Human periprostatic white adipose tissue is rich in stromal progenitor cells and a potential source of prostate tumor stroma
Bibliographic record
Abstract
A body of growing evidence now implicates white adipose tissue as a relevant source of stromal progenitor cells recruited to the tumor microenvironment to form supportive tumor stroma. While the role of periprostatic (PP) adipose tissue in prostate cancer progression has been barely appreciated, we sought to determine the progenitor cell population in PP adipose tissue and the association with prostate cancer. We isolated and characterized CD31(-)CD34(+)CD45(-)CD146(-) progenitor cells (adipose-derived stem cells [ASC]) in paired samples of PP and preperitoneal visceral adipose tissue from prostate tissue and peripheral blood mononuclear cells of prostate cancer and nodular prostatic hyperplasia patients. ASC were quantified by flow cytometry and confirmed through target gene expression. Here we show a significantly higher amount of ASC in PP than in visceral adipose tissue, independent of body mass index and prostatic disease. In the prostate, ASC are increased in cancer compared with prostatic nodular hyperplasia patients. Concordantly, adipsin gene (CFD) expression, which is known to be up-regulated in adipose stem cells, was overexpressed in PP adipose tissue, in the prostate of cancer patients and in prostate CD31(-)CD34(+)CD45(-)CD146(-) sorted cells. ASC were found at higher levels in the blood of prostate cancer patients simultaneously overweight/obese. Present findings indicate that PP adipose tissue is a reservoir of progenitor cells with the potential to migrate towards prostate tumors, although its clinical significance merits further evaluation.
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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 | 0.000 | 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) | 0.002 | 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".