S100 proteins and their influence on pro-survival pathways in cancer
Bibliographic record
Abstract
The S100 gene family is composed of at least 20 members that share a common structure defined in part by the Ca2+ binding EF-hand motif. These genes which are expressed in a discriminate fashion in specific cells and tissues, have been described to have either an intracellular or extracellular function, or both. S100 proteins are implicated in the immune response, differentiation, cytoskeleton dynamics, enzyme activity, Ca2+ homeostasis and growth. A potential role for S100 proteins in neoplasia stems from these activities and from the observation that several S100 proteins have altered levels of expression in different stages and types of cancer. While the precise role and importance of S100 proteins in the development and promotion of cancer is poorly understood, it appears that the binding of Ca2+ is essential for exposing amino acid residues that are important in forming protein-protein interactions with effector molecules. The identity of some of these effector molecules has also now begun to emerge, and with this the elucidation of the signaling pathways that are modulated by these proteins. Some of these interactions are consistent with the diverse functions noted above. Others suggest that, many S100s may also promote cancer progression through specific roles in cell survival and apoptosis pathways. This review summarizes these findings and their implications.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".