The dermcidin gene in cancer: role in cachexia, carcinogenesis and tumour cell survival
Bibliographic record
Abstract
PURPOSE OF REVIEW: The diverse protein products of the dermcidin gene are relevant to immunity, cancer cell progression and cancer cachexia. This article evaluates recent developments/controversies around dermcidin. RECENT FINDINGS: Dermcidin has recently been shown to act as a survival/proliferation factor in hepatoma and prostate cancer cell lines. Recent studies suggest that the Y-P30 subunit of the dermcidin polypeptide offers a survival advantage in such cancer cells. Nevertheless, the relevance of Y-P30 to cancer growth in vivo, and mechanisms of action remain unknown. In mice, tumour cells appear to glycosylate the Y-P30 subunit, transforming it into a potent skeletal muscle proteolysis-inducing factor. Recent work has described a receptor and signal transduction pathways for murine glycosylated proteolysis-inducing factor. The absence of classical N-glycosylation sites in the human proteolysis-inducing factor peptide and the lack of specific tools for the detection of the key carbohydrate moieties conferring the proteolysis-inducing activity, however, remain barriers to confirming glycosylated proteolysis-inducing factor as a pro-cachectic factor in humans. SUMMARY: There is a growing body of evidence illustrating dermcidin as an oncogene and Y-P30 as a survival factor. The biology of murine proteolysis-inducing factor as a pro-cachectic factor continues to evolve; however, its role in human biology remains speculative.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".