Prognostic value of human leukocyte antigen G expression in solid tumors: a systematic review and meta-analysis
Bibliographic record
Abstract
Introduction: Identification of modulators of the immune response with inhibitory properties that could be susceptible for therapeutic intervention is a key goal in cancer research. An example is the human leukocyte antigen G (HLA-G), a nonclassical major histocompatibility complex (MHC) class I molecule, involved in cancer progression. Methods: In this article we performed a systematic review and meta-analysis on the association between HLA-G expression and outcome in solid tumors. This study was performed in accordance with PRISMA guidelines and registered in PROSPERO. Results: A total of 25 studies met the inclusion criteria. These studies comprised data from 4871 patients reporting overall survival (OS), and 961 patients, reporting disease free survival (DFS). HLA-G expression was associated with worse OS (HR 2.09, 95% CI = 1.67 to 2.63; P < .001), that was higher in gastric (HR = 3.40; 95% CI = 1.64 to 7.03), pancreatic (HR = 1.72; 95% CI = 0.79 to 3.74) and colorectal (HR = 1.55; 95% CI = 1.16 to 2.07) cancer. No significant differences were observed between the most commonly utilized antibody (4H84) and other methods of detection. HLA-G expression was associated with DFS which approached but did not meet statistical significance. Discussion: In summary, we describe the first meta-analysis associating HLA-G expression and worse survival in a variety of solid tumors. Systematic Review Registration: https://www.crd.york.ac.uk/PROSPERO/, identifier CRD42022311973.
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.028 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".