Background: Cell–cell communications of various cell populations within tumor microenvironment play an essential role in primary tumor growth, metastasis evolution, and immune escape. Nevertheless, comprehensive investigation of cell–cell communications in the ccRCC (Clear cell renal carcinoma) microenvironment and how this interplay affects prognosis still remains limited. Methods: Intercellular communications were characterized by single-cell data. Firstly, we employed “CellChat” package to characterize intercellular communications across all types of cells in microenvironment in VHL mutated and non-mutated samples from 8 patients, respectively. And pseudotime trajectory analyses were performed with monocle analyses. Finally clinical prognosis and immunotherapy efficacy with different landscapes of intercellular interplay are evaluated by TCGA-KIRC and immunotherapy cohort. Results: Firstly, the VHL phenotype may be related to the intercellular communication landscape. And trajectory analysis reveals the potential relationship of cell–cell communication molecules with T cells and Myeloid cells differentiation. Furthermore, those molecules also correlate with the infiltration of T cells and Myeloid cells. A tumor cluster with highly expressed ligands was defined by quantitative analysis and transcription factor enrichment analysis, which was identified to be pivotal for intercellular communications in tumor microenvironment. Finally, bulk data indicates bulk that different clusters with different intercellular communications have significant predictive value for prognosis and distinguished immunotherapy efficiency. Conclusions: The intercellular communication landscapes of VHL wild and VHL mutant ccRCC vary. Intercellular communications within the tumor microenvironment also influence T cell and myeloid cell development and infiltration, as well as predict clinical prognosis and immunotherapy efficacy in ccRCC.

Background: Muscle invasive bladder cancer (MIBC) has a poor prognosis even after radical cystectomy (RC). Postoperative survival stratification based on radiomics and deep learning (DL) algorithms may be useful for treatment decision-making and follow-up management. This study was aimed to develop and validate a DL model based on preoperative computed tomography (CT) for predicting postcystectomy overall survival (OS) in patients with MIBC. Methods: MIBC patients who underwent RC were retrospectively included from four centers, and divided into the training, internal validation, and external validation sets. A DL model incorporated the convolutional block attention module (CBAM) was built for predicting OS using preoperative CT images. The authors assessed the prognostic accuracy of the DL model and compared it with classic handcrafted radiomics model and clinical model. Then, a deep learning radiomics nomogram (DLRN) was developed by combining clinicopathological factors, radiomics score (Rad-score) and deep learning score (DL-score). Model performance was assessed by C-index, KM curve, and time-dependent ROC curve. Results: A total of 405 patients with MIBC were included in this study. The DL-score achieved a much higher C-index than Rad-score and clinical model (0.690 vs. 0.652 vs. 0.618 in the internal validation set, and 0.658 vs. 0.601 vs. 0.610 in the external validation set). After adjusting for clinicopathologic variables, the DL-score was identified as a significantly independent risk factor for OS by the multivariate Cox regression analysis in all sets (all P<0.01). The DLRN further improved the performance, with a C-index of 0.713 (95% CI: 0.627–0.798) in the internal validation set and 0.685 (95% CI: 0.586–0.765) in external validation set, respectively. Conclusions: A DL model based on preoperative CT can predict survival outcome of patients with MIBC, which may help in risk stratification and guide treatment decision-making and follow-up management.

Using nephrographic phase CT images combined with pathology diagnosis, we aim to develop and validate a fusion feature-based stacking ensemble machine learning model to distinguish malignant renal neoplasms from cystic renal lesions (CRLs). This retrospective research includes 166 individuals with CRLs for model training and 47 individuals with CRLs in another institution for model testing. Histopathology results are adopted as diagnosis criterion. Nephrographic phase CT scans are selected to build the fusion feature-based machine learning algorithms. The pretrained 3D-ResNet50 CNN model and radiomics methods are selected to extract deep features and radiomics features, respectively. Fivefold cross-validated least absolute shrinkage and selection operator (LASSO) regression methods are adopted to identify the most discriminative candidate features in the development cohort. Intraclass correlation coefficients and interclass correlation coefficients are employed to evaluate feature’s reproducibility. Pearson correlation coefficients for normal distribution features and Spearman’s rank correlation coefficients for non-normal distribution features are used to eliminate redundant features. After that, stacking ensemble machine learning models are developed in the training cohort. The area under the receiver operator characteristic curve (ROC), calibration curve, and decision curve analysis (DCA) are adopted in the testing cohort to evaluate the performance of each model. The stacking ensemble machine learning algorithm reached excellent diagnostic performance in the testing dataset. The calibration plot shows good stability when using the stacking ensemble model. Net benefits presented by DCA are higher than the Bosniak 2019 version classification when employing any machine learning algorithm. The fusion feature-based machine learning algorithm accurately distinguishes malignant renal neoplasms from CRLs, which outperformed the Bosniak 2019 version classification, and proves to be more applicable for clinical decision-making.

Background and Objective: Chronic Prostatitis/Chronic Pelvic Pain Syndrome (CP/CPPS) poses a significant threat to male urinary health and has an unclear pathogenesis. High-mobility group box 1 (HMGB1), a danger-associated molecular pattern that has been identified as a key mediator in various inflammatory diseases. However, its role in CP/CPPS remains unclear. This study aimed to investigate HMGB1’s potential contributions to the pathogenesis of CP/CPPS, offering new perspectives for innovative treatments. Materials and Methods: We have successfully extracted prostate antigens from Sprague–Dawley rat prostate tissue and established an experimental autoimmune prostatitis (EAP) mouse model in non-obese diabetic (NOD) mice. Subsequently, EAP mice were treated with recombinant HMGB1 protein (rmHMGB1) or the HMGB1-specific inhibitor glycyrrhizin for 14 days. Behavioral test was performed to assess the chronic pelvic pain. Hematoxylin and eosin (H&E) staining was employed to assess the extent of inflammatory cell infiltration in the prostate, and enzyme-linked immunosorbent assay (ELISA) was performed to assess levels of inflammatory cytokines. Co-immunofluorescence was used to analyze the functional phenotype of macrophages and spatial localization of HMGB1 in prostate of EAP mice. To further validate these findings, we conducted in vitro experiments. In these experiments, lipopolysaccharide (LPS) was used to induce an inflammatory environment in RAW264.7 cells. Interventions included administering rmHMGB1, silencing HMGB1 gene expression with siRNA, and treating cells with the TRAF6 inhibitor C25-140. After interventions, Western blot and immunofluorescence were employed to evaluate the impact on M1 macrophage polarization and inflammation. Results: In this study, we demonstrate that HMGB1 is highly expressed in the prostate tissues of EAP mice. Treating EAP mice with rmHMGB1 significantly increased prostate histological scores (2.83 vs 1.83, p < 0.05) and the sensitivity to pain stimuli, as evidenced by a higher response frequency to von Frey filament stimulation at 4 g (68.33% vs 53.33%, p < 0.05). This treatment also increased the levels of inflammatory proteins IL-6 and TNF-α. In contrast, suppressing HMGB1 with glycyrrhizin significantly reduced inflammation, as indicated by decreased histological scores (0.50 vs 2.17, p < 0.05), and attenuated pain sensitivity, as evidenced by a lower response frequency to von Frey filament stimulation at 4 g (30.83% vs 52.50%, p < 0.05). Glycyrrhizin treatment also reduced IL-6 and TNF-α levels. Furthermore, the proportion of CD11b⁺iNOS⁺ cells, indicative of M1 macrophage polarization, was significantly reduced after glycyrrhizin treatment. In vitro, HMGB1 can regulate the activity of TRAF6 by partially modulating its ubiquitination and degradation, thereby amplifying TRAF6-mediated NF-κB activation, promoting M1 macrophage polarization, and exacerbating inflammation. Discussion and Conclusions: HMGB1 can enhance TRAF6-mediated NF-κB activation, thereby driving M1 macrophage polarization and exacerbating prostate inflammation in EAP mice. Inhibiting HMGB1 expression with glycyrrhizin can suppress M1 polarization of macrophages to alleviate prostate inflammation. This study suggests that targeting the HMGB1/TRAF6/NF-κB signaling pathway may be an effective therapeutic approach for CP/CPPS.

Background and Purpose: The early diagnosis and differential diagnosis of renal cell carcinoma (RCC) has always been a clinical difficulty and a research focus. Carbonic anhydrase IX (CA IX) is highly expressed on the cell membrane of RCC but is not expressed in normal renal tissues. In this study, nanobubbles (NBs) targeting CA IX with ultrasound and photoacoustic multimodal imaging capabilities were prepared to explore a new method for the diagnosis and differential diagnosis of RCC. Methods: Indocyanine green (ICG)-loaded lipid NBs (ICG-NBs) were prepared by using the filming rehydration method, and anti-CA IX polypeptides (ACPs) were attached to their surfaces to prepare CA IX-targeted NBs (ACP/ICG-NBs). The particle size, zeta potential and ICG encapsulation efficiency of these nanobubbles were measured, and their specific targeting and binding abilities to RCC cells were determined. The in vitro and in vivo ultrasound, photoacoustic and fluorescence imaging characteristics of these nanobubbles were also assessed. Results: The particle size of the ACP/ICG-NBs was 475.9 nm in diameter, and their zeta potential was − 2.65 mV. Laser confocal microscopy and flow cytometry both confirmed that ACP/ICG-NBs had specific binding activity and ideal affinity to CA IX-positive RCC cells (786-O) but not to CA IX-negative RCC cells (ACHN). The intensities of the in vitro ultrasound, photoacoustic and fluorescence imaging were positively correlated with the concentrations of ACP/ICG-NBs. In in vivo ultrasound and photoacoustic imaging experiments, ACP/ICG-NBs exhibited specific enhanced ultrasound and photoacoustic imaging effects in 786-O xenograft tumors. Conclusion: The ICG- and ACP-loaded targeted nanobubbles that we prepared had the capability of ultrasound, photoacoustic and fluorescence multimodal imaging and could specifically enhance the ultrasound and photoacoustic imaging of RCC xenograft tumors. This outcome has potential clinical application value for the diagnosis of RCC at the early stage and the differential diagnosis of benign and malignant kidney tumors.

Background: Current prognostic models have limited predictive abilities for the growing number of localized (stage I–III) ccRCCs. It is, therefore, crucial to explore novel preoperative recurrence prediction models to accurately stratify patients and optimize clinical decisions. The purpose of this study was to develop and externally validate a computed tomography (CT)-based deep learning (DL) model for presurgical disease-free survival (DFS) prediction. Methods: Patients with localized ccRCC were retrospectively enrolled from six independent medical centers. Three-dimensional (3D) tumor regions from CT images were utilized as input to architect a ResNet 50 model, which outputted DL computed risk score (DLCR) of each patient for DFS prediction later. The predictive performance of DLCR was assessed and compared to the radiomics model (Rad-Score), the clinical model the authors built and two existing prognostic models (UISS and Leibovich). The complementary value of DLCR to the UISS, Leibovich, as well as Rad-Score were evaluated by stratified analysis. Results: Seven hundred seven patients with localized ccRCC were finally enrolled for models’ training and validating. The DLCR the authors established can perfectly stratify patients into low-risks, intermediate-risks, and high-risks, and outperformed the Rad-Score, clinical model, UISS and Leibovich score in DFS prediction, with a C-index of 0.754 (0.689–0.821) in the external testing set. Furthermore, the DLCR presented excellent risk stratification capacity in subgroups defined by almost all clinic-pathological features. Moreover, patients classified as low-risk by the UISS/Leibovich score/Rad-Score but as intermediate - or high-risk by DLCR were significantly more likely to experience ccRCC recurrence than those stratified as intermediate- or high-risk by UISS/Leibovich score/Rad-Score but as low-risk by DLCR (all Log-rank P-values<0.05). Conclusions: Our DL model, derived from preoperative CT, is superior to radiomics and current models in precisely DFS predicting of localized ccRCC, and can provide complementary values to them, which may assist more informed clinical decisions and adjuvant therapies adoptions.