Seminal plasma metabolomics reveals lysine and serine dysregulation as unique features distinguishing between prostate cancer tumors of Gleason grades 6 and 7
Bibliographic record
Abstract
Abstract Background Prostate cancer (PCa) is a metabolic disease. Most men are diagnosed with low grade indolent disease and differentiating these men from those who have life threatening cancer is a challenging but important clinical dilemma. There are currently limited biomarkers that can distinguish between the indolent Gleason grade 6 and higher‐grade disease. Moreover, some individuals initially diagnosed with low grade disease progress to higher grade disease. Currently prostate biopsies are the only reliable methods of stratifying risk, but biopsies can cause significant morbidity, sample only a small portion of the gland and are costly. Therefore, biomarkers distinguishing between indolent and aggressive patterns of PCa are urgently required to minimize biopsy‐associated morbidity, prevent over‐treatment of indolent PCa and to better stratify patients for appropriate treatment. Methods Seminal fluid samples were collected from normal individuals (n = 13) Before infertility treatment and histologically confirmed PCa patients (n = 51). 1H Nuclear magnetic resonance spectroscopy and orthogonal partial least square discriminant analysis were used to compare the populations. Results Alterations in amino acids levels, specifically lysine and serine and changes in glycolytic intermediates were the most significant metabolic features associated with differences between healthy controls and PCa and between Gleason grade 6 (GS6) and Gleason grade 7 (GS7) samples. Orthogonal partial least square plots discriminated healthy controls from PCa samples (R 2 = 0.54, Q 2 = 0.31; area under the receiver operating characteristics curve [AUC] = 0.96), and GS6 from GS7 samples (R 2 = 0.62, Q 2 = 0.49; AUC = 0.98) based on lysine and serine content. Conclusion This study suggests that seminal plasma metabolomics profiling of seminal fluid is a promising means of differentiating indolent from aggressive disease. Particularly, lysine and serine levels may be able to differentiate GS6 from GS7 disease.
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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".