<i>SRC</i> gene expression in human cancer: the role of transcriptional activation
Bibliographic record
Abstract
Human pp60c-Src (or c-Src) is a 60 kDa nonreceptor tyrosine kinase encoded by the SRC gene and is the cellular homologue to the potent transforming v-Src viral oncogene. c-Src functions at the hub of a vast array of signal transduction cascades that influence cellular proliferation, differentiation, motility, and survival. c-Src activation has been documented in upwards of 50% of tumors derived from the colon, liver, lung, breast, and pancreas. Therefore, a major focus has been to understand the mechanisms of c-Src activation in human cancer. Early studies concentrated on post-translational mechanisms that lead to increased c-Src kinase activity, which often correlated with overexpression of c-Src protein. More recently, the discovery of an activating SRC mutation in a small subset of advanced colon tumors has been reported. In addition, elevated SRC transcription has been identified as yet another mechanism contributing significantly to c-Src activation in a subset of human colon cancer cell lines. Interestingly, histone deacetylase (HDAC) inhibitors, agents with well-documented anti-cancer activity, repress SRC transcription in a wide variety of human cancer cell lines. Analysis of the mechanisms behind HDAC inhibitor mediated repression could be utilized in the future to specifically inhibit SRC gene expression in human cancer.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".