Artificial intelligence-assisted system for the assessment of Forrest classification of peptic ulcer bleeding: a multicenter diagnostic study.

BACKGROUND: Inaccurate Forrest classification may significantly affect clinical outcomes, especially in high risk patients. Therefore, this study aimed to develop a real-time deep convolutional neural network (DCNN) system to assess the Forrest classification of peptic ulcer bleeding (PUB). METHODS: A training dataset (3868 endoscopic images) and an internal validation dataset (834 images) were retrospectively collected from the 900th Hospital, Fuzhou, China. In addition, 521 images collected from four other hospitals were used for external validation. Finally, 46 endoscopic videos were prospectively collected to assess the real-time diagnostic performance of the DCNN system, whose diagnostic performance was also prospectively compared with that of three senior and three junior endoscopists. RESULTS: The DCNN system had a satisfactory diagnostic performance in the assessment of Forrest classification, with an accuracy of 91.2% (95%CI 89.5%-92.6%) and a macro-average area under the receiver operating characteristic curve of 0.80 in the validation dataset. Moreover, the DCNN system could judge suspicious regions automatically using Forrest classification in real-time videos, with an accuracy of 92.0% (95%CI 80.8%-97.8%). The DCNN system showed more accurate and stable diagnostic performance than endoscopists in the prospective clinical comparison test. This system helped to slightly improve the diagnostic performance of senior endoscopists and considerably enhance that of junior endoscopists. CONCLUSION: The DCNN system for the assessment of the Forrest classification of PUB showed satisfactory diagnostic performance, which was slightly superior to that of senior endoscopists. It could therefore effectively assist junior endoscopists in making such diagnoses during gastroscopy.

Identification of prognostic risk score of disulfidptosis-related genes and molecular subtypes in glioma.

Background: Programmed cell death is closely related to glioma. As a novel kind of cell death, the mechanism of disulfidptosis in glioma remains unclear. Therefore, it is of great importance to study the role of disulfidptosis-related genes (DRGs) in glioma. Methods: We first investigated the genetic and transcriptional alterations of 15 DRGs. Two consensus cluster analyses were used to evaluate the association between DRGs and glioma subtypes. In addition, we constructed prognostic DRG risk scores to predict overall survival (OS) in glioma patients. Furthermore, we developed a nomogram to enhance the clinical utility of the DRG risk score. Finally, the expression levels of DRGs were verified by immunohistochemistry (IHC) staining. Results: Most DRGs (14/15) were dysregulated in gliomas. The 15 DRGs were rarely mutated in gliomas, and only 50 of 987 samples (5.07 %) showed gene mutations. However, most of them had copy number variation (CNV) deletions or amplifications. Two distinct molecular subtypes were identified by cluster analysis, and DRG alterations were found to be related to the clinical characteristics, prognosis, and tumor immune microenvironment (TIME). The DRG risk score model based on 12 genes was developed and showed good performance in predicting OS. The nomogram confirmed that the risk score had a particularly strong influence on the prognosis of glioma. Furthermore, we discovered that low DRG scores, low tumor mutation burden, and immunosuppression were features of patients with better prognoses. Conclusion: The DRG risk model can be used for the evaluation of clinical characteristics, prognosis prediction, and TIME estimation of glioma patients. These DRGs may be potential therapeutic targets in glioma.

The prognostic and immunological role of MCM3 in pan-cancer and validation of prognosis in a clinical lower-grade glioma cohort.

Background: Previous studies have shown that MCM3 plays a key role in initiating DNA replication. However, the mechanism of MCM3 function in most cancers is still unknown. The aim of our study was to explore the expression, prognostic role, and immunological characteristics of MCM3 across cancers. Methods: We explored the expression pattern of MCM3 across cancers. We subsequently explored the prognostic value of MCM3 expression by using univariate Cox regression analysis. Spearman correlation analysis was performed to determine the correlations between MCM3 and immune-related characteristics, mismatching repair (MMR) signatures, RNA modulator genes, cancer stemness, programmed cell death (PCD) gene expression, tumour mutation burden (TMB), microsatellite instability (MSI), and neoantigen levels. The role of MCM3 in predicting the response to immune checkpoint blockade (ICB) therapy was further evaluated in four immunotherapy cohorts. Single-cell data from CancerSEA were analysed to assess the biological functions associated with MCM3 in 14 cancers. The clinical correlation and independent prognostic significance of MCM3 were further analysed in the TCGA and CGGA lower-grade glioma (LGG) cohorts, and a prognostic nomogram was constructed. Immunohistochemistry in a clinical cohort was utilized to validate the prognostic utility of MCM3 expression in LGG. Results: MCM3 expression was upregulated in most tumours and strongly associated with patient outcomes in many cancers. Correlation analyses demonstrated that MCM3 expression was closely linked to immune cell infiltration, immune checkpoints, MMR genes, RNA modulator genes, cancer stemness, PCD genes and the TMB in most tumours. There was an obvious difference in outcomes between patients with high MCM3 expression and those with low MCM3 expression in the 4 ICB treatment cohorts. Single-cell analysis indicated that MCM3 was mainly linked to the cell cycle, DNA damage and DNA repair. The expression of MCM3 was associated with the clinical features of LGG patients and was an independent prognostic indicator. Finally, the prognostic significance of MCM3 in LGG was validated in a clinical cohort. Conclusion: Our study suggested that MCM3 can be used as a potential prognostic marker for cancers and may be associated with tumour immunity. In addition, MCM3 is a promising predictor of immunotherapy responses.

CNPY4 is a potential promising prognostic-related biomarker and correlated with immune infiltrates in gliomas.

Glioblastomas are classified into primary and secondary; primary glioblastomas develop rapidly and aggressively, whereas secondary glioblastomas are more common in grade II and III gliomas. Here, we aimed to demonstrate the role of the CNPY4 gene as a potential biomarker in immune infiltration in gliomas. Based on gene expression profile interaction analysis (GEPIA), we studied the survival model of CNPY4 and evaluated its effect on patients with glioma. The glioma dataset was downloaded from The Cancer Genome Atlas (TCGA) database. Logistic regression was used to analyze the relationship between clinical data and CNPY4 expression. Univariate and multivariate Cox proportional-hazards models were used to compare clinical features and patient survival. The relationship between CNPY4 and immune infiltration in glioma was studied using GEPIA and CIBERSORT online tools. TCGA data were analyzed using gene set enrichment analysis (GSEA). Finally, TIMER was used to analyze the expression and immune infiltration of CNPY4 in glioma to study the cumulative survival rate. Univariate logistic regression analysis showed that increased CNPY4 expression was associated with tumor age, grade, IDH status, and 1p/19q codeletion. Multivariate analysis showed that that downregulation of CNPY4 expression was an independent and satisfactory prognostic factor. CNPY4 expression was correlated with the infiltration level of dendritic cells in glioblastoma. In contrast, in low-grade gliomas, the infiltration level of B cells, dendritic cells, macrophages, neutrophils, and CD4+ T cells was significantly correlated with CNPY4 expression. The GSEA results showed that CNPY4 played an immunoregulatory role in immune-related phenotypic pathways between lymphoid and nonlymphoid cells. The intestinal immune networks for IgA production, rabbit thyroid disease, primary immunodeficiencies, and cancer immunotherapy were enriched by PD-1 blockade. High CNPY4 expression is a biomarker of glioma prognosis and is associated with the immune invasion of glioma.

Establishment and Validation of a Ferroptosis-Related lncRNA Signature for Prognosis Prediction in Lower-Grade Glioma.

Background: The prognosis of lower-grade glioma (LGG) is highly variable, and more accurate predictors are still needed. The aim of our study was to explore the prognostic value of ferroptosis-related long non-coding RNAs (lncRNAs) in LGG and to develop a novel risk signature for predicting survival with LGG. Methods: We first integrated multiple datasets to screen for prognostic ferroptosis-related lncRNAs in LGG. A least absolute shrinkage and selection operator (LASSO) analysis was then utilized to develop a risk signature for prognostic prediction. Based on the results of multivariate Cox analysis, a prognostic nomogram model for LGG was constructed. Finally, functional enrichment analysis, single-sample gene set enrichment analysis (ssGSEA), immunity, and m6A correlation analyses were conducted to explore the possible mechanisms by which these ferroptosis-related lncRNAs affect survival with LGG. Results: A total of 11 ferroptosis-related lncRNAs related to the prognosis of LGG were identified. Based on prognostic lncRNAs, a risk signature consisting of 8 lncRNAs was constructed and demonstrated good predictive performance in both the training and validation cohorts. Correlation analysis suggested that the risk signature was closely linked to clinical features. The nomogram model we constructed by combining the risk signature and clinical parameters proved to be more accurate in predicting the prognosis of LGG. In addition, there were differences in the levels of immune cell infiltration, immune-related functions, immune checkpoints, and m6A-related gene expression between the high- and low-risk groups. Conclusion: In summary, our ferroptosis-related lncRNA signature exhibits good performance in predicting the prognosis of LGG. This study may provide useful insight into the treatment of LGG.

A novel risk signature with 6 RNA binding proteins for prognosis prediction in patients with glioblastoma.

ABSTRACT: Recent studies suggested that RNA binding proteins (RBPs) were related to the tumorigenesis and progression of glioma. This study was conducted to identify prognostic RBPs of glioblastoma (GBM) and construct an RBP signature to predict the prognosis of GBM.Univariate Cox regression analysis was carried out to identify the RBPs associated with overall survival of GBM in the The Cancer Genome Atlas (TCGA), GSE16011, and Repository for Molecular Brain Neoplasia data (Rembrandt) datasets, respectively. Overlapping RBPs from the TCGA, GSE16011, and Rembrandt datasets were selected. The biological role of prognostic RBPs was assessed by Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and protein-protein interaction analyses. Least absolute shrinkage and selection operator regression analysis and multivariate Cox regression analysis were used to construct an RBP-related risk signature. The prognostic value of RBP signature was measured by Kaplan-Meier method and time-dependent receiver operating characteristic curve. A nomogram based on independent prognostic factors was established to predict survival for GBM. The CGGA cohort was used as the validation cohort for external validation.This study identified 27 RBPs associated with the prognosis of GBM and constructed a 6-RPBs signature. Kaplan-Meier curves suggested that high-risk score was associated with a poor prognosis. Area under the curve of 1-, 3-, and 5-year overall survival was 0.618, 0.728, and 0.833 for TCGA cohort, 0.655, 0.909, and 0.911 for GSE16011 cohort, and 0.665, 0.792, and 0.781 for Rembrandt cohort, respectively. A nomogram with 4 parameters (age, chemotherapy, O6-methylguanine-DNA methyltransferase promoter status, and risk score) was constructed. The calibration curve showed that the nomogram prediction was in good agreement with the actual observation.The 6-RBPs signature could effectively predict the prognosis of GBM, and our findings supplemented the prognostic index of GBM to a certain extent.

A nomogram combining inflammatory markers and clinical factors predicts survival in patients with diffuse glioma.

ABSTRACT: In this study, we aimed to investigate the prognostic value of neutrophil/lymphocyte ratio (NLR), monocyte/lymphocyte ratio (MLR), and platelet/lymphocyte ratio (PLR) in diffuse glioma, and to establish a prognostic nomogram accordingly.The hematologic and clinicopathological data of 162 patients with primary diffuse glioma who received surgical treatment from January 2012 to December 2018 were retrospectively analyzed. Receiver operator characteristic (ROC) curve was carried out to determine the optimal cut-off values for NLR, MLR, PLR, age, and Ki-67 index, respectively. Kaplan-Meier method was used to investigate the correlation between inflammatory indicators and prognosis of glioma patients. Univariate and multivariate Cox regression were performed to evaluate the independent prognostic value of each parameter in glioma. Then, a nomogram was developed to predict 1-, 3-, and 5-year postoperative survival in diffuse glioma patients based on independent prognostic factors. Subsequent time-dependent ROC curve, calibration curve, decision curve analysis (DCA), and concordance index (C-index) were performed to assess the predictive performance of the nomogram.The Kaplan-Meier curve indicated that patients with high levels of NLR, MLR, and PLR had a poor prognosis. In addition, we found that NLR level was associated with World Health Organization (WHO) grade and IDH status of glioma. The multivariate Cox analysis indicated that resection extent, WHO grade, and NLR level were independent prognostic factors, and we established a nomogram that included these three parameters. The evaluation of the nomogram indicated that the nomogram had a good predictive performance, and the addition of NLR could improve the accuracy.NLR, MLR, and PLR were prognostic factors of diffuse glioma. In addition, the nomogram including NLR was reliable for predicting survival of diffuse glioma patients.

Prognostic lncRNAs, miRNAs, and mRNAs Form a Competing Endogenous RNA Network in Colon Cancer.

Purpose: To develop a multi-RNA-based model to provide survival risk prediction for colon cancer by constructing a competing endogenous RNAs (ceRNAs) network. Methods: The prognostic information and expression of the lncRNAs, miRNAs, and mRNAs in colon cancer specimens from The Cancer Genome Atlas (TCGA) were assessed. Constructing prognostic models used the differentially expressed RNAs. Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses and Gene Ontology were used to identify the functional role of the ceRNA network in the prognosis of colon cancer. Results: Five lncRNAs (AC007384.1, AC002511.1, AC012640.1, C17orf82, and AP001619.1), 8 miRNAs (hsa-mir-141, hsa-mir-150, hsa-mir-375, hsa-mir-96, hsa-mir-107, hsa-mir-106a, hsa-mir-200a, and hsa-mir-1271), and 5 mRNAs (BDNF, KLF4, SESN2, SMOC1, and TRIB3) were highly correlated with tumor status and tumor stage. Three prognostic models based on the 5 lncRNAs, 8 miRNAs, and 5 mRNAs were constructed. The prognostic ability was 0.850 for the lncRNA-based model, 0.811 for the miRNA-based model, and 0.770 for the mRNA-based model. Patients with high-risk scores revealed worse overall survival. The KEGG pathways were significantly enriched in the "neuroactive ligand-receptor interaction." Conclusion: This study identified several potential prognostic biomarkers to construct a multi-RNA-based prognostic model for colon cancer.

Measurement of serum antibodies against NY-ESO-1 by ELISA: A guide for the treatment of specific immunotherapy for patients with advanced colorectal cancer.

NY-ESO-1 has been identified as one of the most immunogenic antigens; thus, is a highly attractive target for cancer immunotherapy. The present study analyzed the expression of serum antibodies (Abs) against NY-ESO-1 in patients with advanced colorectal cancer (CRC), with the aim of guiding the treatment of NY-ESO-1-based specific-immunotherapy for these patients. Furthermore, the present study was the first to evaluate the kinetic expression of anti-NY-ESO-1 Abs and investigate the possible influencing factors. A total of 239 serum samples from 155 pathologically confirmed patients with advanced CRC (stages III and IV) were collected. The presence of spontaneous Abs against NY-ESO-1 was analyzed using an enzyme-linked immunosorbent assay (ELISA). The results demonstrated that 24.5% (38/155) of the investigated patients were positive for NY-ESO-1-specific Abs. No statistically significant correlations were identified between the expression of anti-NY-ESO-1 Abs and clinicopathological parameters, including age and gender, location, grading, local infiltration, lymph node status, metastatic status and K-ras mutation status (P>0.05). In 59 patients, the kinetic expression of anti-NY-ESO-1 Abs was analyzed, of which 14 patients were initially positive and 45 patients were initially negative. Notably, 16/59 (27.1%) patients changed their expression status during the study period, and the initially positive patients were more likely to change compared with the initially negative patients (85.7 vs. 8.8%; P<0.001). Therefore, monitoring serum Abs against NY-ESO-1 by ELISA is an easy and feasible method. The high expression rate of NY-ESO-1-specific Abs in CRC patients indicates that measuring the levels of serum Abs against NY-ESO-1 may guide the treatment of NY-ESO-1-based specific immunotherapy for patients with advanced CRC.