Diabetic Retinopathy Gene Analysis via Transcriptomics and GraphBAN

Zhenghong Jin's research article from 2026 presents an integrative bioinformatics analysis identifying two de novo lipogenesis-associated genes, AHR and SLC1A5, in diabetic retinopathy. The work combines public transcriptomics data, single-cell RNA sequencing from DR fibrous membranes, and GraphBAN modeling for compound interaction prediction. Validation includes RT-qPCR on whole-blood samples from DR patients and control subjects.

Use Cases

• Validate the upregulation of AHR and SLC1A5 gene expression in diabetic retinopathy patient samples using RT-qPCR data.
• Apply the GraphBAN model to predict gene-compound interactions for AHR and SLC1A5, as described for drug screening.
• Analyze single-cell RNA sequencing data to map AHR and SLC1A5 expression to specific cell populations like cDC2 and classical monocytes.
• Utilize the functional enrichment analysis results to explore biological pathways implicated by the identified signature genes.
• Replicate the construction of a clinical risk prediction nomogram based on the AHR and SLC1A5 gene signatures.

Strengths

• Integrates multiple data modalities: public transcriptomics, single-cell RNA-seq, and computational GraphBAN modeling.
• Identifies two specific candidate genes (AHR and SLC1A5) with RT-qPCR validation in patient samples.
• Analysis includes functional enrichment and cellular mapping to cDC2 and monocyte populations.

Limitations

• Dataset is a 13.3 KB document containing analysis results, not the primary raw sequencing data.
• Sample size for the RT-qPCR validation is not specified, limiting assessment of statistical power.
• Temporal and geographic coverage of the source public data is unknown.

Provenance

Source

Zhenghong Jin via figshare.

Collection Method

Integrated bioinformatics analysis of public databases, single-cell RNA sequencing, GraphBAN modeling, and experimental RT-qPCR validation.

Freshness

Last updated in April 2026.