Evidence map›Paper›PMID 23319948›Full record

ArticleJournal of oncology2012

Early detection biomarkers for ovarian cancer.

Sreeja Sarojini, Ayala Tamir, Heejin Lim, Shihong Li, Shifang Zhang, Andre Goy, Andrew Pecora, K Stephen Suh

Open access · hybridAbstract read
In one paragraph

Article in Journal of oncology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers.

0numbers the graph read from it
0cells of the map it votes in
49citing papers in PubMed
7.2field-weighted citation impact, top 3% of its field
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

49 citing papers in PubMed, 91 citations in OpenAlex.

  1. Article
  2. Review
  3. Review
  4. Article
  5. High-MannoseJournal of proteome research · 2026
    Article
  6. Article
  7. Review
  8. Review
  9. Article
  10. Article
  11. Review
  12. Review
  13. Nanobodies for the Early Detection of Ovarian Cancer.International journal of molecular sciences · 2022
    Article
  14. Article
  15. Article
  16. Review of biomarker systems as an alternative for early diagnosis of ovarian carcinoma.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2021
    Review
  17. Review
  18. Expression of Circulating MicroRNA-141 in Epithelial Ovarian Cancer.The Malaysian journal of medical sciences : MJMS · 2020
    Article
  19. Article
  20. Article
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 at 1 institution in 1 country.

Sreeja SarojiniThe Genomics and Biomarker Program, The John Theurer Cancer Center, Hackensack University Medical Center, Hackensack, NJ 07601, USA.
Ayala Tamir
Heejin Lim
Shihong Li
Shifang Zhang
Andre Goy
Andrew Pecora
K Stephen Suh
Hackensack University Medical Center · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Despite the widespread use of conventional and contemporary methods to detect ovarian cancer development, ovarian cancer remains a common and commonly fatal gynecological malignancy. The identification and validation of early detection biomarkers highly specific to ovarian cancer, which would permit development of minimally invasive screening methods for detecting early onset of the disease, are urgently needed. Current practices for early detection of ovarian cancer include transvaginal ultrasonography, biomarker analysis, or a combination of both. In this paper we review recent research on novel and robust biomarkers for early detection of ovarian cancer and provide specific details on their contributions to tumorigenesis. Promising biomarkers for early detection of ovarian cancer include KLK6/7, GSTT1, PRSS8, FOLR1, ALDH1, and miRNAs.

Identifiers

PMID23319948
PMCPMC3540796
OpenAlexW1985280742

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.