Protein Profiling of Microdissected Prostate Tissue Links Growth Differentiation Factor 15 to Prostate Carcinogenesis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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 it