The incidence proportion of erectile dysfunction in patients treated with cryotherapy for prostate cancer: a meta-analysis.

OBJECTIVES: With the maturity of cryotherapy for prostate cancer, the complications after operation are also decreasing, which can improve the prognosis of patients. However, erectile dysfunction (ED) is still one of the main complications after cryotherapy. Therefore, we performed a meta-analysis to evaluate the incidence of erectile dysfunction in patients after cryotherapy. MATERIALS AND METHODS: A comprehensive literature search was performed in August 2018. PUBMED and EMBASE databases were searched to collect studies reporting the incidence rate of ED after cryotherapy from 2002 to 2018. Two reviewers independently screened the literatures, extracted data and assessed the risk of bias of included studies. Pooled ratio and its 95% confidence intervals (95% CIs) were performed by Stata 12.1. RESULTS: Of the 157 articles identified on August 1st 2018, 23 studies which reported ED after cold ablative therapy were identified, however, only 12 used validated outcome measures and met inclusion criteria. A total of 12 studies were included in this meta-analysis. Overall, the results of this meta-analysis showed that the pooled incidence rate of ED was 0.27 (95% CI 0.26-0.28) which means that the incidence rate of ED after cryotherapy for prostate cancer was not high, but we still found that there are great heterogeneity between the 12 articles. By subgroup analysis, we found a statistically significant incidence rate of ED in primarily localized PCa which was 0.49 (95% CI 0.30-0.68), which is clearly lower than the incidence of recurrent prostate cancer after failed primary radiotherapy 0.61 (95% CI 0.43-0.79). CONCLUSION: ED is one of the major complications after cryotherapy for PCa. Furthermore, subgroup analysis revealed a higher incidence rate in PCa undergoing radiotherapy. Significantly, with the development of cryotherapy technology, the incidence of ED after cryotherapy for prostate cancer is decreasing. While we still need further researches to advance knowledge in this field.

Identification a novel set of 6 differential expressed genes in prostate cancer that can potentially predict biochemical recurrence after curative surgery.

PURPOSE: Approximately, 30% patients after radical prostatectomy (RP) will undergo post-operative biochemical recurrence (BCR). Present stratification method by TNM staging and Gleason score was not adequate to screen high-risk patients. In this study, we intended to identify a novel set of differentially expressed gene (DEG) signature that can predict BCR after RP. MATERIALS/PATIENTS: 358 patients after RP with follow-up data were extracted from The Cancer Genome Atlas (TCGA), among which 61 patients had undergone BCR. Key DEGs were confirmed by the intersection of GSE35988 and TCGA_PCa dataset, and their gene expression data were also extracted from TCGA_PCa dataset. Kaplan-Meier plot and Cox proportion hazard regression model were applied to assess the relationship between risk score and survival outcome (BCR). RESULTS: 310 DEGs were confirmed in two prostate cancer dataset. 6 DEGs (SMIM22, NINL, NRG2, TOP2A, REPS2, and TPCN2) were selected to construct a risk score formula. The risk score was a powerful predictive factor independent of TNM stage (HR 3.045, 95% CI 1.655-5.602, p < 0.001). CONCLUSION: In this study, a novel 6-gene signature with robust predictive ability on post-operative BCR was constructed and 4 genes (SMIM22, NRG2, NINL and TPCN2) in the 6-gene signature were not reported to be associated with prostate cancer.

A computational method to predict carbonylation sites in yeast proteins.

Several post-translational modifications (PTM) have been discussed in literature. Among a variety of oxidative stress-induced PTM, protein carbonylation is considered a biomarker of oxidative stress. Only certain proteins can be carbonylated because only four amino acid residues, namely lysine (K), arginine (R), threonine (T) and proline (P), are susceptible to carbonylation. The yeast proteome is an excellent model to explore oxidative stress, especially protein carbonylation. Current experimental approaches in identifying carbonylation sites are expensive, time-consuming and limited in their abilities to process proteins. Furthermore, there is no bioinformational method to predict carbonylation sites in yeast proteins. Therefore, we propose a computational method to predict yeast carbonylation sites. This method has total accuracies of 86.32, 85.89, 84.80, and 86.80% in predicting the carbonylation sites of K, R, T, and P, respectively. These results were confirmed by 10-fold cross-validation. The ability to identify carbonylation sites in different kinds of features was analyzed and the position-specific composition of the modification site-flanking residues was discussed. Additionally, a software tool has been developed to help with the calculations in this method. Datasets and the software are available at https://sourceforge.net/projects/hqlstudio/ files/CarSpred.Y/.