Expression of IRAK1 in Hepatocellular Carcinoma, Its Clinical Significance, and Docking Characteristics with Selected Natural Compounds
Why this work is in the frame
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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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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 | 0.001 |
| 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 it