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Biomedical subjects

Lifeng Yang

Publications and source records attributed to Lifeng Yang.

3 recordsLinked to original sources

Reprogramming neuroblastoma by diet-enhanced polyamine depletion.

Neuroblastoma is a highly lethal childhood tumour derived from differentiation-arrested neural crest cells1,2. Like all cancers, its growth is fuelled by metabolites obtained from either circulation or local biosynthesis3,4. Neuroblastomas depend on local polyamine biosynthesis, and the inhibitor difluoromethylornithine has shown clinical activity5. Here we show that such inhibition can be augmented by dietary restriction of upstream amino acid substrates, leading to disruption of oncogenic protein translation, tumour differentiation and profound survival gains in the Th-MYCN mouse model. Specifically, an arginine- and proline-free diet decreases the amount of the polyamine precursor ornithine and enhances tumour polyamine depletion by difluoromethylornithine. This polyamine depletion causes ribosome stalling, unexpectedly specifically at codons with adenosine in the third position. Such codons are selectively enriched in cell cycle genes and low in neuronal differentiation genes. Thus, impaired translation of these codons, induced by combined dietary and pharmacological intervention, favours a pro-differentiation proteome. These results suggest that the genes of specific cellular programmes have evolved hallmark codon usage preferences that enable coherent translational rewiring in response to metabolic stresses, and that this process can be targeted to activate differentiation of paediatric cancers.

Animals

Comprehensive analysis of diagnostic biomarkers related to histone acetylation in acute myocardial infarction.

BACKGROUND: Acute myocardial infarction (AMI) has become a serious disease that endangers human health, with high morbidity and mortality. Numerous studies have reported histone acetylation can result in the occurrence of cardiovascular diseases. This article aims to explore the potential biomarkers of histone acetylation regulatory genes (ARGs) in AMI patients. METHODS: Five AMI datasets were downloaded from the Gene Expression Omnibus (GEO) database. Next, ARG-related genes were gathered by gene set variation analysis (GSVA) and Spearman's correlation analysis. Subsequently, weighted gene co-expression network analysis (WGCNA) was performed to identify the module genes related to histone acetylation regulation. In the GSE60993 and GSE48060 datasets, the common differentially expressed genes (DEGs) between AMI and control samples were screened. Importantly, the intersecting genes were obtained by overlapping ARGs-related genes, common DEGs, and module genes. Then, the biomarkers in AMI were determined by machine learning, receiver operating characteristic (ROC) curves, and quantitative PCR (qPCR). In addition, immune analysis, drug prediction, molecular docking, and the lncRNA-miRNA-mRNA regulatory network targeting the biomarkers were analyzed, respectively. RESULTS: Here, a total of 18 intersecting genes were identified by overlapping 7,349 ARGs-related genes, 5,565 module genes, and 25 common DEGs. Further, five biomarkers (AQP9, HLA-DQA1, MCEMP1, NKG7, and S100A12) were obtained, and a nomogram was constructed and verified based on these biomarkers. Notably, the biomarkers were significantly associated with CD8 T cells and neutrophils. In addition, the drugs related to biomarkers were predicted, and ATOGEPANT with the molecular target (S100A12) had a high binding affinity (docking score = -10 kcal/mol). CONCLUSION: AQP9, HLA-DQA1, MCEMP1, NKG7, and S100A12 were identified as biomarkers related to ARGs in AMI, which provides a new perspective to study the relationship between ARGs and AMI.

Humans