Decoding hepatobiliary-specific immune gene patterns in gastrointestinal cancers via gene ontology fingerprints, multi-omics, and experimental integr a tion

Abstract

Background: Gastrointestinal (GI) cancers are characterized by high malignancy and poor prognosis. Tumors in different locations exhibit both commonalities and differences. Although immunotherapy has made progress in some GI cancers, the specific immune-related patterns in hepatobiliary tumors have not yet been fully elucidated.

Methods: Using our developed explainable gene ontology fingerprint (XGOF) method, a GI cancer GOF was established. By integrating omics data from 20 hepatocellular carcinoma (HCC) and 15 intrahepatic cholangiocarcinoma (ICC) tissues in our clinic with public databases, immune-related patterns specifically expressed in hepatobiliary tumors were identified via RNA, protein, methylation, tumor microenvironment (TME) analysis, and experimental verification.

Results: XGOF showed that GI cancers are related to diverse immune functions, especially macrophage migration. Compared to others, hepatobiliary tumors exhibit distinct patterns of gene expression, mutation, and methylation. Seven genes (APOA1, LBP, FGA, C9, APCS, ARG1, and MBL2) were identified as immune-related genes specifically decreased in hepatobiliary cancer. The impact of APOA1 on TME, prognosis, and genomic landscape in HCC was explored in prior research. In this work, the experiment confirmed the down-regulation of six genes in cancerous tissues. Moreover, LBP promoter methylation was elevated in cholangiocarcinoma. Single-cell analysis revealed downregulated immune genes in hepatocytes of HCC and cholangiocytes of ICC, enriched in humoral immunity and complement pathways. Additionally, the macrophage migration inhibitory factor (MIF) pathway was identified as a key signal in interactions between ICC tumor cells and microenvironmental cells.

Conclusion: This study identified immune-related gene patterns in hepatobiliary cancer, contributing to the discovery of novel immunotherapy targets and tumor biomarkers for future research.

Keywords

gastrointestinal cancer / hepatobiliary tumor / immune-related gene / multidimensional analysis

Acknowledgments

This work was supported by the National Natural Science Foundation of China (grant Nos. 32370694 and 81972914), Shanghai Public Health Research Project (grant No. 2024GKM25), and the Innovation Group Project of Shanghai Municipal Health Commission (grant No. 2019CXJQ03).

Author contributions

Honglian Huang (Formal Analysis, Methodology, Visualization, Writing - original draft, Writing - review & editing), Yueping Zhan (Validation), Hui Zong (Methodology), Chenjun Huang (Data curation), Fan Yang (Validation), Ziyi Wei (Data curation), Xin Qin (Data curation), M. James C. Crabbe (Writing - review & editing), Ying Wang (Funding acquisition, Methodology, Writing - review & editing), Xiaoyan Zhang (Funding acquisition, Writing - review & editing).

Supplementary data

Supplementary data is available at PCMEDI Journal online.

Conflict of interest

None declared.

Ethics statement

The studies involving human participants were reviewed and approved by the Institutional Ethics Committee of the leading medical center (Shanghai Eastern Hepatobiliary Surgery Hospital, EHBHKY2020-02-012).

Availability of data and materials

Data sources and handling of the publicly available datasets used in this study are described in the Materials and Methods section. Further details and other data that supports the findings of this study are available from the corresponding authors upon request.

References

[1] F erla y J, Soerjomataram I, Dikshit R et al. Cancer incidence and mortality w orldwide: sour ces, methods and major patterns in GLOBOCAN 2012. Int J Cancer 2015; 136: E359-86. https://doi.org/10.1002/ijc.29210

[2] Craig AJ, von Felden J, Garcia-Lezana T et al. Tumour evolu-tion in hepatocellular carcinoma. Nat Rev Gastroenterol Hepatol 2019; 17: 139-52. https://doi.org/10.1038/s41575-019-0229-4

[3] Negrini S, Gorgoulis VG, Halazonetis TD. Genomic instability—An evolving hallmark of cancer. Nat Rev Mol Cell Biol 2010; 11: 220-28. https://doi.org/10.1038/nrm2858

[4] Tong Y, Gao H, Qi Q et al. High fat diet, gut microbiome and gas-trointestinal cancer. Theranostics 2021; 11: 5889-910. https://doi.org/10.7150/thno.56157

[5] Wang Y, Tong Y, Zhang Z et al. ViMIC: a database of human disease-related virus mutations, integration sites and cis-effects. Nucleic Acids Res 2022; 50: D918-D27. https://doi.org/10.1093/nar/gkab779

[6] Yang M, Lin W, Huang J et al. Novel immunotherapeu-tic a ppr oac hes in gastric cancer. Precision Clinical Medicine 2024; 7: pbae020. https://doi.org/10.1093/pcmedi/pbae020

[7] Wooden B, Goossens N, Hoshida Y et al. Using big data to dis-cov er dia gnostics and ther a peutics for gastr ointestinal and liv er diseases. Gastroenterology 2017; 152: 53-67. https://doi.org/10.1053/j.gastro.2016.09.065

[8] Zhang X, Li M, Ye S et al. CBD2:A functional biomarker database for colorectal cancer.. Imeta 2024; 3: e155. https://doi.org/10.1002/imt2.155

[9] Slaughter DP, Southwick HW, Smejkal W. Field cancerization in or al str atified squamous epithelium; clinical implications of multicentric origin. Cancer 1953; 6: 963-8.

[10] Baba Y, Ishimoto T, Kurashige J et al. Epigenetic field cancer-ization in gastrointestinal cancers. Cancer Lett 2016; 375: 360-66. https://doi.org/10.1016/j.canlet.2016.03.009

[11] Dakubo GD, Jakupciak JP, Birc h-Mac hin MA et al. Clinical impli-cations and utility of field cancerization. Cancer Cell Int 2007; 7: 2. https://doi.org/10.1186/1475-2867-7-2

[12] Damania D, Roy HK, Subr amanian H et al. Nanocytology of r ectal colonocytes to assess risk of colon cancer based on field cancer-ization. Cancer Res 2012; 72: 2720-7. https://doi.org/10.1158/0008-5472.CAN-11-3807

[13] Hyatt BJ, Paull PE, Wassef W. Gastric oncology: an update. Curr Opin Gastroenterol 2009; 25: 570-8. https://doi.org/10.1097/MOG.0b013e328331b5c9

[14] Pr e vo LJ, Sanc hez CA, Galipeau PC et al. p53-m utant clones and field effects in Barrett’s esophagus. Cancer Res 1999; 59: 4784-7.

[15] Jung Y, Lee S, Choi HS et al. Clinical validation of colorectal can-cer biomarkers identified from bioinformatics analysis of public expression data. Clin Cancer Res 2011; 17: 700-9. https://doi.org/10.1158/1078-0432.CCR-10-1300

[16] Bailey P, Chang DK, Nones K et al. Genomic analyses identify molecular subtypes of pancreatic cancer. Nature 2016; 531: 47-52. https://doi.org/10.1038/nature16965

[17] Hoshida Y, Nijman SM, K oba yashi M et al. Integr ativ e tr anscrip-tome anal ysis r e v eals common molecular subclasses of human hepatocellular carcinoma. Cancer Res 2009; 69: 7385-92. https://doi.org/10.1158/0008-5472.CAN-09-1089

[18] Jiang X, Wang J, Deng X et al. Role of the tumor micr oenvir on-ment in PD-L1/PD-1-mediated tumor immune escape. Mol Cancer 2019; 18: 10. https://doi.org/10.1186/s12943-018-0928-4

[19] Yamamoto K, Venida A, Yano J et al. Autopha gy pr omotes im-m une e v asion of pancr eatic cancer by degr ading MHC-I. Nature 2020; 581: 100-5. https://doi.org/10.1038/s41586-020-2229-5

[20] Li X, Sun Z, Peng G et al. Single-cell RNA sequencing re-veals a pro-invasive cancer-associated fibroblast subgroup as-sociated with poor clinical outcomes in patients with gastric cancer. Theranostics 2022; 12: 620-38. https://doi.org/10.7150/thno.60540

[21] W ang Y, W ang Y, W ang S et al. GIDB: a knowledge database for the automated curation and multidimensional analysis of molecular signatures in gastrointestinal cancer. Database 2019; 2019 :baz051. https://doi.org/10.1093/database/baz051

[22] Wang Y, Zong H, Yang F et al. A kno wledge empo w ered explain-able gene ontology fingerprint approach to improve gene func-tional explication and prediction. iScience 2023; 26: 106356. https://doi.org/10.1016/j.isci.2023.106356

[23] Hu C, Li T, Xu Y et al. CellMarker 2.0:an updated database of manuall y cur ated cell markers in human/mouse and web tools based on scRNA-seq data. Nucleic Acids Res 2023; 51: D870-6. https://doi.org/10.1093/nar/gkac947

[24] Jiang S, Qian Q, Zhu T et al. Cell Taxonomy: a cur ated r epository of cell types with multifaceted characterization. Nucleic Acids Res 2023; 51: D853-D60. https://doi.org/10.1093/nar/gkac816

[25] Yu G, Wang L-G, Han Y et al. clusterProfiler: an R package for comparing biological themes among gene clusters. Omics 2012; 16: 284-7. https://doi.org/10.1089/omi.2011.0118

[26] Zhou Y, Zhou B, P ac he L et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun 2019; 10: 1523. https://doi.org/10.1038/s41467-019-09234-6

[27] Jin S, Guerr er o-Juar ez CF, Zhang L et al. Inference and anal-ysis of cell-cell communication using CellChat. Nat Commun 2021; 12: 1088. https://doi.org/10.1038/s41467-021-21246-9

[28] Wang Y, Chen S, Xiao X et al. Impact of a polipopr otein A1 on tumor imm une micr oenvir onment, clinical pr ognosis and ge-nomic landscape in hepatocellular carcinoma. Precision Clinical Medicine 2023; 6: pbad021. https://doi.org/10.1093/pcmedi/pbad021

[29] Wang Y, Han G, Wang K et al. Tumor-derived GM-CSF promotes inflammatory colon carcinogenesis via stimulating epithelial re-lease of VEGF. Cancer Res 2014; 74: 716-26. https://doi.org/10.1158/0008-5472.Can-13-1459

[30] Pan P, Oshima K, Huang YW et al. Loss of FFAR2 promotes colon cancer by epigenetic dysregulation of inflammation suppressors. Int J Cancer 2018; 143: 886-96. https://doi.org/10.1002/ijc.31366

[31] Wang-Bishop L, Chen Z, Gomaa A et al. Inhibition of AURKA re-duces pr olifer ation and surviv al of gastr ointestinal cancer cells with activated KRAS by preventing activation of RPS6KB1. Gas-troenterology 2019; 156: 662-75.https://doi.org/10.1053/j.gastro.2018.10.030

[32] Singhal A, Li BT, O’Reilly EM. Targeting KRAS in cancer. Nat Med 2024; 30: 969-83. https://doi.org/10.1038/s41591-024-02903-0

[33] Ar afeh R, Sam uels Y. PIK3CA in cancer: the past 30 years. Semin Cancer Biol 2019; 59: 36-49. https://doi.org/10.1016/j.semcancer.2019.02.002

[34] Zhao M, Mishra L, Deng C-X. The role of TGF-β/SMAD4 signaling in cancer. International Journal of Biological Sciences 2018; 14: 111-23. https://doi.org/10.7150/ijbs.23230

[35] Mossmann D, Müller C, Park S et al. Arginine reprograms metabolism in liver cancer via RBM39. Cell 2023; 186: 5068-83. https://doi.org/10.1016/j.cell.2023.09.011

[36] Chen L, Zhang C, Xue R et al. Deep whole-genome analysis of 494 hepatocellular carcinomas. Nature 2024; 627: 586-93. https://doi.org/10.1038/s41586-024-07054-3

[37] Ferrín G, Ranchal I, Llamoza C et al. Identification of candi-date biomarkers for hepatocellular carcinoma in plasma of HCV-infected cirrhotic patients by 2-D DIGE. Liver Int 2013; 34: 438-46. https://doi.org/10.1111/liv.12277

[38] Liao H, Yang J, Xu Y et al. Mannose-binding lectin 2 as a potential ther a peutic tar get for hepatocellular carcinoma: m ulti-omics analysis and experimental validation. Cancers 2023; 15: 4900. https://doi.org/10.3390/cancers15194900

[39] Zhang Q, Shen X, Yuan X et al. Lipopol ysacc haride binding pro-tein resists hepatic oxidative stress by regulating lipid droplet homeostasis. Nat Commun 2024; 15: 3213. https://doi.org/10.1038/s41467-024-47553-5

[40] Karin M. New insights into the pathogenesis and treatment of non-viral hepatocellular carcinoma: a balancing act be-tween imm unosuppr ession and imm unosurv eillance. Precision Clinical Medicine 2018; 1: 21-8. https://doi.org/10.1093/pcmedi/pby005

[41] Li L, Yang H, Li Y et al. Hypoxia r estr ains the expr ession of com-plement component 9 in tumor-associated macr opha ges promoting non-small cell lung cancer progression. Cell Death Discovery 2018; 4: 63. https://doi.org/10.1038/s41420-018-0064-3

[42] Noe JT, Mitchell RA. MIF-dependent control of tumor immunity. Front Immunol 2020; 11: 609948. https://doi.org/10.3389/fimmu.2020.609948

[43] Al-Abed Y, VanPatten S. MIF as a disease tar get: ISO-1 as a pr oof-of-concept ther a peutic. Future Medicinal Chemistry 2010; 3: 45-63. https://doi.org/10.4155/fmc.10.281