Neuropilin-1: A Multifaceted Target for Cancer Therapy
Bibliographic record
Abstract
Neuropilin-1 (NRP1), initially identified as a neuronal guidance protein, has emerged as a multifaceted regulator in cancer biology. Beyond its role in axonal guidance and angiogenesis, NRP1 is increasingly recognized for its significant impact on tumor progression and therapeutic outcomes. This review explores the diverse functions of NRP1 in cancer, encompassing its influence on tumor cell proliferation, migration, invasion, and metastasis. NRP1 interacts with several key signaling pathways, including vascular endothelial growth factor (VEGF), semaphorins, and transforming growth factor-beta (TGF-β), modulating the tumor microenvironment and promoting angiogenesis. Moreover, NRP1 expression correlates with poor prognosis in various malignancies, underscoring its potential as a prognostic biomarker. Therapeutically, targeting NRP1 holds promise as a novel strategy to inhibit tumor growth and enhance the efficacy of regular treatments such as chemotherapy and radiotherapy. Strategies involving NRP1-targeted therapies, including monoclonal antibodies, small molecule inhibitors, and gene silencing techniques, are being actively investigated in preclinical and clinical settings. Despite challenges in specificity and delivery, advances in understanding NRP1 biology offer new avenues for personalized cancer therapy. Although several types of cancer cells can express NRPs, the role of NRPs in tumor pathogenesis is largely unknown. Future investigations are needed to enhance our understanding of the effects and mechanisms of NRPs on the proliferation, apoptosis, and migration of neuronal, endothelial, and cancer cells. The novel frameworks or multi-omics approaches integrate data from multiple databases to better understand cancer's molecular and clinical features, develop personalized therapies, and help identify biomarkers. This review highlights the pivotal role of NRP1 in cancer pathogenesis and discusses its implications for developing targeted therapeutic approaches to improve patient outcomes, highlighting the role of OMICS in targeting cancer patients for personalized therapy.
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How this classification was reachedexpand
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) | 0.001 | 0.001 |
| 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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".