Dataset Information

Large-scale analysis reveals a novel risk score to predict overall survival in hepatocellular carcinoma.

ABSTRACT: Background:Hepatocellular carcinoma (HCC) is a major cause of cancer mortality and an increasing incidence worldwide; however, there are very few effective diagnostic approaches and prognostic biomarkers. Materials and methods:One hundred forty-nine pairs of HCC samples from Gene Expression Omnibus (GEO) were obtained to screen differentially expressed genes (DEGs) between HCC and normal samples. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, Gene ontology enrichment analyses, and protein-protein interaction network were used. Cox proportional hazards regression analysis was used to identify significant prognostic DEGs, with which a gene expression signature prognostic prediction model was identified in The Cancer Genome Atlas (TCGA) project discovery cohort. The robustness of this panel was assessed in the GSE14520 cohort. We verified details of the gene expression level of the key molecules through TCGA, GEO, and qPCR and used immunohistochemistry for substantiation in HCC tissues. The methylation states of these genes were also explored. Results:Ninety-eight genes, consisting of 13 upregulated and 85 downregulated genes, were screened out in three datasets. KEGG and Gene ontology analysis for the DEGs revealed important biological features of each subtype. Protein-protein interaction network analysis was constructed, consisting of 64 nodes and 115 edges. A subset of four genes (SPINK1, TXNRD1, LCAT, and PZP) that formed a prognostic gene expression signature was established from TCGA and validated in GSE14520. Next, the expression details of the four genes were validated with TCGA, GEO, and clinical samples. The expression panels of the four genes were closely related to methylation states. Conclusion:This study identified a novel four-gene signature biomarker for predicting the prognosis of HCC. The biomarkers may also reveal molecular mechanisms underlying development of the disease and provide new insights into interventional strategies.

SUBMITTER: Zheng Y

PROVIDER: S-EPMC6252784 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Large-scale analysis reveals a novel risk score to predict overall survival in hepatocellular carcinoma.

Zheng Yujia Y Liu Yulin Y Zhao Songfeng S Zheng Zhetian Z Shen Chunyi C An Li L Yuan Yongliang Y

Cancer management and research 20181121

<h4>Background</h4>Hepatocellular carcinoma (HCC) is a major cause of cancer mortality and an increasing incidence worldwide; however, there are very few effective diagnostic approaches and prognostic biomarkers.<h4>Materials and methods</h4>One hundred forty-nine pairs of HCC samples from Gene Expression Omnibus (GEO) were obtained to screen differentially expressed genes (DEGs) between HCC and normal samples. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, Gene ontology enrichment ...[more]

PMID: 30538557

Similar Datasets

Project description:Abstract Background Hepatocellular carcinoma is one of the most lethal cancers worldwide. Novel prognostic and/or predictive biomarkers are urgently needed to improve patient management. Alpha‐fetoprotein (AFP) is a well‐established and widely used biomarker for hepatocellular carcinoma. However, diagnostic accuracy of static AFP values is limited and the clinical potential is a matter of ongoing scientific discussion. Objective We here evaluated the prognostic impact of pretreatment static and dynamic AFP variables on overall survival of hepatocellular carcinoma patients in a Western cohort. Methods Patients with confirmed hepatocellular carcinoma (n = 809) treated at the Johannes Gutenberg University Mainz between 1998 and 2014 and two available pretreatment AFP‐values (AFP‐slope) were retrospectively analysed. Clinicopathological baseline parameters, pretreatment static values and AFP‐slope were assessed. Prognostic impact was determined by Kaplan–Meier analyses and Cox regression models. Results High static and dynamic AFP variables prior to therapy were associated with reduced survival rates of hepatocellular carcinoma patients. Several known clinical parameters such as Child–Pugh B (p < 0.01) and C stage (p < 0.001), portal vein thrombosis (p < 0.001) and extrahepatic spread (p < 0.001) were confirmed as independent predictors for overall survival. Addition of static and/or dynamic AFP variable resulted in higher time‐dependent area under the curves. Notably, in patients with more favourable prognosis, AFP‐slope prior to therapy was a slightly stronger predictor for overall survival compared with static AFP values. Conclusion Static and dynamic AFP variables prior to therapy are predictive for overall survival of hepatocellular carcinoma patients. Addition of AFP‐slope to established prognostic parameters might improve prognostic classification for a subgroup of hepatocellular carcinoma patients with preserved liver function and without portal vein tumour thrombosis. Key Summary Alpha‐fetoprotein (AFP) is the most commonly used biomarker for hepatocellular carcinomas (HCCs), but accuracy of static and dynamic AFP values is limited and the prognostic significance is under debate. High static and dynamic AFP variables prior to therapy are associated with reduced survival rates of HCC patients across different tumour stages and treatment modalities. In patients with more favourable prognosis, AFP‐slope prior to therapy was a better predictor for overall survival in comparison with static AFP values. Addition of AFP‐slope to established prognostic parameters might improve prognostic classification for a subgroup of HCC.

Project description:BackgroundThe benefits of surgical resection (SR) for various Barcelona Clinic Liver Cancer (BCLC) stages of hepatocellular carcinoma (HCC) remain unclear. We investigated the risk factors of overall survival (OS) and survival benefits of SR over nonsurgical treatments in patients with HCC of various BCLC stages.MethodsOverall, 2316 HCC patients were included, and their clinicopathological data and OS were recorded. OS was analyzed by the Kaplan-Meier method and Cox regression analysis. Propensity score matching (PSM) analysis was performed.ResultsIn total, 66 (2.8%), 865 (37.4%), 575 (24.8%) and 870 (35.0%) patients had BCLC stage 0, A, B, and C disease, respectively. Furthermore, 1302 (56.2%) of all patients, and 37 (56.9%), 472 (54.6%), 313 (54.4%) and 480 (59.3%) of patients with BCLC stage 0, A, B, and C disease, respectively, died. The median follow-up duration time was 20 (range 0-96) months for the total cohort and was subdivided into 52 (8-96), 32 (1-96), 19 (0-84), and 12 (0-79) months for BCLC stages 0, A, B, and C cohorts, respectively. The risk factors for OS were (1) SR and cirrhosis; (2) SR, cirrhosis, and Child-Pugh (C-P) class; (3) SR, hepatitis B virus (HBV) infection, and C-P class; and (4) SR, HBV infection, and C-P class for the BCLC stage 0, A, B, and C cohorts, respectively. Compared to non-SR treatment, SR resulted in significantly higher survival rates in all cohorts. The 5-year OS rates for SR vs. non-SR were 44.0% versus 28.7%, 72.2% versus 42.6%, 42.6% versus 36.2, 44.6% versus 23.5%, and 41.4% versus 15.3% (all P values < 0.05) in the total and BCLC stage 0, A, B, and C cohorts, respectively. After PSM, SR resulted in significantly higher survival rates compared to non-SR treatment in various BCLC stages.ConclusionsSR conferred significant survival benefits to patients with HCC of various BCLC stages and should be considered a recommended treatment for select HCC patients, especially patients with BCLC stage B and C disease.

Dataset Information

Large-scale analysis reveals a novel risk score to predict overall survival in hepatocellular carcinoma.

Publications

Large-scale analysis reveals a novel risk score to predict overall survival in hepatocellular carcinoma.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets