Protein Profiling of Microdissected Prostate Tissue Links Growth Differentiation Factor 15 to Prostate Carcinogenesis
Bibliographic record
Abstract
Identification of proteomic alterations associated with early stages in the development of prostate cancer may facilitate understanding of progression of this highly variable disease. Matched normal, high-grade prostatic intraepithelial neoplasia (hPIN) and prostate cancer cells of predominantly Gleason grade 3 were procured by laser capture microdissection from serial sections obtained from snap-frozen samples dissected from 22 radical prostatectomy specimens. From these cells, protein profiles were generated by surface-enhanced laser desorption/ionization-time of flight mass spectrometry. A 24-kDa peak was observed at low or high intensity in profiles of prostate cancer cells in 19 of 27 lesions and at low intensity in 3 of 8 hPIN lesions but was not detectable in matched normal cells. SDS-PAGE analysis of prostate cancer and matched normal epithelium confirmed expression of a prostate cancer-specific 24-kDa protein. Mass spectrometry and protein data-based analysis identified the protein as the dimeric form of mature growth differentiation factor 15 (GDF15). The increased expression of mature GDF15 protein in prostate cancer cells cannot be explained on the basis of up-regulation of GDF15 mRNA because reverse transcription-PCR analysis showed similar amounts of transcript in normal, hPIN, and prostate cancer cells that were obtained by laser capture microdissection in the same set of serial sections from which the protein profiles were obtained. Our findings suggest that early prostate carcinogenesis is associated with expression of mature GDF15 protein.
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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".