Yukui Gao

Background: Diabetic skin wounds arise from chronic hyperglycaemia, peripheral neuropathy, microvascular dysfunction, and compromised immunity. Chronic ulcers are further complicated by infection and local ischaemia–hypoxia, which collectively impede tissue repair. Conventional interventions, including glycaemic control, anti-infective therapy, debridement, and wound dressings, can only partially relieve clinical symptoms. Since these strategies fail to reverse ischaemia–hypoxia or restore the reparative capacity of endogenous cells, ulcers often recur, healing outcomes remain unsatisfactory, and limb amputation may become unavoidable. To overcome these drawbacks, we fabricated a bilayer electrospun microfibre membrane for diabetic wound repair. Methods: An electrospun microfibre membrane with a bilayer structure was fabricated. The outer layer, composed of polycaprolactone (PCL), provided mechanical support and enabled sustained release of mitomycin C (MMC). The inner layer, formulated with a 7:3 ratio of poly-L-lactic acid (PLLA) to gelatine (Gel), was designed to match the mechanical properties of the wound bed and was loaded with dimethyloxalylglycine (DMOG). Antibacterial assays against Escherichia coli and Staphylococcus aureus, as well as proangiogenic evaluations in ischaemic tissues, were conducted to characterise this composite membrane. Results: The outer PCL layer provided mechanical support and enabled sustained MMC release, producing effective antibacterial effects against E. coli and S. aureus. The DMOG-incorporated inner PLLA/GEL layer displayed favourable mechanical compatibility with wound tissue and successfully promoted angiogenesis under ischaemic conditions. By remodelling the wound microenvironment, the biomaterial improved vascularisation and decreased the bacterial load, ultimately accelerating the repair of diabetic skin wounds. Conclusions: This bilayer electrospun membrane integrates antibacterial and pro-angiogenic functions, showing promising application potential for the treatment of diabetic skin wounds.

As a predominant form of cancer affecting male populations, prostate cancer (PCa) demonstrates notably high incidence rates globally. The significant heterogeneity in tumor microenvironment (TME) composition (including epithelial and diverse cell populations) hinders clear interpretation of gene and biomarker roles in disease advancement and immune response modulation. Through combined analysis of bulk and single-cell RNA sequencing data, this investigation evaluates prostate cancer-related genes’ clinical relevance and prognostic potential. We applied approaches like FindAllMarkers, Dseq2 R package, ssGSEA, and WGCNA with both single-cell and bulk transcriptome scales to uncover genes associated with prognosis. Furthermore, we developed a machine learning approach integrating 14 algorithms and 162 algorithmic combinations to support the formation of consensus immune and prognostic-related signatures (IPRS). The IPRS underwent systematic validation in both training and test cohorts, and a multivariate nomogram was constructed to demonstrate its potential utility in prognosis quantification. Through comprehensive multi-omics analyses, which included genomic, single-cell transcriptomic, and bulk transcriptomic data, we sought to achieve a thorough understanding of prognostic characteristics. Furthermore, we evaluated the clinical applicability of the IPRS in the context of immunotherapy and personalized drug selection. Additionally, we examined and confirmed both the gene expression associated with IPRS and the expression level and function of B-cell adhesion molecule (BCAM) within prostate cancer cells and tissue. We discovered 91 genes associated with prognosis in the TME, with 15 of these genes connected to biochemical recurrence. The consensus IPRS constructed based on a machine learning computational framework demonstrates potential value in prognosis prediction and clinical relevance. Multivariate analysis further supports the possibility of IPRS serving as an independent prognostic marker for prostate cancer disease progression. Significant differences in biological functions, immune infiltration, and genomic mutations were also observed among different risk groups. Significantly, the submap method revealed enhanced immunotherapy responsiveness in high-risk patients while highlighting potential pharmacological targets for certain risk subgroups. We selected a collection of genes relevant to PCa prognosis and immune characteristics, which may serve as potential biomarkers with certain clinical translational value.

Purpose: To evaluate the diagnostic performance of apparent diffusion coefficient (ADC) values for the detection of clinically significant prostate cancer (csPCa) in patients with equivocal Prostate Imaging-Reporting and Data System (PI-RADS) 3 lesions. Materials and methods: In this multicenter retrospective study, data from 460 eligible patients meeting predefined inclusion criteria were analyzed. Following the establishment of a standardized region of interest delineation protocol, ADC measurements were obtained for all PI-RADS 3 lesions. Univariate and multivariate logistic regression analyses were performed to identify independent predictors. Diagnostic performance was evaluated using receiver operating characteristic (ROC) curves with calculation of the area under the curve (AUC). The multivariate model’s discriminative ability was assessed through ROC analysis, while calibration was evaluated using calibration plots. Clinical utility was quantified via decision curve analysis. A risk stratification system was subsequently developed to optimize clinical decision-making. Results: For the 460 patients with PI-RADS 3 lesions, 108 (23.5%) were diagnosed with any grade prostate cancer, and 62 (13.5%) were diagnosed with csPCa. The results of the multivariate analysis indicated that prostate volume (OR = 0.957, 95% CI: 0.931–0.984, P = 0.002), minimum ADC (ADCmin) (OR = 0.009, 95% CI: P = 0.014), and lesions in the peripheral zone (OR = 6.269, 95% CI: 2.332–16.850, P < 0.001) were independent predictors of csPCa. Among the ADC parameters, ADCmin demonstrated superior diagnostic accuracy with an AUC of 0.773 (95% CI: 0.717–0.823) for csPCa. The multivariate prediction model incorporating prostate volume, ADCmin and lesion location showed good discrimination and satisfactory calibration in validation cohorts. Applying the threshold prostate volume −3 mm ² /s as the diagnostic criteria of csPCa achieved very high sensitivity (93.5%) and negative predictive value (98.3%). Conclusions: Among the ADC parameters, ADCmin exhibits the highest diagnostic accuracy for identifying csPCa in patients presenting with PI-RADS 3 lesions. Furthermore, we developed a prediction model and a risk stratification system to aid in clinical decision-making regarding prostate biopsy.

Castration-resistant prostate cancer (CRPC) presents significant therapeutic challenges due to its aggressive nature and poor prognosis. Targeting Aurora-A kinase (AURKA) has shown promise in cancer treatment. This study investigates the efficacy of ART-T cell membrane-encapsulated AMS@AD (CM-AMS@AD) nanoparticles (NPs) in a photothermal–chemotherapy–immunotherapy combination for CRPC. Bioinformatics analysis of the Cancer Genome Atlas-prostate adenocarcinoma (TCGA-PRAD) dataset revealed overexpression of AURKA in PCa, correlating with poor clinical outcomes. Single-cell RNA sequencing data from the GEO database showed a significant reduction in immune cells in CRPC. Experimentally, T cell membrane-biomimetic NPs loaded with the AURKA inhibitor Alisertib and chemotherapy drug DTX were synthesized and characterized by dynamic light scattering and transmission electron microscopy, showing good stability and uniformity (average diameter: 158 nm). In vitro studies demonstrated that these NPs inhibited CRPC cell proliferation, increased the G2/M cell population, and elevated apoptosis, confirmed by γH2AX expression. In vivo, CM-AMS@AD NPs accumulated in tumor tissues, significantly slowed tumor growth, decreased proliferation, increased apoptosis, and improved the immune environment, enhancing dendritic cell (DC) maturation and increasing CD8 + /CD4 + ratios. These findings suggest that CM-AMS@AD NPs offer a promising triple-combination therapy for CRPC, integrating photothermal, chemotherapy, and immunotherapy, with significant potential for future clinical applications.

Background: Understanding the molecular mechanisms and identifying prognostic markers across various subtypes and stages of prostate cancer (PCa) are crucial for improving therapeutic strategies against the disease. This study focuses on discovering novel immune-related biomarkers that could aid in the evaluation and prognosis of PCa at different stages and serve as promising therapeutic targets. Methods: Transcriptomic and clinical data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were analyzed to identify differentially expressed genes (DEGs) linked to PCa progression. The relationship between immune cell infiltration in the tumor microenvironment (TME) and the expression levels of baculoviral inhibitor of apoptosis protein repeat containing 5 (BIRC5) and hyaluronan-mediated motility receptor (HMMR) were examined using xCELL and quanTIseq algorithms. Results: The analysis identified ten key hub genes, with survival analysis indicating that higher expressions of BIRC5 and HMMR were associated with poor outcomes and may contribute to tumor progression. Notably, the expressions of BIRC5 and HMMR showed a significant correlation with tumor-infiltrating lymphocytes (TILs) in various PCa subgroups. Immunohistochemistry (IHC) evaluations further corroborated the bioinformatics findings. Conclusions: This study confirms BIRC5 and HMMR as potential biomarkers for predicting the prognosis of PCa, providing important evidence for the development of future therapeutic strategies. Through further research, these biomarkers may be utilized in clinical practice to improve patient management and treatment outcomes.

Some studies have reported that the gut microbiota can influence adrenal-related hormone levels. However, the causal effects of the gut microbiota on adrenal function remain unknown. Therefore, we employed a two-sample Mendelian randomization (MR) study to systematically investigate the impact of gut microbiota on the function of different regions of the adrenal gland. The summary statistics for gut microbiota and adrenal-related hormones used in the two-sample MR analysis were derived from publicly available genome-wide association studies (GWAS). In the MR analysis, inverse variance weighting (IVW) was used as the primary method, with MR-Egger, weighted median, and cML-MA serving as supplementary methods for causal inference. Sensitivity analyses such as the MR-Egger intercept test, Cochran’s Q test, and leave-one-out analysis were used to assess pleiotropy and heterogeneity. We identified 27 causal relationships between 23 gut microbiota and adrenal function using the IVW method. Among these, Sellimonas enhanced the function of the adrenal cortex reticularis zone (beta = 0.008, 95% CI: 0.002–0.013, P = 0.0057). The cML-MA method supported our estimate (beta = 0.009, 95% CI: 0.004–0.013, P = 2 × 10^− 4). Parasutterella, Sutterella, and Anaerofilum affect the functioning of different regions of the adrenal gland. Notably, pleiotropy was not observed. Our findings revealed that the gut microbiota is causally associated with adrenal function. This enhances our understanding of the gut-microbiota-brain axis and provides assistance in the early diagnosis and treatment of adrenal-related diseases in clinical practice.

Pheochromocytoma/paraganglioma (PGPG) is a rare neuroendocrine tumor. Amino acid metabolism is crucial for energy production, redox balance, and metabolic pathways in tumor cell proliferation. This study aimed to build a risk model using amino acid metabolism-related genes, enhancing PGPG diagnosis and treatment decisions. We analyzed RNA-sequencing data from the PCPG cohort in the GEO dataset as our training set and validated our findings using the TCGA dataset and an additional clinical cohort. WGCNA and LASSO were utilized to identify hub genes and develop risk prediction models. The single-sample gene set enrichment analysis, MCPCOUNTER, and ESTIMATE algorithm calculated the relationship between amino acid metabolism and immune cell infiltration in PCPG. The TIDE algorithm predicted the immunotherapy efficacy for PCPG patients. The analysis identified 292 genes with differential expression, which are involved in amino acid metabolism and immune pathways. Six genes (DDC, SYT11, GCLM, PSMB7, TYRO3, AGMAT) were identified as crucial for the risk prediction model. Patients with a high-risk profile demonstrated reduced immune infiltration but potentially higher benefits from immunotherapy. Notably, DDC and SYT11 showed strong diagnostic and prognostic potential. Validation through quantitative Real-Time Polymerase Chain Reaction and immunohistochemistry confirmed their differential expression, underscoring their significance in PCPG diagnosis and in predicting immunotherapy response. This study’s integration of amino acid metabolism-related genes into a risk prediction model offers critical clinical insights for PCPG risk stratification, potential immunotherapy responses, drug development, and treatment planning, marking a significant step forward in the management of this complex condition.

Background: Disulfidptosis and the disulfidptosis-related gene SLC7A11 have recently attracted significant attention for their role in tumorigenesis and tumour management. However, its association with adrenocortical carcinoma (ACC) is rarely discussed. Methods: Differential analysis, Cox regression analysis, and survival analysis were used to screen for the hub gene SLC7A11 in the TCGA and GTEx databases and disulfidptosis-related gene sets. Then, we performed an association analysis between SLC7A11 and clinically relevant factors in ACC patients. Univariate and multivariate Cox regression analyses were performed to evaluate the prognostic value of SLC7A11 and clinically relevant factors. Weighted gene coexpression analysis was used to find genes associated with SLC7A11. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses and the LinkedOmics database were used to analyse the functions of SLC7A11-associated genes. The CIBERSORT and Xcell algorithms were used to analyse the relationship between SLC7A11 and immune cell infiltration in ACC. The TISIDB database was applied to search for the correlation between SLC7A11 expression and immune chemokines. In addition, we performed a correlation analysis for SLC7A11 expression and tumour mutational burden and immune checkpoint-related genes and assessed drug sensitivity based on SLC7A11 expression. Immunohistochemistry and RT‒qPCR were used to validate the upregulation of SLC7A11 in the ACC. Results: SLC7A11 is highly expressed in multiple urological tumours, including ACC. SLC7A11 expression is strongly associated with clinically relevant factors (M-stage and MYL6 expression) in ACC. SLC7A11 and the constructed nomogram can accurately predict ACC patient outcomes. The functions of SLC7A11 and its closely related genes are tightly associated with the occurrence of disulfidptosis in ACC. SLC7A11 expression was tightly associated with various immune cell infiltration disorders in the ACC tumour microenvironment (TME). It was positively correlated with the expression of immune chemokines (CXCL8, CXCL3, and CCL20) and negatively correlated with the expression of immune chemokines (CXCL17 and CCL14). SLC7A11 expression was positively associated with the expression of immune checkpoint genes (NRP1, TNFSF4, TNFRSF9, and CD276) and tumour mutation burden. The expression level of SLC7A11 in ACC patients is closely associated withcthe drug sensitivity. Conclusion: In ACC, high expression of SLC7A11 is associated with migration, invasion, drug sensitivity, immune infiltration disorders, and poor prognosis, and its induction of disulfidptosis is a promising target for the treatment of ACC.

Purpose The objective of this investigation was to explore the diagnostic capability of Prostate Specific Antigen Mass Ratio (PSAMR) combined with Prostate Imaging Reporting and Data System (PI-RADS) scoring for clinically significant prostate cancer (CSPC), develop and validate a Nomogram prediction model for the probability of prostate cancer occurrence in patients who have not undergone prostate biopsy. Methods Initially, we retrospectively collected clinical and pathological data of patients who underwent trans-perineal prostate puncture at Yijishan Hospital of Wanan Medical College from July 2021 to January 2023. Through logistic univariate and multivariate regression analysis, independent risk factors for CSPC were determined. Receiver Operating Characteristic (ROC) curves were generated to compare the ability of different factors for diagnosis of CSPC. Then, we split the dataset into a training set and validation set, compared their heterogeneity, and developed a Nomogram prediction model based on the training set. Finally, we validated the Nomogram prediction model in terms of discrimination, calibration, and clinical usefulness. Results Logistic multivariate regression analysis illustrated that age [64–69 (OR = 2.736, P = 0.029); 69–75 (OR = 4.728, P = 0.001); > 75 (OR = 11.344, P < 0.001)], PSAMR [0.44–0.73 (OR = 4.144, P = 0.028); 0.73–1.64(OR = 13.022, P < 0.001); > 1.64(OR = 50.541, P < 0.001)], and PI-RADS score [4 points (OR = 7.780, P < 0.001); 5 points (OR = 24.533, P < 0.001)] were independent risk factors for CSPC. The Area Under the Curve (AUC) of the ROC curves of PSA, PSAMR, PI-RADS score, and PSAMR combined with PI-RADS score were respectively 0.797, 0.874, 0.889, and 0.928. The performance of PSAMR and PI-RADS score for diagnosis of CSPC was superior to PSA, but inferior to PSAMR combined with PI-RADS. Age, PSAMR, and PI-RADS were included in the Nomogram prediction model. The AUCs of the training set ROC curve and the validation set ROC curve were 0.943 (95% CI 0.917–0.970) and 0.878 (95% CI 0.816–0.940), respectively, in the discrimination validation. The calibration curve showed good consistency, and the decision analysis curve suggested the model had good clinical efficacy. Conclusions We found that PSAMR combined with PI-RADS scoring had a strong diagnostic capability for CSPC, and provided a Nomogram prediction model to predict the probability of prostate cancer occurrence combined with clinical data.