Identification of a Ubiquitin Related Genes Signature for Predicting Prognosis of Prostate Cancer

Background: Ubiquitin and ubiquitin-like (UB/UBL) conjugations are one of the most important post-translational modifications and involve in the occurrence of cancers. However, the biological function and clinical significance of ubiquitin related genes (URGs) in prostate cancer (PCa) are still unclear.Methods: The transcriptome data and clinicopathological data were downloaded from The Cancer Genome Atlas (TCGA), which was served as training cohort. The GSE21034 dataset was used to validate. The two datasets were removed batch effects and normalized using the “sva” R package. Univariate Cox, LASSO Cox, and multivariate Cox regression were performed to identify a URGs prognostic signature. Then Kaplan-Meier curve and receiver operating characteristic (ROC) curve analyses were used to evaluate the performance of the URGs signature. Thereafter, a nomogram was constructed and evaluated.Results: A six-URGs signature was established to predict biochemical recurrence (BCR) of PCa, which included ARIH2, FBXO6, GNB4, HECW2, LZTR1 and RNF185. Kaplan-Meier curve and ROC curve analyses revealed good performance of the prognostic signature in both training cohort and validation cohort. Univariate and multivariate Cox analyses showed the signature was an independent prognostic factor for BCR of PCa in training cohort. Then a nomogram based on the URGs signature and clinicopathological factors was established and showed an accurate prediction for prognosis in PCa.Conclusion: Our study established a URGs prognostic signature and constructed a nomogram to predict the BCR of PCa. This study could help with individualized treatment and identify PCa patients with high BCR risks.

Development and Validation of an Insulin Resistance Predicting Model Using a Machine-Learning Approach in a Population-Based Cohort in Korea

Background: Insulin resistance is a common etiology of metabolic syndrome, but receiver operating characteristic (ROC) curve analysis shows a weak association in Koreans. Using a machine learning (ML) approach, we aimed to generate the best model for predicting insulin resistance in Korean adults aged > 40 of the Ansan/Ansung cohort using a machine learning (ML) approach. Methods: The demographic, anthropometric, biochemical, genetic, nutrient, and lifestyle variables of 8842 participants were included. The polygenetic risk scores (PRS) generated by a genome-wide association study were added to represent the genetic impact of insulin resistance. They were divided randomly into the training (n = 7037) and test (n = 1769) sets. Potentially important features were selected in the highest area under the curve (AUC) of the ROC curve from 99 features using seven different ML algorithms. The AUC target was ≥0.85 for the best prediction of insulin resistance with the lowest number of features. Results: The cutoff of insulin resistance defined with HOMA-IR was 2.31 using logistic regression before conducting ML. XGBoost and logistic regression algorithms generated the highest AUC (0.86) of the prediction models using 99 features, while the random forest algorithm generated a model with 0.82 AUC. These models showed high accuracy and k-fold values (>0.85). The prediction model containing 15 features had the highest AUC of the ROC curve in XGBoost and random forest algorithms. PRS was one of 15 features. The final prediction models for insulin resistance were generated with the same nine features in the XGBoost (AUC = 0.86), random forest (AUC = 0.84), and artificial neural network (AUC = 0.86) algorithms. The model included the fasting serum glucose, ALT, total bilirubin, HDL concentrations, waist circumference, body fat, pulse, season to enroll in the study, and gender. Conclusion: The liver function, regular pulse checking, and seasonal variation in addition to metabolic syndrome components should be considered to predict insulin resistance in Koreans aged over 40 years.

AK098656: a new biomarker of coronary stenosis severity in hypertensive and coronary heart disease patients

Background AK098656 may be an adverse factor for coronary heart disease (CHD), especially in patients with hypertension. This study aimed to analyze the effect of AK098656 on CHD and CHD with various complications. Methods A total of 117 CHD patients and 27 healthy control subjects were enrolled in the study. Plasma AK098656 expression was determined using the quantitative real-time polymerase chain reaction. Student’s t-test was used to compare AK098656 expression levels in different groups. Receiver operating characteristic (ROC) curve and area under the curve (AUC) were used to quantify the discrimination ability between CHD patients and health controls and between CHD and CHD + complications patients. The relationship between AK098656 and coronary stenosis was analyzed using Spearman’s correlation. Results AK098656 expression was remarkably higher in CHD patients than in healthy controls (P = 0.03). The ROC curve revealed an effective predictive AK098656 expression value for CHD risk, with an AUC of 0.656 (95% CI 0.501–0.809). Moreover, AK098656 expression was increased in CHD + complications patients compared to CHD patients alone (P = 0.005), especially in patients with hypertension (CHD + hHTN, P = 0.030). The ROC curve revealed a predictive AK098656 prognostic value for discriminating between CHD and CHD + hHTN patients, with an AUC of 0.666 (95% CI 0.528–0.805). There was no significant difference in AK098656 expression in CHD patients with diabetes mellitus compared to CHD patients alone. In addition, AK098656 expression in CHD patients was positively correlated with stenosis severity (R = 0.261, P = 0.006). Conclusion AK098656 expression was significantly increased in patients with CHD, especially those with hypertension, and its expression level was positively correlated with the degree of coronary stenosis. This implied that AK098656 may be a risk factor for CHD and can potentially be applied in clinical diagnosis or provide a novel target for treatment.

Predictors of perceived success in quitting smoking by vaping: A machine learning approach

Prior research has suggested that a set of unique characteristics may be associated with adult cigarette smokers who are able to quit smoking using e-cigarettes (vaping). In this cross-sectional study, we aimed to identify and rank the importance of these characteristics using machine learning. During July and August 2019, an online survey was administered to a convenience sample of 889 adult smokers (age ≥ 20) in Ontario, Canada who tried vaping to quit smoking in the past 12 months. Fifty-one person-level characteristics, including a Vaping Experiences Score, were assessed in a gradient boosting machine model to classify the status of perceived success in vaping-assisted smoking cessation. This model was trained using cross-validation and tested using the receiver operating characteristic (ROC) curve. The top five most important predictors were identified using a score between 0% and 100% that represented the relative importance of each variable in model training. About 20% of participants (N = 174, 19.6%) reported success in vaping-assisted smoking cessation. The model achieved relatively high performance with an area under the ROC curve of 0.865 and classification accuracy of 0.831 (95% CI [confidence interval] 0.780 to 0.874). The top five most important predictors of perceived success in vaping-assisted smoking cessation were more positive experiences measured by the Vaping Experiences Score (100%), less previously failed quit attempts by vaping (39.0%), younger age (21.9%), having vaped 100 times (16.8%), and vaping shortly after waking up (15.8%). Our findings provide strong statistical evidence that shows better vaping experiences are associated with greater perceived success in smoking cessation by vaping. Furthermore, our study confirmed the strength of machine learning techniques in vaping-related outcomes research based on observational data.

Diagnosis and Localization of Prostate Cancer via Automated Multiparametric MRI Equipped with Artificial Intelligence

Prostate MRI scans for pre-biopsied patients are important. However, fewer radiologists are available for MRI diagnoses, which requires multi-sequential interpretations of multi-slice images. To reduce such a burden, artificial intelligence (AI)-based, computer-aided diagnosis is expected to be a critical technology. We present an AI-based method for pinpointing prostate cancer location and determining tumor morphology using multiparametric MRI. The study enrolled 15 patients who underwent radical prostatectomy between April 2008 and August 2017 at our institution. We labeled the cancer area on the peripheral zone on MR images, comparing MRI with histopathological mapping of radical prostatectomy specimens. Likelihood maps were drawn, and tumors were divided into morphologically distinct regions using the superpixel method. Likelihood maps consisted of pixels, which utilize the cancer likelihood value computed from the T2-weighted, apparent diffusion coefficient, and diffusion-weighted MRI-based texture features. Cancer location was determined based on the likelihood maps. We evaluated the diagnostic performance by the area under the receiver operating characteristic (ROC) curve according to the Chi-square test. The area under the ROC curve was 0.985. Sensitivity and specificity for our approach were 0.875 and 0.961 (p < 0.01), respectively. Our AI-based procedures were successfully applied to automated prostate cancer localization and shape estimation using multiparametric MRI.

High serum levels of CML are associated with poor coronary collateralization in type 2 diabetic patients with chronic total occlusion of coronary artery

Background: The formation of advanced glycation end-products (AGEs) is a crucial risk factor for the pathogenesis of cardiovascular diseases. We investigated whether N-e-carboxy-methyl-lysine (CML), a major form of AGEs in vivo, was associated with poor coronary collateral vessel (CCV) formation in patients with type 2 diabetes mellitus (T2DM) and chronic total occlusion (CTO) of coronary artery.Methods: This study consisted of 242 T2DM patients with angiographically documented CTO. Blood samples were obtained and demographic/clinical characteristics were documented. The collateralization of these patients was defined according to Rentrop score. Receiver operating characteristic (ROC) curve and multivariable regression analysis were performed.Results: 242 patients were categorized into poor CCV group (Rentrop score 0 and 1)(n = 107) and good CCV group (Rentrop score 2 and 3)(n = 135). Serum CML levels were significantly higher in poor CCV group (110.0 ± 83.35 ng/ml) than in good CCV group (62.95 ± 58.83 ng/ml, P<0.001). Moreover, these CML levels were also significantly different across the Rentrop score 0, 1, 2 and 3 groups (P <0.001). In ROC curve for ascertaining poor CCV, AUCs were 0.70 (95% CI 0.64-0.77) for CML. In multivariable logistic regression, CML levels (P<0.001) remained independent determinants of poor CCV after adjustment of traditional risk factors. Conclusions: This study suggests that higher CML levels are associated to poor CCV in T2DM patients with CTO. Inhibition of AGEs including CML is a strategy in antagonizing poor CCV in diabetic patients.

SIRT7 is a prognostic biomarker in kidney renal clear cell carcinoma that is correlated with immune cell infiltration

Background: SIRT7 has been shown to be expressed in many cancer types, including kidney renal clear cell carcinoma (KIRC), but its functional role in this oncogenic context remains to be firmly defined. This study was designed to explore correlations between SIRT7 and KIRC characteristics using the TCGA database. Methods: Relationships between SIRT7 expression and KIRC patient clinicopathological characteristics were assessed through Kruskal-Wallis tests, Wilcoxon signed-rank tests, and logistic regression analyses. Area under the ROC curve (AUC) values were used to assess the prognostic value of SIRT7 as a means of classifying KIRC patients. The functional role of SIRT7 in this cancer type was assessed through GO/KEGG enrichment analyses and immune cell infiltration analyses. Results: In KIRC patients, higher levels of SIRT7 expression were associated with Race, M stage, T stage (all P < 0.05). SIRT7 offered significant diagnostic value in ROC curve analyses (AUC = 0.912), and elevated SIRT7 levels were linked to worse patient overall survival (OS; P < 0.001). The expression of SIRT7 was independently related with KIRC patient OS (HR: 1.827; 95%CI: 1.346-2.481; P<0.001). In GO/KEGG analyses, SIRT7 was found to be associated with ubiquitin-mediated proteolysis and nucleotide excision repair. Higher SIRT7 expression was related to the enhanced infiltration of certain immune cells.Conclusions: Increased SIRT7 expression was associated with a worse KIRC patient prognosis, and immune infiltrates, suggesting it may offer value as a prognostic biomarker for this cancer type.

Development of a Computational Tool for the Estimation of Alveolar Bone Loss in Oral Radiographic Images

The present study evaluated a newly developed computational tool (CT) to assess the alveolar bone space and the alveolar crest angle and compares it to dentist assessment (GT). The novel tool consisted of a set of processes initiated with image enhancement, points localization, and angle and area calculations. In total, we analyzed 148 sites in 39 radiographic images, and among these, 42 sites were selected and divided into two groups of non-periodontitis and periodontitis. The alveolar space area (ASA) and alveolar crest angle (ACA) were estimated. The agreement between the computer software and the ground truth was analyzed using the Bland–Altman plot. The sensitivity and specificity of the computer tool were measured using the ROC curve. The Bland–Altman plot showed an agreement between the ground truth and the computational tool in all of the parameters assessed. The ROC curve showed 100% sensitivity and 100% specificity for 12.67 mm of the alveolar space area. The maximum percentage of sensitivity and specificity were 80.95% for 13.63 degrees of the alveolar crest angle. Computer tool assessment provides accurate disease severity and treatment monitoring for evaluating the alveolar space area (ASA) and the alveolar crest angle (ACA).

Predictive Role of Admission Venous Lactate Level in Patients with Upper Gastrointestinal Bleeding: A Prospective Observational Study

Upper gastrointestinal bleeding (UGIB) is one of the most common emergencies. Risk stratification is essential in patients with this potentially life-threatening condition. The aim of this prospective study was to evaluate the usefulness of the admission venous lactate level in predicting clinical outcomes in patients with UGIB. All consecutive adult patients hospitalized due to UGIB were included in the study. The clinical data included the demographic characteristics of the observed population, etiology of UGIB, need for surgical intervention and intensive care, bleeding recurrence, and mortality rates. Venous lactate was measured in all patients on admission. Logistic regression analyses were used to calculate the odds ratios (OR) of lactate levels for all outcomes. The receiver operating characteristic (ROC) curve was used to determine the accuracy of lactate levels in measuring clinical outcomes, while Youden index was used to calculate the best cut-off points. A total of 221 patients were included in the study (151M; 70F). There were 24 cases of UGIB recurrence (10.8%), 19 patients (8.6%) required surgery, and 37 individuals (16.7%) required intensive care. Mortality rate was 11.3% (25 cases). The logistic regression analysis showed statistically significant association between admission venous lactate and all clinical outcomes: mortality (OR = 1.39, 95%CI: 1.22–1.58, p < 0.001), recurrence of bleeding (OR = 1.16, 95%CI: 1.06; 1.28, p = 0.002), surgical intervention (OR = 1.17, 95%CI: 1.06–1.3, p = 0.002) and intensive care (OR = 1.33, 95%CI: 1.19–1.5, p < 0.001). The ROC curve analysis showed a high predictive value of lactate levels for all outcomes, especially mortality: cut-off point 4.3 (AUC = 0.82, 95%CI: 0.72–0.92, p < 0.001) and intensive care: cut-off point 4.2 (AUC = 0.76, 95%CI: 0.66–0.85, p < 0.001). Admission venous lactate level may be a useful predictive factor of clinical outcomes in patients with UGIB.

NMB as a Novel Prognostic Biomarker in Colorectal Cancer Correlating with Immune Infiltrates

Background:Neuromedin B(NMB) is associated with the occurrence and development of a variety of cancers, However, the role of NMB in colorectal cancer is lacking in further studies.Methods:Transcriptome data and clinical data of CRC were downloaded and analyzed from the TCGA database and GEO database to study the differential expression of NMB. We analyzed the relationship between NMB expression and survival in patients with colorectal cancer using 8 public datasets from the Gene Expression Integration (GEO) database and the TCGA database. Meta-analysis was performed on the analysis results of TCGA and GEO data to determine the role of NMB in CRC. The receiver operating characteristic (ROC) curve was used to evaluate the accuracy of NMB in predicting survival rate in CRC patients. Wilcox. Test and Kruskal. Tests were used to study the relationship between clinicopathological features and the expression of NMB. Cox regression analysis was used to analyze the effect of NMB expression on survival. Gene collection enrichment analysis (GSEA) was performed using the TCGA database to screen the signaling pathway regulated by NMB. The Linkedomics platform was used to identify NMB co-expressed genes and explore the potential mechanisms of NMB mediation. Tumor Immune Estimation Resource (TIMER) site database was used to analyze the relationship between NMB expression level and immune infiltration. Related genes were identified by co-expression analysis, and four genes (NDUFB10, SERF2, DPP7, and NAPRT) were screened out as a prognostic signature. The relationship between risk score and OS were studied to explore the predictive value of risk score for CRC. Nomogram was constructed to predict 1 - and 3-year survival in colorectal cancer patients.Results:NMB was highly expressed in colorectal cancer, suggested a poor prognosis. The ROC curve proved that NMB had a high accuracy in predicting the survival rate of CRC patients. Multivariate regression analysis demonstrated that NMB was an independent predictor of survival in patients with CRC.GSEA identified the pathways involved in NMB regulation, including the P53 Signaling pathway, VEGF Signaling pathway, JAK-STAT Signaling pathway, MAPK Signaling pathway,mTOR Signaling pathway, TGF-BETA Signaling pathway, and WNT Signaling pathway, etc. Then,6512 co-expressed genes were identified through the Linkedomics Platform to investigate the potential mechanisms of NMB regulation, including Hepatocellular carcinoma cell cycle, EGF/EGFR Signaling Pathway, VEGFA-VEGFR2 Signaling Pathway, etc. We also conclude that NMB is correlated with T cells CD8, T cells CD4 memory resting, Macrophages M0. Different mutational forms of NMB were associated with the immune infiltration of 6 leukocytes. We determined the relationship between NMB and immune marker sets in colorectal cancer, such as CCR7, CD3E, CTLA4, HAVCR2, HLA-DPB1. The predictive ability of the risk score was significantly better than that of T, N, and M stages. A new nomogram for predicting the 1-year and 3-year OS of CRC patients was constructed, showing good reliability and accuracy for improved treatment decisions. In addition, NMB may contribute to drug resistance in CRC.Conclusion:NMB is highly expressed in CRC and provides a potential biomarker for the diagnosis and prognosis of CRC.