HLA-G Gene Variability Is Associated with Papillary Thyroid Carcinoma Morbidity and the HLA-G Protein Profile
Bibliographic record
Abstract
Human leukocyte antigen (HLA)-G is an immune checkpoint molecule that is highly expressed in papillary thyroid carcinoma (PTC). The HLA-G gene presents several functional polymorphisms distributed across the coding and regulatory regions (5′URR: 5′ upstream regulatory region and 3′UTR: 3′ untranslated region) and some of them may impact HLA-G expression and human malignancy. To understand the contribution of the HLA-G genetic background in PTC, we studied the HLA-G gene variability in PTC patients in association with tumor morbidity, HLA-G tissue expression, and plasma soluble (sHLA-G) levels. We evaluated 185 PTC patients and 154 healthy controls. Polymorphic sites defining coding, regulatory and extended haplotypes were characterized by sequencing analyses. HLA-G tissue expression and plasma soluble HLA-G levels were evaluated by immunohistochemistry and ELISA, respectively. Compared to the controls, the G0104a(5′URR)G*01:04:04(coding)UTR-03(3’UTR) extended haplotype was underrepresented in the PTC patients, while G0104a(5′URR)G*01:04:01(coding)UTR-03(3′UTR) was less frequent in patients with metastatic and multifocal tumors. Decreased HLA-G tissue expression and undetectable plasma sHLA-G were associated with the G010102a(5′URR)G*01:01:02:01(coding)UTR-02(3′UTR) extended haplotype. We concluded that the HLA-G variability was associated with PTC development and morbidity, as well as the magnitude of the encoded protein expression at local and systemic levels.
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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 | 0.001 |
| 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".