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

Interrogating differences in expression of targeted gene sets to predict breast cancer outcome

2 Jul 2013

Abstract excerpt

BACKGROUND: Genomics provides opportunities to develop precise tests for diagnostics, therapy selection and monitoring. From analyses of our studies and those of published results, 32 candidate genes were identified, whose expression appears related to clinical outcome of breast cancer. Expression of these genes was validated by qPCR and correlated with clinical follow-up to identify a gene subset for development...

Read the complete abstract on PubMed

Topics

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

Share to Breast Cancer Treatment Studies
Share to Ferroptosis and cancer prognosis
Share to Mechanisms of cancer metastasis

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Source

Identifiers

PMID: 23819905
DOI: 10.1186/1471-2407-13-326

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 8 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Interrogating differences in expression of targeted gene sets to predict breast cancer outcomePMID 23819905Read ↗

Development and Validation of a Gene Profile Predicting Benefit of Postmastectomy Radiotherapy in Patients with High-Risk Breast Cancer: A Study of Gene Expression in the DBCG82bc CohortSemantic proximity Read ↗

Predicting Outcomes of Hormone and Chemotherapy in the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) Study by Biochemically-inspired Machine LearningSemantic proximity Read ↗

Prognostic biomarkers related to breast cancer recurrence identified based on Logit model analysisSemantic proximity Read ↗

PreciseBreast is prognostic for breast cancer recurrence in a 21-gene assay managed cohortSemantic proximity Read ↗

Molecular Classification of Breast Cancer Utilizing Long Non-Coding RNA (lncRNA) Transcriptomes Identifies Novel Diagnostic lncRNA Panel for Triple-Negative Breast CancerSemantic proximity Read ↗

Genomic Machine Learning Model Predicts Radiation Therapy Benefit in Early-Stage Breast Cancer Patients with High AccuracySemantic proximity Read ↗

Integrative Transcriptomic Analysis Identifies a Novel 22-Gene Signature Driving Breast Cancer Progression via the Proteasome-Chemokine AxisSemantic proximity Read ↗

Integrative Transcriptomic Analysis Identifies a Novel 22-Gene Signature Driving Breast Cancer Progression via the Proteasome-Chemokine AxisSemantic proximity Read ↗

Bridging and analytical validation of the Prosigna <sup>®</sup> Breast Risk of Recurrence Test as a whole-transcriptome NGS lab developed testSemantic proximity Read ↗

Breast medical oncologists' use of standard prognostic factors to predict a 21-gene recurrence score.Semantic proximity Read ↗

Integrated proteomics and transcriptomics analysis reveals key regulatory genes between ER-positive/PR-positive and ER-positive/PR-negative breast cancerSemantic proximity Read ↗

Integrative Bioinformatics Approach to Identify Prognostic Gene Signatures for Risk Stratification in Thyroid CarcinomaSemantic proximity Read ↗

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