Plasma concentrations of VCAM‐1 and PAI‐1: A predictive biomarker for post‐operative recurrence in colorectal cancer
Bibliographic record
Abstract
This prospective study used antibody suspension bead arrays to identify biomarkers capable of predicting post-operative recurrence with distal metastasis in patients with colorectal cancer. One hundred colorectal cancer patients who underwent surgery were enrolled in this study. The median follow-up period was 3.9 years. The pre-operative plasma concentrations of 24 angiogenesis-related molecules were analyzed with regard to the TNM stage and the development of post-operative recurrence. The concentrations of half of the examined molecules (13/24) increased significantly according to the TNM stage (P < 0.05). Meanwhile, a multivariate logistic regression analysis revealed that the concentrations of vascular cell adhesion molecule 1 (VCAM-1) and plasminogen activator inhibitor-1 (PAI-1) were significantly higher in the post-operative recurrence group. The VCAM-1 and PAI-1 model discriminated post-operative recurrence with an area under the curve of 0.82, a sensitivity of 0.75, and a specificity of 0.73. A leave-one-out cross-validation was applied to the model to assess the prediction performance, and the result indicated that the cross-validated error rate was 12.5% (12/96). In conclusion, our results demonstrate that antibody suspension bead arrays are a powerful tool to screen biomarkers in the clinical setting, and the plasma levels of VCAM-1 and PAI-1 together may be a promising biomarker for predicting post-operative recurrence in patients with colorectal cancer.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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 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".