Elevated high mobility group A2 expression in liver cancer predicts poor patient survival
Bibliographic record
Abstract
BACKGROUND: liver cancer is a malignant tumor with a high morbidity and mortality that endangers human health. High mobility group A2 (HMGA2) is a chromosome associated protein that participates in embryogenesis, tissue development, tumorigenesis and development. OBJECTIVE: to explore the relationship between HMGA2 expression and the clinicopathological parameters and survival of liver cancer patients using The Cancer Genome Atlas Liver Hepatocellular Carcinoma (HCC) data. METHODS: RNA-sequencing data and the corresponding clinical characteristics of the patients were downloaded from the Atlas database. The Chi-squared test was used to assess the relationship between HMGA2 expression and clinical variables. Cox regression analysis was used to compare survival rates between the high- and low-expressing groups; the p-values and Kaplan-Meier survival curves were compared using the log-rank test. RESULTS: RNA-seq data from 373 cases of liver cancer cases were analyzed. HMGA2 was overexpressed in liver cancer and significantly associated with gender (p = 0.0357), T classification (p = 0.0063), clinical classification (p = 0.0026) and overall survival (p = 0.0386). According to the multivariate analysis, HMGA2 could independently predict overall survival in liver cancer. CONCLUSIONS: HMGA2 independently predicts poor prognosis in liver cancer and serves as a molecular marker to determine disease prognosis.
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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) | 0.002 | 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".