Subcellular proteomics analysis of different stages of colorectal cancer cell lines
Bibliographic record
Abstract
Studying cell differentiation and transformation allows a better understanding of the mechanisms involved in the initiation and the evolution of cancer. The role of proteins which participate in these processes is dependent on their location within the cell. Determining the subcellular localization of proteins or the changes in localization is, therefore, paramount in elucidating their role. Using quantitative mass spectrometry, we characterized the protein expression and subcellular localization of nearly 5000 proteins from seven different colorectal cancer (CRC) cell lines, as well as normal colon fibroblasts and intestinal epithelial cells. This cellular characterization allowed the identification of colon cancer-associated proteins with differential expression patterns as well as deregulated protein networks and pathways. Indeed, our results demonstrate differential expression of proteins involved in cell adhesion, cytoskeleton, and transcription in colon cancer cells compared to normal colon-derived cells. Pathway analyses identified different cellular functions, including endocytosis and eIF2 signaling, whose deregulation correlates with mutations found in the different CRC phenotypes. Our results provide an unbiased, quantitative and high-throughput approach to measure changes in protein expression and subcellular protein locations in different CRC cell lines.
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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) | 0.001 | 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".