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Record W3158360527 · doi:10.1186/s40364-021-00286-9

Pathophysiological roles of myristoylated alanine-rich C-kinase substrate (MARCKS) in hematological malignancies

2021· review· en· W3158360527 on OpenAlexafffund
Deepak Iyer, Omar Faruq, Lun Zhang, Nasrin Rastgoo, Aijun Liu, Hong Chang

Bibliographic record

VenueBiomarker Research · 2021
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Kinase Regulation and GTPase Signaling
Canadian institutionsToronto General HospitalUniversity of TorontoUniversity Health Network
FundersCancer Research Society
KeywordsMARCKSMyristoylationCell biologyBiologyCarcinogenesisCancer researchKinaseProtein kinase CPhosphorylationMedicineImmunologyCancerGenetics

Abstract

fetched live from OpenAlex

The myristoylated alanine-rich C-kinase substrate (MARCKS) protein has been at the crossroads of multiple signaling pathways that govern several critical operations in normal and malignant cellular physiology. Functioning as a target of protein kinase C, MARCKS shuttles between the phosphorylated cytosolic form and the unphosphorylated plasma membrane-bound states whilst regulating several molecular partners including, but not limited to calmodulin, actin, phosphatidylinositol-4,5-bisphosphate, and phosphoinositide-3-kinase. As a result of these interactions, MARCKS directly or indirectly modulates a host of cellular functions, primarily including cytoskeletal reorganization, membrane trafficking, cell secretion, inflammatory response, cell migration, and mitosis. Recent evidence indicates that dysregulated expression of MARCKS is associated with the development and progression of hematological cancers. While it is understood that MARCKS impacts the overall carcinogenesis as well as plays a part in determining the disease outcome in blood cancers, we are still at an early stage of interpreting the pathophysiological roles of MARCKS in neoplastic disease. The situation is further complicated by contradictory reports regarding the role of phosphorylated versus an unphosphorylated form of MARCKS as an oncogene versus tumor suppressor in blood cancers. In this review, we will investigate the current body of knowledge and evolving concepts of the physical properties, molecular network, functional attributes, and the likely pathogenic roles of MARCKS in hematological malignancies. Key emphasis will also be laid upon understanding the novel mechanisms by which MARCKS determines the overall disease prognosis by playing a vital role in the induction of therapeutic resistance. Additionally, we will highlight the importance of MARCKS as a valuable therapeutic target in blood cancers and will discuss the potential of existing strategies available to tackle MARCKS-driven blood cancers.

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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.120
GPT teacher head0.395
Teacher spread0.275 · 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

Citations20
Published2021
Admission routes2
Has abstractyes

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