Autophagic Heterogeneity in Gastric Adenocarcinoma
Bibliographic record
Abstract
Background and Aim Gastric/gastroesophageal junction (GEJ) adenocarcinoma is a heterogeneous disease, with various etiologies and with tumors encompassing a spectrum of histologic and molecular subtypes. “Autophagy” includes two related but distinct homeostatic processes that promote cell survival under adverse conditions, namely macro- and chaperone-mediated autophagy. There is increasing evidence of the roles autophagy may play in tumorigenesis. Methods Autophagic pathways were examined in the context of the heterogeneity intrinsic to gastric/GEJ adenocarcinoma, utilizing immunohistochemistry targeting specific proteins within the pathways (p62, LAMP2A, LC3B). We examined whole sections of normal and dysplastic gastric mucosa, as well as a tissue microarray of adenocarcinomas. Results Dysplastic gastric epithelium was marked by frequent nuclear p62 and aberrant LAMP2A expression compared to normal. Examining the pattern of LC3B/cytoplasmic p62 immuno-reactivity in gastric adenocarcinoma demonstrated a predominant pattern of LC3BHigh/p62High staining (56/86, 65.1%), which has been previously associated with active, but impaired macroautophagy. There were no statistically significant associations seen between LC3B/cytoplasmic p62 staining patterns with tumor grade, histotype, or approximated TCGA molecular subtype. LAMP2A and nuclear p62 and staining patterns were also heterogeneous across the cohort, but with no statistically significant associations seen. The prognostic significance of the three proteins was limited, however high nuclear p62 levels were associated with worse overall survival (log-rank p-value = 0.0396). Conclusion Our data demonstrate the dynamic nature of autophagic proteins in the gastric epithelium, and we expand the biological heterogeneity observed in gastric/GEJ adenocarcinoma to include autophagy.
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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 | 0.000 |
| 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 | 0.001 | 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".