Current and future approaches for the therapeutic targeting of metastasis (Review)
Bibliographic record
Abstract
Metastasis is the process whereby cancer cells disseminate and establish secondary tumors at distant sites from the primary tumor and is estimated to be responsible for approximately 90% of all cancer deaths. Cancers with metastatic spread are frequently resistant to conventional chemotherapeutic approaches, underlining the urgent need for novel treatments in these diseases. Recent advances in understanding the mechanisms underlining both the intrinsic cellular and extrinsic micro-environmental factors contributing to the metastatic process have resulted in the identification of a number of molecular targets for the development of specific anti-metastatic therapeutic strategies. These targets include intracellular enzymes such as the protein tyrosine kinases, cell surface receptors and their ligands, and elements of the extracellular matrix such as pro-angiogenic factors, protease enzymes and cytokines. Many of these pathways interact with each other, with the possibility of multiple downstream antineoplastic consequences as well as the potential for synergistic effects by targeting more than one of these factors. This review outlines several of the promising targets, and provides examples, of how these targets are being exploited as anti-metastatic therapies in conjunction with conventional treatments.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.008 |
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".