MétaCan
Menu
Back to cohort
Record W3155216659 · doi:10.1016/j.canlet.2021.04.002

LncRNA-miRNA axes in breast cancer: Novel points of interaction for strategic attack

2021· review· en· W3155216659 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueCancer Letters · 2021
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer-related molecular mechanisms research
Canadian institutionsDalhousie University
FundersCanadian Institutes of Health Research
KeywordsmicroRNABiologyBreast cancerComputational biologySuppressorRegulation of gene expressionGeneEpithelial–mesenchymal transitionCancerBioinformaticsCancer researchGeneticsMetastasis

Abstract

fetched live from OpenAlex

Therapeutic effectiveness in breast cancer can be limited by the underlying mechanisms of pathogenesis, including epithelial-mesenchymal transition (EMT), cancer stem cells (CSCs) and drug resistance. Long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) are master regulators of gene expression and are functionally important mediators in these mechanisms of pathogenesis. Intricate crosstalks between these non-coding RNAs form complex regulatory networks of post-transcriptional gene regulation. Depending on the specific lncRNA/miRNA interaction, the lncRNA-miRNA axis can have tumor suppressor or oncogenic effects, thus defining the lncRNA-miRNA axis is important for determining targetability. Herein, we summarize the current literature describing lncRNA-miRNA interactions that are critical in the molecular mechanisms that regulate EMT, CSCs and drug resistance in breast cancer. Further, we review both the well-studied and potential novel mechanisms of lncRNA-miRNA interactions in breast cancer.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.958
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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 head0.078
GPT teacher head0.401
Teacher spread0.323 · 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