Xianning Central Hospital

BackgroundHepatocellular carcinoma (HCC) exhibits significant molecular heterogeneity and complex immune microenvironment, which to some extent limits the accuracy of prognosis assessment and the formulation of individualized treatment strategies. This study aims to identify immune-derived molecular signatures based on multi-omics data and machine learning methods for the prognosis prediction and risk stratification of HCC.MethodsBased on weighted gene co-expression network analysis(WGCNA) and differential gene analysis,immune-derived molecular signature (IDMS) were screened in both single-cell and bulk transcriptomes. Prognostic model was constructed by multi-machine learning approachs. Subsequently, we investigated the differences in mutations, biological functions, and immune cell infiltration within the tumor microenvironment between the high- and low-risk groups.In addition, we comprehensively analyzed the drug sensitivity of IDMS and predicted potential drugs.ResultsWe identified seven hub genes at the single-cell and bulk transcriptome levels. Based on multiple machine learning, we constructed a prognostic model that demonstrated excellent performance in predicting overall survival for patients with HCC. IDMS -integrated normograms provide a promising and quantitative tool for clinical risk management.Notably, a significant difference in microsatellite instability (MSI) was observed between the high- and low-risk groups. This indicates that patients in the high-risk group might have a better response to immunotherapy. Additionally, we predicted potential drugs targeting to these risk subgroups.ConclusionOur research developed an IDMS that could serve as an effective tool for patient stratification management and prognosis prediction. This signature could provide a reference for immunotherapy for patients with HCC and improve their prognosis.

Background: Aortic dissection (AD) is a life-threatening vascular emergency with limited effective pharmacological treatments. Recent studies have identified Src homology 2 domain-containing transforming protein C1 (p66Shc) as a crucial mediator of oxidative stress, apoptosis, and inflammation in aortic cells, thereby contributing to cellular dysfunction and vascular remodeling implicated in AD development and progression. Despite its established role in promoting vascular dysfunction and remodeling, the protective potential of targeting p66Shc in AD remains unclear.Methods: We quantified activated protein C (aPC) levels in clinical plasma samples from control subjects and AD patients using enzyme-linked immunosorbent assay (ELISA). To evaluate changes in p66Shc expression, we analyzed aortic tissues by Western blotting (WB), immunohistochemistry (IHC), and immunofluorescence (IF) staining. An in vivo AD model was established in thrombomodulin (TM)-mutant ApoE-/- mice, which display impaired TM-dependent PC activation, and exogenous PC was administered to evaluate its therapeutic effect. In parallel, mechanistic studies were performed in human endothelial cells using WB, co-immunoprecipitation (Co-IP), dual-label IF staining, chromatin immunoprecipitation (ChIP), luciferase reporter assays, and mitochondrial functional analyses.Results: In this study, we demonstrate that aPC, a coagulation protease with known cytoprotective properties, downregulates p66Shc expression through epigenetic modifications. Additionally, aPC can modulate the expression of a cold shock protein Y-box-binding protein 1 (YB1), which acts as a transcription factor, leading to elevated O-linked N-acetylglucosamine transferase (OGT) levels. This upregulation enhances the O-glycosylation of p66Shc on its 29th tyrosine residue, preventing its mitochondrial translocation, preserving mitochondrial membrane potential, and reducing reactive oxygen species (ROS) production. Consequently, these molecular mechanisms inhibit the onset and progression of AD.Conclusions: aPC epigenetically represses p66Shc transcription and promotes its O-glycosylation at Thr29 via the YB-1/OGT axis, thereby inhibiting mitochondrial ROS production and preventing vascular injury.

Background:Atherosclerosis (AS) is an important risk factor for macrovascular complications in diabetes mellitus and also serves as an independent risk factor for adverse cardiovascular and cerebrovascular events.Objectives:This study aims to investigate the association between serum meteorin-like protein (Metrnl) levels and carotid artery plaques in patients with type 2 diabetes mellitus (T2DM).Design:This study is a cross-sectional study that recruited patients with type 2 diabetes from our hospital’s endocrinology department and healthy subjects from the physical examination center. Carotid ultrasound and glucose tolerance test were performed, and subjects were grouped and analyzed based on the results of the carotid ultrasound and glucose tolerance test.Methods:A total of 208 participants were enrolled in this study. Serum Metrnl concentrations were measured using an enzyme-linked immunosorbent assay. The predictive value of Metrnl for carotid plaque development in T2DM patients was evaluated using receiver operating characteristic (ROC) curve analysis.Results:Serum Metrnl levels were elevated in the T2DM with carotid plaque group compared to those without (p < 0.05). Multivariate stepwise regression analysis revealed that Metrnl was an independent influencing factor for carotid intima-media thickness. Logistic regression analysis demonstrated that serum Metrnl remained independently associated with carotid plaque formation after adjusting for multiple confounders. ROC curve analysis indicated that the area under the curve for Metrnl in predicting carotid plaque in T2DM patients was 0.724 (95% confidence interval: 0.648–0.800), with a sensitivity of 71.3% and specificity of 67.9%.Conclusion:Elevated Metrnl levels are a risk factor for carotid plaque formation in T2DM patients, suggesting a potential role of Metrnl in the initiation and progression of carotid atherosclerosis in diabetic individuals.

ObjectiveThis study explored the level of soluble CD13 (sCD13) and its correlation with angiogenic factors, evaluating the predictive efficacy of sCD13 in wet age-related macular degeneration (wAMD).Methods200 patients were included (58 in Non AMD group, 42 in Early AMD group, and 100 in wAMD group). Detailed routine and ophthalmologic examinations were performed on all subjects, and the central retinal thickness (CRT) and ganglion cell-inner plexiform layer (GCIPL) were determined. The concentration of sCD13 was compared. The correlation of sCD13 with PDGF, hsCRP and IL-8 was analyzed. ROC curves were plotted and the diagnostic value of sCD13 was assessed by area under the curve (AUC).ResultsThe sCD13 concentration of patients in the wAMD group (20.41 ± 5.86 U/mL) was higher. Age, history of smoking, CRT, hsCRP and IL-8 were higher in the wAMD group, while mean GCIPL, BCVA, and PDGF were lower. sCD13 was positively correlated with hsCRP (r = 0.505) and IL-8 (r = 0.193) and negatively correlated with PDGF (r = −0.241). sCD13 had predictive efficacy in distinguishing wAMD from non AMD and early AMD, with AUC values of 0.74 and 0.61, respectively (P < 0.05).ConclusionsCD13 concentration in the affected eyes of wAMD patients is abnormally elevated and associated with elevated serum hsCRP and IL-8 levels and decreased PDGF. These results suggest that elevated sCD13 may promote the development of wAMD, emphasizing the importance of early control of sCD13 levels.

Osteosarcoma is a highly aggressive bone tumor with a complex tumor microenvironment (TME) that contributes to its progression and therapeutic resistance. In this study, we integrated single‐cell RNA sequencing (scRNA‐seq) and bulk RNA‐seq datasets to characterize the TME and identify key prognostic genes in osteosarcoma. Using scRNA‐seq data from 16 osteosarcoma samples, we defined eight major cell types within the TME and performed functional enrichment analyses. Through weighted gene co‐expression network analysis (WGCNA) focused on an inflammation‐related gene signature, we identified the yellow module as the most correlated with inflammation. By intersecting tumor‐upregulated genes with WGCNA‐derived genes, we identified BNIP3 as the only significant prognostic gene associated with poor survival in both the TARGET and GSE21257 cohorts. Functional annotation revealed that high BNIP3 expression is negatively correlated with immune‐related pathways and immune cell infiltration, including T cells, B cells, NK cells, and neutrophils. Additionally, BNIP3‐high patients exhibited a reduced sensitivity to several potential therapeutic agents. Our findings highlight BNIP3 as a hazardous gene in osteosarcoma, with important roles in immune evasion and prognosis, suggesting its potential as a therapeutic target.

Metabolic dysfunction-associated steatotic liver disease (MASLD) is a leading chronic liver disorder closely linked to diabetes mellitus (DM) and its cardiovascular and renal complications. Early identification of diabetes risk in this population is essential for timely intervention. To develop machine learning (ML) models to predict diabetes risk in individuals with MASLD and to identify key predictive factors using a nationally representative dataset. Data from 6310 MASLD participants (2007-2018) were analysed and classified into DM and non-DM groups. Feature selection was performed using Random Forest, Least Absolute Shrinkage and Selection Operator and Support Vector Machine Recursive Feature Elimination. Based on selected features, nine ML models were developed. Model performance was evaluated using accuracy, sensitivity, area under the curve, F1 score, Rank Score and Brier Score. SHapley Additive exPlanations (SHAP) were used for interpretability. Eight key variables (age, urinary albumin (Ualb), total cholesterol (TC), lipid accumulation product (LAP), urinary creatinine, white blood cell count, uric acid and Visceral Adiposity Index) were identified and used for model construction. Among nine algorithms, the Light Gradient Boosting Machine (LightGBM) model showed superior predictive performance. SHAP analysis revealed that Ualb, age, TC and LAP were the most influential predictors. Our ML-based model effectively identifies individuals with MASLD at high risk for developing DM. The LightGBM algorithm outperformed other models in both accuracy and interpretability. Key predictors such as Ualb and LAP highlight the importance of renal and metabolic markers in early diabetes risk prediction, offering a new approach for individualised intervention and clinical decision-making.

ABSTRACTThis study evaluated the effect of ascorbic acid (AA) combined with dihydroartemisinin (DHA) in lung adenocarcinoma (LUAD) and the underlying mechanisms to determine whether this combination therapy provides a new therapeutic direction for the treatment of LUAD. The CCK‐8, colony formation and transwell assays were used to assess the vitality, proliferation, invasion and migratory capabilities of LUAD cells after various treatments. Furthermore, a xenograft study was performed to assess the effects on tumour inhibition. Transmission electron microscopy (TEM) was used to investigate the changes in mitochondrial architecture in LUAD cells. Additionally, the levels of reactive oxygen species (ROS), divalent iron, malondialdehyde (MDA), mitochondrial membrane potential, and glutathione (GSH) in LUAD cells were quantified using a detection assay kit. GPX4 and SLC7A11 expression levels were assessed using immunohistochemistry, western blotting, and quantitative polymerase chain reaction. The study results showed the inhibitory effect of AA plus DHA on the viability and progression of tumour cells in vitro; the combined therapy reduced cell proliferation, increased cell death, restricted cell invasion and migration, and significantly reduced tumour development in vivo. Furthermore, we observed an excess of iron inside cells, accumulation of ROS, over‐expression of MDA, a reduction in the mitochondrial membrane potential, and depletion of GSH in response to combined therapy. Three ferroptosis‐related inhibitors partially reversed AA plus DHA‐induced cell death. TEM showed changes associated with ferroptosis in the mitochondria. In addition, the administration of AA with DHA reduced the expression of SLC7A11 and GPX4. Finally, the abovementioned effects of ferroptosis could be regulated by influencing the SLC7A11 gene and GPX4. To our knowledge, this is the first study to show that AA and DHA induced ferroptosis in LUAD via the SLC7A11/GPX4 signalling pathway.

This study investigated whether the microrchidia family CW-type zinc finger 2(MORC2) decreases cellular response to paclitaxel by modulating glycolysis in ovarian cancer.In resistant ovarian cancer cell sublines for paclitaxel, including A2780/Taxol and SKOV3/Taxol, and parental cell lines, including A2780 and SKOV3, the differential expression of MORC2 was confirmed via qRT-PCR at the mRNA level and through western blot analysis at the protein level. shRNA interference was used to downregulate MORC2 in paclitaxel-resistant cells, and the actions of MORC2 reduction on paclitaxel resistance, cell cycle distribution, the uptake of glucose, and lactate production were investigated.As shown in A2780/Taxol and SKOV3/Taxol cells, MORC2 was highly expressed, and MORC2 protein expression was related to the time and concentration of paclitaxel. MORC2 downregulation made paclitaxel-resistant ovarian cancer cells more sensitive to paclitaxel, which indicated that more cells were arrested at the G0/G1 phase, suppressed glucose metabolism-related indicators (GLUT4, LDHA, and HK2). MORC2 knockdown reduced tumor growth and glycolysis, and enhanced paclitaxel sensitivity in vivo.These findings suggest MORC2 is upregulated in paclitaxel-resistant ovarian cancer cells, and that downregulation of MORC2 may reduce glycolysis and improve sensitivity to paclitaxel should be further examined.