ReviewCancer science2008
Translational microarray systems for outcome prediction of hepatocellular carcinoma.
Review in Cancer science, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed, 25 citations in OpenAlex.
- Transcriptome analysis creates a new era of precision medicine for managing recurrent hepatocellular carcinoma.World journal of gastroenterology · 2023Review
- Fructose-Bisphosphate Aldolase A Regulates Hypoxic Adaptation in Hepatocellular Carcinoma and Involved with Tumor Malignancy.Digestive diseases and sciences · 2019Article
- Prediction of Early Recurrence of Liver Cancer by a Novel Discrete Bayes Decision Rule for Personalized Medicine.BioMed research international · 2016Article
- High expression of ribonucleotide reductase subunit M2 correlates with poor prognosis of hepatocellular carcinoma.Gut and liver · 2014Article
- Article
- Integrative genomics: liver regeneration and hepatocellular carcinoma.Journal of cellular biochemistry · 2012Article
- Gene signatures in hepatocellular carcinoma (HCC).Seminars in cancer biology · 2011Review
- Microarray-based gene expression analysis of hepatocellular carcinoma.Current genomics · 2010Article
- Molecular signatures of noncancerous liver tissue can predict the risk for late recurrence of hepatocellular carcinoma.Journal of gastroenterology · 2010Review
- Distinct expression patterns in hepatitis B virus- and hepatitis C virus-infected hepatocellular carcinoma.World journal of gastroenterology · 2008Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
DNA microarray technology has revolutionized our understanding of the molecular basis of hepatocellular carcinoma (HCC), one of the most fatal human cancers with a high recurrence rate. Many researchers have used DNA microarray technology to reclassify HCC with respect to metastatic potential and to develop predictors for the outcome of HCC. However, developed predictors have reached the level only of small retrospective studies, and their current status is far from that required for clinical use. This is due to the lack of transparent data, the high cost and data instability associated with the high dimensionality of the technique, the infancy of bioinformatics, and the complicated nature of recurrent HCC. This comprehensive review summarizes: (i) class comparison studies to identify genes or pathways involved in HCC metastasis (ii) class discovery studies that have resulted in the identification of a new molecular subclass of HCC with respect to metastasis, and (iii) class prediction studies to develop multidimensional predictors for HCC outcome. We also discuss issues that need to be addressed so that the power of array-based predictors can be estimated prospectively in large independent cohorts of HCC patients.
Indexed as
Identifiers
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.