Proteomic signatures of radioresistance: Alteration of inflammation, angiogenesis and metabolism-related factors in radioresistant oesophageal adenocarcinoma
Bibliographic record
Abstract
The clinical management of locally advanced oesophageal adenocarcinoma (OAC) involves neoadjuvant chemoradiotherapy (CRT), but as radioresistance remains a major clinical challenge, complete pathological response to CRT only occurs in 20-30% of patients. In this study we used an established isogenic cell line model of radioresistant OAC to detect proteomic signatures of radioresistance to identify novel molecular and cellular targets of radioresistance in OAC. A total of 5785 proteins were identified of which 251 were significantly modulated in OE33R cells, when compared to OE33P. Gene ontology and pathway analysis of these significantly modulated proteins demonstrated altered metabolism in radioresistant cells accompanied by an inhibition of apoptosis. In addition, inflammatory and angiogenic pathways were positively regulated in radioresistant cells compared to the radiosensitive cells. In this study, we demonstrate, for the first time, a comprehensive proteomic profile of the established isogenic cell line model of radioresistant OAC. This analysis provides insights into the molecular and cellular pathways which regulate radioresistance in OAC. Furthermore, it identifies pathway specific signatures of radioresistance that will direct studies on the development of targeted therapies and personalised approaches to radiotherapy.
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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 | 0.000 |
| 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".