Human Leukocyte Antigen G Polymorphism and Expression Are Associated with an Increased Risk of Non-Small-Cell Lung Cancer and Advanced Disease Stage
Bibliographic record
Abstract
Human leukocyte antigen (HLA)-G acts as negative regulator of the immune responses and its expression may enable tumor cells to escape immunosurveillance. The purpose of this study was to investigate the influence of HLA-G allelic variants and serum soluble HLA-G (sHLA-G) levels on risk of non-small-cell lung cancer (NSCLC). We analyzed 191 Caucasian adults with NSCLC and 191 healthy subjects recruited between January 2009 and March 2014 in Ariana (Tunisia). Serum sHLA-G levels were measured by immunoassay and HLA-G alleles were determined using a direct DNA sequencing procedures. The heterozygous genotypes of HLA-G 010101 and -G 010401 were associated with increased risks of both NSCLC and advanced disease stages. In contrast, the heterozygous genotypes of HLA-G 0105N and -G 0106 were associated with decreased risks of NSCC and clinical disease stage IV, respectively. Serum sHLA-G levels were significantly higher in patients with NSCLC and particularly in those with advanced disease stages compared to healthy subjects. The area under the receiver-operating characteristic (ROC) curves was 0.82 for controls vs patients. Given 100% specificity, the highest sensitivity achieved to detect NSCLC was 52.8% at a cutoff value of 24.9 U/ml. Patients with the sHLA-G above median level (≥ 50 U/ml) had a significantly shorter survival time. This study demonstrates that HLA-G allelic variants are independent risk factors for NSCLC. Serum sHLA-G levels in NSCLC patients could be useful biomarkers for the diagnostic and prognosis of NSCLC.
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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) | 0.002 | 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".