Urosepsis is a severe infectious complication of retrograde intrarenal surgery (RIRS) that can progress to septic shock and become life-threatening. Early detection and prompt intervention are crucial for enhancing the prognosis of patients at risk for urosepsis. This study seeks to develop and verify a prediction model for post-RIRS urosepsis utilizing machine learning (ML) techniques. Electronic health record (EHR) data from 124 urosepsis patients and 1,786 non-urosepsis patients, all of whom had upper urinary tract stones and underwent RIRS, were analyzed. Participants were randomly allocated to a training cohort (80

Objectives To investigate the association between exposure to specific endocrine-disrupting chemicals (EDCs), air pollutants and the risk of localised prostate cancer (PCa) and to evaluate the combined effect of mixed EDC exposures. Design A case–control study. Setting Secondary care; a single tertiary hospital in Western China. Participants A total of 580 patients with histologically confirmed localised PCa who underwent radical prostatectomy between May 2021 and June 2023 were included as cases. They were matched 1:2 with 1160 cancer-free controls from the same cohort on age (±5 years) and date of biological sample collection. Key exclusion criteria were a history of other malignancies, androgen deprivation therapy prior to surgery or acute urinary infection. Primary and secondary outcome measures The primary outcome was the odds of PCa associated with individual and mixed exposures. Primary exposures were urinary concentrations of nine bisphenols (BPs) and nine phthalate metabolites (quantified via high-performance liquid chromatography–tandem mass spectrometry) and estimated ambient air pollution exposure (sulphur dioxide (SO 2 ) and PM ₂.₅ ) based on geocoded residential addresses. Results A total of 580 cases and 1160 controls were included in the final analysis. Exposure to the highest quartile of sulphur dioxide (SO 2 ) over 5 years (9.37–28.79 µg/m 3 ) was significantly associated with PCa, yielding an OR of 1.65 (95% CI 1.08 to 2.72; p<0.001) compared with the lowest quartile (3.61–6.89 µg/m 3 ). Urinary concentrations of multiple phthalate metabolites (MECPP, MEHHP and MEHP) and BPs (bisphenol A (BPA) and BPZ) were significantly higher in cases (all p<0.05). Weighted quantile sum (WQS) regression analysis demonstrated a significant positive association between the mixture of EDCs and PCa risk (WQS OR=1.26, 95% CI 1.20 to 1.33; p<0.001). Conclusions Exposure to SO 2 and specific EDCs (both individually and as a mixture) is significantly associated with localised PCa. Trial registration number ChiCTR1900024623.

Drug resistance remains a formidable obstacle in the clinical management of solid tumors, including bladder cancer. In this study, building upon our previously developed theranostic platform, we synthesized a streamlined nanoparticle, PLZ4@SeD, which exclusively encapsulates the organoselenium derivative SeD-1b. By focusing on the intrinsic potency of the organic moiety, we explored its potential to overcome therapeutic resistance through metabolic and redox intervention. Our findings demonstrate that PLZ4@SeD is a potent ferroptosis inducer that triggers robust endogenous ROS production and iron mobilization within bladder cancer cells. Mechanistically, PLZ4@SeD activates the oxidative phosphorylation (OXPHOS)-related regulatory axis, which serves as a metabolic engine to fuel mitochondrial ROS generation and drive the ferroptotic cascade. This metabolic rewiring concurrently induces ferroptotic cell death—characterized by GSH depletion and GPX4 downregulation—and suppresses PD-L1 expression. Using bladder cancer cell lines, patient-derived organoids (PDOs), and syngeneic mouse models, we show that this dual-action strategy not only exerts direct cytotoxicity but also significantly enhances the therapeutic efficacy of doxorubicin (DOX) and anti-PD-1 therapy. Furthermore, PLZ4@SeD treatment remodels the tumor microenvironment (TME) by promoting the infiltration of CD4+ and CD8+ T cells while reducing immunosuppressive F4/80+ macrophages, effectively converting “cold” tumors into an immune-responsive “hot” state. In summary, our study provides a compelling mechanistic rationale for utilizing PLZ4@SeD as a novel intervention to overcome multidrug resistance, offering a promising and innovative approach for the precision treatment of bladder cancer.

Advanced bladder cancer (BCa) is associated with a poor prognosis, highlighting the urgent need for novel therapeutic strategies with improved efficacy and reduced toxicity. Nectin-4 (PVRL4, here after referred to as N4) is a clinically validated therapeutic target; however, existing antibody-based agents are limited by inadequate tumor penetration and off-target toxicities. Nanobodies (Nbs) possess favorable characteristics, including small molecular size, enhanced tissue penetration, and ease of sengineering. This study aimed to identify and preliminarily characterize N4-targeting Nb candidates for BCa using a yeast surface display-based screening strategy. N4 was highly expressed in BCa and significantly associated with an unfavorable prognosis. The YSD platform supported efficient library expansion, surface display, and iterative enrichment of N4-binding clones. Among the identified Nbs, Nb-80 exhibited apparent binding activity, with an EC₅₀ of 0.150 μg/mL. BLI analysis demonstrated rapid association–dissociation kinetics. An N4-binding Nb candidate was identified and preliminarily characterized using a YSD-based screening strategy. These findings support the utility of the YSD platform for Nb discovery and provide a preliminary basis for further validation of N4-targeting Nb candidates in BCa. Not applicable.

In this retrospective analysis, we used routinely collected clinical variables to develop a machine learning model for liver cancer diagnosis and examined the variables that contributed to model performance. We studied 3,629 people who were assessed because they were suspected to have liver cancer or other liver diseases. Patients who had pathologically confirmed liver cancer, and controls comprised of healthy subjects, patients with chronic hepatitis B, chronic hepatitis C, or cirrhosis. From 87 clinical parameters, least absolute shrinkage and selection operator regression was applied to identify candidate predictors. Nine machine learning algorithms were then trained and tested through 10-fold cross-validation and external validation in an independent cohort. Performance of the models was measured based on area under the receiver operating characteristic curve, sensitivity, specificity, calibration and decision curve analysis. The variables that contributed most to discrimination included carbohydrate antigen 19-9, alpha-fetoprotein-related indicators, liver function parameters, and inflammatory markers. Among the nine algorithms, Extreme Gradient Boosting achieved the highest discriminative performance, with an AUC of 1.000 in the training cohort and 0.937 in the validation cohort. In the SHapley Additive exPlanations analysis, carbohydrate antigen 19-9 made the largest contribution to the final model. These findings suggest that machine learning algorithms, particularly Extreme Gradient Boosting, may integrate heterogeneous clinical indicators and provide a useful auxiliary tool for distinguishing liver cancer from non-liver cancer conditions in individuals undergoing clinical assessment. Further prospective validation in high-risk surveillance cohorts is required before the model can be applied to early risk prediction or population-level screening.

Accurate prostate cancer (PCa) diagnosis remains difficult because of tumor heterogeneity and the challenge of integrating multimodal clinical information. We developed Prost-LM, a multimodal large language model that jointly embeds MRI-derived features, numerical PSA values, and free-text clinical reports into a unified semantic space to enable deep cross-modal reasoning. Trained and validated on a large multi-center cohort of 3940 patients, Prost-LM achieved strong diagnostic performance, with an internal validation AUC of 0.954 for distinguishing PCa from benign conditions, outperforming MRI-only models (AUC = 0.868, P < 0.001). For detecting clinically significant PCa (Gleason score ≥ 7), Prost-LM reached an AUC of 0.955. Additionally, the model provides interpretable diagnostic decisions to support clinical verification. These results suggest Prost-LM can improve automated PCa diagnosis and support precision oncology through multimodal AI.

Benign prostatic hyperplasia (BPH) is a prevalent age-related disorder characterized by chronic inflammation, metabolic dysregulation, and abnormal cellular proliferation. Protein lactylation, an emerging post-translational modification closely associated with cellular metabolism, has been implicated in the pathogenesis of various diseases. However, its role and associated molecular features in BPH remain uncharacterized. We integrated publicly available single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq datasets derived from BPH and normal prostate tissues. Utilizing machine learning algorithms (LASSO, Random Forest, and Boruta) strictly as feature selection tools, we identified key lactylation-related candidate genes. Subsequently, employing the BPH-1 cell line in vitro, we preliminarily investigated the potential biological functions of the prioritized epithelial target, ANXA2, through siRNA-mediated knockdown, Western blotting, ELISA, and cell proliferation assays. We computationally identified ANXA2 and IFI27 as key lactylation-related candidate genes, both of which were significantly downregulated in BPH tissues. scRNA-seq data revealed their cell-type specificity, demonstrating that ANXA2 is predominantly expressed in epithelial cells, whereas IFI27 is primarily expressed in endothelial cells. Furthermore, we uncovered a potential link between metabolism and inflammation involving ANXA2. Specifically, ANXA2 knockdown in BPH-1 cells was associated with alterations in glycolytic gene expression and a concomitant reduction in lactate production. Our data suggest that this decreased lactate level is associated with elevated secretion of the proinflammatory cytokine IL-6 and enhanced cellular proliferation. Additionally, cellular trajectory and communication analyses indicated that ANXA2⁺ epithelial cells and IFI27⁺ endothelial cells represent distinct subpopulations characterized by enhanced intercellular signaling capacities, suggesting their potential roles as critical molecular hubs within the BPH microenvironment. This study presents the first single-cell resolution landscape of lactylation-related gene activity in BPH. We computationally identified ANXA2 and IFI27 as key lactylation-related candidate genes. Importantly, our study suggests a potential link whereby ANXA2 deficiency is associated with BPH cell proliferation and potential metabolic and inflammatory alterations. These findings provide valuable new insights into the underlying pathophysiological mechanisms of BPH and lay the groundwork for the development of targeted therapeutic strategies.

Urinary extracellular vesicles (uEVs) are promising biomarkers for prostate cancer (PCa). Although novel uEV-based biomarkers have advanced early diagnosis, their detection processes remain cumbersome and cost-prohibitive. The physical parameters of uEVs offer untapped predictive potential for PCa, providing a more convenient, rapid, and cost-effective diagnostic approach. This study aims to construct a predictive model integrating uEV physical parameters with machine learning (ML) algorithms to enhance the early diagnosis of PCa. Urine samples were collected from 222 eligible participants. uEVs were isolated using a commercial kit, and their concentration and size parameters were quantified via nanoparticle tracking analysis (NTA). Utilizing these physical parameters, five ML algorithms were trained and evaluated to identify the optimal diagnostic model for PCa. Model performance was systematically assessed using the area under the receiver operating characteristic curve (AUC), learning curves, calibration curves, and decision curve analysis (DCA). Additionally, SHapley Additive exPlanations (SHAP) were employed to visualize the contributions of key predictors. Analysis of uEV physical parameters revealed that, compared to benign controls, PCa patients exhibited a significantly higher proportion of 30-150 nm uEVs and a smaller overall particle size (p < 0.001). Among the evaluated algorithms, the eXtreme Gradient Boosting (XGBoost) model demonstrated superior performance. For discriminating PCa from benign prostatic hyperplasia (BPH), the XGBoost model achieved AUC values of 0.934 and 0.864 in the training and testing cohorts, respectively. DCA demonstrated that the XGBoost model yielded a higher clinical net benefit across a threshold probability range of 0-60%. Overall, the diagnostic efficacy and clinical utility of the XGBoost model significantly outperformed routine clinical parameters, including prostate-specific antigen (PSA) and prostate-specific antigen density (PSAD). This study successfully developed and validated a noninvasive ML-based diagnostic model utilizing the physical parameters of uEVs. This approach serves as a preliminary adjunctive tool to assist clinicians in accurately identifying prostate cancer, thereby potentially reducing the incidence of unnecessary prostate biopsies.

Bladder cancer (BLCA) is a urinary system malignant tumor with high morbidity and mortality worldwide. There is an urgent need for new biomarkers and therapeutic targets. By integrating single-cell RNA sequencing, MitoCarta database and the Cancer Genome Atlas data, this study found that mitochondrial ribosomal protein L37 (MRPL37) was significantly up-regulated in BLCA, and its expression level was related to disease progression and poor prognosis. Functional experiments in vitro and in vivo confirmed that knockdown of MRPL37 could inhibit the proliferation, invasion, migration, metabolism and cisplatin resistance of BLCA cells by cell experiments, mouse subcutaneous tumor model, mouse orthotopic bladder cancer model and patient-derived organoid (PDO) model. A novel MRPL37-LRPPRC-ROS-AKT signaling axis was identified: loss of MRPL37 leads to the disorder of branched-chain amino acid metabolism, down-regulates the key mitochondrial protein LRPPRC, and then causes mitochondrial membrane potential damage and reactive oxygen species accumulation, which ultimately exert anti-cancer effect by inhibiting the AKT pathway. This study not only advances the understanding of the mechanism by which mitochondrial translation defects drive tumorigenesis, but also highlights the potential of MRPL37 as a target in BLCA prognosis and targeted therapy.

Five novel 5,7-dihalo-substituted 8-quinolinoline zinc(II) coordination compounds, namely, [Zn(LD1)2(qph)]·3H2O (GXU-1·3H2O; GXU = Guangxi University), [Zn(LD2)2(qph)]·2H2O·CH2Cl2 (GXU-2·2H2O·CH2Cl2), [Zn(LD3)2(qph)] (GXU-3), [Zn(iph)(LD1)2]⋅5CH3OH (GXU-4⋅5CH3OH), and [Zn(iph)(LD2)2]⋅0.5CH3OH⋅2CH2Cl2 (GXU-5⋅0.5CH3OH⋅2CH2Cl2), were synthesized and characterized. Here, H-LD1 = 5,7-dibromo-8-hydroxyquinoline, H-LD2 = 5,7-dichloro-8-quinolinol, H-LD3 = 5,7-dichloro-8‑hydroxy-2-methylquinoline, qph = 2-(quinolin-2-yl)-1H-imidazo [4,5-f] [1,10]phenanthroline, and iph = 6-(1H-imidazo [4,5-f] [1,10]phenanthrolin-2-yl)-2H-chromen-2-one. Complexes GXU-1–GXU-5 exhibited significantly enhanced antiproliferative cytotoxicity against human malignant melanoma (SK-MEL-5) tumor cells, with half maximal inhibitory concentration (IC50) values in the range of 0.11–2.34 μM, compared with the free ligands H-LD1–H-LD3, iph, and qph. In addition, we initially confirmed that the antiproliferative activity of GXU-1 and GXU-3 in SK-MEL-5 cells is associated with the induction of cell senescence and apoptosis, which correlate with induction of DNA damage, inhibition of human telomerase reverse transcriptase targeting, and activation of cleaved caspase-3/7 (c-caspase-3/7). Moreover, GXU-3, the most potent compound against SK-MEL-5 cells (IC50 = 0.11 ± 0.02 µM), exhibited superior antitumor efficacy (approximately 56.8%) in an SK-MEL-5 xenograft model with no remarkable systemic toxicity. Overall, GXU-3 exhibits superior cytotoxicity and a more robust antitumor mechanism compared to the other zinc(II) coordination compounds. This performance enhancement stems from the key roles of the H-LD3 and qph ligands within GXU-3.

Abstract Background Urinary catheterization is a routine procedure after ureteroscopy lithotripsy (URSL), but it often causes catheter-related bladder discomfort (CRBD) and urethral pain, which aggravates patients’ postoperative discomfort. This study finds out the effect of topical anesthesia on CRBD and urethra pain in patients undergoing ureteroscopy lithotripsy and urinary catheterization. Methods In this study, 330 patients undergoing ureteroscopy lithotripsy enrolled, with 160 cases in the control group and 170 cases in the experimental group. The experimental group divided into two subgroups based on the local anesthetic used: Tetracaine Hydrochloride Gel subgroup and Oxybuprocaine Gel subgroup. Postoperative assessments conducted using CRBD scores and urethra pain numerical rating scale (NRS) score. CRBD and urethra pain NRS scores measured at T0, T1, T2, T3, T4, T5, and T6. Results Compared to the control group, the use of local anesthetics significantly reduced both CRBD scores and urethra pain NRS scores in the experimental group, with the differences being statistically significant (P < 0.01). In male patients, patients who used local anesthetics markedly decreased CRBD scores and urethra pain NRS scores compared to those not receiving local anesthetics, showing statistical significance (P < 0.01), whereas no significant difference followed in female patients. No statistically significant differences found between Rigid ureteroscopy lithotripsy (R-URSL) and Flexible ureteroscopy lithotripsy (F-URSL) regardless of the use of local anesthetics. Within the experimental group, the effects of different local anesthetics were similar, with comparable impacts on CRBD scores and urethra pain NRS scores, and no statistical differences noted. These findings suggest that local anesthetics are effective in reducing postoperative CRBD scores and urethra pain NRS scores, especially in male patients. Conclusion Topical anesthesia following ureteroscopy lithotripsy reduces CRBD scores and urethra pain NRS scores in patients undergoing urinary catheterization, especially in male patients. Trial registration Chinese Clinical Trial Registry (No. ChiCTR2500105477, https://www.chictr.org.cn/ . date: July 4, 2025).

Background Advanced bladder cancer (BCa) is associated with a poor prognosis, highlighting the urgent need for novel therapeutic strategies with improved efficacy and reduced toxicity. Nectin-4 (PVRL4, here after referred to as N4) is a clinically validated therapeutic target; however, existing antibody-based agents are limited by inadequate tumor penetration and off-target toxicities. Nanobodies (Nbs) possess favorable characteristics, including small molecular size, enhanced tissue penetration, and ease of engineering. This study aimed to identify and validate N4-specific Nbs for BCa using a yeast surface display-based screening strategy. Results N4 was highly expressed in BCa and significantly associated with an unfavorable prognosis. The YSD platform supported efficient library expansion, surface display, and iterative enrichment of N4-binding clones. Among the identified Nbs, Nb-80 exhibited favorable binding activity, with an EC₅₀ of 0.150 µg/mL. BLI analysis demonstrated rapid association–dissociation kinetics. Conclusions An N4-targeting Nb was successfully identified using a YSD-based screening strategy. These findings support the utility of the YSD platform for Nb discovery and provide a foundation for developing Nb-based N4-targeted strategies in BCa.

Background The development of resistance to anti-androgen therapies, such as bicalutamide (Bic), poses a significant challenge in the treatment of prostate cancer (PCa), and the intercellular complex mechanisms are difficult to comprehend. Emerging evidence suggests that stem cells and their secreted exosomes (Exo) contribute to tumor microenvironment-mediated drug resistance. However, the role of these vesicles in promoting bicalutamide resistance in prostate cancer remains elusive. This work aimed to clarify the mechanism by which exosomes from bicalutamide-stimulated mesenchymal stem cells (Bic-MSCs-Exo) provide resistance to bicalutamide in prostate cancer. Methods This work aimed to clarify the function of exosomes produced from Bic-MSCs in bicalutamide resistance in PCa and its molecular underpinnings. The impact of Bic-MSCs supernatants and their exosomes on cell proliferation, clone formation, migratory capacity, and apoptosis was assessed by the co-culturing of these supernatants and exosomes with PCa cells to elucidate their pro-resistance phenotype. Additional short RNA sequencing of exosomes, together with bioinformatics and single-cell data analysis, was conducted to identify and characterize the pivotal molecule lncRNA SILC1.The downstream regulatory network was validated by dual-luciferase reporter assays, functional rescue and loss-of-function tests, quantitative PCR, and Western blotting; the molecular processes were confirmed at both the in vivo and clinical levels using an organoid model and clinical samples analyzed via FISH. Results We found that Bic-MSCs-derived exosomes (Bic-MSCs-Exo) promote prostate cancer (PCa) cell survival and drug resistance. Using sequencing and quantitative PCR (qPCR) analyses, we found that exosomes carry the drug-resistant molecule SILC1, which binds miR-577and functions as a competing endogenous RNA (ceRNA). Combining multi-database analyses with experimental data, we confirmed that miR-577 targets and inhibits RHOA expression. Notably, high expression levels of RHOA are associated with increased proliferation, invasion, and enhanced clonogenic ability in prostate cancer cells. Furthermore, we demonstrated that the knockdown of SILC1 leads to the downregulation of RHOA. In organoid models of hormone-sensitive prostate cancer (HSPC) and Castration-Resistant Prostate Cancer (CRPC), it was found that RHOA was considerably overexpressed in CRPC. Additionally, a fluorescence in situ hybridization (FISH) study of clinical specimens demonstrated a link among the expression of SILC1, miR-577, and RHOA. Conclusions The discovery of the lncRNA SILC1 /RHOA as a promising marker of PCa resistance sheds light on the molecular pathogenesis of PCa resistance and provides potential therapeutic targets for the disease.

Urosepsis is one of the most severe complications after percutaneous nephrolithotomy (PNL). This study aimed to develop and validate a preoperative machine learning (ML) model for predicting post-PNL urosepsis using electronic medical record (EMR) data. In this retrospective study, we included 360 patients with urosepsis and 2,636 without urosepsis. Multimodal clinical parameters were collected, including demographic data, admission vital signs, imaging reports, and laboratory results. The cohort was randomly split into a training set (80

To develop and validate a predictive model for distinguishing benign and malignant renal masses using machine learning (ML) algorithms. We analyzed data from 1084 patients diagnosed with renal masses between June 2020 and November 2023. Patients were randomly divided into training and validation cohorts at an 8:2 ratio. Seven ML algorithms were employed to construct prediction models for malignant tumors. The area under the receiver operating characteristic curve (AUC) was used as the primary evaluation metric to identify the best model. Comprehensive model evaluation included AUC, accuracy, sensitivity, specificity, and other performance indicators, with validation on an independent cohort. The SHapley Additive exPlanation (SHAP) algorithm was applied to calculate and visualize feature importance. Eight variables were included in the final model: gender, cystic/solid nature of the mass, maximum mass diameter, uric acid levels, endogenous creatinine clearance, albumin, albumin-to-globulin ratio, and direct/total bilirubin. Among the seven ML models, the Gradient Boosting Decision Tree (GBDT) model performed best, achieving an AUC of 0.88 (95

The specific mechanisms of N6-methyladenosine (m6A) in castration-resistant prostate cancer (CRPC) remain incompletely understood. Wilms' tumor 1 and pyruvate kinase M2-like protein (WTAP) serve as a major regulatory factor of m6A. However, whether it regulates CRPC through m6A mechanisms is unclear. This research revealed that WTAP stands out as a key regulator among m6A factors, and considerably influences the development and behavior of CRPC. WTAP was downregulated in CRPC. A low WTAP expression predicts poor survival and a high WTAP promotes the flutamide drug sensitivity of CRPC cells. WTAP-modulated m6A modification, which can be recognized by YTHDF2, contributes to the post-transcriptional inactivation of nuclear receptor subfamily 3 group C member 1 (NR3C1). In vitro and in vivo experiments unveiled the key role of NR3C1, a rarely studied oncoprotein, in CRPC. The WTAP/YTHDF2/NR3C1 axis was actively involved in CRPC malignancy and the flutamide drug sensitivity of CRPC cells. The clinical correlation of WTAP, YTHDF2, and NR3C1 was further demonstrated in CRPC tissues and castration-dependent prostate cancer tissues. Our study uncovered a novel molecular mechanism by which the m6A-induced WTAP/YTHDF2/NR3C1 axis promotes CRPC flutamide drug sensitivity. This finding suggests the potential of WTAP as a promising prognostic marker and therapeutic target against flutamide drug sensitivity in CRPC.

Cancer remains a leading global cause of mortality, making early detection crucial for improving survival outcomes. The study aims to develop a machine learning-enabled blood-derived exosomal RNA profiling platform for multi-cancer detection and localization. In this multi-phase, multi-center study, we analyzed RNA from exosomes derived from peripheral blood plasma in 818 participants across eight cancer types during the discovery phase. Machine learning techniques were applied to identify potential pan-cancer biomarkers. During the screening and model validation phases, the sample size was progressively expanded to 1,385 participants in two steps, while the candidate biomarkers were refined into a set of 12 exosomal tumor RNA signatures (ETR.sig). In the subsequent model construction phase, diagnostic models were developed using the expanded cohort and ETR.sig. Statistical analyses included the calculation of receiver operating characteristic (ROC) curves and AUC values to assess the models' ability to distinguish cancer cases from controls and determine tumor origins. To further validate and explore the biological relevance of the identified biomarkers, we integrated tissue RNA-seq, single-cell data, and clinical information. Machine learning analysis initially identified 33 candidate biomarkers, which were narrowed down to 20 ETR.sig in the screening phase and 12 ETR.sig in the validation phase. In the model construction phase, a diagnostic model based on ETR.sig, built using the Random Forest (RF) algorithm, showed excellent performance with an AUC of 0.915 for distinguishing pan-cancer from controls. The multi-class classification model also demonstrated strong classification power, with macro-average and micro-average AUCs of 0.983 and 0.985, respectively, for differentiating between eight cancer types. Additionally, tumor origin classification using the RF-based diagnostic models achieved high AUC values: BRCA 0.976, COAD 0.98, KIRC 0.947, LIHC 0.967, LUAD 0.853, OV 0.972, PAAD 0.977, and PRAD 0.898. Integration of tissue RNA-seq, single-cell data, and clinical information revealed key associations between ETR.sig-related genes and tumor development. The study demonstrates the robust potential of exosomal RNA as a minimally invasive biomarker resource for cancer detection. The developed ETR.sig platform offers a promising tool for precision oncology and broad-spectrum cancer screening, integrating advanced computational models with nanoscale vesicle biology for accurate and rapid diagnosis.

OBJECTIVE:Early detection and treatment of nasopharyngeal carcinoma (NPC) are critical for improving patient prognosis. The aim of this study is to develop and compare multiple machine learning (ML) models using multimodal clinical data to identify a predictive model for NPC risk, increase diagnostic accuracy, and guide personalized treatment strategies. METHODS:Clinical data were retrospectively collected from 1337 patients suspected of having NPC at the First People's Hospital of Yulin. Feature selection was performed using the least absolute shrinkage and selection operator (LASSO) regression. Patients were divided into training and test sets (80:20 ratio), and seven ML models were developed based on the training set. Model performance was assessed using metrics such as the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. The best-performing model was further evaluated through decision curve analysis (DCA), calibration, and learning curves. SHapley Additive exPlanations (SHAP) were used to interpret key clinical features. RESULTS:Seven models were developed using 17 clinical features selected from 53 parameters. The gradient boosting decision tree (GBDT) model demonstrated superior performance (AUC of 0.95 in the training cohort and 0.82 in the validation cohort). Calibration curves and DCA confirmed the model's strong accuracy and clinical benefit. SHAP analysis revealed that age, lymphocyte percentage, serum albumin, sex, and EBV IgM were the five most significant predictors of NPC risk. CONCLUSION:The GBDT-based ML model, using multimodal clinical data, accurately identifies patients at high risk for NPC, providing a valuable tool for early screening and personalized treatment strategies.

In recent years, immunotherapy has made significant progress. However, the understanding of the heterogeneity and function of T cells, particularly CD8 + T cells, in cervical cancer (CESC) microenvironment remains insufficient. We aim to characterize the heterogeneity, developmental trajectory, regulatory network, and intercellular communication of CD8 + T cells in cervical squamous cell carcinoma and to construct a prognostic risk model based on the transcriptomic characteristics of CD8 + T cells. We integrated single-cell RNA sequencing data from CESC tumor samples with bulk transcriptome data from TCGA and GEO databases. We identified CD8 + T cell subsets in the CESC microenvironment, revealing significant interactions between CD8 + T cells and other cell types through intercellular communication analysis. Pseudotime trajectory analysis revealed dynamic transcriptional regulation during CD8 + T cell differentiation and functional acquisition processes. We constructed a transcriptional regulatory network for CESC CD8 + T cells, identifying key transcription factors. Based on CD8 + T cell-related genes, a prognostic risk model comprising eight core genes was developed and validated using machine learning. We identified four distinct CD8 + T cell subsets, namely progenitor, intermediate, proliferative, and terminally differentiated, each exhibiting unique transcriptomic characteristics and functional properties. CD8 + T cell subsets interact with macrophages through different ligand-receptor networks, including the CCL-CCR signaling pathway and costimulatory molecules. Sorafenib was identified as a potential immunotherapeutic drug through drug screening. Experimental validation demonstrated that sorafenib enhances the cytotoxicity of CD8 + T cells by increasing the secretion of IFN-γ and TNF-α, thereby significantly inhibiting the invasiveness and survival of CESC cells. Our study provides valuable insights into the heterogeneity and functional diversity of CD8 + T cells in CESC. We demonstrate that a CD8 + T cell-related prognostic signature may serve as a potential tool for risk stratification in patients with CESC. Additionally, our finding suggests that sorafenib could be a promising therapeutic candidate for improving antitumor immunity in this patient population.

Human EXO1 is a 5' → 3' exonuclease that plays a critical role in regulating cell cycle checkpoints, maintaining replication forks and participating in post-replication DNA repair pathways. Previous studies have highlighted the importance of EXO1 in cancers such as liver and breast cancer. However, there is a gap in the comprehensive analysis of EXO1 in a wide range of cancers, and its precise role in cancer patient prognosis and immune response remains unclear. This study aims to systematically investigate the potential associations between EXO1, immune infiltration and prognostic value in different cancer types and to gain a deeper understanding of its mechanisms of action.We conducted a comprehensive investigation into the overarching role of EXO1 across various cancers by utilizing multiple databases. Our analysis encompassed scrutinizing EXO1 expression and evaluating its correlation with clinical survival, immune checkpoints, Tumor Stemness Score, Prognostic Value, immunomodulators, genomic profiles, immunological characteristics, immunotherapy, and functional enrichment. EXO1 is expressed in various normal tissues and at significantly higher levels in most tumors compared to non-tumor tissues. High EXO1 expression is associated with poor prognosis in certain cancers. Genetic mutations and RNA modifications of EXO1 were also investigated, as well as its correlation with tumor immunity and genomic stability. We investigated the potential role of EXO1 in immunotherapy and identified differing drug responses based on EXO1 expression levels in Skin Cutaneous Melanoma (SKCM), which holds promise for personalized treatment. Furthermore, we analyzed EXO1-related genes and pathways, revealing its potential involvement in crucial biological processes such as DNA repair and cell cycle regulation. These findings provide a comprehensive understanding of the role of EXO1 in cancer initiation and progression, offering valuable insights for personalized cancer therapy and precision medicine. This study highlights the diverse functions of EXO1 in cancer, including its involvement in cancer initiation, prognosis, immune regulation, and response to immunotherapy. These findings provide valuable insights into the potential of EXO1 as a biomarker and therapeutic target, as well as its role in determining treatment response in cancer management. This information is crucial for guiding personalized cancer therapy and precision medicine.