Evidence map›Paper›PMID 20713493›Full record

ArticleClinical cancer research : an official journal of the American Association for Cancer Research2010

New strategies in hepatocellular carcinoma: genomic prognostic markers.

Augusto Villanueva, Yujin Hoshida, Sara Toffanin, Anja Lachenmayer, Clara Alsinet, Radoslav Savic, Helena Cornella, Josep M Llovet

Abstract read
In one paragraph

Article in Clinical cancer research : an official journal of the American Association for Cancer Research, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 69 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
69citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

69 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Guideline
  2. Trial
  3. Plasma biomarkers as predictors of outcome in patients with advanced hepatocellular carcinoma.Clinical cancer research : an official journal of the American Association for Cancer Research · 2012
    Trial
  4. Review
  5. Article
  6. Article
  7. Review
  8. Article
  9. Article
  10. Review
  11. Article
  12. Review
  13. Article
  14. Radiomics of hepatocellular carcinoma.Abdominal radiology (New York) · 2021
    Review
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Radiomics and radiogenomics of primary liver cancers.Clinical and molecular hepatology · 2019
    Review

9 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Augusto VillanuevaInstitut d'Investigacions Biomèdiques Agusto Pi i Sunyer, Hospital Clinic, Barcelona, Spain.
Yujin Hoshida
Sara Toffanin
Anja Lachenmayer
Clara Alsinet
Radoslav Savic
Helena Cornella
Josep M Llovet

Funding

Integrative genomic analysis in hepatocellular carcinomaR01DK076986 · NIDDK · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI LLOVET, JOSEP M · 2007 to 2011
$1.3M
NIDDK NIH HHS 1R01DK076986-01NIDDK NIH HHS R01 DK076986
6 · The paper itself

Abstract

Accurate prognosis prediction in oncology is critical. In patients with hepatocellular carcinoma (HCC), unlike most solid tumors, the coexistence of two life-threatening conditions, cancer and cirrhosis, makes prognostic assessments difficult. Despite the usefulness of clinical staging systems for HCC in routine clinical decision making (e.g., Barcelona-Clinic Liver Cancer algorithm), there is still a need to refine and complement outcome predictions. Recent data suggest the ability of gene signatures from the tumor (e.g., EpCAM signature) and adjacent tissue (e.g., poor-survival signature) to predict outcome in HCC (either recurrence or overall survival), although independent external validation is still required. In addition, novel information is being produced by alternative genomic sources such as microRNA (miRNA; e.g., miR-26a) or epigenomics, areas in which promising preliminary data are thoroughly explored. Prognostic models need to contemplate the impact of liver dysfunction and risk of subsequent de novo tumors in a patient's life expectancy. The challenge for the future is to precisely depict genomic predictors (e.g., gene signatures, miRNA, or epigenetic biomarkers) at each stage of the disease and their specific influence to determine patient prognosis.

Indexed as

Biomarkers, TumorCarcinoma, HepatocellularGenetic MarkersHumansLiver NeoplasmsPredictive Value of TestsPrognosisTreatment OutcomeBiomarkers, TumorGenetic Markers

Identifiers

PMID20713493
PMCPMC3395071

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.