Identifying Key Genes to the Early Diagnosis of Inflammatory Bowel Disease by Integrating Analysis at the Blood and Tissue Levels
Bibliographic record
Abstract
Background: Inflammatory bowel disease (IBD) encompasses Crohn's disease (CD) and ulcerative colitis (UC), is challenging to diagnose, and frequently relapses, significantly affecting patients' quality of life. Despite extensive efforts, the pathogenesis of IBD remains unclear. Methods: In this study, we integrated bioinformatics analysis and animal disease model to investigate IBD from two dimensions to identify potential diagnostic biomarkers and explore the pathogenesis of distinct conditions at tissue-specific levels. Results: Firstly, we identified dysferlin (DYSF) and C-X-C motif chemokine ligand 2 (CXCL2) as crucial biomarkers for IBD, with 11 and 13 putative biomarkers for CD and UC, respectively, identified by peripheral blood testing only. CXCL8 and S100 calcium-binding protein A8 (S100A8) were determined to be critical hub genes and validated by real-time polymerase chain reaction (RT-PCR). Secondly, in CD, the differentially expressed genes (DEGs) were mainly associated with immunity based on Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, while the metabolism of multiple substances and substance transport activity were dominant in UC. Thirdly, essential genes in the pathological progression of CD and UC were identified through protein-protein interaction networks and molecular complex detection (MCODE) analysis. Finally, pathological examination and quantitative analysis of IBD models confirmed the above results. Conclusions: Our findings could contribute to understanding the molecular mechanism of IBD, hold clinical significance for early diagnosis and prevention, and provide effective targets for treating IBD.
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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 | 0.002 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".