Identification of metabolism-associated biomarkers with a focus on ketone body pathways in diabetic retinopathy by integrating transcriptomics and single-cell analysis
- 1Department of Ophthalmology, The First Affiliated Hospital of Xi'an Jiaotong University
- 2Department of Ophthalmology, Xi'an Union Hospital
Abstract
Background: To identify potential biomarkers of ketone body metabolism (KBM) associated with diabetic retinopathy (DR), and to explore possible underlying mechanism between KBM and DR.
Methods: Differentially expressed genes (DEGs) between DR and healthy normal control (NC) samples were acquired in GSE221521.Then, DEGs and KBM-related genes (KBM-RGs) were interleaved to discern candidate genes, and the key feature genes were derived through two algorithms of machine learning. Next, biomarkers were generated through receiver operating characteristic (ROC) curves analysis and expression validation in GSE221521 and GSE189005.Then, enrichment analysis, immune infiltration and single-cell RNA sequencing analysis were conducted.Finally, experimental validation of biomarkers was conducted through reverse transcription-quantitative polymerase chain reaction (RT-qPCR).
Results: ACACA and WDTC1 were recognized as DR-associated candidate biomarkers. ACACA was enriched in Reactome RNA polymerase II transcription pathway, and WDTC1 was enriched in Reactome eukaryotic translation initiation pathway. Two types of immune cells, namely activated NK cells and monocytes, were significantly different between DR and NC samples in bulk immune infiltration analysis. Next, NK cells and T cells were identified as key cell types based on cell proportion analysis. Both ACACA and WDTC1 were highly expressed in NK cells and T cells. Cell-cell communication patterns changed in the DR group. ACACA and WDTC1 showed stable expression during the differentiation of T cells and NK cells. Finally, RT-qPCR results showed that ACACA and WDTC1 were up-regulated in DR samples. This concurred with the results yielded by bioinformatics analysis.
Conclusion: This study identified two metabolism-associated biomarkers (ACACA and WDTC1) that may serve as DR-associated candidate biomarkers. These preliminary findings suggest potential molecular leads for further investigation, though prospective validation in larger cohorts is needed.
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BibTeX
@article{cheng2026identification,
title = {{Identification of metabolism-associated biomarkers with a focus on ketone body pathways in diabetic retinopathy by integrating transcriptomics and single-cell analysis}},
author = {Qiaochu Cheng and Yuyao Qu and Minbo Chen and Li Qin and Jingming Li and Yaguang Hu},
journal = {Frontiers in Endocrinology},
year = {2026},
month = nov,
doi = {10.3389/fendo.2026.1929190},
url = {https://www.frontiersin.org/articles/10.3389/fendo.2026.1929190/full},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Identification of metabolism-associated biomarkers with a focus on ketone body pathways in diabetic retinopathy by integrating transcriptomics and single-cell analysis
AU - Qiaochu Cheng
AU - Yuyao Qu
AU - Minbo Chen
AU - Li Qin
AU - Jingming Li
AU - Yaguang Hu
PY - 2026
DA - 2026/11/05
JO - Frontiers in Endocrinology
DO - 10.3389/fendo.2026.1929190
UR - https://www.frontiersin.org/articles/10.3389/fendo.2026.1929190/full
AB - Background: To identify potential biomarkers of ketone body metabolism (KBM) associated with diabetic retinopathy (DR), and to explore possible underlying mechanism between KBM and DR. Methods: Differentially expressed genes (DEGs) between DR and healthy normal control (NC) samples were acquired in GSE221521.Then, DEGs and KBM-related genes (KBM-RGs) were interleaved to discern candidate genes, and the key feature genes were derived through two algorithms of machine learning. Next, biomarkers were generated through receiver operating characteristic (ROC) curves analysis and expression validation in GSE221521 and GSE189005.Then, enrichment analysis, immune infiltration and single-cell RNA sequencing analysis were conducted.Finally, experimental validation of biomarkers was conducted through reverse transcription-quantitative polymerase chain reaction (RT-qPCR). Results: ACACA and WDTC1 were recognized as DR-associated candidate biomarkers. ACACA was enriched in Reactome RNA polymerase II transcription pathway, and WDTC1 was enriched in Reactome eukaryotic translation initiation pathway. Two types of immune cells, namely activated NK cells and monocytes, were significantly different between DR and NC samples in bulk immune infiltration analysis. Next, NK cells and T cells were identified as key cell types based on cell proportion analysis. Both ACACA and WDTC1 were highly expressed in NK cells and T cells. Cell-cell communication patterns changed in the DR group. ACACA and WDTC1 showed stable expression during the differentiation of T cells and NK cells. Finally, RT-qPCR results showed that ACACA and WDTC1 were up-regulated in DR samples. This concurred with the results yielded by bioinformatics analysis. Conclusion: This study identified two metabolism-associated biomarkers (ACACA and WDTC1) that may serve as DR-associated candidate biomarkers. These preliminary findings suggest potential molecular leads for further investigation, though prospective validation in larger cohorts is needed.
SN - 1664-2392
ER -