Bladder cancer was a malignant disease in patients, our research aimed at discovering the possible biomarkers for the diseases. The gene chip GSE31684, including 93samples, was downloaded from the GEO datasets and co-expression network was constructed by the data. Molecular complex detection(MCODE) was used to identify hub genes. The most significant cluster including 16 genes: CDH11, COL3A1, COL6A3, COL5A1, AEBP1, COL1A2, NTM, COL11A1, THBS2, COL8A1, COL1A1, BGN, MMP2, PXDN, THY1, and TGFB1I1 was identified. After annotated by BiNGO, they were suggested associated with collagen fibril organization and blood vessel development. In addition, the Kaplan Meier curves were obtained by UALCAN. The high expression of THY1, AEBP1, CDH11, COL1A1, COL1A2, COL11A1, MMP2, PXDN, BGN, COL5A1, COL8A1, and TGFB1I1 indicated poor prognosis of the patients(P < 0.05). Finally, we examined genes’ expression between low and high tumor stage by the Wilcoxon test(P < 0.05), TGFB1I1 was excluded. THY1, AEBP1, CDH11, COL1A1, COL1A2, COL11A1, MMP2, PXDN, BGN, COL5A1, COL8A1 associated with the tumor stage as well as tumor patients’ prognosis. COL5A1, COL8A1(P < 0.01) may serve as therapeutic targets for the disease.

Bladder cancer (BLCA) is the fifth most common cancer and has the features of low survival rate and high morbidity and mortality. The Cancer Genome Atlas (TCGA) is a pool of global gene expression profile and contains huge amounts of cancer genomics data, which makes it possible to inquire the relationship between gene expression and prognosis of a series of malignant tumors including BLCA. Immune and stromal cells are two major components of tumor microenvironment (TME) which play an important role in judging the prognosis of tumor and influencing the progression of malignant, inflammatory, and metabolic disorders. In our study, we conducted a quantitative analysis of immune and stromal elements based on the ESTIMATE algorithm and thus divided BLCA cases into high and low groups. Then the differentially expressed genes closely related to tumor prognosis between groups were identified and had been shown to correlate with immune response and stromal alterations, which was further confirmed by functional enrichment analysis and protein‐protein interaction networks. We validated those genes through BLCA dates downloaded from ArrayExpress and thus got the marker genes to predict prognosis of BLCA. Additionally, immune cell infiltration analysis explored the correlation between the verified genes and immune cells. In conclusion, we identified a series of TME‐related genes that assess the prognosis and explored the interaction between TME and tumor prognosis to guide clinical individualized treatment.

Background: Despite previous literature exploring the factors influencing lower urinary tract symptoms (LUTS), few studies have examined the relationship between nutritional status and LUTS.Objectives: The objective of this research was to evaluate the relationship between LUTS and Geriatric Nutritional Risk Index (GNRI) in middle-aged and older men.Methods: We included 2,607 men in the NHANES 2005–2006 and 2007–2008 cycles for cross-sectional analysis. We screened for LUTS based on four specific questions on the relevant questionnaire. We calculated GNRI according to the relevant calculation formula and included other covariates. Multivariate logistic analysis using GNRI as the principal independent variable and adjusting for other covariates were used to determine the association with LUTS, nocturia, and daytime LUTS.Results: According to the responses to the questionnaire, out of 2,607 eligible participants, 471 had LUTS, 906 had nocturia, and 819 had daytime LUTS. In the unadjusted regression model, LUTS (OR = 0.93, 95% CI = 0.91–0.96, p < 0.001), nocturia (OR = 0.90, 95% CI = 0.88–0.93, p < 0.001), and daytime LUTS (OR = 0.96, 95% CI = 0.94–0.99, p = 0.002) were significantly negatively associated with GNRI. After adjustment by adding covariates, LUTS (OR = 0.97,95% CI =0.94–0.99, p = 0.026) and nocturia (OR = 0.94, 95% CI =0.91–0.93, p < 0.001) were significantly negatively associated with GNRI.Conclusion: Low GNRI was associated with the development of LUTS. In the prevention and treatment of LUTS, urologists should consider the impact of nutritional status on LUTS, and interventions for nutritional status may prevent and improve LUTS.

Objective: To explore SA levels in the serum of urothelial tumor patients and their correlation with clinical pathological features and localization.