Development and Validation of a Carbohydrate Metabolism-Related Model for Predicting Prognosis and Immune Landscape in Hepatocellular Carcinoma Patients

Abstract

Objective

The activities and products of carbohydrate metabolism are involved in key processes of cancer. However, its relationship with hepatocellular carcinoma (HCC) is unclear.

Methods

The cancer genome atlas (TCGA)-HCC and ICGC-LIRI-JP datasets were acquired via public databases. Differentially expressed genes (DEGs) between HCC and control samples in the TCGA-HCC dataset were identified and overlapped with 355 carbohydrate metabolism-related genes (CRGs) to obtain differentially expressed CRGs (DE-CRGs). Then, univariate Cox and least absolute shrinkage and selection operator (LASSO) analyses were applied to identify risk model genes, and HCC samples were divided into high/low-risk groups according to the median risk score. Next, gene set enrichment analysis (GSEA) was performed on the risk model genes. The sensitivity of the risk model to immunotherapy and chemotherapy was also explored.

Results

A total of 8 risk model genes, namely, G6PD, PFKFB4, ACAT1, ALDH2, ACYP1, OGDHL, ACADS, and TKTL1, were identified. Moreover, the risk score, cancer status, age, and pathologic T stage were strongly associated with the prognosis of HCC patients. Both the stromal score and immune score had significant negative/positive correlations with the risk score, reflecting the important role of the risk model in immunotherapy sensitivity. Furthermore, the stromal and immune scores had significant negative/positive correlations with risk scores, reflecting the important role of the risk model in immunotherapy sensitivity. Eventually, we found that high-/low-risk patients were more sensitive to 102 drugs, suggesting that the risk model exhibited sensitivity to chemotherapy drugs. The results of the experiments in HCC tissue samples validated the expression of the risk model genes.

Conclusion

Through bioinformatic analysis, we constructed a carbohydrate metabolism-related risk model for HCC, contributing to the prognosis prediction and treatment of HCC patients.

References

[1] GillesH, GarbuttT, LandrumJ. Hepatocellular Carcinoma. Crit Care Nurs Clin North Am, 2022, 34(3): 289-301

[2] VogelA, MeyerT, SapisochinG, et al.. Hepatocellular carcinoma. Lancet, 2022, 400(10360): 1345-1362

[3] VivarelliM, MontaltiR, RisalitiA. Multimodal treatment of hepatocellular carcinoma on cirrhosis: an update. World J Gastroenterol, 2013, 19(42): 7316-7326

[4] LlovetJM, CastetF, HeikenwalderM, et al.. Immunotherapies for hepatocellular carcinoma. Nat Rev Clin Oncol, 2022, 19(3): 151-172

[5] VogelA, MartinelliE. Updated treatment recommendations for hepatocellular carcinoma (HCC) from the ESMO Clinical Practice Guidelines. Ann Oncol, 2021, 32(6): 801-805

[6] SiaD, VillanuevaA, FriedmanSL, et al.. Liver Cancer Cell of Origin, Molecular Class, and Effects on Patient Prognosis. Gastroenterology, 2017, 152(4): 745-761

[7] ChanLK, TsuiYM, HoDW, et al.. Cellular heterogeneity and plasticity in liver cancer. Semin Cancer Biol, 2022, 82: 134-149

[8] RafalskiVA, ManciniE, BrunetA. Energy metabolism and energy-sensing pathways in mammalian embryonic and adult stem cell fate. J Cell Sci, 2012, 125: 5597-5608 Pt 23)

[9] PavlovaNN, ZhuJ, ThompsonCB. The hallmarks of cancer metabolism: Still emerging. Cell Metab, 2022, 34(3): 355-377

[10] HanahanD. Hallmarks of Cancer: New Dimensions. Cancer Discov, 2022, 12(1): 31-46

[11] ChandelNS. Carbohydrate Metabolism. Cold Spring Harb Perspect Biol, 2021, 13(1): a040568

[12] CrabtreeHG. Observations on the carbohydrate metabolism of tumors. Biochem J, 1929, 23(3): 536-545

[13] Vander HeidenMG, DeBerardinisRJ. Understanding the Intersections between Metabolism and Cancer Biology. Cell, 2017, 168(4): 657-669

[14] YouM, XieZ, ZhangN, et al.. Signaling pathways in cancer metabolism: mechanisms and therapeutic targets. Signal Transduct Target Ther, 2023, 8(1): 196

[15] TangL, WeiF, WuY, et al.. Role of metabolism in cancer cell radioresistance and radiosensitization methods. J Exp Clin Cancer Res, 2018, 37(1): 87

[16] WarburgO, WindF, NegeleinE. The metabolism of tumors in the body. J Gen Physiol, 1927, 8(6): 519-530

[17] WeiQ, QianY, YuJ, et al.. Metabolic rewiring in the promotion of cancer metastasis: mechanisms and therapeutic implications. Oncogene, 2020, 39(39): 6139-6156

[18] StinconeA, PrigioneA, CramerT, et al.. The return of metabolism: biochemistry and physiology of the pentose phosphate pathway. Biol Rev Camb Philos Soc, 2015, 90(3): 927-963

[19] AndersonNM, MuckaP, KernJG, et al.. The emerging role and targetability of the TCA cycle in cancer metabolism. Protein Cell, 2018, 9(2): 216-237

[20] FengJ, LiJ, WuL, et al.. Emerging roles and the regulation of aerobic glycolysis in hepatocellular carcinoma. J Exp Clin Cancer Res, 2020, 39(1): 126

[21] ShahUA, IyengarNM. Plant-Based and Ketogenic Diets As Diverging Paths to Address Cancer: A Review. JAMA Oncol, 2022, 8(8): 1201-1208

[22] XiaL, OyangL, LinJ, et al.. The cancer metabolic reprogramming and immune response. Mol Cancer, 2021, 20(1): 28

[23] ZhangQ, HeY, LuoN, et al.. Landscape and Dynamics of Single Immune Cells in Hepatocellular Carcinoma. Cell, 2019, 179(4): 829-845.e20

[24] LuC, RongD, ZhangB, et al.. Current perspectives on the immunosuppressive tumor microenvironment in hepatocellular carcinoma: challenges and opportunities. Mol Cancer, 2019, 18(1): 130

[25] ZhangY, SunY, RaoE, et al.. Fatty acid-binding protein E-FABP restricts tumor growth by promoting IFN-β responses in tumor-associated macrophages. Cancer Res, 2014, 74(11): 2986-2998

[26] YangY, YangY, LiuJ, et al.. Establishment and validation of a carbohydrate metabolism-related gene signature for prognostic model and immune response in acute myeloid leukemia. Front Immunol, 2022, 13: 1038570

[27] LiuS, WangZ, ZhuR, et al.. Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2. J Vis Exp, 2021, 18(175): e62528

[28] YuG, WangLG, HanY, et al.. clusterProfiler: an R package for comparing biological themes among gene clusters. Omics, 2012, 16(5): 284-287

[29] MinSH, ZhouJ. smplot: An R Package for Easy and Elegant Data Visualization. Front Genet, 2021, 12: 802894

[30] RobinX, TurckN, HainardA, et al.. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics, 2011, 12: 77

[31] MosmannT. Rapid colorimetric assay for cellular growth and survival: application to proliferation and cytotoxicity assays. J Immunol Methods, 1983, 65(1–2): 55-63

[32] LoweWL, BoydFT, ClarkeDW, et al.. Development of brain insulin receptors: structural and functional studies of insulin receptors from whole brain and primary cell cultures. Endocrinology, 1986, 119(1): 25-35

[33] WuJ, LiL, ZhangH, et al.. A risk model developed based on tumor microenvironment predicts overall survival and associates with tumor immunity of patients with lung adenocarcinoma. Oncogene, 2021, 40(26): 4413-4424

[34] XuQ, ChenS, HuY, et al.. Landscape of Immune Microenvironment Under Immune Cell Infiltration Pattern in Breast Cancer. Front Immunol, 2021, 12: 711433

[35] MaeserD, GruenerRF, HuangRS. oncoPredict: an R package for predicting in vivo or cancer patient drug response and biomarkers from cell line screening data. Brief Bioinform, 2021, 22(6): bbab260

[36] YangC, HuangX, LiuZ, et al.. Metabolism-associated molecular classification of hepatocellular carcinoma. Mol Oncol, 2020, 14(4): 896-913

[37] XuK, XiaP, LiuP. A six lipid metabolism related gene signature for predicting the prognosis of hepatocellular carcinoma. Sci Rep, 2022, 12(1): 20781

[38] MouL, PuZ, LuoY, et al.. Construction of a lipid metabolism-related risk model for hepatocellular carcinoma by single cell and machine learning analysis. Front Immunol, 2023, 14: 1036562

[39] XuQ, MiaoD, SongX, et al.. Glycolysis-Related Gene Signature Can Predict Survival and Immune Status of Hepatocellular Carcinoma. Ann Surg Oncol, 2022, 29(6): 3963-3976

[40] HuB, QuC, QiWJ, et al.. Development and verification of the glycolysis-associated and immune-related prognosis signature for hepatocellular carcinoma. Front Genet, 2022, 13: 955673

[41] KongJ, YuG, SiW, et al.. Identification of a glycolysis-related gene signature for predicting prognosis in patients with hepatocellular carcinoma. BMC Cancer, 2022, 22(1): 142

[42] DengT, YeQ, JinC, et al.. Identification and validation of a glycolysis-associated multiomics prognostic model for hepatocellular carcinoma. Aging, 2021, 13(5): 7481-7498

[43] YangHC, WuHY, YenWC, et al.. The Redox Role of G6PD in Cell Growth, Cell Death, and Cancer. Cells, 2019, 8(9): 1055

[44] CaoF, LuoA, YangC. G6PD inhibits ferroptosis in hepatocellular carcinoma by targeting cytochrome P450 oxidoreductase. Cellular Signal, 2021, 87: 110098

[45] KamCS, HoDWH, MingIVS, et al.. PFKFB4 Drives the Oncogenicity in TP53-Mutated Hepatocellular Carcinoma in a Phosphatase-Dependent Manner. Cell Mol Gastroenterol Hepatol, 2023, 15(6): 1325-1350

[46] GuL, ZhuY, LinX, et al.. Stabilization of FASN by ACAT1-mediated GNPAT acetylation promotes lipid metabolism and hepatocarcinogenesis. Oncogene, 2020, 39(11): 2437-2449

[47] ZhangH, FuL. The role of ALDH2 in tumorigenesis and tumor progression: Targeting ALDH2 as a potential cancer treatment. Acta Pharm Sin B, 2021, 11(6): 1400-1411

[48] YaoS, YinX, ChenT, et al.. ALDH2 is a prognostic biomarker and related with immune infiltrates in HCC. Am J Cancer Res, 2021, 11(11): 5319-5337

[49] SeoW, GaoY, HeY, et al.. ALDH2 deficiency promotes alcohol-associated liver cancer by activating oncogenic pathways via oxidized DNA-enriched extracellular vesicles. J Hepatol, 2019, 71(5): 1000-1011

[50] LiguriG, CamiciG, ManaoG, et al.. A new acylphosphatase isoenzyme from human erythrocytes: purification, characterization, and primary structure. Biochemistry, 1986, 25(24): 8089-8094

[51] WangS, ZhouL, JiN, et al.. Targeting ACYP1-mediated glycolysis reverses lenvatinib resistance and restricts hepatocellular carcinoma progression. Drug Resist Updat, 2023, 69: 100976

[52] JiangX, PengJ, XieY, et al.. Oxoglutarate dehydrogenase-like inhibits the progression of hepatocellular carcinoma by inducing DNA damage through noncanonical function. Cell Death Differ, 2023, 30(8): 1931-1942

[53] DaiW, LiY, SunW, et al.. Silencing of OGDHL promotes liver cancer metastasis by enhancing hypoxia inducible factor 1 α protein stability. Cancer Sc, 2023, 114(4): 1309-1323

[54] DaiW, XuL, YuX, et al.. OGDHL silencing promotes hepatocellular carcinoma by reprogramming glutamine metabolism. J Hepatol, 2020, 72(5): 909-923

[55] ChenD, FengX, LvZ, et al.. ACADS acts as a potential methylation biomarker associated with the proliferation and metastasis of hepatocellular carcinomas. Aging, 2019, 11(20): 8825-8844

[56] ZhangS, YangJH, GuoCK, et al.. Gene silencing of TKTL1 by RNAi inhibits cell proliferation in human hepatoma cells. Cancer Lett, 2007, 253(1): 108-114

[57] SangroB, SarobeP, Hervás-StubbsS, et al.. Advances in immunotherapy for hepatocellular carcinoma. Nat Rev Gastroenterol Hepatol, 2021, 18(8): 525-543

[58] FoersterF, GairingSJ, IlyasSI, et al.. Emerging immunotherapy for HCC: A guide for hepatologists. Hepatology, 2022, 75(6): 1604-1626

[59] FuJ, XuD, LiuZ, et al.. Increased regulatory T cells correlate with CD8 T-cell impairment and poor survival in hepatocellular carcinoma patients. Gastroenterology, 2007, 132(7): 2328-2339

[60] BoutilierAJ, ElsawaSF. Macrophage Polarization States in the Tumor Microenvironment. Int J Mol Sci, 2021, 22(13): 6995

[61] BaiR, LiY, JianL, et al.. The hypoxia-driven crosstalk between tumor and tumor-associated macrophages: mechanisms and clinical treatment strategies. Mol Cancer, 2022, 21(1): 177

[62] LinY, XuJ, LanH. Tumor-associated macrophages in tumor metastasis: biological roles and clinical therapeutic applications. J Hematol Oncol, 2019, 12(1): 76

[63] YeungO, LoC, LingC, et al.. Alternatively activated (M2) macrophages promote tumour growth and invasiveness in hepatocellular carcinoma. J Hepatol, 2015, 62(3): 607-616

[64] El-KhoueiryAB, SangroB, YauT, et al.. Nivolumab in patients with advanced hepatocellular carcinoma (CheckMate 040): an open-label, non-comparative, phase 1/2 dose escalation and expansion trial. Lancet, 2017, 389(10088): 2492-2502

[65] ZhuAX, FinnRS, EdelineJ, et al.. Pembrolizumab in patients with advanced hepatocellular carcinoma previously treated with sorafenib (KEYNOTE-224): a non-randomised, open-label phase 2 trial. Lancet Oncol, 2018, 19(7): 940-952

[66] KelleyRK, SangroB, HarrisW, et al.. Safety, Efficacy, and Pharmacodynamics of Tremelimumab Plus Durvalumab for Patients With Unresectable Hepatocellular Carcinoma: Randomized Expansion of a Phase I / II Study. J Clin Oncol, 2021, 39(27): 2991-3001

[67] GautronAS, Dominguez-VillarM, de MarckenM, et al.. Enhanced suppressor function of TIM-3+ FoxP3+ regulatory T cells. Eur J Immunol, 2014, 44(9): 2703-2711

[68] LiZ, LiN, LiF, et al.. Immune checkpoint proteins PD-1 and TIM-3 are both highly expressed in liver tissues and correlate with their gene polymorphisms in patients with HBV-related hepatocellular carcinoma. Medicine, 2016, 95(52): e5749

[69] AndrewsLP, MarciscanoAE, DrakeCG, et al.. LAG3 (CD223) as a cancer immunotherapy target. Immunol Rev, 2017, 276(1): 80-96

[70] ZhouG, SprengersD, BoorPPC, et al.. Antibodies Against Immune Checkpoint Molecules Restore Functions of Tumor-Infiltrating T Cells in Hepatocellular Carcinomas. Gastroenterology, 2017, 153(4): 1107-1119.e10

[71] WangJ, SanmamedMF, DatarI, et al.. Fibrinogen-like Protein 1 Is a Major Immune Inhibitory Ligand of LAG-3. Cell, 2019, 176(1–2): 334-347.e12