Schizophrenia Fatty Acid Metabolism Gene Expression and Diagnostic Model

A 2026 study by Cui Zhao integrates single-cell and bulk RNA-seq data from 9 schizophrenia patients and 14 controls to investigate cell type-specific alterations in fatty acid metabolism. The dataset includes a five-gene diagnostic model for schizophrenia, validated with an area under the ROC curve of 0.856 in the training cohort. The model was further validated using an MK-801-induced mouse model of schizophrenia.

Use Cases

• Validate diagnostic biomarkers for schizophrenia based on the five identified key genes (ACAA1, ACAT2, ACSS1, PSME1, S100A10).
• Analyze cell type-specific gene expression patterns in neuronal subpopulations like CUX2+ NeuN and OPRM1+ NeuN.
• Investigate correlations between fatty acid metabolism genes and inflammatory responses in schizophrenia.
• Benchmark new diagnostic models against the published LASSO regression model with an AUC of 0.856.

Strengths

• Includes a validated five-gene diagnostic model with an area under the ROC curve of 0.856 in the training cohort.
• Integrates data from multiple sources, including single-cell sequencing (23 samples) and bulk RNA-seq datasets GSE174407 and GSE107638.
• Key findings were validated in an independent mouse model of schizophrenia.

Limitations

• Row count and column-level documentation are unknown, which may limit suitability assessment.
• The dataset is very small at 9.6 KB, indicating limited scope.
• Description metadata is limited; actual data quality and structure require manual inspection after download.

Provenance

Source

figshare, author Cui Zhao

Collection Method

Integrated analysis of single-cell and bulk RNA-seq data using Seurat, SCENIC, and Monocle 2, with model construction via LASSO regression.

Freshness

Last updated 2026-04-21 05:29:59; freshness should be verified.