Dataset Information

Development and validation of a new tumor-based gene signature predicting prognosis of HBV/HCV-included resected hepatocellular carcinoma patients.

ABSTRACT: BACKGROUND:Due to the phenotypic and molecular diversity of hepatocellular carcinomas (HCC), it is still a challenge to determine patients' prognosis. We aim to identify new prognostic markers for resected HCC patients. METHODS:274 patients were retrospectively identified and samples collected from Zhongshan hospital, Fudan University. We analyzed the gene expression patterns of tumors and compared expression patterns with patient survival times. We identified a "9-gene signature" associated with survival by using the coefficient and regression formula of multivariate Cox model. This molecular signature was then validated in three patients cohorts from internal cohort (n?=?69), TCGA (n?=?369) and GEO dataset (n?=?80). RESULTS:We identified 9-gene signature consisting of ZC2HC1A, MARCKSL1, PTGS1, CDKN2B, CLEC10A, PRDX3, PRKCH, MPEG1 and LMO2. The 9-gene signature was used, combined with clinical parameters, to fit a multivariable Cox model to the training cohort (concordance index, ci?=?0.85), which was successfully validated (ci?=?0.86 for internal cohort; ci?=?0.78 for in silico cohort). The signature showed improved performance compared with clinical parameters alone (ci?=?0.70). Furthermore, the signature predicted patient prognosis than previous gene signatures more accurately. It was also used to stratify early-stage, HBV or HCV-infected patients into low and high-risk groups, leading to significant differences in survival in training and validation (P?

SUBMITTER: Zhu GQ

PROVIDER: S-EPMC6582497 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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Development and validation of a new tumor-based gene signature predicting prognosis of HBV/HCV-included resected hepatocellular carcinoma patients.

Zhu Gui-Qi GQ Yang Yi Y Chen Er-Bao EB Wang Biao B Xiao Kun K Shi Shi-Ming SM Zhou Zheng-Jun ZJ Zhou Shao-Lai SL Wang Zheng Z Shi Ying-Hong YH Fan Jia J Zhou Jian J Liu Tian-Shu TS Dai Zhi Z

Journal of translational medicine 20190618 1

<h4>Background</h4>Due to the phenotypic and molecular diversity of hepatocellular carcinomas (HCC), it is still a challenge to determine patients' prognosis. We aim to identify new prognostic markers for resected HCC patients.<h4>Methods</h4>274 patients were retrospectively identified and samples collected from Zhongshan hospital, Fudan University. We analyzed the gene expression patterns of tumors and compared expression patterns with patient survival times. We identified a "9-gene signature" ...[more]

PMID: 31215439

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Project description:BackgroundHepatocellular carcinoma (HCC) is a malignancy with a bleak prognosis. Although emerging research increasingly supports the involvement of chromatin regulators (CRs) in cancer development, CRs in HCC patients have not received proportionate attention. This study aimed to investigate the role and prognostic significance of CRs in HCC patients, providing new insights for clinical diagnosis and treatment strategies.MethodsWe analyzed 424 samples in The Cancer Genome Atlas-Liver hepatocellular carcinoma (TCGA-LIHC) data to identify key CR genes associated with HCC prognosis by differential expression and univariate Cox regression analyses. LASSO-multivariate Cox regression method was used for construction of a prognostic signature and development of a CR-related prognosis model. The prognosis capacity of the model was evaluated via Kaplan-Meier method. Relationship between the model and tumor microenvironment (TME) was evaluated. Additionally, clinical variables and the model were incorporated to create a nomogram. The role of the prognostic gene MRG-binding protein (MRGBP) in HCC was elucidated by immunohistochemistry and semiquantitative analysis.ResultsA risk score model, comprising B-lymphoma Mo-MLV insertion region 1 (BMI1), chromobox 2 (CBX2), and MRGBP, was constructed. The area under the curve (AUC) of the CR-based signature is 0.698 (P<0.05), exhibiting robust predictive power. Functional and pathway analyses illuminated the biological relevance of these genes. Immune microenvironment analysis suggested potential implications for immunotherapy. Drug sensitivity analysis identified agents for targeted treatment. Clinical samples show that MRGBP is highly expressed in HCC tissues.ConclusionsThis CR-based signature shows promise as a valuable prognostic tool for HCC patients. It demonstrates predictive capabilities, independence from other clinical factors, and potential clinical applicability. In addition, we need more experiments to validate our findings. These findings offer insights into HCC prognosis and treatment, with implications for personalized medicine and improved patient outcomes.

Project description:BackgroundHepatocellular carcinoma (HCC) is a highly lethal disease. Effective prognostic tools to guide clinical decision-making for HCC patients are lacking.ObjectiveWe aimed to establish a robust prognostic model based on differentially expressed genes (DEGs) in HCC.MethodsUsing datasets from The Cancer Genome Atlas (TCGA), the Gene Expression Omnibus (GEO), and the International Genome Consortium (ICGC), DEGs between HCC tissues and adjacent normal tissues were identified. Using TCGA dataset as the training cohort, we applied the least absolute shrinkage and selection operator (LASSO) algorithm and multivariate Cox regression analyses to identify a multi-gene expression signature. Proportional hazard assumptions and multicollinearity among covariates were evaluated while building the model. The ICGC cohort was used for validation. The Pearson test was used to evaluate the correlation between tumor mutational burden and risk score. Through single-sample gene set enrichment analysis, we investigated the role of signature genes in the HCC microenvironment.ResultsA total of 274 DEGs were identified, and a six-DEG prognostic model was developed. Patients were stratified into low- or high-risk groups based on risk scoring by the model. Kaplan-Meier analysis revealed significant differences in overall survival and progression-free interval. Through univariate and multivariate Cox analyses, the model proved to be an independent prognostic factor compared to other clinic-pathological parameters. Time-dependent receiver operating characteristic curve analysis revealed satisfactory prediction of overall survival, but not progression-free interval. Functional enrichment analysis showed that cancer-related pathways were enriched, while immune infiltration analyses differed between the two risk groups. The risk score did not correlate with levels of PD-1, PD-L1, CTLA4, or tumor mutational burden.ConclusionsWe propose a six-gene expression signature that could help to determine HCC patient prognosis. These genes may serve as biomarkers in HCC and support personalized disease management.

Dataset Information

Development and validation of a new tumor-based gene signature predicting prognosis of HBV/HCV-included resected hepatocellular carcinoma patients.

Publications

Development and validation of a new tumor-based gene signature predicting prognosis of HBV/HCV-included resected hepatocellular carcinoma patients.

Similar Datasets

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