ReviewJournal of hepatology2010
Cancer gene discovery in hepatocellular carcinoma.
Review in Journal of hepatology, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 86 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
86 citing papers in PubMed, 183 citations in OpenAlex.
- Association of epidermal growth factor and epidermal growth factor receptor polymorphisms with the risk of hepatitis B virus-related hepatocellular carcinoma in the population of North China.Genetic testing and molecular biomarkers · 2013Trial
- Peretinoin, an acyclic retinoid, improves the hepatic gene signature of chronic hepatitis C following curative therapy of hepatocellular carcinoma.BMC cancer · 2013Trial
- PRC1, CDCA3, and CDC20 are upregulated and Treg numbers are decreased in canine massive hepatocellular carcinoma.BMC veterinary research · 2025Article
- NUB1 reduction promotes PCNA-mediated tumor growth by disturbing the PCNA polyubiquitination/NEDDylation in hepatocellular carcinoma cells.Cell death & disease · 2025Article
- Early Enhancement in Contrast-Enhanced Computed Tomography Is an Index ofVeterinary sciences · 2025Article
- Autophagy-based therapy for hepatocellular carcinoma: from standard treatments to combination therapy, oncolytic virotherapy, and targeted nanomedicines.Clinical and experimental medicine · 2024Review
- Ras-related associated with diabetes genes for biomarker-based therapeutics in cancer: A comparative evolutionary genomic study.Saudi medical journal · 2024Article
- Prognostic value of RNA methylation-related genes in gastric adenocarcinoma based on bioinformatics.PeerJ · 2024Article
- Semaglutide reduces tumor burden in the GAN diet-induced obese and biopsy-confirmed mouse model of NASH-HCC with advanced fibrosis.Scientific reports · 2023Article
- Mitochondrial Lipid Metabolism Genes as Diagnostic and Prognostic Indicators in Hepatocellular Carcinoma.Current genomics · 2023Article
- Article
- Ring Finger Protein 125 Is an Anti-Proliferative Tumor Suppressor in Hepatocellular Carcinoma.Cancers · 2022Article
- Update on the role of C1GALT1 in cancer.Oncology letters · 2022Review
- Targeting of noncoding RNAs encoded by a novel MYC enhancers inhibits the proliferation of human hepatic carcinoma cells in vitro.Scientific reports · 2022Article
- SCUBE3 downregulation modulates hepatocellular carcinoma by inhibiting CCNE1 via TGFβ/PI3K/AKT/GSK3β pathway.Cancer cell international · 2022Article
- Article
- MAPK/ERK Signaling Pathway in Hepatocellular Carcinoma.Cancers · 2021Review
- Ameliorative effects of colostrum against DMBA hepatotoxicity in rats.Saudi journal of biological sciences · 2021Article
- Review
- Peretinoin, an Acyclic Retinoid, for the Secondary Prevention of Hepatocellular Carcinoma.Molecules (Basel, Switzerland) · 2021Review
26 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 3 countries.
Funding
Abstract
Hepatocellular carcinoma (HCC) is a deadly cancer, whose incidence is increasing worldwide. Albeit the main risk factors for HCC development have been clearly identified, such as hepatitis B and C virus infection and alcohol abuse, there is still preliminary understanding of the key drivers of this malignancy. Recent data suggest that genomic analysis of cirrhotic tissue - the pre-neoplastic carcinogenic field - may provide a read-out to identify at risk populations for cancer development. Given this contextual complexity, it is of utmost importance to characterize the molecular pathogenesis of this disease, and pinpoint the dominant pathways/drivers by integrative oncogenomic approaches and/or sophisticated experimental models. Identification of the dominant proliferative signals and key aberrations will allow for a more personalized therapy. Pathway-based approaches and functional experimental studies have aided in identifying the activation of different signaling cascades in HCC (e.g. epidermal growth factor, insulin-like growth factor, RAS, MTOR, WNT-betacatenin, etc.). However, the introduction of new high-throughput genomic technologies (e.g. microarrays, deep sequencing, etc.), and increased sophistication of computational biology (e.g. bioinformatics, biomodeling, etc.), opens the field to new strategies in oncogene and tumor suppressor discovery. These oncogenomic approaches are framed within emerging new disciplines such as systems biology, which integrates multiple inputs to explain cancer onset and progression. In addition, the consolidation of sophisticated animal models, such as mosaic cancer mouse models or the use of transposons for mutagenesis screens, have been instrumental for the identification of novel tumor drivers. We herein review some classical as well as some recent fast track approaches for oncogene discovery in HCC, and provide a comprehensive landscape of the currently known spectrum of molecular aberrations involved in hepatocarcinogenesis.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.