Using a novel gene signature for predicting the efficacy of the transarterial chemoembolization in patients with hepatocellular carcinoma.
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE104580 | GEO | 2020/10/01
REPOSITORIES: GEO
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Project description:These paired HCC and non-tumorous liver tissues were used to determine highly differentially expressed genes in HCC and non-tumorous liver tissue. Hepatocellular carcinoma (HCC) is a malignancy with poor survival outcome. Genes showing extreme differential expression between paired human HCC and adjacent non-tumorous liver tissue were investigated. PLVAP was identified as a gene specifically expressed in vascular endothelial cells of HCC but not in non-tumorous liver tissues. This finding was confirmed by RT-PCR analysis of micro-dissected cells and immunohistochemical staining of tissue sections. A recombinant monoclonal anti-PLVAP Fab fragment co-expressing extracellular domain of human tissue factor (TF) was developed. The potential therapeutic effect and toxicity to treat HCC were studied using a Hep3B HCC xenograft model in SCID mice. Infusion of recombinant monoclonal anti-PLVAP Fab-TF into the tumor feeding artery induced tumor vascular thrombosis and extensive tumor necrosis at doses between 2.5 µg and 12 µg. Tumor growth was suppressed for 40 days after a single treatment. Systemic administration did not induce tumor necrosis. Little systemic toxicity was noted for this therapeutic agent. The results of this study suggest that anti-PLVAP Fab-TF may be used to treat HCC cases for which transcatheter arterial chemoembolization (TACE) is currently used, but without major drawbacks of TACE. Anti-PLVAP Fab-TF may improve therapeutic outcome and be a viable therapeutic agent in patients with more advanced disease and compromised liver function. Frozen hepatocellular carcinoma and adjacent non-tumorous liver tissues were used for gnee expression profiling study. Affymetrix U133A genechips were used for gene expression profiling. This dataset is part of the TransQST collection.
2015-01-01 | E-GEOD-60502 | biostudies-arrayexpress
Project description:These paired HCC and non-tumorous liver tissues were used to determine highly dfferentially expressed genes in HCC and non-tumorous liver tissue. Hepatocellular carcinoma (HCC) is a malignancy with poor survival outcome. Genes showing extreme differential expression between paired human HCC and adjacent non-tumorous liver tissue were investigated. PLVAP was identified as a gene specifically expressed in vascular endothelial cells of HCC but not in non-tumorous liver tissues. This finding was confirmed by RT-PCR analysis of micro-dissected cells and immunohistochemical staining of tissue sections. A recombinant monoclonal anti-PLVAP Fab fragment co-expressing extracellular domain of human tissue factor (TF) was developed. The potential therapeutic effect and toxicity to treat HCC were studied using a Hep3B HCC xenograft model in SCID mice. Infusion of recombinant monoclonal anti-PLVAP Fab-TF into the tumor feeding artery induced tumor vascular thrombosis and extensive tumor necrosis at doses between 2.5 µg and 12 µg. Tumor growth was suppressed for 40 days after a single treatment. Systemic administration did not induce tumor necrosis. Little systemic toxicity was noted for this therapeutic agent. The results of this study suggest that anti-PLVAP Fab-TF may be used to treat HCC cases for which transcatheter arterial chemoembolization (TACE) is currently used, but without major drawbacks of TACE. Anti-PLVAP Fab-TF may improve therapeutic outcome and be a viable therapeutic agent in patients with more advanced disease and compromised liver function.
2015-01-01 | GSE60502 | GEO
Project description:Background & Aims: Hepatocellular carcinoma (HCC) is the second most lethal cancer due to lack of effective therapies. Although promising, HCC molecular classification, which enriches potential responders to specific therapies, has not yet been assessed in clinical trials of anti-HCC drugs. We aimed to overcome these challenges by developing clinicopathological surrogate indices of HCC molecular classification. Methods: HCC classification defined in our previous transcriptome meta-analysis (S1, S2, and S3 subclasses) was implemented in an FDA-approved diagnostic platform (Elements assay, NanoString). Ninety-six HCC tumors (training set) were assayed to develop molecular subclass-predictive indices based on clinicopathological features, which were independently validated in 99 HCC tumors (validation set). Molecular deregulations associated with the histopathological features were determined by pathway analysis. Sample sizes for HCC clinical trials enriched with specific molecular subclasses were determined. Results: HCC subclass-predictive indices were: steatohepatitic (SH)-HCC variant and immune cell infiltrate for S1 subclass, macrotrabecular/compact pattern, lack of pseudoglandular pattern, and high serum alpha-fetoprotein (>400 ng/mL) for S2 subclass, and microtrabecular pattern, lack of SH-HCC and clear cell variants, and lower histological grade for S3 subclass. Macrotrabecular/compact pattern, a predictor of S2 subclass, was associated with activation of therapeutically targetable oncogene YAP and stemness markers EPCAM/KRT19. BMP4 was associated with pseudoglandular pattern. Subclass-predictive indices-based patient enrichment reduced clinical trial sample sizes from 121, 184, and 53 to 30, 43, and 22 for S1, S2, and S3 subclass-targeting therapies, respectively. Conclusions: HCC molecular subclasses can be enriched by clinicopathological indices tightly associated with deregulation of therapeutically targetable molecular pathways.
2015-06-17 | GSE59548 | GEO
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
Project description:Hepatocellular carcinoma (HCC) is one of the leading causes of cancer deaths worldwide. Recently, microRNAs (miRNAs) are reported to be altered and act as potential biomarkers in various cancers. However, miRNA biomarkers for predicting the stage of HCC are limitedly discovered. Hence, we sought to identify a novel miRNA signature associated with cancer stage in HCC. We proposed a support vector machine (SVM)-based cancer stage prediction method, SVM-HCC, which uses an inheritable bi-objective combinatorial genetic algorithm for selecting a minimal set of miRNA biomarkers while maximizing the accuracy of predicting the early and advanced stages of HCC. SVM-HCC identified a 23-miRNA signature that is associated with cancer stages in patients with HCC and achieved a 10-fold cross-validation accuracy, sensitivity, specificity, Matthews correlation coefficient, and area under the receiver operating characteristic curve (AUC) of 92.59%, 0.98, 0.74, 0.80, and 0.86, respectively; and test accuracy and test AUC of 74.28% and 0.73, respectively. We prioritized the miRNAs in the signature based on their contributions to predictive performance, and validated the prognostic power of the prioritized miRNAs using Kaplan-Meier survival curves. The results showed that seven miRNAs were significantly associated with prognosis in HCC patients. Correlation analysis of the miRNA signature and its co-expressed miRNAs revealed that hsa-let-7i and its 13 co-expressed miRNAs are significantly involved in the hepatitis B pathway. In clinical practice, a prediction model using the identified 23-miRNA signature could be valuable for early-stage detection, and could also help to develop miRNA-based therapeutic strategies for HCC.
| S-EPMC7467934 | biostudies-literature
Project description:Hepatocellular carcinoma (HCC) is frequently diagnosed in patients with late-stage disease who are ineligible for curative surgical therapies. Furthermore, the majority of patients become resistant to sorafenib. Recently, computational methods for drug repurposing have shown great promise to accelerate the discovery of new uses for existing drugs. In order to identify novel drugs for use against sorafenib resistant (SR)-HCC, we employed a transcriptomics-based drug repurposing method termed connectivity mapping. We conducted a comprehensive analysis of available in vitro and in vivo gene signatures of (SR)-HCC, and generated our own in vitro model using the Huh7 HCC cell line. We compared coverage of SR-HCC gene signatures across seven patient-derived HCC gene expression datasets, and observed that patients harboring the Huh7 SR-HCC gene signature had significantly reduced survival. Utilizing the Huh7 SR-HCC gene signature, we applied connectivity mapping to drug-induced gene expression profiles (n= 3,740 drugs) in the HepG2 HCC cell line from the LINCS database in order to find drugs that could oppose sorafenib resistance. We validated the use of two non-receptor tyrosine kinase inhibitors, dasatinib and fostamatinib, to reduce viability of sorafenib-resistant HCC cells and confirmed up-regulated activity of Src family kinases, the targets of dasatinib, in our SR-HCC models. We prospectively validated predicted gene expression changes in fostamatinib treated Huh7-SR via RNA-seq analysis.
2019-09-01 | GSE113005 | GEO
Project description:Vascular invasion is considered as the critical risk factors for tumor recurrence of hepatocellular carcinoma (HCC). To reveal the molecular mechanisms underlying macrovascular invasion (MaVI) and metastasis in HCC, we performed an iTRAQ based proteomic study to identify notably dysregulated proteins in 53 HCC patients with differential vascular invasion. In patients with MaVI, 47 proteins were significantly down-regulated in HCC tumor tissue. More importantly, 30 of them were not changed in HCC without MaVI. Gene ontology analysis of these 47 proteins shows the top 3 enriched pathways are urea cycle, gluconeogenesis and arginine biosynthetic process. We validated 9 remarkably dysregulated candidates in HCC patients with MaVI by Western blot, including 8 down-regulated proteins (CPS1, ASS1, ASL, ARG1, BHMT, DMGDH, Annexin A6 and CES1) and 1 up-regulated protein (CKAP4). Furthermore, dysregulation of CPS1, ASL and ARG1, key enzymes involved in urea cycle, together with Annexin A6 and CES1, major proteins in regulating cholesterol homeostasis and fatty acid ester metabolism were verified using immunohistochemical staining. The significant down-regulation of urea cycle generates clinically relevant proteomic signature in HCC patients with macrovascular invasion, which may provide possible insights into the molecular mechanisms of metastasis and new therapeutic targets of HCC.
2022-03-02 | PXD011846 | Pride
Project description:Background: The presence of vascular invasion (VI) in pathology specimens has been widely described as closely linked to poor outcome in hepatocellular carcinoma (HCC) patients after tumor resection. Previous attempts have been conducted to achieve molecular markers or signatures to predict HCC recurrence in HCC. Here, we aim to develop a diagnostic model combining clinical and genomic variables able to detect the presence of VI prior to surgery and link it to survival estimation. Methods: Seventy-nine HCV related HCC samples from patients that underwent surgical resection as a treatment for HCC were subjected to Genome-wide gene expression profiling and a predictive model of vascular invasion was constructed. The model was tested in an independent-validation set of 153 fixed tissue samples of resected HCC. Quantitative RTPCR and inmunohistochemistry were performed in HCC samples to test a potential biomarker. Results: A 39-gene signature was able to accurately (72%) identify vascular invasion in HCC patients treated with resection. A model including tumor size and the signature is able to predict presence of VI with 85% accuracy in HCV-related HCC patients, and is able to exclude VI in up to 87% cases in HCC from all etiologies. Conclusions: Using the VI gene signature together with tumor size, VI can be successfully detected in HCC patients. The diagnostic model, integrated in a previously reported survival chart is able to provide an estimated survival for selected cases. Clinical implications of this fact are relevant to provide objective data to further apply expanded indication of curative treatments in HCC. Gene-expression profiling was performed using formalin-fixed, paraffin-embedded hepatocellular carcinoma tissues obtained at the time of surgical resection.
2011-07-30 | E-GEOD-20017 | biostudies-arrayexpress
Project description:Molecular targeted therapy has shown potential in hepatocellular carcinoma (HCC) patients, and immunotherapy applications are developing rapidly. However, clinical guidance for making individualized therapy decisions for HCC patients remains lacking. To facilitate individualized and reasonable clinical guidance for each HCC patient, we constructed the MDH score.
2022-09-22 | GSE213615 | GEO
Project description:Background: Hepatocellular carcinoma (HCC) is regarded as one of the most common cancers in the world with a poor prognosis. Patients with HCC often have abnormal purine and uric acid metabolism, but their relationship with prognosis is unclear. Methods: Here, we collected the data of peripheral blood uric acid and clinical data in 50 patients with HCC and analyzed the relationship with prognosis. At the same time, the transcriptome sequencing data of TCGA and GEO databases were collected to analyze the changes in purine metabolic pathway activity and construct a prognosis prediction model. Based on the prognosis prediction model related to purine metabolism, we further looked for the differences in the immune microenvironment and molecular level and provided possible drug targets. Results: We found that the level of serum uric acid was positively correlated with the prognosis of HCC. At the same time, purine metabolism and purine biosynthesis pathway activities were significantly activated in patients with a poor prognosis of HCC. The prognosis prediction model of HCC based on purine metabolism and purine biosynthesis pathway can accurately evaluate the prognosis of patients with HCC. Meanwhile, we found that there were significant changes in tumor immune infiltration microenvironment and biological function at the molecular level in patients with over-activation of purine metabolism and purine biosynthesis pathway. In addition, we found that uric acid level was positively correlated with peripheral blood leukocytes in HCC patients. Conclusion: In this study, we found that the level of peripheral blood uric acid in patients with HCC is correlated with their prognosis. The prognosis of patients with HCC can be accurately predicted through the metabolic process of uric acid and purine.
| S-EPMC9315342 | biostudies-literature