TGFB1-mediated autophagy facilitates oxaliplatin resistance in stomach adenocarcinoma
Bibliographic record
Abstract
Increasing evidence has indicated that transforming growth factor beta 1 (TGFB1) is engaged in tumorigenesis and progression. Nevertheless, the underlying role and mechanism of TGFB1 in stomach adenocarcinoma (STAD) chemotherapy remains unknown. TGFB1 levels in various types of cancers were first analyzed by the TCGA database. Next, the degree of cellular damage, apoptosis and autophagy were detected by lactate dehydrogenase kit, flow cytometry, autophagy fluorescence analysis, and Western blot assay. The gene highly correlated with TGFB1 expression was searched by LinkedOmics and KEGG. We disclosed TGFB1 was enhanced in STAD. Besides, TGFB1 was remarkably higher in STAD patients in oxaliplatin (OXA) chemoresistant group than sensitive group. Additionally, the half maximal inhibitory concentration (IC50) values of OXA-resistant cells were markedly elevated. Furthermore, TGFB1 reduced AGS-OXA and HGC27-OXA cell injury, inhibited apoptosis and induced cellular autophagy. The addition of the autophagy inhibitor 3-methyladenine hindered this phenomenon. Further studies revealed that muscle RAS oncogene homolog (MRAS) is a downstream target gene of TGFB1. TGFB1 accelerated MRAS level in OXA cells, and MRAS knockdown reversed the effects of TGFB1 on OXA cell function. TGFB1 induces cellular autophagy via MRAS, thereby promoting STAD OXA resistance.
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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 | 0.000 | 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".