Table 6: Biomarker Identification in Diabetic Neuropathy via Mendelian Randomization

Jiannan Li published a dataset on 2026-04-27 containing results from a Mendelian randomization and bioinformatics analysis of diabetic neuropathy. The study identified seven plasma proteins and three key genes (HSPB1, KRT14, SFN) as potential diagnostic biomarkers. The analysis integrated GWAS data on 4,907 proteins with a gene expression dataset (GSE148061) from 106 individuals.

Use Cases

• Validate potential plasma protein biomarkers for diabetic neuropathy based on the seven identified proteins.
• Investigate gene expression patterns in diabetic neuropathy using the integrated GEO dataset GSE148061.
• Apply machine learning for biomarker discovery based on the LASSO and Random Forest methods described.
• Perform functional enrichment analysis on key genes based on the identified Wnt and MAPK signaling pathways.

Strengths

• Analysis is based on GWAS data for 4,907 plasma proteins.
• Validation includes a gene expression dataset (GSE148061) with 53 patients and 53 healthy donors.
• Findings were further validated using a diabetic neuropathy mouse model.

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 GWAS and GEO sources used.

Provenance

Source

figshare, author Jiannan Li

Collection Method

Mendelian randomization using GWAS data, bioinformatics analysis of GEO dataset GSE148061, and validation with animal models.

Freshness

Last updated 2026-04-27 04:14:50; freshness should be verified.