Prognostic Outcomes and Risk Factors for Patients with Renal Cell Carcinoma and Venous Tumor Thrombus after Radical Nephrectomy and Thrombectomy: The Prognostic Significance of Venous Tumor Thrombus Level.

IntroductionTo evaluate the prognostic outcomes and risk factors for renal cell carcinoma (RCC) patients with venous tumor thrombus in China.Materials and methodsWe reviewed the clinical information of 169 patients who underwent radical nephrectomy and thrombectomy. Overall and cancer-specific survival rates were analyzed. Univariate and multivariate analyses were used to investigate the potential prognostic factors.ResultsThe median survival time was 63 months. The five-year overall survival and cancer-specific survival rate were 53.6% and 54.4% for all patients. For all patients, significant survival difference was only observed between early (below hepatic vein) and advanced (above hepatic vein) tumor thrombus. However, significant differences existed between both RV/IVC and early/advanced tumor thrombus groups in N0M0 patients. Multivariate analysis demonstrated that higher tumor thrombus level (p = 0.016, RR = 1.58), N (p = 0.013, RR = 2.60), and M (p < 0.001, RR = 4.14) stages and adrenal gland invasion (p = 0.001, RR = 4.91) were the most significant negative prognostic predictors.ConclusionsIn this study, we reported most cases of RCC patients with venous extension in China. We proved that patients with RCC and venous tumor thrombus may have relative promising long-term survival rate, especially those with early tumor thrombus

Physician preferences for nonmetastatic castration-resistant prostate cancer treatment in China

IntroductionThe treatment preferences of Chinese physicians who treat nonmetastatic castration-resistant prostate cancer (nmCRPC) and how they weigh the benefits and risks of nmCRPC treatment are still unknown. This study aimed to evaluate Chinese physicians’ benefit–risk treatment preferences for nmCRPC and assist in setting nmCRPC treatment goals.MethodsA paper-based discrete choice experiment (DCE) survey was administered to 80 nmCRPC-treating physicians. DCE responses were analyzed to produce the preference weight and the relative importance score for each attribute level. The marginal rate of substitution (MRS) was used to quantify the amount of overall survival (OS) physicians were willing to trade for a reduction in treatment-related adverse events (AEs). We further conducted the exploratory analysis, stratifying physicians from 5 perspectives into different subgroups and examining the treatment preferences and OS trade-off in each subgroup.ResultsIn terms of efficacy attributes, physicians placed greater emphasis on OS than time to pain progression. With regard to safety attributes, serious fracture was perceived as the most important AE by physicians, followed by serious fall, cognitive problems, skin rash, and fatigue. In the exploratory analysis, we found generally that physicians with less clinical practice experience and those from more economically developed regions placed more emphasis on AEs and were willing to give up more of their patients’ OS to reduce the risk of AEs.ConclusionPhysicians from mainland China value the importance of minimizing treatment-related AEs when considering different treatment options for patients with nmCRPC, and they are willing to trade a substantial amount of OS to avoid AEs

Active module identification in intracellular networks using a memetic algorithm with a new binary decoding scheme

BACKGROUND: Active modules are connected regions in biological network which show significant changes in expression over particular conditions. The identification of such modules is important since it may reveal the regulatory and signaling mechanisms that associate with a given cellular response. RESULTS: In this paper, we propose a novel active module identification algorithm based on a memetic algorithm. We propose a novel encoding/decoding scheme to ensure the connectedness of the identified active modules. Based on the scheme, we also design and incorporate a local search operator into the memetic algorithm to improve its performance. CONCLUSION: The effectiveness of proposed algorithm is validated on both small and large protein interaction networks

Predicting Drug-Target Interaction Networks Based on Functional Groups and Biological Features

Background: Study of drug-target interaction networks is an important topic for drug development. It is both timeconsuming and costly to determine compound-protein interactions or potential drug-target interactions by experiments alone. As a complement, the in silico prediction methods can provide us with very useful information in a timely manner. Methods/Principal Findings: To realize this, drug compounds are encoded with functional groups and proteins encoded by biological features including biochemical and physicochemical properties. The optimal feature selection procedures are adopted by means of the mRMR (Maximum Relevance Minimum Redundancy) method. Instead of classifying the proteins as a whole family, target proteins are divided into four groups: enzymes, ion channels, G-protein- coupled receptors and nuclear receptors. Thus, four independent predictors are established using the Nearest Neighbor algorithm as their operation engine, with each to predict the interactions between drugs and one of the four protein groups. As a result, the overall success rates by the jackknife cross-validation tests achieved with the four predictors are 85.48%, 80.78%, 78.49%, and 85.66%, respectively. Conclusion/Significance: Our results indicate that the network prediction system thus established is quite promising an

Clinical features of ST-segment elevation myocardial infarction in young Chinese patients

Background: To investigate the clinical characteristics, angiographic findings and clinical outcomes (in-hospital) of young adults with acute myocardium infarction in a Chinese population. Methods: This was an observational study. Five hundred and forty-nine patients who suffered with ST-segment elevation myocardial infarction (STEMI) firstly between January 2013 and December 2015 were enrolled consecutively. All patients were divided into two groups: “young group” patients were ≤ 50 years old; and “non-young group” patients were > 50 years old. Clinical features were compared, angiographic findings and clinical outcomes were observed between the two groups. Results: There were 131 and 418 patients included in the young group and the non-young group, respectively. Twenty-eight patients suffered deaths during the hospital stay and only one death occurred in the young group. Compared with non-young group, the young group was associated with male, smoke, fewer chronic diseases, Killip class I on admission, lower level of N-terminal pro B-type natriuretic peptide (NT-proBNP), higher level of triglyceride and lower level of high-density lipoprotein cholesterol (HDL-C), single-vessel lesion and intracoronary thrombus (p < 0.005). The average length of hospital stay of non-young group was 1.5 days longer than the young group. Compared with the non-young group, the young group inclined not to use or use only one stent (p = 0.026). Multivariable logistic regression analysis showed that older age, shorter hospital stay, advanced Killip class III/IV, increased white blood cell and NT-proBNP were independent risk factors for survival in acute STEMI patients during hospitalization (p < 0.005). Conclusions: Compared with non-young group, the young group was associated with male, smoke, higher level of triglyceride and lower level of HDL-C. The condition of patients in young group were relatively mild and the risk of death during hospitalization was lower than the other group

Contralateral upper tract urothelial carcinoma after nephroureterectomy: the predictive role of DNA methylation

Abstract Background Aberrant methylation of genes is one of the most common epigenetic modifications involved in the development of urothelial carcinoma. However, it is unknown the predictive role of methylation to contralateral new upper tract urothelial carcinoma (UTUC) after radical nephroureterectomy (RNU). We retrospectively investigated the predictive role of DNA methylation and other clinicopathological factors in the contralateral upper tract urothelial carcinoma (UTUC) recurrence after radical nephroureterectomy (RNU) in a large single-center cohort of patients. Methods In a retrospective design, methylation of 10 genes was analyzed on tumor specimens belonging to 664 consecutive patients treated by RNU for primary UTUC. Median follow-up was 48 mo (range: 3–144 mo). Gene methylation was accessed by methylation-sensitive polymerase chain reaction, and we calculated the methylation index (MI), a reflection of the extent of methylation. The log-rank test and Cox regression were used to identify the predictor of contralateral UTUC recurrence. Results Thirty (4.5%) patients developed a subsequent contralateral UTUC after a median follow-up time of 27.5 (range: 2–139) months. Promoter methylation for at least one gene promoter locus was present in 88.9% of UTUC. Fewer methylation and lower MI (P = 0.001) were seen in the tumors with contralateral UTUC recurrence than the tumors without contralateral recurrence. High MI (P = 0.007) was significantly correlated with poor cancer-specific survival. Multivariate analysis indicated that unmethylated RASSF1A (P = 0.039), lack of bladder recurrence prior to contralateral UTUC (P = 0.009), history of renal transplantation (P < 0.001), and preoperative renal insufficiency (P = 0.002) are independent risk factors for contralateral UTUC recurrence after RNU. Conclusions Our data suggest a potential role of DNA methylation in predicting contralateral UTUC recurrence after RNU. Such information could help identify patients at high risk of new contralateral UTUC recurrence after RNU who need close surveillance during follow up.http://deepblue.lib.umich.edu/bitstream/2027.42/110306/1/13046_2015_Article_120.pd

Prediction of Deleterious Non-Synonymous SNPs Based on Protein Interaction Network and Hybrid Properties

Non-synonymous SNPs (nsSNPs), also known as Single Amino acid Polymorphisms (SAPs) account for the majority of human inherited diseases. It is important to distinguish the deleterious SAPs from neutral ones. Most traditional computational methods to classify SAPs are based on sequential or structural features. However, these features cannot fully explain the association between a SAP and the observed pathophysiological phenotype. We believe the better rationale for deleterious SAP prediction should be: If a SAP lies in the protein with important functions and it can change the protein sequence and structure severely, it is more likely related to disease. So we established a method to predict deleterious SAPs based on both protein interaction network and traditional hybrid properties. Each SAP is represented by 472 features that include sequential features, structural features and network features. Maximum Relevance Minimum Redundancy (mRMR) method and Incremental Feature Selection (IFS) were applied to obtain the optimal feature set and the prediction model was Nearest Neighbor Algorithm (NNA). In jackknife cross-validation, 83.27% of SAPs were correctly predicted when the optimized 263 features were used. The optimized predictor with 263 features was also tested in an independent dataset and the accuracy was still 80.00%. In contrast, SIFT, a widely used predictor of deleterious SAPs based on sequential features, has a prediction accuracy of 71.05% on the same dataset. In our study, network features were found to be most important for accurate prediction and can significantly improve the prediction performance. Our results suggest that the protein interaction context could provide important clues to help better illustrate SAP's functional association. This research will facilitate the post genome-wide association studies