scholarly journals A gene-based risk score model for predicting recurrence-free survival in patients with hepatocellular carcinoma

2020 ◽  
Author(s):  
Wenhua Wang ◽  
Lingchen Wang ◽  
Xinsheng Xie ◽  
Yehong Yan ◽  
Yue Li ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Liver Cancer ◽  
Risk Score ◽  
Prognostic Model ◽  
Validation Dataset ◽  
Recurrence Free Survival ◽  
Free Survival ◽  
Liver Cancers ◽  
Score Model ◽  
Robust Likelihood

Abstract Background Hepatocellular carcinoma (HCC) remains the most frequent liver cancer, accounting for approximately 90% of primary liver cancers worldwide. The recurrence-free survival (RFS) of HCC patients is a critical factor in devising a personal treatment plan. Thus, it is necessary to accurately forecast the prognosis of HCC patients in clinical practice.Methods Using The Cancer Genome Atlas (TCGA) dataset, we identified genes associated with RFS. A robust likelihood-based survival modeling approach was used to select the best genes for the prognostic model. Then, the GSE76427 dataset was used to evaluate the prognostic model’s effectiveness. Results We identified 1331 differentially expressed genes associated with RFS. Seven of these genes were selected to generate the prognostic model. The validation in both the TCGA cohort and GEO cohort demonstrated that the 7-gene prognostic model can predict the RFS of HCC patients. Meanwhile, the results of the multivariate Cox regression analysis showed that the 7-gene risk score model could function as an independent prognostic factor. In addition, according to the time-dependent ROC curve, the 7-gene risk score model performed better in predicting the RFS of the training set and the external validation dataset than the classical TNM staging and BCLC. Furthermore, these seven genes were found to be related to the occurrence and development of liver cancer by exploring three other databases. Conclusion Our study identified a seven-gene signature for HCC RFS prediction that can be used as a novel and convenient prognostic tool. These seven genes might be potential target genes for metabolic therapy and the treatment of HCC.

scholarly journals A gene-based risk score model for predicting recurrence-free survival in patients with hepatocellular carcinoma

2020 ◽  
Author(s):  
WENHUA WANG ◽  
LINGCHEN WANG ◽  
XINSHENG XIE ◽  
YEHONG YAN ◽  
YUE LI ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Liver Cancer ◽  
Risk Score ◽  
Prognostic Model ◽  
Validation Dataset ◽  
Recurrence Free Survival ◽  
Free Survival ◽  
Liver Cancers ◽  
Score Model ◽  
Robust Likelihood

Abstract BackgroundHepatocellular carcinoma (HCC) remains the most frequent liver cancer, accounting for approximately 90% of primary liver cancers worldwide. The recurrence-free survival (RFS) of HCC patients is a critical factor in devising a personal treatment plan. Thus, it is necessary to accurately forecast the prognosis of HCC patients in clinical practice.MethodsUsing The Cancer Genome Atlas (TCGA) dataset, we identified genes associated with RFS. A robust likelihood-based survival modeling approach was used to select the best genes for the prognostic model. Then, the GSE76427 dataset was used to evaluate the prognostic model’s effectiveness.ResultsWe identified 1331 differentially expressed genes associated with RFS. Seven of these genes were selected to generate the prognostic model. The validation in both the TCGA cohort and GEO cohort demonstrated that the 7-gene prognostic model can predict the RFS of HCC patients. Meanwhile, the results of the multivariate Cox regression analysis showed that the 7-gene risk score model could function as an independent prognostic factor. In addition, according to the time-dependent ROC curve, the 7-gene risk score model performed better in predicting the RFS of the training set and the external validation dataset than the classical TNM staging and BCLC. Furthermore, these seven genes were found to be related to the occurrence and development of liver cancer by exploring three other databases.ConclusionOur study identified a seven-gene signature for HCC RFS prediction that can be used as a novel and convenient prognostic tool. These seven genes might be potential target genes for metabolic therapy and the treatment of HCC.

scholarly journals A gene-based risk score model for predicting recurrence-free survival in patients with hepatocellular carcinoma

2020 ◽  
Author(s):  
WENHUA WANG ◽  
LINGCHEN WANG ◽  
XINSHENG XIE ◽  
YEHONG YAN ◽  
YUE LI ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Liver Cancer ◽  
Risk Score ◽  
Prognostic Model ◽  
Validation Dataset ◽  
Recurrence Free Survival ◽  
Free Survival ◽  
Liver Cancers ◽  
Score Model ◽  
Robust Likelihood

Abstract BackgroundHepatocellular carcinoma (HCC) remains the most frequent liver cancer, accounting for approximately 90% of primary liver cancers worldwide. The recurrence-free survival (RFS) of HCC patients is a critical factor in devising a personal treatment plan. Thus, it is necessary to accurately forecast the prognosis of HCC patients in clinical practice. MethodsUsing The Cancer Genome Atlas (TCGA) dataset, we identified genes associated with RFS. A robust likelihood-based survival modeling approach was used to select the best genes for the prognostic model. Then, the GSE76427 dataset was used to evaluate the prognostic model’s effectiveness. ResultsWe identified 1331 differentially expressed genes associated with RFS. Seven of these genes were selected to generate the prognostic model. The validation in both the TCGA cohort and GEO cohort demonstrated that the 7-gene prognostic model can predict the RFS of HCC patients. Meanwhile, the results of the multivariate Cox regression analysis showed that the 7-gene risk score model could function as an independent prognostic factor. In addition, according to the time-dependent ROC curve, the 7-gene risk score model performed better in predicting the RFS of the training set and the external validation dataset than the classical TNM staging and BCLC. Furthermore, these seven genes were found to be related to the occurrence and development of liver cancer by exploring three other databases. ConclusionOur study identified a seven-gene signature for HCC RFS prediction that can be used as a novel and convenient prognostic tool. These seven genes might be potential target genes for metabolic therapy and the treatment of HCC.

scholarly journals A gene-based risk score model for predicting recurrence-free survival in patients with hepatocellular carcinoma

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Wenhua Wang ◽  
Lingchen Wang ◽  
Xinsheng Xie ◽  
Yehong Yan ◽  
Yue Li ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Liver Cancer ◽  
Risk Score ◽  
Prognostic Model ◽  
Validation Dataset ◽  
Recurrence Free Survival ◽  
Free Survival ◽  
Liver Cancers ◽  
Score Model ◽  
Robust Likelihood

Abstract Background Hepatocellular carcinoma (HCC) remains the most frequent liver cancer, accounting for approximately 90% of primary liver cancers worldwide. The recurrence-free survival (RFS) of HCC patients is a critical factor in devising a personal treatment plan. Thus, it is necessary to accurately forecast the prognosis of HCC patients in clinical practice. Methods Using The Cancer Genome Atlas (TCGA) dataset, we identified genes associated with RFS. A robust likelihood-based survival modeling approach was used to select the best genes for the prognostic model. Then, the GSE76427 dataset was used to evaluate the prognostic model’s effectiveness. Results We identified 1331 differentially expressed genes associated with RFS. Seven of these genes were selected to generate the prognostic model. The validation in both the TCGA cohort and GEO cohort demonstrated that the 7-gene prognostic model can predict the RFS of HCC patients. Meanwhile, the results of the multivariate Cox regression analysis showed that the 7-gene risk score model could function as an independent prognostic factor. In addition, according to the time-dependent ROC curve, the 7-gene risk score model performed better in predicting the RFS of the training set and the external validation dataset than the classical TNM staging and BCLC. Furthermore, these seven genes were found to be related to the occurrence and development of liver cancer by exploring three other databases. Conclusion Our study identified a seven-gene signature for HCC RFS prediction that can be used as a novel and convenient prognostic tool. These seven genes might be potential target genes for metabolic therapy and the treatment of HCC.

scholarly journals A prognostic model for predicting recurrence-free survival in hepatocellular carcinoma patients

2020 ◽  
Author(s):  
Wenhua Wang ◽  
Lingchen Wang ◽  
Xinsheng Xie ◽  
Yehong Yan ◽  
Yue Li ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Liver Cancer ◽  
Prognostic Model ◽  
Treatment Plan ◽  
External Validation ◽  
Validation Dataset ◽  
Recurrence Free Survival ◽  
Free Survival ◽  
Liver Cancers ◽  
Robust Likelihood

Abstract Hepatocellular carcinoma (HCC) remains the most frequent liver cancer, accounting for approximately 90% of primary liver cancers worldwide. The recurrence-free survival (RFS) of HCC patients is a critical factor in devising a personal treatment plan. Thus, it is necessary to accurately forecast the prognosis of HCC patients in clinical practice. Using the The Cancer Genome Atlas （TCGA） dataset, we identified genes that are associated with RFS. A robust likelihood-based survival modeling approach was used to select the best genes for the prognostic model. Then, the GSE76427 dataset was used to evaluate the prognostic model’s effectiveness. We identified 1331 differentially expressed genes associated with RFS. Seven of these genes were selected to generate the prognostic model. Validation in both the TCGA cohort and the GEO cohort demonstrated that the 7-gene prognostic model has the capability of predicting the RFS of HCC patients. Meanwhile, the result of multivariate Cox regression showed that the 7-gene prognostic model could work as an independent prognostic factor. In addition, according to the time dependent ROC curve, the 7-gene prognostic model performed better in predicting the RFS of the training set and the external validation dataset than the classical TNM staging and BCLC. What’s more, these seven genes were found to be related to the occurrence and development of liver cancer by exploring other three databases. Our study identified a seven-gene signature for HCC RFS prediction that is a novel and convenient prognostic tool. The seven genes might provide potential target genes for metabolic therapy and the treatment of HCC.

scholarly journals Identification of a novel four-lncRNA signature as a prognostic indicator in cirrhotic hepatocellular carcinoma

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7413 ◽  
Author(s):  
Linkun Ma ◽  
Cunliang Deng
Keyword(s):  
Hepatocellular Carcinoma ◽  
Liver Cancer ◽  
Risk Score ◽  
Cox Regression ◽  
Prognostic Score ◽  
Prognostic Significance ◽  
Training Set ◽  
Score Model ◽  
Stratified Analysis ◽  
Testing Set

Background Many studies have shown that long noncoding RNAs (lncRNA) are closely associated with the occurrence and development of various tumors and have the potential to be prognostic markers. Moreover, cirrhosis is an important prognostic risk factors in patients with liver cancer. Some studies have reported that lncRNA-related prognostic models have been used to predict overall survival (OS) and recurrence-free survival (RFS) in patients with hepatocellular carcinoma (HCC). However, no one has constructed a prognostic lncRNA model only in patients with cirrhotic HCC. Thus, it is necessary to screen novel potential lncRNA markers for improve the prognosis of cirrhotic HCC patients. Methods The probe expression profile dataset (GSE14520–GPL3921) from the Gene Expression Omnibus (GEO), which included 204 cirrhotic HCC samples, was reannotated and the lncRNA and mRNA expression dataset was obtained. The patients were randomly assigned to either the training set (n = 103) and testing set (n = 100). Univariate cox regression and the least absolute shrinkage and selection operator (LASSO) model were applied to screen lncRNAs linked to the OS of cirrhotic HCC in the training set. The lncRNAs having significant correlation with OS were then selected and the multivariate Cox regression model was implemented to construct the prognostic score model. Whether or not this model was related to RFS in the training set was simultaneously determined. The testing set was used to validate the lncRNA risk score model. A risk score based on the lncRNA signature was used for stratified analysis of different clinical features to test their prognostic performance. The prognostic lncRNA-related protein genes were identified by the co-expression matrix of lncRNA-mRNA, and the function of these lncRNAs was predicted through the enrichment of these co-expression genes. Results The signature consisted of four lncRNAs:AC093797.1,POLR2J4,AL121748.1 and AL162231.4. The risk model was closely correlated with the OS of cirrhotic HCC in the training cohort, with a hazard ratio (HR) of 3.650 (95% CI [1.761–7.566]) and log-rank P value of 0.0002. Moreover, this model also showed favorable prognostic significance for RFS in the training set (HR: 2.392, 95% CI [1.374–4.164], log-rank P = 0.0015). The predictive performance of the four-lncRNA model for OS and RFS was verified in the testing set. Furthermore, the results of stratified analysis revealed that the four-lncRNA model was an independent factor in the prediction of OS and RFS of patients with clinical characteristics such as TNM (Tumor, Node, Metastasis system) stages I–II, Barcelona Clinic Liver Cancer (BCLC) stages 0–A, and solitary tumors in both the training set and testing set. The results of functional prediction showed that four lncRNAs may be potentially involve in multiple metabolic processes, such as amino acid, lipid, and glucose metabolism in cirrhotic HCC.

scholarly journals Risk Score Model for Microvascular Invasion in Hepatocellular Carcinoma: The Role of Tumor Burden and Alpha-Fetoprotein

Cancers ◽  
2021 ◽  
Vol 13 (17) ◽  
pp. 4403
Author(s):  
Jin-Chiao Lee ◽  
Hao-Chien Hung ◽  
Yu-Chao Wang ◽  
Chih-Hsien Cheng ◽  
Tsung-Han Wu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Risk Score ◽  
Treatment Plan ◽  
Significant Risk ◽  
Disease Free Survival ◽  
Microvascular Invasion ◽  
Alpha Fetoprotein ◽  
Validation Dataset ◽  
Score Model ◽  
Preoperative Factors

Microvascular invasion (MVI) is a significant risk factor for the recurrence of hepatocellular carcinoma, but it is a histological feature that needs to be confirmed after hepatectomy or liver transplantation. The preoperative prediction of MVI can optimize the treatment plan of HCC, but an easy and widely applicable model is still lacking. The aim of our study was to predict the risk of MVI using objective preoperative factors. We retrospectively collected 1153 patients who underwent liver resection for HCC, and MVI was found to be associated with significantly poor disease-free survival. The patients were randomly split in a 3:1 ratio into training (n = 864) and validation (n = 289) datasets. The multivariate analysis of the training dataset found preoperative total tumor volume (TTV) and alpha-fetoprotein (AFP) to be independent risk factors for MVI. We built a risk score model with cutoff points of TTV at 30, 60, and 300 cm3 and AFP at 160 and 2000 ng/mL, and the model stratified the risk of MVI into low risk (14.1%), intermediate risk (36.4%), and high risk (60.5%). The validation of the risk score model with the validation dataset showed moderate performance (the concordance statistic: 0.731). The model comprised simple and objective preoperative factors with good applicability, which can help to guide treatment plans for HCC and future study design.

scholarly journals Downregulation of CYP2A6 and CYP2C8 in Tumor Tissues Is Linked to Worse Overall Survival and Recurrence-Free Survival from Hepatocellular Carcinoma

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Xiaojing Ren ◽  
Yuanyuan Ji ◽  
Xuhua Jiang ◽  
Xun Qi
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
Tumor Staging ◽  
Gene Expression Omnibus ◽  
Intrahepatic Metastasis ◽  
Clinicopathological Features ◽  
Validation Dataset ◽  
Recurrence Free Survival ◽  
Free Survival ◽  
Tumor Tissues

Objective. This study aimed to evaluate the links between CYP450 family genes in tumor tissues and hepatocellular carcinoma (HCC) outcomes.Methods. Gene Expression Omnibus (GEO) databases GSE14520 and GSE36376 were used to identify differential expressed CYP450 genes between tumor and nontumor tissues and related to HCC clinicopathological features and survivals.Results. Seven CYP450 genes including CYP1A2, CYP2A6, CYP2C8, CYP2C9, CYP2E1, CYP3A4, and CYP4A11 were downregulated in tumor tissues, which were validated in both GSE14520 and GSE36376. HCC patients with CYP2A6 and CYP2C8 low levels in tumor tissues suffered from poorer overall survival (OS) compared to those with high CYP2A6 and CYP2C8 in GSE14520 profile (log ranksP= 0.01 andP= 0.006, respectively). In addition, HCC patients with lower CYP2A6 and CYP2C8 in tumors had worse recurrence-free survival (RFS) than those with higher CYP2A6 and CYP2C8 (log ranksP= 0.02 andP= 0.012, respectively). In GSE36376 validation dataset, HCC patients with lower CYP2A6 and CYP2C8 had worse OS and RFS than those with higher CYP2A6 and CYP2C8 (allP< 0.05), in line with results in GSE14520 dataset. Additionally, lower CYP2A6 and CYP2C8 are associated with advanced clinicopathological features including tumor staging, vascular invasion, intrahepatic metastasis, and high alpha fetoprotein (allP< 0.05).Conclusion. Downregulation of CYP2A6 and CYP2C8 in tumor tissues links to poorer OS and RFS in HCC patients.

scholarly journals Integrated analysis of methylation-driven genes and pretreatment prognostic factors in patients with hepatocellular carcinoma

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Dongsheng He ◽  
Shengyin Liao ◽  
Lifang Cai ◽  
Weiming Huang ◽  
Xuehua Xie ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Prognostic Factors ◽  
Risk Score ◽  
Prognostic Model ◽  
Cox Regression ◽  
The Cancer Genome Atlas ◽  
Integrated Analysis ◽  
Calibration Plot ◽  
Clinical Parameters ◽  
T Stage

Abstract Background The potential reversibility of aberrant DNA methylation indicates an opportunity for oncotherapy. This study aimed to integrate methylation-driven genes and pretreatment prognostic factors and then construct a new individual prognostic model in hepatocellular carcinoma (HCC) patients. Methods The gene methylation, gene expression dataset and clinical information of HCC patients were downloaded from The Cancer Genome Atlas (TCGA) database. Methylation-driven genes were screened with a Pearson’s correlation coefficient less than − 0.3 and a P value less than 0.05. Univariable and multivariable Cox regression analyses were performed to construct a risk score model and identify independent prognostic factors from the clinical parameters of HCC patients. The least absolute shrinkage and selection operator (LASSO) technique was used to construct a nomogram that might act to predict an individual’s OS, and then C-index, ROC curve and calibration plot were used to test the practicability. The correlation between clinical parameters and core methylation-driven genes of HCC patients was explored with Student’s t-test. Results In this study, 44 methylation-driven genes were discovered, and three prognostic signatures (LCAT, RPS6KA6, and C5orf58) were screened to construct a prognostic risk model of HCC patients. Five clinical factors, including T stage, risk score, cancer status, surgical method and new tumor events, were identified from 13 clinical parameters as pretreatment-independent prognostic factors. To avoid overfitting, LASSO analysis was used to construct a nomogram that could be used to calculate the OS in HCC patients. The C-index was superior to that from previous studies (0.75 vs 0.717, 0.676). Furthermore, LCAT was found to be correlated with T stage and new tumor events, and RPS6KA6 was found to be correlated with T stage. Conclusion We identified novel therapeutic targets and constructed an individual prognostic model that can be used to guide personalized treatment in HCC patients.

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