Diabetic Retinopathy Candidate Genes from Integrated Transcriptomic Analysis

An Excel table presents results from an integrative bioinformatics study identifying de novo lipogenesis-associated genes in diabetic retinopathy. The data includes two candidate genes, AHR and SLC1A5, validated via RT-qPCR, and predictions of gene-compound interactions from a GraphBAN model. The dataset was authored by Zhenghong Jin and uploaded to figshare in April 2026.

Use Cases

• Validate RT-qPCR-confirmed upregulation of AHR and SLC1A5 genes in diabetic retinopathy patient samples.
• Analyze functional enrichment results linking identified genes to specific DR-associated biological pathways.
• Explore GraphBAN model predictions for 2 compounds interacting with AHR and 14 compounds interacting with SLC1A5.
• Map single-cell RNA-seq expression patterns of candidate genes to cDC2 and classical monocyte cell populations.
• Use the provided nomogram scores to assess predicted risk of diabetic retinopathy.

Strengths

• Contains two experimentally validated candidate genes (AHR, SLC1A5) via RT-qPCR.
• Integrates predictions from multiple analysis methods: GraphBAN, single-cell RNA-seq, and functional enrichment.
• Data is structured in a single Excel file for direct access.

Limitations

• The dataset is very small at 11.5 KB, containing only analysis results, not raw sequencing data.
• Row counts and specific column names for the underlying data are not provided.
• Findings are preliminary, based on an exploratory study as noted by the author.

Provenance

Source

figshare, author Zhenghong Jin.

Collection Method

Integrated bioinformatics analysis of public transcriptomic data, single-cell RNA sequencing, and GraphBAN modeling, validated by RT-qPCR.

Freshness

Last updated April 2026.