Immune–related biomarkers shared by inflammatory bowel disease and liver cancer
Bibliographic record
Abstract
It has been indicated that there is an association between inflammatory bowel disease (IBD) and hepatocellular carcinoma (HCC). However, the molecular mechanism underlying the risk of developing HCC among patients with IBD is not well understood. The current study aimed to identify shared genes and potential pathways and regulators between IBD and HCC using a system biology approach. By performing the different gene expression analyses, we identified 871 common differentially expressed genes (DEGs) between IBD and HCC. Of these, 112 genes overlapped with immune genes were subjected to subsequent bioinformatics analyses. The results revealed four hub genes (CXCL2, MMP9, SPP1 and SRC) and several other key regulators including six transcription factors (FOXC1, FOXL1, GATA2, YY1, ZNF354C and TP53) and five microRNAs (miR-124-3p, miR-34a-5p, miR-1-3p, miR-7-5p and miR-99b-5p) for these disease networks. Protein-drug interaction analysis discovered the interaction of the hub genes with 46 SRC-related and 11 MMP9- related drugs that may have a therapeutic effect on IBD and HCC. In conclusion, this study sheds light on the potential connecting mechanisms of HCC and IBD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 teacher head, 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".