Positive Correlation of Insulin-Like Growth Factor-II with Proliferating Cell Index in Patients with Colorectal Neoplasia
Bibliographic record
Abstract
BACKGROUND: Insulin-like growth factor-II (IGF-II) stimulates cell proliferation and is considered a potential risk factor for colorectal cancer. Tumor levels of IGF-II seem to positively correlate with colorectal cancer cell proliferation. This investigation examined the association of circulating IGF-II to the proliferating cell index (PCI) of tumor and matched normal mucosa in patients with colorectal neoplasia. METHODS: Circulating IGF-II level (ng/mL) was determined in the peripheral blood plasma by ELISA. The proliferating cells in tumor or matched normal mucosa were immunohistochemically stained using the primary antibody against Ki-67. Computer image analysis was used and PCI was expressed as the percentage of Ki-67-positive cells/total counted cells. RESULTS: Sixty-four patients were evaluated; 45 had colorectal neoplasia (27 males/18 females; mean age, 66.8 +/- 11.8 years) and 19 had hyperplastic polyps (6 males and 13 females; mean age, 58.4 +/- 14.4 years). Among patients with colorectal neoplasia, blood IGF-II levels were positively correlated with PCI in the matched normal mucosa (r = 0.46, P < 0.05) but not in the tumor. In patients with hyperplastic polyps, blood IGF-II levels were not correlated with the PCI in the polyps. Blood IGF-II levels were higher in colorectal cancer patients with Dukes' C/D stage (P < 0.01) or with positive lymph nodes (P < 0.01). CONCLUSION: Circulating IGF-II positively correlated with PCI in normal colonic mucosa of patients with colorectal neoplasia, suggesting that IGF-II may have a role in initiating the carcinogenic pathway by stimulating cell proliferation. Blood IGF-II was increased in advanced colorectal cancer, indicating that it might enhance colorectal cancer progression and be a useful marker of poor prognosis.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".