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The Role of the IGF Axis in Epithelial-to-Mesenchymal Transition during the Progression of Prostate Cancer

2015· article· en· W2218712189 on OpenAlexvenueno aff
Rehanna Mansor, Amit Bahl, Jeff M.P. Holly, Claire M. Perks

Bibliographic record

VenueJournal of Analytical Oncology · 2015
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsnot available
Fundersnot available
KeywordsProstate cancerEpithelial–mesenchymal transitionVimentinCancerCancer researchProstateMedicineTumor progressionOncologyInternal medicineMetastasisImmunohistochemistry

Abstract

fetched live from OpenAlex

Prostate cancer is the second most common lethal cancer in men worldwide. Despite the fact that the prognosis for patients with localized disease is good, many patients succumb to metastatic disease with the development of resistance to hormone treatments. This is normally termed castration-resistant prostate cancer (CRPC). The development of metastatic, castration-resistant prostate cancer has been associated with epithelial-to-mesenchymal transition (EMT), a process where cancer cells acquire a more mesenchymal phenotype with enhanced migratory potential, invasiveness and elevated resistance to apoptosis. The main event in EMT is the repression of epithelial markers such as E-cadherin and upregulation of mesenchymal markers such as N-cadherin, vimentin and fibronectin. The insulin-like growth factor (IGF) signalling axis is essential for normal development and maintenance of tissues, including that of the prostate, and dysregulation of this pathway contributes to prostate cancer progression and malignant transformation. It is becoming increasingly clear that one of the ways in which the IGF axis impacts upon cancer progression is through promoting EMT. This review will explore the role of EMT in prostate cancer progression with a specific focus on the involvement of the IGF axis and its downstream signalling pathways in regulating EMT in prostate 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.

How this classification was reachedexpand

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.233
Threshold uncertainty score0.179

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.024
GPT teacher head0.379
Teacher spread0.355 · 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 teacher head, 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

Citations1
Published2015
Admission routes1
Has abstractyes

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