scholarly journals A Prognostic Model Based on RNA Binding Protein Predicts Clinical Outcomes in Hepatocellular Carcinoma Patients

2021 ◽  
Vol 10 ◽  
Author(s):  
Zhongsong Man ◽  
Yongqiang Chen ◽  
Lu Gao ◽  
Guowei Xei ◽  
Quanfu Li ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Prognostic Model ◽  
Rna Binding ◽  
Cancer Genome ◽  
Low Risk ◽  
Cox Regression Analysis ◽  
High Risk Patients ◽  
Model Based ◽  
Risk Patients

Dysregulation of RNA binding proteins (RBPs) is closely associated with tumor events. However, the function of RBPs in hepatocellular carcinoma (HCC) has not been fully elucidated. The RNA sequences and relevant clinical data of HCC were retrieved from the The Cancer Genome Atlas (TCGA) database to identify distinct RBPs. Subsequently, univariate and multivariate cox regression analysis was performed to evaluate the overall survival (OS)-associated RBPs. The expression levels of prognostic RBP genes and survival information were analyzed using a series of bioinformatics tool. A total of 365 samples with 1,542 RBPs were included in this study. One hundred and eighty-seven differently RBPs were screened, including 175 up-regulated and 12 down-regulated. The independent OS-associated RBPs of NHP2, UPF3B, and SMG5 were used to develop a prognostic model. Survival analysis showed that low-risk patients had a significantly longer OS and disease-free survival (DFS) when compared to high-risk patients (HR: 2.577, 95% CI: 1.793–3.704, P < 0.001 and HR: 1.599, 95% CI: 1.185–2.159, P = 0.001, respectively). The International Cancer Genome Consortium (ICGC) database was used to externally validate the model, and the OS of low-risk patients were found to be longer than that of high-risk patients (P < 0.001). The Nomograms of OS and DFS were plotted to help in clinical decision making. These results showed that the model was effective and may help in prognostic stratification of HCC patients. The prognostic prediction model based on RBPs provides new insights for HCC diagnosis and personalized treatment.

scholarly journals A Novel Epigenetic Machine Learning Model to Define Risk of Progression for Hepatocellular Carcinoma Patients

2021 ◽  
Vol 22 (3) ◽  
pp. 1075
Author(s):  
Luca Bedon ◽  
Michele Dal Bo ◽  
Monica Mossenta ◽  
Davide Busato ◽  
Giuseppe Toffoli ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Progression Free Survival ◽  
Low Risk ◽  
Functional Enrichment ◽  
Free Survival ◽  
High Risk Patients ◽  
Risk Of Progression ◽  
Risk Patients

Although extensive advancements have been made in treatment against hepatocellular carcinoma (HCC), the prognosis of HCC patients remains unsatisfied. It is now clearly established that extensive epigenetic changes act as a driver in human tumors. This study exploits HCC epigenetic deregulation to define a novel prognostic model for monitoring the progression of HCC. We analyzed the genome-wide DNA methylation profile of 374 primary tumor specimens using the Illumina 450 K array data from The Cancer Genome Atlas. We initially used a novel combination of Machine Learning algorithms (Recursive Features Selection, Boruta) to capture early tumor progression features. The subsets of probes obtained were used to train and validate Random Forest models to predict a Progression Free Survival greater or less than 6 months. The model based on 34 epigenetic probes showed the best performance, scoring 0.80 accuracy and 0.51 Matthews Correlation Coefficient on testset. Then, we generated and validated a progression signature based on 4 methylation probes capable of stratifying HCC patients at high and low risk of progression. Survival analysis showed that high risk patients are characterized by a poorer progression free survival compared to low risk patients. Moreover, decision curve analysis confirmed the strength of this predictive tool over conventional clinical parameters. Functional enrichment analysis highlighted that high risk patients differentiated themselves by the upregulation of proliferative pathways. Ultimately, we propose the oncogenic MCM2 gene as a methylation-driven gene of which the representative epigenetic markers could serve both as predictive and prognostic markers. Briefly, our work provides several potential HCC progression epigenetic biomarkers as well as a new signature that may enhance patients surveillance and advances in personalized treatment.

scholarly journals Hub gene identification and prognostic model establishment for patients with HBV-related hepatocellular carcinoma

2021 ◽  
Author(s):  
Lianmei Wang ◽  
Jing Meng ◽  
Shasha Qin ◽  
Aihua Liang
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Prognostic Model ◽  
Expression Profiles ◽  
Cyclin B1 ◽  
Mitotic Checkpoint ◽  
Cancer Genome ◽  
Hub Genes ◽  
Cox Regression Analysis ◽  
Model Based

Abstract Hepatocellular carcinoma (HCC) is associated with poor 5-year survival. Chronic infection with hepatitis B virus (HBV) contributes to ~50% of HCC cases. Identification of biomarkers is pivotal for the therapy of HBV-related HCC (HBV–HCC). We downloaded gene-expression profiles from Gene expression omnibus (GEO) datasets with HBV-HCC patients and the corresponding controls. Integration of these differentially expressed genes (DEGs) was achieved with the Robustrankaggreg (RRA) method. DEGs functional analyses and pathway analyses was performed using the Gene ontology (GO) database, and the Kyoto encyclopedia of genes and genomes (KEGG) database respectively. Cyclin-dependent kinase 1 (CDK1), Cyclin B1 (CCNB1), Forkhead box M1 (FOXM1), Aurora kinase A (AURKA), Cyclin B2 (CCNB2), Enhancer of zeste homolog 2 (EZH2), Cell division cycle 20 (CDC20), DNA topoisomerase II alpha (TOP2A), BUB1 mitotic checkpoint serine/threonine kinase B (BUB1B), and ZW10 interactor (ZWINT), were identified as the top-ten hub genes. The expression of hub-genes was verified in the liver cancer-riken, JP project from international cancer genome consortium (ICGC-LIRI-JP), the cancer genome atlas (TCGA) HCC cohort, and Human protein profiles dataset. A four-gene prognostic related model based on the expression of ZWINT, EZH2, FOXM1 and CDK1 were established through Cox regression analysis in ICGC-LIRI-JP project, and verified in TCGA-HCC cohort. Furthermore, a nomogram model based on pathology stage, gender and four-genes prognostic model was built to predict the prognosis for HBV–HCC patients. In conclusion, ZWINT, EZH2, FOXM1 and CDK1 play a pivotal role in HBV-HCC, and are potential therapeutic targets of HBV HCC.

scholarly journals Identification and Validation of a Prognostic Model Based on Three Autophagy-Related Genes in Hepatocellular Carcinoma

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Fanbo Qin ◽  
Junyong Zhang ◽  
Jianping Gong ◽  
Wenfeng Zhang
Keyword(s):  
Hepatocellular Carcinoma ◽  
Regression Analysis ◽  
Risk Score ◽  
Prognostic Value ◽  
Prognostic Model ◽  
Cox Regression ◽  
Cancer Genome ◽  
Clinical Parameters ◽  
Cox Regression Analysis ◽  
Model Based

Background. Accumulating studies have demonstrated that autophagy plays an important role in hepatocellular carcinoma (HCC). We aimed to construct a prognostic model based on autophagy-related genes (ARGs) to predict the survival of HCC patients. Methods. Differentially expressed ARGs were identified based on the expression data from The Cancer Genome Atlas and ARGs of the Human Autophagy Database. Univariate Cox regression analysis was used to identify the prognosis-related ARGs. Multivariate Cox regression analysis was performed to construct the prognostic model. Receiver operating characteristic (ROC), Kaplan-Meier curve, and multivariate Cox regression analyses were performed to test the prognostic value of the model. The prognostic value of the model was further confirmed by an independent data cohort obtained from the International Cancer Genome Consortium (ICGC) database. Results. A total of 34 prognosis-related ARGs were selected from 62 differentially expressed ARGs identified in HCC compared with noncancer tissues. After analysis, a novel prognostic model based on ARGs (PRKCD, BIRC5, and ATIC) was constructed. The risk score divided patients into high- or low-risk groups, which had significantly different survival rates. Multivariate Cox analysis indicated that the risk score was an independent risk factor for survival of HCC after adjusting for other conventional clinical parameters. ROC analysis showed that the predictive value of this model was better than that of other conventional clinical parameters. Moreover, the prognostic value of the model was further confirmed in an independent cohort from ICGC patients. Conclusion. The prognosis-related ARGs could provide new perspectives on HCC, and the model should be helpful for predicting the prognosis of HCC patients.

scholarly journals Identifying Eight Ferroptosis-Related Signatures for Prediction of Hepatocellular Carcinoma Overall Survival

2021 ◽  
Author(s):  
Dandong Luo ◽  
Qiang Tao ◽  
HuiJuan Jiang ◽  
JunLing Zhu ◽  
Ning Zhang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
High Risk ◽  
Cox Regression ◽  
Risk Group ◽  
Cancer Genome ◽  
Low Risk ◽  
Functional Enrichment ◽  
Cox Regression Analysis ◽  
Alternative Treatment Option

Abstract Background Hepatocellular carcinoma (HCC) is characterized by widespread epidemiology and extraordinary heterogeneity, with challenging prognosis prediction. Ferroptosis is a regulatory cell death driven by iron-dependent lipid peroxidation. The main aim of this study was to determine the predictive value of ferroptosis-related genes (FRGs) in HCC. Methods Herein, the data of HCC patients were downloaded from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) public databases. In the ICGC cohort, a multigenic signature was constructed using the LASSO Cox regression model. Next, patients in the TCGA cohort were used to verify the reliability of the model. Results Results showed that 30.07% of the differentially expressed genes (DEGs) in the ICGC cohort were associated with ferroptosis. Among them, 35 genes were identified as intersected genes associated with overall survival in both cohorts. Moreover, an 8-gene signature for prediction of HCC patients was constructed and the patients were divided it into low-risk and high-risk groups. The results indicated that the overall survival (OS) of patients in the high-risk group was lower than OS of patients in the low-risk group (P < 0.001 in both cohorts). Multivariate Cox regression analysis indicated that the risk score was an independent predictor of OS (HR > 1, P < 0.001). Receiver operating curves (ROC) demonstrated the predictive power of the signature. Furthermore, functional enrichment analysis revealed the existence of significantly correlated immune-related pathways, and their immune states were different between groups. Conclusions In summary, the genetic signature described in this study was associated with ferroptosis and it can be used to predict the prognosis of HCC. Therefore, targeted treatment of ferroptosis may be an alternative treatment option for HCC.

scholarly journals A 5-Genomic Mutation Signature Can Predict the Survival for Patients With NSCLC Receiving Atezolizumab

2021 ◽  
Vol 12 ◽  
Author(s):  
Jiamao Lin ◽  
Xiaohui Wang ◽  
Chenyue Zhang ◽  
Shuai Bu ◽  
Chenglong Zhao ◽  
...  
Keyword(s):  
High Risk ◽  
Gene Mutation ◽  
Training Group ◽  
Low Risk ◽  
Survival Prediction ◽  
Cox Regression Analysis ◽  
High Risk Patients ◽  
Risk Patients ◽  
Genomic Mutation ◽  
Testing Group

BackgroundAt present, there is a lack of studies focusing on the survival prediction of patients with non-small cell lung cancer (NSCLC) receiving atezolizumab in light of gene mutation characteristic.MethodsPatients with NSCLC receiving atezolizumab from the OAK study were defined as the training group. LASSO Cox regressions were applied to establish the gene mutation signature model to predict the overall survival (OS) rate of the training group. NSCLC patients receiving atezolizumab from the POPLAR study were defined as the testing group to validate the gene mutation signature model. In addition, we compared the OS rate between patients receiving atezolizumab and docetaxel classified according to their risk score based on our gene mutation signature model.ResultsWe successfully established a 5-genomic mutation signature that included CREBBP, KEAP1, RAF1, STK11 and TP53 mutations. We found it was superior to the blood tumor mutation burden (bTMB) score and programmed death ligand 1 (PDL1) expression in the prediction of the OS rate for patients receiving atezolizumab. High-risk patients receiving atezolizumab had a worse OS rate compared with low-risk patients in the training (P = 0.0004) and testing (P = 0.0001) groups. In addition, low-risk patients using atezolizumab had a better OS rate compared with those in use of docetaxel for the training (P &lt;0.0001) and testing groups (P = 0.0095). High-risk patients of the training group (P = 0.0265) using atezolizumab had a better OS rate compared with those using docetaxel. However, the OS difference between atezolizumab and docetaxel was not found in high-risk patients from the testing group (P = 0.6403). Multivariate Cox regression analysis showed that the risk model in light of 5-genomic mutation signature was an independent prognostic factor on OS for patients receiving atezolizumab (P &lt;0.0001). In addition, significant OS benefit could only be found in low-risk patients receiving atezolizumab compared with docetaxel (P &lt;0.0001).ConclusionsThe 5-genomic mutation signature could predict OS benefit for patients with NSCLC receiving atezolizumab. Therefore, the establishment of the 5-genomic mutation panel will guide clinicians to identify optimal patients who could benefit from atezolizumab treatment.

scholarly journals A novel RNA binding protein-associated prognostic model to predict overall survival in hepatocellular carcinoma patients

2020 ◽  
Author(s):  
Ye Liu ◽  
Zhixiang Qin ◽  
Hai Yang ◽  
Yang Gu ◽  
Kun Li
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
High Risk ◽  
Prognostic Model ◽  
Rna Binding ◽  
Cox Regression ◽  
Risk Group ◽  
Differentially Expressed ◽  
Data Set ◽  
Cox Regression Analysis

Abstract Background Hepatocellular carcinoma (HCC) represents one of the deadliest malignancies worldwide. Despite significant advances in diagnosis and treatment, the mortality rate from HCC persists at a substantial level. This research strives to establish a prognostic model based on the RNA binding proteins (RBPs) that can predict HCC patients’ OS. Methods There was an RNA-seq data set derived from the Cancer Genome Atlas (TCGA) databank which was included in our research as well as a Microarray data set (GSE14520). The differentially expressed RBPs between HCC and normal tissues were investigated in TCGA dataset. Subsequently, the TCGA data set was randomly split into a training and a testing cohort. The prognostic model of the training cohort was developed by applying univariate Cox regression and lasso Cox regression analyses and multivariate Cox regression analysis. In order to evaluate the prognostic value of the model, a comprehensive survival assessment was conducted. Results A total of 133 differentially expressed RBPs were identified. Five RBPs (RPL10L, EZH2, PPARGC1A, ZNF239, IFIT1) were used to construct the model. The model accurately predicted the prognosis of liver cancer patients in both the TCGA cohort and the GSE14520 validation cohort. HCC patients could be assigned into a high-risk group and a low-risk group by this model, and the overall survival of these two groups was significantly different. Furthermore, the risk scores obtained by our model were highly correlated with immune cell infiltration. . Conclusions Five RBPs-related prognostic models were constructed and validated to predict OS reliably in HCC individuals. It helps to identify patients at high risk of mortality with the risk prediction score, which optimizes personalized therapeutic decision-making.

scholarly journals Accuracy of upper endoscopies with random biopsies to identify patients with gastric premalignant lesions who can safely be exempt from surveillance

2021 ◽  
Vol 24 (3) ◽  
pp. 680-690
Author(s):  
Michiel C. Mommersteeg ◽  
Stella A. V. Nieuwenburg ◽  
Wouter J. den Hollander ◽  
Lisanne Holster ◽  
Caroline M. den Hoed ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
High Risk ◽  
Low Risk ◽  
Premalignant Lesions ◽  
High Risk Patients ◽  
European Guidelines ◽  
Progression Of Disease ◽  
Risk Patients ◽  
Progression Risk

Abstract Introduction Guidelines recommend endoscopy with biopsies to stratify patients with gastric premalignant lesions (GPL) to high and low progression risk. High-risk patients are recommended to undergo surveillance. We aimed to assess the accuracy of guideline recommendations to identify low-risk patients, who can safely be discharged from surveillance. Methods This study includes patients with GPL. Patients underwent at least two endoscopies with an interval of 1–6 years. Patients were defined ‘low risk’ if they fulfilled requirements for discharge, and ‘high risk’ if they fulfilled requirements for surveillance, according to European guidelines (MAPS-2012, updated MAPS-2019, BSG). Patients defined ‘low risk’ with progression of disease during follow-up (FU) were considered ‘misclassified’ as low risk. Results 334 patients (median age 60 years IQR11; 48.7% male) were included and followed for a median of 48 months. At baseline, 181/334 (54%) patients were defined low risk. Of these, 32.6% were ‘misclassified’, showing progression of disease during FU. If MAPS-2019 were followed, 169/334 (51%) patients were defined low risk, of which 32.5% were ‘misclassified’. If BSG were followed, 174/334 (51%) patients were defined low risk, of which 32.2% were ‘misclassified’. Seven patients developed gastric cancer (GC) or dysplasia, four patients were ‘misclassified’ based on MAPS-2012 and three on MAPS-2019 and BSG. By performing one additional endoscopy 72.9% (95% CI 62.4–83.3) of high-risk patients and all patients who developed GC or dysplasia were identified. Conclusion One-third of patients that would have been discharged from GC surveillance, appeared to be ‘misclassified’ as low risk. One additional endoscopy will reduce this risk by 70%.

scholarly journals A prognostic model based on seven immune-related genes predicts the overall survival of patients with hepatocellular carcinoma

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Qian Yan ◽  
Wenjiang Zheng ◽  
Boqing Wang ◽  
Baoqian Ye ◽  
Huiyan Luo ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
High Risk ◽  
Risk Score ◽  
Immune Cells ◽  
Prognostic Model ◽  
Risk Groups ◽  
Low Risk ◽  
Prognostic Performance ◽  
Immune Related Genes

Abstract Background Hepatocellular carcinoma (HCC) is a disease with a high incidence and a poor prognosis. Growing amounts of evidence have shown that the immune system plays a critical role in the biological processes of HCC such as progression, recurrence, and metastasis, and some have discussed using it as a weapon against a variety of cancers. However, the impact of immune-related genes (IRGs) on the prognosis of HCC remains unclear. Methods Based on The Cancer Gene Atlas (TCGA) and Immunology Database and Analysis Portal (ImmPort) datasets, we integrated the ribonucleic acid (RNA) sequencing profiles of 424 HCC patients with IRGs to calculate immune-related differentially expressed genes (DEGs). Survival analysis was used to establish a prognostic model of survival- and immune-related DEGs. Based on genomic and clinicopathological data, we constructed a nomogram to predict the prognosis of HCC patients. Gene set enrichment analysis further clarified the signalling pathways of the high-risk and low-risk groups constructed based on the IRGs in HCC. Next, we evaluated the correlation between the risk score and the infiltration of immune cells, and finally, we validated the prognostic performance of this model in the GSE14520 dataset. Results A total of 100 immune-related DEGs were significantly associated with the clinical outcomes of patients with HCC. We performed univariate and multivariate least absolute shrinkage and selection operator (Lasso) regression analyses on these genes to construct a prognostic model of seven IRGs (Fatty Acid Binding Protein 6 (FABP6), Microtubule-Associated Protein Tau (MAPT), Baculoviral IAP Repeat Containing 5 (BIRC5), Plexin-A1 (PLXNA1), Secreted Phosphoprotein 1 (SPP1), Stanniocalcin 2 (STC2) and Chondroitin Sulfate Proteoglycan 5 (CSPG5)), which showed better prognostic performance than the tumour/node/metastasis (TNM) staging system. Moreover, we constructed a regulatory network related to transcription factors (TFs) that further unravelled the regulatory mechanisms of these genes. According to the median value of the risk score, the entire TCGA cohort was divided into high-risk and low-risk groups, and the low-risk group had a better overall survival (OS) rate. To predict the OS rate of HCC, we established a gene- and clinical factor-related nomogram. The receiver operating characteristic (ROC) curve, concordance index (C-index) and calibration curve showed that this model had moderate accuracy. The correlation analysis between the risk score and the infiltration of six common types of immune cells showed that the model could reflect the state of the immune microenvironment in HCC tumours. Conclusion Our IRG prognostic model was shown to have value in the monitoring, treatment, and prognostic assessment of HCC patients and could be used as a survival prediction tool in the near future.

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