Multi-omics analysis reveals critical metabolic regulators in bladder cancer.

BACKGROUND: The crosstalk between genomic alterations and metabolic dysregulation in bladder cancer is largely unknown. A deep understanding of the interactions between cancer drivers and cancer metabolic changes will provide novel opportunities for targeted therapeutic strategies. METHODS: Three primary bladder cancer specimens with paired normal tissues or blood samples were subjected to whole-exome sequencing, DNA methylation array and whole-transcriptome sequencing by next-generation sequencing technology. We applied the methods to multi-omics data combining the Cancer Genome Atlas (TCGA) bladder cancer samples, including somatic mutation, DNA copy number, DNA methylation and gene expression profile for validation. RESULTS: We identified 34 mutated cancer driver genes in bladder cancer. KDM6A was the most significantly mutated cancer driver gene. Metabolic pathways were enriched in both differentially methylated regions (DMRs) and differentially expressed genes. Twenty-nine DMRs in the TSS200 region were highly correlated with the upregulation of gene expression, and 24 DMRs in the genome were highly correlated with the downregulation of gene expression. A total of 201 genes had highly correlated DNA methylation and expression. Thirty-four genes, including the known metabolic genes CXXC5, PRR5, ABCB8 and BAHD1, were further validated in the TCGA cohort. Multi-omics alterations identified two new candidate driver genes, WIPI2 and GFM2, that warrant future studies. CONCLUSIONS: This study provides a comprehensive and systematic analysis, focusing on identifying key regulatory factors that may lead to cancer metabolic heterogeneity. Further understanding and verification of the cancer genes driving metabolic reprogramming and their role in the progression of bladder cancer will help to identify new therapeutic targets.

Diabetes mellitus, systemic inflammation and overactive bladder.

Background: Increasing evidence emphasizes the potential relationship between diabetes and OAB (overactive bladder). However, large population epidemiology is still lacking. Methods: This cross-sectional study included six cycle NHANES surveys, with a total of 23863 participants. Logistic regression models were constructed to analyze the association between diabetes mellitus, diabetes-related markers, and inflammatory biomarkers with OAB. Restricted cubic splines were used to analyze the non-linear associations. Mediating analysis was performed to test the effect of inflammatory biomarkers on the relationship between diabetes-related markers and OAB. Finally, machine learning models were applied to predict the relative importance and construct the best-fit model. Results: Diabetes mellitus participants' OAB prevalence increased by 77% compared with non-diabetes. As the quartiles of diabetes-related markers increased, the odds of OAB monotonically increased in three models (all p for trend < 0.001). Glycohemoglobin exhibited a linear association with OAB (p for nonlinearity > 0.05). White blood cells significantly mediated the associations between diabetes-related markers (glycohemoglobin, fasting glucose, and insulin) with OAB, and the proportions were 7.23%, 8.08%, and 17.74%, respectively (all p < 0.0001). Neutrophils partly mediated the correlation between (glycohemoglobin, fasting glucose, and insulin) and OAB at 6.58%, 9.64%, and 17.93%, respectively (all p < 0.0001). Machine learning of the XGBoost model constructs the best fit model, and XGBoost predicts glycohemoglobin is the most important indicator on OAB. Conclusion: Our research revealed diabetes mellitus and diabetes-related markers were remarkably associated with OAB, and systemic inflammation was an important mediator of this association.

Multi-omic profiling of clear cell renal cell carcinoma identifies metabolic reprogramming associated with disease progression.

Clear cell renal cell carcinoma (ccRCC) is a complex disease with remarkable immune and metabolic heterogeneity. Here we perform genomic, transcriptomic, proteomic, metabolomic and spatial transcriptomic and metabolomic analyses on 100 patients with ccRCC from the Tongji Hospital RCC (TJ-RCC) cohort. Our analysis identifies four ccRCC subtypes including De-clear cell differentiated (DCCD)-ccRCC, a subtype with distinctive metabolic features. DCCD cancer cells are characterized by fewer lipid droplets, reduced metabolic activity, enhanced nutrient uptake capability and a high proliferation rate, leading to poor prognosis. Using single-cell and spatial trajectory analysis, we demonstrate that DCCD is a common mode of ccRCC progression. Even among stage I patients, DCCD is associated with worse outcomes and higher recurrence rate, suggesting that it cannot be cured by nephrectomy alone. Our study also suggests a treatment strategy based on subtype-specific immune cell infiltration that could guide the clinical management of ccRCC.

Identification of Key Differentially Expressed mRNAs, miRNAs, lncRNAs, and circRNAs for Xp11 Translocation Renal Cell Carcinoma (RCC) Based on Whole-Transcriptome Sequencing.

We carried out whole transcriptome sequencing (WTS) on the tumor and the matching adjacent normal tissues from five patients having Xp11 translocation renal cell carcinoma (RCC). This was performed in terms of obtaining more understanding of the genomic panorama and molecular basis of this cancer. To examine gene-regulatory networks in XP11 translocation RCC, variance expression analysis was carried out, followed by functional enrichment analysis. Gene Expression Omnibus (GEO) of Xp11 translocation RCC data was used to validate the results. As per inclusion criteria, a total of 1886 differentially expressed mRNAs (DEmRNAs), 56 differentially expressed miRNAs (DEmiRNAs), 223 differentially expressed lncRNAs (DElncRNAs), and 1764 differentially expressed circRNAs (DEcircRNAs) were found. KEGG enrichment study of DEmiRNA, DElncRNA, and DEcircRNA target genes identified the function of protein processing in the endoplasmic reticulum, lysosome, and neutrophil-mediated immunity. Three subnetwork modules integrated from the PPI network also revealed the genes involved in protein processing in the endoplasmic reticulum, lysosome, and protein degradation processes, which may regulate the Xp11 translocation RCC process. The ceRNA complex network was created by Cytoscape, which included three upregulated circRNAs, five upregulated lncRNAs, 24 upregulated mRNAs, and two downregulated miRNAs (hsa-let-7d-5p and hsa-miR-433-3p). The genes as a prominent component of the complex ceRNA network may be key factors in the pathogenesis of Xp11 translocation RCC. Our findings clarified the genomic and transcriptional complexity of Xp11 translocation RCC while also pointing to possible new targets for Xp11 translocation RCC characterization.

Systematic analysis between inflammation-related index and sex hormones in American adults: cross-sectional research based NHANES 2013-2016.

Background: A series of novel inflammation-related indexes has been confirmed to be efficient indicators of human immune and inflammatory status, with great potential as predictors for a variety of diseases. However, the association between inflammation-related indexes and sex hormones in the general population remained uncertain. Methods: We incorporated data from the NHANES 2013-2016 survey of American adults. On the basis of distribution and comparison analysis, we chose to undertake separate analyses of men and women (including premenopausal and postmenopausal groups). Multivariable weighted linear regression models, eXtreme Gradient Boosting (XGBoost) models, generalized linear analysis, stratified models, logistic regression models and sensitivity analysis were utilized to assess the relationships between inflammation-related indexes and sex hormones. Results: Total 9372 participants out of 20146 were fitted into our research. We conducted separate gender analysis due to different distribution. Multivariable weighted linear regression indicated every component of the inflammation-related index was negatively correlated with at least one component of the male hormone indexes. However, SII, NLR, PPN, and NC were associated positively with female estradiol. XGBoost identify SII, PLR and NLR were the critical indexes on sex hormones. Inflammation-related indexes was associated with Testosterone deficiency in male and postmenstrual group and associated with Excessive Estradiol in premenstrual group. Finally, the subgroup analysis revealed that the association between sex hormones and inflammatory indicators was prominent in American adults over the age of 60 or those with BMI (>28 kg/m2). Conclusion: In all, inflammation-related indexes act as independent risks associated with sex hormone alterations and metabolic disorder in both genders. Using multiple models, we revealed the relative importance of inflammation-related indexes. Subgroup analysis also identified the high-risk population. More prospective and experimental research should be conducted to validate the results.

Association between N, N-diethyl-m-toluamide exposure and the odds of kidney stones in US adults: a population-based study.

Background: Currently, there is limited research on the specific relationship between N, N-diethyl-m-toluamide (DEET) exposure and the odds of kidney stones. We aimed to investigate the relationship between DEET exposure and the prevalence of kidney stones. Methods: We included 7,567 qualified participants in our research from the 2007-2016 NHANES survey. We carried out three logistic regression models to explore the potential association between DEET exposure and the odds of kidney stones. Spline smoothing with generalized additive models (GAM) was utilized to assess the non-linear relationship and restricted cubic spline (RCS) curves was to determine the dose-response association. Multivariate regression models were used to conduct stratified analysis and sensitivity analysis. Results: Baseline characteristics of study participants presented the distribution of covariables. Regression analysis revealed that the odds of kidney stones were positively associated with the main metabolites of 3-diethyl-carbamoyl benzoic acid (DCBA) (log2) (OR = 1.05, 95% CI 1.02 to 1.08). The fourth quartile of urine DCBA showed a greater risk of kidney stones in the fully adjusted model (OR = 1.36, 95% CI 1.08 to 1.72). Another DEET metabolite of N, N-diethyl-3-hydroxymethylbenzamide (DHMB) was used to confirm the accuracy and stability of the results. The spline smoothing curve represented two main DEET metabolites had similar no-linear relationships and a positive trend with kidney stones proportion. RCS implied that the incidence of kidney stones rose with increasing levels of DEET exposure. High-risk groups on kidney stones were exhibited by stratified analysis under DEET exposure. Conclusion: Our study suggests that DEET exposure is positively associated with odds of kidney stones. Further investigation into the underlying processes of this association is required to guide the prevention and treatment of kidney stones.

Single cell sequencing reveals that CD39 inhibition mediates changes to the tumor microenvironment.

Single-cell sequencing technologies have noteworthily improved our understanding of the genetic map and molecular characteristics of bladder cancer (BC). Here we identify CD39 as a potential therapeutic target for BC via single-cell transcriptome analysis. In a subcutaneous tumor model and orthotopic bladder cancer model, inhibition of CD39 (CD39i) by sodium polyoxotungstate is able to limit the growth of BC and improve the overall survival of tumor-bearing mice. Via single cell RNA sequencing, we find that CD39i increase the intratumor NK cells, conventional type 1 dendritic cells (cDC1) and CD8 + T cells and decrease the Treg abundance. The antitumor effect and reprogramming of the tumor microenvironment are blockaded in both the NK cells depletion model and the cDC1-deficient Batf3-/- model. In addition, a significant synergistic effect is observed between CD39i and cisplatin, but the CD39i + anti-PD-L1 (or anti-PD1) strategy does not show any synergistic effects in the BC model. Our results confirm that CD39 is a potential target for the immune therapy of BC.

Association between Serum Triglycerides and Prostate Specific Antigen (PSA) among U.S. Males: National Health and Nutrition Examination Survey (NHANES), 2003-2010.

(1) Background: Increasing evidence indicates that lipid metabolism may influence the concentration of prostate-specific antigen (PSA). However, the association between triglycerides and PSA remains unclear and complicated. Hence, we evaluated the correlation between triglycerides and PSA based on the U.S. National Health and Nutrition Examination Survey (NHANES) database. (2) Methods: A total of 2910 participants out of 41,156 participants fit into our study after conducting the screening from the 2003 to 2010 NHANES survey. Serum triglycerides were the independent variable of our study, and PSA was the dependent variable; (3) Results: In our study, the average age of chosen participants was 59.7 years (±12.7). After adjusting for covariates, the result indicated that for each additional unit of serum triglyceride (mg/dL), the PSA concentrations were reduced by 0.0043 ng/mL (-0.0082, -0.0005) with a statistical difference. Furthermore, we used machine learning of the XGBoost model to determine the relative importance of selected variables as well as constructed a smooth curve based on the fully adjusted model to investigate the possible linear relationship between the triglyceride and PSA concentrations. (4) Conclusions: The serum triglyceride is independently and negatively correlated with PSA among American males, which may make it hard to detect asymptomatic prostate cancer and diagnose at an advance stage with higher triglycerides due to detection bias.

Multiple statistical models reveal specific volatile organic compounds affect sex hormones in American adult male: NHANES 2013-2016.

Background: Some VOCs are identified as endocrine-disrupting chemicals (EDCs), interfering with the effect of sex hormones. However, no studies focused on the common spectrum of environmental VOCs exposure affecting sex hormones in the average male population. Objectives: We aimed to explore the association between VOCs and sex hormones in American adult males using multiple statistical models. Methods: The generalized linear (GLM), eXtreme Gradient Boosting (XGBoost), weighted quantile sum (WQS), Bayesian kernel machine regression (BKMR) and stratified models were used to evaluate the associations between Specific Volatile Organic Compounds and sex hormones in American adult male from NHANES 2013-2016. Results: Pearson correlation model revealed the potential co-exposure pattern among VOCs. XGBoost algorithm models and the WQS model suggested the relative importance of VOCs. BKMR models reveal that co-exposure to the VOCs was associated with increased Testosterone (TT), Estradiol (E2), SHBG and decreased TT/E2. GLM models revealed specific VOC exposure as an independent risk factor causing male sex hormones disorders. Stratified analysis identified the high-risk group on the VOCs exposures. We found Blood 2,5-Dimethylfuran in VOCs was the most significant effect on sex hormones in male. Testosterone increased by 213.594 (ng/dL) (124.552, 302.636) and estradiol increased by 7.229 (pg/mL) for each additional unit of blood 2,5-Dimethylfuran (ng/mL). Conclusion: This study is an academic illustration of the association between VOCs exposure and sex hormones, suggesting that exposure to VOCs might be associated with sex hormone metabolic disorder in American adult males.