The functions and mechanisms of RNA modification in prostate: Current status and future perspectives.

The increasing incidence and mortality of prostate cancer worldwide significantly impact the life span of male patients, emphasizing the urgency of understanding its pathogenic mechanism and associated molecular changes that regulate tumor progression for effective prevention and treatment. RNA modification, an important post-transcriptional regulatory process, profoundly influences tumor cell growth and metabolism, shaping cell fate. Over 170 RNA modification methods are known, with prominent research focusing on N6-methyladenosine, N7-methylguanosine, N1-methyladenosine, 5-methylcytidine, pseudouridine, and N4-acetylcytidine modifications. These alterations intricately regulate coding and non-coding RNA post-transcriptionally, affecting the stability of RNA and protein expression levels. This article delves into the latest advancements and challenges associated with various RNA modifications in prostate cancer tumor cells, tumor microenvironment, and core signaling molecule androgen receptors. It aims to provide new research targets and avenues for molecular diagnosis, treatment strategies, and improvement of the prognosis in prostate cancer.

The development and assessment of a predicting nomogram for the recovery of immediate urinary continence following laparoscopic radical prostatectomy.

Purpose: This study aimed to develop a nomogram to predict the recovery of immediate urinary continence in laparoscopic radical prostatectomy (LRP) patients. Methods: A prediction model was developed based on a dataset of 154 LRP patients. Immediate urinary continence was defined as free from using pads within 7 days after the removal of the urinary catheter. The least absolute shrinkage and selection operator regression (LASSO) model was applied to screen the features. Multivariate logistic regression analysis was used to establish prediction model integrating the features selected from the LASSO regression analysis. Receiver operating curve (ROC), calibration and decision curve analysis (DCA) were used to assess the model's discrimination, calibration and clinical utility. Results: The identified features of the prediction model included age, body mass index (BMI) and three pelvic anatomic parameters measured by MRI: membranous urethral length (MUL), intravesical prostatic protrusion length (IPPL) and puborectalis muscle width (PMW). The nomogram showed good discrimination with an are under the curve(AUC) of 0.914 (95% CI, 0.865-0.959, p < 0.001). Moreover, good calibration was showed in the model. Lastly, DCA showed that the nomogram was clinically useful. Conclusion: The developed novel nomogram that can predict the possibility for post-prostatectomy patients to recover immediate urinary continence could be used as a counseling tool to explain urinary incontinence to patients after LRP.

The value of contrast-enhanced ultrasound and magnetic resonance imaging in the diagnosis of bladder cancer.

OBJECTIVE: Imaging examination, tumor marker detection, bladder biopsy, and other methods are the common methods for the diagnosis of bladder cancer (BC). This study was aimed to assess the value of contrast-enhanced ultrasound (CEUS) and magnetic resonance imaging (MRI) in the diagnosis of BC. MATERIALS AND METHODS: Fifty-nine patients with BC were recruited in our hospital from September 2012 to December 2015, who had CEUS and magnetic resonance diffusion-weighted imaging (MRI + DWI). All patients underwent surgical treatment and definite pathological stage. The series and parallel combined diagnosis methods were applied to calculate the diagnostic sensitivity, specificity, and accuracy through using quantitative apparent diffusion coefficient (ADC) and receiver operating characteristic curve. RESULTS: The accuracies of CEUS and MRI + DWI examination for T staging of BC were 74.6% and 76.3%, respectively. Compared with the single diagnostic methods, the two combined diagnosis accuracy was 91.5%, which was significantly improved in diagnosis accuracy (P < 0.05). The diagnostic accuracies of CEUS, MRI + DWI, and ADC for muscle invasion of BC were 81.4%, 83.1%, and 84.7%, respectively. The diagnostic accuracy of CEUS parallel combined with MRI + DWI (91.5%) was obviously enhanced, compared with that with the single diagnostic method. CONCLUSION: The accuracy of CEUS and MRI + DWI combined diagnosis was higher than that with the single diagnostic method. CEUS and MRI + DWI combined diagnosis was a feasible and effective method for the clinical diagnosis of BC.

Magnetic resonance imaging-based radiomics signature for preoperative prediction of Ki67 expression in bladder cancer.

PURPOSE: The Ki67 expression is associated with the advanced clinicopathological features and poor prognosis in bladder cancer (BCa). We aimed to develop and validate magnetic resonance imaging (MRI)-based radiomics signatures to preoperatively predict the Ki67 expression status in BCa. METHODS AND MATERIALS: We retrospectively collected 179 BCa patients with Ki67 expression and preoperative MRI. Radiomics features were extracted from T2-weighted (T2WI) and dynamic contrast-enhancement (DCE) images. The synthetic minority over-sampling technique (SMOTE) was used to balance the minority group (low Ki67 expression group) in the training set. Minimum redundancy maximum relevance was used to identify the best features associated with Ki67 expression. Support vector machine and Least Absolute Shrinkage and Selection Operator algorithms (LASSO) were used to construct radiomics signatures in training and SMOTE-training sets, and diagnostic performance was assessed by the area under the curve (AUC) and accuracy. The decision curve analyses (DCA) and calibration curve and were used to investigate the clinical usefulness and calibration of radiomics signatures, respectively. The Kaplan-Meier test was performed to investigate the prognostic value of radiomics-predicted Ki67 expression status. RESULTS: 1218 radiomics features were extracted from T2WI and DCE images, respectively. The SMOTE-LASSO model based on nine features achieved the best predictive performance in the SMOTE-training (AUC, 0.859; accuracy, 80.3%) and validation sets (AUC, 0.819; accuracy, 81.5%) with a good calibration performance and clinical usefulness. Immunohistochemistry-based high Ki67 expression and radiomics-predicted high Ki67 expression based on the SMOTE-LASSO model were significantly associated with poor disease-free survival in training and validation sets (all P < 0.05). CONCLUSIONS: The SMOTE-LASSO model could predict the Ki67 expression status and was associated with survival outcomes of the BCa patients, thereby may aid in clinical decision-making.

A Genomic-Clinicopathologic Nomogram for the Prediction of Lymph Node Invasion in Prostate Cancer.

BACKGROUND: Lymph node status is important for treatment decision making in prostate cancer (PCa). We aimed to develop a genomic-clinicopathologic nomogram for the prediction of lymph node invasion (LNI) in PCa. METHODS: Differentially expressed genes between LNI and non-LNI PCa samples were identified in the Cancer Genome Atlas database. Univariate Cox regression analysis and minimum redundancy maximum relevance were performed for gene selection. The synthetic minority oversampling technique (SMOTE) was conducted to balance the minority group (LNI group). Machine learning models were constructed in the training set and assessed in the validation set. Univariable logistic regression and multivariable logistic regression were applied to build a nomogram. Furthermore, the RNA-sequence data from our center were used to validate the expression levels of hub genes between five matched primary PCa and the corresponding LNI samples. RESULTS: The 37-gene-based support vector machine (SVM) model had the optimal synthesized performance in the SMOTE-balanced training (area under the curve (AUC): 0.947) and validation (AUC: 0.901) sets. Incorporating the SVM-based risk score and the Gleason grade, the genomic-clinicopathologic nomogram demonstrated good prediction and calibration both in the SMOTE-balanced training (AUC: 0.946) and validation (AUC: 0.910) sets. The dysregulated expression of hub genes between PCa and LNI samples was also validated. CONCLUSION: The proposed nomogram combining the 37-gene-based SVM model with the Gleason grade had the potential to preoperatively predict LNI in PCa. Some of the hub genes should be prioritized for functional studies and mechanistic analyses.

Combining Multiparametric MRI Radiomics Signature With the Vesical Imaging-Reporting and Data System (VI-RADS) Score to Preoperatively Differentiate Muscle Invasion of Bladder Cancer.

BACKGROUND: The treatment and prognosis for muscle-invasive bladder cancer (MIBC) and non-muscle-invasive bladder cancer (NMIBC) are different. We aimed to construct a nomogram based on the multiparametric MRI (mpMRI) radiomics signature and the Vesical Imaging-Reporting and Data System (VI-RADS) score for the preoperative differentiation of MIBC from NMIBC. METHOD: The retrospective study involved 185 pathologically confirmed bladder cancer (BCa) patients (training set: 129 patients, validation set: 56 patients) who received mpMRI before surgery between August 2014 to April 2020. A total of 2,436 radiomics features were quantitatively extracted from the largest lesion located on the axial T2WI and from dynamic contrast-enhancement images. The minimum redundancy maximum relevance (mRMR) algorithm was used for feature screening. The selected features were introduced to construct radiomics signatures using three classifiers, including least absolute shrinkage and selection operator (LASSO), support vector machines (SVM) and random forest (RF) in the training set. The differentiation performances of the three classifiers were evaluated using the area under the curve (AUC) and accuracy. Univariable and multivariable logistic regression were used to develop a nomogram based on the optimal radiomics signature and clinical characteristics. The performance of the radiomics signatures and the nomogram was assessed and validated in the validation set. RESULTS: Compared to the RF and SVM classifiers, the LASSO classifier had the best capacity for muscle invasive status differentiation in both the training (accuracy: 90.7%, AUC: 0.934) and validation sets (accuracy: 87.5%, AUC: 0.906). Incorporating the radiomics signature and VI-RADS score, the nomogram demonstrated better discrimination and calibration both in the training set (accuracy: 93.0%, AUC: 0.970) and validation set (accuracy: 89.3%, AUC: 0.943). Decision curve analysis showed the clinical usefulness of the nomogram. CONCLUSIONS: The mpMRI radiomics signature may be useful for the preoperative differentiation of muscle-invasive status in BCa. The proposed nomogram integrating the radiomics signature with the VI-RADS score may further increase the differentiation power and improve clinical decision making.

Integrating multiparametric MRI radiomics features and the Vesical Imaging-Reporting and Data System (VI-RADS) for bladder cancer grading.

PURPOSE: Pathological grade is important for the treatment selection and outcome prediction in bladder cancer (BCa). We aimed to construct a radiomics-clinical nomogram to preoperatively differentiate high-grade BCa from low-grade BCa. METHODS: A total of 185 BCa patients who received multiparametric MRI (mpMRI) before surgery between August 2014 and April 2020 were enrolled in our study. Radiomics features were extracted from the largest tumor located on dynamic contrast-enhancement and T2WI images. After feature selection, the synthetic minority over-sampling technique (SMOTE) was performed to balance the minority group (low-grade group). Radiomics signatures were constructed in the training set and assessed in the validation set. Univariable and multivariable logistic regression were applied to build a nomogram. RESULTS: The radiomics signature generated by the least absolute shrinkage and selection operator model achieved the optimal performance for BCa grading in both the SMOTE-balanced training [accuracy: 93.2%, area under the curve (AUC): 0.961] and validation sets (accuracy: 89.9%, AUC: 0.952). A radiomics-clinical nomogram incorporating the radiomics signature and the Vesical Imaging-Reporting and Data System (VI-RADS) score had novel calibration and discrimination both in the training (AUC: 0.956) and validation sets (AUC: 0.958). Decision curve analysis presented the clinical utility of the nomogram for decision-making. CONCLUSIONS: The mpMRI-based radiomics signature had the potential to preoperatively predict the pathological grade of BCa. The proposed nomogram combining the radiomics signature with the VI-RADS score improved the diagnostic power, which may aid in clinical decision-making.

Computed Tomography-Based Radiomics Signature: A Potential Indicator of Epidermal Growth Factor Receptor Mutation in Pulmonary Adenocarcinoma Appearing as a Subsolid Nodule.

BACKGROUND: Lung adenocarcinoma (LADC) with epidermal growth factor receptor (EGFR) mutation is considered a subgroup of lung cancer sensitive to EGFR-targeted tyrosine kinase inhibitors. We aimed to develop and validate a computed tomography (CT)-based radiomics signature for prediction of EGFR mutation status in LADC appearing as a subsolid nodule. MATERIALS AND METHODS: A total of 467 eligible patients were divided into training and validation cohorts (n = 306 and 161, respectively). Radiomics features were extracted from unenhanced CT images by using Pyradiomics. A CT-based radiomics signature for distinguishing EGFR mutation status was constructed using the random forest (RF) method in the training cohort and then tested in the validation cohort. A combination of the radiomics signature with a clinical factors model was also constructed using the RF method. The performance of the model was evaluated using the area under the curve (AUC) of a receiver operating characteristic curve. RESULTS: In this study, 64.2% (300/467) of the patients showed EGFR mutations. L858R mutation of exon 21 was the most common mutation type (185/301). We identified a CT-based radiomics signature that successfully discriminated between EGFR positive and EGFR negative in the training cohort (AUC = 0.831) and the validation cohort (AUC = 0.789). The radiomics signature combined with the clinical factors model was not superior to the simple radiomics signature in the two cohorts (p > .05). CONCLUSION: As a noninvasive method, the CT-based radiomics signature can be used to predict the EGFR mutation status of LADC appearing as a subsolid nodule. IMPLICATIONS FOR PRACTICE: Lung adenocarcinoma (LADC) with epidermal growth factor receptor (EGFR) mutation is considered a subgroup of lung cancer that is sensitive to EGFR-targeted tyrosine kinase inhibitors. However, some patients with inoperable subsolid LADC are unable to undergo tissue sampling by biopsy for molecular analysis in clinical practice. A computed tomography-based radiomics signature may serve as a noninvasive biomarker to predict the EGFR mutation status of subsolid LADCs when mutational profiling is not available or possible.

[Study and application of transmission tracking analysis technique during incubation period of respiratory infectious diseases].

OBJECTIVE: To develop a new transmission tracking analysis technique during incubation period of respiratory infectious diseases, and to discuss its practical value in the field survey of infectious diseases. METHODS: The classical epidemiological theory was integrated with geographic information system. The transmission tracking analysis technique was established based on the modeling platform ArcGIS Engine Developer Kit 9.3, using the techniques of address matching, shortest path analysis and buffer analysis, and programming by Visual C++. Eight serious sever acute respiratory syndrome (SARS) cases in Shanghai in year 2003 were then chose as prototype to set up the test cases A-H. The electronic map and population density data were separately collected from Institute of Surveying and Mapping in Shanghai and Shanghai statistical yearbook 2003, to calculate and explore the parameters as length of transmission path, area of buffer zone and key departments by single and multi case analysis module. RESULTS: The single case transmission tracking analysis showed that the length of transmission track of case A was 129.89 km during April 25th to 29th in 2003, including 12 tracing point and 108 intimate contacts, and the total area of buffer zone was 7.11 km(2) including 81 important institutes, naming 72 schools, 6 kindergartens and 3 gerocomiums. The multi-case transmission tracking analysis showed that the 8 cases shared 5 tracks without any temporal communication. However, there was a spatial communication whose length was 1.42 km and area was 0.60 km(2). There were no important institutes found in this communication area. CONCLUSION: Transmission tracking technique is practicable and efficient to trace the source of infection, analyze the transmission tracks, establish the isolation buffer area and explore the important geographic positions in epidemiological investigation.