Diabetic Retinopathy Gene Expression Analysis with Single-Cell Data

Two candidate genes, AHR and SLC1A5, were identified through integrative analysis of transcriptomics and single-cell RNA sequencing data from diabetic retinopathy patients. The 58.6 KB Excel file contains results from a bioinformatics study by Zhenghong Jin, published on figshare in April 2026. It includes a predictive nomogram, functional enrichment results, and GraphBAN model predictions for gene-compound interactions.

Use Cases

• Validate the predictive nomogram for DR risk using the total scores derived from AHR and SLC1A5 expression levels.
• Analyze the functional enrichment results to explore biological pathways like metabolic processes implicated in DR pathogenesis.
• Investigate the predicted gene-compound interactions for AHR and SLC1A5 from the GraphBAN model for drug discovery.
• Map the dynamic expression patterns of AHR and SLC1A5 to specific cell populations like cDC2 and classical monocytes using the single-cell RNA sequencing data.

Strengths

• Integrates multiple data types: transcriptomics, single-cell RNA-seq, and computational GraphBAN predictions.
• Candidate genes AHR and SLC1A5 were validated via RT-qPCR in patient blood samples.
• Data is openly shared under a CC-BY-4.0 license, facilitating reuse.

Limitations

• The dataset is very small at 58.6 KB, indicating limited raw data or summary-level results only.
• Sample size for the validating RT-qPCR experiment is not specified, limiting assessment of statistical power.
• Unknown row and column counts prevent evaluation of the dataset's granularity and feature scope.

Provenance

Source

Public databases for DR-related transcripts and DNL-associated genes, with validation from original RT-qPCR.

Collection Method

Integrated bioinformatics analysis, single-cell RNA sequencing, GraphBAN modeling, and laboratory validation.

Freshness

Last updated April 2026, representing recent research findings.