Expression and Signaling Pathways of Nerve Growth Factor (NGF) and Pro-NGF in Breast Cancer: A Systematic Review
Bibliographic record
Abstract
Breast cancer represents the most common type of cancer and is the leading cause of death due to cancer among women. Thus, the prevention and early diagnosis of breast cancer is of primary urgency, as well as the development of new treatments able to improve its prognosis. Nerve Growth Factor (NGF) is a neurotrophic factor involved in the regulation of neuronal functions through the binding of the Tropomyosin receptor kinase A (TrkA) and the Nerve Growth Factor receptor or Pan-Neurotrophin Receptor 75 (NGFR/p75NTR). In addition, its precursor (pro-NGF) can extert biological activity by forming a trimeric complex with NGFR/p75NTR and sortilin, or by binding to TrkA receptors with low affinity. Several examples of in vitro and in vivo evidence show that NGF is both synthesized and released by breast cancer cells, and has mitogen, antiapoptotic and angiogenic effects on these cells through the activation of different signaling cascades that involve TrkA and NGFR/p75NTR receptors. Conversely, pro-NGF signaling has been related to breast cancer invasion and metastasis. Other studies suggested that NGF and its receptors could represent a good diagnostic and prognostic tool, as well as promising therapeutic targets for breast cancer. In this paper, we comprehensively summarize and systematically review the current experimental evidence on this topic. INPLASY ID: INPLASY2022100017.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| 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) | 0.005 | 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".