Pathophysiological roles of myristoylated alanine-rich C-kinase substrate (MARCKS) in hematological malignancies
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".