An asparagine metabolism-based classification reveals the metabolic and immune heterogeneity of hepatocellular carcinoma

• Published in: BMC medical genomics Vol. 15; no. 1; pp. 1 - 222
• Main Authors: Bai, Jianguo, Tang, Ruifeng, Zhou, Keyu, Chang, Jialei, Wang, Hongyue, Zhang, Qixin, Shi, Jiahui, Sun, Chao
• Format: Journal Article
• Language: English
• Published: London BioMed Central Ltd 25.10.2022; BioMed Central; BMC
• Subjects: Algorithms; Amino acids; Asparagine; Asparagine metabolism; Biomarkers; Cancer; Cancer therapies; Care and treatment; Cell survival; Chemotherapy; Datasets; DNA damage; DNA damage response; Energy metabolism; Gene expression; Gene set enrichment analysis; Genetic aspects; Genetic markers; Genomes; Genomics; Glutamine; GOT2; Health aspects; Hepatocellular carcinoma; Hepatoma; Immunoregulation; Infiltration; Kinases; Liver cancer; Lymphocytes; Lymphocytes T; Medical prognosis; Metabolism; Metastases; Patient outcomes; Prognosis; Protein synthesis; Proteins; Response rates; RNA sequencing; Software; Stem cells; T cell receptors; Therapeutic targets; Transaminase; Tumor microenvironment; Tumors; China
• ISSN: 1755-8794; 1755-8794
• DOI: 10.1186/s12920-022-01380-z

hepatocellular carcinoma (HCC) is the major form of liver cancer with a poor prognosis. Amino acid metabolism has been found to alter in cancers and contributes to malignant progression. However, the asparagine metabolism status and relevant mechanism in HCC were barely understood. By conducting consensus clustering and the least absolute shrinkage and selection operator regression of HCC samples from three cohorts, we classified the HCC patients into two subtypes based on asparagine metabolism level. The Gene Ontology, Kyoto Encyclopedia of Genes and Genomes analyses and Gene Set Enrichment Analysis of the differentially expressed genes between two subgroups were conducted. Immune cell infiltration was evaluated using CIBERSORT algorithm. The prognostic values of genes were analyzed by univariate and multivariate cox regression, ROC curve and Kaplan-Meier survival estimate analyses. Cell types of sing-cell RNA sequencing (scRNA-seq) data were clustered utilizing UMAP method. HCC patients with higher asparagine metabolism level have worse prognoses. Moreover, we found the distinct energy metabolism patterns, DNA damage response (DDR) pathway activating levels, drug sensitivities to DDR inhibitors, immune cell compositions in the tumor microenvironment and responses to immune therapy between two subgroups. Further, we identified a potential target gene, glutamic-oxaloacetic transaminase 2 (GOT2). GOT2 downregulation was associated with worse HCC prognosis and increased infiltration of T regulatory cells (Tregs). ScRNA-seq revealed the GOT2 downregulation in cancer stem cells compared with HCC cells. Taken together, HCC subtype which is more reliant on asparagine and glutamine metabolism has a worse prognosis, and a core gene of asparagine metabolism GOT2 is a potential prognostic marker and therapeutic target of HCC. Our study promotes the precision therapy of HCC and may improve patient outcomes.