Predictive value of cadherin-11 for subsequent recurrence and progression in non-muscle invasive bladder cancer.

BACKGROUND: Cadherin-11 (CDH11) is a type II cadherin and reported to function as an oncogene in various cancers. Our present study aims to investigate the role of CDH11 in bladder cancer (BCA). METHODS: Bioinformatics analysis was performed in four independent microarray data including 56 non-muscle-invasive bladder cancer (NMIBC) and 132 muscle-invasive bladder cancer (MIBC) tissues from Gene Expression Omnibus to screen out differentially expressed genes. Next, we detected CDH11 expression in BCA specimens and cell lines by qPCR and western blotting assays. Immunohistochemical analyses were performed in 209 paraffin-embedded BCA samples and 30 adjacent normal bladder tissues. RESULTS: Bioinformatics analysis revealed that CDH11 had a higher expression level in MIBC tissues than in NMIBC, which was consistent with our clinical BCA specimens and cell lines at both mRNA and protein levels. Immunohistochemical analysis demonstrated that over-expression of CDH11 was closely related to the histological grade, pT status, tumour size and poor outcomes of BCA patients. What's more, CDH11 (area under curve (AUC) = 0.673 and 0.735) had a better predictive value than E-cadherin (AUC = 0.629 and 0.629) and a similar discrimination with the European Organization for Research and Treatment of Cancer (EORTC) score system (AUC = 0.719 and 0.667) in evaluating potential recurrence and progression of NMIBC. Moreover, combination of CDH11 and EORTC score system was the best predictive model in predicting recurrence of NMIBC (AUC = 0.779) among the three models. CONCLUSIONS: CDH11 was a reliable therapeutic target in BCA and a useful index to predict the possibilities of recurrence and progression in NMIBC patients.

Optimal Starting Age and Baseline Level for Repeat Tests: Economic Concerns of PSA Screening for Chinese Men - 10-Year Experience of a Single Center.

OBJECTIVE: To investigate the optimal age for the baseline serum prostate-specific antigen (PSA) test and for repeat screening and its economic burden in a single center in China. MATERIALS AND METHODS: 35,533 men with PSA screening were retrospectively enrolled in this study. Follow-ups were conducted in 1,586 men with PSA >4 ng/mL, and receiver-operating characteristic (ROC) curves were employed to investigate the optimal cutoffs. RESULTS: ROC analysis indicated that the optimal age for initial PSA screening was 57.5 years (AUC = 0.84), 62.5 years (AUC = 0.902), 60.5 years (AUC = 0.909), and 61.5 years (AUC = 0.890) for individuals with PSA >4 and >10 ng/mL, a diagnosis of prostate cancer (PCa), and clinically significant PCa defined as the focus events, respectively. For Chinese men aged 50-59, 60-69, and >70 years, the initial PSA levels of 1.305 ng/mL (AUC = 0.699), 1.975 ng/mL (AUC = 0.711), and 2.740 ng/mL (AUC = 0.720) might have a PSA velocity >0.75 ng/mL per year during the follow-up. In addition, the total cost amounts to CNY 13,609,260 in these cases, but only 60 of the 35,533 (0.17%) men gained benefit from PSA screening. CONCLUSION: In our opinion, the optimal starting age for initial PSA testing was 57.5 years. The necessity for repeat screening should be based on the first PSA level depending on age. A cost--benefit analysis should be included in population-based screening.

Experimental study on failure characteristics and rheological properties of pillar-like rock samples with different shapes.

The stability of coal pillar is extremely important to the control of rock strata movement and surface subsidence. It is of great significance for mining design to analyze the stability and failure characteristics of coal and rock pillars left after mining and to study the failure characteristics and rheological properties of coal and rock with different shapes. In this paper, based on uniaxial compression and rheological tests on rock samples, the rheological properties of rock samples with different shapes were discussed by using the nonlinear theoretical mechanics and damage theory, and the rheological mechanical characteristics of coarse yellow sandstone samples under the action of different free surface areas and the same loading contact area were investigated by means of experimental research, theoretical analysis and numerical simulation. The following conclusions were drawn: the failure characteristics and dynamic change process of rock samples with different shapes under the same loading contact area are obtained by uniaxial compression test and multi-stage rheological loading. The uniaxial compressive strengths of rock samples with the same loading contact surface area and different free surface areas are inversely proportional to their free surface areas. For the round sample, the stress level in the rheological test is obviously lower than the instantaneous peak uniaxial compression strength, while for the other samples, the stress level in the rheological test is close to the instantaneous peak uniaxial compression strength. For rock all these samples, both the ratio of steady-state rheological time to final failure time and the deformation degree decrease with the increase of free surface area.

Plasma cell subtypes analyzed using artificial intelligence algorithm for predicting biochemical recurrence, immune escape potential, and immunotherapy response of prostate cancer.

Background: Plasma cells as an important component of immune microenvironment plays a crucial role in immune escape and are closely related to immune therapy response. However, its role for prostate cancer is rarely understood. In this study, we intend to investigate the value of a new plasma cell molecular subtype for predicting the biochemical recurrence, immune escape and immunotherapy response in prostate cancer. Methods: Gene expression and clinicopathological data were collected from 481 prostate cancer patients in the Cancer Genome Atlas. Then, the immune characteristics of the patients were analyzed based on plasma cell infiltration fractions. The unsupervised clustering based machine learning algorithm was used to identify the molecular subtypes of the plasma cell. And the characteristic genes of plasma cell subtypes were screened out by three types of machine learning models to establish an artificial neural network for predicting plasma cell subtypes. Finally, the prediction artificial neural network of plasma cell infiltration subtypes was validated in an independent cohort of 449 prostate cancer patients from the Gene Expression Omnibus. Results: The plasma cell fraction in prostate cancer was significantly decreased in tumors with high T stage, high Gleason score and lymph node metastasis. In addition, low plasma cell fraction patients had a higher risk of biochemical recurrence. Based on the differential genes of plasma cells, plasma cell infiltration status of PCa patients were divided into two independent molecular subtypes(subtype 1 and subtype 2). Subtype 1 tends to be immunosuppressive plasma cells infiltrating to the PCa region, with a higher likelihood of biochemical recurrence, more active immune microenvironment, and stronger immune escape potential, leading to a poor response to immunotherapy. Subsequently, 10 characteristic genes of plasma cell subtype were screened out by three machine learning algorithms. Finally, an artificial neural network was constructed by those 10 genes to predict the plasma cell subtype of new patients. This artificial neural network was validated in an independent validation set, and the similar results were gained. Conclusions: Plasma cell infiltration subtypes could provide a potent prognostic predictor for prostate cancer and be an option for potential responders to prostate cancer immunotherapy.

Comprehensive signature analysis of drug metabolism differences in the White, Black and Asian prostate cancer patients.

The drug response sensitivity and related prognosis of prostate cancer varied from races, while the original mechanism remains rarely understood. In this study, the comprehensive signature including transcriptomics, epigenome and single nucleotide polymorphisms (SNPs) of 485 PCa cases- including 415 Whites, 58 Blacks and 12 Asians from the TCGA database were analyzed to investigate the drug metabolism differences between races. We found that Blacks and Whites had a more prominent drug metabolism, cytotoxic therapy resistance, and endocrine therapy resistance than Asians, while Whites were more prominent in drug metabolism, cytotoxic therapy resistance and endocrine therapy resistance than Blacks. Subsequently, the targeted regulation analysis indicated that the racial differences in cytotoxic therapy resistance, endocrine therapy resistance, might originate from drug metabolisms, and 19 drug metabolism-related core genes were confirmed in the multi-omics network for subsequent analysis. Furthermore, we verified that CYP1A1, CYP3A4, CYP2B6, UGT2B17, UGT2B7, UGT1A8, UGT2B11, GAS5, SNHG6, XIST significantly affected antineoplastic drugs sensitivities in PCa cell lines, and these genes also showed good predictive efficiency of drug response and treatment outcomes for PCa in this cohort of patients. These findings revealed a comprehensive signature of drug metabolism differences for the Whites, Blacks and Asians, and it may provide some evidence for making individualized treatment strategies.

Effects of typical and atypical antipsychotics on glucose-insulin homeostasis and lipid metabolism in first-episode schizophrenia.

RATIONALE: Glucose and lipid metabolism dysfunction is a significant side effect associated with antipsychotics. Although there are many studies about the linkages between drugs and metabolic dysfunction, most of these studies have compared the effects of two antipsychotics on only one metabolic measure: either glucose or lipid metabolism. OBJECTIVES: The present study aimed to investigate the effects of clozapine, olanzapine, risperidone, and sulpiride on glucose and lipid metabolism in first-episode schizophrenia. MATERIALS AND METHODS: One hundred twelve schizophrenics were assigned randomly to receive clozapine, olanzapine, risperidone, or sulpiride for 8 weeks. Planned assessments included body mass index (BMI), waist-to-hip ratio, fasting glucose, insulin, C-peptide, insulin resistance index (IRI), cholesterol, and triglyceride. All measures were collected at baseline and at the end of the 8-week treatment. RESULTS: After treatment, insulin, C-peptide, and IRI were significantly increased in the four groups, but not fasting glucose levels. Cholesterol and triglyceride levels were significantly increased in the clozapine and olanzapine groups. Patients treated with clozapine and olanzapine had higher fasting insulin, C-peptide, and IRI levels than those treated with risperidone and sulpiride. Among the four antipsychotics, the increases of mean BMI from high to low were as follows: clozapine, olanzapine, sulpiride, and risperidone. CONCLUSIONS: This study confirmed that the four antipsychotic drugs were associated with an increase of insulin, C-peptide, and IRI. It was found that clozapine and olanzapine were associated with an increase in cholesterol and triglyceride levels. The effects of clozapine and olanzapine on the glucose and lipid metabolism outweighed those of risperidone and sulpiride.