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Abstract A2-61: Functional enrichment and pathway analysis of the transcriptome related to lymphovascular invasion and recurrence in axillary-node negative breast cancer

2015· article· en· W4244385207 on OpenAlexaff
Mathieu Blais, Shelley B. Bull, Dushanthi Pinnaduwage, Irene L. Andrulis

Bibliographic record

VenueCancer Research · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBreast Cancer Treatment Studies
Canadian institutionsLunenfeld-Tanenbaum Research InstituteUniversity of Toronto
Fundersnot available
KeywordsLymphovascular invasionKEGGTranscriptomeBreast cancerBiologyCarcinogenesisGene expression profilingGeneCancerGene expressionCancer researchComputational biologyGeneticsMetastasis

Abstract

fetched live from OpenAlex

Abstract We previously reported that the presence of Lymphovascular invasion (LVI) is an independent predictor of recurrence in axillary node-negative breast cancer (ANNBC). We produced a unique set of gene expression microarrays specifically designed to identify genes with altered expression in LVI+ tumors (n= 37) compared to LVI- tumors (n = 68). We further examined expression differences in a second analysis designed to discover, within LVI+ tumors alone, clinically relevant genes involved in early recurrence (n = 4, smaller than 4 years) vs. no recurrence for at least 10 years (n = 41). To identify genes and pathways involved in LVI and recurrence in ANNBC, we performed a functional analysis of the expression data. We used the publicly available database tool via WebGestalt to perform a web-based enrichment analysis including Gene ontology, KEGG and the Cytogenetic band. In parallel, using Ingenuity pathway® (IPA), we identified networks, upstream regulators and canonical pathways. IPA provides the probability that the association between the genes in the dataset and the canonical pathways could be explained by chance alone through a right-tailed Fisher's exact test p-value. IPA use a z-score algorithm for predicting activation which reduces the chance that random data will generate significant predictions. In LVI+ vs LVI- analysis, the top IPA activated biofunctions are related to proliferation and migration. In LVI+REC+ vs LVI+REC- analysis, they are related to cell survival and migration as well as lipid synthesis and metabolism. The subset of genes identified in both comparisons include those involved in polarization of cells, oxidation of lipids, cellular homeostasis and growth of the lymphatic system component, with decreased cell death and apoptosis biofunctions. Our bioinformatics analyses are consistent with a migratory phenotype for cancer cells associated with LVI. In addition, we observed that inflammation related pathways are enriched in the LVI+ subgroup that undergoes early recurrence. In the upstream regulator IPA analysis, TGFb NFKb and PI3K are involved in the LVI+ in respect to LVI tumors as well as in LVI+REC+ tumors in respect to LVI+REC- tumors. The results suggest important pathways as well as upstream regulators associated with LVI and recurrence that are involved in invasion and cell fate biofunctions. The findings are currently being evaluated through mRNA and protein validation as well as functional studies. Citation Format: Mathieu Blais, Shelley B. Bull, Dushanthi Pinnaduwage, Irene L. Andrulis. Functional enrichment and pathway analysis of the transcriptome related to lymphovascular invasion and recurrence in axillary-node negative breast cancer. [abstract]. In: Proceedings of the AACR Special Conference on Translation of the Cancer Genome; Feb 7-9, 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 1):Abstract nr A2-61.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.048
GPT teacher head0.336
Teacher spread0.288 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2015
Admission routes1
Has abstractyes

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