MétaCan
Menu
Back to cohort
Record W2990275983 · doi:10.3324/haematol.2019.227579

Targeting CD47/TNFAIP8 by miR-155 overcomes drug resistance and inhibits tumor growth through induction of phagocytosis and apoptosis in multiple myeloma

2019· article· en· W2990275983 on OpenAlexafffund
Nasrin Rastgoo, Jian Wu, Mariah Liu, Maryam Pourabdollah, Eshetu G. Atenafu, Donna Reece, Wei‐Min Chen, Hong Chang

Bibliographic record

VenueHaematologica · 2019
Typearticle
Languageen
FieldImmunology and Microbiology
TopicPhagocytosis and Immune Regulation
Canadian institutionsUniversity Health NetworkUniversity of Toronto
FundersLeukemia and Lymphoma ResearchLeukemia and Lymphoma Society of CanadaCancer Research Society
KeywordsCD47Multiple myelomaBortezomibCancer researchPhagocytosisApoptosisDownregulation and upregulationDrug resistanceIn vivoMedicineBiologyImmunology

Abstract

fetched live from OpenAlex

The mechanisms of drug resistance in multiple myeloma are poorly understood. Here we show that CD47, an integrin-associated receptor, is significantly upregulated in drug resistant myeloma cells in comparison with parental cells, and that high expression of CD47 detected by immunohistochemistry is associated with shorter progression free and overall survivals in multiple myeloma patients. We show that miR-155 is expressed at low levels in drug resistant myeloma cells and is a direct regulator of CD47 through its 3'UTR. Furthermore, low miR-155 levels are associated with advanced stages of disease. MiR-155 overexpression suppressed CD47 expression on myeloma cell surface, leading to induction of phagocytosis of myeloma cells by macrophages and inhibition of tumor growth. MiR-155 overexpression also re-sensitized drug-resistant myeloma cells to bortezomib leading to cell death through targeting TNFAIP8, a negative mediator of apoptosis in vitro and in vivo. Thus, miR-155 mimics may serve as a promising new therapeutic modality by promoting phagocytosis and inducing apoptosis in patients with refractory/relapsed multiple myeloma.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.816

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.011
GPT teacher head0.217
Teacher spread0.206 · 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 designBench or experimental
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

Citations66
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueHaematologicaSame topicPhagocytosis and Immune RegulationFrench-language works237,207