Causal relationship in gut microbiota and upper urinary urolithiasis using Mendelian randomization.

Background: Several reports in recent years have found an association between gut microbiota and upper urinary urolithiasis. However, the causal relationship between them remains to be clarified. Methods: Genetic variation is used as a tool in Mendelian randomization for inference of whether exposure factors have a causal effect on disease outcomes. We selected summary statistics from a large genome-wide association study of the gut microbiome published by the MiBioGen consortium with a sample size of 18,340 as an exposure factor and upper urinary urolithiasis data from FinnGen GWAS with 4,969 calculi cases and 213,445 controls as a disease outcome. Then, a two-sample Mendelian randomization analysis was performed by applying inverse variance-weighted, MR-Egger, maximum likelihood, and weighted median. In addition, heterogeneity and horizontal pleiotropy were excluded by sensitivity analysis. Results: IVW results confirmed that class Deltaproteobacteria (OR = 0.814, 95% CI: 0.666-0.995, P = 0.045), order NB1n (OR = 0.833, 95% CI: 0.737-0.940, P = 3.15 × 10-3), family Clostridiaceae1 (OR = 0.729, 95% CI: 0.581-0.916, P = 6.61 × 10-3), genus Barnesiella (OR = 0.695, 95% CI: 0.551-0.877, P = 2.20 × 10-3), genus Clostridium sensu_stricto_1 (OR = 0.777, 95% CI: 0.612-0.986, P = 0.0380), genus Flavonifractor (OR = 0.711, 95% CI: 0.536-0.944, P = 0.0181), genus Hungatella (OR = 0.829, 95% CI: 0.690-0.995, P = 0.0444), and genus Oscillospira (OR = 0.758, 95% CI: 0.577-0.996, P = 0.0464) had a protective effect on upper urinary urolithiasis, while Eubacterium xylanophilum (OR =1.26, 95% CI: 1.010-1.566, P = 0.0423) had the opposite effect. Sensitivity analysis did not find outlier SNPs. Conclusion: In summary, a causal relationship was found between several genera and upper urinary urolithiasis. However, we still need further randomized controlled trials to validate.

Systematic Construction and Validation of a Novel Macrophage Differentiation-Associated Prognostic Model for Clear Cell Renal Cell Carcinoma.

Background: Clear cell renal cell carcinoma (ccRCC) is a malignant tumor of the human urinary system. Macrophage differentiation is associated with tumorigenesis. Therefore, exploring the prognostic value of macrophage differentiation-associated genes (MDGs) may contribute to better clinical management of ccRCC patients. Methods: The RNA sequence data of ccRCC were obtained from The Cancer Genome Atlas (TCGA) database. Differentially expressed MDGs were unveiled in ccRCC and normal samples. The prognostic model was established according to the univariate and multivariate Cox regression analyses. By combining clinico-pathological features and prognostic genes, a nomogram was established to predict individual survival probability. The Tumor Immune Estimation Resource (TIMER) database was utilized to analyze the correlation between prognostic genes and immune infiltrating cells. Eventually, the mRNA and protein expression levels of prognostic genes were verified. Results: A total of 52 differentially expressed prognosis-related MDGs were identified in ccRCC. Afterward, a six-gene prognostic model (ABCG1, KDF1, KITLG, TGFA, HAVCR2, and CD14) was constructed through the Cox analysis. The overall survival in the high-risk group was relatively poor. Moreover, the risk score was identified as an independent prognostic factor. We constructed a prognostic nomogram with a well-fitted calibration curve based on risk score and clinical data. Furthermore, the prognostic genes were significantly related to the level of immune cell infiltration including B cells, CD8+T cells, CD4+T cells, macrophages, neutrophils, and dendritic cells. Finally, the mRNA expression of prognostic genes in clinical ccRCC tissues showed that the ABCG1, HAVCR2, CD14, and TGFA mRNA in tumor samples were increased compared with the adjacent control tissue samples, while KDF1 and KITLG were decreased, which was consistent with the verification results in the GSE53757. Conclusion: In conclusion, this study identified and validated a macrophage differentiation-associated prognostic model for ccRCC that could be used to predict the outcomes of the ccRCC patients.

DNA Methylation Modification Regulator-Mediated Molecular Clusters and Tumor Metabolic Characterization in Prostate Cancer.

BACKGROUND: An increasing number of studies have indicated a close link between DNA methylation and tumor metabolism. However, the overall influence of DNA methylation on tumor metabolic characteristics in prostate cancer (PCa) remains unclear. METHODS: We first explored the subtypes of DNA methylation modification regulators and tumor metabolic features of 1,205 PCa samples using clustering analysis and gene set variation analysis based on the mRNA levels of DNA methylation modification regulators. A DNA methylation-related score (DMS) was calculated using principal component analysis and the DNA methylation modification-related gene signatures to quantify DNA methylation characteristics. We then performed a meta-analysis to identify the hazard ratio of DMS in the six cohorts. In addition, a nomogram was drawn using univariate and multivariate Cox analyses based on the DMS and clinical variables. Finally, a drug sensitivity analysis of the DMS was performed based on the genomics of drug sensitivity in cancer datasets. RESULTS: Three PCa clusters showing different DNA methylation modification patterns and tumor metabolic features were identified. A DMS system was established to quantify the characteristics of DNA methylation modification. PCa samples showed a differential metabolic landscape between the high and low DMS groups. The prognostic value of the DMS and nomogram was independently validated in multiple cohorts. A high DMS was associated with increases in the tumor mutation burden, copy number variation, and microsatellite instability; high tumor heterogeneity; and poor prognosis. Finally, DMS was closely related to different types of antitumor treatment. CONCLUSION: Improving the understanding of tumor metabolism by characterizing DNA methylation modification patterns and using the DMS may help clinicians predict prognosis and aid in more personalized antitumor therapy strategies for PCa.

Metabolic characterization and metabolism-score of tumor to predict the prognosis in prostate cancer.

Tumor metabolism patterns have been reported to be associated with the prognosis of many cancers. However, the metabolic mechanisms underlying prostate cancer (PCa) remain unknown. This study aimed to explore the metabolic characteristics of PCa. First, we downloaded mRNA expression data and clinical information of PCa samples from multiple databases and quantified the metabolic pathway activity level using single-sample gene set enrichment analysis (ssGSEA). Through unsupervised clustering and principal component analyses, we explored metabolic characteristics and constructed a metabolic score for PCa. Then, we independently validated the prognostic value of our metabolic score and the nomogram based on the metabolic score in multiple databases. Next, we found the metabolic score to be closely related to the tumor microenvironment and DNA mutation using multi-omics data and ssGSEA. Finally, we found different features of drug sensitivity in PCa patients in the high/low metabolic score groups. In total, 1232 samples were analyzed in the present study. Overall, an improved understanding of tumor metabolism through the characterization of metabolic clusters and metabolic score may help clinicians predict prognosis and aid the development of more personalized anti-tumor therapeutic strategies for PCa.

Novel CFD modeling approaches to assessing urine flow in prostatic urethra after transurethral surgery.

Assessment of the pressure and velocity of urine flow for different diameter ratios of prostatic urethra (RPU) after transurethral surgery using computational fluid dynamics (CFD). A standardized and idealized two-dimensional CFD model after transurethral surgery (CATS-1st) was developed for post-surgery mid-voiding. Using CATS-1st, 210 examples were amplified according to an array of size [3][5][14], which contained three groups of longitudinal diameters of prostatic urethra (LD-PU). Each of these groups contained five subgroups of transverse diameters of the bladder neck (TD-BN), each with 14 examples of transverse diameters of PU (TD-PU). The pressure and velocity of urine flow were monitored through flow dynamics simulation, and the relationship among RPU-1 (TD-PU/TD-BN), RPU-2 (RPU-1/LD-PU), the transverse diameter of the vortex, and the midpoint velocity of the external urethral orifice (MV-EUO) was determined. A total of 210 CATS examples, including CATS-1st examples, were analyzed. High (bladder and PU) and medium/low (the rest of the urethra) pressure zones, and low (bladder), medium (PU), and high (the rest of the urethra) velocity zones were determined. The rapid changes in the velocity were concentrated in and around the PU. Laminar flow was present in all the examples. The vortices appeared and then gradually shrank with reducing RPU on both the sides of PU in 182 examples. In the vortex examples, minimum RPU-1 and RPU-2 reached close to the values of 0.79 and 0.02, respectively. MV-EUO increased gradually with decreasing RPU. In comparison to the vortex examples, the non-vortex examples exhibited a significantly higher (p < 0.01) MV-EUO. The developed CFD models (CATS) presented an effective simulation of urine flow behavior within the PU after transurethral surgery for benign prostatic hyperplasia (BPH). These models could prove to be useful for morphological repair in PU after transurethral surgery.

Identification of Metabolism-Associated Prostate Cancer Subtypes and Construction of a Prognostic Risk Model.

BACKGROUND: Despite being the second most common tumor in men worldwide, the tumor metabolism-associated mechanisms of prostate cancer (PCa) remain unclear. Herein, this study aimed to investigate the metabolism-associated characteristics of PCa and to develop a metabolism-associated prognostic risk model for patients with PCa. METHODS: The activity levels of PCa metabolic pathways were determined using mRNA expression profiling of The Cancer Genome Atlas Prostate Adenocarcinoma cohort via single-sample gene set enrichment analysis (ssGSEA). The analyzed samples were divided into three subtypes based on the partitioning around medication algorithm. Tumor characteristics of the subsets were then investigated using t-distributed stochastic neighbor embedding (t-SNE) analysis, differential analysis, Kaplan-Meier survival analysis, and GSEA. Finally, we developed and validated a metabolism-associated prognostic risk model using weighted gene co-expression network analysis, univariate Cox analysis, least absolute shrinkage and selection operator, and multivariate Cox analysis. Other cohorts (GSE54460, GSE70768, genotype-tissue expression, and International Cancer Genome Consortium) were utilized for external validation. Drug sensibility analysis was performed on Genomics of Drug Sensitivity in Cancer and GSE78220 datasets. In total, 1,039 samples and six cell lines were concluded in our work. RESULTS: Three metabolism-associated clusters with significantly different characteristics in disease-free survival (DFS), clinical stage, stemness index, tumor microenvironment including stromal and immune cells, DNA mutation (TP53 and SPOP), copy number variation, and microsatellite instability were identified in PCa. Eighty-four of the metabolism-associated module genes were narrowed to a six-gene signature associated with DFS, CACNG4, SLC2A4, EPHX2, CA14, NUDT7, and ADH5 (p <0.05). A risk model was developed, and external validation revealed the strong robustness our risk model possessed in diagnosis and prognosis as well as the association with the cancer feature of drug sensitivity. CONCLUSIONS: The identified metabolism-associated subtypes reflected the pathogenesis, essential features, and heterogeneity of PCa tumors. Our metabolism-associated risk model may provide clinicians with predictive values for diagnosis, prognosis, and treatment guidance in patients with PCa.

Epigenetic regulation of Elf5 is associated with epithelial-mesenchymal transition in urothelial cancer.

E74-like factor 5 (Elf5) has been associated with tumor suppression in breast cancer. However, its role in urothelial cancer (UC) is completely unknown. Immunohistochemistry (IHC) and methylation specific PCR (MSP) were done to detect Elf5 expression level and its promoter methylation. Results revealed that low expression of Elf5 on protein and mRNA levels were associated with tumor progression, early relapse and poor survival. In vitro, down-regulation of Elf5 can increase epithelial-mesenchymal transition (EMT). Aberrant Elf5 methylation was identified as major mechanism for Elf5 gene silence. Accordingly, restoration of Elf5 by infection or demethylating treatment effectively reversed EMT processes. In conclusion, we identified Elf5 as a novel biomarker of UC on several biological levels and established a causative link between Elf5 and EMT in UC.