Clustering Multi-Omics Data in Gleason Scores $3+4$ and $4+3$ Reveals 3 Patterns
Bibliographic record
Abstract
The Gleason Score (GS) is widely used to classify prostate cancer (Pca), with Gleason 7 (GS7) tumors typically being classified into Gleason$3+4$and Gleason$4+3$. However, this classification may overlook other molecular subtypes, limiting risk stratification and personalized treatment. Using multi-omics data analysis and unsupervised machine learning, this study investigates GS7 omics patterns. A total of 341 prostate cancer samples were clustered using five clustering techniques -$k-\text{Means}$, hierarchical clustering, Gaussian Mixture Model (GMM), DBSCAN, and Mean Shift Clustering, and cluster validity was determined using PCA visualization, dendrograms, and silhouettes. To further validate the biological relevance of the clusters, supervised classification models, including Random Forest and Support Vector Machine (SVM), were employed, achieving high classification accuracy (95.50% with Random Forest, 97.30% with SVM). GS7 tumors consistently showed three distinct subtypes, supporting the hypothesis of an unidentified molecular group. This finding challenges conventional classification paradigms and has significant implications for risk assessment and treatment strategies in prostate cancer. Future studies integrating larger patient cohorts and clinical validation are essential to establish the translational significance of this novel subtype.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".