The emerging role of FAM46C as a biomarker and therapeutic target in gastric adenocarcinoma
Bibliographic record
Abstract
On a global scale, gastric adenocarcinoma (GCa) accounts for a large burden of death from cancer. Despite advances in systemic therapy and surgical technique, the fatality rate for GCa remains unacceptably high in Europe and North America, where diagnosis is typically made at an advanced stage. Biomarkers that can accurately predict response to new therapies and provide novel therapeutic strategies are urgently sought. FAM46C, a putative noncanonical nucleotidyltransferase, has garnered interest for its tumor suppressor function in multiple myeloma. A frequent and profound depletion of FAM46C has been described in GCa patients from China, Japan and now Canada. Furthermore, the degree of FAM46C depletion meaningfully portends cancer recurrence following resection, and death from GCa. In this review, we provide an updated summary of the literature regarding FAM46C as a biomarker in GCa and explore the potential mechanism(s) through which FAM46C depletion promotes GCa progression, including dis-inhibition of oncogenic Plk4 kinase activity. We highlight the potential for restoration of FAM46C levels as a therapeutic strategy. Norcantharidin, a synthetic analogue of the traditional Chinese medicine cantharidin derived from the blister beetle, is the only bio-available compound presently known to upregulate FAM46C expression and is under investigation in phase one trials in cancer patients.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".