[Relationship between long non-coding RNA MALAT1 and prostate cancer].

Prostate cancer (PCa), as a malignant tumor originating in the prostate glandular epithelium, has become a global "killer" that threatens the health of elderly men. PCa-related studies have been focusing on the progression mechanisms and treatment strategies of the malignancy, particularly on the role of long non-coding RNA (lncRNA) in recent years. The lncRNA metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) plays a key role in the progression and treatment of PCa, as well as in its metastasis and invasion and cell proliferation. lncRNA MALAT1 not only influences the biological characteristics of PCa, but also has a regulatory effect on the medicinal treatment of the disease, its action mechanisms involving ceRNA and AR signaling pathways. This review focuses on the relationship between lncRNA MALAT1 and PCa, aiming to provide a new research direction for the diagnosis and treatment of the malignancy.

[Transperitoneal versus extraperitoneal robot-assisted radical prostatectomy for localized prostate cancer].

OBJECTIVE: To compare the clinical effects of transperitoneal (Tp) versus extraperitoneal (Ep) robot-assisted radical prostatectomy (RARP) in the treatment of localized prostate cancer. METHODS: We searched PubMed, EMBASE, Web of Science, EBSCO, Cochrane Library, Wanfang, CNKI, and CBM for the articles comparing the clinical effect Tp-RARP with that of Ep-RARP in the treatment of localized prostate cancer published from January 2000 to November 2016. All the articles must meet the inclusion criteria, that is, dealing with at least one of the following aspects: operation time, intraoperative blood loss, postoperative catheterization time, length of bed confinement, perioperative complications, positive surgical margins, bowel-related complications, postoperative anastomotic leakage, and postoperative urinary continence. We subjected the data obtained to statistical analysis using the RevMan5.3 software. RESULTS: Two randomized controlled trials and six case-control studies were included in this meta-analysis, involving 451 cases of Tp-RARP and 676 cases of Ep-RARP. Compared with Tp-RARP, Ep-RARP showed significantly shorter operation time (WMD = 21.39, 95% CI: 7.54－35.24, P = 0.002), shorter length of bed confinement (WMD = 0.85, 95% CI: 0.61－1.09, P <0.001), and lower rate of bowel-related complications (RR = 9.74, 95% CI: 3.26－29.07, P <0.001). However, no statistically significant differences were found between the two strategies in intraoperative blood loss (WMD = －8.12, 95% CI: －27.86－11.63, P = 0.42), postoperative catheterization time (WMD = 0.17, 95% CI: －0.55－0.21, P = 0.38), or the rates of perioperative complications (RR = 1.34, 95% CI: －0.97－1.87, P = 0.08), positive surgical margins (RR = 1.24, 95% CI: 0.95－1.61, P = 0.12), anastomotic leakage (RR = 0.98, 95% CI: 0.46－2.10, P = 0.95), urinary continence at 3 months (RR = 0.96, 95% CI: 0.91－1.00, P = 0.05) and urinary continence at 6 months (RR = 1.00, 95% CI: 0.97－1.02, P = 0.82). CONCLUSIONS: Ep-RARP has the advantages of shorter operation time, shorter length of bed confinement and lower rate of bowel-related complications over Tp-RARP, and therefore may be a better option for the treatment of localized prostate cancer. However, more multi-centered randomized controlled clinical trials are needed for further evaluation of these two approaches.

Deep learning algorithm-based multimodal MRI radiomics and pathomics data improve prediction of bone metastases in primary prostate cancer.

PURPOSE: Bone metastasis is a significant contributor to morbidity and mortality in advanced prostate cancer, and early diagnosis is challenging due to its insidious onset. The use of machine learning to obtain prognostic information from pathological images has been highlighted. However, there is a limited understanding of the potential of early prediction of bone metastasis through the feature combination method from various sources. This study presents a method of integrating multimodal data to enhance the feasibility of early diagnosis of bone metastasis in prostate cancer. METHODS AND MATERIALS: Overall, 211 patients diagnosed with prostate cancer (PCa) at Gansu Provincial Hospital between January 2017 and February 2023 were included in this study. The patients were randomized (8:2) into a training group (n = 169) and a validation group (n = 42). The region of interest (ROI) were segmented from the three magnetic resonance imaging (MRI) sequences (T2WI, DWI, and ADC), and pathological features were extracted from tissue sections (hematoxylin and eosin [H&E] staining, 10 × 20). A deep learning (DL) model using ResNet 50 was employed to extract deep transfer learning (DTL) features. The least absolute shrinkage and selection operator (LASSO) regression method was utilized for feature selection, feature construction, and reducing feature dimensions. Different machine learning classifiers were used to build predictive models. The performance of the models was evaluated using receiver operating characteristic curves. The net clinical benefit was assessed using decision curve analysis (DCA). The goodness of fit was evaluated using calibration curves. A joint model nomogram was eventually developed by combining clinically independent risk factors. RESULTS: The best prediction models based on DTL and pathomics features showed area under the curve (AUC) values of 0.89 (95% confidence interval [CI], 0.799-0.989) and 0.85 (95% CI, 0.714-0.989), respectively. The AUC for the best prediction model based on radiomics features and combining radiomics features, DTL features, and pathomics features were 0.86 (95% CI, 0.735-0.979) and 0.93 (95% CI, 0.854-1.000), respectively. Based on DCA and calibration curves, the model demonstrated good net clinical benefit and fit. CONCLUSION: Multimodal radiomics and pathomics serve as valuable predictors of the risk of bone metastases in patients with primary PCa.

Multimodal data integration for predicting progression risk in castration-resistant prostate cancer using deep learning: a multicenter retrospective study.

Purpose: Patients with advanced prostate cancer (PCa) often develop castration-resistant PCa (CRPC) with poor prognosis. Prognostic information obtained from multiparametric magnetic resonance imaging (mpMRI) and histopathology specimens can be effectively utilized through artificial intelligence (AI) techniques. The objective of this study is to construct an AI-based CRPC progress prediction model by integrating multimodal data. Methods and materials: Data from 399 patients diagnosed with PCa at three medical centers between January 2018 and January 2021 were collected retrospectively. We delineated regions of interest (ROIs) from 3 MRI sequences viz, T2WI, DWI, and ADC and utilized a cropping tool to extract the largest section of each ROI. We selected representative pathological hematoxylin and eosin (H&E) slides for deep-learning model training. A joint combined model nomogram was constructed. ROC curves and calibration curves were plotted to assess the predictive performance and goodness of fit of the model. We generated decision curve analysis (DCA) curves and Kaplan-Meier (KM) survival curves to evaluate the clinical net benefit of the model and its association with progression-free survival (PFS). Results: The AUC of the machine learning (ML) model was 0.755. The best deep learning (DL) model for radiomics and pathomics was the ResNet-50 model, with an AUC of 0.768 and 0.752, respectively. The nomogram graph showed that DL model contributed the most, and the AUC for the combined model was 0.86. The calibration curves and DCA indicate that the combined model had a good calibration ability and net clinical benefit. The KM curve indicated that the model integrating multimodal data can guide patient prognosis and management strategies. Conclusion: The integration of multimodal data effectively improves the prediction of risk for the progression of PCa to CRPC.

The functional roles of m6A modification in prostate cancer.

Prostate cancer (PCa) is the most prevalent malignancy of the male genitourinary system, and its etiology suggests that genetics is an essential risk factor for its development and progression, while exogenous factors may have an significant impact on this risk. Initial diagnosis of advanced PCa is relatively frequent, and androgen deprivation therapy (ADT) is the predominant standard of care for PCa and the basis for various novel combination therapy regimens, and is often required throughout the patient's subsequent treatment. Although diagnostic modalities and treatment options are evolving, some patients suffer from complications, including biochemical relapse, metastasis and treatment resistance. Mechanisms of PCa pathogenesis and progression have been the focus of research. N6-methyladenosine (m6A) is an RNA modification involved in cell physiology and tumor metabolism. It has been observed to affect the evolution of diverse cancers through the regulation of gene expression. Genes associated with m6A are prominent in PCa and are involved in multiple aspects of desmoresistant PCa occurrence, progression, PCa bone metastasis (BM), and treatment resistance. Here, we explore the role of m6A modifications in promoting PCa.

COVID-19 Vaccine in Renal Transplant Recipients: A Bibliometric-Based Analysis of Trends.

BACKGROUND: The global community has been affected by COVID-19, which emerged in December 2019. Since then, many studies have been conducted on kidney transplant recipients (KTRs) and COVID-19. This study aimed to perform a bibliometric and visual analysis of the published relationship between KTRs and COVID-19. OBJECTIVE: To discuss the current status, hot spots, and development trend of research on KTRs vaccination with the COVID-19 vaccine and to provide a reference for researchers in related fields. METHODS: Visual analysis of countries/regions, institutions, authors, references cited, and keywords for 2020 to 2023 via Microsoft Office Excel 2019 and CiteSpace (6.1.R6) based on the Web of Science core database. RESULTS: A total of 366 publications were included after screening, with a rapid increase in the global literature studying the COVID-19 vaccine of KTRs. The US has the highest number of publications, indicating that it is the leading country in this field of research. Charite University of Medicine Berlin and Schrezenmeier E are the most published institutions and authors, respectively. "Antibody Response After a Third Dose of the messenger RNA-1273 SARS-CoV-2 Vaccine in Kidney Transplant Recipients With Minimal Serologic Response to 2 Doses" is the most central co-cited reference; The keywords "kidney transplant recipient," "covid 19 vaccine," and "mortality" have become hot topics of research. The keywords "humoral response" and "bnt162b2" are the latest research frontiers for detecting bursts. CONCLUSIONS: This paper analyzed the current status and trends of vaccination studies in KTRs through bibliometric analysis. Several studies support the vaccination of KTRs with the COVID-19 vaccine. However, the evidence for improving vaccine efficacy by adjustment of immunosuppression is still limited, and future studies on vaccination will remain a hot topic in this field.

Adipocyte-derived extracellular vesicles: bridging the communications between obesity and tumor microenvironment.

By the year 2035 more than 4 billion people might be affected by obesity and being overweight. Adipocyte-derived Extracellular Vesicles (ADEVs/ADEV-singular) are essential for communication between the tumor microenvironment (TME) and obesity, emerging as a prominent mechanism of tumor progression. Adipose tissue (AT) becomes hypertrophic and hyperplastic in an obese state resulting in insulin resistance in the body. This modifies the energy supply to tumor cells and simultaneously stimulates the production of pro-inflammatory adipokines. In addition, obese AT has a dysregulated cargo content of discharged ADEVs, leading to elevated amounts of pro-inflammatory proteins, fatty acids, and carcinogenic microRNAs. ADEVs are strongly associated with hallmarks of cancer (proliferation and resistance to cell death, angiogenesis, invasion, metastasis, immunological response) and may be useful as biomarkers and antitumor therapy strategy. Given the present developments in obesity and cancer-related research, we conclude by outlining significant challenges and significant advances that must be addressed expeditiously to promote ADEVs research and clinical applications.

Multiparametric MRI radiomics in prostate cancer for predicting Ki-67 expression and Gleason score: a multicenter retrospective study.

PURPOSE: Prostate cancer (PCa) with high Ki-67 expression and high Gleason Scores (GS) tends to have aggressive clinicopathological characteristics and a dismal prognosis. In order to predict the Ki-67 expression status and the GS in PCa, we sought to construct and verify MRI-based radiomics signatures. METHODS AND MATERIALS: We collected T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) images from 170 PCa patients at three institutions and extracted 321 original radiomic features from each image modality. We used support vector machine (SVM) and least absolute shrinkage and selection operator (LASSO) logistic regression to select the most informative radiomic features and built predictive models using up sampling and feature selection techniques. Using receiver operating characteristic (ROC) analysis, the discriminating power of this feature was determined. Subsequent decision curve analysis (DCA) assessed the clinical utility of the radiomic features. The Kaplan-Meier (KM) test revealed that the radiomics-predicted Ki-67 expression status and GS were prognostic factors for PCa survival. RESULT: The hypothesized radiomics signature, which included 15 and 9 selected radiomics features, respectively, was significantly correlated with pathological Ki-67 and GS outcomes in both the training and validation datasets. Areas under the curve (AUC) for the developed model were 0.813 (95% CI 0.681,0.930) and 0.793 (95% CI 0.621, 0.929) for the training and validation datasets, respectively, demonstrating discrimination and calibration performance. The model's clinical usefulness was verified using DCA. In both the training and validation sets, high Ki-67 expression and high GS predicted by radiomics using SVM models were substantially linked with poor overall survival (OS). CONCLUSIONS: Both Ki-67 expression status and high GS correlate with PCa patient survival outcomes; therefore, the ability of the SVM classifier-based model to estimate Ki-67 expression status and the Lasso classifier-based model to assess high GS may enhance clinical decision-making.

[Effects of cyclopamine on the proliferation and cycle of prostate cancer cell line DU145].

OBJECTIVE: To investigate the inhibitory effect of the Hedgehog signal pathway blocker (cyclopamine) on DU145 cells. METHODS: We interfered DU145 cells with cyclopamine at the concentrations of 1, 10, 50 and 100 micromol/L and detected its inhibitory effect on the cells by MTT colorimetry assay at 24, 48 and 72 hours, as well as its effect on the cell cycle by flow cytometry. We also determined the difference in the mRNA expression of cyclin E between the experimental and control groups by RT-PCR at 48 hours after 50 +/- micromol/L cyclopamine intervention. RESULTS: Cyclopamine inhibited the DU145 cells in a time- and dose-dependent manner, with inhibition rates of 7.42, 12.70 and 59.15% in the 10, 50 and 100 micromol/L groups respectively at 24 hours, significantly different from that of the blank control group (P < 0.05). It markedly suppressed the proliferation of the DU145 cells at >10 micromol/L at 24 hours. Flow cytometry showed an obviously increased proportion of stage G1 cells at the concentration of >10 micromol/L after 48-hour intervention, with statistically significant differences from the G1 cell proportions in the control, 10 micromol/L and 50 micromol/ L groups, which were (52.17 +/- 2.21)%, (60.13 +/- 2.75)% and (74.30 +/- 3.52)% respectively (P < 0.01). The apoptotic peak was elevated with the increased concentration of cyclopamine. The cyclin E mRNA expression of the DU145 cells was decreased by 61.90% at 48 hours after 50 micromol/L cyclopamine intervention as compared with the blank control group (P < 0.01). CONCLUSION: Cyclopamine can inhibit the proliferation of DU145 cells, and the mechanism may be related with its effect of down-regulating the cyclin E mRNA expression of DU145 cells and blocking them in stage G1. Cyclopamine can also induce the apoptosis of DU145 cells.

Standard-tract percutaneous nephrolithotomy for complicated calculi within horseshoe kidneys: A report of one case.

A 48-year-old man was presented in the local hospitalized where he lived because of lower back pain one month ago. Then he came to our hospital for kidney stones on the right side within horseshoe kidneys and hydronephrosis diagnosed by imaging and abdominal ultrasound. After we proceeded single standard percutaneous nephrolithotomy with holmium laser combined EMS, his stones were totally removed with little intraoperative bleeding. No eventful post-operative complications occurred and the curative effect was very satisfied by KUB after stay in bed for 3 days reviewed. And a long-term follow up showed his recovery was quite well.