Characterization of the Lipid Metabolism in Bladder Cancer to Guide Clinical Therapy

Higher genetically predicted triglyceride level increases the bladder cancer risk independent of LDL and HDL levels

The causal relationship between lipid levels and bladder cancer is still inconclusive currently. We aimed to reveal the causal relationship between triglycerides, HDL, and LDL and the risk of bladder cancer by univariable and multivariable Mendelian randomization (MR) analysis. The single nucleotide polymorphisms (SNPs) of exposure (triglycerides: 441,016 samples; HDL: 403,943 samples; LDL: 440,546 samples) were obtained from UK Biobank. The Genetic variation related to bladder cancer included 1554 cases and 359,640 controls. Univariable and multivariable MR methods were conducted with subsequent analysis, and smoking was regarded as a confounder. The inverse-variance weighted (IVW), MR-Egger, weighted-median method, Cochran's Q test, and MR-PRESSO were considered the main MR analysis and sensitivity analysis methods. Univariable MR analysis results suggested the triglycerides level (P = 0.011, OR = 1.001, 95% CI = 1.000-1.002) was causally associated with increased risk of bladder cancer. Multivariable MR results indicated that higher triglyceride levels could still increase the risk of bladder cancer after adjusting the effects of HDL, LDL, and smoking (P = 0.042, OR = 1.001, 95% CI = 1.000-1.002). Our findings supported that triglyceride level is causally associated with an increased risk of bladder cancer independent of LDL and HDL at the genetic level. Timely attention to changes in blood lipid levels might reduce the risk of bladder cancer.

Cisplatin-Resistant Urothelial Bladder Cancer Cells Undergo Metabolic Reprogramming beyond the Warburg Effect

Advanced urothelial bladder cancer (UBC) patients are tagged by a dismal prognosis and high mortality rates, mostly due to their poor response to standard-of-care platinum-based therapy. Mediators of chemoresistance are not fully elucidated. This work aimed to study the metabolic profile of advanced UBC, in the context of cisplatin resistance. Three isogenic pairs of parental cell lines (T24, HT1376 and KU1919) and the matching cisplatin-resistant (R) sublines were used. A set of functional assays was used to perform a metabolic screening on the cells. In comparison to the parental sublines, a tendency was observed towards an exacerbated glycolytic metabolism in the cisplatin-resistant T24 and HT1376 cells; this glycolytic phenotype was particularly evident for the HT1376/HT1376R pair, for which the cisplatin resistance ratio was higher. HT1376R cells showed decreased basal respiration and oxygen consumption associated with ATP production; in accordance, the extracellular acidification rate was also higher in the resistant subline. Glycolytic rate assay confirmed that these cells presented higher basal glycolysis, with an increase in proton efflux. While the results of real-time metabolomics seem to substantiate the manifestation of the Warburg phenotype in HT1376R cells, a shift towards distinct metabolic pathways involving lactate uptake, lipid biosynthesis and glutamate metabolism occurred with time. On the other hand, KU1919R cells seem to engage in a metabolic rewiring, recovering their preference for oxidative phosphorylation. In conclusion, cisplatin-resistant UBC cells seem to display deep metabolic alterations surpassing the Warburg effect, which likely depend on the molecular signature of each cell line.

Identification of Hydroxysteroid Dehydrogenase Type 1 As a Potential Bladder Tumor Marker

Background

The 17beta-hydroxysteroid dehydrogenase type 1 (HSD17B) family has been implicated in the prognosis and treatment prediction of various malignancies; however, its association with bladder cancer (BLCA) remains unclear. This study aimed to evaluate the potential of HSD17B1, as a prognostic biomarker, for the survival of patients with BLCA and to determine its effectiveness as a supplemental biomarker for BLCA.

Methods

A series of bioinformatics techniques were applied to investigate the expression of HSD17B1 in different types of cancer and its potential association with the prognosis of BLCA patients using diverse databases. The UALCAN, Human Protein Atlas, cBioPortal, Metascape, GEPIA, MethSurv, and TIMER were employed to analyze expression differences, mutation status, enrichment analysis, overall survival, methylation, and immune-infiltrating cells. The real-time reverse transcription-PCR (qRT-PCR) was implemented to detect the messenger ribonucleic acid (mRNA) expression levels of HSD17B1 in vitro.

Results

Elevated mRNA and protein levels of HSD17B1, surpassing normal levels, were observed in BLCA samples. In addition, the BLCA patients with higher mRNA expression level of HSD17B1 significantly reduced the overall survival. Also, several immune infiltrating cells, including mast cell resting CIBERSORT-ABS, have been identified as tumor-associated biomarker genes, with the potential to significantly influence the immunological environment. Finally, qRT-PCR analysis revealed a significant upregulation of HSD17B1 mRNA expression level in the cancer cells compared to the human 293T cells, which was consistent with the bioinformatics data.

Conclusion

There is a strong correlation between the elevated HSD17B1 expression and positive prognosis in patients with BLCA. Therefore, HSD17B1 can be used as a prognostic biomarker in these patients.

Elafibranor emerged as a potential chemotherapeutic drug for non-muscle invasive bladder cancer

Intravesical infusion of chemotherapeutics is highly recommended by several clinical guidelines for treating nonmuscle invasive bladder cancer (NMIBC). However, cytotoxic chemotherapeutics can cause a series of side effects, which greatly limits their application. Herein, a starvation therapy strategy was proposed, and elafibranor (ELA) was validated as a safe chemotherapeutic for NMIBC. The results showed that 20 μM ELA was sufficient to inhibit the proliferation and migration of bladder cancer cells and increase the level of intracellular reactive oxygen species (ROS). Furthermore, 2 mg/kg ELA treatment blocked the growth of primary tumors in an immunodeficient model by inhibiting proliferation and inducing apoptosis and improved the survival time of immunocompetent model mice. ELA treatment up to 10 mg/kg met the general safety requirements. We also established a patient-derived conditional reprogramming cell (CRC) model to assess the clinical translational potential of ELA. The antitumor effect and antitumor specificity of ELA treatment were confirmed. This work not only identified a promising chemotherapeutic for NMIBC but also provided a potential methodological system for drug discovery.

Identification of tryptophan metabolism- and immune-related genes signature and prediction of immune infiltration landscape in bladder urothelial carcinoma

Introduction

Tryptophan metabolism is indirectly involved in immune tolerance and promotes response to anticancer drugs. However, the mechanisms underlying tryptophan metabolism and immune landscape in bladder urothelial carcinoma (BLCA) are not fully understood.

Methods

A BLCA dataset containing 406 tumor samples with clinical survival information and 19 normal samples were obtained from the Cancer Genome Atlas database. The validation set, GSE32894, contained 223 BLCA tumor samples with survival information, and the single-cell dataset, GSE135337, included seven BLCA tumor samples; both were obtained from the gene expression omnibus database. Univariate and multivariate Cox regression analyses were conducted to evaluate clinical parameters and risk scores. Immune infiltration and checkpoint analyses were performed to explore the immune landscape of BLCA. Single-cell analysis was conducted to further identify the roles of model genes in BLCA. Finally, NAMPT expression in BLCA and adjacent tissues was detected using RT-qPCR, CCK-8 and Transwell assays were conducted to determine the role of NAMPT in BLCA cells.

Results

Six crossover genes (TDO2, ACAT1, IDO1, KMO, KYNU, and NAMPT) were identified by overlap analysis of tryptophan metabolism-related genes, immune-related genes, and differentially expressed genes (DEGs). Three biomarkers, NAMPT, IDO1, and ACAT1, were identified using Cox regression analysis. Accordingly, a tryptophan metabolism- and immune-related gene risk model was constructed, and the patients were divided into high- and low-risk groups. There were significant differences in the clinical parameters, prognosis, immune infiltration, and immunotherapy response between the risk groups. RT-qPCR revealed that NAMPT was upregulated in BLCA samples. Knocking down NAMPT significantly inhibited BLCA cell proliferation, migration, and invasion.

Discussion

In our study, we constructed a tryptophan metabolism- and immune-related gene risk model based on three biomarkers, namely NAMPT, IDO1, and ACAT1, that were significantly associated with the progression and immune landscape of BLCA. The risk model could effectively predict patient prognosis and immunotherapy response and can guide individualized immunotherapy.

Identification of metabolic biomarkers associated with nonalcoholic fatty liver disease

BACKGROUND

Nonalcoholic fatty liver disease (NAFLD) is the most common liver disease. Metabolism-related genes significantly influence the onset and progression of the disease. Hence, it is necessary to screen metabolism-related biomarkers for the diagnosis and treatment of NAFLD patients.

METHODS

GSE48452, GSE63067, and GSE89632 datasets including nonalcoholic steatohepatitis (NASH) and healthy controls (HC) analyzed in this study were retrieved from the Gene Expression Omnibus (GEO) database. First, differentially expressed genes (DEGs) between NASH and HC samples were obtained. Next, metabolism-related DEGs (MR-DEGs) were identified by overlapping DEGs and metabolism-related genes (MRG). Further, a protein-protein interaction (PPI) network was developed to show the interaction among MR-DEGs. Subsequently, the "Least absolute shrinkage and selection operator regression" and "Random Forest" algorithms were used to screen metabolism-related genes (MRGs) in patients with NAFLD. Next, immune cell infiltration and gene set enrichment analyses (GSEA) were performed on these metabolism-related genes. Finally, the expression of metabolism-related gene was determined at the transcription level.

RESULTS

First, 129 DEGs related to NAFLD development were identified among patients with nonalcoholic steatohepatitis (NASH) and healthy control. Next, 18 MR-DEGs were identified using the Venn diagram. Subsequently, four genes, including AMDHD1, FMO1, LPL, and P4HA1, were identified using machine learning algorithms. Moreover, a regulatory network consisting of four genes, 25 microRNAs (miRNAs), and 41 transcription factors (TFs) was constructed. Finally, a significant increase in FMO1 and LPL expression levels and a decrease in AMDHD1 and P4HA1 expression levels were observed in patients in the NASH group compared to the HC group.

CONCLUSION

Metabolism-related genes associated with NAFLD were identified, containing AMDHD1, FMO1, LPL, and P4HA1, which provide insights into diagnosing and treating patients with NAFLD.