The enigmatic interplay of immune cells and abnormal spermatozoa through Mendelian randomization.

PURPOSE: Abnormal spermatozoa significantly impact reproductive health, affecting fertility rates, potentially prolonging conception time, and increasing the risk of miscarriages. This study employs Mendelian randomization to explore their potential link with immune cells, aiming to reveal their potential causal association and wider implications for reproductive health. METHODS: We conducted forward and reverse Mendelian randomization analyses to explore the potential causal connection between 731 immune cell signatures and abnormal spermatozoa. Using publicly available genetic data, we investigated various immune signatures such as median fluorescence intensities (MFI), relative cell (RC), absolute cell (AC), and morphological parameters (MP). Robustness was ensured through comprehensive sensitivity analyses assessing consistency, heterogeneity, and potential horizontal pleiotropy. The MR study produced a statistically significant p-value of .0000684, Bonferroni-corrected for the 731 exposures. RESULTS: The Mendelian randomization analysis revealed strong indications of a reciprocal relationship between immune cell pathways and sperm integrity. When examining immune cell exposure, a potential causal link with abnormal sperm was observed in 35 different types of immune cells. Conversely, the reverse Mendelian randomization results indicated that abnormal sperm might causally affect 39 types of immune cells. These outcomes suggest a potential mutual influence between alterations in immune cell functionality and the quality of spermatozoa. CONCLUSION: This study highlights the close link between immune responses and sperm development, suggesting implications for reproductive health and immune therapies. Further research may offer crucial insights into male fertility and immune disorders.

FATP2 activates PI3K/Akt/mTOR pathway by inhibiting ATF3 and promotes the occurrence and development of bladder cancer.

Bladder cancer (BLCA) is ranked among the main causes of mortality in male cancer patients, and research into targeted therapies guided by its genomics and molecular biology has been a prominent focus in BLCA studies. Fatty acid transporter protein 2 (FATP2), a member of the FATPs family,is a key contributor to the progression of cancers such as hepatocellular carcinomas and melanomas.However,its role in BLCA remains poorly understand. This study delved into the function of FATP2 in BLCA through a succession of experiments in vivo and in vitro, employing techniques as quantitative real-time polymerase chain reaction (qRT-PCR), RNA sequencing, transwell assays, immunofluorescence, western blot,and others to dissect its mechanistic actions. The findings revealed that an oncogenic function is executed by FATP2 in bladder cancer, significantly impacting the proliferation and migration capabilities, thereby affecting the prognosis of BLCA patients. Furthermore, A suppression that relies on both time and concentration of BLCA proliferation and migration, trigger of apoptosis, and blockage of the cell cycle at the G2/M phase were observed when the inhibitor of FATP2, Lipofermata, was applied. It was unveiled through subsequent investigations that ATF3 expression is indirectly promoted by Lipofermata through the inhibition of FATP2, ultimately inhibiting the signal transduction of the PI3K/Akt/mTOR pathway. This effect was also responsible for the inhibitory impact on BLCA proliferation. Therefore, FATP2 emerges as an auspicious and emerging molecular target with potential applications in precision therapy in BLCA.

Trends in suicide mortality among prostate cancer survivors in the United States, 1975-2019.

BACKGROUND: Suicide was an important cause of death in prostate cancer. This study intended to investigate trends in suicide mortality among prostate cancer (PCa) survivors from 1975 to 2019 in the United States. METHOD: We identified PCa survivors from the Surveillance, Epidemiology, and End Results (SEER) program from January 1975 to December 2019. Standardized mortality rate (SMR) was calculated d to assess the relative risk of suicide in PCa survivors compared with the general men population. Poisson regression model was performed to test for trend of SMRs. The cumulative mortality rate of suicide was calculated to assess the clinical burden of suicide mortality. RESULTS: 7108 (0.2%) cases were death from suicide cause, and 2,308,923(65.04%%) cases recorded as dying from non-suicidal causes. Overall, a slightly higher suicide mortality rate among PCa survivors was observed compared with general male population (SMR: 1.15, 95%CI: 1.09-1.2). The suicide mortality rate declined significantly relative to the general population by the calendar year of diagnosis, from an SMR of 1.74(95%CI: 1.17-2.51) in 1975-1979 to 0.99(0.89-1.1) in 2015-2019 (Ptrend < 0.001). PCa survivors with aged over 84 years, black and other races, registered in registrations (including Utah, New Mexico, and Hawaii) failed to observe a decrease in suicide mortality (Ptrend > 0.05). The cumulative suicide mortality during 1975-1994 was distinctly higher than in 1995-2019(P < 0.001). CONCLUSION: The trend in suicide mortality declined significantly from 1975 to 2019 among PCa survivors compared with the general male population in the United States. Notably, part of PCa survivors had no improvement in suicide mortality, and additional studies in the future were needed to explore it.

The association between serum testosterone level and congestive heart failure in US male adults: data from National Health and Nutrition Examination Survey (NHANES) 2011-2016.

BACKGROUND: This study aimed to investigate the relationship between serum testosterone levels and the risk of congestive heart failure (CHF) in adult males. Previous research has suggested a potential link between serum testosterone and cardiovascular health, but the findings have been inconclusive. METHODS: This study was cross-sectional, and the data were obtained from the 2011-2016 cycle of the National Health and Nutrition Examination Survey (NHANES), which included a sample of 6,841 male participants. Serum testosterone levels were measured using a standardized assay, and CHF status was assessed through self-reporting. Covariates such as age, ethnicity, lifestyle factors, and health conditions were considered in the analysis. RESULTS: Among the participants, 242 individuals had a documented history of CHF. We observed a linear correlation between serum testosterone levels and CHF occurrence, with higher serum testosterone levels associated with a decreased risk of CHF (Q4 vs. Q1, OR = 0.29, 95% CI: 0.19-0.47, P < 0.001). After adjusting for confounding variables, multivariate analysis revealed that high serum testosterone levels remained significantly associated with a lower risk of CHF (OR: 0.47, 95% CI: 0.27-0.80, P = 0.01). Subgroup analysis indicated a significant association between high serum testosterone levels and reduced CHF risk in individuals over 50 years old. CONCLUSION: Our findings suggest that the serum testosterone level was positively associated with CHF in adult males. This study highlights the potential role of serum testosterone in cardiovascular health, particularly in older individuals. Further research is needed to elucidate the underlying mechanisms and explore the clinical implications of these findings.

Causal relationship between prostate cancer and 12 types of cancers: multivariable and bidirectional Mendelian randomization analyses.

BACKGROUND: Previous observational studies have shown an association between certain cancers and the subsequent risk of prostate cancer (PCa). However, the causal relationship between these cancers and PCa is still unclear. This study aimed to investigate the causal relationship between 12 common cancers and the risk of PCa. METHODS: We employed genome-wide association studies (GWAS) to perform forward and reverse Mendelian randomization (MR) within two-sample frameworks. Furthermore, we conducted multivariable MR analyses to investigate the relationships between different types of cancer. In addition, multiple sensitivity analysis methods were employed to assess the robustness of our findings. RESULTS: Our univariable MR analysis showed that genetically predicted hematological cancer was associated with a reduced risk of PCa (OR: 0.911, 95% CI 0.89-0.922, P = 0.03). Furthermore, MR analysis demonstrates that genetically predicted occurrence of thyroid gland and endocrine gland cancer also raised the risk of PCa (all P < 0.05). Multivariable analysis showed that thyroid gland cancer exhibited a higher incidence of PCa (OR: 1.12, 95% CI: 1.08-1.16, P = 0.008). In the reverse MR analysis, we found no significant inverse causal associations between PCa and 12 types of cancers. CONCLUSION: In summary, this study provided insights into the causal relationships between various types of cancer and PCa. Hematological cancer was suggested to associate with a lower risk of PCa, while thyroid gland cancer and endocrine gland cancer might increase the risk. These findings contribute to the understanding of genetic factors related to PCa and its potential associations with other cancers.

CircFSCN1 induces tumor progression and triggers epithelial-mesenchymal transition in bladder cancer through augmentation of MDM2-mediated p53 silencing.

BACKGROUND: Compelling evidences indicated that circular RNA (circRNA) was a novel class of non-coding RNA that played critical and distinct roles in various human cancers. Their roles and underlying mechanisms, however, in bladder cancer (BC) remained largely unknown. METHODS: A novel circRNA derived from oncogene FSCN1, namely circFSCN1, was selected from a microarray analysis. The phenotypic alterations were assessed with functional experiments in vitro and in vivo. RNA immunoprecipitation, RNA pull-down, luciferase reporter assay, and rescue experiments were sequentially proceeded to clarify the interactions among circFSCN1, miR-145-5p, MDM2, and p53. RESULTS: We observed that the expression of circFSCN1 was elevated in BC cell lines and tissues. Next, we validated the fundamental properties of circFSCN1. In the meanwhile, we noticed that elevated circFSCN1 level, pathological T stage, and tumor grade were identified as independent factors associated with cancer-specific survivals of patients with BC，as determined by univariate and multivariable COX regression analyses. Phenotype studies demonstrated the promoting effects of circFSCN1 on the proliferation, migration, invasion, and epithelial-mesenchymal transition (EMT) of BC cells. Mechanistically, we elucidated that circFSCN1, primarily localized in the cytoplasm, upregulated the expression of MDM2, a well-known inhibitor of p53, by directly binding to miR-145-5p. CONCLUSIONS: Elevated circFSCN1 induces tumor progression and EMT in BC via enhancing MDM2-mediated silencing of p53 by sponging miR-145-5p. Targeting circFSCN1, a novel identified target, may be conducive in impeding BC progression and providing survival benefits for patients with BC.

Deciphering the immunological and prognostic features of bladder cancer through platinum-resistance-related genes analysis and identifying potential therapeutic target P4HB.

Objectives: To identify the molecular subtypes and develop a scoring system for the tumor immune microenvironment (TIME) and prognostic features of bladder cancer (BLCA) based on the platinum-resistance-related (PRR) genes analysis while identifying P4HB as a potential therapeutic target. Methods: In this study, we analyzed gene expression data and clinical information of 594 BLCA samples. We used unsupervised clustering to identify molecular subtypes based on the expression levels of PRR genes. Functional and pathway enrichment analyses were performed to understand the biological activities of these subtypes. We also assessed the TIME and developed a prognostic signature and scoring system. Moreover, we analyzed the efficacy of immune checkpoint inhibitors. Then we conducted real-time fluorescence quantitative polymerase chain reaction (RT-qPCR) experiments to detect the expression level of prolyl 4-hydroxylase subunit beta (P4HB) in BLCA cell lines. Transfection of small interference ribonucleic acid (siRNA) was performed in 5637 and EJ cells to knock down P4HB, and the impact of P4HB on cellular functions was evaluated through wound-healing and transwell assays. Finally, siRNA transfection of P4HB was performed in the cisplatin-resistant T24 cell to assess its impact on the sensitivity of BLCA to platinum-based chemotherapy drugs. Results: In a cohort of 594 BLCA samples (TCGA-BLCA, n=406; GSE13507, n=188), 846 PRR-associated genes were identified by intersecting BLCA expression data from TCGA and GEO databases with the PRR genes from the HGSOC-Platinum database. Univariate Cox regression analysis revealed 264 PRR genes linked to BLCA prognosis. We identified three molecular subtypes (Cluster A-C) and the PRR scoring system based on PRR genes. Cluster C exhibited a better prognosis and lower immune cell infiltration compared to the other Clusters A and B. The high PRR score group was significantly associated with an immunosuppressive tumor microenvironment, poor clinical-pathological features, and a poor prognosis. Furthermore, the high PRR group showed higher expression of immune checkpoint molecules and a poorer response to immune checkpoint inhibitors than the low PRR group. The key PRR gene P4HB was highly expressed in BLCA cell lines, and cellular functional experiments in vitro indicate that P4HB may be an important factor influencing BLCA migration and invasion. Conclusion: Our study demonstrates that the PRR signatures are significantly associated with clinical-pathological features, the TIME, and prognostic features. The key PRR gene, P4HB, s a biomarker for the individualized treatment of BLCA patients.

Development and validation of a nomogram to predict lung metastasis in patients with testicular germ cell tumors.

Background: Lung metastatic tumor (LM) is one of testicular germ cell tumors' most common metastatic sites. Our study aimed to develop a nomogram for predicting the risk of LM among patients with testicular germ cell tumors (TGCTs). Methods: Clinicopathological information of 4078 patients with TGCT between 2010 and 2015 was obtained from SEER. Univariate and multivariate logistic regression analyses were performed to identify risk factors for LM, and a nomogram was developed based on these factors. Calibration curves, area under the receiver operating curve (AUC), and decision curve analysis (DCA) were used to evaluate the accuracy and discrimination of the model. Results: Study participants included 4078 people with TGCTs, including 305 people with LM. They were randomly divided into two groups (training cohort = 2854 and validation cohort = 1224) at a ratio of 7:3. The following variables were incorporated in the nomogram: marital status, tumor histological type, T stage, brain metastasis, liver metastasis, lactate dehydrogenase (LDH), and chemotherapy. Besides, the AUC of it was 0.922 in the training cohort, while was 0.930 in the validation cohort. Training and validation cohort calibrations showed that the nomogram had excellent predictive abilities. DCA suggested it was more clinically relevant than the traditional TN staging. Conclusion: We have established a nomogram to predict the risk of LM in patients with TGCTs. Doctors and patients can use this nomogram to monitor and identify lung metastasis of tumors through active monitoring and follow-up.

Development and validation of a contrast-enhanced CT-based radiomics nomogram for differentiating mass-like thymic hyperplasia and low-risk thymoma.

PURPOSE: To explore the efficiency of a contrast-enhanced CT-based radiomics nomogram integrated with radiomics signature and clinically independent predictors to distinguish mass-like thymic hyperplasia (ml-TH) from low-risk thymoma (LRT) preoperatively. METHODS: 135 Patients with histopathology confirmed ml-TH (n = 65) and LRT (n = 70) were randomly divided into training set (n = 94) and validation set (n = 41) at a ratio of 7:3. The least absolute shrinkage and selection operator (LASSO) algorithm was used to obtain the optimal features. Based on the selected features, four machine learning models, support vector machine (SVM), logistic regression (LR), extreme gradient boosting (XGBOOST), and random forest (RF) were constructed. Multivariate logistic regression was used to establish a radiomics nomogram containing clinically independent predictors and radiomics signature. Receiver operating characteristic (ROC), DeLong test, and calibration curves were used to detect the performance of the radiomics nomogram in training set and validation set. RESULTS: In the validation set, the area under the curve (AUC) value of LR (0.857; 95% CI: 0.741, 0.973) was the highest of the four machine learning models. Radiomics nomogram containing radiomics signature and clinically independent predictors (including age, shape, and net enhancement degree) had better calibration and identification in the training set (AUC: 0.959; 95% CI: 0.922, 0.996) and validation set (AUC: 0.895; 95% CI: 0.795, 0.996). CONCLUSION: We constructed a contrast-enhanced CT-based radiomics nomogram containing clinically independent predictors and radiomics signature as a noninvasive preoperative prediction method to distinguish ml-TH from LRT. The radiomics nomogram we constructed has potential for preoperative clinical decision making.

Value of the application of computed tomography-based radiomics for preoperative prediction of unfavorable pathology in initial bladder cancer.

OBJECTIVES: To construct and validate unfavorable pathology (UFP) prediction models for patients with the first diagnosis of bladder cancer (initial BLCA) and to compare the comprehensive predictive performance of these models. MATERIALS AND METHODS: A total of 105 patients with initial BLCA were included and randomly enrolled into the training and testing cohorts in a 7:3 ratio. The clinical model was constructed using independent UFP-risk factors determined by multivariate logistic regression (LR) analysis in the training cohort. Radiomics features were extracted from manually segmented regions of interest in computed tomography (CT) images. The optimal CT-based radiomics features to predict UFP were determined by the optimal feature filter and the least absolute shrinkage and selection operator algorithm. The radiomics model consist with the optimal features was constructed by the best of the six machine learning filters. The clinic-radiomics model combined the clinical and radiomics models via LR. The area under the curve (AUC), accuracy, sensitivity, specificity, positive and negative predictive value, calibration curve and decision curve analysis were used to evaluate the predictive performance of the models. RESULTS: Patients in the UFP group had a significantly older age (69.61 vs. 63.93 years, p = 0.034), lager tumor size (45.7% vs. 11.1%, p = 0.002) and higher neutrophil to lymphocyte ratio (NLR; 2.76 vs. 2.33, p = 0.017) than favorable pathologic group in the training cohort. Tumor size (OR, 6.02; 95% CI, 1.50-24.10; p = 0.011) and NLR (OR, 1.50; 95% CI, 1.05-2.16; p = 0.026) were identified as independent predictive factors for UFP, and the clinical model was constructed using these factors. The LR classifier with the best AUC (0.817, the testing cohorts) was used to construct the radiomics model based on the optimal radiomics features. Finally, the clinic-radiomics model was developed by combining the clinical and radiomics models using LR. After comparison, the clinic-radiomics model had the best performance in comprehensive predictive efficacy (accuracy = 0.750, AUC = 0.817, the testing cohorts) and clinical net benefit among UFP-prediction models, while the clinical model (accuracy = 0.625, AUC = 0.742, the testing cohorts) was the worst. CONCLUSION: Our study demonstrates that the clinic-radiomics model exhibits the best predictive efficacy and clinical net benefit for predicting UFP in initial BLCA compared with the clinical and radiomics model. The integration of radiomics features significantly improves the comprehensive performance of the clinical model.

Development of a butyrate metabolism-related gene-based molecular subtypes and scoring system for predicting prognosis and immunotherapy response in bladder cancer.

PURPOSE: In recent times, multiple molecular subtypes with varying prognoses have been identified in bladder cancer (BLCA). However, the attributes of butyrate metabolism-related (BMR) molecular subtypes and their correlation with immunotherapy response remain inadequately explored in BLCA. METHODS: We utilized 594 samples of BLCA to investigate the molecular subtypes mediated by BMR genes and their correlation with the immunotherapy response. To quantify the BMR features of individual tumors, we developed a BMR score through the COX and LASSO regression methods. Clinical-related, tumor microenvironment, drug-sensitive and immunotherapy analyses were used to comprehensively analyze BMR scores. RESULTS: Two distinct molecular subtypes related to butyrate metabolism were identified in BLCA, each with unique prognostic implications and immune microenvironments. BMR score was constructed based on 7 BMR genes and was used to classify the patients into two score groups. Clinical analysis revealed that the BMR score was an independent prognostic factor. The higher the score, the worse the prognosis. The BMR score can also predict tumor immunity. The results demonstrated that a low BMR score was associated with higher efficacy of immunotherapy, which was also validated by an external dataset. CONCLUSION: Our study proposes both molecular subtypes and a BMR-based score as promising prognostic classifications in BLCA. These findings may offer new insights for the development of precise targeted cancer therapies.

EIF4A3 serves as a prognostic and immunosuppressive microenvironment factor and inhibits cell apoptosis in bladder cancer.

EIF4A3 (Eukaryotic translation initiation factor 4A3 (EIF4A3) was recently recognized as an oncogene; however, its role in BLCA (bladder cancer) remains unclear. We explored EIF4A3 expression and its prognostic value in BLCA in public datasets, including the TCGA (The Cancer Genome Atlas) and GEO (Gene Expression Omnibus). Thereafter, the association between EIF4A3 expression and the infiltration of immune cells and immune-checkpoint expression was determined using TIMER2 (Tumor Immune Estimation Resource 2) tool. Additionally, the impact of EIF4A3 on cellular proliferation and apoptosis events in BLCA cell lines was determined by siRNA technology. In this study, EIF4A3 was found to be significantly upregulated in BLCA, upregulated expression of EIF4A3 was related to poor prognosis, advanced histologic grade, subtype, pathological stage, white race, and poor primary therapy outcome. The immune infiltration analysis revealed that EIF4A3 expression was negatively associated with CD8+ and CD4+ T cells and positively with myeloid-derived suppressor cells, macrophage M2, cancer-associated fibroblasts, and Treg cells. Moreover, EIF4A3 was coexpressed with PD-L1 (programmed cell death 1-ligand 1) and its expression was higher in patients responding to anti-PD-L1 therapy. EIF4A3 knockdown significantly inhibited proliferation and promoted apoptosis in 5,637 and T24 cells. In summary, BLCA patients with elevated EIF4A3 expression had an unfavorable prognosis and immunosuppressive microenvironment, and EIF4A3 may facilitate BLCA progression by promoting cell proliferation and inhibiting apoptosis. Furthermore, our study suggests that EIF4A3 is a potential biomarker and therapeutic target for BLCA.

Machine learning models combining computed tomography semantic features and selected clinical variables for accurate prediction of the pathological grade of bladder cancer.

Objective: The purpose of this research was to develop a radiomics model that combines several clinical features for preoperative prediction of the pathological grade of bladder cancer (BCa) using non-enhanced computed tomography (NE-CT) scanning images. Materials and methods: The computed tomography (CT), clinical, and pathological data of 105 BCa patients attending our hospital between January 2017 and August 2022 were retrospectively evaluated. The study cohort comprised 44 low-grade BCa and 61 high-grade BCa patients. The subjects were randomly divided into training (n = 73) and validation (n = 32) cohorts at a ratio of 7:3. Radiomic features were extracted from NE-CT images. A total of 15 representative features were screened using the least absolute shrinkage and selection operator (LASSO) algorithm. Based on these characteristics, six models for predicting BCa pathological grade, including support vector machine (SVM), k-nearest neighbor (KNN), gradient boosting decision tree (GBDT), logical regression (LR), random forest (RF), and extreme gradient boosting (XGBOOST) were constructed. The model combining radiomics score and clinical factors was further constructed. The predictive performance of the models was evaluated based on the area under the receiver operating characteristic (ROC) curve, DeLong test, and decision curve analysis (DCA). Results: The selected clinical factors for the model included age and tumor size. LASSO regression analysis identified 15 features most linked to BCa grade, which were included in the machine learning model. The SVM analysis revealed that the highest AUC of the model was 0.842. A nomogram combining the radiomics signature and selected clinical variables showed accurate prediction of the pathological grade of BCa preoperatively. The AUC of the training cohort was 0.919, whereas that of the validation cohort was 0.854. The clinical value of the combined radiomics nomogram was validated using calibration curve and DCA. Conclusion: Machine learning models combining CT semantic features and the selected clinical variables can accurately predict the pathological grade of BCa, offering a non-invasive and accurate approach for predicting the pathological grade of BCa preoperatively.

MTX-211 Inhibits GSH Synthesis through Keap1/NRF2/GCLM Axis and Exerts Antitumor Effects in Bladder Cancer.

Globally, bladder cancer (BLCA) is still the leading cause of death in patients with tumors. The function and underlying mechanism of MTX-211, an EFGR and PI3K kinase inhibitor, have not been elucidated. This study examined the function of MTX-211 in BLCA cells using in vitro and in vivo assays. RNA sequencing, quantitative real-time polymerase chain reaction, Western blotting, co-immunoprecipitation, and immunofluorescence were performed to elucidate the underlying mechanism. Our observations revealed that MTX-211 has a time- and concentration-dependent inhibitory effect on bladder cancer cell proliferation. Flow cytometry analysis showed that cell apoptosis and G0/G1 cell cycle arrest were significantly induced by MTX-211. MTX-211 inhibited intracellular glutathione (GSH) metabolism, leading to a decrease in GSH levels and an increase in reactive oxygen species. GSH supplementation partly reversed the inhibitory effects of MTX-211. Further experiments verified that MTX-211 promoted NFR2 protein ubiquitinated degradation via facilitating the binding of Keap1 and NRF2, subsequently resulting in the downregulated expression of GCLM, which plays a vital role in GSH synthesis. This study provided evidence that MTX-211 effectively inhibited BLCA cell proliferation via depleting GSH levels through Keap1/NRF2/GCLM signaling pathway. Thus, MTX-211 could be a promising therapeutic agent for cancer.

Pathological diagnostic nomograms for predicting malignant histology and unfavorable pathology in patients with endophytic renal tumor.

Purpose: To develop and validate nomograms for pre-treatment prediction of malignant histology (MH) and unfavorable pathology (UP) in patients with endophytic renal tumors (ERTs). Methods: We retrospectively reviewed the clinical information of 3245 patients with ERTs accepted surgical treatment in our center. Eventually, 333 eligible patients were included and randomly enrolled into training and testing sets in a ratio of 7:3. We performed univariable and multivariable logistic regression analyses to determine the independent risk factors of MH and UP in the training set and developed the pathological diagnostic models of MH and UP. The optimal model was used to construct a nomogram for MH and UP. The area under the receiver operating characteristics (ROC) curves (AUC), calibration curves and decision curve analyses (DCA) were used to evaluate the predictive performance of models. Results: Overall, 172 patients with MH and 50 patients with UP were enrolled in the training set; and 74 patients with MH and 21 patients with UP were enrolled in the validation set. Sex, neutrophil-to-lymphocyte ratio (NLR), R score, N score and R.E.N.A.L. score were the independent predictors of MH; and BMI, NLR, tumor size and R score were the independent predictors of UP. Single-variable and multiple-variable models were constructed based on these independent predictors. Among these predictive models, the malignant histology-risk nomogram consisted of sex, NLR, R score and N score and the unfavorable pathology-risk nomogram consisted of BMI, NLR and R score performed an optimal predictive performance, which reflected in the highest AUC (0.842 and 0.808, respectively), the favorable calibration curves and the best clinical net benefit. In addition, if demographic characteristics and laboratory tests were excluded from the nomograms, only the components of the R.E.N.A.L. Nephrometry Score system were included to predict MH and UP, the AUC decreased to 0.781 and 0.660, respectively (P=0.001 and 0.013, respectively). Conclusion: In our study, the pathological diagnostic models for predicting malignant and aggressive histological features for patients with ERTs showed outstanding predictive performance and convenience. The use of the models can greatly assist urologists in individualizing the management of their patients.

Application of a combined radiomics nomogram based on CE-CT in the preoperative prediction of thymomas risk categorization.

Objective: This study aimed to establish a combined radiomics nomogram to preoperatively predict the risk categorization of thymomas by using contrast-enhanced computed tomography (CE-CT) images. Materials and Methods: The clinical, pathological, and CT data of 110 patients with thymoma (50 patients with low-risk thymomas and 60 patients with high-risk thymomas) collected in our Hospital from July 2017 to March 2022 were retrospectively analyzed. The study subjects were randomly divided into the training set (n = 77) and validation set (n = 33) in a 7:3 ratio. Radiomics features were extracted from the CT images, and the least absolute shrinkage and selection operator (LASSO) algorithm was performed to select 13 representative features. Five models, including logistic regression (LR), support vector machine (SVM), random forest (RF), decision tree (DT), and gradient boosting decision tree (GBDT) were constructed to predict thymoma risks based on these features. A combined radiomics nomogram was further established based on the clinical factors and radiomics scores. The performance of the models was evaluated using receiver operating characteristic (ROC) curve, DeLong tests, and decision curve analysis. Results: Maximum tumor diameter and boundary were selected to build the clinical factors model. Thirteen features were acquired by LASSO algorithm screening as the optimal features for machine learning model construction. The LR model exhibited the highest AUC value (0.819) among the five machine learning models in the validation set. Furthermore, the radiomics nomogram combining the selected clinical variables and radiomics signature predicted the categorization of thymomas at different risks more effectively (the training set, AUC = 0.923; the validation set, AUC = 0.870). Finally, the calibration curve and DCA were utilized to confirm the clinical value of this combined radiomics nomogram. Conclusion: We demonstrated the clinical diagnostic value of machine learning models based on CT semantic features and the selected clinical variables, providing a non-invasive, appropriate, and accurate method for preoperative prediction of thymomas risk categorization.

Partial Nephrectomy Versus Radical Nephrectomy for Endophytic Renal Tumors: Comparison of Operative, Functional, and Oncological Outcomes by Propensity Score Matching Analysis.

Purpose: The study aimed to compare operative, functional, and oncological outcomes between partial nephrectomy (PN) and radical nephrectomy (RN) for entophytic renal tumors (ERTs) by propensity score matching (PSM) analysis. Methods: A total of 228 patients with ERTs who underwent PN or RN between August 2014 and December 2021 were assessed. A PSM in a 1:1 ratio was conducted to balance the differences between groups. Perioperative characteristics, renal functional, and oncological outcomes were compared between groups. Univariate and multivariate logistic and Cox proportional hazard regression analyses were used to determine the predictors of functional and survival outcomes. Results: After PSM, 136 cases were matched to the PN group (n = 68) and the RN group (n = 68). Patients who underwent RN had shorter OT, less EBL, and lower high-grade complications (all p <0.05) relative to those who underwent PN. However, better perseveration of renal function was observed in the PN group, which was reflected in 48-h postoperative AKI (44.1% vs. 70.6%, p = 0.002), 1-year postoperative 90% eGFR preservation (45.6% vs. 22.1%, p = 0.004), and new-onset CKD Stage ≥III at last follow-up (2.9% vs. 29.4%, p <0.001). RN was the independent factor of short-term (OR, 2.812; 95% CI, 1.369-5.778; p = 0.005) and long-term renal function decline (OR, 10.242; 95% CI, 2.175-48.240; p = 0.003). Furthermore, PN resulted in a better OS and similar PFS and CSS as compared to RN (p = 0.042, 0.15, and 0.21, respectively). RN (OR, 7.361; 95% CI, 1.143-47.423; p = 0.036) and pT3 stage (OR, 4.241; 95% CI, 1.079-16.664; p = 0.039) were independent predictors of overall mortality. Conclusion: Among patients with ERTs, although the PN group showed a higher incidence of high-grade complications than RN, when technically feasible and with experienced surgeons, PN is recommended for better preservation of renal function, longer OS, and similar oncological outcomes.

Mechanisms of SARS-CoV-2 Infection-Induced Kidney Injury: A Literature Review.

The severe acute respiratory coronavirus 2 (SARS-CoV-2) has become a life-threatening pandemic. Clinical evidence suggests that kidney involvement is common and might lead to mild proteinuria and even advanced acute kidney injury (AKI). Moreover, AKI caused by coronavirus disease 2019 (COVID-19) has been reported in several countries and regions, resulting in high patient mortality. COVID-19-induced kidney injury is affected by several factors including direct kidney injury mediated by the combination of virus and angiotensin-converting enzyme 2, immune response dysregulation, cytokine storm driven by SARS-CoV-2 infection, organ interactions, hypercoagulable state, and endothelial dysfunction. In this review, we summarized the mechanism of AKI caused by SARS-CoV-2 infection through literature search and analysis.

New Prognostic Gene Signature and Immune Escape Mechanisms of Bladder Cancer.

Background: The immune microenvironment profoundly affects tumor prognosis and therapy. The present study aimed to reveal potential immune escape mechanisms and construct a novel prognostic signature via systematic bioinformatic analysis of the bladder cancer (BLCA) immune microenvironment. Patients and Methods: The transcriptomic data and clinicopathological information for patients with BLCA were obtained from The Cancer Genome Atlas (TCGA). Consensus clustering analysis based on the CIBERSORT and ESTIMATE algorithms was performed with patients with BLCA, which divided them into two clusters. Subsequently, the differentially expressed genes (DEGs) in the two were subjected to univariate Cox and least absolute shrinkage and selection operator (LASSO) regression analyses to identify prognostic genes, which were used to construct a prognostic model. The predictive performance of the model was verified by receiver operating characteristic (ROC) and Kaplan-Meier (K-M) analyses. In addition, we analyzed the differentially altered immune cells, mutation burden, neoantigen load, and subclonal genome fraction between the two clusters to reveal the immune escape mechanism. Results: Based on the ESTIMATE and clustering analyses, patients with BLCA were classified into two heterogeneous clusters: ImmuneScoreH and ImmuneScoreL. Univariate Cox and LASSO regression analyses identified CD96 (HR = 0.83) and IBSP (HR = 1.09), which were used to construct a prognostic gene signature with significant predictive accuracy. Regarding potential immune escape mechanisms, ImmuneScoreH and ImmuneScoreL were characterized by inactivation of innate immune cell chemotaxis. In ImmuneScoreL, a low tumor antigen load might contribute to immune escape. ImmuneScoreH featured high expression of immune checkpoint molecules. Conclusion: CD96 and IBSP were considered prognostic factors for BLCA. Innate immune inactivation and a low tumor antigen load may be associated with immune escape mechanisms in both clusters. Our research complements the exploration of the immune microenvironment in BLCA.

Novel circular RNA circ_0086722 drives tumor progression by regulating the miR-339-5p/STAT5A axis in prostate cancer.

Recent studies validate that circular RNAs have played critical regulatory functions in cancer biology. However, their contribution to prostate cancer (PCa) remained largely unclear. Our study aimed to explore the regulatory function and underlying mechanisms of circ_0086722 in PCa. The circ_0086722 levels in PCa tissues and cell lines were detected through qRT-PCR. In vivo and in vitro experiments were performed to evaluate the functions of circ_0086722 in PCa. Subsequently, the underlying mechanisms of circ_0086722 were explored with qRT-PCR, RNA immunoprecipitation, miRNA pull-down, and luciferase reporter assay experiments. Results revealed that circ_0086722 was highly expressed in PCa tissues and cell lines. Furthermore, high levels of circ_0086722 were positively correlated with pT3 stage, higher specimen Gleason score (>7), and worse biochemical recurrence-free survivals of PCa patients. Then, functional experiments revealed that circ_0086722 accelerated PCa proliferation and progression. Moreover, we demonstrated that circ_0086722 could mechanistically relieve the repressive effects of miR-339-5p on its target STAT5A, which facilitates PCa development. In conclusion, our findings revealed that circ_0086722 drives PCa development via the miR-339-5p/STAT5A axis, and it may function as a potential prognostic biomarker or therapeutic target for PCa treatment. Further studies with large sample sizes and sufficiently long follow-up periods are necessary to confirm the predictive role of circ_0086722 for the prognosis of PCa patients.