Dataset Information

A Gene Expression Signature Associated with Overall Survival in Patients with Hepatocellular Carcinoma Suggests a New Treatment Strategy.

ABSTRACT: Despite improvements in the management of liver cancer, the survival rate for patients with hepatocellular carcinoma (HCC) remains dismal. The survival benefit of systemic chemotherapy for the treatment of liver cancer is only marginal. Although the reasons for treatment failure are multifactorial, intrinsic resistance to chemotherapy plays a primary role. Here, we analyzed the expression of 377 multidrug resistance (MDR)-associated genes in two independent cohorts of patients with advanced HCC, with the aim of finding ways to improve survival in this poor-prognosis cancer. Taqman-based quantitative polymerase chain reaction revealed a 45-gene signature that predicts overall survival (OS) in patients with HCC. Using the Connectivity Map Tool, we were able to identify drugs that converted the gene expression profiles of HCC cell lines from ones matching patients with poor OS to profiles associated with good OS. We found three compounds that convert the gene expression profiles of three HCC cell lines to gene expression profiles associated with good OS. These compounds increase histone acetylation, which correlates with the synergistic sensitization of those MDR tumor cells to conventional chemotherapeutic agents, including cisplatin, sorafenib, and 5-fluorouracil. Our results indicate that it is possible to modulate gene expression profiles in HCC cell lines to those associated with better outcome. This approach also increases sensitization of HCC cells toward conventional chemotherapeutic agents. This work suggests new treatment strategies for a disease for which few therapeutic options exist.

SUBMITTER: Gillet JP

PROVIDER: S-EPMC4727122 | biostudies-literature | 2016 Feb

REPOSITORIES: biostudies-literature

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A Gene Expression Signature Associated with Overall Survival in Patients with Hepatocellular Carcinoma Suggests a New Treatment Strategy.

Gillet Jean-Pierre JP Andersen Jesper B JB Madigan James P JP Varma Sudhir S Bagni Rachel K RK Powell Katie K Burgan William E WE Wu Chung-Pu CP Calcagno Anna Maria AM Ambudkar Suresh V SV Thorgeirsson Snorri S SS Gottesman Michael M MM

Molecular pharmacology 20151214 2

Despite improvements in the management of liver cancer, the survival rate for patients with hepatocellular carcinoma (HCC) remains dismal. The survival benefit of systemic chemotherapy for the treatment of liver cancer is only marginal. Although the reasons for treatment failure are multifactorial, intrinsic resistance to chemotherapy plays a primary role. Here, we analyzed the expression of 377 multidrug resistance (MDR)-associated genes in two independent cohorts of patients with advanced HCC, ...[more]

PMID: 26668215

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Project description:BackgroundHepatocellular carcinoma (HCC) remains a major challenge for public health worldwide. Considering the great heterogeneity of HCC, more accurate prognostic models are urgently needed. To identify a robust prognostic gene signature, we conduct this study.Materials and methodsLevel 3 mRNA expression profiles and clinicopathological data were obtained in The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC). GSE14520 dataset from the gene expression omnibus (GEO) database was downloaded to further validate the results in TCGA. Differentially expressed mRNAs between HCC and normal tissue were investigated. Univariate Cox regression analysis and lasso Cox regression model were performed to identify and construct the prognostic gene signature. Time-dependent receiver operating characteristic (ROC), Kaplan-Meier curve, multivariate Cox regression analysis, nomogram, and decision curve analysis (DCA) were used to assess the prognostic capacity of the six-gene signature. The prognostic value of the gene signature was further validated in independent GSE14520 cohort. Gene Set Enrichment Analyses (GSEA) was performed to further understand the underlying molecular mechanisms. The performance of the prognostic signature in differentiating between normal liver tissues and HCC were also investigated.ResultsA novel six-gene signature (including CSE1L, CSTB, MTHFR, DAGLA, MMP10, and GYS2) was established for HCC prognosis prediction. The ROC curve showed good performance in survival prediction in both the TCGA HCC cohort and the GSE14520 validation cohort. The six-gene signature could stratify patients into a high- and low-risk group which had significantly different survival. Cox regression analysis showed that the six-gene signature could independently predict OS. Nomogram including the six-gene signature was established and shown some clinical net benefit. Furthermore, GSEA revealed several significantly enriched oncological signatures and various metabolic process, which might help explain the underlying molecular mechanisms. Besides, the prognostic signature showed a strong ability for differentiating HCC from normal tissues.ConclusionsOur study established a novel six-gene signature and nomogram to predict overall survival of HCC, which may help in clinical decision making for individual treatment.

Project description:Hepatocellular carcinoma (HCC) is one of the most common public health challenges, worldwide. Because of molecular complexity and tumor heterogeneity, there are no effective predictive models for prognosis of HCC. This underlines the unmet need for accurate prognostic models for HCC. Analysis of GSE14520 data from gene omnibus (GEO) database identified multiple differentially expressed mRNAs (DEMs) between HCC and normal tissues. After randomly stratifying the patients into the training and testing groups, we performed univariate, lasso, and multivariable Cox regression analyses to delineate the prognostic gene signature in training set. We then used Kaplan-Meier plot, time-dependent receiver operating characteristic (ROC), multivariable Cox regression analysis of clinical information, nomogram, and decision curve analysis (DCA) to evaluate the predictive and overall survival value of a novel five-gene signature (CNIH4, SOX4, SPP1, SORBS2, and CCL19) within and across sets, separately and combined. We also validated the prognostic value of the five-gene signature using The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC), GSE54236 and International Cancer Genome Consortium (ICGC) sets. Multivariable Cox regression analysis revealed that the five-gene signature and tumor node metastasis (TNM) stage were independent prognostic factors for overall survival of HCC patients in GSE14520 and TCGA-LIHC. Combining TNM stage clinical pathological parameters and nomogram greatly improved the prognosis prediction of HCC. Further gene set enrichment analysis (GSEA) revealed enrichment of KEGG pathways related to cell cycle in the high-risk group and histidine metabolism in the low-risk group. Finally, all these five mRNAs are overexpressed between 12 pairs of HCC and adjacent normal tissues by quantitative real-time PCR validation. In brief, a five-gene prognostic signature and a nomogram were identified and constructed, respectively, and further validated for their HCC prognostic value. The five-gene risk score together with TNM stage models could aid in rationalizing customized therapies in HCC patients.

Dataset Information

A Gene Expression Signature Associated with Overall Survival in Patients with Hepatocellular Carcinoma Suggests a New Treatment Strategy.

Publications

A Gene Expression Signature Associated with Overall Survival in Patients with Hepatocellular Carcinoma Suggests a New Treatment Strategy.

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