STAT3 Expression, Molecular Features, Inflammation Patterns, and Prognosis in a Database of 724 Colorectal Cancers
Bibliographic record
Abstract
PURPOSE: STAT3 is a transcription factor that is constitutively activated in some cancers. It seems to play crucial roles in cell proliferation and survival, angiogenesis, tumor-promoting inflammation, and suppression of antitumor host immune response in the tumor microenvironment. Although the STAT3 signaling pathway is a potential drug target, clinical, pathologic, molecular, or prognostic features of STAT3-activated colorectal cancer remain uncertain. EXPERIMENTAL DESIGN: Utilizing a database of 724 colon and rectal cancer cases, we evaluated phosphorylated STAT3 (p-STAT3) expression by immunohistochemistry. The Cox proportional hazards model was used to compute mortality HR, adjusting for clinical, pathologic, and molecular features, including microsatellite instability (MSI), the CpG island methylator phenotype (CIMP), LINE-1 methylation, 18q LOH, TP53 (p53), CTNNB1 (β-catenin), JC virus T-antigen, and KRAS, BRAF, and PIK3CA mutations. RESULTS: Among the 724 tumors, 131 (18%) showed high-level p-STAT3 expression (p-STAT3-high), 244 (34%) showed low-level expression (p-STAT3-low), and the remaining 349 (48%) were negative for p-STAT3. p-STAT3 overexpression was associated with significantly higher colorectal cancer-specific mortality [log-rank P = 0.0020; univariate HR (p-STAT3-high vs. p-STAT3-negative): 1.85, 95% CI: 1.30-2.63, P(trend) = 0.0005; multivariate HR: 1.61, 95% CI: 1.11-2.34, P(trend) = 0.015]. p-STAT3 expression was positively associated with peritumoral lymphocytic reaction (multivariate OR: 3.23; 95% CI: 1.89-5.53, P < 0.0001). p-STAT3 expression was not associated with MSI, CIMP, or LINE-1 hypomethylation. CONCLUSIONS: STAT3 activation in colorectal cancer is associated with adverse clinical outcome, supporting its potential roles as a prognostic biomarker and a chemoprevention and/or therapeutic target.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".