Identifying and prioritizing disease-related genes based on the network topological features.

Identifying and prioritizing disease-related genes are the most important steps for understanding the pathogenesis and discovering the therapeutic targets. The experimental examination of these genes is very expensive and laborious, and usually has a higher false positive rate. Therefore, it is highly desirable to develop computational methods for the identification and prioritization of disease-related genes. In this study, we develop a powerful method to identify and prioritize candidate disease genes. The novel network topological features with local and global information are proposed and adopted to characterize genes. The performance of these novel features is verified based on the 10-fold cross-validation test and leave-one-out cross-validation test. The proposed features are compared with the published features, and fused strategy is investigated by combining the current features with the published features. And, these combination features are also utilized to identify and prioritize Parkinson's disease-related genes. The results indicate that identified genes are highly related to some molecular process and biological function, which provides new clues for researching pathogenesis of Parkinson's disease. The source code of Matlab is freely available on request from the authors.

mTOR-Dependent Suppression of Remnant Liver Regeneration in Liver Failure After Massive Liver Resection in Rats.

BACKGROUND AND AIMS: Massive hepatectomy often leads to fatal liver failure because of a small remnant liver volume. The aim of this study was to investigate the potential mechanisms leading to liver failure. METHODS: Sprague-Dawley rats had performed a sham operation, 85 % partial hepatectomy (PH) or 90 % PH, and all had free access to water with or without supplemented glucose. Liver function and survival were evaluated. Liver parenchymal injury was assessed by evaluating hepatic pathology, blood biochemistry, and apoptotic and necrotic alterations. The regeneration response was assessed by the weight gain of the remnant liver, hepatocyte proliferation markers, and regeneration-related molecules. RESULTS: The 90 % hepatectomy resulted in a significantly lower survival rate and impaired liver function; however, no significant more serious liver parenchymal injuries were detected. TNF-α, HGF, myc and IL-6 were either similarly expressed or overexpressed; however, the increase in remnant liver weight, mitotic index, and the presence of Ki-67 and PCNA were significantly lower in the 90 %-hepatectomized rats. mTOR, p70S6K and 4EBP1 were not activated in the remnant liver after a 90 % hepatectomy as obviously as those after an 85 % hepatectomy, which was concomitant with the higher expression of phospho-AMPK and a lower intrahepatic ATP level. Glucose treatment significantly improved the survival rate of 90 %-hepatectomized rats. CONCLUSIONS: Suppression of remnant liver regeneration was observed in the 90 % PH and contributed to fatal liver failure. This suppressed liver regenerative capacity was related to the inhibited activation of mTOR signaling.

Comparison of different preoperative objective nutritional indices for evaluating 30-d mortality and complications after liver transplantation.

BACKGROUND: The nutritional status is closely related to the prognosis of liver transplant recipients, but few studies have reported the role of preoperative objective nutritional indices in predicting liver transplant outcomes. AIM: To compare the predictive value of various preoperative objective nutritional indicators for determining 30-d mortality and complications following liver transplantation (LT). METHODS: A retrospective analysis was conducted on 162 recipients who underwent LT at our institution from December 2019 to June 2022. RESULTS: This study identified several independent risk factors associated with 30-d mortality, including blood loss, the prognostic nutritional index (PNI), the nutritional risk index (NRI), and the control nutritional status. The 30-d mortality rate was 8.6%. Blood loss, the NRI, and the PNI were found to be independent risk factors for the occurrence of severe postoperative complications. The NRI achieved the highest prediction values for 30-d mortality [area under the curve (AUC) = 0.861, P < 0.001] and severe complications (AUC = 0.643, P = 0.011). Compared to those in the high NRI group, the low patients in the NRI group had lower preoperative body mass index and prealbumin and albumin levels, as well as higher alanine aminotransferase and total bilirubin levels, Model for End-stage Liver Disease scores and prothrombin time (P < 0.05). Furthermore, the group with a low NRI exhibited significantly greater incidences of intraabdominal bleeding, primary graft nonfunction, and mortality. CONCLUSION: The NRI has good predictive value for 30-d mortality and severe complications following LT. The NRI could be an effective tool for transplant surgeons to evaluate perioperative nutritional risk and develop relevant nutritional therapy.

Risk Score Based on Two microRNAs as a Prognostic Marker of Hepatocellular Carcinoma and the Corresponding Competitive Endogenous RNA Network.

PURPOSE: Liver transplantation (LT) currently yields the best outcomes for hepatocellular carcinoma (HCC). However, tumor recurrence still occurs in some patients. Identifying markers that predict HCC recurrence after LT is an unmet medical need. METHODS: In this study, differential expression analysis was used to identify differentially expressed microRNAs (DEmiRs) between HCC and liver tissues in the The Cancer Genome Atlas database and in data from patients with recurrent or non-recurrent HCC in the GSE64989 dataset. The expression profiles of the overlap DEmiRs were used to construct an miRNA-based risk score to predict prognosis using Cox regression analysis. The target genes of the miRNAs of interest were predicted, and they were analyzed for functional enrichment. Furthermore, we used the miRNAs of interest to construct a competitive endogenous RNA (ceRNA) network of long non-coding RNAs (lncRNAs), miRs and mRNAs. RESULTS: Four up-regulated and three down-regulated miRNAs in HCC and recurrent HCC after LT were considered as candidate miRs. MiR-3200-3p and miR-3690 were selected to construct the miR-based risk score, which was found to be associated with poor overall survival and progression-free survival. Furthermore, it proved to be an independent prognostic factor after adjusting for other clinicopathological factors. The corresponding ceRNA networks of these two miRs that we constructed may help to understand their regulatory mechanisms in HCC. CONCLUSION: We propose a risk score based on miR-3200-3p and miR-3690 that may be useful as a prognostic marker to predict HCC recurrence after LT. We generated a ceRNA network involving these miRNAs, which may help reveal their regulatory roles in HCC.

Outcomes of liver transplantation for end-stage biliary disease: A comparative study with end-stage liver disease.

AIM: To evaluate the outcomes of patients with end-stage biliary disease (ESBD) who underwent liver transplantation, to define the concept of ESBD, the criteria for patient selection and the optimal operation for decision-making. METHODS: Between June 2002 and June 2014, 43 patients with ESBD from two Chinese organ transplantation centres were evaluated for liver transplantation. The causes of liver disease were primary biliary cirrhosis (n = 8), cholelithiasis (n = 8), congenital biliary atresia (n = 2), graft-related cholangiopathy (n = 18), Caroli's disease (n = 2), iatrogenic bile duct injury (n = 2), primary sclerosing cholangitis (n = 1), intrahepatic bile duct paucity (n = 1) and Alagille's syndrome (n = 1). The patients with ESBD were compared with an end-stage liver disease (ESLD) case control group during the same period, and the potential prognostic values of multiple demographic and clinical variables were assessed. The examined variables included recipient age, sex, pre-transplant clinical status, pre-transplant laboratory values, operation condition and postoperative complications, as well as patient and allograft survival rates. Survival analysis was performed using Kaplan-Meier curves, and the rates were compared using log-rank tests. All variables identified by univariate analysis with P values < 0.100 were subjected to multivariate analysis. A Cox proportional hazard regression model was used to determine the effect of the study variables on outcomes in the study group. RESULTS: Patients in the ESBD group had lower model for end-stage liver disease (MELD)/paediatric end-stage liver disease (PELD) scores and a higher frequency of previous abdominal surgery compared to patients in the ESLD group (19.2 ± 6.6 vs 22.0 ± 6.5, P = 0.023 and 1.8 ± 1.3 vs 0.1 ± 0.2, P = 0.000). Moreover, the operation time and the time spent in intensive care were significantly higher in the ESBD group than in the ESLD group (527.4 ± 98.8 vs 443.0 ± 101.0, P = 0.000, and 12.74 ± 6.6 vs 10.0 ± 7.5, P = 0.000). The patient survival rate in the ESBD group was not significantly different from that of the ESBD group at 1, 3 and 5 years (ESBD: 90.7%, 88.4%, 79.4% vs ESLD: 84.9%, 80.92%, 79.0%, χ(2) = 0.194, P = 0.660). The graft-survival rates were also similar between the two groups at 1, 3 and 5 years (ESBD: 90.7%, 85.2%, 72.7% vs ESLD: 84.9%, 81.0%, 77.5%, χ(2) = 0.003, P = 0.958). Univariate analysis identified MELD/PELD score (HR = 1.213, 95%CI: 1.081-1.362, P = 0.001) and bleeding volume (HR = 0.103, 95%CI: 0.020-0.538, P = 0.007) as significant factors affecting the outcomes of patients in the ESBD group. However, multivariate analysis revealed that MELD/PELD score (HR = 1.132, 95%CI: 1.005-1.275, P = 0.041) was the only negative factor that was associated with short survival time. CONCLUSION: MELD/PELD criteria do not adequately measure the clinical characteristics and staging of ESBD. The allocation system based on MELD/PELD criteria should be re-evaluated for patients with ESBD.

Identifying functions of protein complexes based on topology similarity with random forest.

Elucidating the functions of protein complexes is critical for understanding disease mechanisms, diagnosis and therapy. In this study, based on the concept that protein complexes with similar topology may have similar functions, we firstly model protein complexes as weighted graphs with nodes representing the proteins and edges indicating interaction between proteins. Secondly, we use topology features derived from the graphs to characterize protein complexes based on the graph theory. Finally, we construct a predictor by using random forest and topology features to identify the functions of protein complexes. Effectiveness of the current method is evaluated by identifying the functions of mammalian protein complexes. And then the predictor is also utilized to identify the functions of protein complexes retrieved from human protein-protein interaction networks. We identify some protein complexes with significant roles in the occurrence of tumors, vesicles and retinoblastoma. It is anticipated that the current research has an important impact on pathogenesis and the pharmaceutical industry. The source code of Matlab and the dataset are freely available on request from the authors.

Identifying subcellular localizations of mammalian protein complexes based on graph theory with a random forest algorithm.

In the post-genome era, one of the most important and challenging tasks is to identify the subcellular localizations of protein complexes, and further elucidate their functions in human health with applications to understand disease mechanisms, diagnosis and therapy. Although various experimental approaches have been developed and employed to identify the subcellular localizations of protein complexes, the laboratory technologies fall far behind the rapid accumulation of protein complexes. Therefore, it is highly desirable to develop a computational method to rapidly and reliably identify the subcellular localizations of protein complexes. In this study, a novel method is proposed for predicting subcellular localizations of mammalian protein complexes based on graph theory with a random forest algorithm. Protein complexes are modeled as weighted graphs containing nodes and edges, where nodes represent proteins, edges represent protein-protein interactions and weights are descriptors of protein primary structures. Some topological structure features are proposed and adopted to characterize protein complexes based on graph theory. Random forest is employed to construct a model and predict subcellular localizations of protein complexes. Accuracies on a training set by a 10-fold cross-validation test for predicting plasma membrane/membrane attached, cytoplasm and nucleus are 84.78%, 71.30%, and 82.00%, respectively. And accuracies for the independent test set are 81.31%, 69.95% and 81.00%, respectively. These high prediction accuracies exhibit the state-of-the-art performance of the current method. It is anticipated that the proposed method may become a useful high-throughput tool and plays a complementary role to the existing experimental techniques in identifying subcellular localizations of mammalian protein complexes. The source code of Matlab and the dataset can be obtained freely on request from the authors.

Identification of potential host proteins for influenza A virus based on topological and biological characteristics by proteome-wide network approach.

A proteome-wide network approach was performed to characterize significant patterns of influenza A virus (IAV)-human interactions, and to further identify potentially valuable targets for prophylactic and therapeutic interventions. Topological analysis demonstrated a strong tendency for IAV to interplay with highly connected and central proteins located in sparsely connected sub-networks. Additionally, functional analysis based on biological process revealed a number of functional groups overrepresented for IAV interactions, in which regulation of cell death and apoptosis, and phosphorus metabolic process is the most highly enriched. In order to investigate whether these topological and biological features are significant enough to distinguish IAV targets from human proteome, a discrimination model was constructed based on these features using support vector machine coupled with genetic algorithm. The average result of overall prediction accuracy is 71.04% by leave-one-out across validation test. The optimized classifier was then applied to 9706 human proteins. As a result, 1418 novel genes were identified from human interactome, some of which were experimentally validated by others' works to be important for IAV infection. The findings presented in this study might be important in discovering new drug targets for therapeutic treatments as well as revealing topological features and functional properties specific for viral infection.

Identification of human protein complexes from local sub-graphs of protein-protein interaction network based on random forest with topological structure features.

In the post-genomic era, one of the most important and challenging tasks is to identify protein complexes and further elucidate its molecular mechanisms in specific biological processes. Previous computational approaches usually identify protein complexes from protein interaction network based on dense sub-graphs and incomplete priori information. Additionally, the computational approaches have little concern about the biological properties of proteins and there is no a common evaluation metric to evaluate the performance. So, it is necessary to construct novel method for identifying protein complexes and elucidating the function of protein complexes. In this study, a novel approach is proposed to identify protein complexes using random forest and topological structure. Each protein complex is represented by a graph of interactions, where descriptor of the protein primary structure is used to characterize biological properties of protein and vertex is weighted by the descriptor. The topological structure features are developed and used to characterize protein complexes. Random forest algorithm is utilized to build prediction model and identify protein complexes from local sub-graphs instead of dense sub-graphs. As a demonstration, the proposed approach is applied to protein interaction data in human, and the satisfied results are obtained with accuracy of 80.24%, sensitivity of 81.94%, specificity of 80.07%, and Matthew's correlation coefficient of 0.4087 in 10-fold cross-validation test. Some new protein complexes are identified, and analysis based on Gene Ontology shows that the complexes are likely to be true complexes and play important roles in the pathogenesis of some diseases. PCI-RFTS, a corresponding executable program for protein complexes identification, can be acquired freely on request from the authors.