Model containing sarcopenia and visceral adiposity can better predict the prognosis of hepatocellular carcinoma: a multicenter study.

AIM: This study aimed to explore whether the addition of sarcopenia and visceral adiposity could improve the accuracy of model predicting progression-free survival (PFS) in hepatocellular carcinoma (HCC). METHODS: In total, 394 patients with HCC from five hospitals were divided into the training and external validation datasets. Patients were initially treated by liver resection or transarterial chemoembolization. We evaluated adipose and skeletal muscle using preoperative computed tomography imaging and then constructed three predictive models, including metabolic (ModelMA), clinical-imaging (ModelCI), and combined (ModelMA-CI) models. Their discrimination, calibration, and decision curves were compared, to identify the best model. Nomogram and subgroup analysis was performed for the best model. RESULTS: ModelMA-CI containing sarcopenia and visceral adiposity had good discrimination and calibrations (integrate area under the curve for PFS was 0.708 in the training dataset and 0.706 in the validation dataset). ModelMA-CI had better accuracy than ModelCI and ModelMA. The performance of ModelMA-CI was not affected by treatments or disease stages. The high-risk subgroup (scored > 198) had a significantly shorter PFS (p < 0.001) and poorer OS (p < 0.001). CONCLUSIONS: The addition of sarcopenia and visceral adiposity improved accuracy in predicting PFS in HCC, which may provide additional insights in prognosis for HCC in subsequent studies.

Aeromonas veronii and aerolysin are important for the pathogenesis of motile aeromonad septicemia in cyprinid fish.

Aeromonas species are ubiquitous inhabitants of freshwater environments, and are responsible for fish motile aeromonad septicemia (MAS). A. hydrophila is implicated as the primary etiologic agent of MAS. Here, we analysed MAS epidemiological data for cyprinid fish in southern China, and found that A. veronii infections dominated. Consistent with this observation, A. veronii isolates were generally more virulent than A. hydrophila isolates when infecting germ-free zebrafish larvae via continuous immersion challenge. Through in vivo screening of the transposon library of the A. veronii strain Hm091, aerolysin was identified as the key virulence factor. Further results indicated that A. veronii Hm091 aerolysin disrupts the intestinal barrier of zebrafish, enabling systematic invasion by not only A. veronii Hm091 in a mono-infection, but also A. hydrophila NJ-1 in a mixed infection. Moreover, the differences in aerolysin expression and activity were the major contributor to the observed differences between the A. veronii and A. hydrophila strains regarding invasion efficacy via intestine. Together, our results provide new insights into the aetiology and pathogenesis of Aeromonas infections, and highlight the importance of A. veronii-targeted treatments in future efforts against MAS.

The Hepatotoxicity of Palmitic Acid in Zebrafish Involves the Intestinal Microbiota.

Background: Palmitic acid (PA) is the main saturated fatty acid naturally occurring in animal fats and vegetable oils. In recent decades, palm oil, an alternative lipid source containing high amounts of PA, has been widely used to replace fish oil in aquafeed. Objective: We investigated the hepatotoxicity of PA in zebrafish and the underlying mechanism. Methods: One-month-old zebrafish fed a high-fat diet (HFD) containing 16% soybean oil and 3 PA-incorporated HFDs [4%, 8%, and 12% PA (12PA)] for 2 wk (experiment 1) and 4 wk (experiment 2) were used to evaluate PA-induced liver damage and endoplasmic reticulum (ER) stress. Germ-free (GF) zebrafish fed low-fat, high-fat, or 12PA diets for 5 d were used to study the direct effects of PA on liver damage (experiment 3). GF zebrafish colonized with HFD or 12PA microbiota for 48 h were used to elucidate the indirect effects of PA-altered microbiota on liver damage (experiment 4). Last, GF zebrafish colonized with HFD or 12PA microbiota were used to evaluate the effects of different microbiotas on PA absorption (experiment 5). Results: In experiment 1, the proportion of PA in the liver linearly increased as its percentage in dietary lipid increased (r2 = 0.83, P < 0.05). In experiment 2, the expression of glucose-regulated protein 78 (Grp78) and C/EBP-homologous protein (Chop) was higher in the 12PA group than in the HFD group (2.2- and 2.7-fold, respectively; P < 0.05). The activity of caspase-12 was increased by 61.1% in the 12PA group compared with the HFD group (P < 0.05). In experiment 3, caspase-12 activity was higher in the 12PA group than in the HFD group (P < 0.05). In experiment 4, GF zebrafish colonized with PA-altered microbiota had higher caspase-12 activity (P < 0.05) than those colonized by HFD microbiota. In experiment 5, PA-altered microbiota promoted PA absorption (P < 0.05) and aggravated ER stress and liver damage in the context of high-PA feeding. Conclusions: The PA-altered microbiota indirectly induced ER stress and liver damage in zebrafish. Moreover, the PA microbiota promoted the absorption of PA, leading to enhanced PA overflow into the liver and aggravated hepatotoxicity of PA in zebrafish.

Bioinformatics and expression analysis of finTRIM genes in grass carp, Ctenopharyngodon idella.

The tripartite motifs (TRIMs) constitute a large family of proteins containing a Really Interesting New Gene (RING) domain, a B-box domain and coiled-coil region followed by different C-terminal domains. TRIM proteins play multiple roles in various cellular processes, including cell growth, differentiation, apoptosis and antiviral immunity. Fish novel large multigene TRIM genes (finTRIM/ftr) appear only in teleosts and play a vital role in antiviral responses. Phylogenetic analysis revealed the existence of different subsets of novel fish TRIM 14 genes (finTRIM14/ftr14), ftr51, ftr67, ftr72, ftr82, ftr83, and ftr99 in grass carp (Ctenopharyngodon idella), suggesting lineage-specific diversification events. Therefore, the number of finTRIM genes varies greatly among species. The ftr genes in grass carp, which are closely related to zebrafish and possess various evolutionary branches, have evolved faster than human TRIMs. The predicted protein domains were almost identical RING zinc finger domains, with the exception of ftr72, the B-box domain (excluding ftr67, ftr82, ftr83), and the B30.2 domain, which evolved under positive selection (with the exception of ftr67, and ftr72). The genes were predominantly expressed in the spleen, gill and head kidney. These findings indicate that the ftr genes in grass carp are involved diverse cellular processes, including innate immune responses.

ID2 Promotes Lineage Transition of Prostate Cancer through FGFR and JAK-STAT Signaling.

The use of androgen receptor pathway inhibitors (ARPIs) has led to an increase in the proportion of AR-null prostate cancer, including neuroendocrine prostate cancer (NEPC) and double-negative prostate cancer (DNPC), but the mechanism underlying this lineage transition has not been elucidated. We found that ID2 expression was increased in AR-null prostate cancer. In vitro and in vivo studies confirmed that ID2 promotes PCa malignancy and can confer resistance to enzalutamide in PCa cells. We generated an ID2 UP50 signature, which is capable of determining resistance to enzalutamide and is valuable for predicting patient prognosis. Functional experiments showed that ID2 could activate stemness-associated JAK/STAT and FGFR signaling while inhibiting the AR signaling pathway. Our study indicates a potentially strong association between ID2 and the acquisition of a stem-like phenotype in adenocarcinoma cells, leading to resistance to androgen deprivation therapy (ADT) and next-generation ARPIs in prostate cancer.

A dynamic nomogram integrated with blood inflammation markers for predicting overall survival in patients with upper tract urothelial carcinoma.

Background: Upper tract urothelial carcinoma (UTUC) is a relatively rare disease with a poor prognosis. A growing body of evidence demonstrates that inflammation and the inflammatory microenvironment play a crucial role in tumorigenesis and tumor progression. Our aim was to evaluate the prognostic value of blood inflammation markers and develop a prediction model that incorporates inflammation markers in order to predict overall survival (OS) of UTUC. Methods: We included 304 localized UTUC patients from two medical institutions who had undergone radical nephroureterectomy (RNU) (167 in the training cohort, 137 in the validation cohort). Univariate and multivariate Cox regression analyses were performed to screen the prognostic factors, and a nomogram and a web-based calculator were generated based on these predictors. The Harrell's concordance index (C-index), the area under the receiver operating characteristic (ROC) curve, the calibration curve, and decision curve analysis (DCA) were used to evaluate the performance of the nomogram. Results: Independent predictors incorporated in the nomogram were pathological stage, surgical margin, albumin-to-globulin ratio (AGR), and hemoglobin-to-red cell distribution width ratio (HRR). The c-index value was 0.726 in the training cohort and 0.761 in the validation cohort. The area under the ROC of the nomogram at 1-, 3- and 5-year in the training and validation sets were 0.765, 0.755, 0.763, and 0.791, 0.833, 0.802, respectively. Both the internal and external validation calibration plots showed a subtle distinction between the predicted and the actual probabilities. And it appears to provide incremental benefits for clinical decision-making in comparison to the American Joint Committee of Cancer (AJCC) staging system. Conclusions: In patients with UTUC after RNU, lower preoperative AGR and HRR were independent predictors of inferior survival. In addition, we created a novel blood inflammation marker-based dynamic nomogram that may be useful for surgeons or oncologists in risk stratification and patient selection for more intensive therapy and closer follow-up.

A seven-gene prognosis model to predict biochemical recurrence for prostate cancer based on the TCGA database.

Background: The incidence rate of prostate cancer is increasing rapidly. This study aims to explore the gene-associated mechanism of prostate cancer biochemical recurrence (BCR) after radical prostatectomy and to construct a biochemical recurrence of prostate cancer prognostic model. Methods: The DEseq2 R package was used for the differential expression of mRNA. The ClusterProfiler R package was used to analyze the functional enrichment of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) to explore related mechanisms. The Survival, Survminer, and My.stepwise R packages were used to construct the prognostic model to predict the biochemical recurrence-free probability. The RMS R package was used to draw the nomogram. For evaluating the prognostic model, the timeROC R package was used to draw the time-dependent ROC curve (receiver operating characteristic curve). Result: To investigate the association between mRNA and prostate cancer, we performed differential expression analysis on the TCGA (The Cancer Genome Atlas) database. Seven protein-coding genes (VWA5B2, ARC, SOX11, MGAM, FOXN4, PRAME, and MMP26) were picked as independent prognostic genes by regression analysis. Based on their Cox coefficient, a risk score formula was proposed. According to the risk scores, patients were divided into high- and low-risk groups based on the median score. Kaplan-Meier plot curves showed that the low-risk group had a better biochemical recurrence-free probability compared to the high-risk group. The 1-year, 3-year, and 5-year AUCs (areas under the ROC curve) of the model were 77%, 81%, and 86%, respectively. In addition, we built a nomogram based on the result of multivariate Cox regression analysis. Furthermore, we select the GSE46602 dataset as our external validation. The 1-year, 3-year, and 5-year AUCs of BCR-free probability were 83%, 82%, and 80%, respectively. Finally, the levels of seven genes showed a difference between PRAD tissues and adjacent non-tumorous tissues. Conclusions: This study shows that establishing a biochemical recurrence prediction prognostic model comprising seven protein-coding genes is an effective and precise method for predicting the progression of prostate cancer.

Identifying Protein Complexes from Dynamic Temporal Interval Protein-Protein Interaction Networks.

Identification of protein complex is very important for revealing the underlying mechanism of biological processes. Many computational methods have been developed to identify protein complexes from static protein-protein interaction (PPI) networks. Recently, researchers are considering the dynamics of protein-protein interactions. Dynamic PPI networks are closer to reality in the cell system. It is expected that more protein complexes can be accurately identified from dynamic PPI networks. In this paper, we use the undulating degree above the base level of gene expression instead of the gene expression level to construct dynamic temporal PPI networks. Further we convert dynamic temporal PPI networks into dynamic Temporal Interval Protein Interaction Networks (TI-PINs) and propose a novel method to accurately identify more protein complexes from the constructed TI-PINs. Owing to preserving continuous interactions within temporal interval, the constructed TI-PINs contain more dynamical information for accurately identifying more protein complexes. Our proposed identification method uses multisource biological data to judge whether the joint colocalization condition, the joint coexpression condition, and the expanding cluster condition are satisfied; this is to ensure that the identified protein complexes have the features of colocalization, coexpression, and functional homogeneity. The experimental results on yeast data sets demonstrated that using the constructed TI-PINs can obtain better identification of protein complexes than five existing dynamic PPI networks, and our proposed identification method can find more protein complexes accurately than four other methods.

A method for identifying protein complexes with the features of joint co-localization and joint co-expression in static PPI networks.

Identifying protein complexes in static protein-protein interaction (PPI) networks is essential for understanding the underlying mechanism of biological processes. Proteins in a complex are co-localized at the same place and co-expressed at the same time. We propose a novel method to identify protein complexes with the features of joint co-localization and joint co-expression in static PPI networks. To achieve this goal, we define a joint localization vector to construct a joint co-localization criterion of a protein group, and define a joint gene expression to construct a joint co-expression criterion of a gene group. Moreover, the functional similarity of proteins in a complex is an important characteristic. Thus, we use the CC-based, MF-based, and BP-based protein similarities to devise functional similarity criterion to determine whether a protein is functionally similar to a protein cluster. Based on the core-attachment structure and following to seed expanding strategy, we use four types of biological data including PPI data with reliability score, protein localization data, gene expression data, and gene ontology annotations, to identify protein complexes. The experimental results on yeast data show that comparing with existing methods our proposed method can efficiently and exactly identify more protein complexes, especially more protein complexes of sizes from 2 to 6. Furthermore, the enrichment analysis demonstrates that the protein complexes identified by our method have significant biological meaning.