High FA2H and UGT8 transcript levels predict hydroxylated hexosylceramide accumulation in lung adenocarcinoma
Bibliographic record
Abstract
Lung cancer causes more deaths than any other cancer. Sphingolipids encompass metabolically interconnected species whose balance has pivotal effects on proliferation, migration, and apoptosis. In this study, we paralleled quantification of sphingolipid species with quantitative (q)PCR analyses of metabolic enzymes in order to identify dysregulated routes of sphingolipid metabolism in different subtypes of lung cancers. Lung samples were submitted to histopathological reexamination in order to confirm cancer type/subtype, which included adenocarcinoma histological subtypes and squamous cell and neuroendocrine carcinomas. Compared with benign lesions and tumor-free parenchyma, all cancers featured decreased sphingosine-1-phosphate and SMs. qPCR analyses evidenced differential mechanisms leading to these alterations between cancer types, with neuroendocrine carcinomas upregulating SGPL1 , but CERT1 being downregulated in adenocarcinomas and squamous cell carcinomas. 2-Hydroxyhexosylceramides (2-hydroxyHexCers) were specifically increased in adenocarcinomas. While UDP-glycosyltransferase 8 ( UGT8 ) transcript levels were increased in all cancer subtypes, fatty acid 2-hydroxylase ( FA2H ) levels were higher in adenocarcinomas than in squamous and neuroendocrine carcinomas. As a whole, we report differing mechanisms through which all forms of lung cancer achieve low SM and lysosphingolipids. Our results also demonstrate that FA2H upregulation is required for the accumulation of 2-hydroxyHexCers in lung cancers featuring high levels of UGT8 .
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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) | 0.002 | 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".