Discover Community Create You

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

Correlations among ERCC1, XPB, UBE2I, EGF, TAL2 and ILF3 revealed by gene signatures of histological subtypes of patients with epithelial ovarian cancer

3 Oct 2011

Abstract excerpt

The aim of this study was to better understand the mechanisms of tumor development and disease progression in human epithelial ovarian cancer. Fifty genes were screened for gene signature; 20 expressed genes were assessed in tumor and normal samples of EOC patients by RT-PCR. Expression of UBE2I, EGF, TAL2 and ILF3 was validated by qPCR on the ABI Prism 7000 Detection System. ERCC1 and XPB expression was...

Read the complete abstract on PubMed

Topics

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

Share to Cancer-related molecular mechanisms research
Share to Ovarian cancer diagnosis and treatment
Share to RNA modifications and cancer

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Source

Identifiers

PMID: 21971700
DOI: 10.3892/or.2011.1483

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Correlations among ERCC1, XPB, UBE2I, EGF, TAL2 and ILF3 revealed by gene signatures of histological subtypes of patients with epithelial ovarian cancerPMID 21971700Read ↗

Identification of potential biomarkers in subtypes of epithelial ovarian cancerSemantic proximity Read ↗

High UBE2T mRNA expression and its prognostic significance in Ovarian cancer: a study based on data miningSemantic proximity Read ↗

High UBE2T mRNA expression and its prognostic significance in Ovarian cancer: a study based on data miningSemantic proximity Read ↗

Integrated bioinformatic analysis identifies UBE2Q1 as a potential prognostic marker for high grade serous ovarian cancerSemantic proximity Read ↗

Identification of Ovarian Cancer Gene Expression Patterns Associated with Disease Progression and MortalitySemantic proximity Read ↗

Meta-analysis based gene expressional profiling reveals functional genes in ovarian cancerSemantic proximity Read ↗

Comparison of expression profiles in ovarian epithelium in vivo and ovarian cancer identifies novel candidate genes involved in disease pathogenesis.Semantic proximity Read ↗

Predictive modeling of gene mutations for the survival outcomes of epithelial ovarian cancer patients.Semantic proximity Read ↗

Identification of a novel epithelial-mesenchymal transition-related gene signature for the prognosis prediction of endometrial carcinomaSemantic proximity Read ↗

Machine learning and bioinformatics models to identify gene expression patterns of ovarian cancer associated with disease progression and mortalitySemantic proximity Read ↗

Increased expression of UBE2T predicting poor survival of ovarian cancer: based on comprehensive analysis of UBE2s, clinical samples and the GEO databaseSemantic proximity Read ↗

Gene expression profiling differentiates germ cell tumors from other cancers and defines subtype-specific signaturesSemantic proximity Read ↗

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