MDD Biomarker Data: DCBLD2 and Immune-Related Genes from Blood, Brain, and Rat Models

A six-gene diagnostic signature (DCBLD2, FZD5, GP1BA, MMP8, RNF144B, SOCS1) for major depressive disorder was identified with an AUC of 0.83. The dataset includes transcriptomic data from human peripheral blood, post-mortem prefrontal cortex, and a chronic unpredictable stress rat model, authored by Xinyu Wu and last updated on 2026-04-30. It was compiled from the Gene Expression Omnibus database using Mendelian randomization and machine learning methods like LASSO and random forest.

Use Cases

• Identifying diagnostic biomarkers for major depressive disorder based on the six-gene signature.
• Analyzing immune cell infiltration differences between MDD patients and healthy controls.
• Validating candidate gene expression in independent human cohorts and animal stress models.
• Investigating cell-type-specific gene expression localization using single-cell RNA sequencing data.

Strengths

• Includes multi-source validation from human peripheral blood, post-mortem brain tissue, and a rat model.
• The six-gene diagnostic signature demonstrated an AUC of 0.83 for discriminating MDD.
• Analysis leveraged multiple machine learning methods including LASSO regression and random forest.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Data may reflect bias inherent to the specific transcriptomic datasets retrieved from GEO.

Provenance

Source

Gene Expression Omnibus (GEO) database, human cohort, and rat model.

Collection Method

Mendelian randomization, differential gene expression analysis, and machine learning (logistic regression, LASSO, random forest).

Freshness

Last updated 2026-04-30 05:41:45; freshness should be verified.