Sig27 stratifies prostate cancer recurrence by assessing the immunosuppressive properties of tumors
Bibliographic record
Abstract
Prostate cancer (PC) remains a leading cause of cancer-related mortality in men, with recurrence contributing significantly to poor outcomes. Its molecular heterogeneity complicates effective risk stratification. We evaluated Sig27, a novel 27-gene panel, across 13 bulk RNA-seq datasets (n = 3,133 tumors) and 6 single-cell RNA-seq (scRNA-seq) datasets (n = 53 patients). Sig27 expression was elevated in PC compared to normal tissue and further increased in high-grade Gleason tumors, node-positive, and recurrent tumors. Sig27 demonstrated recurrence prediction comparable to Oncotype DX, with strong enrichment in immune regulatory pathways. To further investigate immune associations, we developed SigIC, a 22-gene immune checkpoint panel. Sig27 showed strong correlations with SigIC and individual immune checkpoints (e.g., HAVCR2, CD96, TIGIT) in both primary and metastatic PC. In scRNA-seq data, Sig27 was enriched in tumor-associated monocytes/macrophages (TAMs) and endothelial cells. We identified five key Sig27 genes - TFEC, FPR3, NOD2, LAMP3, and MCTP1 - and constructed Sig27IMG, a multigene panel formed by these five genes, and demonstrated their robust correlations with immune checkpoints and their strong enrichment in TAMs and endothelial cells. Sig27IMG strongly predicted PC recurrence and was dominantly expressed in TAMs, dendritic cells, and endothelial cells across 26 cancer types (n = 386 patients) in scRNA-seq studies and 17 cancer types (n = 5,672 patients) in bulk RNA-seq investigations. Notably, Sig27IMG stratified patients with a poor prognosis risk in these 17 cancer types. In summary, Sig27 and its derivative panel, Sig27IMG, offer a robust assessment of PC recurrence, highlighting immunosuppressive features mediated by TAMs, dendritic cells, and endothelial cells across multiple cancer types.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".