Targeting uridine–cytidine kinase 2 induced cell cycle arrest through dual mechanism and could improve the immune response of hepatocellular carcinoma
Bibliographic record
Abstract
BACKGROUND: Pyrimidine metabolism is critical for tumour progression. Uridine-cytidine kinase 2 (UCK2), a key regulator of pyrimidine metabolism, is elevated during hepatocellular carcinoma (HCC) development and exhibits carcinogenic effects. However, the key mechanism of UCK2 promoting HCC and the therapeutic value of UCK2 are still undefined. The aim of this study is to investigate the potential of UCK2 as a therapeutic target for HCC. METHODS: Gene expression matrices were obtained from public databases. RNA-seq, co-immunoprecipitation and RNA-binding protein immunoprecipitation were used to determine the mechanism of UCK2 promoting HCC. Immune cell infiltration level and immune-related functional scores were evaluated to assess the link between tumour microenvironment and UCK2. RESULTS: In HCC, the expression of UCK2 was upregulated in part by TGFβ1 stimulation. UCK2 promoted cell cycle progression of HCC by preventing the degradation of mTOR protein and maintaining the stability of PDPK1 mRNA. We also identified UCK2 as a novel RNA-binding protein. Downregulation of UCK2 induced cell cycle arrest and activated the TNFα/NFκB signalling pathway-related senescence-associated secretory phenotype to modify the tumour microenvironment. Additionally, UCK2 was a biomarker of the immunosuppressive microenvironment. Downregulated UCK2 induced a secretory phenotype, which could improve the microenvironment, and decreased UCK2 remodelling metabolism could lower the resistance of tumour cells to T-cell-mediated killing. CONCLUSIONS: Targeting UCK2 inhibits HCC progression and could improve the response to immunotherapy in patients with HCC. Our study suggests that UCK2 could be an ideal target for 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 | 0.000 |
| 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".