High‐throughput Assay and In Vivo Screen Identify <b>α</b> ‐2,3‐sialylation of CD98 by ST3GAL1 and ST3GAL2 as Essential to Melanoma Survival
Bibliographic record
Abstract
Melanoma is an aggressive type of skin cancer that accounts for most skin cancer deaths, and the incidence of melanoma has increased rapidly over the past decades. Early‐staged disease can be cured by surgery, however, a lack of curative treatments for patients with established melanoma metastasis results in significantly low survival rates. Thus, identification of key drivers in melanoma progression is essential to our understanding of melanoma biology. Glycosylation is a hallmark of cancer biology and altered glycosylation influences multiple facets of both tumor growth and progression. Herein, we utilized lectin microarrays to compare the glycomes of early transformation and melanoma progression. We found common glycan signatures, including an increase of α‐2,3‐sialosides, in both biological processes, and revealed glycans associated with site‐specific metastasis. Tandem analysis of using an innovative functional in vivo growth screening of essential glycogenes identified the underlying sialyltransferases ST3GAL1 and ST3GAL2 as essential for melanoma growth. We confirmed upregulation of ST3GAL1 and ST3GAL2 in melanoma via examination of transcriptomic datasets and human tissue microarrays. Proteomic analysis identified CD98 as a candidate glycoprotein responsible for promoting melanoma proliferation. Our studies reveal glycans may act as molecular drivers that functionally contribute to melanoma biology and opens up novel paths to develop glycan‐based therapeutics to treat melanoma patients.
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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 | 0.000 | 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".