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Objective: Diabetic retinopathy (DR) is a prevalent microvascular complication of diabetes, contributing to vision impairment and related retinal diseases. Growing evidence indicates that cellular senescence (CS) under high-glucose conditions plays a role in the pathogenesis of DR. This study aims to identify key biomarkers of CS in DR by integrating transcriptomics, single-cell sequencing data, and experimental validation, thereby offering insights for understanding the disease mechanism and developing novel therapeutic strategies.
Methods: DR-related datasets and CS-related genes (CSRGs) were retrieved from the Gene Expression Omnibus (GEO) and CellAge databases. The characteristic gene set for DR-CS was obtained by intersecting differentially expressed genes (DEGs), Weighted Gene Co-expression Network Analysis (WGCNA) results, and CSRGs. Subsequent analyses involved constructing protein-protein interaction (PPI) network, cytoHubba screening, enrichment analysis, and immune infiltration analysis. Machine learning methods were used to identify key biomarkers from the DR-CS characteristic gene set, which were then validated using external datasets. Single-cell sequencing and gene set enrichment analysis (GSEA) were employed to determine the cellular location and biological functions of DR-CS key biomarkers, and animal experiments further validated these biomarkers.
Results: A total of 67 DR-CS-related characteristic genes were identified. Enrichment analysis highlighted pathways like cellular senescence and the Advanced Glycation Endproducts-Receptor for Advanced Glycation Endproducts (AGE-RAGE) signaling pathway in diabetic complications as being closely related to DR development. A set of 13 characteristic genes was selected through a combination of PPI network and six cytoHubba algorithms. Further analysis using machine learning, expression analysis, and Receiver Operating Characteristic (ROC) analysis revealed MYC and LOX as key biomarkers of DR-CS. The expression characteristics of MYC and LOX in various cells were examined using single-cell RNA sequencing. Animal experiments demonstrated that the expression levels of MYC and LOX in the retina were significantly higher in DR group than in the control group ( < 0.05).
Conclusion: MYC and LOX were identified as key biomarkers of DR-CS. Thus, investigating these genes may provide new therapeutic targets for DR treatment by targeting cellular senescence.
Supplementary Information: The online version contains supplementary material available at 10.1186/s13098-025-01899-y.
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http://dx.doi.org/10.1186/s13098-025-01899-y | DOI Listing |
Geroscience
September 2025
Department of Urology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
This study aims to investigate the predictive value of combined phenotypic age and phenotypic age acceleration (PhenoAgeAccel) for benign prostatic hyperplasia (BPH) and develop a machine learning-based risk prediction model to inform precision prevention and clinical management strategies. The study analyzed data from 784 male participants in the US National Health and Nutrition Examination Survey (NHANES, 2001-2008). Phenotypic age was derived from chronological age and nine serum biomarkers.
View Article and Find Full Text PDFJ Cancer Res Clin Oncol
September 2025
Inner Mongolia Medical University Affiliated Hospital, Hohhot, 010030, Inner Mongolia, China.
Purpose: Lung cancer is currently the most common malignant tumor worldwide and one of the leading causes of cancer-related deaths, posing a serious threat to human health. MicroRNAs (miRNAs) are a class of endogenous non-coding small RNA molecules that regulate gene expression and are involved in various biological processes associated with lung cancer. Understanding the mechanisms of lung carcinogenesis and detecting disease biomarkers may enable early diagnosis of lung cancer.
View Article and Find Full Text PDFBr J Cancer
September 2025
Department of Genetics, Institut Curie, PSL Research University, Paris, France.
Background: Identifying molecular alterations specific to advanced lung adenocarcinomas could provide insights into tumour progression and dissemination mechanisms.
Method: We analysed tumour samples, either from locoregional lesions or distant metastases, from patients with advanced lung adenocarcinoma from the SAFIR02-Lung trial by targeted sequencing of 45 cancer genes and comparative genomic hybridisation array and compared them to early tumours samples from The Cancer Genome Atlas.
Results: Differences in copy-number alterations frequencies suggest the involvement in tumour progression of LAMB3, TNN/KIAA0040/TNR, KRAS, DAB2, MYC, EPHA3 and VIPR2, and in metastatic dissemination of AREG, ZNF503, PAX8, MMP13, JAM3, and MTURN.
J Immunother Cancer
September 2025
Harold C Simmons Comprehensive Cancer Center, The University of Texas Southwestern Medical Center, Dallas, Texas, USA
Background: While highly efficacious for numerous cancers, immune checkpoint inhibitors (ICIs) can cause unpredictable and potentially severe immune-related adverse events (irAEs), underscoring the need to understand irAE biology.
Methods: We used a multidimensional approach incorporating single-cell RNA sequencing, mass cytometry, multiplex cytokine assay, and antinuclear antibody (ANA) profiling to characterize the peripheral immune landscape of patients receiving ICI therapy according to irAE development.
Results: Analysis of 162 patients revealed that individuals who developed clinically significant irAEs exhibited a baseline proinflammatory, autoimmune-like state characterized by a significantly higher abundance of CD57 T and natural killer (NK) T cells, plasmablasts, proliferating and activated CXCR3 lymphocytes, CD8 effector and terminal effector memory T cells, along with reduced NK cells and elevated plasma ANA levels.
J Immunother Cancer
September 2025
CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
Neoadjuvant immunochemotherapy (nICT) has demonstrated significant potential in improving pathological response rates and survival outcomes for patients with locally advanced esophageal squamous cell carcinoma (ESCC). However, substantial interindividual variability in therapeutic outcomes highlights the urgent need for more precise predictive tools to guide clinical decision-making. Traditional biomarkers remain limited in both predictive performance and clinical feasibility.
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