Extracellular heparan 6-O-endosulfatases SULF1 and SULF2 in HNSC and other malignancies
Bibliographic record
Abstract
ABSTRACT SULF1 and SULF2 are oncogenic in a number of human malignancies, including head and neck squamous cell carcinoma (HNSC). The function of these two heparan sulfate editing enzymes was previously considered largely redundant but the biology of cancer suggests differences that we explore in our RNAseq and RNAScope studies of HNSC and in a pan cancer analysis using the TCGA and CPTAC (proteomics) data. Our studies document a consistent upregulation of SULF1 and SULF2 in HNSC which is associated with poor survival outcomes. SULF2 expression increases in multiple malignancies but less consistently than SULF1, which uniformly increases in the tumor tissues and negatively impacts survival in several types of cancer. Meanwhile, SULF1 showed low expression in cancer cell lines and a scRNAseq study of HNSC shows that SULF1 is not supplied by epithelial tumor cells, like SULF2, but is secreted by cancer associated fibroblasts. Our RNAScope and PDX analysis of the HNSC tissues fully confirm the stromal source of SULF1 and explain the uniform impact of this enzyme on the biology of multiple malignancies. In summary, the SULF1 enzyme, supplied by a subset of cancer associated fibroblasts, is upregulated and negatively impacts HNSC survival at an early stage of the disease progression while the SULF2 enzyme, supplied by tumor cells, impacts survival at later stages of HNSC. This paradigm is common to multiple malignancies and suggests a potential for diagnostic and therapeutic targeting of the heparan sulfatases in cancer diseases.
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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".