Proteomics and phosphoproteomics of chordoma biopsies reveal alterations in multiple pathways and aberrant kinases activities
Bibliographic record
Abstract
Background: Chordoma is a slow-growing but malignant subtype of bone sarcoma with relatively high recurrence rates and high resistance to chemotherapy. It is urgent to understand the underlying regulatory networks to determine more effective potential targets. Phosphorylative regulation is currently regarded as playing a significant role in tumorigenesis, and the use of tyrosine kinase inhibitors in clinical practice has yielded new promise for the treatment of a variety of sarcoma types. Materials and methods: We performed comprehensive proteomic and phosphoproteomic analyses of chordoma using four-dimensional label-free liquid chromatography-tandem mass spectrometry (LC-MS/MS) and bioinformatics analysis. The potential aberrantly expressed kinases and their functions were validated using western blotting and CCK-8 assays. Results: Compared with paired normal muscle tissues, 1,139 differentially expressed proteins (DEPs) and 776 differentially phosphorylated proteins (DPPs) were identified in chordoma tumor tissues. The developmentally significant Wnt-signaling pathway and oxidative phosphorylation were aberrant in chordoma. Moreover, we predicted three kinases (AURA, CDK9, and MOK) with elevated activity by kinase-pathway network analysis (KiPNA) and verified their increased expression levels. The knockdown of these kinases markedly suppressed chordoma cell growth, and this was also the case for cells treated with the CDK9 inhibitor AZD4573. We additionally examined 208 proteins whose expression and phosphorylation levels were synergetically altered. Conclusions: We herein depicted the collective protein profiles of chordomas, providing insight into chordomagenesis and the potential development of new therapeutic targets.
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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 | 0.000 |
| 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) | 0.001 | 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".