Changes in the Apoptotic and Survival Signaling in Cancer Cells and Their Potential Therapeutic Implications
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".