Identifying Hepatocellular Carcinoma Driver Genes by Integrative Pathway Crosstalk and Protein Interaction Network
Bibliographic record
Abstract
In this study, we mined out hepatocellular carcinoma (HCC) driver genes from MEDLINE literatures by bioinformatics methods of pathway crosstalk and protein interaction network. Furthermore, the relationship between driver genes and their clinicopathological characteristics, as well as classification effectiveness was verified in the public databases. We identified 560 human genes reported to be associated with HCC in 1074 published articles. Functional analysis revealed that biological processes and biochemical pathways relating to tumor pathogenesis, cancer disease, tumor cell molecule, and hepatic disease were enriched in these genes. Pathway crosstalk analysis indicated that significant pathways could be divided into three modules: cancer disease, virus infection, and tumor signaling pathway. The HCC-related protein-protein interaction network comprised 10,212 nodes, and 56,400 edges were mined out to identify 18 modules corresponding to 14 driver genes. We verified that these 14 driver genes have high classification effectiveness to distinguish cancer samples from normal samples and the classification effectiveness was better than that of randomly selected genes. Present study provided pathway crosstalk and protein interaction network for understanding potential tumorigenesis genes underlying HCC. The 14 driver genes identified from this study are of great translational value in HCC diagnosis and treatment, as well as in clinical study on the pathogenesis of HCC.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".