Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Predicting biomarkers for ovarian cancer using gene-expression microarrays.

British journal of cancer - 9 Feb 2004

Adib T R, Henderson S, Perrett C, Hewitt D, Bourmpoulia D, Ledermann J, Boshoff C

Abstract excerpt

Ovarian cancer has the highest mortality rate of gynaecological cancers. This is partly due to the lack of effective screening markers. Here, we used oligonucleotide microarrays complementary to approximately 12 000 genes to establish a gene-expression microarray (GEM) profile for normal ovarian...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Forecasting
Share to Mass Screening
Share to Myelin Proteins

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Neoplasm Proteins

Share to Oligonucleotide Array Sequence Analysis

Share to Ovarian Neoplasms

Share to Proteolipids

Share to Uteroglobin

Source

Open in PubMed Open full text

Identifiers

PMID: 14760385
DOI: 10.1038/sj.bjc.6601603
PMCID: PMC2409606

Genes

GEM

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 1 Sept 2026. Prepared record publication-14760385-2026-09-01.1. Manifest publication-graph-cohort-2026-09-01.1.

Source receipt: PubMed via Helena Literature Corpus, retrieved 1 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 11 closest

Predicting biomarkers for ovarian cancer using gene-expression microarrays.PMID 14760385Read ↗

A distinct molecular profile associated with mucinous epithelial ovarian cancer.Semantic proximity Read ↗

Gene-expression signatures in ovarian cancer: Promise and challenges for patient stratificationSemantic proximity Read ↗

Identification of genes associated with ovarian cancer metastasis using microarray expression analysisSemantic proximity Read ↗

DNA microarrays for assessing ovarian cancer gene expression.Semantic proximity Read ↗

Microarray-Based Gene Expression Studies in Ovarian CancerSemantic proximity Read ↗

Molecular characterization of ovarian cancer by gene-expression profilingSemantic proximity Read ↗

Multidrug Resistance–Linked Gene Signature Predicts Overall Survival of Patients with Primary Ovarian Serous CarcinomaSemantic proximity Read ↗

Secretoglobin expression in ovarian carcinoma: lipophilin B gene upregulation as an independent marker of better prognosis.Semantic proximity Read ↗

Identification of differentially expressed proteins in ovarian cancer using high-density protein microarraysSemantic proximity Read ↗

A multiplex platform for the identification of ovarian cancer biomarkersSemantic proximity Read ↗

Validation of LRG1 as a Potential Biomarker for Detection of Epithelial Ovarian Cancer by a Blinded StudySemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.