Statistical Classification of Diseases and Related Health Problems 10th Revision (ICD-10), 396 inpatients diagnosed with ATPD were collected in this study. The Cox proportional-hazards regression model was performed using demographic data, clinical characteristics, and inflammatory markers to identify independent predictors for subsequent diagnostic transition (SDT) to SCZ.

Results

During the follow-up period, 43.69% (n = 173) of ATPD patients had their diagnoses revised to SCZ. The multivariate Cox regression analysis identified post-treatment monocyte count, post-treatment monocyte/lymphocyte ratio (MLR), and the presence of schizophreniform symptoms as significant predictors for the diagnostic revision. Time-dependent receiver operating characteristic (TimeROC) analyses were developed. The AUC value at the 5-year follow-up was 0.728 for combined predictors, 0.702 for post-treatment monocyte count, 0.764 for post-treatment MLR, and 0.535 for the presence of schizophreniform symptoms.

Conclusion

The combined predictors had good predictive ability for the diagnostic transition from acute and transient psychotic disorder to schizophrenia.

Peripheral Blood Mononuclear Cell Biomarkers for Major Depressive Disorder: A Transcriptomic Approach

Background: Major depressive disorder (MDD) is a complex condition characterized by persistent depressed mood, loss of interest or pleasure, loss of energy or fatigue, and, in severe case, recurrent thoughts of death. Despite its prevalence, reliable diagnostic biomarkers for MDD remain elusive. Identifying peripheral biomarkers for MDD is crucial for early diagnosis, timely intervention, and ultimately reducing the risk of suicide. Metabolic changes in peripheral blood mononuclear cells (PBMCs) have been observed in animal models of depression, suggesting that PBMC could serve as a valuable matrix for exploring potential peripheral biomarkers in MDD. Methods: We performed a transcriptomic analysis of PBMCs from patients with MDD and age- and sex-matched healthy controls (n = 20 per group). Results: Our analysis identified 270 differentially expressed genes in PBMCs from MDD patients compared to controls, which correlated with the Hamilton Depression Rating Scale scores. These genes are involved in several KEGG pathways, including the herpes simplex virus 1 infection pathway, NOD-like receptor signaling pathway, antigen processing and presentation, and glycerophospholipid metabolism-all of which are linked to various aspects of the immune response. Further machine learning analysis and quantitative real-time PCR (qPCR) validation identified three key genes-TRPV2, ZNF713, and CTSL-that effectively distinguish MDD patients from healthy controls. Conclusions: The immune dysregulation observed in PBMCs is closely related to the pathogenesis of MDD. The candidate biomarkers TRPV2, ZNF713, and CTSL, identified and validated through machine learning and qPCR, hold promise for the objective diagnosis of MDD. Trial Registration: Clinical Trial Registry identifier: ChiCTR2300076589.