Mismatch repair genes as prognosis biomarkers for hepatocellular carcinoma
Bibliographic record
Abstract
Abstract Introduction: Great progress was made in early diagnosis and in the treatment of hepatocellular carcinoma (HCC) in most countries, but the mortality rate is still very high. The outcome for HCC patients is influenced not just by the traits of the tumor, but also by its cause, liver functionality, and individual variations, leading to past models not yielding optimal outcomes. Mismatch repair is highly related to the prognosis and progression of liver cancer. However, the prediction model of liver cancer progression by mismatch repair pathway-related genes has not been established. Methods: In this study, mismatch repair pathway-related genes were screened from the TCGA and ICGC databases. We employed both univariate analysis and lasso Cox regression analysis to pinpoint eight genes and formulate a risk score. The model's clinical utility was subsequently confirmed through Cox regression analyses. Results: We chose eight genes (YBX1, PSMD14, NOP58, RUVBL1, HMMR, KPNA2, BSG, and IRAK1) from the set of mismatch repair genes and utilized them to create a prognostic risk factor, which was subsequently validated by using TCGA database. The results indicated a big difference in prognosis between risk groups, categorized based on median risk coefficient. Additionally, we employed a nomogram to predict overall survival. Furthermore, when we conducted functional enrichment analysis, it revealed a connection between the high-risk group and cell cycle process and DNA replication synthesis. Further analysis also suggested that differences in prognosis between various risk groups could be attributed to an immunosuppressed tumor microenvironment. Discussion: The prognostic model composed of 8 mismatch repair pathway-related genes has potential application value and good predictive performance. The related genes may be biomarkers for HCC treatment, which can provide new strategy in guiding the clinical prediction of 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".