High expression of MAGE-A9 correlates with unfavorable survival in hepatocellular carcinoma
Bibliographic record
Abstract
Melanoma-associated antigens (MAGE)-A9 has been reported to play important roles in the development of human cancers. However, the association between MAGE-A9 expression and the clinicopathological characteristics of hepatocellular carcinoma (HCC) is not well understood. The study was to detect the expression of MAGE-A9 in human HCC and investigate the association between its expression and the clinicopathological characteristics of HCC. Reverse transcription-polymerase chain reaction (RT-PCR), one-step quantitative -PCR (qPCR) and immunohistochemistry (IHC) analyses were performed to characterize the expression of MAGE-A9 in HCC cell lines and tissues. Kaplan-Meier survival and Cox regression analyses were employed to evaluate the prognosis of 100 HCC patients. The results showed that the expression of MAGE-A9 in HCC was significantly higher than that in non-cancerous cells and tissues. Moreover, the expression level of the MAGE-A9 protein in HCC was related to the pathological grade (p = 0.003), portal vein invasion (p = 0.001), distant metastasis (p = 0.022) and TNM stage (p = 0.005). Cox regression analysis further revealed that MAGE-A9 expression is an independent prognostic factor for disease-free survival (p = 0.006) and overall survival (p = 0.022). These data are the first to indicate that MAGE-A9 expression is a valuable prognostic biomarker for HCC and that high MAGE-A9 expression suggests unfavorable survival outcomes in HCC 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".