Rationale and objectivesBone metastasis (BM) is pivotal in prostate cancer (PCa) management. This study developed a multimodal model integrating MRI-derived deep features from the prostate gland (PG) and periprostatic adipose tissue (PPAT) with clinical variables for BM risk assessment.MethodsRetrospectively, 464 patients were recruited and randomly divided into training and internal test cohorts at a ratio of 7:3. Deep features were extracted from PG and PPAT regions on T2-weighted MRI using a pretrained ResNet-50 as a fixed feature extractor. Clinical, PG, and PPAT component models were developed using patient-level cross-validation. Their out-of-fold probabilities were integrated by a logistic-regression meta-learner to construct the Deep feature-based PG-PPAT-Clinical (DPPC) model. Model performance was evaluated using ROC-AUC, average precision, calibration analysis, decision-curve analysis, and SHAP analysis.ResultsThe DPPC model achieved ROC-AUCs of 0.922 (95% CI, 0.887–0.954) in the training cohort and 0.928 (95% CI, 0.864–0.978) in the internal test cohort. Its ROC-AUC was significantly higher than that of the Clinical model in the training cohort and the PPAT model in the internal test cohort, whereas the remaining pairwise differences were not statistically significant. At a probability threshold of 0.5, the internal-test sensitivity and specificity were 65.1% and 95.9%, respectively. The observed BM rates were 87.5% in the high-risk group and 13.9% in the low-risk group.ConclusionBy synergizing deep learning signatures from PG and PPAT with clinical factors, the DPPC model demonstrates promising performance for BM risk stratification, and external validation and further calibration assessment are required.

Although recent advancements have shed light on the crucial role of coordinated evolution among cell subpopulations in influencing disease progression, the full potential of these insights has not yet been fully harnessed in the clinical application of personalized precision medicine for prostate cancer (PCa). In this study, we utilized single-cell sequencing to identify the evolutionary characteristics of tumoral cell states and employed comprehensive bulk RNA sequencing to evaluate their potential as prognostic indicators and therapeutic targets. Leveraging advancements in artificial intelligence, we integrated machine learning with multi-omics to develop and validate the tumor evolutionary characteristic predictive indicator (TECPI). TECPI not only demonstrated superior prognostic performance compared to traditional clinical predictors and 81 previously published models but also improved patient outcomes by accurately identifying individuals who would benefit from immunotherapy and targeted therapies. Furthermore, we experimentally validated the critical role of AMOTL1 in PCa pharmacodynamics through its interaction with AR, pivotal for modulating the sensitivity to AR antagonist. Additionally, we demonstrated the generalizability and applicability of TECPI across pan-cancers. In summary, this study emphasizes the importance of understanding cellular diversity and dynamics within the tumor microenvironment to predict PCa progression and to guide targeted therapy effectively.

Background:U2AF homology motif kinase 1 (UHMK1) has been associated with RNA processing and protein phosphorylation, thereby influencing tumor progression. The study aimed to explore its regulatory mechanisms and biological functions in human prostate cancer (PCa). Methods:In this study, we systematically evaluated the expression and prognostic significance of UHMK1 in public databases, followed by validation through immunohistochemistry (IHC) in PCa specimens. Both gain-of-function and loss-of-function experiments were conducted to elucidate the role of UHMK1 in vitro and in vivo. Additionally, a series of molecular and biochemical assays were performed to investigate the regulatory mechanisms underlying UHMK1 activity. Results:Our findings revealed that UHMK1 expression was significantly upregulated in PCa tissues and correlated with poor patient prognosis, as demonstrated by analysis of public datasets and confirmed by immunohistochemical staining. Functional studies showed that UHMK1 depletion suppressed tumor cell proliferation and metastasis, while its overexpression promoted these processes. Mechanistically, we identified that UHMK1 phosphorylates nuclear receptor coactivator 3 (NCOA3), which subsequently activates activating transcription factor 4 (ATF4) to upregulate methylenetetrahydrofolate dehydrogenase 2 (MTHFD2) transcription. Interestingly, MTHFD2 was found to reciprocally enhance UHMK1 expression, establishing a positive feedback loop. Conclusions:In conclusion, our data suggest that the UHMK1-MTHFD2 axis forms a positive feedback loop that drives PCa progression. Targeting this loop represents a promising therapeutic strategy for restraining prostate cancer development and progression.

INTRODUCTION:Smoking is a significant risk factor for prostate cancer (PCa), a major health threat for aging males globally. This study evaluates the worldwide burden of smoking-related PCa from 1990 to 2021 and projects trends to 2031. METHODS:Using Global Burden of Disease (GBD) 2021 data, we analyzed age-standardized rates (ASRs) and estimated annual percentage changes for mortality, years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life years (DALYs) across different age groups, sociodemographic index (SDI) levels, regions, and countries, employing hierarchical clustering and autoregressive integrated moving average (ARIMA) modeling. RESULTS:From 1990 to 2021, global smoking-related prostate cancer burden declined, with annual reductions in ASRs for mortality, YLLs, and DALYs, while YLDs initially increased before declining. Age-specific analysis revealed the highest ASRs for mortality, YLLs, and DALYs in the 90-94 years age group, whereas YLDs peaked at 70-74 years of age. SDI regions exhibited elevated ASRs but the most pronounced declines, and were the only areas with negative YLD trends. The disparity in disability rates between high and low SDI countries diminished from 7.33 (95% CI: 6.04-8.63) in 1990 to 3.78 (95% CI: 2.64-4.92) in 2021, and the concentration index decreased from 0.34 (95% CI: 0.28-0.39) to 0.15 (95% CI: 0.10-0.20). The ARIMA models predict that DALYs will decrease from 3.215 (95% CI: 3.169-3.26) in 2022 to 2.69 (95% CI: 2.159-3.221) in 2031, YLLS will decrease from 2.827 (95% CI: 2.787-2.866) to 2.336 (95% CI: 1.855-2.817), YLDs and deaths will stabilize in a gradually decreasing trend. CONCLUSIONS:Despite improved global equity in smoking-related PCa burden, targeted interventions for elderly populations, enhanced tobacco control policies, and region-specific prevention strategies remain essential to further reduce this preventable disease burden worldwide.

PURPOSE:To evaluate a radiomics-based nomogram using peri-prostatic adipose tissue (PPAT) features for predicting bone metastasis (BM) in newly diagnosed prostate cancer (PCa) patients. METHODS:A retrospective study of 151 PCa patients (October 2010-November 2022) was conducted. Radiomic features were extracted from axial T2-weighted MRI of PPAT, and normalized PPAT was calculated as the ratio of PPAT volume to prostate volume. A radiomics score (Radscore) was developed using logistic regression with 16 features selected via LASSO regression. Independent predictors identified through univariate and multivariate logistic regression were used to construct a nomogram. Predictive performance was assessed using ROC curves, and internal validation involved 1000 bootstrapped iterations. RESULTS:The Radscore, based on 16 features, showed significant association with BM and outperformed normalized PPAT in predictive value. Independent predictors of BM included Radscore, alkaline phosphatase (ALP), and clinical N stage (cN). A nomogram integrating these factors demonstrated strong discrimination (C-index: 0.908; 95% CI: 0.851-0.966) and calibration, with consistent results in validation (C-index: 0.903; 95% CI: 0.897-0.916). Decision curve analysis confirmed its clinical utility. CONCLUSIONS:Radscore, cN, and ALP were identified as independent BM predictors. The developed nomogram enables accurate risk stratification and personalized BM predictions for newly diagnosed PCa patients.

ObjectivesTo investigate the role of MRI measurements of peri-prostatic adipose tissue (PPAT) in predicting bone metastasis (BM) in patients with newly diagnosed prostate cancer (PCa).MethodsWe performed a retrospective study on 156 patients newly diagnosed with PCa by prostate biopsy between October 2010 and November 2022. Clinicopathologic characteristics were collected. Measurements including PPAT volume and prostate volume were calculated by MRI, and the normalized PPAT (PPAT volume/prostate volume) was computed. Independent predictors of BM were determined by univariate and multivariate logistic regression analysis, and a new nomogram was developed based on the predictors. Receiver operating characteristic (ROC) curves were used to estimate predictive performance.ResultsPPAT and normalized PPAT were associated with BM (P<0.001). Normalized PPAT positively correlated with clinical T stage(cT), clinical N stage(cN), and Grading Groups(P<0.05). The results of ROC curves indicated that PPAT and normalized PPAT had promising predictive value for BM with the AUC of 0.684 and 0.775 respectively. Univariate and multivariate analysis revealed that high normalized PPAT, cN, and alkaline phosphatase(ALP) were independently predictors of BM. The nomogram was developed and the concordance index(C-index) was 0.856.ConclusionsNormalized PPAT is an independent predictor for BM among with cN, and ALP. Normalized PPAT may help predict BM in patients with newly diagnosed prostate cancer, thus providing adjunctive information for BM risk stratification and bone scan selection.

Ferroptosis induction has emerged as a promising therapeutic approach for prostate cancer (PCa), either as a monotherapy or in combination with hormone therapy. Therefore, identifying the mechanisms regulating ferroptosis in PCa cells is essential. Our previous study demonstrated that HJURP, an oncogene upregulated in PCa cells, plays a role in tumor proliferation. Here, we expand these findings by elucidating a novel mechanism by which HJURP inhibits sensitivity to ferroptosis inducers in PCa cells via the PRDX1/reactive oxygen species (ROS) pathway in vitro and in vivo. Mechanistically, HJURP forms disulfide-linked intermediates with PRDX1 through Cys327 and Cys457 residues. This disulfide binding promotes PRDX1 redox cycling and inhibits its hyperoxidation. As a result, HJURP enhances the peroxidase activity of PRDX1, leading to a decrease in ROS levels and subsequently suppressing lipid peroxidation induced by ferroptosis inducers. These findings reveal the potential of HJURP/PRDX1 as novel therapeutic targets and biomarkers of ferroptosis in PCa patients.

Background: Benign prostatic hyperplasia (BPH) is prevalent among the aging male population and often presents with distressing lower urinary tract symptoms. There is emerging evidence that commercial oral poly-herbal traditional Chinese medicine (TCM) formulation combined with Western medicine (WM) may offer enhanced therapeutic effects compared to WM alone in BPH treatment. Nevertheless, determining the optimal formulations for BPH remains controversial. We aimed to employ a network meta-analysis to compare and assess differences among commonly used and recommended poly-herbal TCM formulations outlined in the Chinese guidelines for BPH treatment, providing clinical medication recommendations and guidance.Methods: We extensively searched for RCTs of BPH patients that had oral poly-herbal TCM formulations and WM treatment, covering both English and Chinese databases up to 31 October 2023. The quality of the included studies was evaluated using the Cochrane risk-of-bias tool Version 2 (ROB2). A Bayesian network meta-analysis was performed to assess the effectiveness of various formulations, followed by sensitivity and subgroup analyses.Results: Our meta-analysis included 107 RCTs involving 11,037 patients across 16 oral poly-herbal TCM formulations. The quality of the selected studies was assessed as “Some concerns”. Most formulations combined with WM demonstrated superior therapeutic efficacy compared to WM alone. For clinical effective rate, Jingui Shenqi pill (JGSQ) + WM had the highest-ranking probability (87.38%). Concerning International Prostate Symptom Score (IPSS) and maximum flow rate of urine, Guizhi Fuling capsule (GZFL) + WM was most effective (91.10% and 98.55%). Regarding the quality of life score and postvoid residual urine, Pulean tablet (PLA) + WM ranked first (86.71% and 91.81%). In controlling prostate volume, Huange capsule (HE) + WM demonstrated the highest efficacy (95.65%). Additionally, among the interventions, Lingze (LZ) + WM capsule exhibited the lowest incidence of adverse drug reactions (2.32%).Conclusion: Combining oral poly-herbal TCM formulations with WM may provide greater therapeutic benefits in BPH treatment compared to WM alone. JGSQ, GZFL, PLA, and HE emerged as promising treatment options. However, further rigorous empirical studies are essential to substantiate these findings.Systematic Review Registration:https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=459651, CRD 42023459651.

IntroductionMacrophages are components of the innate immune system and can play an anti-tumor or pro-tumor role in the tumor microenvironment owing to their high heterogeneity and plasticity. Meanwhile, prostate cancer (PCa) is an immune-sensitive tumor, making it essential to investigate the value of macrophage-associated networks in its prognosis and treatment.MethodsMacrophage-related marker genes (MRMGs) were identified through the comprehensive analysis of single-cell sequencing data from GSE141445 and the impact of macrophages on PCa was evaluated using consensus clustering of MRMGs in the TCGA database. Subsequently, a macrophage-related marker gene prognostic signature (MRMGPS) was constructed by LASSO-Cox regression analysis and grouped based on the median risk score. The predictive ability of MRMGPS was verified by experiments, survival analysis, and nomogram in the TCGA cohort and GEO-Merged cohort. Additionally, immune landscape, genomic heterogeneity, tumor stemness, drug sensitivity, and molecular docking were conducted to explore the relationship between MRMGPS and the tumor immune microenvironment, therapeutic response, and drug selection.ResultsWe identified 307 MRMGs and verified that macrophages had a strong influence on the development and progression of PCa. Furthermore, we showed that the MRMGPS constructed with 9 genes and the predictive nomogram had excellent predictive ability in both the TCGA and GEO-Merged cohorts. More importantly, we also found the close relationship between MRMGPS and the tumor immune microenvironment, therapeutic response, and drug selection by multi-omics analysis.DiscussionOur study reveals the application value of MRMGPS in predicting the prognosis of PCa patients. It also provides a novel perspective and theoretical basis for immune research and drug choices for PCa.

BACKGROUND:Prostate cancer (PCa), a globally prevalent malignancy, displays intricate heterogeneity within its epithelial cells, closely linked with disease progression and immune modulation. However, the clinical significance of genes and biomarkers associated with these cells remains inadequately explored. To address this gap, this study aimed to comprehensively investigate the roles and clinical value of epithelial cell-related genes in PCa.METHODS:Leveraging single-cell sequencing data from GSE176031, we conducted an extensive analysis to identify epithelial cell marker genes (ECMGs). Employing consensus clustering analysis, we evaluated the correlations between ECMGs, prognosis, and immune responses in PCa. Subsequently, we developed and validated an optimal prognostic signature, termed the epithelial cell marker gene prognostic signature (ECMGPS), through synergistic analysis from 101 models employing 10 machine learning algorithms across five independent cohorts. Additionally, we collected clinical features and previously published signatures from the literature for comparative analysis. Furthermore, we explored the clinical utility of ECMGPS in immunotherapy and drug selection using multi-omics analysis and the IMvigor cohort. Finally, we investigated the biological functions of the hub gene, transmembrane p24 trafficking protein 3 (TMED3), in PCa using public databases and experiments.RESULTS:We identified a comprehensive set of 543 ECMGs and established a strong correlation between ECMGs and both the prognostic evaluation and immune classification in PCa. Notably, ECMGPS exhibited robust predictive capability, surpassing traditional clinical features and 80 published signatures in terms of both independence and accuracy across five cohorts. Significantly, ECMGPS demonstrated significant promise in identifying potential PCa patients who might benefit from immunotherapy and personalized medicine, thereby moving us nearer to tailored therapeutic approaches for individuals. Moreover, the role of TMED3 in promoting malignant proliferation of PCa cells was validated.CONCLUSIONS:Our findings highlight ECMGPS as a powerful tool for improving PCa patient outcomes and supply a robust conceptual framework for in-depth examination of PCa complexities. Simultaneously, our study has the potential to develop a novel alternative for PCa diagnosis and prognostication.

Background: Prostate cancer (PCa) ranks as the second most prevalent malignancy among males on a global scale. Accumulating evidence suggests that inflammation has an intricate relationship with tumorigenesis, tumor progression and tumor immune microenvironment. However, the overall impact of inflammation-related genes on the clinical prognosis and tumor immunity in PCa remains unclear. Methods: Machine learning methods were utilized to construct and validate a signature using The Cancer Genome Atlas (TCGA) for training, while the Memorial Sloan Kettering Cancer Center (MSKCC) and GSE70769 cohorts for independent validation. The efficacy of the signature in predicting outcomes and its clinical utility were assessed through a series of investigations encompassing in vitro experiments, survival analysis, and nomogram development. The association between the signature and precision medicine was explored via tumor immunity, genomic heterogeneity, therapeutic response, and molecular docking analyses, using bulk and single-cell RNA-sequencing data. Results: We identified 7 inflammation-related genes with prognostic significance and developed an inflammation-related prognostic signature (IRPS) with 6 genes. Furthermore, we demonstrated that both the IRPS and a nomogram integrating risk score and pathologic T stage exhibited excellent predictive ability for the survival outcomes in PCa patients. Moreover, the IRPS was found to be significantly associated with the tumor immune, genomic heterogeneity, therapeutic response, and drug selection. Conclusion: IRPS can serve as a reliable predictor for PCa patients. The signature may provide clinicians with valuable information on the efficacy of therapy and help personalize treatment for PCa patients.

Background. This study sought to perform a survival analysis and construct a prognostic nomogram model based on the Gleason grade, total prostate-specific antigen (tPSA), alkaline phosphate (ALP), and TNM stage in patients with prostate cancer (PCa). Methods. The progression-free survival (PFS) of 255 PCa patients was analyzed in this study. The prognostic value of tPSA and ALP was evaluated using the Kaplan-Meier survival curves and Cox regression analysis, and a nomogram model based on the Gleason grade, tPSA, ALP, and TNM stage was further established for PFS prediction in PCa patients. Results. PCa patients with different Gleason grades, tPSA and ALP levels, and TNM stages presented distinct PFS. The Gleason grade, tPSA, ALP, and TNM stage were four independent prognostic indicators. The C-index of the established nomogram was 0.705 for PFS in the test cohort and 0.687 for the validation cohort, and the calibration curves indicated a good consistency between predicted and actual PFS in PCa patients. Conclusion. The data of this study demonstrated that the Gleason grade, tPSA, ALP, and TNM stage of PCa patients are independently correlated with PFS, and a nomogram model based on these indicators may be valuable for the PFS prediction in PCa patient.

Objective:To investigate the expression of G2 and S phase-expressed-1 (GTSE1) in prostate cancer(PCa) tissues and its regulation in PCa metastasis.Methods:GEPIA, UALCAN and FireBrowse databases were analyzed to determine the correlation between GTSE1 expression and clinical indexes and metastasis in patients with PCa. The co-expression genes and possible pathways of GTSE1 were further analyzed. Wound healing and transwell invasion assays were used to detect the effect of GTSE1 on the migration and invasion of PCa cells in vitro. Western blotting assay was used to analyze the effects of GTSE1 on epithelial-to-mesenchymal transition (EMT) markers.Results:The expression of GTSE1 was significantly increased in PCa tissues, especially in metastatic PCa, and was closely related to the disease progression and lymph node metastasis. Overexpression of GTSE1 enhanced the ability of migration and invasion of PCa cells, and promoted the formation of EMT, which showed the opposite effects with GTSE1 knockdown. GTSE1 may act on the microtubules of PCa cells and cooperate with TPX2, TOP2A and CENPF to drive EMT and metastasis of PCa. Conclusion:GTSE1 is highly expressed in PCa tissues and can promote the invasion and migration of PCa cells, which may be developed as a new molecular target for the treatment of metastatic PCa.

Objective:To investigate the relationship between the serum alkaline phosphatase (ALP), prostate specific antigen (PSA) and bone metastasis in initially diagnosed prostate cancer (PCa) patients in different ISUP(International Society of Urological Pathology)groups.Methods:The 368 initial diagnosed prostate cancer patients recruited from January 2013 to December 2018 were retrospectively analyzed, including 247 cases in the Third Affiliated Hospital of Sun Yat-sen University, 111 cases in the Yuebei People's Hospital Affiliated to Medical College of Shantou University and 10 cases in Shenzhen Hospital of Southern Medical University. According to whether there was bone metastasis at the initial diagnosis, it was divided into 230 cases in the bone metastasis group and 138 cases in the non bone metastasis group. There was no significant difference between the two groups in age [(71.9±9.4) years and (71.2±8.7) years], body mass index (BMI) [(23.1±3.7) kg/m 2 and (23.7±2.6) kg/m 2]. There were significant differences in PSA [(307.3±847.0) ng/ml and (84.5±257.3) ng/ml] and ALP [(174.5±270.8) U/L and (71.0±23.2) U/L] between the two groups. In different PSA subgroups, there were 45 cases in PSA <10 ng/ml, 35 cases in PSA 10-20 ng/ml and 288 cases in PSA >20 ng/ml. The differences of ALP and PSA between bone metastasis group and non-bone metastasis group based on different ISUP stratification were analyzed, ROC curves were used to predict their risks of bone metastasis. Results:There were 3(1.3%), 22(9.6%), 34(14.8%), 85(37.0%) and 86 (37.4%) prostate cancer patients with bone metastasis from ISUP group 1 to 5, and 14(10.1%), 19(13.8%), 29(21.0%), 32(23.2%) and 44(31.9%) without bone metastasis, respectively. There was significant difference in the serum ALP levels between the bone metastasis group and the boneless metastasis group in the ISUP group 4(157.6±207.7 vs. 66.5±17.0) and 5(189.4±257.5 vs. 69.2±18.4)( P<0.001) and PSA levels had difference in the ISUP group 3(240.3±313.0 vs. 42.4±42.1), 4(152.3±184.5 vs. 44.7±33.3) and 5(435.2±1006.3 vs. 60.8±84.8)( P<0.001). There was statistically significant between the bone metastasis group and the without(336.1±882.2 vs. 139.3±328.1) when PSA>20 ng/ml( P=0.006). ROC curve analysis: the cut-off values of ALP were 115.5, 109.0, 75.5 and 86.0 U/L from ISUP group 2 to 5 respectively, the sensitivity was 23.8%, 56.5%, 66.4% and 50.6% respectively, the specificity was 99.7%, 93.4%, 78.3% and 89.2% respectively, and the accuracy were 59.4%, 73.1%, 69.7% and 63.3%, respectively. The cut-off values of PSA were 39.5, 93.1, 54.2 and 28.9 ng/ml from ISUP group 2 to 5 respectively, the sensitivity was 64.4%, 68.4%, 87.4% and 88.3% respectively, and the specificity was 79.5%, 90.6%, 63.7% and 61.6% respectively, and the accuracy were 71.6%, 78.1%, 80.1% and 79.2%, respectively. Conclusion:ALP increased significantly in ISUP group ≥4 and PSA in ISUP group ≥3, which related to bone metastasis in patients with initial diagnosed prostate cancer.

Genes with cross-cancer aberrations are most likely to be functional genes or potential therapeutic targets. Here, we found a total of 137 genes were ectopically expressed in eight cancer types, of which Holliday junction recognition protein (HJURP) was significantly upregulated in prostate cancer (PCa). Moreover, patients with higher HJURP mRNA and protein levels had poorer outcomes, and the protein levels served as an independent prognosis factor for the overall survival of PCa patients. Functionally, ectopic HJURP expression promoted PCa cells proliferation in vitro and in vivo. Mechanistically, HJURP increased the ubiquitination of cyclin-dependent kinase inhibitor 1 (CDKN1A) via the GSK3β/JNK signaling pathway and decreased its stability. This study investigated the role of HJURP in PCa proliferation and may provide a novel prognostic and therapeutic target for PCa.

目的 分析血浆纤维蛋白原(FIB)与前列腺癌临床病理特征的关系,探讨FIB在前列腺癌患者病情评估中的临床价值.方法 回顾性分析从2006年1月至2017年12月间珠江医院456例前列腺癌(PCa)患者及322例前列腺增生(BPH)患者的临床病理资料.分析前列腺癌患者术前血浆纤维蛋白原水平与纤维蛋白原(FIB)、Gleason评分、总前列腺特异抗原(tPSA)值、游离前列腺特异抗原(fPSA)值、游离前列腺特异抗原百分比(f/tPSA)、年龄、体质量指数(BMI)、临床T分期(cT)、有无淋巴结转移、有无骨转移、危险度分级之间的相关性.结果 两组患者的术前血浆FIB中位水平分别为3.52(2.97～4.35)g/L、3.12(2.61～3.45)g/L.前列腺癌组显著高于前列腺增生组,差异有统计学意义(P<0.05).前列腺癌患者术前血浆FIB水平与年龄、tPSA、Gleason评分、cT分期改变呈正相关性(P<0.05).结论 前列腺癌患者术前血浆纤维蛋白原水平与肿瘤病理评分、临床T分期以及相关血清指标有密切关系,可以联合相关影像及实验室检查结果预测患者疾病进展情况及其转归.

目的 探讨DVPV数字化手术辅助影像导航技术在泌尿外科复杂手术中的应用价值.方法 应用DVPV系统的三维可视化数字重建、虚拟现实应用、全息医疗专业影像平台,在普通CT或MRI数据的基础上进行三维图像重建,术前制定最优的手术方案,术中通过虚拟现实眼镜观看病灶影像并为手术操作进行导航.记录手术操作时间、疗效与围手术期并发症.结果 7例手术均顺利完成.3例肾血管平滑肌瘤(2例为较大体积,1例为肾门肿瘤)被完整切除,患侧肾脏均被保留.无损伤肾门血管和肾盂,平均出血量30 ml,平均手术时间105 min.在肾结石病例中,术中影像导航下避开肾脏重要血管,切开肾皮质的薄弱处取净鹿角型结石,手术时间120 min,出血量约50 ml.大体积中叶前列腺癌用腹腔镜技术完成根治术,出血量约75 ml.前列腺巨大囊性肿物行经直肠穿刺抽液+肿物活检术.肾上腺肿瘤用腹腔镜切除.全部患者平均住院日4.5 d,术中术后无并发症,恢复良好.结论 DVPV数字化手术影像导航系统有助于术前疾病评估,明确病变位置,术中虚拟现实影像导航,可提高医师操作的精准性,降低手术出血量、操作难度与风险,缩短手术时间,让患者获益.

Objective:To investigate the role of Smad4 as a target gene regulated by microRNA (miRNA, miR)-301a and the mediating effect of high glucose in promoting the proliferation of prostate cancer cells.Methods:Real-time PCR was used to examine the expression of miR-301 in prostate cancer cells. Bioinformatics prediction by using software of TargetScan and PicTar and luciferase reporter gene assay was utilized for the identification of the target genes of miR-301. The expression of Smad4 was detected by real-time PCR and Western bloting [25 μg, 10% sodium dodecyl sulfate polyacrylamide gel electropheresis (SDS-PAGE)] after the miR-301 mimic was transfected 24 h later. After miR-301 mimics were transfected or co-transfected with Smad4 over-expression plasmid for 24 h, the proliferation and cell cycle of prostate cancer cells (PC-3 and DUl45) were examined by cell counting kit-8 (CCK-8) and flow cytometry, respectively.Results:Bioinformatics prediction and luciferase reporter gene assay demonstrated that Smad4 was the target of miR-301a. After up-regulating miR-301a, the expression of Smad4 mRNA and protein was significantly reduced. The cell proliferation ability increased by 160% ( P<0.05) after up-regulating the expression of both miR-301a and Smad4 for 96 h. MiR-301a could inhibit the expression of Smad4, thereby promoting the cell transition from the G 1 phase to the S phase. In PC-3 cells, before overexpression of miR-301a, the percentages of different phases were G 0/G 1 62.60%, S 28.00%, G 2/M 9.40%; after overexpression, the percentages were G 0/G 1 49.97%, S 41.04%, G 2/M 8.99%, the differences in the phases of G0 and S were significant ( P<0.05). In DU-145cells, before overexpression of miR-301a, the percentages of different phases were G 0/G 1 65.16%, S 24.54%, G 2/M 10.30%; after overexpression, the percentages were G 0/G 1 53.34%, S 36.67%, G 2/M 10.00%, the differences in the phases of G 0 and S were significant ( P<0.05). In addition, knockdown of Smad4 shortened the G 1 phase and prolonged the S phase, which was consistent with the results of miR-301a overexpression. Overexpression of Smad4 could block the effect of miR-301a inducing transformation of G 1/S phase. Conclusion:MiR-301 can regulate the proliferation of prostate cancer cells by targeting Smad4 gene. Smad4 plays an important role in the regulation of hyperglycemia associated prostate cancer.

Purpose Renal cell carcinoma (RCC) is the most common type of kidney cancer in adults. Exosomes are membrane-enclosed extracellular vesicles, and exosomal RNA can be a biomarker for cancer diagnosis and prognosis in RCC patients. We aim to identify differences in miRNA expression profiles in peripheral blood exosomes between RCC patients and healthy subjects as well as to investigate novel markers of RCC. Methods We performed exosomal miRNA sequencing of plasma samples obtained from five RCC patients and five control subjects, subsequently 22 RCC patients and 16 control subjects were investigated using qPCR to confirm the differential miRNA which from plasma exosomal RNA sequencing. ROC curves were constructed to assess the diagnostic accuracy of exosomal miRNAs as diagnostic biomarkers of RCC. Results Exosomes were isolated with the exoeasy maxi kit and confirmed using TEM and NTA. They have a spherical structure with a diameter of approximately 40–180 nm. The exosomal miRNA sequence results showed that a total of 2357 miRNAs were detected, and 245 miRNAs were differentially expressed between RCC patients and healthy controls (p<0.001, average counts >5, log|fc|>1). Further analysis revealing that, versus the control, 17 miRNAs are up-regulated and 5 miRNAs are down-regulated under selection conditions with average miRNAs counts >100. qPCR was performed using 38 subjects—the results showed that the expression levels of hsa-mir-149-3p and hsa-mir-424-3p were upregulated; the expression levels of hsa-mir-92a-1-5p were significantly downregulated in the plasma exosomes of RCC. For diagnosis of RCC, the AUC of hsa-mir-92a-1-5p, hsa-mir-149-3p and hsa-mir-424-3p was 0.8324, 0.7188 and 0.7727, with the sensitivity of 0.875, 0.750 and 0.750, and the specificity of 0.773,0.727 and 0.818, respectively, at the best cutoff value. Conclusion Our study revealed that the expression levels of hsa-mir-92a-1-5p, hsa-mir-149-3p and hsa-mir-424-3p were significantly abnormal in RCC patients, which may be novel biomarkers for RCC diagnosis.