Expression of IRAK1 in Hepatocellular Carcinoma, Its Clinical Significance, and Docking Characteristics with Selected Natural Compounds
Bibliographic record
Abstract
This study aimed to explore clinical significance of interleukin-1 receptor-associated kinase 1 (IRAK1) in the diagnosis, prognosis, and targeted therapy of hepatocellular carcinoma. A systematic analysis based on the cancer genome atlas (TCGA) indicated that IRAK1 was highly expressed in 18 cancer types (p < 0.01) and may be a pan-cancer biomarker. In hepatocellular carcinoma, the alteration rate of IRAK1 was rather high (62.4%), in which mRNA high relative to normal predominated (58.9%). Higher expression was associated with shorter overall survival (p < 0.01). IRAK1 expression correlated positively with pathology stage and tumor grade (for the latter there was only a slight trend). Interestingly, it correlated positively with TP53 mutation (p < 0.001), suggesting a possible strategy for targeting TP53 via IRAK1. Immunohistochemistry experiments confirmed a higher positive rate of IRAK1 in carcinoma than in para-carcinoma tissues (χ2 = 18.006, p < 0.001). Higher tumor grade correlated with more strongly positive staining. Molecular docking revealed cryptotanshinone, matrine, and harmine as the best hit compounds with inhibition potential for IRAK1. Our findings suggest that IRAK1 may play biologically predictive roles in hepatocellular carcinoma. The suppression of IRAK1/NF-κB signaling via inhibition of IRAK1 by the hit compounds can be a potential strategy for the targeted therapy.
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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 | 0.000 |
| 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 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".