Protein Kinases in Phagocytosis: Promising Genetic Biomarkers for Cancer
Bibliographic record
Abstract
Abstract Cancer is a complex disease characterized by genetic and molecular diversity, often involving dysregulation of critical cellular pathways. Recent advances in pan-cancer research have highlighted the importance of shared oncogenic mechanisms across different cancer types, providing new avenues for therapeutic exploration. Protein kinases, particularly those involved in phagocytosis, play pivotal roles in cellular homeostasis and immune response. This study systematically examines the genetic alterations and expression profiles of protein kinases associated with phagocytosis across various cancer types, using data from The Cancer Genome Atlas (TCGA) and other publicly available resources. We analyzed single nucleotide variations (SNVs), copy number variations (CNVs), methylation patterns, and mRNA expression to identify recurring alterations and their associations with survival outcomes. Our findings reveal that MET and MERTK are the most frequently mutated genes, with missense mutations dominating across cancers. CNV analysis shows significant correlations with survival in cancers like UCEC, KIRP, and KIRC, while methylation analysis indicates cancer-specific regulatory patterns affecting gene expression. Differential expression analysis highlights distinct cancer-type-specific expression profiles, with genes like MET and BTK displaying significant variation. Crosstalk pathway analysis further reveals the involvement of these kinases in key cancer-related pathways, such as epithelial-mesenchymal transition (EMT) and apoptosis. Drug sensitivity analysis identifies potential therapeutic targets, with gene expression correlating significantly with cancer cell line responsiveness to specific compounds. These findings underscore the importance of the phagocytotic kinome in cancer biology and suggest potential therapeutic strategies targeting protein kinases to enhance immune response and improve treatment outcomes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".