Identification of secreted proteins regulated by cAMP in glioblastoma cells using glycopeptide capture and label‐free quantification
Bibliographic record
Abstract
Exposure of glioblastoma U87MG cells to a cAMP analog leads to a decrease in proliferation, invasion, and angiogenic potential. Here, we apply a label-free MS-based approach to identify formerly N-linked glycopeptides that change in abundance upon cAMP treatment. Over 150 unique glycopeptides in three biological repetitions were quantified, leading to the identification of 14 upregulated proteins and 21 downregulated proteins due to cAMP treatment. Of these, eight have been validated, either through comparison with microarray data or by Western blot. We estimate our ability to identify differentially expressed peptides at greater than 85% in a single biological repetition, while the analysis of multiple biological repetitions lowers the false positive rate to approximately 2%. Many of the proteins identified in this study are involved in cell signaling and some, such as Tenascin C, Cathepsin L, Neuroblastoma suppressor of tumorigenicity, and AXL/UFO tyrosine-protein kinase receptor, have been previously shown to be involved in glioblastoma progression. We also identify several semitryptic peptides that increase in abundance upon cAMP treatment, suggesting that cAMP regulates protease activity in these cells. Overall, these results demonstrate the benefits of using a highly specific enrichment method for quantitative proteomic experiments.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".