Identification of the Myosin Heavy Polypeptide 9 as a Downstream Effector of the Proprotein Convertases in the Human Colon Carcinoma HT-29 Cells
Bibliographic record
Abstract
In addition to the large spectrum of the protein precursors processed and activated by the proprotein convertases (PCs) that are crucial for the maintenance of the malignant phenotype of colon cancer cells such as matrix metalloproteases, adhesion molecules, growth factors, and growth factor receptors, the PCs also regulate the expression and the activity of other proteins that are not PC substrates and involved in the acquisition of the metastatic and tumorigenic potential of these tumor cells. The identification in colon cancer cells of such proteins is thereby crucial for the understanding of the cascade of molecular events regulated by the PCs leading to tumorigenesis and metastasis and thus may constitute potential candidates for new colon cancer-specific targets and/or biomarkers. Using the human colon cancer cells HT-29 and ProteinChip arrays analysis that apply the surface-enhanced laser desorption ionization time-of-flight mass spectrometry (SELDI-TOF-MS), we identified the myosin heavy polypeptide 9 as new downstream effector of PCs in these cells. This protein was reported to be involved in the processes of malignant epithelial transformation and its role in colon cancer is unknown.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".