BackgroundDespite 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.MethodsThe 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.ResultsThree 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.ConclusionsThe 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.
BackgroundSeveral 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.MethodsGenetic 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.ResultsIVW 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.ConclusionIn summary, a causal relationship was found between several genera and upper urinary urolithiasis. However, we still need further randomized controlled trials to validate.
Background Gastric cancer is a common but lethal cancer owing to deficient in effective treatment. Substantial evidences have proved that immune infiltration plays a key role in progression of gastric cancer. This study aimed to establish a signature based on immune related genes that can predict clinical outcomes and therapeutic efficacy. Methods The expression data from The Cancer Genome Atlas database and 4617 immune related genes from previously published 160 immune gene sets were collected for development and validation of the signature. Cox proportional hazard regression model was used to construct the signature. The reliability and forecasting ability were evaluated by two independent datasets from GEO. Results A gene model consisting of 47 immune related genes was used as our signature. Risk scores were calculated based on the coefficient and the expression level of each gene in this model. The low risk score group had an obviously favorable prognosis than the other group in all cohorts. Both of univariate and multivariate analysis suggested that our immune gene signature was an independent prognostic factor. Single sample gene Set Enrichment Analysis (ssGSEA) revealed that high risk score was associated with high Th17 cell infiltration, low mast cell and pro- angiogenesis immune cell infiltration. More importantly, patients with high risk score presented high tumor mutation burden (TMB), which is an essential element for predicting therapeutic efficacy of immune check point inhibitor. Conclusion This signature is a promising tool to predict prognosis and screen out population who can get benefit from immune check point inhibitor.
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