Network Analysis in the Identification of Genes Conferring Metastatic Potential in Hepatocellular Carcinoma
Objectives: Hepatocellular carcinoma (HCC) is a common liver cancer accounting with high mortality rate owing to metastasis. Anti-metastatic treatment is scant while proposed mechanisms are in excess, yet specific molecular drivers of HCC remain at large. Therefore, our study aims to identify drivers of HCC metastasis using protein-protein interaction (PPI) networks to identify key driver genes associated with HCC metastasis.
Methods: From differential expression genes (DEGs) analysis using GSE45114 microarray dataset, four main hub genes that correlated with patient survival were identified. The first hub gene, SERPINC1 had the highest centrality parameter in impeding HCC metastasis, implicating thrombin mediation through thrombin-induced tumor growth and angiogenesis.
Results: Our study reveals that thrombin was not differentially expressed, hence, suggesting the involvement of other, less-well studied pathways in impeding metastasis, such as KNG1, PAH, AMBP, and TTR. Findings for CD44 were consistent with existing literature. Meanwhile, FGG and APOA5, both less studied genes in the context cancer metastasis studies, were found to be crucial in impeding HCC metastasis.
Conclusion: This study identified four potential proteins (SERPINC1, CD44, FGG and APOA5) to be therapeutic targets or biomarkers and demonstrates the use of PPI networks for understanding HCC metastasis at a more profound level.
1. Llovet JM, Kelley RK, Villanueva A, Singal AG, Pikarsky E, Roayaie S, et al. Hepatocellular carcinoma. Nat Rev Dis Primers 2021;7:6. [CrossRef]
2. WHO. International Agency for Research on Cancer. Malaysia Globocan 2020. Available at: https://gco.iarc.fr/today/data/factsheets/populations/458-malaysia-fact-sheets.pdf. Accessed Jan 3, 2023.
3. Raihan R, Azzeri A, H Shabaruddin F, Mohamed R. Hepatocellular carcinoma in Malaysia and its changing trend. Euroasian J Hepatogastroenterol 2018;8:54–6. [CrossRef]
4. Wu W, He X, Andayani D, Yang L, Ye J, Li Y, et al. Pattern of distant extrahepatic metastases in primary liver cancer: a SEER based study. J Cancer 2017;8:2312–8.
5. Zhang M, Lv X, Jiang Y, Li G, Qiao Q. Identification of aberrantly methylated differentially expressed genes in glioblastoma multiforme and their association with patient survival. Exp Ther Med 2019;18:2140–52. [CrossRef]
6. Xiang ZL, Zeng ZC, Tang ZY, Fan J, He J, Zeng HY, et al. Potential prognostic biomarkers for bone metastasis from hepatocellular carcinoma. Oncologist 2011;16:1028–39.
7. Xiang ZL, Zeng ZC, Fan J, Tang ZY, Zeng HY, Gao DM. Gene expression profiling of fixed tissues identified hypoxia-inducible factor-1α, VEGF, and matrix metalloproteinase-2 as biomarkers of lymph node metastasis in hepatocellular carcinoma. Clin Cancer Res 2011;17:5463–72. [CrossRef]
8. Wei L, Lian B, Zhang Y, Li W, Gu J, He X, Xie L. Application of microRNA and mRNA expression profiling on prognostic biomarker discovery for hepatocellular carcinoma. BMC Genomics 2014;15:S13. [CrossRef]
9. Viacava Follis A. Centrality of drug targets in protein networks. BMC Bioinformatics. 2021;22:527. [CrossRef]
10. Grinchuk OV, Yenamandra SP, Iyer R, Singh M, Lee HK, Lim KH, et al. Tumor-adjacent tissue co-expression profile analysis reveals pro-oncogenic ribosomal gene signature for prognosis of resectable hepatocellular carcinoma. Mol Oncol 2018;12:89–113. [CrossRef]
11. Davis S, Meltzer PS. GEOquery: a bridge between the Gene Expression Omnibus (GEO) and BioConductor. Bioinformatics 2007;23:1846–7. [CrossRef]
12. Wickham H, Averick M, Bryan J, Chang W, McGowan LD, François R, et al. Welcome to the Tidyverse. J Open Source Softw 2019;4:1686. [CrossRef]
13. Pagès H, Carlson, M, Falcon S, Li N. Manipulation of SQLitebased annotations in Bioconductor. Available at: https://bioconductor.org/packages/AnnotationDbi. Accessed Jan 3, 2023.
14. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res 2015;43:e47.
15. Law CW, Alhamdoosh M, Su S, Dong X, Tian L, Smyth GK, et al. RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR. F1000Res 2016;5:ISCB Comm J-1408. [CrossRef]
16. Dolgalev I. msigdbr: MSigDB gene sets for multiple organisms in a tidy data format. Available at: https://cran.r-project.org/package=msigdbr. Accessed Jan 3, 2023.
17. Korotkevich G, Sukhov V, Budin N, Shpak B, Artyomov MN, Sergushichev A, et al. Fast gene set enrichment analysis. bioRxiv. 2021 Feb 1. Doi: 10.1101/060012. [Epub ahead of print].
18. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage Det al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 2003;13:2498–504. [CrossRef]
19. Doncheva NT, Morris JH, Gorodkin J, Jensen LJ. Cytoscape StringApp: Network analysis and visualization of proteomics data. J Proteome Res 2019;18:623–32. [CrossRef]
20. Makrodimitris S, Reinders M, van Ham R. A thorough analysis of the contribution of experimental, derived and sequence-based predicted protein-protein interactions for functional annotation of proteins. PLoS One 2020;15:e0242723.
21. Assenov Y, Ramírez F, Schelhorn SE, Lengauer T, Albrecht M. Computing topological parameters of biological networks. Bioinformatics 2008;24:282–4. [CrossRef]
22. Therneau TM. Survival: Survival Analysis. Available at: https://cran.r-project.org/package=survival. Accessed Jan 3, 2023.
23. Kassambara A, Kosinski M, Biecek P, Fabian S. Survminer: Drawing survival curves using 'ggplot2'. Available at: https://cran.r-project.org/package=survminer. Accessed Jan 3, 2023.
24. Tang Z, Kang B, Li C, Chen T, Zhang Z. GEPIA2: an enhanced web server for large-scale expression profiling and interactive analysis. Nucleic Acids Res 2019;47:556–60. [CrossRef]
25. Merico D, Isserlin R, Stueker O, Emili A, Bader GD. Enrichment map: a network-based method for gene-set enrichment visualization and interpretation. PLoS One 2010;5:e13984.
26. Kucera M, Isserlin R, Arkhangorodsky A, Bader GD. AutoAnnotate: A Cytoscape app for summarizing networks with semantic annotations. F1000Res 2016;5:1717. [CrossRef]
27. Xu D, Wu J, Dong L, Luo W, Li L, Tang D, et al. Serpinc1 Acts as a tumor suppressor in hepatocellular carcinoma through inducing apoptosis and blocking macrophage polarization in an ubiquitin-proteasome manner. Front Oncol 2021;11:738607.
28. Kurata M, Okajima K, Kawamoto T, Uchiba M, Ohkohchi N. Antithrombin reduces reperfusion-induced hepatic metastasis of colon cancer cells. World J Gastroenterol 2006;12:60–5.
29. Iwako H, Tashiro H, Amano H, Tanimoto Y, Oshita A, Kobayashi T, et al. Prognostic significance of antithrombin III levels for outcomes in patients with hepatocellular carcinoma after curative hepatectomy. Ann Surg Oncol 2012;19:2888–96.
30. Rullier A, Senant N, Kisiel W, Bioulac-Sage P, Balabaud C, Le Bail B, et al. Expression of protease-activated receptors and tissue factor in human liver. Virchows Arch 2006;448:46–51.
31. Korita PV, Wakai T, Shirai Y, Matsuda Y, Sakata J, Cui X, et al. Overexpression of osteopontin independently correlates with vascular invasion and poor prognosis in patients with hepatocellular carcinoma. Hum Pathol 2008;39:1777–83. [CrossRef]
32. Noe JT, Mitchell RA. MIF-dependent control of tumor immunity. Front Immunol 2020;11:609948. [CrossRef]
33. Mawhinney L, Armstrong ME, O' Reilly C, Bucala R, Leng L, Fingerle-Rowson G, et al. Macrophage migration inhibitory factor (MIF) enzymatic activity and lung cancer. Mol Med 2015;20:729–35. [CrossRef]
34. Guda MR, Rashid MA, Asuthkar S, Jalasutram A, Caniglia JL, Tsung AJ, et al. Pleiotropic role of macrophage migration inhibitory factor in cancer. Am J Cancer Res 2019;9:2760–73.
35. Meyer-Siegler KL, Cox J, Leng L, Bucala R, Vera PL. Macrophage migration inhibitory factor anti-thrombin III complexes are decreased in bladder cancer patient serum: Complex formation as a mechanism of inactivation. Cancer Lett 2010;290:49– 57. [CrossRef]
36. Takafuji V, Forgues M, Unsworth E, Goldsmith P, Wang XW. An osteopontin fragment is essential for tumor cell invasion in hepatocellular carcinoma. Oncogene 2007;26,6361–71.
37. Deyell M, Garris CS, Laughney AM. Cancer metastasis as a non-healing wound. Br J Cancer 2021;124:1491–502. [CrossRef]
38. Priebatsch KM, Kvansakul M, Poon IK, Hulett MD. functional regulation of the plasma protein histidine-rich glycoprotein by Zn2+ in settings of tissue injury. Biomolecules 2017;7:22.
39. Matus CE, Bhoola KD, Figueroa CD. Kinin b1 receptor signaling in skin homeostasis and wound healing. Yale J Biol Med 2020;93:175–85.
40. Mishra A, Suman KH, Nair N, Majeed J, Tripathi V. An updated review on the role of the CXCL8-CXCR1/2 axis in the progression and metastasis of breast cancer. Mol Biol Rep 2021;48:6551–61.
41. Arnold SA, Brekken RA. SPARC: a matricellular regulator of tumorigenesis. J Cell Commun Signal 2009;3:255–73. [CrossRef]
42. Muresan XM, Sticozzi C, Belmonte G, Savelli V, Evelson P, Valacchi G, et al. Modulation of cutaneous scavenger receptor B1 levels by exogenous stressors impairs “in vitro” wound closure. Mech Ageing Dev 2018;172:78–85. [CrossRef]
43. Berger H, Wodarz A, Borchers A. PTK7 faces the Wnt in development and disease. Front Cell Dev Biol 2017;5:31.
44. Ricca TI, Liang G, Suenaga AP, Han SW, Jones PA, Jasiulionis MG. Tissue inhibitor of metalloproteinase 1 expression associated with gene demethylation confers anoikis resistance in early phases of melanocyte malignant transformation. Transl Oncol 2009;2:329–40. [CrossRef]
45. Löffek S, Schilling O, Franzke CW. Series "matrix metalloproteinases in lung health and disease": Biological role of matrix metalloproteinases: a critical balance. Eur Respir J 2011;38:191–208.
46. Shen H, Wu H, Sun F, Qi J, Zhu Q. A novel four-gene of iron metabolism-related and methylated for prognosis prediction of hepatocellular carcinoma. Bioengineered 2021;12:240–51.
47. Desoteux M, Louis C, Bévant K, Glaise D, Coulouarn C. A minimal subset of seven genes associated with tumor hepatocyte differentiation predicts a poor prognosis in human hepatocellular carcinoma. Cancers (Basel) 2021;13:5624. [CrossRef]
48. Gacche RN, Meshram RJ. Targeting tumor micro-environment for design and development of novel anti-angiogenic agents arresting tumor growth. Prog Biophys Mol Biol 2013;113:333– 54. [CrossRef]
49. Sharma M, Khan S, Rahman S, Singh LR. The extracellular protein, transthyretin is an oxidative stress biomarker. Front Physiol 2019;10:5. [CrossRef]
50. Xu J, Fang J, Cheng Z, Fan L, Hu W, Zhou F, et al. Overexpression of the Kininogen-1 inhibits proliferation and induces apoptosis of glioma cells. J Exp Clin Cancer Res 2018;37:180.
51. Chen C, Zhao S, Karnad A, Freeman JW. The biology and role of CD44 in cancer progression: therapeutic implications. J Hematol Oncol 2018;11:64. [CrossRef]
52. Senbanjo LT, Chellaiah MA. CD44: A multifunctional cell surface adhesion receptor is a regulator of progression and metastasis of cancer cells. Front Cell Dev Biol 2017;5:18.
53. Hong W, Guan KL. The YAP and TAZ transcription co-activators: key downstream effectors of the mammalian Hippo pathway. Semin Cell Dev Biol 2012;23:785–93. [CrossRef]
54. Shin E, Kim J. The potential role of YAP in head and neck squamous cell carcinoma. Exp Mol Med 2020;52:1264–74
55. Zhang J, He X, Wan Y, Zhang H, Tao T, Zhang M, et al. CD44 promotes hepatocellular carcinoma progression via upregulation of YAP. Exp Hematol Oncol 2021;10:54.
56. Preca BT, Bajdak K, Mock K, Sundararajan V, Pfannstiel J, Maurer J, et al. A self-enforcing CD44s/ZEB1 feedback loop maintains EMT and stemness properties in cancer cells. Int J Cancer 2015;137:2566–77. [CrossRef]
57. Li J, Zhang Y, Ruan R, He W, Qian Y. The novel interplay between CD44 standard isoform and the caspase-1/IL1B pathway to induce hepatocellular carcinoma progression. Cell Death Dis 2020;11:961. [CrossRef]
58. Thiery JP, Acloque H, Huang RY, Nieto MA. Epithelial-mesenchymal transitions in development and disease. Cell 2009;139:871–90. [CrossRef]
59. Paszek MJ, DuFort CC, Rossier O, Bainer R, Mouw JK, Godula K, et al. The cancer glycocalyx mechanically primes integrin-mediated growth and survival. Nature 2014;511:319–25.
60. Gonzalez-Avila G, Sommer B, Mendoza-Posada DA, Ramos C, Garcia-Hernandez AA, Falfan-Valencia R. Matrix metalloproteinases participation in the metastatic process and their diagnostic and therapeutic applications in cancer. Crit Rev Oncol Hematol 2019;137:57–83. [CrossRef]
61. Winkler J, Abisoye-Ogunniyan A, Metcalf KJ, Werb Z. Concepts of extracellular matrix remodelling in tumour progression and metastasis. Nat Commun. 2020 Oct 9;11(1):5120.
62. Seubert B, Grünwald B, Kobuch J, Cui H, Schelter F, Schaten S, et al. Tissue inhibitor of metalloproteinases (TIMP)-1 creates a premetastatic niche in the liver through SDF-1/CXCR4-dependent neutrophil recruitment in mice. Hepatology 2015;61:238–48. [CrossRef]
63. Li X, Sun X, Kan C, Chen B, Qu N, Hou N, et al. COL1A1: A novel oncogenic gene and therapeutic target in malignancies. Pathol Res Pract 2022 236:154013. [CrossRef]
64. Zhang Z, Wang Y, Zhang J, Zhong J, Yang R. COL1A1 promotes metastasis in colorectal cancer by regulating the WNT/PCP pathway. Mol Med Rep 2018;17:5037–42.
65. Simons M, Mlodzik M. Planar cell polarity signaling: from fly development to human disease. Annu Rev Genet 2008;42:517– 40. [CrossRef]
66. Harrold JM, Ramanathan M, Mager DE. Network-based approaches in drug discovery and early development. Clin Pharmacol Ther 2013;94:651–8. [CrossRef]
67. Mosesson MW. Fibrinogen and fibrin structure and functions. J Thromb Haemost 2005;3:1894–904. [CrossRef]
68. Zhang X, Wang F, Huang Y, Ke K, Zhao B, Chen L, et al. FGG promotes migration and invasion in hepatocellular carcinoma cells through activating epithelial to mesenchymal transition. Cancer Manag Res 2019;11:1653–65.
69. Yue H, Hu Z, Hu R, Guo Z, Zheng Y, Wang Y, et al. ALDH1A1 in cancers: Bidirectional function, drug resistance, and regulatory mechanism. Front Oncol 2022;12:918778.
70. Ren L, Yi J, Li W, Zheng X, Liu J, Wang J, et al. Apolipoproteins and cancer. Cancer Med 2019;8:7032–43. [CrossRef]
71. Li H, Long J, Xie F, Kang K, Shi Y, Xu W, et al. Transcriptomic analysis and identification of prognostic biomarkers in cholangiocarcinoma. Oncol Rep 2019;42:1833–42. [CrossRef]
72. Ma XL, Gao XH, Gong ZJ, Wu J, Tian L, Zhang CY, et al.. Apolipoprotein A1: a novel serum biomarker for predicting the prognosis of hepatocellular carcinoma after curative resection. Oncotarget 2016;7:70654–68. [CrossRef]
73. Hoshida Y, Fuchs BC, Tanabe KK. Prevention of hepatocellular carcinoma: potential targets, experimental models, and clinical challenges. Curr Cancer Drug Targets 2012;12:1129–59.
74. Tai CS, Lin YR, Teng TH, Lin PY, Tu SJ, Chou CH, et al. Haptoglobin expression correlates with tumor differentiation and fiveyear overall survival rate in hepatocellular carcinoma. PLoS One 2017;12:e0171269. [CrossRef]
75. Wang YB, Zhou BX, Ling YB, Xiong ZY, Li RX, Zhong YS, et al. Decreased expression of ApoF associates with poor prognosis in human hepatocellular carcinoma. Gastroenterol Rep (Oxf) 2019;7:354–60. [CrossRef]