Using a novel gene signature for predicting the efficacy of the transarterial chemoembolization in patients with hepatocellular carcinoma.
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ABSTRACT:
PROVIDER: PRJNA413131 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:This SuperSeries is composed of the following subset Series: GSE9843: Gene expression profiling of 91 hepatocellular carcinomas with hepatitis C virus etiology, Samples with "vascular invasion: Yes/No" were included in the study. GSE20017: Gene Signature to Identify Vascular Invasion in Hepatocellular Carcinoma Refer to individual Series
2011-07-30 | E-GEOD-20238 | biostudies-arrayexpress
Project description:The risk of locoregional or distant failure in advanced HPV-negative head and neck squamous cell carcinoma (HNSCC) patients is high. However, no suitable markers for stratification are clinically available. Thus, we aimed to identify a microRNA(miRNA)-signature predicting disease recurrence. For this purpose the miRNA profiles from 162 HNSCC samples were analysed with regard to identification of a low-complex porgnostic signature. The data set consists of a discovery dataset (n=85) and a validation dataset (n=77). The study resulted in a prognostic 5-miRNA signature significantly predicting the relevant clinical endpoint freedom from recurrence.
2018-09-06 | E-MTAB-5793 | biostudies-arrayexpress
Project description:Sorafenib is the only approved targeted drug for hepatocellular carcinoma (HCC), but its effect on patients’ survival gain is limited and varies over a wide range depending on patho-genetic conditions. Thus, enhancing the efficacy of sorafenib and finding a reliable predictive biomarker are crucuial to achieve efficient control of HCCs. In this study, we employed a systems approach by combining transcriptome analysis of the mRNA changes in HCC cell lines in response to sorafenib with network analysis to investigate the action and resistance mechanism of sorafenib. Gene ontology and gene set analysis revealed that proteotoxic stress and apoptosis modules are activated in the presence of sorafenib. Further analysis of the endoplasmic reticulum (ER) stress network model combined with in vitro experiments showed that introducing an additional stress by treating the orally active protein disulfide isomerase (PDI) inhibitor (PACMA 31) can synergistically increase the efficacy of sorafenib in vitro and in vivo, which was confirmed using a mouse xenograft model. We also found that HCC patients with high PDI expression show resistance to sorafenib and poor clinical outcomes, compared to the low PDI expression group. These results suggest that PDI is a promising therapeutic target for enhancing the efficacy of sorafenib and can also be a biomarker for predicting sorafenib responsiveness.
2017-03-19 | GSE96795 | GEO
Project description:Sorafenib is the only approved targeted drug for hepatocellular carcinoma (HCC), but its effect on patients’ survival gain is limited and varies over a wide range depending on patho-genetic conditions. Thus, enhancing the efficacy of sorafenib and finding a reliable predictive biomarker are crucuial to achieve efficient control of HCCs. In this study, we employed a systems approach by combining transcriptome analysis of the mRNA changes in HCC cell lines in response to sorafenib with network analysis to investigate the action and resistance mechanism of sorafenib. Gene ontology and gene set analysis revealed that proteotoxic stress and apoptosis modules are activated in the presence of sorafenib. Further analysis of the endoplasmic reticulum (ER) stress network model combined with in vitro experiments showed that introducing an additional stress by treating the orally active protein disulfide isomerase (PDI) inhibitor (PACMA 31) can synergistically increase the efficacy of sorafenib in vitro and in vivo, which was confirmed using a mouse xenograft model. We also found that HCC patients with high PDI expression show resistance to sorafenib and poor clinical outcomes, compared to the low PDI expression group. These results suggest that PDI is a promising therapeutic target for enhancing the efficacy of sorafenib and can also be a biomarker for predicting sorafenib responsiveness.
2017-03-19 | GSE96794 | GEO
Project description:Sorafenib is the only approved targeted drug for hepatocellular carcinoma (HCC), but its effect on patients’ survival gain is limited and varies over a wide range depending on patho-genetic conditions. Thus, enhancing the efficacy of sorafenib and finding a reliable predictive biomarker are crucuial to achieve efficient control of HCCs. In this study, we employed a systems approach by combining transcriptome analysis of the mRNA changes in HCC cell lines in response to sorafenib with network analysis to investigate the action and resistance mechanism of sorafenib. Gene ontology and gene set analysis revealed that proteotoxic stress and apoptosis modules are activated in the presence of sorafenib. Further analysis of the endoplasmic reticulum (ER) stress network model combined with in vitro experiments showed that introducing an additional stress by treating the orally active protein disulfide isomerase (PDI) inhibitor (PACMA 31) can synergistically increase the efficacy of sorafenib in vitro and in vivo, which was confirmed using a mouse xenograft model. We also found that HCC patients with high PDI expression show resistance to sorafenib and poor clinical outcomes, compared to the low PDI expression group. These results suggest that PDI is a promising therapeutic target for enhancing the efficacy of sorafenib and can also be a biomarker for predicting sorafenib responsiveness.
2017-03-19 | GSE96793 | GEO
Project description:Sorafenib is the only approved targeted drug for hepatocellular carcinoma (HCC), but its effect on patients’ survival gain is limited and varies over a wide range depending on patho-genetic conditions. Thus, enhancing the efficacy of sorafenib and finding a reliable predictive biomarker are crucuial to achieve efficient control of HCCs. In this study, we employed a systems approach by combining transcriptome analysis of the mRNA changes in HCC cell lines in response to sorafenib with network analysis to investigate the action and resistance mechanism of sorafenib. Gene ontology and gene set analysis revealed that proteotoxic stress and apoptosis modules are activated in the presence of sorafenib. Further analysis of the endoplasmic reticulum (ER) stress network model combined with in vitro experiments showed that introducing an additional stress by treating the orally active protein disulfide isomerase (PDI) inhibitor (PACMA 31) can synergistically increase the efficacy of sorafenib in vitro and in vivo, which was confirmed using a mouse xenograft model. We also found that HCC patients with high PDI expression show resistance to sorafenib and poor clinical outcomes, compared to the low PDI expression group. These results suggest that PDI is a promising therapeutic target for enhancing the efficacy of sorafenib and can also be a biomarker for predicting sorafenib responsiveness.
2017-03-19 | GSE96792 | GEO
Project description:A novel genomic signature predicting FDG uptake in diverse metastatic tumors
| PRJNA421282 | ENA
Project description:A genomic enhancer signature associates with Hepatocellular Carcinoma prognosis
| PRJNA875090 | ENA
Project description:Molecular Signature of Hepatitis D Virus-Associated Hepatocellular Carcinoma
| PRJNA419216 | ENA
Project description:Hepatocellular carcinoma (HCC) has a dismal long-term outcome. We aimed to construct a multi-gene model for prognosis prediction to inform HCC management. The cancer-specific differentially expressed genes (DEGs) were identified using RNA-seq data of paired tumor and normal tissue. A prognostic signature was built by LASSO regression analysis. Gene set enrichment analysis (GSEA) was performed to further understand the underlying molecular mechanisms. A 10-gene signature was constructed to stratify the TCGA and ICGC cohorts into high- and low-risk groups where prognosis was significantly worse in the high-risk group across cohorts (P < 0.001 for all). The 10-gene signature outperformed all previously reported models for both C-index and the AUCs for 1-, 3-, 5-year survival prediction (C-index, 0.84 vs 0.67 to 0.73; AUCs for 1-, 3- and 5-year OS, 0.84 vs 0.68 to 0.79, 0.81 to 0.68 to 0.80, and 0.85 vs 0.67 to 0.78, respectively). Multivariate Cox regression analysis revealed risk group and tumor stage to be independent predictors of survival in HCC. A nomogram incorporating tumor stage and signature-based risk group showed better performance for 1- and 3-year survival than for 5-year survival. GSEA revealed enrichment of pathways related to cell cycle regulation among high-risk samples and metabolic processes in the low-risk group. Our 10-gene model is robust for prognosis prediction and may help inform clinical management of HCC.
| S-EPMC7372135 | biostudies-literature