Temporal proteomic analysis of IGF‐1R signalling in MCF‐7 breast adenocarcinoma cells
Bibliographic record
Abstract
Abstract Dysregulation of the insulin‐like growth factor 1 receptor signalling network is implicated in tumour growth and resistance to chemotherapy. We explored proteomic changes resulting from insulin‐like growth factor 1 stimulation of MCF‐7 adenocarcinoma cells as a function of time. Quantitative analysis using iTRAQ™ reagents and 2‐D LC‐MS/MS analysis of three biological replicates resulted in the identification of 899 proteins (p≤0.05) with an estimated mean false‐positive rate of 2.6%. Quantitative protein expression was obtained from 681 proteins. Further analysis by supervised k‐means clustering identified five temporal clusters, which were submitted to the FuncAssociate server to assign overrepresented gene ontology terms. Proteins associated with vesicle transport were significantly overrepresented. We further analyzed our data set for proteins showing temporal significance using the software, extraction and analysis of differential gene expression, resulting in 20 significantly and temporally changing proteins (p≤0.1). These significant proteins play roles in, among others, altered glucose metabolism (lactate dehydrogenase A and pyruvate kinase M1/M2) and cellular stress (nascent polypeptide‐associated complex subunit α and heat shock (HSC70) proteins). We used multiple reaction monitoring to validate these interesting proteins and have revealed several differences in relative peptide expression corresponding to protein isoforms and variants.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 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".