Quantitative profiling of LNCaP prostate cancer cells using isotope‐coded affinity tags and mass spectrometry
Bibliographic record
Abstract
Androgens are involved in the pathogenesis of diseases including adenocarcinoma of the prostate. These hormones are important for growth, maintenance, and integrity of structure and function of the prostate. Androgen-deprivation is currently the only effective systemic therapy for prostate cancer but the effects of androgens on the proteome are still poorly described. Here we quantitatively profile changes in the proteome of LNCaP human prostate cancer cells in response to androgen using the newly developed isotope-coded affinity tag (ICAT) labeling and two-dimensional liquid chromatography-tandem mass spectroscopy (2-D LC-MS/MS). ICAT enables the concurrent identification and comparative quantitative analysis of proteins present in various biological samples including human cell and tissue extracts. Quantification and identification of 139 proteins in complex protein mixtures obtained from androgen-stimulated and unstimulated LNCaP cells were achieved. Changes in levels of 77 proteins in response to androgens were detected. Some of these proteins have been previously reported to be regulated by androgens and include spermine synthase, fatty acid synthase and calreticulin precursor. A large number of proteins that have not been previously reported to be expressed in prostate cells were also quantitatively identified. Examples of these include members of the dual specificity protein phosphatase subfamily, "similar" to hypothetical protein DKFZp434B0328.1, "similar" to 14-3-3 protein zeta and "similar" to hypothetical protein 458, components of the actin cytoskeleton and a range of unknown/uncharacterized proteins. This catalogue of proteins provides an overview of the proteome of prostate cancer cells and the global changes that occur in response to androgens.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".