Role of Epigenetic Factors in Determining the Biological Behavior and Prognosis of Hepatocellular Carcinoma
Abstract
:1. Introduction
2. Materials and Methods
2.1. Microarray Data Analysis for Transcriptome Profiling
2.1.1. Data Set and Basic Features
2.1.2. Transcriptome Profiling with Microarrays
2.2. Bioinformatics Analyses of Whole Genome Bisulfite Sequencing (WGBS)
2.3. Bioinformatics Analyses of MeDIP-Sequencing
2.4. Ethical and Financial Aspects of the Study
3. Results
3.1. Results of Transcriptome Profiling with Microarrays Analysis
3.2. Results of Whole Genome Bisulfite Sequencing Analysis
3.3. Results of MeDIP-Seq Analysis
3.4. Summary of the Molecular and Metabolic Pathways of the Genes That Showed Differences in Expression and Methylation Profile
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Allaire, M.; Bruix, J.; Korenjak, M.; Manes, S.; Maravic, Z.; Reeves, H.; Salem, R.; Sangro, B.; Sherman, M. What to do about hepatocellular carcinoma: Recommendations for health authorities from the International Liver Cancer Association. JHEP Rep. 2022, 4, 100578. [Google Scholar] [CrossRef]
- Satilmis, B.; Sahin, T.T.; Cicek, E.; Akbulut, S.; Yilmaz, S. Hepatocellular Carcinoma Tumor Microenvironment and Its Implications in Terms of Anti-tumor Immunity: Future Perspectives for New Therapeutics. J. Gastrointest. Cancer 2021, 52, 1198–1205. [Google Scholar] [CrossRef]
- Bardakçı, M.; Ergün, Y.; Yalçın, K. Retrospective Analysis of Demographic and Laboratory Data of Patients with Hepatocellular Carcinoma: Single Center Experience. Acta Oncol. Turc. 2019, 52, 64–72. [Google Scholar] [CrossRef]
- Sung, H.; Ferlay, J.; Siegel, R.L.; Laversanne, M.; Soerjomataram, I.; Jemal, A.; Bray, F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J. Clin. 2021, 71, 209–249. [Google Scholar] [CrossRef] [PubMed]
- Akinyemiju, T.; Abera, S.; Ahmed, M.; Alam, N.; Alemayohu, M.A.; Allen, C.; Al-Raddadi, R.; Alvis-Guzman, N.; Amoako, Y.; Artaman, A.; et al. The Burden of Primary Liver Cancer and Underlying Etiologies from 1990 to 2015 at the Global, Regional, and National Level: Results from the Global Burden of Disease Study 2015. JAMA Oncol. 2017, 3, 1683–1691. [Google Scholar] [CrossRef] [PubMed]
- McGlynn, K.A.; Petrick, J.L.; London, W.T. Global epidemiology of hepatocellular carcinoma: An emphasis on demographic and regional variability. Clin. Liver Dis. 2015, 19, 223–238. [Google Scholar] [CrossRef]
- Vo Quang, E.; Shimakawa, Y.; Nahon, P. Epidemiological projections of viral-induced hepatocellular carcinoma in the perspective of WHO global hepatitis elimination. Liver Int. 2021, 41, 915–927. [Google Scholar] [CrossRef] [PubMed]
- Nguyen, P.H.D.; Ma, S.; Phua, C.Z.J.; Kaya, N.A.; Lai, H.L.H.; Lim, C.J.; Lim, J.Q.; Wasser, M.; Lai, L.; Tam, W.L.; et al. Intratumoural immune heterogeneity as a hallmark of tumour evolution and progression in hepatocellular carcinoma. Nat. Commun. 2021, 12, 227. [Google Scholar] [CrossRef] [PubMed]
- Vessoni, A.T.; Filippi-Chiela, E.C.; Lenz, G.; Batista, L.F.Z. Tumor propagating cells: Drivers of tumor plasticity, heterogeneity, and recurrence. Oncogene 2020, 39, 2055–2068. [Google Scholar] [CrossRef]
- Ni, J.; Bucci, J.; Malouf, D.; Knox, M.; Graham, P.; Li, Y. Exosomes in Cancer Radioresistance. Front. Oncol. 2019, 9, 869. [Google Scholar] [CrossRef] [PubMed]
- Chaffer, C.L.; Weinberg, R.A. How does multistep tumorigenesis really proceed? Cancer Discov. 2015, 5, 22–24. [Google Scholar] [CrossRef] [PubMed]
- Rotondo, J.C.; Borghi, A.; Selvatici, R.; Magri, E.; Bianchini, E.; Montinari, E.; Corazza, M.; Virgili, A.; Tognon, M.; Martini, F. Hypermethylation-Induced Inactivation of the IRF6 Gene as a Possible Early Event in Progression of Vulvar Squamous Cell Carcinoma Associated with Lichen Sclerosus. JAMA Dermatol. 2016, 152, 928–933. [Google Scholar] [CrossRef]
- Shen, H.; Laird, P.W. Interplay between the cancer genome and epigenome. Cell 2013, 153, 38–55. [Google Scholar] [CrossRef]
- Nagaraju, G.P.; Dariya, B.; Kasa, P.; Peela, S.; El-Rayes, B.F. Epigenetics in hepatocellular carcinoma. Semin. Cancer Biol. 2022, 86, 622–632. [Google Scholar] [CrossRef] [PubMed]
- Wilson, C.L.; Mann, D.A.; Borthwick, L.A. Epigenetic reprogramming in liver fibrosis and cancer. Adv. Drug Deliv. Rev. 2017, 121, 124–132. [Google Scholar] [CrossRef]
- Erkekoglu, P.; Oral, D.; Chao, M.W.; Kocer-Gumusel, B. Hepatocellular Carcinoma and Possible Chemical and Biological Causes: A Review. J. Environ. Pathol. Toxicol. Oncol. 2017, 36, 171–190. [Google Scholar] [CrossRef] [PubMed]
- Lee, J.; Molley, T.G.; Seward, C.H.; Abdeen, A.A.; Zhang, H.; Wang, X.; Gandhi, H.; Yang, J.L.; Gaus, K.; Kilian, K.A. Geometric regulation of histone state directs melanoma reprogramming. Commun. Biol. 2020, 3, 341. [Google Scholar] [CrossRef]
- Cheishvili, D.; Boureau, L.; Szyf, M. DNA demethylation and invasive cancer: Implications for therapeutics. Br. J. Pharmacol. 2015, 172, 2705–2715. [Google Scholar] [CrossRef]
- Chik, F.; Szyf, M.; Rabbani, S.A. Role of epigenetics in cancer initiation and progression. Adv. Exp. Med. Biol. 2011, 720, 91–104. [Google Scholar] [CrossRef]
- Taniai, M. Alcohol and hepatocarcinogenesis. Clin. Mol. Hepatol. 2020, 26, 736–741. [Google Scholar] [CrossRef]
- Ding, X.; He, M.; Chan, A.W.H.; Song, Q.X.; Sze, S.C.; Chen, H.; Man, M.K.H.; Man, K.; Chan, S.L.; Lai, P.B.S.; et al. Genomic and Epigenomic Features of Primary and Recurrent Hepatocellular Carcinomas. Gastroenterology 2019, 157, 1630–1645.E6. [Google Scholar] [CrossRef] [PubMed]
- Liu, X.; McEachron, T.A.; Schwartzentruber, J.; Wu, G. Histone H3 mutations in pediatric brain tumors. Cold Spring Harb. Perspect. Biol. 2014, 6, a018689. [Google Scholar] [CrossRef] [PubMed]
- Bayat, A. Science, medicine, and the future: Bioinformatics. BMJ 2002, 324, 1018–1022. [Google Scholar] [CrossRef]
- Goffeau, A.; Barrell, B.G.; Bussey, H.; Davis, R.W.; Dujon, B.; Feldmann, H.; Galibert, F.; Hoheisel, J.D.; Jacq, C.; Johnston, M.; et al. Life with 6000 genes. Science 1996, 274, 546–567. [Google Scholar] [CrossRef]
- Parkhill, J.; Wren, B.W.; Thomson, N.R.; Titball, R.W.; Holden, M.T.; Prentice, M.B.; Sebaihia, M.; James, K.D.; Churcher, C.; Mungall, K.L.; et al. Genome sequence of Yersinia pestis, the causative agent of plague. Nature 2001, 413, 523–527. [Google Scholar] [CrossRef] [PubMed]
- Gao, Z.; Xu, Z.; Hung, M.S.; Lin, Y.C.; Wang, T.; Gong, M.; Zhi, X.; Jablon, D.M.; You, L. Promoter demethylation of WIF-1 by epigallocatechin-3-gallate in lung cancer cells. Anticancer Res. 2009, 29, 2025–2030. [Google Scholar]
- Li, L.; Liang, Y.; Kang, L.; Liu, Y.; Gao, S.; Chen, S.; Li, Y.; You, W.; Dong, Q.; Hong, T.; et al. Transcriptional Regulation of the Warburg Effect in Cancer by SIX1. Cancer Cell 2018, 33, 368–385.e367. [Google Scholar] [CrossRef]
- Kojima, K.; April, C.; Canasto-Chibuque, C.; Chen, X.; Deshmukh, M.; Venkatesh, A.; Tan, P.S.; Kobayashi, M.; Kumada, H.; Fan, J.B.; et al. Transcriptome profiling of archived sectioned formalin-fixed paraffin-embedded (AS-FFPE) tissue for disease classification. PLoS ONE 2014, 9, e86961. [Google Scholar] [CrossRef]
- Villanueva, A.; Portela, A.; Sayols, S.; Battiston, C.; Hoshida, Y.; Méndez-González, J.; Imbeaud, S.; Letouzé, E.; Hernandez-Gea, V.; Cornella, H.; et al. DNA methylation-based prognosis and epidrivers in hepatocellular carcinoma. Hepatology 2015, 61, 1945–1956. [Google Scholar] [CrossRef]
- Durinck, S.; Spellman, P.T.; Birney, E.; Huber, W. Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt. Nat. Protoc. 2009, 4, 1184–1191. [Google Scholar] [CrossRef]
- Gautier, L.; Cope, L.; Bolstad, B.M.; Irizarry, R.A. affy—Analysis of Affymetrix GeneChip data at the probe level. Bioinformatics 2004, 20, 307–315. [Google Scholar] [CrossRef] [PubMed]
- Ritchie, M.E.; Phipson, B.; Wu, D.; Hu, Y.; Law, C.W.; Shi, W.; Smyth, G.K. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015, 43, e47. [Google Scholar] [CrossRef]
- Metsalu, T.; Vilo, J. ClustVis: A web tool for visualizing clustering of multivariate data using Principal Component Analysis and heatmap. Nucleic Acids Res. 2015, 43, W566–W570. [Google Scholar] [CrossRef]
- Reimand, J.; Isserlin, R.; Voisin, V.; Kucera, M.; Tannus-Lopes, C.; Rostamianfar, A.; Wadi, L.; Meyer, M.; Wong, J.; Xu, C.; et al. Pathway enrichment analysis and visualization of omics data using g:Profiler, GSEA, Cytoscape and EnrichmentMap. Nat. Protoc. 2019, 14, 482–517. [Google Scholar] [CrossRef] [PubMed]
- Szklarczyk, D.; Franceschini, A.; Wyder, S.; Forslund, K.; Heller, D.; Huerta-Cepas, J.; Simonovic, M.; Roth, A.; Santos, A.; Tsafou, K.P.; et al. STRING v10: Protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 2015, 43, D447–D452. [Google Scholar] [CrossRef] [PubMed]
- Garcia-Moreno, A.; Lopez-Dominguez, R.; Villatoro-Garcia, J.A.; Ramirez-Mena, A.; Aparicio-Puerta, E.; Hackenberg, M.; Pascual-Montano, A.; Carmona-Saez, P. Functional Enrichment Analysis of Regulatory Elements. Biomedicines 2022, 10, 590. [Google Scholar] [CrossRef]
- García-Ruiz, S.; Gil-Martínez, A.L.; Cisterna, A.; Jurado-Ruiz, F.; Reynolds, R.H.; NABEC (North America Brain Expression Consortium); Cookson, M.R.; Hardy, J.; Ryten, M.; Botía, J.A. CoExp: A Web Tool for the Exploitation of Co-expression Networks. Front. Genet. 2021, 12, 630187. [Google Scholar] [CrossRef]
- Cardozo, L.E.; Russo, P.S.T.; Gomes-Correia, B.; Araujo-Pereira, M.; Sepúlveda-Hermosilla, G.; Maracaja-Coutinho, V.; Nakaya, H.I. webCEMiTool: Co-expression Modular Analysis Made Easy. Front. Genet. 2019, 10, 146. [Google Scholar] [CrossRef]
- Bolger, A.M.; Lohse, M.; Usadel, B. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics 2014, 30, 2114–2120. [Google Scholar] [CrossRef]
- Chen, S.; Zhou, Y.; Chen, Y.; Gu, J. fastp: An ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 2018, 34, i884–i890. [Google Scholar] [CrossRef]
- Ewels, P.; Magnusson, M.; Lundin, S.; Käller, M. MultiQC: Summarize analysis results for multiple tools and samples in a single report. Bioinformatics 2016, 32, 3047–3048. [Google Scholar] [CrossRef] [PubMed]
- Shen, W.; Le, S.; Li, Y.; Hu, F. SeqKit: A Cross-Platform and Ultrafast Toolkit for FASTA/Q File Manipulation. PLoS ONE 2016, 11, e0163962. [Google Scholar] [CrossRef] [PubMed]
- Langmead, B.; Salzberg, S.L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 2012, 9, 357–359. [Google Scholar] [CrossRef] [PubMed]
- Li, H.; Handsaker, B.; Wysoker, A.; Fennell, T.; Ruan, J.; Homer, N.; Marth, G.; Abecasis, G.; Durbin, R.; 1000 Genome Project Data Processing Subgroup. The Sequence Alignment/Map format and SAMtools. Bioinformatics 2009, 25, 2078–2079. [Google Scholar] [CrossRef]
- Krueger, F.; Andrews, S.R. Bismark: A flexible aligner and methylation caller for Bisulfite-Seq applications. Bioinformatics 2011, 27, 1571–1572. [Google Scholar] [CrossRef]
- Quinlan, A.R.; Hall, I.M. BEDTools: A flexible suite of utilities for comparing genomic features. Bioinformatics 2010, 26, 841–842. [Google Scholar] [CrossRef]
- Ge, S.X.; Jung, D.; Yao, R. ShinyGO: A graphical gene-set enrichment tool for animals and plants. Bioinformatics 2020, 36, 2628–2629. [Google Scholar] [CrossRef]
- Lienhard, M.; Grasse, S.; Rolff, J.; Frese, S.; Schirmer, U.; Becker, M.; Börno, S.; Timmermann, B.; Chavez, L.; Sültmann, H.; et al. QSEA-modelling of genome-wide DNA methylation from sequencing enrichment experiments. Nucleic Acids Res. 2017, 45, e44. [Google Scholar] [CrossRef]
- Cavalcante, R.G.; Sartor, M.A. annotatr: Genomic regions in context. Bioinformatics 2017, 33, 2381–2383. [Google Scholar] [CrossRef]
- Balogh, J.; Victor, D., III; Asham, E.H.; Burroughs, S.G.; Boktour, M.; Saharia, A.; Li, X.; Ghobrial, R.M.; Monsour, H.P., Jr. Hepatocellular carcinoma: A review. J. Hepatocell. Carcinoma 2016, 3, 41–53. [Google Scholar] [CrossRef]
- Villanueva, A.; Minguez, B.; Forner, A.; Reig, M.; Llovet, J.M. Hepatocellular carcinoma: Novel molecular approaches for diagnosis, prognosis, and therapy. Annu. Rev. Med. 2010, 61, 317–328. [Google Scholar] [CrossRef] [PubMed]
- Karczewski, K.J.; Snyder, M.P. Integrative omics for health and disease. Nat. Rev. Genet. 2018, 19, 299–310. [Google Scholar] [CrossRef] [PubMed]
- Lin, K.T.; Shann, Y.J.; Chau, G.Y.; Hsu, C.N.; Huang, C.Y. Identification of latent biomarkers in hepatocellular carcinoma by ultra-deep whole-transcriptome sequencing. Oncogene 2014, 33, 4786–4794. [Google Scholar] [CrossRef]
- Gurnett, C.A.; Alaee, F.; Kruse, L.M.; Desruisseau, D.M.; Hecht, J.T.; Wise, C.A.; Bowcock, A.M.; Dobbs, M.B. Asymmetric lower-limb malformations in individuals with homeobox PITX1 gene mutation. Am. J. Hum. Genet. 2008, 83, 616–622. [Google Scholar] [CrossRef]
- Eun, J.W.; Jang, J.W.; Yang, H.D.; Kim, J.; Kim, S.Y.; Na, M.J.; Shin, E.; Ha, J.W.; Jeon, S.; Ahn, Y.M.; et al. Serum Proteins, HMMR, NXPH4, PITX1 and THBS4; A Panel of Biomarkers for Early Diagnosis of Hepatocellular Carcinoma. J. Clin. Med. 2022, 11, 2128. [Google Scholar] [CrossRef]
- Li, F.; Liu, T.; Xiao, C.Y.; Yu, J.X.; Lu, L.G.; Xu, M.Y. FOXP1 and SPINK1 reflect the risk of cirrhosis progression to HCC with HBV infection. Biomed. Pharmacother. 2015, 72, 103–108. [Google Scholar] [CrossRef] [PubMed]
- Marshall, A.; Lukk, M.; Kutter, C.; Davies, S.; Alexander, G.; Odom, D.T. Global gene expression profiling reveals SPINK1 as a potential hepatocellular carcinoma marker. PLoS ONE 2013, 8, e59459. [Google Scholar] [CrossRef]
- Huang, K.; Xie, W.; Wang, S.; Li, Q.; Wei, X.; Chen, B.; Hua, Y.; Li, S.; Peng, B.; Shen, S. High SPINK1 Expression Predicts Poor Prognosis and Promotes Cell Proliferation and Metastasis of Hepatocellular Carcinoma. J. Investig. Surg. 2021, 34, 1011–1020. [Google Scholar] [CrossRef]
- Feng, J.; Lu, P.Z.; Zhu, G.Z.; Hooi, S.C.; Wu, Y.; Huang, X.W.; Dai, H.Q.; Chen, P.H.; Li, Z.J.; Su, W.J.; et al. ACSL4 is a predictive biomarker of sorafenib sensitivity in hepatocellular carcinoma. Acta Pharmacol. Sin. 2021, 42, 160–170. [Google Scholar] [CrossRef]
- Chen, J.; Ding, C.; Chen, Y.; Hu, W.; Yu, C.; Peng, C.; Feng, X.; Cheng, Q.; Wu, W.; Lu, Y.; et al. ACSL4 reprograms fatty acid metabolism in hepatocellular carcinoma via c-Myc/SREBP1 pathway. Cancer Lett. 2021, 502, 154–165. [Google Scholar] [CrossRef] [PubMed]
- Luo, J.P.; Wang, J.; Huang, J.H. CDKN2A is a prognostic biomarker and correlated with immune infiltrates in hepatocellular carcinoma. Biosci. Rep. 2021, 41, BSR20211103. [Google Scholar] [CrossRef]
- Yeter, D.; Deth, R. ITPKC susceptibility in Kawasaki syndrome as a sensitizing factor for autoimmunity and coronary arterial wall relaxation induced by thimerosal’s effects on calcium signaling via IP3. Autoimmun. Rev. 2012, 11, 903–908. [Google Scholar] [CrossRef]
- Marquez, J.; Kohli, M.; Arteta, B.; Chang, S.; Li, W.B.; Goldblatt, M.; Vidal-Vanaclocha, F. Identification of hepatic microvascular adhesion-related genes of human colon cancer cells using random homozygous gene perturbation. Int. J. Cancer 2013, 133, 2113–2122. [Google Scholar] [CrossRef]
- Oshi, M.; Newman, S.; Murthy, V.; Tokumaru, Y.; Yan, L.; Matsuyama, R.; Endo, I.; Takabe, K. ITPKC as a Prognostic and Predictive Biomarker of Neoadjuvant Chemotherapy for Triple Negative Breast Cancer. Cancers 2020, 12, 2758. [Google Scholar] [CrossRef]
- Xu, D.; Wang, Y.; Wu, J.; Zhang, Z.; Chen, J.; Xie, M.; Tang, R.; Chen, C.; Chen, L.; Lin, S.; et al. ECT2 overexpression promotes the polarization of tumor-associated macrophages in hepatocellular carcinoma via the ECT2/PLK1/PTEN pathway. Cell Death Dis. 2021, 12, 162. [Google Scholar] [CrossRef]
- Kepser, L.J.; Khudayberdiev, S.; Hinojosa, L.S.; Macchi, C.; Ruscica, M.; Marcello, E.; Culmsee, C.; Grosse, R.; Rust, M.B. Cyclase-associated protein 2 (CAP2) controls MRTF-A localization and SRF activity in mouse embryonic fibroblasts. Sci. Rep. 2021, 11, 4789. [Google Scholar] [CrossRef]
- Fu, J.; Li, M.; Wu, D.C.; Liu, L.L.; Chen, S.L.; Yun, J.P. Increased Expression of CAP2 Indicates Poor Prognosis in Hepatocellular Carcinoma. Transl. Oncol. 2015, 8, 400–406. [Google Scholar] [CrossRef]
- Cai, H.; Shao, B.; Zhou, Y.; Chen, Z. High expression of TOP2A in hepatocellular carcinoma is associated with disease progression and poor prognosis. Oncol. Lett. 2020, 20, 232. [Google Scholar] [CrossRef]
- Wang, T.; Lu, J.; Wang, R.; Cao, W.; Xu, J. TOP2A promotes proliferation and metastasis of hepatocellular carcinoma regulated by miR-144-3p. J. Cancer 2022, 13, 589–601. [Google Scholar] [CrossRef]
- Wang, A.; Chen, X.; Li, D.; Yang, L.; Jiang, J. METTL3-mediated m6A methylation of ASPM drives hepatocellular carcinoma cells growth and metastasis. J. Clin. Lab. Anal. 2021, 35, e23931. [Google Scholar] [CrossRef]
- Zeng, Y.; He, H.; Zhang, Y.; Wang, X.; Yang, L.; An, Z. CCNB2, TOP2A, and ASPM Reflect the Prognosis of Hepatocellular Carcinoma, as Determined by Weighted Gene Coexpression Network Analysis. Biomed Res. Int. 2020, 2020, 4612158. [Google Scholar] [CrossRef] [PubMed]
- Zhang, H.; Yang, X.; Zhu, L.; Li, Z.; Zuo, P.; Wang, P.; Feng, J.; Mi, Y.; Zhang, C.; Xu, Y.; et al. ASPM promotes hepatocellular carcinoma progression by activating Wnt/β-catenin signaling through antagonizing autophagy-mediated Dvl2 degradation. FEBS Open Bio 2021, 11, 2784–2799. [Google Scholar] [CrossRef] [PubMed]
- Gu, Y.; Chen, B.; Guo, D.; Pan, L.; Luo, X.; Tang, J.; Yang, W.; Zhang, Y.; Zhang, L.; Huang, J.; et al. Up-Regulation of RACGAP1 Promotes Progressions of Hepatocellular Carcinoma Regulated by GABPA via PI3K/AKT Pathway. Oxid. Med. Cell. Longev. 2022, 2022, 3034150. [Google Scholar] [CrossRef]
- Ma, W.; Zhang, X.; Ma, C.; Liu, P. Highly expressed FAM189B predicts poor prognosis in hepatocellular carcinoma. Pathol. Oncol. Res. 2022, 28, 1610674. [Google Scholar] [CrossRef]
- Patil, V.; Ward, R.L.; Hesson, L.B. The evidence for functional non-CpG methylation in mammalian cells. Epigenetics 2014, 9, 823–828. [Google Scholar] [CrossRef]
- Li, S.; Han, J.; Guo, G.; Sun, Y.; Zhang, T.; Zhao, M.; Xu, Y.; Cui, Y.; Liu, Y.; Zhang, J. Voltage-gated sodium channels β3 subunit promotes tumorigenesis in hepatocellular carcinoma by facilitating p53 degradation. FEBS Lett. 2020, 594, 497–508. [Google Scholar] [CrossRef]
- Chavez-Lopez, M.d.G.; Zuniga-Garcia, V.; Perez-Carreon, J.I.; Avalos-Fuentes, A.; Escobar, Y.; Camacho, J. Eag1 channels as potential early-stage biomarkers of hepatocellular carcinoma. Biol. Targets Ther. 2016, 10, 139–148. [Google Scholar] [CrossRef]
- Yasen, M.; Kajino, K.; Kano, S.; Tobita, H.; Yamamoto, J.; Uchiumi, T.; Kon, S.; Maeda, M.; Obulhasim, G.; Arii, S.; et al. The up-regulation of Y-box binding proteins (DNA binding protein A and Y-box binding protein-1) as prognostic markers of hepatocellular carcinoma. Clin. Cancer Res. 2005, 11, 7354–7361. [Google Scholar] [CrossRef] [PubMed]
- Kim, S.H.; Hwang, S.; Song, G.W.; Jung, D.H.; Moon, D.B.; Yang, J.D.; Yu, H.C. Identification of key genes and carcinogenic pathways in hepatitis B virus-associated hepatocellular carcinoma through bioinformatics analysis. Ann. Hepato-Biliary-Pancreat. Surg. 2022, 26, 58–68. [Google Scholar] [CrossRef]
- Li, Y.; Li, J.; He, T.; Song, Y.; Wu, J.; Wang, B. Significance of Identifying Key Genes Involved in HBV-Related Hepatocellular Carcinoma for Primary Care Surveillance of Patients with Cirrhosis. Genes 2022, 13, 2331. [Google Scholar] [CrossRef]
- Sha, M.; Cao, J.; Zong, Z.P.; Xu, N.; Zhang, J.J.; Tong, Y.; Xia, Q. Identification of genes predicting unfavorable prognosis in hepatitis B virus-associated hepatocellular carcinoma. Ann. Transl. Med. 2021, 9, 975. [Google Scholar] [CrossRef]
- Xing, T.; Yan, T.; Zhou, Q. Identification of key candidate genes and pathways in hepatocellular carcinoma by integrated bioinformatical analysis. Exp. Ther. Med. 2018, 15, 4932–4942. [Google Scholar] [CrossRef] [PubMed]
- Zhou, Z.; Li, Y.; Hao, H.; Wang, Y.; Zhou, Z.; Wang, Z.; Chu, X. Screening Hub Genes as Prognostic Biomarkers of Hepatocellular Carcinoma by Bioinformatics Analysis. Cell Transpl. 2019, 28, 76S–86S. [Google Scholar] [CrossRef] [PubMed]
- Qiu, L.; Zhan, K.; Malale, K.; Wu, X.; Mei, Z. Transcriptomic profiling of peroxisome-related genes reveals a novel prognostic signature in hepatocellular carcinoma. Genes Dis. 2022, 9, 116–127. [Google Scholar] [CrossRef] [PubMed]
- Zhang, H.; Su, X.; Burley, S.K.; Zheng, X.F.S. mTOR regulates aerobic glycolysis through NEAT1 and nuclear paraspeckle-mediated mechanism in hepatocellular carcinoma. Theranostics 2022, 12, 3518–3533. [Google Scholar] [CrossRef]
- Bedoui, Y.; Guillot, X.; Sélambarom, J.; Guiraud, P.; Giry, C.; Jaffar-Bandjee, M.C.; Ralandison, S.; Gasque, P. Methotrexate an Old Drug with New Tricks. Int. J. Mol. Sci. 2019, 20, 5023. [Google Scholar] [CrossRef]
- Xing, X.; Zhang, B.; Li, D.; Wang, T. Comprehensive Whole DNA Methylome Analysis by Integrating MeDIP-seq and MRE-seq. Methods Mol. Biol. 2018, 1708, 209–246. [Google Scholar] [CrossRef]
- Li, D.; Zhang, B.; Xing, X.; Wang, T. Combining MeDIP-seq and MRE-seq to investigate genome-wide CpG methylation. Methods 2015, 72, 29–40. [Google Scholar] [CrossRef]
- Ye, Z.; Wang, Y.; Yuan, R.; Ding, R.; Hou, Y.; Qian, L.; Zhang, S. Vesicle-mediated transport-related genes predict the prognosis and immune microenvironment in hepatocellular carcinoma. J. Cancer 2024, 15, 3645–3662. [Google Scholar] [CrossRef]
- Snaebjornsson, M.T.; Janaki-Raman, S.; Schulze, A. Greasing the Wheels of the Cancer Machine: The Role of Lipid Metabolism in Cancer. Cell Metab. 2020, 31, 62–76. [Google Scholar] [CrossRef]
- Bian, X.; Qian, Y.; Tan, B.; Li, K.; Hong, X.; Wong, C.C.; Fu, L.; Zhang, J.; Li, N.; Wu, J.-L. In-depth mapping carboxylic acid metabolome reveals the potential biomarkers in colorectal cancer through characteristic fragment ions and metabolic flux. Anal. Chim. Acta 2020, 1128, 62–71. [Google Scholar] [CrossRef] [PubMed]
GEO Access ID | Platform ID | Number of Samples | The Aim of the Study |
---|---|---|---|
GSE46444 [28] | GPL13369 (a) | 136 | Transcriptomic analyses were performed using the archived FFPE and fresh HCC and non-tumorous cirrhotic tissues that were compared with each other. A total of 88 FFPE HCC versus 48 fresh cirrhotic liver tissue samples were analyzed. The results showed that old FFPE HCC tissue could be used in transcriptomic analysis for the diagnosis and classification of the disease. The study provided no data regarding the molecular processes of HCC. |
GSE63898 [29] | GPL13667 (b) | 396 | The sample number used in this study is high, with 228 HCC and 168 cirrhotic liver tissue samples. Transcriptomic microarray analyses were performed. The important characteristic of the study was the presence of microarray transcriptomic analyses together with methylation analysis. The study provides detailed data, including epi-driver genes, and are correlated with the survival data of the patients. |
Tissue Type | Number of Samples (n) | Average Number of Measurements | Mean Number of Nucleotides |
---|---|---|---|
Tumor (HCC) tissue | 34 | 10,690,595 | 744,661,764 |
Healthy liver tissue | 27 | 5,132,889 | 518,421,848 |
Adjacent non-tumor liver tissue | 7 | 23,028,257 | 1,278,260,601 |
Cirrhotic liver tissue | 8 | 30,104,184 | 1,535,313,410 |
Tissue Type | Number of Samples (n) | Average Number of Measurements | Mean Number of Nucleotides |
---|---|---|---|
Tumor (HCC) tissue | 26 | 22,254,325 | 836,263,008 |
Adjacent non-tumor liver tissue | 26 | 23,025,232 | 865,227,639 |
Downregulated Pathways in HCC | Upregulated Pathways in HCC | Molecular Function Genes That Had a Change in Methylation Profile in HCC |
---|---|---|
Olefinic compound metabolic processes | Arachidonic acid epoxygenase activity | Voltage-gated potassium channel activity |
Monocarboxylic acid metabolic processes | Aromatase activity | Potassium channel activity |
Carboxylic acid metabolic processes | Steroid hydroxylase activity | Voltage-gated ion channel activity |
Oxoacid metabolic processes | Oxidoreductase activity | Potassium ion transmembrane transporter activity |
Lipid metabolic processes | Monooxygenase activity | Extracellular matrix structural constituent |
Blood microparticle | Heme binding | Gated channel activity |
Secretory granule lumen | Tetrapyrrole binding | Ion channel activity |
Cytoplasmic vesicle lumen | Iron ion binding | Cis-regulatory region sequence-specific DNA binding |
Vesicle lumen | Transition metal ion binding | RNA polymerase II cis-regulatory region sequence-specific DNA binding |
Drug metabolism | Channel activity | |
Mineral absorption | Transcription cis-regulatory region binding | |
Retinol metabolism | Transcription regulatory region nucleic acid binding | |
Metabolism of xenobiotics by cytochrome P450 | DNA-binding transcription factor activity. RNA polymerase II-specific | |
Chemical carcinogenesis | Sequence-specific DNA binding | |
Fatty acid degradation | Sequence-specific double-stranded DNA binding | |
Bile secretion | RNA polymerase II transcription regulatory region sequence-specific DNA binding | |
Complement and coagulation cascades | DNA-binding transcription factor activity | |
PI3K-Akt signaling pathway | Calcium ion binding | |
Pathways in cancer | Double-stranded DNA binding |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Akbulut, S.; Kucukakcali, Z.; Sahin, T.T.; Colak, C.; Yilmaz, S. Role of Epigenetic Factors in Determining the Biological Behavior and Prognosis of Hepatocellular Carcinoma. Diagnostics 2024, 14, 1925. https://doi.org/10.3390/diagnostics14171925
Akbulut S, Kucukakcali Z, Sahin TT, Colak C, Yilmaz S. Role of Epigenetic Factors in Determining the Biological Behavior and Prognosis of Hepatocellular Carcinoma. Diagnostics. 2024; 14(17):1925. https://doi.org/10.3390/diagnostics14171925
Chicago/Turabian StyleAkbulut, Sami, Zeynep Kucukakcali, Tevfik Tolga Sahin, Cemil Colak, and Sezai Yilmaz. 2024. "Role of Epigenetic Factors in Determining the Biological Behavior and Prognosis of Hepatocellular Carcinoma" Diagnostics 14, no. 17: 1925. https://doi.org/10.3390/diagnostics14171925