Abstract
Metabolites are important indicators of cancer and mutations in genes involved in amino acid metabolism may influence tumorigenesis. Immunotherapy is an effective cancer treatment option; however, its relationship with amino acid metabolism has not been reported. In this study, RNA-seq data for 371 liver cancer patients were acquired from TCGA and used as the training set. Data for 231 liver cancer patients were obtained from ICGC and used as the validation set to establish a gene signature for predicting liver cancer overall survival outcomes and immunotherapeutic responses. Four reliable groups based on 132 amino acid metabolism-related DEGs were obtained by consistent clustering of 371 HCC patients and a four-gene signature for prediction of liver cancer survival outcomes was developed. Our data show that in different clinical groups, the overall survival outcomes in the high-risk group were markedly low relative to the low-risk group. Univariate and multivariate analyses revealed that the characteristics of the 4-gene signature were independent prognostic factors for liver cancer. The ROC curve revealed that the risk characteristic is an efficient predictor for 1-, 2-, and 3-year HCC survival outcomes. The GSVA and KEGG pathway analyses revealed that high-risk score tumors were associated with all aspects of the degree of malignancy in liver cancer. There were more mutant genes and greater immune infiltrations in the high-risk groups. Assessment of the three immunotherapeutic cohorts established that low-risk score patients significantly benefited from immunotherapy. Then, we established a prognostic nomogram based on the TCGA cohort. In conclusion, the 4-gene signature is a reliable diagnostic marker and predictor for immunotherapeutic efficacy.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data Availability
The data underlying this study are freely available from the TCGA, ICGC, and the GEO databases. The R code and raw data of this article can be obtained from the following link https://www.jianguoyun.com/p/DRReQzUQufXRCxi9xYYFIAA. The data from TCGA, ICGC, and GEO are all publicly available. Therefore, this study was exempted from the approval of the local ethics committee. The current study follows TCGA, ICGC, and GEO data access policies and publication guidelines.
Abbreviations
- aDC:
-
Activated dendritic cell
- AFP:
-
Alpha fetoprotein
- AMGs:
-
Amino acid metabolism-related genes
- APC:
-
Antigen-presenting cell
- AUC:
-
Area under the curve
- CCR:
-
Cytokine–cytokine receptor
- CI:
-
Confidence interval
- DEGs:
-
Differentially expressed genes
- FDR:
-
False discovery rate
- HCC:
-
Hepatocellular carcinoma
- HLA:
-
Human leukocyte antigen
- HR:
-
Hazard ratio
- ICGC:
-
International Cancer Genome Consortium
- iDC:
-
Immature dendritic cell
- KEGG:
-
Kyoto Encyclopedia of Genes and Genomes
- LASSO:
-
Least absolute shrinkage and selection operator
- OS:
-
Overall survival
- GO:
-
Gene ontology
- PCA:
-
Principal component analysis
- pDC:
-
Plasmacytoid dendritic cell
- ROC:
-
Receiver operating characteristic
- ssGSEA:
-
Single-sample gene set enrichment analysis
- TCGA:
-
The Cancer Genome Atlas
- Tfh:
-
T follicular helper cell
- TIL:
-
Tumor-infiltrating lymphocyte
- t-SNE:
-
T-distributed stochastic neighbor embedding
References
Alawyia B, Constantinou C (2023) Hepatocellular Carcinoma: a narrative review on current knowledge and future prospects. Curr Treat Options Oncol. https://doi.org/10.1007/s11864-023-01098-9
Charoentong P, Finotello F, Angelova M et al (2017) Pan-cancer immunogenomic analyses reveal genotype-immunophenotype relationships and predictors of response to checkpoint blockade. Cell Rep 18:248–262. https://doi.org/10.1016/j.celrep.2016.12.019
Dasgupta P, Henshaw C, Youlden DR et al (2020) Global trends in incidence rates of primary adult liver cancers: a systematic review and meta-analysis. Front Oncol 10:171. https://doi.org/10.3389/fonc.2020.00171
Fasano M, Corte C, Liello RD et al (2022) Immunotherapy for head and neck cancer: present and future. Crit Rev Oncol Hematol 174:103679. https://doi.org/10.1016/j.critrevonc.2022.103679
Fu S, Xu S, Zhang S (2023) The role of amino acid metabolism alterations in pancreatic cancer: from mechanism to application. Biochim Biophys Acta Rev Cancer 1878:188893. https://doi.org/10.1016/j.bbcan.2023.188893
Grohmann U, Bronte V (2010) Control of immune response by amino acid metabolism. Immunol Rev 236:243–264. https://doi.org/10.1111/j.1600-065X.2010.00915.x
Hanzelmann S, Castelo R, Guinney J (2013) GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics 14:7. https://doi.org/10.1186/1471-2105-14-7
Hu B, Yang XB, Sang XT (2020) Construction of a lipid metabolism-related and immune-associated prognostic signature for hepatocellular carcinoma. Cancer Med 9:7646–7662. https://doi.org/10.1002/cam4.3353
Huang AC, Zappasodi R (2022) A decade of checkpoint blockade immunotherapy in melanoma: understanding the molecular basis for immune sensitivity and resistance. Nat Immunol 23:660–670. https://doi.org/10.1038/s41590-022-01141-1
Iasonos A, Schrag D, Raj GV et al (2008) How to build and interpret a nomogram for cancer prognosis. J Clin Oncol 26:1364–1370. https://doi.org/10.1200/JCO.2007.12.9791
Jain M, Nilsson R, Sharma S et al (2012) Metabolite profiling identifies a key role for glycine in rapid cancer cell proliferation. Science 336:1040–1044. https://doi.org/10.1126/science.1218595
Lee JE, Kim MY (2022) Cancer epigenetics: past, present and future. Semin Cancer Biol 83:4–14. https://doi.org/10.1016/j.semcancer.2021.03.025
Li Z, Zhang H (2016) Reprogramming of glucose, fatty acid and amino acid metabolism for cancer progression. Cell Mol Life Sci 73:377–392. https://doi.org/10.1007/s00018-015-2070-4
Li X, Zhang HS (2023) Amino acid metabolism, redox balance and epigenetic regulation in cancer. Febs J. https://doi.org/10.1111/febs.16803
Liu GM, Xie WX, Zhang CY et al (2020) Identification of a four-gene metabolic signature predicting overall survival for hepatocellular carcinoma. J Cell Physiol 235:1624–1636. https://doi.org/10.1002/jcp.29081
Mates JM, Campos-Sandoval JA, de Los SJ et al (2020) Glutaminases regulate glutathione and oxidative stress in cancer. Arch Toxicol 94:2603–2623. https://doi.org/10.1007/s00204-020-02838-8
Mayakonda A, Lin DC, Assenov Y et al (2018) Maftools: efficient and comprehensive analysis of somatic variants in cancer. Genome Res 28:1747–1756. https://doi.org/10.1101/gr.239244.118
Pavlova NN, Thompson CB (2016) The emerging hallmarks of cancer metabolism. Cell Metab 23:27–47. https://doi.org/10.1016/j.cmet.2015.12.006
Reda M, Ngamcherdtrakul W, Nelson MA et al (2022) Development of a nanoparticle-based immunotherapy targeting PD-L1 and PLK1 for lung cancer treatment. Nat Commun 13:4261. https://doi.org/10.1038/s41467-022-31926-9
Rooney MS, Shukla SA, Wu CJ et al (2015) Molecular and genetic properties of tumors associated with local immune cytolytic activity. Cell 160:48–61. https://doi.org/10.1016/j.cell.2014.12.033
Shen R, Li P, Li B et al (2019) Identification of distinct immune subtypes in colorectal cancer based on the stromal compartment. Front Oncol 9:1497. https://doi.org/10.3389/fonc.2019.01497
Tang C, Ma J, Liu X et al (2020) Identification of a prognostic signature of nine metabolism-related genes for hepatocellular carcinoma. Peerj 8:e9774. https://doi.org/10.7717/peerj.9774
Thakur C, Chen F (2019) Connections between metabolism and epigenetics in cancers. Semin Cancer Biol 57:52–58. https://doi.org/10.1016/j.semcancer.2019.06.006
Tibshirani R (1997) the lasso method for variable selection in the cox model. Stat Med 16:385–395. https://doi.org/10.1002/(SICI)1097-0258(19970228)16:4%3c385::AID-SIM380%3e3.0.CO;2-3
Torrecilla S, Sia D, Harrington AN et al (2017) Trunk mutational events present minimal intra- and inter-tumoral heterogeneity in hepatocellular carcinoma. J Hepatol 67:1222–1231. https://doi.org/10.1016/j.jhep.2017.08.013
Vettore L, Westbrook RL, Tennant DA (2020) New aspects of amino acid metabolism in cancer. Br J Cancer 122:150–156. https://doi.org/10.1038/s41416-019-0620-5
Vickers AJ, Elkin EB (2006) Decision curve analysis: a novel method for evaluating prediction models. Med Decis Making 26:565–574. https://doi.org/10.1177/0272989X06295361
Villanueva A (2019) Hepatocellular carcinoma. N Engl J Med 380:1450–1462. https://doi.org/10.1056/NEJMra1713263
Wilkerson MD, Hayes DN (2010) ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking. Bioinformatics 26:1572–1573. https://doi.org/10.1093/bioinformatics/btq170
Wu T, Hu E, Xu S et al (2021a) clusterProfiler 4.0: a universal enrichment tool for interpreting omics data. Innovation (NY) 2:100141. https://doi.org/10.1016/j.xinn.2021.100141
Wu X, Lan T, Li M et al (2021) Six metabolism related mRNAs predict the prognosis of patients with hepatocellular carcinoma. Front Mol Biosci 8:621232. https://doi.org/10.3389/fmolb.2021.621232
Xing M, Wang X, Kiken RA et al (2021) Immunodiagnostic biomarkers for hepatocellular carcinoma (HCC): the first step in detection and treatment. Int J Mol Sci 22:6139. https://doi.org/10.3390/ijms22116139
Yang JD, Hainaut P, Gores GJ et al (2019) A global view of hepatocellular carcinoma: trends, risk, prevention and management. Nat Rev Gastro Hepat 16:589–604. https://doi.org/10.1038/s41575-019-0186-y
Yao C, Zhang T, Wu T et al (2022) Facts and hopes for immunotherapy in renal cell carcinoma. Clin Cancer Res 28:5013–5020. https://doi.org/10.1158/1078-0432.CCR-21-2372
Zhang Z, Ma L, Goswami S et al (2019) Landscape of infiltrating B cells and their clinical significance in human hepatocellular carcinoma. Oncoimmunology 8:e1571388. https://doi.org/10.1080/2162402X.2019.1571388
Zhang S, Liu Z, Wu D et al (2020) Single-cell RNA-Seq analysis reveals microenvironmental infiltration of plasma cells and hepatocytic prognostic markers in HCC with cirrhosis. Front Oncol 10:596318. https://doi.org/10.3389/fonc.2020.596318
Zhao Y, Zhang J, Wang S et al (2021) Identification and validation of a nine-gene amino acid metabolism-related risk signature in HCC. Front Cell Dev Biol 9:731790. https://doi.org/10.3389/fcell.2021.731790
Zhu P, Li FF, Zeng J et al (2021) Integrative analysis of the characteristics of lipid metabolism-related genes as prognostic prediction markers for hepatocellular carcinoma. Eur Rev Med Pharmacol Sci 25:116–126. https://doi.org/10.26355/eurrev_202101_24355
Zhu L, Zhu X, Wu Y (2022) Effects of glucose metabolism, lipid metabolism, and glutamine metabolism on tumor microenvironment and clinical implications. Biomolecules 12:580. https://doi.org/10.3390/biom12040580
Zhu J, Xu X, Jiang M et al (2023) Comprehensive characterization of ferroptosis in hepatocellular carcinoma revealing the association with prognosis and tumor immune microenvironment. Front Oncol 13:1145380. https://doi.org/10.3389/fonc.2023.1145380
Acknowledgements
The authors are grateful for the invaluable support and useful discussions with other members of the Department of Youjiang Medical College for Nationalities. We thank the TCGA and GEO databases as well as IMvigor210 package for the availability of the data. And we also want to thank the support of the Key Laboratory of Molecular Pathology (For Hepatobiliary Diseases) of Guangxi, Affiliated Hospital of Youjiang Medical University for Nationalities.
Funding
This study was supported by the Basic Ability Improvement Project for Young and Middle-aged Teachers in Colleges and Universities of Guangxi (Grant No. 2022KY0542), and the Basic Ability Improvement Project for Young and Middle-aged Teachers in Colleges and Universities of Guangxi (Grant No. 2022KY0532). The work here was also supported by the Project of Basic Scientific Research and Technology Development Plan in 2021 (Grant No. 20212348) and the Project of Baise Scientific Research and Technology Development Plan in 2021 (Grant No. 20212347).
Author information
Authors and Affiliations
Contributions
Designed study and wrote the paper: Ming-you Dong, Lu-sheng Liao, and Run-lei Du. Analyzed data: Lu-sheng Liao, Ming-you Dong, and Jun-li Wang. Performed research: Zi-jun Xiao, Ting-jun Liu, Feng-die Huang, Yan-ping Zhong, Xin Zhang, and Ke-heng Chen. Contributed to methodology: Lu-sheng Liao, Ming-you Dong, and Run-lei Du. All the authors contributed to the article and approved the submitted version.
Corresponding authors
Ethics declarations
Conflict of interest
The authors have not disclosed any conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
10528_2023_10502_MOESM3_ESM.jpg
Figure S1. Identification and enrichment analysis of differentially expressed AMGs. (A) Heatmap of differentially expressed AMGs in normal and tumor tissues. (B) Volcano plot of differentially expressed AMGs. (C) The 10 most significant signal pathways identified by GO. (D) The 10 most significant signal pathways identified by KEGG enrichment. Supplementary file3 (JPG 600 KB)
10528_2023_10502_MOESM4_ESM.jpg
Figure S2. Consensus clustering of the TCGA dataset by molecular subgroups based on prognostic AMGs. (A-C) Consensus clustering matrices of 131 prognostic AMGs in TCGA dataset for k = 2-4. (D) Cumulative distribution function (CDF) curve. (E) CDF Delta area curve shows relative changes in area under CDF curve for every category number k, relative to k-1. (F) PCA analysis of four clusters in HCC. (G) Heatmap and clinicopathological features of four;. (H) Kaplan–-Meier analysis of OS in the four clusters in the TCGA- HCC dataset. Supplementary file4 (JPG 2815 KB)
10528_2023_10502_MOESM5_ESM.jpg
Figure S3. Associations between the 4-gene signature and patients’ clinicopathological features in the TCGA dataset. Supplementary file5 (JPG 524 KB)
10528_2023_10502_MOESM6_ESM.jpg
Figure S4. Correlation between the 4-gene signatures, immune cells (A), and immune-associated roles (B) in HCC. Supplementary file6 (JPG 1416 KB)
10528_2023_10502_MOESM7_ESM.jpg
Figure S5. Illustrative findings of GSVA (A, C) and KEGG (B, D) analysis in the TCGA and ICGC datasets. The most shared or significant KEGG pathways in TCGA (A-B) and ICGC (C-D) datasets. Pink rectangles denote immune-associated pathways that overlap between the datasets. Supplementary file7 (JPG 1891 KB)
10528_2023_10502_MOESM8_ESM.jpg
Figure S6. Prediction of chemotherapy sensitivity in low- and high-risk score patients. (A) Spearman correlation and differential drug response analysis of four CTRP compounds. (B) Spearman correlation and differential drug response analysis of seven PRISM compounds. Note: the lower the value on the y-axis, the higher the drug sensitivity. Supplementary file8 (JPG 215 KB)
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Liao, Ls., Xiao, Zj., Wang, Jl. et al. A Four Amino Acid Metabolism-Associated Genes (AMGs) Signature for Predicting Overall Survival Outcomes and Immunotherapeutic Efficacy in Hepatocellular Carcinoma. Biochem Genet (2023). https://doi.org/10.1007/s10528-023-10502-w
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10528-023-10502-w