Changes in the Apoptotic and Survival Signaling in Cancer Cells and Their Potential Therapeutic Implications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In normal healthy tissues, an equilibrium is established between cell death and survival. This equilibrium ensures that cells survive in the right milieu, but undergo programmed cell death (apoptosis) when damaged, or when the environment is no longer supportive. Diseases may occur with alterations in this homeostasis. For example, cancer cells may survive in an environment in which they would not normally exist. This is accomplished by alterations in the expressions or functions of genes controlling both survival and apoptotic signaling pathways. Survival signaling pathways involve the activation of cell surface receptors, serine threonine kinases, transcription factors as well as other molecules. In breast and ovarian cancers, the ErbB2 growth factor receptor is overexpressed and this contributes to the progression of these cancers, in part by constitutively activating survival signaling pathways. In contrast, apoptotic signal transduction pathways are often inhibited in cancer. For example, overexpression of Bcl-2 blocks apoptosis and this contributes to the accumulation of cells in follicular lymphomas and chronic lymphocytic leukemia. Furthermore, alterations in these signaling pathways in cancer cells may lead to drug resistance. Recent advances in molecular targeted therapies have taken advantage of alterations in survival and apoptotic signaling pathways in cancer to specifically target aberrantly regulated molecules. For example, Herceptin trade mark inhibits ErbB2 function and anti-sense oligonucleotides against Bcl-2 reduce Bcl-2 expression. These agents can thus induce apoptosis in the specific cancer cell against which they have been targeted. In this review, we will discuss alteration in survival and apoptotic signal transduction pathways in cancer and the development of novel chemotherapeutic drugs to target these pathways.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it