Molecular mechanisms of hepatic metastasis in colorectal cancer
Bibliographic record
Abstract
BACKGROUND: Colorectal cancer currently accounts for 11% of all cancers in the United States and is the second leading cause of cancer-related death, with the majority of deaths attributable to hepatic metastases. Many new studies are elucidating the complex molecular factors involved in this event, which could be used to generate clinically applicable screening and therapeutic tools. METHODS: An initial Pubmed and Medline literature search using keywords such as, molecular factor, colorectal cancer, hepatic metastasis/es, and main headings, such as angiogenesis, was reviewed. Since there are many molecular factors involved in this process not all could be included in this review. The list of discussed gene products was limited to the most studied factors, identified by the number of references in the literature search, and additional recently discovered gene products with in-vivo evidence of strong metastasis association. RESULTS: Twenty molecular factors were identified and included in the discussion of this review article. The molecular factors were separated into four groups based on their function, they are: proteolysis, adhesion, angiogenesis, and cell survival. All factors have a promising role as a screening or therapeutic target. CONCLUSION: This review has identified the many recent advances in elucidating the pathways involved in colorectal cancer hepatic metastasis. By better understanding the many complex molecular events involved in metastasis, novel screening and therapeutic tools may be developed with the ultimate goal of preventing metastasis and increasing patient survival.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".