2026-10-11T15:10:10Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33998926 2026-09-25T17:37:43Z category_4 category_8 category_12 category_13 category_14 category_873 category_46 category_734 category_61 category_64 category_132 portal_5 item_type_3 month_year_09_2026

Drug prediction results of CA11. Rong Feng (9161534) Xia Wang (36364) Xiaodong Li (181281) Zhifang Zhao (4687054) Bendan Long (25111366) Hui Zhang (7197) Fengjuan Yue (13188370) Biochemistry Microbiology Cell Biology Genetics Molecular Biology Chemical Sciences not elsewhere classified Immunology Biological Sciences not elsewhere classified Developmental Biology Cancer Infectious Diseases values exceeding 0 regulatory network analysis including schizandrin b expression consistency analysis thirty candidate compounds candidate compound prediction spliceosome &# 8221 significant negative correlation proteasome &# 8221 kdm5b &# 8211 immune cell infiltration autophagy &# 8221 related expression scores derive candidate genes performed using machine m2 macrophages demonstrating identify biomarkers associated xlink "> med19 related datasets gse189005 xlink "> m2 macrophages related genes significant downregulation differential infiltration &# 8220 candidate biomarkers thus qualifying therapeutic intervention study aimed published studies public databases potential framework pirinixic acid mechanistic investigation learning algorithms functional enrichment exhibiting area diabetic retinopathy control groups consistently downregulated biological relevance <div> BackgroundEmerging evidence indicates that hypoxia- and cuproptosis-related molecular alterations may contribute to diabetic retinopathy (DR). This study aimed to identify biomarkers associated with these processes and investigate their biological relevance in DR. MethodsThe DR-related datasets GSE189005 and GSE221521 were sourced from public databases, while hypoxia-related and cuproptosis-related genes were extracted from published studies. Genes consistently differentially expressed across both datasets were intersected with those associated with hypoxia- and cuproptosis-related expression scores to derive candidate genes. Selection of candidate biomarkers was performed using machine-learning algorithms and expression consistency analysis, with diagnostic performance assessed via receiver operating characteristic (ROC) curves. Functional enrichment, immune cell infiltration, regulatory network analysis, candidate compound prediction, and reverse transcription quantitative PCR (RT-qPCR) were also conducted. ResultsIntersection screening yielded 13 candidate genes. Machine-learning analysis identified three candidate biomarkers. Notably, MED19 and CA11 were consistently downregulated in DR, exhibiting area under the curve (AUC) values exceeding 0.70 in both datasets, thus qualifying as biomarkers. Functional enrichment analysis suggested MED19's involvement in “proteasome” and “spliceosome” pathways, while CA11 was linked to “regulation of autophagy” and “basal cell carcinoma” pathways. Differential infiltration of three immune cell populations (eosinophils, M2 macrophages, activated natural killer cells) was noted between DR and control groups in GSE189005, with M2 macrophages demonstrating a significant negative correlation with both biomarkers. Regulatory network analysis highlighted several candidate transcriptional regulators for MED19 and CA11, including ELF1–MED19 and KDM5B–CA11. Thirty candidate compounds/interventions were identified, including schizandrin B and pirinixic acid. RT-qPCR confirmed the significant downregulation of MED19 and CA11 in DR samples, providing preliminary support. ConclusionMED19 and CA11 were identified as biomarkers, presenting a potential framework for therapeutic intervention in DR.</div> 2026-09-25T17:37:39Z Dataset Dataset 10.1371/journal.pone.0358182.s003 https://figshare.com/articles/dataset/_p_Drug_prediction_results_of_CA11_p_/33998926 CC BY 4.0