Target Screening and Single Cell Analysis of Diabetic Retinopathy and Hepatocarcinoma

The association between liver cancer and diabetes has been a longstanding focus in medical research. Current evidence suggests that diabetes is an independent risk factor for the development of liver cancer. Diabetic retinopathy (DR), a prevalent neurovascular complication of diabetes, has yet to be fully characterised concerning liver cancer. Therefore, this study seeks to identify shared genes and pathways between liver cancer and DR to uncover potential therapeutic targets. Immune infiltration and cell communication in liver cancer were analysed using the GEO single-cell dataset GSM7494113. Single-cell RNA sequencing data from rat retinas were obtained from the GEO datasets GSE209872 and GSE160306. Ferritin phagocytosis-related genes were retrieved from the GeneCards database. The SeuratR package was employed for single-cell clustering analysis, while the CellChat package assessed differences in intercellular communication. Genes shared between DR and liver cancer were identified, and the DGIDB database was consulted to predict potential drug-gene interactions targeting membrane proteins involved in ferritin phagocytosis. Key ferritin phagocytosis (FRHG) genes were further validated using quantitative real-time polymerase chain reaction (qRT-PCR). After annotating the single-cell data through dimensionality reduction and clustering, the expression of genes associated with membrane protein-related ferritinophagy was notably elevated in both HCC and DR samples. Based on the expression of ferritinophagy-related genes, the ferritin deposition score in Müller cells from the DR group was significantly higher than that in the control group. Cell communication analysis revealed that central hub genes associated with ferritinophagy, such as PSAP and MK, along with other signalling pathways, were significantly upregulated in the high Müller group compared to the low Müller group. In contrast, VEGF expression was enhanced in the low Müller group. Importantly, the machine learning model constructed using these key hub genes demonstrated high diagnostic efficacy for both HCC and DR. Finally, by simulating a hyperosmotic diabetic microenvironment, we confirmed in vitro that high glucose conditions significantly stimulate the expression of the shared key hub genes in both HCC and DR. The present study identified the connection between ferritinophagy-related subgroups of cells and key hub genes in both HCC and DR, providing new insights into DR-associated biomarkers and the shared pathological regulatory pathways with HCC. These findings further suggest potential therapeutic targets for both diseases.