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An asparagine metabolism-based classification reveals the metabolic and immune heterogeneity of hepatocellular carcinoma
INTRODUCTION AND OBJECTIVES: 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...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9594908/ https://www.ncbi.nlm.nih.gov/pubmed/36284275 http://dx.doi.org/10.1186/s12920-022-01380-z |
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author | Bai, Jianguo Tang, Ruifeng Zhou, Keyu Chang, Jialei Wang, Hongyue Zhang, Qixin Shi, Jiahui Sun, Chao |
author_facet | Bai, Jianguo Tang, Ruifeng Zhou, Keyu Chang, Jialei Wang, Hongyue Zhang, Qixin Shi, Jiahui Sun, Chao |
author_sort | Bai, Jianguo |
collection | PubMed |
description | INTRODUCTION AND OBJECTIVES: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12920-022-01380-z. |
format | Online Article Text |
id | pubmed-9594908 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-95949082022-10-26 An asparagine metabolism-based classification reveals the metabolic and immune heterogeneity of hepatocellular carcinoma Bai, Jianguo Tang, Ruifeng Zhou, Keyu Chang, Jialei Wang, Hongyue Zhang, Qixin Shi, Jiahui Sun, Chao BMC Med Genomics Research INTRODUCTION AND OBJECTIVES: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12920-022-01380-z. BioMed Central 2022-10-25 /pmc/articles/PMC9594908/ /pubmed/36284275 http://dx.doi.org/10.1186/s12920-022-01380-z Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Bai, Jianguo Tang, Ruifeng Zhou, Keyu Chang, Jialei Wang, Hongyue Zhang, Qixin Shi, Jiahui Sun, Chao An asparagine metabolism-based classification reveals the metabolic and immune heterogeneity of hepatocellular carcinoma |
title | An asparagine metabolism-based classification reveals the metabolic and immune heterogeneity of hepatocellular carcinoma |
title_full | An asparagine metabolism-based classification reveals the metabolic and immune heterogeneity of hepatocellular carcinoma |
title_fullStr | An asparagine metabolism-based classification reveals the metabolic and immune heterogeneity of hepatocellular carcinoma |
title_full_unstemmed | An asparagine metabolism-based classification reveals the metabolic and immune heterogeneity of hepatocellular carcinoma |
title_short | An asparagine metabolism-based classification reveals the metabolic and immune heterogeneity of hepatocellular carcinoma |
title_sort | asparagine metabolism-based classification reveals the metabolic and immune heterogeneity of hepatocellular carcinoma |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9594908/ https://www.ncbi.nlm.nih.gov/pubmed/36284275 http://dx.doi.org/10.1186/s12920-022-01380-z |
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