WISP1 is associated to advanced disease, EMT and an inflamed tumor microenvironment in multiple solid tumors
Bibliographic record
Abstract
Background: WNT1-Inducible Signaling Pathway Protein 1 (WISP1) is implicated in prostate cancer growth and metastasis and the regulation of inflammation in diverse benign diseases. The objectives of this study were to assess the prognostic value of WISP1, its association to inflammation and its relevance as a biomarker for immune checkpoint blockade (ICB) response.Methods: Publicly available RNA-seq datasets were used to evaluate the prognostic value of WISP1 gene expression and its association with tumor-infiltrating lymphocytes, inflamed tumor microenvironment, and anti-PD-1 ICB response. A tissue microarray (TMA) including 285 radical prostatectomy specimens was used to confirm these associations in prostate cancer. The effect of recombinant WISP1 (rWISP1) on inflammatory cytokines was assessed in vitro.Results: High levels of WISP1 correlated with BCR-free survival in prostate adenocarcinoma and overall survival in primary melanoma, low-grade glioma, and kidney papillary cell carcinoma. Some effects could be accounted for by higher WISP1 expression in advanced disease. High WISP1 expression in prostate adenocarcinoma was correlated with CD8+ cells density. In vitro, rWISP1 increased inflammatory cytokine production. High WISP1 gene expression in RNA-seq datasets was correlated with gene signatures of multiple immune cell types as well as an inflammatory cytokine, immune checkpoint, and epithelial-mesenchymal transition (EMT) gene expression. WISP1 mRNA expression was associated with primary resistance to ICB in datasets showing EMT.Conclusions: Our results support an association between WISP1 expression and advanced disease, EMT and an inflamed tumor microenvironment in multiple solid tumors. The consequences of WISP1 expression on cancer immunotherapy remains to be addressed.
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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".