ArticleMetabolites2023
A Nucleotide Metabolism-Related Gene Signature for Risk Stratification and Prognosis Prediction in Hepatocellular Carcinoma Based on an Integrated Transcriptomics and Metabolomics Approach.
Article in Metabolites, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 5 citations in OpenAlex.
- Multiomics Analysis of Nucleotide Metabolism Highlights the Important Role of Adenylate Kinase 4 in Pancreatic Cancer.Human mutation · 2026Article
- Identification of a Novel Subtype-Derived Prognostic Signature Based on Nucleotide Metabolism-Related Molecular Classification for Predicting Lung Adenocarcinoma Prognosis.International journal of genomics · 2026Article
- Article
- Comprehensive pan-cancer analysis reveals EPHB2 is a novel predictive biomarker for prognosis and immunotherapy response.BMC cancer · 2024Article
- Glutathione metabolism-related gene signature predicts prognosis and treatment response in low-grade glioma.Aging · 2024Article
- Identifying Proteins and Amino Acids Associated with Liver Cancer Risk: A Study Utilizing Mendelian Randomization and Bulk RNA Sequencing Analysis.Journal of personalized medicine · 2024Article
Corrections and comments
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Authors and funding
9 authors at 1 institution in 1 country.
Funding
Abstract
Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality worldwide. The in-depth study of genes and metabolites related to nucleotide metabolism will provide new ideas for predicting the prognosis of HCC patients. This study integrated the transcriptome data of different cancer types to explore the characteristics and significance of nucleotide metabolism-related genes (NMGRs) in different cancer types. Then, we constructed a new HCC classifier and prognosis model based on HCC samples from TCGA and GEO, and detected the gene expression level in the model through molecular biology experiments. Finally, nucleotide metabolism-related products in serum of HCC patients were examined using untargeted metabolomics. A total of 97 NMRGs were obtained based on bioinformatics techniques. In addition, a clinical model that could accurately predict the prognostic outcome of HCC was constructed, which contained 11 NMRGs. The results of PCR experiments showed that the expression levels of these genes were basically consistent with the predicted trends. Meanwhile, the results of untargeted metabolomics also proved that there was a significant nucleotide metabolism disorder in the development of HCC. Our results provide a promising insight into nucleotide metabolism in HCC, as well as a tailored prognostic and chemotherapy sensitivity prediction tool for patients.
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Registered trials
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