Peripheral Biomarkers in Schizophrenia: A Meta-Analysis of Microarray Gene Expression Datasets
Bibliographic record
Abstract
BACKGROUND: Schizophrenia is a severe psychiatric disorder with a complex pathophysiology. Given its prevalence, high risk of mortality, early onset, and high levels of disability, researchers have attempted to develop early detection strategies for facilitating timely pharmacological and/or nonpharmacological interventions. Here, we performed a meta-analysis of publicly available gene expression datasets in peripheral tissues in schizophrenia and healthy controls to detect consistent patterns of illness-associated gene expression. We also tested whether our earlier finding of a downregulation of NPTX2 expression in the brain of schizophrenia patients replicated in peripheral tissues. METHODS: We conducted a systematic search in the Gene Expression Omnibus repository (https://www.ncbi.nlm.nih.gov/gds/) and identified 3 datasets matching our inclusion criteria: GSE62333, GSE18312, and GSE27383. After quality controls, the total sample size was: schizophrenia (n = 71) and healthy controls (n = 57) (schizophrenia range: n = 12-40; healthy controls range: n = 8-29). RESULTS: The results of the meta-analysis conducted with the GeneMeta package revealed 2 genes with a false discovery rate < 0.05: atlastin GTPase 3 (ATL3) (upregulated) and arachidonate 15-lipoxygenase, type B (ALOX15B) (downregulated). The result for ATL3 was confirmed using the weighted Z test method, whereas we found a suggestive signal for ALOX15B (false discovery rate < 0.10). CONCLUSIONS: These data point to alterations of peripheral expression of ATL3 in schizophrenia, but did not confirm the significant association signal found for NPTX2 in postmortem brain samples. These findings await replication in newly recruited schizophrenia samples as well as complementary analysis of their encoded peptides in blood.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.026 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".