A Novel Post-Operative ALRI Model Accurately Predicts Clinical Outcomes of Resected Hepatocellular Carcinoma Patients

BackgroundHepatocellular carcinoma (HCC) is one of the leading malignant tumors worldwide. Prognosis and long-term survival of HCC remain unsatisfactory, even after radical resection, and many non-invasive predictors have been explored for post-operative patients. Most prognostic prediction models were based on preoperative clinical characteristics and pathological findings. This study aimed to investigate the prognostic value of a newly constructed nomogram, which incorporated post-operative aspartate aminotransferase to lymphocyte ratio index (ALRI).MethodsA total of 771 HCC patients underwent radical resection from three medical centers were enrolled and grouped into the training cohort (n = 416) and validation cohort (n = 355). Prognostic prediction potential of ALRI was assessed by receiver operating curve (ROC) analysis. The Cox regression model was used to identify independent prognostic factors. Nomograms for overall survival (OS) and disease-free survival (DFS) were constructed and further validated externally.ResultsThe ROC analysis ranked ALRI as the most effective prediction marker for resected HCC patients, with the cut-off value determined at 22.6. Higher ALRI level positively correlated with larger tumor size, higher tumor node metastasis (TNM) stage, and inversely with lower albumin level and shorter OS and DFS. Nomograms for OS and DFS were capable of discriminating HCC patients into different risk-groups.ConclusionsPost-operative ALRI was of prediction value for HCC prognosis. This novel nomogram may categorize HCC patients into different risk groups, and offer individualized surveillance reference for post-operative patients.

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An Oxidative Stress Index-Based Score for Prognostic Prediction in Colorectal Cancer Patients Undergoing Surgery

Oxidative Medicine and Cellular Longevity ◽

10.1155/2021/6693707 ◽

2021 ◽

Vol 2021 ◽

pp. 1-15

Author(s):

Yinghao Cao ◽

Shenghe Deng ◽

Lizhao Yan ◽

Junnan Gu ◽

Fuwei Mao ◽

...

Keyword(s):

Oxidative Stress ◽

Colorectal Cancer ◽

Prediction Models ◽

Cox Regression ◽

Prognostic Index ◽

Disease Free Survival ◽

Tnm Stage ◽

Cox Regression Analysis ◽

T Stage ◽

Prognostic Prediction

Oxidative stress plays an important role in the development of colorectal cancer (CRC). This study is aimed at developing and validating a novel scoring system, based on oxidative stress indexes, for prognostic prediction in CRC patients. A retrospective analysis of 1422 CRC patients who underwent surgical resection between January 2013 and December 2017 was performed. These patients were randomly assigned to the training set ( n = 1022 ) or the validation set ( n = 400 ). Cox regression model was used to analyze the laboratory parameters. The CRC-Integrated Oxidative Stress Score (CIOSS) was developed from albumin (ALB), direct bilirubin (DBIL), and blood urea nitrogen (BUN), which were significantly associated with survival in CRC patients. Furthermore, a survival nomogram was generated by combining the CIOSS with other beneficial clinical characteristics. The CIOSS generated was as follows: 0.074 × albumin (g/L), − 0.094 × bilirubin (μmol/L), and - 0.099 × blood urea nitrogen (mmol/L), based on the multivariable Cox regression analysis. Using 50% (0.1025) and 85% (0.481) of CIOSS as cutoff values, three prognostically distinct groups were formed. Patients with high CIOSS experienced worse overall survival (OS) ( hazard ratio HR = 4.33 ; 95% confidence interval [CI], 2.80-6.68; P < 0.001 ) and worse disease-free survival (DFS) ( HR = 3.02 ; 95% CI, 1.96-4.64; P < 0.001 ) compared to those with low CIOSS. This predictive nomogram had good calibration and discrimination. ROC analyses showed that the CIOSS possessed excellent performance ( AUC = 0.818 ) in predicting DFS. The AUC of the OS nomogram based on CIOSS, TNM stage, T stage, and chemotherapy was 0.812, while that of the DFS nomogram based on CIOSS, T stage, and TNM stage was 0.855. Decision curve analysis showed that these two prediction models were clinically useful. CIOSS is a CRC-specific prognostic index based on the combination of available oxidative stress indexes. High CIOSS is a powerful indicator of poor prognosis. The CIOSS also showed better predictive performance compared to TNM stage in CRC patients.

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Prognostic Value of Serum Osteoprotegerin Level in Patients With Hepatocellular Carcinoma Following Surgical Resection

Frontiers in Oncology ◽

10.3389/fonc.2021.731989 ◽

2021 ◽

Vol 11 ◽

Author(s):

Chihao Zhang ◽

Jiayun Lin ◽

Xiaochun Ni ◽

Hongjie Li ◽

Lei Zheng ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Cox Regression ◽

Survival Rates ◽

Disease Free Survival ◽

Radical Resection ◽

Sensitivity Analyses ◽

Patients With Cancer ◽

Kaplan Meier ◽

Restricted Mean Survival Time ◽

Cox Analysis

BackgroundMultiple studies have reported that tissue or serum osteoprotegerin (OPG) level is a prognostic factor for patients with cancer. However, little is known about the role of serum OPG in hepatocellular carcinoma (HCC). In this study, we aimed to investigate whether serum OPG concentration has an effect on HCC patients’ prognosis.MethodsA total of 386 eligible HCC patients undergoing radical hepatectomy were enrolled from Shanghai Ninth People’s Hospital and Zhongshan Hospital between 2010 and 2018. Kaplan-Meier curves, Cox regression model, and the restricted mean survival time (RMST) were used to estimate the association of OPG and HCC patients’ survival outcome. In addition, sensitivity analyses were carried out including subgroup analysis and propensity score matching (PSM).ResultsPatients were separated into two groups according to the cut-off value of OPG calculated by X-tile. Multivariate Cox analysis showed that patients with high OPG level had worse overall survival (OS) (HR: 1.93; 95% CI: 1.40–2.66, p<0.001) and disease-free survival (DFS) (HR: 1.85; 95% CI: 1.39–2.47, p<0.001) before matching. On average, RMST ratio between high and low OPG turned out to be 0.797 (95% CI: 0.716–0.887, p<0.001). In the matched population, we found that OPG level was negatively associated with OS (HR: 1.85; 95% CI: 1.25–2.74, p=0.002) and DFS (HR: 1.71; 95% CI: 1.20–2.44, p=0.003). In addition, a similar trend was further confirmed by subgroup analyses.ConclusionIn a word, HCC patients with high OPG level had poorer survival rates compared with HCC patients with low OPG level. This factor could act as a potential prognostic predictor for HCC patients who underwent radical resection in the future.

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Five metastasis-related mRNAs signature predicting the survival of patients with liver hepatocellular carcinoma

BMC Cancer ◽

10.1186/s12885-021-08431-1 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Chao Chen ◽

Yan Qun Liu ◽

Shi Xiang Qiu ◽

Ya Li ◽

Ning Jun Yu ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Regression Analysis ◽

Clinical Data ◽

Prognostic Model ◽

Cox Regression ◽

Risk Groups ◽

Test Dataset ◽

Cox Regression Analysis ◽

Prognostic Prediction ◽

Liver Hepatocellular Carcinoma

Abstract Backgrounds Liver hepatocellular carcinoma (HCC) is one of the most malignant tumors, of which prognosis is unsatisfactory in most cases and metastatic of HCC often results in poor prognosis. In this study, we aimed to construct a metastasis- related mRNAs prognostic model to increase the accuracy of prediction of HCC prognosis. Methods Three hundred seventy-four HCC samples and 50 normal samples were downloaded from The Cancer Genome Atlas (TCGA) database, involving transcriptomic and clinical data. Metastatic-related genes were acquired from HCMBD website at the same time. Two hundred thirty-three samples were randomly divided into train dataset and test dataset with a proportion of 1:1 by using caret package in R. Kaplan-Meier method and univariate Cox regression analysis and lasso regression analysis were performed to obtain metastasis-related mRNAs which played significant roles in prognosis. Then, using multivariate Cox regression analysis, a prognostic prediction model was established. Transcriptome and clinical data were combined to construct a prognostic model and a nomogram for OS evaluation. Functional enrichment in high- and low-risk groups were also analyzed by GSEA. An entire set based on The International Cancer Genome Consortium(ICGC) database was also applied to verify the model. The expression levels of SLC2A1, CDCA8, ATG10 and HOXD9 are higher in tumor samples and lower in normal tissue samples. The expression of TPM1 in clinical sample tissues is just the opposite. Results One thousand eight hundred ninety-five metastasis-related mRNAs were screened and 6 mRNAs were associated with prognosis. The overall survival (OS)-related prognostic model based on 5 MRGs (TPM1,SLC2A1, CDCA8, ATG10 and HOXD9) was significantly stratified HCC patients into high- and low-risk groups. The AUC values of the 5-gene prognostic signature at 1 year, 2 years, and 3 years were 0.786,0.786 and 0.777. A risk score based on the signature was a significantly independent prognostic factor (HR = 1.434; 95%CI = 1.275–1.612; P < 0.001) for HCC patients. A nomogram which incorporated the 5-gene signature and clinical features was also built for prognostic prediction. GSEA results that low- and high-risk group had an obviously difference in part of pathways. The value of this model was validated in test dataset and ICGC database. Conclusion Metastasis-related mRNAs prognostic model was verified that it had a predictable value on the prognosis of HCC, which could be helpful for gene targeted therapy.

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A Novel Hypoxic-Angiogenesis-Immune-Related Gene Model for Prognostic and Therapeutic Effect Prediction in Hepatocellular Carcinoma Patients

Disease Markers ◽

10.1155/2022/9428660 ◽

2022 ◽

Vol 2022 ◽

pp. 1-27

Author(s):

Wen Lv ◽

Qi Yao

Keyword(s):

Hepatocellular Carcinoma ◽

High Risk ◽

Liver Cancer ◽

Cox Regression ◽

Risk Group ◽

Immune Dysfunction ◽

Risk Groups ◽

High Risk Group ◽

Low Risk ◽

Prognostic Prediction

Background. Hepatocellular carcinoma (HCC) is one of the most heterogeneous malignant tumors that have been discovered so far, which makes the prognostic prediction difficult. The hypoxia, angiogenesis, and immunity-related genes (HAIRGs) are closely related to the development of liver cancer. However, the prognostic and treatment effect of hypoxia, angiogenesis, and immunity-related genes in HCC continues to be further clarified. Methods. The gene expression quantification data and clinical information in patients with liver cancer were downloaded from the TCGA database, and HAIRG signature was built by using the least absolute shrinkage and selection operator (LASSO) technique. Patient from the ICGC database validated the model. Then, tumor immune dysfunction and exclusion (TIDE) algorithm was applied to estimate the clinical response to immunotherapy and the sensitivity of drugs was evaluated by the half-maximal inhibitory concentration (IC50). Result. The HAIRGs were identified between the HCC patients and normal patients in the TCGA database. In univariate Cox regression analysis, seventeen differentially expressed genes (DEGs) were associated with overall survival (OS). An eight HAIRG signature model was constructed and was used to divide the patients into two groups according to the median value of the risk score base on the TCGA dataset. Patients in the high-risk group had a significant reduction in OS compared to those in the low-risk group ( P < 0.001 in the TCGA, P < 0.001 in the ICGC). For TCGA and ICGC databases of univariate Cox regression analyses, the risk score was used as an independent predictor of OS ( HR > 1 , P < 0.001 ). Functional analysis showed that the relevant immune pathways and immune responses were enriched, cellular component analysis showed that the immunoglobulin complex and other related substances were enriched, and immune status existed a difference in the high- and low-risk groups. Then, the tumor immune dysfunction and exclusion (TIDE) algorithm presented differences in immune response in the high- and low-risk groups ( P < 0.05 ), and based on drug sensitivity prediction, patients in the high-risk group were more sensitive to cisplatin compared to those in the low-risk group in both the TCGA and ICGC cohorts ( P < 0.05 ). Conclusions. HAIRG signature can be utilized for prognostic prediction in HCC, while it can be considered a prediction model for clinical evaluation of immunotherapy response and chemotherapy sensitivity in HCC.

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Four Metastasis-Related mRNAs Signature Predicting the Survival of Patients With Liver Hepatocellular Carcinoma

10.21203/rs.3.rs-358201/v1 ◽

2021 ◽

Author(s):

chao chen ◽

ShiXiang Qiu ◽

Ya Li ◽

YanQun Liu ◽

Kang Liu ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Regression Analysis ◽

Clinical Data ◽

Prognostic Model ◽

Cox Regression ◽

Risk Groups ◽

Test Dataset ◽

Cox Regression Analysis ◽

Prognostic Prediction ◽

Liver Hepatocellular Carcinoma

Abstract Backgrounds: Liver hepatocellular carcinoma (LIHC) is one of the most malignant tumors, of which prognosis is unsatisfactory in most cases and metastatic of LIHC often results in poor prognosis. In this study, we aimed to construct a metastasis- related mRNAs prognostic model to increase the accuracy of prediction of LIHC prognosis.Methods: 374 LIHC samples and 50 normal samples were downloaded from TCGA database, involving transcriptomic and clinical data. Metastatic-related genes were acquired from HCMBD website at the same time. 343 samples were randomly divided into train dataset and test dataset with a proportion of 1:1 by using caret package in R. Kaplan-Meier method and univariate Cox regression analysis and lasso regression analysis were performed to obtain metastasis-related mRNAs which played significant roles in prognosis. Then, using multivariate Cox regression analysis, a prognostic prediction model was established. Transcriptome and clinical data were combined to construct a prognostic model and a nomogram for OS evaluation. Functional enrichment in high- and low-risk groups were also analyzed by GSEA. An entire set was applied to verify the model.Results: 1895 metastasis-related mRNAs were screened and 8mRNAs were associated with prognosis. The overall survival (OS)-related prognostic model which was constructed based on 4 MRGs (MMP1, SPP1, STC2, CDCA8) significantly stratified LIHC patients into high- and low-risk groups. The AUC values of the 4-gene prognostic signature at 1 year, 2 years, and 3 years were 0.807,0.729 and 0.673. A risk score based on the signature was a significantly independent prognostic factor (HR=1.295; 95%CI=1.167-1.436; P<0.001) for LIHC patients. A nomogram which incorporated the 4-gene signature and clinical features was also built for prognostic prediction. GSEA results that low- and high-risk group had an obviously difference in part of pathways. The value of this model was validated in test dataset and entire set.Conclusion: Metastasis-related mRNAs prognostic model was verified that it had a predictable value on the prognosis of LIHC, which could be helpful for gene targeted therapy.

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A prognostic nomogram based on LASSO Cox regression in patients with alpha-fetoprotein-negative hepatocellular carcinoma following non-surgical therapy

BMC Cancer ◽

10.1186/s12885-021-07916-3 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Dongdong Zhou ◽

Xiaoli Liu ◽

Xinhui Wang ◽

Fengna Yan ◽

Peng Wang ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Surgical Therapy ◽

Calibration Curve ◽

Cox Regression ◽

Validation Cohort ◽

Risk Group ◽

Risk Groups ◽

High Risk Group ◽

Alpha Fetoprotein ◽

Clinicopathologic Characteristics

Abstract Background Alpha-fetoprotein-negative hepatocellular carcinoma (AFP-NHCC) (< 8.78 ng/mL) have special clinicopathologic characteristics and prognosis. The aim of this study was to apply a new method to establish and validate a new model for predicting the prognosis of patients with AFP-NHCC. Methods A total of 410 AFP-negative patients with clinical diagnosed with HCC following non-surgical therapy as a primary cohort; 148 patients with AFP-NHCC following non-surgical therapy as an independent validation cohort. In primary cohort, independent factors for overall survival (OS) by LASSO Cox regression were all contained into the nomogram1; by Forward Stepwise Cox regression were all contained into the nomogram2. Nomograms performance and discriminative power were assessed with concordance index (C-index) values, area under curve (AUC), Calibration curve and decision curve analyses (DCA). The results were validated in the validation cohort. Results The C-index of nomogram1was 0.708 (95%CI: 0.673–0.743), which was superior to nomogram2 (0.706) and traditional modes (0.606–0.629). The AUC of nomogram1 was 0.736 (95%CI: 0.690–0.778). In the validation cohort, the nomogram1 still gave good discrimination (C-index: 0.752, 95%CI: 0.691–0.813; AUC: 0.784, 95%CI: 0.709–0.847). The calibration curve for probability of OS showed good homogeneity between prediction by nomogram1 and actual observation. DCA demonstrated that nomogram1 was clinically useful. Moreover, patients were divided into three distinct risk groups for OS by the nomogram1: low-risk group, middle-risk group and high-risk group, respectively. Conclusions Novel nomogram based on LASSO Cox regression presents more accurate and useful prognostic prediction for patients with AFP-NHCC following non-surgical therapy. This model could help patients with AFP-NHCC following non-surgical therapy facilitate a personalized prognostic evaluation.

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The Treatment Effect of Liver Transplantation versus Liver Resection for HCC: A Review and Future Perspectives

Cancers ◽

10.3390/cancers13153730 ◽

2021 ◽

Vol 13 (15) ◽

pp. 3730

Author(s):

Berend R. Beumer ◽

Roeland F. de Wilde ◽

Herold J. Metselaar ◽

Robert A. de Man ◽

Wojciech G. Polak ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Liver Transplantation ◽

Liver Resection ◽

Early Stage ◽

Disease Free Survival ◽

Milan Criteria ◽

Term Survival ◽

Equal Distribution ◽

Intention To Treat ◽

Treat Analysis

For patients presenting with hepatocellular carcinoma within the Milan criteria, either liver resection or liver transplantation can be performed. However, to what extent either of these treatment options is superior in terms of long-term survival is unknown. Obviously, the comparison of these treatments is complicated by several selection processes. In this article, we comprehensively review the current literature with a focus on factors accounting for selection bias. Thus far, studies that did not perform an intention-to-treat analysis conclude that liver transplantation is superior to liver resection for early-stage hepatocellular carcinoma. In contrast, studies performing an intention-to-treat analysis state that survival is comparable between both modalities. Furthermore, all studies demonstrate that disease-free survival is longer after liver transplantation compared to liver resection. With respect to the latter, implications of recurrences for survival are rarely discussed. Heterogeneous treatment effects and logical inconsistencies indicate that studies with a higher level of evidence are needed to determine if liver transplantation offers a survival benefit over liver resection. However, randomised controlled trials, as the golden standard, are believed to be infeasible. Therefore, we suggest an alternative research design from the causal inference literature. The rationale for a regression discontinuity design that exploits the natural experiment created by the widely adopted Milan criteria will be discussed. In this type of study, the analysis is focused on liver transplantation patients just within the Milan criteria and liver resection patients just outside, hereby ensuring equal distribution of confounders.

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A long non-coding RNA-based signature predicts early recurrence in hepatocellular carcinoma

Cancer Biomarkers ◽

10.3233/cbm-210193 ◽

2021 ◽

pp. 1-10

Author(s):

Shuai He ◽

Jin-Feng Li ◽

Hao Tian ◽

Ye Sang ◽

Xiao-Jing Yang ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Curative Resection ◽

Risk Model ◽

Cox Regression ◽

Early Recurrence ◽

Support Vector ◽

Term Survival ◽

Cox Regression Analysis ◽

Non Coding Rna ◽

Long Non Coding Rna

BACKGROUND: Early recurrence is the main obstacle for long-term survival of hepatocellular carcinoma (HCC) patients after curative resection. OBJECTIVE: We aimed to develop a long non-coding RNA (lncRNA) based signature to predict early recurrence. METHODS: Using bioinformatics analysis and quantitative reverse transcription PCR (RT-qPCR), we screened for lncRNA candidates that were abnormally expressed in HCC. The expression levels of candidate lncRNAs were analyzed in HCC tissues from 160 patients who underwent curative resection, and a risk model for the prediction of recurrence within 1 year (early recurrence) of HCCs was constructed with linear support vector machine (SVM). RESULTS: A lncRNA-based classifier (Clnc), which contained nine differentially expressed lncRNAs including AF339810, AK026286, BC020899, HEIH, HULC, MALAT1, PVT1, uc003fpg, and ZFAS1 was constructed. In the test set, this classifier reliably predicted early recurrence (AUC, 0.675; sensitivity, 72.0%; specificity, 63.1%) with an odds ratio of 4.390 (95% CI, 2.120–9.090). Clnc showed higher accuracy than traditional clinical features, including tumor size, portal vein tumor thrombus (PVTT) in predicting early recurrence (AUC, 0.675 vs 0.523 vs 0.541), and had much higher sensitivity than Barcelona Clinical Liver Cancer (BCLC; 72.0% vs 50.0%), albeit their AUCs were comparable (0.675 vs 0.678). Moreover, combining Clnc with BCLC significantly increased the AUC, compared with Clnc or BCLC alone in predicting early recurrence (all P< 0.05). Finally, logistic and Cox regression analysis suggested that Clnc was an independent prognostic factor and associated with the early recurrence and recurrence-free survival of HCC patients after resection, respectively (all P= 0.001). CONCLUSIONS: Our lncRNA-based classifier Clnc can predict early recurrence of patients undergoing surgical resection of HCC.

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Development and Validation of Prognostic Nomograms Predicting Recurrence and Survival in Patients with Solitary Hepatocellular Carcinoma Following Curative Liver Resection

10.21203/rs.3.rs-1070685/v1 ◽

2021 ◽

Author(s):

Xinxin Chen ◽

Wenxia Qiu ◽

Xuekun Xie ◽

Zefeng Chen ◽

Zhiwei Han ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Liver Resection ◽

Cox Regression ◽

Disease Free Survival ◽

Clinicopathological Characteristics ◽

Tumor Diameter ◽

Cox Regression Analysis ◽

Tumor Capsule ◽

Calibration Curves ◽

Solitary Hepatocellular Carcinoma

Abstract Background: This work was designed to establish and verify our nomograms integrating clinicopathological characteristics with hematological biomarkers to predict both disease-free survival (DFS) and overall survival (OS) in solitary hepatocellular carcinoma (HCC) patients following hepatectomy.Methods: We scrutinized the data retrospectively from 414 patients with a clinicopathological diagnosis of solitary HCC from Guangxi Medical University Cancer Hospital (Nanning, China) between January 2004 and December 2012. Following the random separation of the samples in a 7:3 ratio into the training set and validation set, the former set was assessed by Cox regression analysis to develop two nomograms to predict the 1-year and 3-year DFS and OS (3-years and 5-years). This was followed by discrimination and calibration estimation employing Harrell’s C-index (C-index) and calibration curves, while the internal validation was also assessed.Results: In the training cohort, the tumor diameter, tumor capsule, macrovascular invasion, and alpha-fetoprotein (AFP) were included in the DFS nomogram. Age, tumor diameter, tumor capsule, macrovascular invasion, microvascular invasion, and aspartate aminotransferase (AST) were included in the OS nomogram. The C-index was 0.691 (95% CI: 0.644-0.738) for the DFS-nomogram and 0.713 (95% CI: 0.670-0.756) for the OS-nomogram. The survival probability calibration curves displayed a fine agreement between the predicted and observed ranges in both data sets. Conclusion: Our nomograms combined clinicopathological features with hematological biomarkers to emerge effective in predicting the DFS and OS in solitary HCC patients following curative liver resection. Therefore, the potential utility of our nomograms for guiding individualized treatment clinically and monitor the recurrence monitoring in these patients.

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NDRG3 overexpression is associated with a poor prognosis in patients with hepatocellular carcinoma

Bioscience Reports ◽

10.1042/bsr20180907 ◽

2018 ◽

Vol 38 (6) ◽

Cited By ~ 1

Author(s):

Ji-sheng Jing ◽

Hongbo Li ◽

Shun-cai Wang ◽

Jiu-ming Ma ◽

La-qing Yu ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Cox Regression ◽

Survival Curve ◽

Disease Free Survival ◽

Prognostic Indicator ◽

Clinicopathological Characteristics ◽

Pcr Analysis ◽

Kaplan Meier ◽

Tumor Tissues ◽

The Relationship

N-myc downstream-regulated gene 3 (NDRG3), an important member of the NDRG family, is involved in cell proliferation, differentiation, and other biological processes. The present study analyzed NDRG3 expression in hepatocellular carcinoma (HCC) and explored the relationship between expression of NDRG3 in HCC patients and their clinicopathological characteristics. We performed quantitative real-time reverse-transcription polymerase chain reaction (qRT-PCR) analysis and immunohistochemistry (IHC) analyses on HCC tissues to elucidate NDRG3 expression characteristics in HCC patients. Kaplan–Meier survival curve and Cox regression analyses were used to evaluate the prognoses of 102 patients with HCC. The results revealed that compared with non-tumor tissues, HCC tissues showed significantly higher NDRG3 expression. In addition, our analyses showed that NDRG3 expression was statistically associated with tumor size (P=0.048) and pathological grade (P=0.001). Survival analysis and Kaplan–Meier curves revealed that NDRG3 expression is an independent prognostic indicator for disease-free survival (P=0.002) and overall survival (P=0.005) in HCC patients. The data indicate that NDRG3 expression may be considered as a oncogenic biomarker and a novel predictor for HCC prognosis.

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