Large scale phosphoproteome analysis of LNCaP human prostate cancer cells
Bibliographic record
Abstract
Prostate cancer is the most frequently diagnosed cancer among men in the western world. The androgen receptor, a phosphoprotein, is suspected to be involved in all stages of the prostate cancer. Androgen receptor activity can be modulated by various kinases such as PKA, MAPK, AKT, and Src. Phosphorylation is an important post-translational modification and serves as a molecular on-off switch to regulate signaling. Disruptions of cellular phosphorylation are associated with various diseases such as cancer and kinases provide important drug targets. Here we present an analysis of the phosphoproteome in LNCaP human prostate cancer cells. The analytical strategy employed here used proteomics based methodologies with a combination of detergents and chaotropic reagents during trypsin digestion followed by titanium dioxide enrichment of phosphopeptides. Over the course of multiple analyses by mass spectrometry we identified a total of 746 phosphorylation sites in 540 phosphopeptides corresponding to 116 phosphoproteins, of which 56 had not been previously reported. Phosphoproteins identified included transcription factors, co-regulators of the androgen receptor, and cancer-related proteins that include β-catenin, USP10, and histone deacetylase-2. The information of signaling pathways, motifs of phosphorylated peptides, biological processes, molecular functions, cellular components, and protein interactions from the identified phosphoproteins established a map of phosphoproteome and signaling pathways in LNCaP cells.
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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 | 0.001 | 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) | 0.001 | 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".