Advances in the diagnosis of early biomarkers for acute kidney injury: a literature reviewYang, Chen, He
et alBMC Nephrol (2025) 26 (1), 115
Abstract: Acute kidney injury (AKI) is a critical condition with diverse manifestations and variable outcomes. Its diagnosis traditionally relies on delayed indicators such as serum creatinine and urine output, making early detection challenging. Early identification is essential to improving patient outcomes, driving the need for novel biomarkers. Recent advancements have identified promising biomarkers across various biological processes. Tubular injury markers, including neutrophil gelatinase-associated lipocalin (NGAL), kidney injury molecule-1 (KIM-1), N-acetyl-β-D-glucosaminidase (NAG), and liver-type fatty acid-binding protein (L-FABP), offer insights into early tubular damage. Inflammatory and repair-associated biomarkers, such as interleukin-18 (IL-18), monocyte chemotactic protein-1 (MCP-1), osteopontin (OPN), and C-C motif chemokine ligand 14 (CCL14), reflect ongoing injury and recovery processes. Additionally, stress and repair markers like tissue inhibitor of metalloproteinase-2 (TIMP-2) and insulin-like growth factor-binding protein-7 (IGFBP-7), alongside filtration markers such as cystatin C (CysC) and proenkephalin (PenKid®) e.tal, further enhance diagnostic precision. Oxidative stress-related markers, including Superoxide Dismutase 1 (SOD1), also contribute valuable information. Emerging candidates, such as microRNAs, soluble urokinase plasminogen activator receptor (SuPAR), and chitinase-3-like protein 1 (CHI3L1), hold substantial promise for AKI detection and prognosis. This review summarizes the progress in AKI biomarker research, highlighting their clinical utility and exploring their potential to refine early diagnosis and management strategies. These findings offer a new perspective for integrating novel biomarkers into routine clinical practice, ultimately improving AKI care.© 2025. The Author(s).
Single-cell atlas of human pancreatic islet and acinar endothelial cells in health and diabetesCraig-Schapiro, Li, Chen
et alNat Commun (2025) 16 (1), 1338
Abstract: Characterization of the vascular heterogeneity within the pancreas has previously been lacking. Here, we develop strategies to enrich islet-specific endothelial cells (ISECs) and acinar-specific endothelial cells (ASECs) from three human pancreases and corroborate these findings with three published pancreatic datasets. Single-cell RNA sequencing reveals the unique molecular signatures of ISECs, including structural genes COL13A1, ESM1, PLVAP, UNC5B, and LAMA4, angiocrine genes KDR, THBS1, BMPs and CXCR4, and metabolic genes ACE, PASK and F2RL3. ASECs display distinct signatures including GPIHBP1, CCL14, CD74, AQP1, KLF4, and KLF2, which may manage the inflammatory and metabolic needs of the exocrine pancreas. Ligand-receptor analysis suggests ISECs and ASECs interact with LUM+ fibroblasts and RGS5+ pericytes and smooth muscle cells via VEGF-A:VEGFR2, CXCL12:CXCR4, and LIF:LIFR pathways. Comparative expression and immunohistochemistry indicate disruption of endothelial-expressed CD74, ESM1, PLVAP, THBD, VWA1, and VEGF-A cross-talk among vascular and other cell types in diabetes. Thus, our data provide a single-cell vascular atlas of human pancreas, enabling deeper understanding of pancreatic pathophysiology in health and disease.© 2025. The Author(s).
Molecular Subtype Identification and Potential Drug Prediction Based on Anoikis-Related Genes Expression in KeratoconusJiang, Zhang, Jia
et alInvest Ophthalmol Vis Sci (2025) 66 (2), 3
Abstract: Anoikis is a special apoptosis accompanied by the loss of extracellular matrix (ECM) environment and the decomposition of ECM is an important process in the occurrence of keratoconus (KC). This study aims to describe the expression profile of anoikis-related genes (ARGs) in KC samples, identify differentially expressed genes (DEGs), characterize the biological functions and immune characteristics of different molecular subtypes of KC and predict potential drugs based on the construction of a co-expression network.First, we identified molecular subtypes by optimal clustering K based on the expression profile of ARGs in the KC dataset and analyzed the differences of functional and immune characteristics. Then a weighted gene co-expression network was constructed based on cluster analysis to obtain hub genes and protein-protein interaction network was constructed to analyze hub nodes and predict potential node-targeting drugs.By comparing the expression profile between disease and normal samples, we found that there were significant differences in ARGs such as BCL2, CAV1, and CEACAM5. Consistent cluster analysis identified two definite clusters on the basis of ARGs expression difference. Kyoto Encyclopedia of Genes and Genomes and Gene Ontology enrichment analysis showed that DEGs were enriched significantly in pathways like ECM receptor interaction, chemokine signal, notch signal, focal adhesion, and functional sets like proteolysis, anoikis, regulation of natural killer and T-cell proliferation. CIBERSORT calculation showed that there were significant differences between the two subtypes on immune cell infiltration (monocytes and plasma) and immune molecules (CCL11, CCL14, HLA-A, HLA-B, and so on). Then, co-expression network was constructed based on cluster phenotype, 5202 genes were selected as hub genes, and 321 HubDEGs were obtained after intersection with significant DEGs. Seven hub nodes, EIF4G1, KHSRP, PABPC1, POLR2A, PTBP1, RPS19, and SMARCA4, were identified and matched drugs or small molecular compounds. Insulin and dexamethasone were identified as potential target drugs.We revealed the differential expression of ARGs in KC samples, and identified two distinct subtypes that showed significant differences in biological function and immune infiltration. The identification of hub gene nodes elucidated their therapeutic value on predicted potential drugs.
Identification and analysis of prognostic ion homeostasis characteristics in kidney renal clear cell carcinomaZhang, Qian, Zhao
et alHeliyon (2025) 11 (2), e41736
Abstract: Kidney renal clear cell carcinoma (KIRC), a prevalent primary malignant tumor within the urinary system, is characterized by significant heterogeneity. It has been shown that ion channels are important targets for anti-tumor therapy. In this study, we screened 70 selected KIRC related ion homeostasis genes with significant differential expression. We established diagnostic and prognostic models for 15 genes (PDK4, JPH4, ATP1A3, CCL7, CYP27B1, ABCB6, TNFSF11, MCHR1, TNNI3, ANGPTL3, Ednrb, SAA1, Chrna9, TMPRSS6, CCL14) by LASSO regression in the TCGA-KIRC cohort. We also provided a nomogram based on ion homeostasis for clinicians to explore the combined effect of the risk model on clinical variables. Patients in the low-risk group have a significant survival advantage. The potential clinical benefit of our predicted 15 gene signatures in clinical strategies was validated by Calibration Curves and DCA curves. Ultimately, the immune microenvironment and enrichment pathways were analyzed among individuals categorized as high-risk and low-risk. The predictable ion homeostasis-associated 15 gene signature established in this study predicts overall survival outcomes in patients with KIRC, to some extent helping clinicians to select personalized treatment regimens.© 2025 The Authors.
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