Different Master Regulators Define Proximal and Distal Gastric Cancer: Insights into Prognosis and Opportunities for Targeted Therapy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background: Gastric cancer (GC) represents a significant global health burden with considerable heterogeneity in clinical and molecular behavior. The anatomical site of tumor origin—proximal versus distal—has emerged as a determinant of prognosis and response to therapy. The aim of this paper is to elucidate the transcriptional and regulatory differences between proximal gastric cancer (PGC) and distal gastric cancer (DGC) through master regulator (MR) analysis. Methods: We analyzed RNA-seq data from TCGA-STAD and microarray data from GEO (GSE62254, GSE15459). Differential gene expression and MR analyses were performed using DESeq2, limma, corto, and RegEnrich pipelines. A harmonized matrix of 4785 genes was used for MR inference following normalization and batch correction. Functional enrichment and survival analyses were conducted to explore prognostic associations. Results: Among 364 TCGA and 492 GEO patients, PGC was associated with more aggressive clinicopathological features and poorer outcomes. We identified 998 DEGs distinguishing PGC and DGC. PGC showed increased FOXM1 (a key regulator of cell proliferation), STAT3, and NF-κB1 activity, while DGC displayed enriched GATA6, CDX2 (a marker of intestinal differentiation), and HNF4A signaling. Functional enrichment highlighted proliferative and inflammatory programs in PGC, and differentiation and metabolic pathways in DGC. MR activity stratified survival outcomes, reinforcing prognostic relevance. Conclusions: PGC and DGC are governed by distinct transcriptional regulators and signaling networks. Our findings provide a biological rationale for location-based stratification and inform targeted therapy development.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 it