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Record W4382982412 · doi:10.3390/cancers15133468

The Hippo Pathway Effectors YAP/TAZ-TEAD Oncoproteins as Emerging Therapeutic Targets in the Tumor Microenvironment

2023· review· en· W4382982412 on OpenAlexafffund
Reza Bayat Mokhtari, Neda Ashayeri, Leili Baghaie, Manpreet Sambi, Kosar Satari, Narges Baluch, Dmitriy A. Bosykh, Myron R. Szewczuk, Sayan Chakraborty

Bibliographic record

VenueCancers · 2023
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHippo pathway signaling and YAP/TAZ
Canadian institutionsHospital for Sick ChildrenQueen's University
FundersUniversity of TorontoQueen's UniversityRoswell Park Cancer Institute
KeywordsHippo signaling pathwayBiologyCancer researchTranscription factorCarcinogenesisSuppressorEffectorTumor microenvironmentEpigeneticsCell biologyCancerTumor cellsGeneticsGene

Abstract

fetched live from OpenAlex

Various cancer cell-associated intrinsic and extrinsic inputs act on YAP/TAZ proteins to mediate the hyperactivation of the TEAD transcription factor-based transcriptome. This YAP/TAZ-TEAD activity can override the growth-limiting Hippo tumor-suppressor pathway that maintains normal tissue homeostasis. Herein, we provide an integrated summary of the contrasting roles of YAP/TAZ during normal tissue homeostasis versus tumor initiation and progression. In addition to upstream factors that regulate YAP/TAZ in the TME, critical insights on the emerging functions of YAP/TAZ in immune suppression and abnormal vasculature development during tumorigenesis are illustrated. Lastly, we discuss the current methods that intervene with the YAP/TAZ-TEAD oncogenic signaling pathway and the emerging applications of combination therapies, gut microbiota, and epigenetic plasticity that could potentiate the efficacy of chemo/immunotherapy as improved cancer therapeutic strategies.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.002

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 head0.027
GPT teacher head0.306
Teacher spread0.279 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations83
Published2023
Admission routes2
Has abstractyes

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