ReviewWorld journal of gastroenterology2007
Microarray-based analysis for hepatocellular carcinoma: from gene expression profiling to new challenges.
Review in World journal of gastroenterology, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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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.
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Who cites it
12 citing papers in PubMed, 42 citations in OpenAlex.
- Genome-wide CRISPR knockout screens identify ADAMTSL3 and PTEN genes as suppressors of HCC proliferation and metastasis, respectively.Journal of cancer research and clinical oncology · 2020Article
- Molecular alterations in the carcinogenesis and progression of hepatocellular carcinoma: Tumor factors and background liver factors.Oncology letters · 2016Article
- Identifying hepatocellular carcinoma-related genes and pathways by system biology analysis.Irish journal of medical science · 2015Article
- Gene Network Analysis of Glucose Linked Signaling Pathways and Their Role in Human Hepatocellular Carcinoma Cell Growth and Survival in HuH7 and HepG2 Cell Lines.BioMed research international · 2015Article
- Transcription addiction: can we garner the Yin and Yang functions of E2F1 for cancer therapy?Cell death & disease · 2014Review
- Article
- Expression and functional role of a transcribed noncoding RNA with an ultraconserved element in hepatocellular carcinoma.Proceedings of the National Academy of Sciences of the United States of America · 2011Article
- Gene signatures derived from a c-MET-driven liver cancer mouse model predict survival of patients with hepatocellular carcinoma.PloS one · 2011Article
- Predicting prognosis in hepatocellular carcinoma after curative surgery with common clinicopathologic parameters.BMC cancer · 2009Article
- Emerging roles of E2Fs in cancer: an exit from cell cycle control.Nature reviews. Cancer · 2009Review
- Quantification of SLIT-ROBO transcripts in hepatocellular carcinoma reveals two groups of genes with coordinate expression.BMC cancer · 2008Article
- Comparative analysis of proteome and transcriptome in human hepatocellular carcinoma using 2D-DIGE and SAGE.The protein journal · 2008Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accumulation of mutations and alterations in the expression of various genes result in carcinogenesis, and the development of microarray technology has enabled us to identify the comprehensive gene expression alterations in oncogenesis. Many studies have applied this technology for hepatocellular carcinoma (HCC), and identified a number of candidate genes useful as biomarkers in cancer staging, prediction of recurrence and prognosis, and treatment selection. Some of these target molecules have been used to develop new serum diagnostic markers and therapeutic targets against HCC to benefit patients. Previously, we compared gene expression profiling data with classification based on clinicopathological features, such as hepatitis viral infection or liver cancer progression. The next era of gene expression analysis will require systematic integration of expression profiles with other types of biological information, such as genomic locus, gene function, and sequence information. We have reported integration between expression profiles and locus information, which is effective in detecting structural genomic abnormalities, such as chromosomal gains and losses, in which we showed that gene expression profiles are subject to chromosomal bias. Furthermore, array-based comparative genomic hybridization analysis and allelic dosage analysis using genotyping arrays for HCC were also reviewed, with comparison of conventional methods.
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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.