Integrated bioinformatics analyses of key genes involved in hepatocellular carcinoma immunosuppression

Huang,Hongyan; Hu,Youwen; Guo,Li; Wen,Zhili

doi:10.3892/ol.2021.13091

Integrated bioinformatics analyses of key genes involved in hepatocellular carcinoma immunosuppression

Authors:
- Hongyan Huang
- Youwen Hu
- Li Guo
- Zhili Wen
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Affiliations: Department of Gastroenterology, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi 330006, P.R. China
Published online on: October 13, 2021 https://doi.org/10.3892/ol.2021.13091
Article Number: 830
Copyright: © Huang et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Hepatocellular carcinoma (HCC) is a typical inflammation‑driven cancer. Chronically unresolved inflammation may remodel the immunosuppressive tumor microenvironment, which is rich in innate immune cells. The mechanisms via which HCC progresses through the evasion of the innate immune surveillance remain unclear. The present study thus aimed to identify key genes involved in HCC immunosuppression and to establish an innate immune risk signature, with the ultimate goal of obtaining new insight into effective immunotherapies. HCC and normal liver tissue mRNA expression and clinicopathological data were obtained from the Cancer Genome Atlas database. The immunosuppressive innate immune‑related genes (IIRGs) in HCC were screened using integrated bioinformatics analyses. Gene expression was then validated using the Gene Expression Omnibus database and the Human Protein Atlas database, and tissues were obtained from patients with HCC who underwent surgery. In total, 3,676 genes were identified as differentially expressed mRNAs after comparing the HCC tissues with the normal liver tissues in TCGA. Gene Set Enrichment Analyses revealed 21 highly expressed IIRGs in HCC tissues. A survival analysis and Cox regression model were used to construct an innate immune risk signature, including three IIRGs: Collectin‑12 (COLEC12), matrix metalloproteinase‑12 (MMP12) and mucin‑12 (MUC12) genes. Univariate and multivariate Cox analyses revealed that the signature of the three IIRGs was a robust independent risk factor in relation to the overall survival (OS) of patients with HCC. The expression of the three aforementioned IIRGs was confirmed through external validation. Moreover, COLEC12 and MMP12 expression significantly correlated with that of immune checkpoint molecules or immunosuppressive cytokines. The tumor immune dysfunction and exclusion tool predicted that the increased expression of the three IIRGs in patients with HCC was significantly associated with the efficacy of relatively poor immune checkpoint blockade therapy. Conclusively, a novel innate immune‑related risk signature for patients with HCC was constructed and validated. This signature may be involved in immunosuppression, and may be used to predict a poor prognosis, functioning as a potential immunotherapeutic target for patients with HCC.

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1	Forner A, Reig M and Bruix J: Hepatocellular carcinoma. Lancet. 391:1301–1314. 2018. View Article : Google Scholar : PubMed/NCBI
2	Villanueva A: Hepatocellular carcinoma. N Engl J Med. 380:1450–1462. 2019. View Article : Google Scholar : PubMed/NCBI
3	Lechel A and Gougelet A: Early HCC treatment: A future strategy against interferon/miR-484 axis to revert precancerous lesions? Gut. 65:1073–1074. 2016. View Article : Google Scholar : PubMed/NCBI
4	Kudo M: Early detection and curative treatment of early-stage hepatocellular carcinoma. Clin Gastroenterol Hepatol. 3 (Suppl 2):S144–S148. 2005. View Article : Google Scholar : PubMed/NCBI
5	Thomas MB, Jaffe D, Choti MM, Belghiti J, Curley S, Fong Y, Gores G, Kerlan R, Merle P, O'Neil B, et al: Hepatocellular carcinoma: Consensus recommendations of the National Cancer Institute Clinical Trials Planning Meeting. J Clin Oncol. 28:3994–4005. 2010. View Article : Google Scholar : PubMed/NCBI
6	Philips GK and Atkins M: Therapeutic uses of anti-PD-1 and anti-PD-L1 antibodies. Int Immunol. 27:39–46. 2015. View Article : Google Scholar : PubMed/NCBI
7	Rowshanravan B, Halliday N and Sansom DM: CTLA-4: a moving target in immunotherapy. Blood. 131:58–67. 2018. View Article : Google Scholar : PubMed/NCBI
8	Zhu AX, Finn RS, Edeline J, Cattan S, Ogasawara S, Palmer D, Verslype C, Zagonel V, Fartoux L, Vogel A, et al KEYNOTE-224 investigators, : Pembrolizumab in patients with advanced hepatocellular carcinoma previously treated with sorafenib (KEYNOTE-224): A non-randomised, open-label phase 2 trial. Lancet Oncol. 19:940–952. 2018. View Article : Google Scholar : PubMed/NCBI
9	Kambhampati S, Bauer KE, Bracci PM, Keenan BP, Behr SC, Gordan JD and Kelley RK: Nivolumab in patients with advanced hepatocellular carcinoma and Child-Pugh class B cirrhosis: Safety and clinical outcomes in a retrospective case series. Cancer. 125:3234–3241. 2019. View Article : Google Scholar : PubMed/NCBI
10	Yu LX, Ling Y and Wang HY: Role of nonresolving inflammation in hepatocellular carcinoma development and progression. NPJ Precis Oncol. 2:62018. View Article : Google Scholar : PubMed/NCBI
11	Ringelhan M, Pfister D, O'Connor T, Pikarsky E and Heikenwalder M: The immunology of hepatocellular carcinoma. Nat Immunol. 19:222–232. 2018. View Article : Google Scholar : PubMed/NCBI
12	Han Q, Zhao H, Jiang Y, Yin C and Zhang J: HCC-derived exosomes: Critical player and target for cancer immune escape. Cells. 8:5582019. View Article : Google Scholar : PubMed/NCBI
13	Tanimine N, Ohira M, Tahara H, Ide K, Tanaka Y, Onoe T and Ohdan H: Strategies for deliberate induction of immune tolerance in liver transplantation: From preclinical models to clinical application. Front Immunol. 11:16152020. View Article : Google Scholar : PubMed/NCBI
14	Sia D, Jiao Y, Martinez-Quetglas I, Kuchuk O, Villacorta-Martin C, Castro de Moura M, Putra J, Camprecios G, Bassaganyas L, Akers N, et al: Identification of an Immune-specific Class of Hepatocellular Carcinoma, Based on Molecular Features. Gastroenterology. 153:812–826. 2017. View Article : Google Scholar : PubMed/NCBI
15	Xiao Y and Yu D: Tumor microenvironment as a therapeutic target in cancer. Pharmacol Ther. 221:1077532021. View Article : Google Scholar : PubMed/NCBI
16	Ngambenjawong C, Gustafson HH and Pun SH: Progress in tumor-associated macrophage (TAM)-targeted therapeutics. Adv Drug Deliv Rev. 114:206–221. 2017. View Article : Google Scholar : PubMed/NCBI
17	Barry KC, Hsu J, Broz ML, Cueto FJ, Binnewies M, Combes AJ, Nelson AE, Loo K, Kumar R, Rosenblum MD, et al: A natural killer-dendritic cell axis defines checkpoint therapy-responsive tumor microenvironments. Nat Med. 24:1178–1191. 2018. View Article : Google Scholar : PubMed/NCBI
18	Lambrechts D, Wauters E, Boeckx B, Aibar S, Nittner D, Burton O, Bassez A, Decaluwé H, Pircher A, Van den Eynde K, et al: Phenotype molding of stromal cells in the lung tumor microenvironment. Nat Med. 24:1277–1289. 2018. View Article : Google Scholar : PubMed/NCBI
19	Liu XN, Cui DN, Li YF, Liu YH, Liu G and Liu L: Multiple ‘Omics’ data-based biomarker screening for hepatocellular carcinoma diagnosis. World J Gastroenterol. 25:4199–4212. 2019. View Article : Google Scholar : PubMed/NCBI
20	Cerami E, Gao J, Dogrusoz U, Gross BE, Sumer SO, Aksoy BA, Jacobsen A, Byrne CJ, Heuer ML, Larsson E, et al: The cBio cancer genomics portal: An open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2:401–404. 2012. View Article : Google Scholar : PubMed/NCBI
21	Yildiz G, Arslan-Ergul A, Bagislar S, Konu O, Yuzugullu H, Gursoy-Yuzugullu O, Ozturk N, Ozen C, Ozdag H, Erdal E, et al: Genome-wide transcriptional reorganization associated with senescence-to-immortality switch during human hepatocellular carcinogenesis. PLoS One. 8:e640162013. View Article : Google Scholar : PubMed/NCBI
22	Zhou W, Ma Y, Zhang J, Hu J, Zhang M, Wang Y, Li Y, Wu L, Pan Y, Zhang Y, et al: Predictive model for inflammation grades of chronic hepatitis B: Large-scale analysis of clinical parameters and gene expressions. Liver Int. 37:1632–1641. 2017. View Article : Google Scholar : PubMed/NCBI
23	Robinson MD, McCarthy DJ and Smyth GK: edgeR: A Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 26:139–140. 2010. View Article : Google Scholar : PubMed/NCBI
24	Dennis G Jr, Sherman BT, Hosack DA, Yang J, Gao W, Lane HC and Lempicki RA: DAVID: Database for annotation, visualization, and integrated discovery. Genome Biol. 4:32003. View Article : Google Scholar : PubMed/NCBI
25	Ito K and Murphy D: Application of ggplot2 to pharmacometric graphics. CPT Pharmacometrics Syst Pharmacol. 2:e792013. View Article : Google Scholar : PubMed/NCBI
26	Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, et al: Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA. 102:15545–15550. 2005. View Article : Google Scholar : PubMed/NCBI
27	Chen HY, Yu SL, Chen CH, Chang GC, Chen CY, Yuan A, Cheng CL, Wang CH, Terng HJ, Kao SF, et al: A five-gene signature and clinical outcome in non-small-cell lung cancer. N Engl J Med. 356:11–20. 2007. View Article : Google Scholar : PubMed/NCBI
28	Lorent M, Giral M and Foucher Y: Net time-dependent ROC curves: A solution for evaluating the accuracy of a marker to predict disease-related mortality. Stat Med. 33:2379–2389. 2014. View Article : Google Scholar : PubMed/NCBI
29	Blanche P, Dartigues JF and Jacqmin-Gadda H: Estimating and comparing time-dependent areas under receiver operating characteristic curves for censored event times with competing risks. Stat Med. 32:5381–5397. 2013. View Article : Google Scholar : PubMed/NCBI
30	Pontén F, Jirström K and Uhlen M: The Human Protein Atlas--a tool for pathology. J Pathol. 216:387–393. 2008. View Article : Google Scholar : PubMed/NCBI
31	Zhang B, Su X, Xie Z, Ding H, Wang T, Xie RY and Wen Z: Inositol-requiring kinase 1 regulates apoptosis via inducing endoplasmic reticulum stress in colitis epithelial cells. Dig Dis Sci. 66:3015–3025. 2021. View Article : Google Scholar : PubMed/NCBI
32	Livak KJ and Schmittgen TD: Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) method. Methods. 25:402–408. 2001. View Article : Google Scholar : PubMed/NCBI
33	Tang Z, Li C, Kang B, Gao G, Li C and Zhang Z: GEPIA: A web server for cancer and normal gene expression profiling and interactive analyses. Nucleic Acids Res. 45:W98–W102. 2017. View Article : Google Scholar : PubMed/NCBI
34	Jiang P, Gu S, Pan D, Fu J, Sahu A, Hu X, Li Z, Traugh N, Bu X, Li B, et al: Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response. Nat Med. 24:1550–1558. 2018. View Article : Google Scholar : PubMed/NCBI
35	Iñarrairaegui M, Melero I and Sangro B: Immunotherapy of hepatocellular carcinoma: Facts and hopes. Clin Cancer Res. 24:1518–1524. 2018. View Article : Google Scholar : PubMed/NCBI
36	Rizvi S, Wang J and El-Khoueiry AB: Liver cancer immunity. Hepatology. 73 (Suppl 1):86–103. 2021. View Article : Google Scholar : PubMed/NCBI
37	Finn OJ: Cancer immunology. N Engl J Med. 358:2704–2715. 2008. View Article : Google Scholar : PubMed/NCBI
38	Jeong JY, Kim TB, Kim J, Choi HW, Kim EJ, Yoo HJ, Lee S, Jun HR, Yoo W, Kim S, et al: Diversity in the extracellular vesicle-derived microbiome of tissues according to tumor progression in pancreatic cancer. Cancers (Basel). 12:23462020. View Article : Google Scholar : PubMed/NCBI
39	Rohr-Udilova N, Klinglmüller F, Schulte-Hermann R, Stift J, Herac M, Salzmann M, Finotello F, Timelthaler G, Oberhuber G, Pinter M, et al: Deviations of the immune cell landscape between healthy liver and hepatocellular carcinoma. Sci Rep. 8:62202018. View Article : Google Scholar : PubMed/NCBI
40	McGreal EP, Miller JL and Gordon S: Ligand recognition by antigen-presenting cell C-type lectin receptors. Curr Opin Immunol. 17:18–24. 2005. View Article : Google Scholar : PubMed/NCBI
41	Li GZ, Deng JF, Qi YZ, Liu R and Liu ZX: COLEC12 regulates apoptosis of osteosarcoma through Toll-like receptor 4-activated inflammation. J Clin Lab Anal. 34:e234692020. View Article : Google Scholar : PubMed/NCBI
42	Chang LL, Hsu WH, Kao MC, Chou CC, Lin CC, Liu CJ, Weng BC, Kuo FC, Kuo CH, Lin MH, et al: Stromal C-type lectin receptor COLEC12 integrates H. pylori, PGE2-EP2/4 axis and innate immunity in gastric diseases. Sci Rep. 8:38212018. View Article : Google Scholar : PubMed/NCBI
43	Moehle C, Ackermann N, Langmann T, Aslanidis C, Kel A, Kel-Margoulis O, Schmitz-Madry A, Zahn A, Stremmel W and Schmitz G: Aberrant intestinal expression and allelic variants of mucin genes associated with inflammatory bowel disease. J Mol Med (Berl). 84:1055–1066. 2006. View Article : Google Scholar : PubMed/NCBI
44	Andrianifahanana M, Moniaux N and Batra SK: Regulation of mucin expression: Mechanistic aspects and implications for cancer and inflammatory diseases. Biochim Biophys Acta. 1765:189–222. 2006.PubMed/NCBI
45	Kataoka H, Uchino H, Iwamura T, Seiki M, Nabeshima K and Koono M: Enhanced tumor growth and invasiveness in vivo by a carboxyl-terminal fragment of alpha1-proteinase inhibitor generated by matrix metalloproteinases: A possible modulatory role in natural killer cytotoxicity. Am J Pathol. 154:457–468. 1999. View Article : Google Scholar : PubMed/NCBI
46	Shapiro SD and Senior RM: Matrix metalloproteinases. Matrix degradation and more. Am J Respir Cell Mol Biol. 20:1100–1102. 1999. View Article : Google Scholar : PubMed/NCBI
47	Jirillo E, Caccavo D, Magrone T, Piccigallo E, Amati L, Lembo A, Kalis C and Gumenscheimer M: The role of the liver in the response to LPS: Experimental and clinical findings. J Endotoxin Res. 8:319–327. 2002. View Article : Google Scholar : PubMed/NCBI
48	Haque TR and Barritt AS IV: Intestinal microbiota in liver disease. Best Pract Res Clin Gastroenterol. 30:133–142. 2016. View Article : Google Scholar : PubMed/NCBI
49	Wiest R, Albillos A, Trauner M, Bajaj JS and Jalan R: Targeting the gut-liver axis in liver disease. J Hepatol. 67:1084–1103. 2017. View Article : Google Scholar : PubMed/NCBI
50	Jiang JW, Chen XH, Ren Z and Zheng SS: Gut microbial dysbiosis associates hepatocellular carcinoma via the gut-liver axis. Hepatobiliary Pancreat Dis Int. 18:19–27. 2019. View Article : Google Scholar : PubMed/NCBI
51	Nejman D, Livyatan I, Fuks G, Gavert N, Zwang Y, Geller LT, Rotter-Maskowitz A, Weiser R, Mallel G, Gigi E, et al: The human tumor microbiome is composed of tumor type-specific intracellular bacteria. Science. 368:973–980. 2020. View Article : Google Scholar : PubMed/NCBI