Inverse Association between Raf Kinase Inhibitory Protein and Signal Transducers and Activators of Transcription 3 Expression in Gastric Adenocarcinoma Patients: Implications for Clinical Outcome
Bibliographic record
Abstract
PURPOSE: Raf Kinase Inhibitory Protein (RKIP) plays a pivotal role in cancer by regulating apoptosis induced by chemotherapeutic agents, or immune-mediated stimuli and is a metastasis suppressor protein. The signal transducer and activator of transcription 3 (STAT3) is a transcription factor that is frequently activated in gastric adenocarcinomas, thereby promoting tumor growth. We examined the expression patterns of RKIP and STAT3 with regard to human gastric cancer, predicting that elevated RKIP status may favor clinical outcome. EXPERIMENTAL DESIGN: Tissue microarrays were created from samples from 143 patients with gastric adenocarcinomas. The microarrays were immunohistochemically stained for RKIP and STAT3, and the intensity and extent of the staining was semiquantitatively scored. RESULTS: In intestinal-type gastric adenocarcinomas, RKIP and STAT3, expression were inversely associated. Cytoplasmic RKIP expression directly correlated with patient survival. Nuclear STAT3 expression inversely correlated with survival. In the diffuse tumor type, no significant correlation was found between RKIP and patient outcome. In the intestinal-type gastric adenocarcinoma, multivariate analysis adjusted for treatment types revealed RKIP and tumor stage to be significant independent predictors of survival. In the diffuse tumor type, stage was the only significant predictor of survival. CONCLUSION: These results indicate the predictive and protective role of cytoplasmic RKIP expression in gastric adenocarcinoma of the intestinal subtype. In contrast, nuclear STAT3 expression is associated with poor patient prognosis in the intestinal subtype. Significantly, we show an inverse association between RKIP and STAT3 and a positive correlation between RKIP and 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 | 0.001 |
| 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) | 0.001 | 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".