Surface Proteomic Analysis Reveals the Presence of Noncanonical Cell Membrane Endoplasmic Reticulum Chaperones in High-Grade Gliomas
Bibliographic record
Abstract
High-grade gliomas (HGG) are highly aggressive tumors, which are predominately fatal for adults and pediatric patients. Identifying cancer-selective therapeutic targets remains a critical unmet need. The overexpression of endoplasmic reticulum (ER) chaperones in various cancers is well documented. Moreover, tumor cells exhibit an atypical surface expression of ER chaperones, suggesting the potential for selective targeting. Our study examined the differences in the mRNA, total protein, and surface expression levels of seven key ER chaperones, compared with those in non-neoplastic samples. Notably, a poor correlation was found between mRNA, protein, and surface protein levels, underscoring the limitations of transcriptomics alone in target discovery. We also highlight the limitations of surfaceome studies which exclude noncanonical membrane proteins, such as ectopically expressed ER chaperones, which often escape detection by conventional bioinformatic pipelines. For the first time, this study advances our understanding of the surface expression of ER chaperones in both adult and pediatric HGG. Our findings highlight the importance of surfaceome analysis in the discovery of cancer selective targets against this devastating disease.
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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".