Estimating disparities of prostate cancer burden and its attributable risk factors for males across the BRICS-plus, 1990-2019: A comparable study of key nations with emerging economies.

BACKGROUNDS: The study aimed to analyze epidemiology burden of male prostate cancer across the BRICS-plus, and identify potential risk factors by assessing the associations with age, period, birth cohorts and sociodemographic index (SDI). METHODS: Data were extracted from the Global Burden of Disease Study 2019. The average annual percent change (AAPC) was calculated to assess long-term trends, and age-period-cohort analysis was used to analyze these three effects on prostate cancer burden. Quantile regression was used to investigate the association between SDI and health outcomes. RESULTS: The higher incidence and mortality were observed in Mercosur and SACU regions, increasing trends were observed in prostate cancer incidence in almost all BRICS-plus countries (AAPC > 0), and EEU's grew by 24.31% (%AAPC range: -0.13-3.03). Mortality had increased in more than half of countries (AAPC > 0), and SACU grew by 1.82% (%AAPC range: 0.62-1.75). Incidence and mortality risk sharply increased with age across all BRICS-plus countries and globally, and the peak was reached in the age group 80-84 years. Rate ratio (RR) of incidence increased with birth cohorts in all BRICS-plus countries except for Kazakhstan where slightly decrease, while mortality RR decreased with birth cohort in most of BRICS-plus countries. SDI presented significantly positive associations with incidence in 50 percentiles. The deaths attributable to smoking declined in most of BRICS-plus nations, and many countries in China-ASEAN-FTA and EEU had higher values. CONCLUSION: Prostate cancer posed a serious public health challenge with an increasing burden among most of BRICS-plus countries. Age had significant effects on prostate cancer burden, and recent birth cohorts suffered from higher incidence risk. SDI presented a positive relationship with incidence, and the smoking-attributable burden was tremendous in China-ASEAN-FTA and EEU region. Secondary prevention should be prioritized in BRICS-plus nations, and health policies targeting important populations should be strengthened based on their characteristics and adaptability.

Exploring the correlation of glycolysis-related chondroitin polymerizing factor (CHPF) with clinical characteristics, immune infiltration, and cuproptosis in bladder cancer.

Bladder cancer (BLCA) is a common malignant neoplasm of the urinary system. Glycolysis is an essential metabolic pathway regulated by various genes with implications for tumor progression and immune escape. Scoring the glycolysis for each sample in the TCGA-BLCA dataset was done using the ssGSEA algorithm for quantification. The results showed that the score in BLCA tissues was markedly greater than those in adjacent tissues. Additionally, the score was found to be correlated with metastasis and high pathological stage. Functional enrichment analyses of the glycolysis-related genes showed they were related to roles associated with tumor metastasis, glucose metabolism, cuproptosis, and tumor immunotherapy in BLCA. Using 3 different machine learning algorithms, we identified chondroitin polymerizing factor (CHPF) as a central glycolytic gene with high expression in BLCA. In addition, we showed CHPF is a valuable diagnostic marker of BLCA with an area under the ROC (AUC) of 0.81. Sequencing BLCA 5637 cells after siRNA-mediated CHPF silencing and bioinformatics revealed that CHPF positively correlated with the markers of epithelial-to-mesenchymal transformation (EMT), glycometabolism-related enzymes, and immune cell infiltration. In addition, CHPF silencing inhibited the infiltration of multiple immune cells in BLCA. Genes that promote cuproptosis negatively correlated with CHPF expression and were up-regulated after CHPF silencing. High CHPF expression was a risk factor for overall and progression-free survival of patients who received immunotherapy for BLCA. Finally, using immunohistochemistry, we demonstrated that the CHPF protein had high expression in BLCA, increasing in high-grade tumors and those with muscle invasion. The CHPF expression levels were also positively associated with 18F-fluorodeoxyglucose uptake in PET/CT images. We conclude that the glycolysis-related gene CHPF is an effective diagnostic and treatment target for BLCA.

A Predictive Model for the 10-year Overall Survival Status of Patients With Distant Metastases From Differentiated Thyroid Cancer Using XGBoost Algorithm-A Population-Based Analysis.

Purpose: To explore clinical and non-clinical characteristics affecting the prognosis of patients with differentiated thyroid cancer with distant metastasis (DTCDM) and establish an accurate overall survival (OS) prognostic model. Patients and methods: Study subjects and related information were obtained from the National Cancer Institute's surveillance, epidemiology, and results database (SEER). Kaplan-Meier analysis, log-rank test, and univariate and multivariate Cox analysis were used to screen for factors influencing the OS of patients with DTCDM. Nine variables were introduced to build a machine learning (ML) model, receiver operating characteristic (ROC) was used to evaluate the recognition ability of the model, calibration plots were used to obtain prediction accuracy, and decision curve analysis (DCA) was used to estimate clinical benefit. Results: After applying the inclusion and exclusion criteria, a total of 3,060 patients with DTCDM were included in the survival analysis from 2004 to 2017. A machine learning prediction model was developed with nine variables: age at diagnosis, gender, race, tumor size, histology, regional lymph node metastasis, primary site surgery, radiotherapy, and chemotherapy. After excluding patients who survived <120 months, variables were sub-coded and machine learning was used to model OS prognosis in patients with DTCDM. Patients 6-50 years of age had the highest scores in the model. Other variables with high scores included small tumor size, male sex, and age 51-76. The AUC and calibration curves confirm that the XGBoost model has good performance. DCA shows that our model can be used to support clinical decision-making in a 10-years overall survival model. Conclusion: An artificial intelligence model was constructed using the XGBoost algorithms to predict the 10-years overall survival rate of patients with DTCDM. After model validation and evaluation, the model had good discriminative ability and high clinical value. This model could serve as a clinical tool to help inform treatment decisions for patients with DTCDM.

Metformin exerts an antitumor effect by inhibiting bladder cancer cell migration and growth, and promoting apoptosis through the PI3K/AKT/mTOR pathway.

BACKGROUND: To observe and explore the effect of metformin on the migration and proliferation of bladder cancer T24 and 5637 cells in vitro. METHODS: Bladder cancer T24 and 5637 cell lines were cultured in vitro, and were divided into group A (blank control group) and group B (metformin group: 5, 10, 15, and 20 mmol/L); both groups were plated on 6-well plates at the same time. Culture in 24-well plates was used for wound healing assays and in 96-well plates for Transwell migration and invasion, and Cell Counting Kit-8 proliferation experiments. We observed and detected the cell migration and proliferation ability of each group at 48 h, and calculated the cell migration area and survival rate. Flow cytometry was used to detect cell apoptosis in the groups. The apoptosis-related proteins, cleaved-caspase 3, cleaved-PARP, and the PI3K/AKT/mTOR signaling pathway member proteins PI3K, phosphorylated (p)-PI3K, AKT, p-AKT, mTOR, and p-mTOR were detected using western blotting. RESULTS: After 48 h of treatment with different concentrations of metformin, the cell migration and proliferation capabilities were significantly lower than those in the blank control group. The proliferation and migration abilities of T24 and 5637 cells decreased in a metformin concentration-dependent manner (P < 0.05). The apoptosis rate under different concentrations of metformin, as detected by flow cytometry, showed a significantly higher rate in the metformin group than in the control group (P < 0.05). Compared with that in the control group, the level of cleaved-caspase 3 and cleaved-PARP protein in the metformin group was increased in each treatment group, and the levels of p-mTOR, p-AKT, and p-PI3K decreased significantly compared with those in the control group (P < 0.05). CONCLUSION: Metformin inhibited bladder cancer T24 and 5637 cell migration and proliferation, and induced their apoptosis. The mechanism might involve inhibition of the activation of the PI3K/AKT/mTOR signaling pathway.

NUDT1 Could Be a Prognostic Biomarker and Correlated with Immune Infiltration in Clear Cell Renal Cell Carcinoma.

Background: Clear cell renal cell carcinoma (ccRCC) is a malignant tumor with high morbidity and mortality. As a member of the Nudix hydrolase superfamily, Nudix (nucleoside diphosphate-linked moiety X)-type motif 1 (NUDT1) is closely related to the occurrence and development of cancer. Our study aims to explore the role of NUDT1 in ccRCC and its relationship with immune infiltration. Methods: The NUDT1 expression matrix and corresponding clinical information were obtained from The Cancer Genome Atlas (TCGA) database. The expression difference of NUDT1 in ccRCC and its relationship with the clinical characteristics were investigated using R software. Kaplan-Meier (K-M) analysis, univariate Cox regression, multivariate Cox regression, receiver operating characteristic (ROC) curve, and nomogram were utilized to evaluate the survival and prognosis of patients. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were utilized to explore the function of differential genes in low- or high-expression group of NUDT1. TCGA dataset and Tumor IMmune Estimation Resource (TIMER) database were utilized to explore the relationship between NUDT1 and immune infiltration. Finally, TCGA dataset was utilized for gene set enrichment analysis (GSEA). Results: NUDT1 was not only overexpressed in ccRCC but also significantly correlated with clinicopathological features (P < 0.05). K-M survival analysis showed that upregulated NUDT1 was closely related to the decrease of overall survival (OS) and progression-free survival (PFS) in ccRCC patients. Multivariate Cox regression revealed that NUDT1 was a independent prognostic indicator (HR = 1.437, 95% CI: 1.065-1.939, P=0.018). The ROC curve showed that NUDT1 had a certain accuracy in predicting the outcome of ccRCC patiens. Furthermore, a total of 150 coexpressed genes and 1,886 differentially expressed genes (DEGs) were identified. GO/KEGG and GSEA results suggested that NUDT1 and its DEGs were involved in the immune-related pathways. NUDT1 expression was positively correlated with infiltrating levels of regulatory T cells (Tregs), CD8+ T cells, follicular helper T cells, and M0 macrophages. In addition, NUDT1 was positively related to immune checkpoints, such as PD-1, LAG3, CTLA4, and CD70, in ccRCC. Conclusion: NUDT1 plays a key role in the prognosis and immune cell infiltration of ccRCC patients, indicating its potential use as a prognostic biomarker and therapeutic target.

METTL3-mediated m6A modification of KIF3C-mRNA promotes prostate cancer progression and is negatively regulated by miR-320d.

The occurrence of distant metastasis is one of the leading causes of death in patients with prostate cancer (PCa). It is confirmed that kinesin protein is associated with a variety of malignancies, and the KIF3 family is related to cancer, but the relationship between KIF3C and prostate cancer is not clear. Our experiments have confirmed that KIF3C is highly expressed in prostate cancer tissues and cell lines. Further, functional tests have proven that KIF3C can promote the growth migration and invasion of PCa. We used Starbase 3.0 to discover that methyltransferase like 3 (METTL3) interacts with KIF3C. Our hypothesis and experiments concluded that METTL3 induced m6A modification on KIF3C, promoting the stabilization of KIF3C-mRNA by IGF2 binding protein 1 (IGF2BP1). The prediction that miR-320d inhibits KIF3C expression by targeting METTL3 using the miRmap website, was later confirmed experimentally. Further, a recovery experiment was used to confirm that miR-320d inhibited the progression of prostate cancer. KIF3C was overexpressed in prostate cancer, promoting its growth migration and invasion was induced by miR-320d/METTL3 in an m6A dependent process.

An Unfolded Protein Response Related Signature Could Robustly Predict Survival Outcomes and Closely Correlate With Response to Immunotherapy and Chemotherapy in Bladder Cancer.

Background: The unfolded protein response (UPR) plays a significant role in maintaining protein hemostasis in tumor cells, which are crucial for tumor growth, invasion, and resistance to therapy. This study aimed to develop a UPR-related signature and explore its correlation with immunotherapy and chemotherapy in bladder cancer. Methods: The differentially expressed UPR-related genes were put into Lasso regression to screen out prognostic genes, which constituted the UPR signature, and were incorporated into multivariate Cox regression to generate risk scores. Subsequently, the predictive performance of this signature was estimated by receiver operating characteristic (ROC) curves. The CIBERSORTx, the maftool, and Gene set enrichment analysis (GSEA) were applied to explore infiltrated immune cells, tumor mutational burden (TMB), and enriched signaling pathways in both risk groups, respectively. Moreover, The Cancer Immunome Atlas (TCIA) and Genomics of Drug Sensitivity in Cancer (GDSC) databases were used to predict responses to chemotherapy and immunotherapy. Results: Twelve genes constituted the UPR-related signature. Patients with higher risk scores had worse overall survival (OS) in training and three validation sets. The UPR-related signature was closely correlated with clinicopathologic parameters and could serve as an independent prognostic factor. M0 macrophages showed a significantly infiltrated difference in both risk groups. TMB analysis showed that the risk score in the wild type and mutation type of FGFR3 was significantly different. GSEA indicated that the immune-, extracellular matrix-, replication and repair associated pathways belonged to the high risk group and metabolism-related signal pathways were enriched in the low risk group. Prediction of immunotherapy and chemotherapy revealed that patients in the high risk group might benefit from chemotherapy, but had a worse response to immunotherapy. Finally, we constructed a predictive model with age, stage, and UPR-related risk score, which had a robustly predictive performance and was validated in GEO datasets. Conclusion: We successfully constructed and validated a novel UPR-related signature in bladder cancer, which could robustly predict survival outcomes and closely correlate with the response to immunotherapy and chemotherapy in bladder cancer.