Association between serum 25-hydroxyvitamin D level and inflammatory markers in hemodialysis-treated patients.

OBJECTIVE: To investigate the relationship between serum 25-hydroxyvitamin D (25(OH)D) level with novel inflammatory markers in hemodialysis-treated patients. METHODS: A total of 167 maintenance hemodialysis-treated patients were enrolled in this cross-sectional study. The patients were divided into vitamin D deficiency (a serum 25(OH)D level <20 ng/mL) and nondeficiency (a serum 25(OH)D level ≥20 ng/mL) groups. The neutrophil to lymphocyte ratio (NLR), platelet to lymphocyte ratio (PLR), and monocyte to lymphocyte ratio (MLR) were calculated by the complete blood cell count. The relationship between 25(OH)D level with other parameters was assessed by bivariate correlation analysis and linear regression analysis. RESULTS: There were significant differences between the two groups in terms of age, diabetes, levels of albumin, creatinine, high-density lipoprotein cholesterol (HDL-C) and low-density lipoprotein cholesterol (LDL-C) as well as NLR and MLR (p = .004, p = .031, p < .001, p = .043, p = .008, p = .006, p = .002, and p < .001, respectively). There exist negative correlations between serum 25(OH)D level with age, diabetes, alkaline phosphatase level, NLR, PLR, and MLR (p = .002, p = .002, p = .037, p = .001, p = .041, and p < .001, respectively) and positive correlations between serum 25(OH)D level with albumin level, creatinine level, phosphorus level, HDL-C, and LDL-C (p < .001, p < .001, p = .013, p = .02, p = .002, respectively). Multiple analysis results showed that sex, diabetes, albumin level and NLR were independently associated with serum 25(OH)D level (p = .021, p = .015, p = .033, and p = .041, respectively). High values of NLR and MLR were associated with patients with serum 25(OH)D deficiency. There were negative interplays between serum 25(OH) D level with NLR, PLR, and MLR and also an independent association between serum 25(OH) D level with NLR. CONCLUSION: Collectively, serum 25(OH)D level has a negative correlation with inflammatory markers.

Identification and verification of a PANoptosis-related long noncoding ribonucleic acid signature for predicting the clinical outcomes and immune landscape in lung adenocarcinoma.

PANoptosis is a type of programmed cell death (PCD) characterised by apoptosis, necroptosis and pyroptosis. Long non-coding ribonucleic acids (lncRNAs) are participating in the malignant behaviour of tumours regulated by PCD. Nevertheless, the function of PANoptosis-associated lncRNAs in lung adenocarcinoma remains to be investigated. In this work, a PANoptosis-related lncRNA signature (PRLSig) was developed based on the least absolute shrinkage and selection operator algorithm. The stability and fitness of PRLSig were confirmed by systematic evaluation of Kaplan-Meier, Cox analysis algorithm, receiver operating characteristic analysis, stratification analysis. In addition, ESTIMATE, single sample gene set enrichment analysis, immune checkpoints and the cancer immunome database confirmed the predictive value of the PRLSig in immune microenvironment and helped to identify populations for which immunotherapy is advantageous. The present research provides novel insights to facilitate risk stratification and optimise personalised treatment for LUAD.

Association between weight-adjusted waist index and serum total testosterone in males aged 6-19 years in the United States: Data from NHANES 2013-2016.

Objective: This study aimed to estimate the association between weight-adjusted waist index and serum total testosterone (sTT) in males aged 6-19 years in the United States. Methods: A cross-sectional study was conducted using data from the National Health and Nutrition Examination Survey (NHANES) 2013-2016. sTT was considered as the response variable, and weight-adjusted waist circumference index (WWI) as the independent variable. Multiple linear regression was performed to estimate the association between the two variables, and subgroup analysis was performed to identify sensitive cohorts. Smoothing curve fitting and threshold effects analysis was carried out to assess possible nonlinear relationships between WWI and sTT. Results: The study included 4207 participants. The mean value of sTT (117.93 ng/dl) was used as the grouping basis, with 1066 participants having serum total testosterone levels above the mean. A negative association was observed between WWI and sTT [beta coefficient (ß) = -72.50, 95% confidence interval (CI): -79.45, -65.55], which decreased as WWI increased (P for trend<0.05). Subgroup analysis indicated a stronger negative correlation in late adolescent (16-19 years) males (ß = -128.94, 95% CI: -146.75, -111.13). The smoothing curve fit analysis revealed a U-shaped curve relationship for the negative correlation between WWI and sTT. Threshold effect analysis suggested a significant change when WWI exceeded 10.09 (ß = -15.82, 95% CI: -24.11, -7.54), and stepwise threshold effect analysis indicated that this negative correlation became less stable when WWI exceeded 11.45 (ß = -0.80, 95% CI: -9.15, 7.56). Conclusions: Participants with higher WWI exhibited lower total testosterone levels, and a negative association was found between WWI and total testosterone, particularly in late adolescent males aged 16-19 years. Among males aged 6-19 years, caution should be exercised regarding the risk of lower testosterone levels associated with elevated WWI, particularly when WWI is below 10.09.

Comprehensive assessment of regulatory T-cells-related scoring system for predicting the prognosis, immune microenvironment and therapeutic response in hepatocellular carcinoma.

INTRODUCTION: Regulatory T cells (Tregs) play important roles in tumor immunosuppression and immune escape. The aim of the present study was to construct a novel Tregs-associated biomarker for the prediction of tumour immune microenvironment (TIME), clinical outcomes, and individualised treatment in hepatocellular carcinoma (HCC). METHODS: Single-cell sequencing data were obtained from the three independent cohorts. Cox and LASSO regression were utilised to develop the Tregs Related Scoring System (TRSSys). GSE140520, ICGC-LIRI and CHCC cohorts were used for the validation of TRSSys. Kaplan-Meier, ROC, and Cox regression were utilised for the evaluation of TRSSys. The ESTIMATE, TIMER 2.0, and ssGSEA algorithm were utilised to determine the value of TRSSys in predicting the TIME. GSVA, GO, KEGG, and TMB analyses were used for mechanistic exploration. Finally, the value of TRSSys in predicting drug sensitivity was evaluated based on the oncoPredict algorithm. RESULTS: Comprehensive validation showed that TRSSys had good prognostic predictive efficacy and applicability. Additionally, ssGSEA, TIMER and ESTIMATE algorithm suggested that TRSSys could help to distinguish different TIME subtypes and determine the beneficiary population of immunotherapy. Finally, the oncoPredict algorithm suggests that TRSSys provides a basis for individualised treatment. CONCLUSIONS: TRSSys constructed in the current study is a novel HCC prognostic prediction biomarker with good predictive efficacy and stability. Additionally, risk stratification based on TRSSys can help to identify the TIME landscape subtypes and provide a basis for individualized treatment options.

Thermodynamic and thermo-economic analyses of a coupled heat-pump system for low-grade exhaust-air heat exchange in mines.

Combining mine exhaust waste heat with existing heat pump technology is a promising technical route to realise the efficient extraction and scientific use of low-grade waste heat resources in mines and to solve the problem of insufficient heat supply in remote mining areas. This study proposes a new type of mine-exhaust-air heat exchange coupled with heat-pump waste-heat-utilisation system based on deep enthalpy heat extraction. Using a mining area in Northwest China as a representative case, this study establishes a systematic exergy analytical model and a thermo-economic model. Through an in-depth analysis of the different evaporation temperatures and condensing temperatures, the system's energy efficiency ratio (COP) reaches its optimal performance, with the total exergy efficiency surpassing 90%. The minimum efficiency of the subsystem return air heat exchanger is 35%. The unit thermal costs of the mine exhaust air waste heat utilisation system and a conventional coal-fired boiler system are 0.1291 and 0.1573 million RMB/kW·h, respectively. This is a thermal economics cost saving of 21%. The studied system demonstrates great economic viability and the potential for energy saving throughout its life cycle.

Identification and validation of a cancer-associated fibroblasts-related scoring system to predict prognosis and immune landscape in hepatocellular carcinoma through integrated analysis of single-cell and bulk RNA-sequencing.

BACKGROUND: Cancer-associated fibroblasts (CAFs) regulate the malignant biological behaviour of hepatocellular carcinoma (HCC) as a significant component of the tumour immune microenvironment (TIME). This study aimed to develop a CAFs-based scoring system to predict the prognosis and TIME of patients with HCC. METHODS: Data for the TCGA-LIHC and GSE14520 cohorts were downloaded from The Cancer Genome Atlas and the Gene Expression Omnibus databases. Single-cell RNA-sequencing data for HCC samples were retrieved from the GSE166635 cohort. The Least Absolute Shrinkage and Selection Operator algorithm was employed to develop a CAFs-related scoring system (CAFRss). The predictive value of the CAFRss was determined using Kaplan-Meier, Cox regression and Receiver Operating Characteristic curves. Additionally, the TIMER platform, single sample Gene Set Enrichment Analysis and the Estimation of STromal and Immune cells in MAlignant Tumour tissues using Expression data algorithms were performed to determine the TIME landscape. Finally, the pRRophic algorithm was utilised for drug sensitivity analysis. RESULTS: The evaluation of the CAFRss system demonstrated its superior ability to predict the clinical outcome of patients with HCC. Additionally, CAFRss effectively distinguished HCC populations with distinct TIME landscapes. Furthermore, CAFRss-based risk stratification identified individuals with immune 'hot tumours' and predicted the survival of patients treated with ICBs. CONCLUSIONS: The developed CAFRss can serve as a predictive tool for determining the clinical outcome of HCC and differentiating populations with diverse TIME characteristics.

An angiogenesis-related three-long non-coding ribonucleic acid signature predicts the immune landscape and prognosis in hepatocellular carcinoma.

The tumour microenvironment is a key determinant of the efficacy of immunotherapy. Angiogenesis is closely linked to tumour immunity. We aimed to screen long non-coding ribonucleic acids (lncRNAs) associated with angiogenesis to predict the prognosis of individuals with hepatocellular carcinoma (HCC) and characterise the tumour immune microenvironment (TIME). Patient data, including transcriptome and clinicopathological parameters, were retrieved from The Cancer Genome Atlas database. Moreover, co-expression algorithm was utilized to obtain angiogenesis-related lncRNAs. Additionally, survival-related lncRNAs were identified using Cox regression and the least absolute shrinkage and selection operator algorithm, which aided in constructing an angiogenesis-related lncRNA signature (ARLs). The ARLs was validated using Kaplan-Meier method, time-dependent receiver operating characteristic analyses, and Cox regression. Additionally, an independent external HCC dataset was used for further validation. Then, gene set enrichment analysis, immune landscape, and drug sensitivity analyses were implemented to explore the role of the ARLs. Finally, cluster analysis divided the entire HCC dataset into two clusters to distinguish different subtypes of TIME. This study provides insight into the involvement of angiogenesis-associated lncRNAs in predicting the TIME characteristics and prognosis for individuals with HCC. Furthermore, the developed ARLs and clusters can predict the prognosis and TIME characteristics in HCC, thereby aiding in selecting the appropriate therapeutic strategies involving immune checkpoint inhibitors and targeted drugs.

The efficacy and safety of an improved percutaneous peritoneal dialysis catheter placement technique in urgent-start peritoneal dialysis patients: a retrospective cohort study.

BACKGROUND: Urgent-start peritoneal dialysis has high catheterization skill requirements and that early complications. The optimal catheter placement method remains debatable in urgent-start peritoneal dialysis patients. Safe and effective peritoneal dialysis catheterization is needed in clinical work. METHODS: We retrospectively analyzed the data of 34 patients diagnosed with end-stage renal disease who opt for peritoneal dialysis, 19 males and 15 females, with an average age of 62.3±14.7 years, peritoneal dialysis catheter implantation was completed by the improved percutaneous catheterization technique. They were followed for 6 months, early and late complications were observed and the survival rate of the catheter technique was calculated. RESULTS: All 34 patients diagnosed with end-stage renal disease successfully underwent catheter placement using the improved percutaneous technique; the catheterization success rate was 100%. No severe organ injuries, such as intestinal perforation and bladder perforation, occurred intraoperatively. Peritoneal dialysis was started immediately after surgery. The early complications included one case of leakage, one case of omental wrapping, and six cases of rectus abdominis hemorrhage. The late complications included one case of pleuro-abdominal fistula and two cases of peritonitis. The 6-month technical survival rate for the catheter was 94.1% (32/34). Compared to previously reported studies, this technique may reduce leakage and early catheter dysfunction, and improve the technical survival of catheters. CONCLUSIONS: The improved percutaneous peritoneal dialysis catheter placement technique might be an effective and safe method for urgent­start peritoneal dialysis patients.

Construction of a Necroptosis-Associated Long Non-Coding RNA Signature to Predict Prognosis and Immune Response in Hepatocellular Carcinoma.

Background: Necroptosis is a form of programmed cell death, and studies have shown that long non-coding RNA molecules (lncRNAs) can regulate the process of necroptosis in various cancers. We sought to screen lncRNAs associated with necroptosis to predict prognosis and tumor immune infiltration status in patients with hepatocellular carcinoma (HCC). Methods: Transcriptomic data from HCC tumor samples and normal tissues were extracted from The Cancer Genome Atlas database. Necroptosis-associated lncRNAs were obtained by co-expression analysis. Necroptosis-associated lncRNAs were then screened by Cox regression and least absolute shrinkage and selection operator methods to construct a risk model for HCC. The models were also validated and evaluated by Kaplan-Meier analysis, univariate and multivariate Cox regression, and time-dependent receiver operating characteristic (ROC) curves. In addition, Gene Ontology, Kyoto Encyclopedia of Genes and Genomes enrichment, gene set enrichment, principal component, immune correlation, and drug sensitivity analyses were applied to assess model risk groups. To further differentiate the immune microenvironment of different HCC subtypes, the entire dataset was divided into three clusters, based on necroptosis-associated lncRNAs, and a series of analyses performed. Results: We constructed a model comprising four necroptosis-associated lncRNAs: POLH-AS1, DUXAP8, AC131009.1, and TMCC1-AS1. Overall survival (OS) duration was significantly longer in patients classified as low-risk than those who were high-risk, according to our model. Univariate and multivariate Cox regression analyses further confirmed risk score stability. The analyzed models had area under the ROC curve values of 0.786, 0.713, and 0.639 for prediction of 1-, 3-, and 5-year OS, respectively, and risk score was significantly associated with immune cell infiltration and ESTIMATE score. In addition, differences between high and low-risk groups in predicted half-maximal inhibitory concentration values for some targeted and chemical drugs, providing a potential basis for selection of treatment approach. Finally, cluster analysis facilitated more refined differentiation of the immune microenvironment in patients with HCC and may allow prediction of the effectiveness of immune checkpoint inhibitors. Conclusions: This study contributes to understanding of the function of necroptosis-related lncRNAs in predicting the prognosis and immune infiltration status of HCC. The risk model constructed and cluster analysis provide a basis for predicting the prognosis of patients with HCC and to inform the selection of immunotherapeutic strategies.

Long-term efficacy, safety, and medication compliance of roxadustat on peritoneal dialysis patients with renal anemia affected by the COVID-19 pandemic: a retrospective study.

BACKGROUND: Current studies have limited data on long-term treatment safety and medication compliance of roxadustat for renal anemia in peritoneal dialysis (PD) patients. We aimed to analyze the long-term efficacy, safety, and medication compliance of roxadustat in the treatment of renal anemia in patients with PD who discontinued recombinant human erythropoietin (rhEPO) treatment due to the corona virus disease 2019 (COVID-19) outbreak. METHODS: We retrospectively collected patients who were switched from rhEPO to roxadustat in our hospital due to the pandemic. The criteria for subject inclusion: aged >18 years with a dialysis vintage >3 months, without malignant tumor, no severe cardiovascular and cerebrovascular diseases, and not combined hemodialysis. Patients were followed up until the end of December 2021. Hemoglobin (Hb), red blood cell (RBC) and hematocrit (Hct) were recorded at baseline, month 1-12 and month 20, and iron parameters at baseline, 3, 6, 9, 12, and 20 months were collected. The Morisky Medication Adherence Scale-8 (MMAS-8) was used to score medication compliance during rhEPO treatment and roxadustat treatment, and adverse reactions occurred during treatment were collected. The efficacy and medication compliance of roxadustat were analyzed using Wilcoxon rank sum test or t-test. RESULTS: The median follow-up time was 21.1 (20.6, 21.7) months. After 1 month of treatment, the Hb level was significantly increased by 9.4 g/L (95% CI: 6.0-12.8 g/L) compared with the baseline, follow up at 20 months showed the Hb level had remained stable, increased by 20.7 g/L (95% CI: 15.9-25.4 g/L) compared with before treatment. At the beginning of treatment, total iron binding capacity increased, transferrin saturation and serum ferritin decreased, serum iron remained stable during treatment. During roxadustat treatment, no patient discontinued treatment due to the pandemic, and the Morisky score was improved compared with that during rhEPO treatment [5.75 (4.25, 6.00) vs. 6.75 (5.75, 7.00), P=0.000]. There were no serious adverse events associated with roxadustat were observed. CONCLUSIONS: Roxadustat can effectively improve anemia and had good tolerance in patients undergoing PD who have difficult using rhEPO, and the medication compliance was better than rhEPO during the COVID-19.

The predictive value of coronary artery calcification score combined with bone mineral density for the 2-year risk of cardiovascular events in maintenance hemodialysis patients.

PURPOSE: Cardiovascular disease is the leading cause of death in maintenance hemodialysis (MHD) patients. The aim of this study is to investigate the predictive value of coronary artery calcification score (CACs) combined with bone mineral density (BMD) for the risk of cardiovascular diseases in MHD patients. METHODS: From January 2017 to January 2019, we enrolled 112 MHD patients and 112 controls in Ningbo First Hospital, and retrospectively counted the cardiovascular events in the next 2 years after enrollment. According to the occurrence of cardiovascular events, the MHD patients were divided into CVD group and non-CVD group. The differences of vertebral BMD and CACs between the two groups were compared. ROC curve, Kaplan-Meier curve and Cox regression analyses were used for assess the predictive value of 2-year cardiovascular events in MHD patients. RESULTS: Among 112 MHD patients, 49 (43.75%) patients had cardiovascular events. The results showed that the average value of BMD in MHD patients was significantly lower than that in the control group (99.88 ± 30.99 VS. 108.35 ± 23.98, P = 0.0231). The CACs in MHD patients were significantly higher than that in the control group (317.81 ± 211.53 VS. 190.03 ± 100.50, P < 0.001). The results between CVD group and the non-CVD group were to the same direction (BMD: 81.12 ± 31.28 VS. 114.48 ± 21.61, P < 0.001; CACs: 447.16 ± 234.11 VS. 217.21 ± 119.03, P < 0.001). Besides, CACs combined with BMD yield an AUC of 0.875 with a sensitivity of 79.60%, a specificity of 82.50%. Kaplan-Meier curve and Cox regression analyses indicated that CACs and BMD were independently associated with high risk of cardiovascular events in MHD patients. CONCLUSION: The combination of CACs and vertebral BMD could predict the occurrence of cardiovascular events in MHD patients to some extent.

Exosomal miRNA-profiling of pleural effusion in lung adenocarcinoma and tuberculosis.

Background: Pleural effusion (PE) caused by lung cancer is prevalent, and it is difficult to differentiate it from PE caused by tuberculosis. Exosome-based liquid biopsy offers a non-invasive technique to diagnose benign and malignant PE. Exosomal miRNAs are potential diagnostic markers and play an essential role in signal transduction and biological processes in tumor development. We hypothesized that exosomal miRNA expression profiles in PE would contribute to identifying its diagnostic markers and elucidating the molecular basis of PE formation in lung cancer. Methods: The exosomes from PE caused by lung adenocarcinoma (LUAD) and pulmonary tuberculosis were isolated and verified by transmission electron microscopy. The exosomal miRNA profiles were identified using deep sequencing and validated with quantitative real-time PCR (qRT-PCR). We performed bioinformatic analysis for differentially expressed miRNAs to explore how exosomal miRNAs regulate pleural effusion. Results: We identified 99 upregulated and 91 downregulated miRNAs in malignant pleural effusion (MPE) compared to tuberculous pleural effusion (TPE). Seven differentially expressed miRNAs (DEmiRNAs) were validated by qRT-PCR, out of which 5 (71.4%) were confirmed through sequencing. Gene Ontology (GO) analysis revealed that most exosomal miRNAs target genes were involved in regulating cellular processes and nitrogen compound metabolism. According to the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, the exosomal miRNAs target genes were mainly involved in Fc gamma R-mediated phagocytosis, Rap1 signaling pathway, and breast cancer. The hub genes, including ITGAM, FOXO1, MAPK14, YWHAB, GRIN1, and PRF1, were screened through plug-in cytoHubba. The PFR1 was identified as a critical gene in MPE formation using single-cell sequencing analysis. Additionally, we hypothesized that tumor cells affected natural killer cells and promoted the generation of PE in LUAD via the exosomal hsa-miR-3120-5p-PRF1 axis. Conclusions: We identified exosomal miRNA profiles in LUAD-MPE and TPE, which may help in the differential diagnosis of MPE and TPE. Bioinformatic analysis revealed that these miRNAs might affect PE generation through tumor immune response in LUAD. Our results provided a new theoretical basis for understanding the function of exosomal miRNAs in LUAD-MPE.

PANX1 is a potential prognostic biomarker associated with immune infiltration in pancreatic adenocarcinoma: A pan-cancer analysis.

Pannexin 1 (PANX1) channel is a critical ATP-releasing pathway that modulates tumor immunity, progression, and prognosis. However, the roles of PANX1 in different cancers remain unclear. We analyzed the expression of PANX1 in human pan-cancer in the Oncomine and GEPIA2.0 databases. The prognostic value of PANX1 expression was determined using Kaplan-Meier plotter and OncoLnc tools. The correlation between PANX1 and tumor-infiltrating immune cells was investigated using the TIMER 2.0. In addition, the relationship between PANX1 and immunomodulators was explored using TISIDB. Finally, gene set enrichment analysis (GSEA) was performed utilizing LinkedOmics. The results indicated that PANX1 was overexpressed in most cancers compared to normal tissues. The high expression of PANX1 was associated with poor prognosis in multiple tumors, especially in pancreatic adenocarcinoma (PAAD). In addition, PANX1 was correlated with a variety of immunomodulators, such as CD274, IL10, CD276, IL2RA, TAP1, and TAP2. PANX1 expression level was significantly related to infiltration of multiple immune cells in many cancers, including cancer associated fibroblast, macrophage, and neutrophil cells. Further analysis revealed that PANX1 was significantly associated with T cells CD8+ (rho = 0.524, P = 1.94e-13) and Myeloid dendritic cell (rho = 0.564, P = 9.45e-16). GSEA results showed that PANX1 was closely associated with leukocyte cell-cell adhesion, endoplasmic reticulum lumen, ECM-receptor interaction, and Focal adhesion pathways in PAAD. PANX1 expression was higher in pan-cancer samples than in normal tissues. The high expression of PANX1 was associated with poor outcome and immune infiltration in multiple cancers, especially in PAAD.

Investigating potential molecular mechanisms of serum exosomal miRNAs in colorectal cancer based on bioinformatics analysis.

Colorectal cancer (CRC) is the most common malignant gastrointestinal tumor worldwide. Serum exosomal microRNAs (miRNAs) play a critical role in tumor progression and metastasis. However, the underlying molecular mechanisms are poorly understood.The miRNAs expression profile (GSE39833) was downloaded from Gene Expression Omnibus (GEO) database. GEO2R was applied to screen the differentially expressed miRNAs (DEmiRNAs) between healthy and CRC serum exosome samples. The target genes of DEmiRNAs were predicted by starBase v3.0 online tool. The gene ontology (GO) and Kyoto Encyclopedia of Genomes pathway (KEGG) enrichment analysis were performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID) online tool. The protein-protein interaction (PPI) network was established by the Search Tool for the Retrieval of Interacting Genes (STRING) visualized using Cytoscape software. Molecular Complex Detection (MCODE) and cytohubba plug-in were used to screen hub genes and gene modules.In total, 102 DEmiRNAs were identified including 67 upregulated and 35 downregulated DEmiRNAs, and 1437 target genes were predicted. GO analysis showed target genes of upregulated DEmiRNAs were significantly enriched in transcription regulation, protein binding, and ubiquitin protein ligase activity. While the target genes of downregulated DEmiRNAs were mainly involved in transcription from RNA polymerase II promoter, SMAD binding, and DNA binding. The KEGG pathway enrichment analyses showed target genes of upregulated DEmiRNAs were significantly enriched in proteoglycans in cancer, microRNAs in cancer, and phosphatidylinositol-3 kinases/Akt (PI3K-Akt) signaling pathway, while target genes of downregulated DEmiRNAs were mainly enriched in transforming growth factor-beta (TGF-beta) signaling pathway and proteoglycans in cancer. The genes of the top 3 modules were mainly enriched in ubiquitin mediated proteolysis, spliceosome, and mRNA surveillance pathway. According to the cytohubba plugin, 37 hub genes were selected, and 4 hub genes including phosphoinositide-3-kinase regulatory subunit 1 (PIK3R1), SRC, cell division cycle 42 (CDC42), E1A binding protein p300 (EP300) were identified by combining 8 ranked methods of cytohubba.The study provides a comprehensive analysis of exosomal DEmiRNAs and target genes regulatory network in CRC, which can better understand the roles of exosomal miRNAs in the development of CRC. However, these findings require further experimental validation in future studies.

Copy Number Aberrations of Multiple Genes Identified as Prognostic Markers for Extrahepatic Metastasis-free Survival of Patients with Hepatocellular Carcinoma.

Extrahepatic metastasis confers unfavorable patient prognosis in patients with hepatocellular carcinoma (HCC), however, reliable markers allowing prediction of extrahepatic metastasis at the time of initial diagnosis are still lacking. This study was to identify gene-level copy number aberrations (CNAs) related to extrahepatic metastasis-free survival of HCC patients, and further examine the associations between CNAs and gene expression. Array comparative genomic hybridization (aCGH) and expression array were used to analyze gene CNAs and expression levels, respectively. The associations between CNAs of a panel of 20 genes and extrahepatic metastasis-free survival were analyzed in 66 patients with follow-up period of 1.6-90.5 months. The gene expression levels between HCCs with and without gene CNA were compared in 109 patients with HCC. We observed that gains at MDM4 and BCL2L1, and losses at APC and FBXW7 were independent prognostic markers for extrahepatic metastasis-free survival of HCC patients. Integration analysis of aCGH and expression data showed that MDM4 and BCL2L1 were significantly upregulated in HCCs with gene gain, while APC and FBXW7 were significantly downregulated in HCCs with gene loss. We concluded that gene gains at MDM4 and BCL2L1, and losses at APC and FBXW7, with concordant expression changes, were associated with extrahepatic metastasis-free survival of HCC patients and have potential to act as novel prognostic markers.

Purification and biochemical characterization of FrsA protein from Vibrio vulnificus as an esterase.

Fermentation-respiration switch protein (FrsA) was thought to play an important role in controlling the metabolic flux between respiration and fermentation pathways, whereas the biochemical function of FrsA was unclear yet. A gene coding for FrsA protein from Vibrio vulnificus was chemically synthesized. The recombinant VvFrsA was expressed as a soluble protein and purified by Ni-NTA affinity chromatography. The protein had a subunit molecular weight of ca. 45 kDa by SDS-PAGE and preferred short-chain esters when p-nitrophenyl alkanoate esters were used as substrates. Optimum condition for VvFrsA was found to be at pH 9.0 and 50 °C. The protein retained high esterase activity at alkaline condition and would denature slowly at over 50 °C. With p-nitrophenyl acetate as the substrate, the Km and kcat were determined to be 18.6 mM and 0.67 s-1, respectively, by steady-state kinetic assay. Molecular dynamics simulation and docking model structure revealed that p-nitrophenyl acetate could be the substrate of VvFrsA. In conclusion our results demonstrated that the protein was able to catalyze the hydrolysis of esters, especially p-nitrophenyl acetate, for the first time.

Large-scale prediction of protein ubiquitination sites using a multimodal deep architecture.

BACKGROUND: Ubiquitination, which is also called "lysine ubiquitination", occurs when an ubiquitin is attached to lysine (K) residues in targeting proteins. As one of the most important post translational modifications (PTMs), it plays the significant role not only in protein degradation, but also in other cellular functions. Thus, systematic anatomy of the ubiquitination proteome is an appealing and challenging research topic. The existing methods for identifying protein ubiquitination sites can be divided into two kinds: mass spectrometry and computational methods. Mass spectrometry-based experimental methods can discover ubiquitination sites from eukaryotes, but are time-consuming and expensive. Therefore, it is priority to develop computational approaches that can effectively and accurately identify protein ubiquitination sites. RESULTS: The existing computational methods usually require feature engineering, which may lead to redundancy and biased representations. While deep learning is able to excavate underlying characteristics from large-scale training data via multiple-layer networks and non-linear mapping operations. In this paper, we proposed a deep architecture within multiple modalities to identify the ubiquitination sites. First, according to prior knowledge and biological knowledge, we encoded protein sequence fragments around candidate ubiquitination sites into three modalities, namely raw protein sequence fragments, physico-chemical properties and sequence profiles, and designed different deep network layers to extract the hidden representations from them. Then, the generative deep representations corresponding to three modalities were merged to build the final model. We performed our algorithm on the available largest scale protein ubiquitination sites database PLMD, and achieved 66.4% specificity, 66.7% sensitivity, 66.43% accuracy, and 0.221 MCC value. A number of comparative experiments also indicated that our multimodal deep architecture outperformed several popular protein ubiquitination site prediction tools. CONCLUSION: The results of comparative experiments validated the effectiveness of our deep network and also displayed that our method outperformed several popular protein ubiquitination site prediction tools. The source codes of our proposed method are available at https://github.com/jiagenlee/deepUbiquitylation .

Detecting Succinylation sites from protein sequences using ensemble support vector machine.

BACKGROUND: Lysine succinylation is a new kind of post-translational modification which plays a key role in protein conformation regulation and cellular function control. To understand the mechanism of succinylation profoundly, it is necessary to identify succinylation sites in proteins accurately. However, traditional methods, experimental approaches, are labor-intensive and time-consuming. Computational prediction methods have been proposed recent years, and they are popular because of their convenience and high speed. In this study, we developed a new method to predict succinylation sites in protein combining multiple features, including amino acid composition, binary encoding, physicochemical property and grey pseudo amino acid composition, with a feature selection scheme (information gain). And then, it was trained using SVM (Support Vector Machine) and an ensemble learning algorithm. RESULTS: The performance of this method was measured with an accuracy of 89.14% and a MCC (Matthew Correlation Coefficient) of 0.79 using 10-fold cross validation on training dataset and an accuracy of 84.5% and a MCC of 0.2 on independent dataset. CONCLUSIONS: The conclusions made from this study can help to understand more of the succinylation mechanism. These results suggest that our method was very promising for predicting succinylation sites. The source code and data of this paper are freely available at https://github.com/ningq669/PSuccE .

Glypre: In Silico Prediction of Protein Glycation Sites by Fusing Multiple Features and Support Vector Machine.

Glycation is a non-enzymatic process occurring inside or outside the host body by attaching a sugar molecule to a protein or lipid molecule. It is an important form of post-translational modification (PTM), which impairs the function and changes the characteristics of the proteins so that the identification of the glycation sites may provide some useful guidelines to understand various biological functions of proteins. In this study, we proposed an accurate prediction tool, named Glypre, for lysine glycation. Firstly, we used multiple informative features to encode the peptides. These features included the position scoring function, secondary structure, AAindex, and the composition of k-spaced amino acid pairs. Secondly, the distribution of distinctive features of the residues surrounding the glycation and non-glycation sites was statistically analysed. Thirdly, based on the distribution of these features, we developed a new predictor by using different optimal window sizes for different properties and a two-step feature selection method, which utilized the maximum relevance minimum redundancy method followed by a greedy feature selection procedure. The performance of Glypre was measured with a sensitivity of 57.47%, a specificity of 90.78%, an accuracy of 79.68%, area under the receiver-operating characteristic (ROC) curve (AUC) of 0.86, and a Matthews's correlation coefficient (MCC) of 0.52 by 10-fold cross-validation. The detailed analysis results showed that our predictor may play a complementary role to other existing methods for identifying protein lysine glycation. The source code and datasets of the Glypre are available in the Supplementary File.

EPuL: An Enhanced Positive-Unlabeled Learning Algorithm for the Prediction of Pupylation Sites.

Protein pupylation is a type of post-translation modification, which plays a crucial role in cellular function of bacterial organisms in prokaryotes. To have a better insight of the mechanisms underlying pupylation an initial, but important, step is to identify pupylation sites. To date, several computational methods have been established for the prediction of pupylation sites which usually artificially design the negative samples using the verified pupylation proteins to train the classifiers. However, if this process is not properly done it can affect the performance of the final predictor dramatically. In this work, different from previous computational methods, we proposed an enhanced positive-unlabeled learning algorithm (EPuL) to the pupylation site prediction problem, which uses only positive and unlabeled samples. Firstly, we separate the training dataset into the positive dataset and the unlabeled dataset which contains the remaining non-annotated lysine residues. Then, the EPuL algorithm is utilized to select the reliably negative initial dataset and then iteratively pick out the non-pupylation sites. The performance of the proposed method was measured with an accuracy of 90.24%, an Area Under Curve (AUC) of 0.93 and an MCC of 0.81 by 10-fold cross-validation. A user-friendly web server for predicting pupylation sites was developed and was freely available at http://59.73.198.144:8080/EPuL.