A Bioinformatics-Based Approach to Discover Novel Biomarkers in Hepatocellular Carcinoma
,
Abstract
Background: Liver hepatocellular carcinoma (LIHC) is a common cancer with a poor prognosis and high recurrence rate. We aimed to identify potential biomarkers for LIHC by investigating the involvement of hub genes, microRNAs (miRNAs), transcription factors (TFs), and protein kinases (PKs) in its occurrence.
Methods: we conducted a bioinformatics analysis using microarray datasets, the TCGA-LIHC dataset, and text mining to identify differentially expressed genes (DEGs) associated with LIHC. They then performed functional enrichment analysis and gene-disease association analysis. The protein-protein interaction network of the genes was established, and hub genes were identified. The expression levels and survival analysis of these hub genes were evaluated, and their association with miRNAs, TFs, and PKs was assessed.
Results: The analysis identified 122 common genes involved in LIHC pathogenesis. Ten hub genes were filtered out, including CDK1, CCNB1, CCNB2, CCNA2, ASPM, NCAPG, BIRC5, RRM2, KIF20A, and CENPF. The expression level of all hub genes was confirmed, and high expression levels of all hub genes were correlated with poor overall survival of LIHC patients.
Conclusion: Identifying potential biomarkers for LIHC can aid in the design of targeted treatments and improve the survival of LIHC patients. The findings of this study provide a basis for further research in the field of LIHC and contribute to the understanding of its molecular pathogenesis.
1. Dimri M, Satyanarayana A (2020). Molecular signaling pathways and therapeutic targets in hepatocellular carcinoma. Cancers (Basel), 12 (2): 491.
2. Khalaf AM, Fuentes D, Morshid AI, et al (2018). Role of Wnt/β-catenin signaling in hepatocellular carcinoma, pathogenesis, and clinical significance. J Hepatocell Carcinoma, 5:61-73.
3. Hanauer DA, Rhodes DR, Sinha-Kumar C, Chinnaiyan AM (2007). Bioinformatics approaches in the study of cancer. Curr Mol Med, 7 (1): 133-41.
4. Banwait JK, Bastola DR (2015). Contribution of bioinformatics prediction in microRNA-based cancer therapeutics. Adv Drug Deliv Rev, 81: 94-103.
5. Xu L, Tong T, Wang Z, et al (2020). Identification of hub genes and analysis of prognostic values in hepatocellular carcinoma by bioinformatics analysis. Am J Med Sci, 359 (4): 226-34.
6. Wang JH, Zhao LF, Wang HF, et al (2019). GenCLiP 3: mining human genes' functions and regulatory networks from PubMed based on co-occurrences and natural language processing. Bioinformatics, btz807.
7. Szklarczyk D, Gable AL, Nastou KC, et al (2021). The STRING database in 2021: customizable protein–protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res, 49 (D1): D605-D12.
8. Shannon P, Markiel A, Ozier O, et al (2003). Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res, 13 (11): 2498-504.
9. Bader GD, Hogue CW (2003). An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics, 4:2.
10. Chin C-H, Chen S-H, Wu H-H, et al (2014). cytoHubba: identifying hub objects and sub-networks from complex interactome. BMC Syst Biol, 8 Suppl 4(Suppl 4):S11.
11. Tang Z, Kang B, Li C, et al (2019). GEPIA2: an enhanced web server for large-scale expression profiling and interactive analysis. Nucleic Acids Res, 47 (W1): W556-W560.
12. Lánczky A, Győrffy B (2021). Web-based survival analysis tool tailored for medical research (KMplot): development and implementation. J Med Internet Res, 23 (7): e27633.
13. Kuleshov MV, Jones MR, Rouillard AD, et al (2016). Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res, 44 (W1): W90-W7.
14. Jin J, Xu H, Li W, et al (2020). LINC00346 acts as a competing endogenous RNA regulating development of hepatocellular carcinoma via modulating CDK1/CCNB1 axis. Front Bioeng Biotechnol, 8: 54.
15. Zou Y, Ruan S, Jin L, et al (2020). CDK1, CCNB1, and CCNB2 are prognostic biomarkers and correlated with immune infiltration in hepatocellular carcinoma. Med Sci Monit, 26: e925289.
16. Zhang H, Liu R, Sun L, et al (2021). Comprehensive analysis of gene expression changes and validation in hepatocellular carcinoma. Onco Targets Ther, 14: 1021-1031.
17. Zhang H, Yang X, Zhu L, et al (2021). ASPM promotes hepatocellular carcinoma progression by activating Wnt/β‐catenin signaling through antagonizing autophagy‐mediated Dvl2 degradation. FEBS Open Bio, 11 (10): 2784-99.
18. Guo Z, Zhu Z (2021). NCAPG is a prognostic biomarker associated with vascular invasion in hepatocellular carcinoma. Eur Rev Med Pharmacol Sci, 25(23):7238-51.
19. Gong C, Ai J, Fan Y, et al (2019). NCAPG promotes the proliferation of hepatocellular carcinoma through PI3K/AKT signaling. Onco Targets Ther, 12:8537-8552.
20. Ai J, Gong C, Wu J, et al (2019). MicroRNA‑181c suppresses growth and metastasis of hepatocellular carcinoma by modulating NCAPG. Cancer Manag Res, 11: 3455-3467.
21. Xu R, Lin L, Zhang B, et al (2021). Identification of prognostic markers for hepatocellular carcinoma based on the epithelial-mesenchymal transition-related gene BIRC5. BMC Cancer, 21 (1): 687.
22. Chen X, Duan N, Zhang C, et al (2016). Survivin and tumorigenesis: molecular mechanisms and therapeutic strategies. J Cancer, 7 (3): 314-23.
23. Wu R, Tang W, Qiu K, et al (2021). An Integrative Pan-Cancer Analysis of the Prognostic and Immunological Role of Casein Kinase 2 Alpha Protein 1 (CSNK2A1) in Human Cancers: A Study Based on Bioinformatics and Immunohistochemical Analysis. Int J Gen Med, 14: 6215-6232.
24. Lee B, Ha SY, Song DH, et al (2014). High expression of ribonucleotide reductase subunit M2 correlates with poor prognosis of hepatocellular carcinoma. Gut Liver, 8 (6): 662-8.
25. He B, Yin J, Gong S, et al (2017). Bioinformatics analysis of key genes and pathways for hepatocellular carcinoma transformed from cirrhosis. Medicine (Baltimore), 96 (25): e6938.
26. Xiong M, Zhuang K, Luo Y, et al (2019). KIF20A promotes cellular malignant behavior and enhances resistance to chemotherapy in colorectal cancer through regulation of the JAK/STAT3 signaling pathway. Aging (Albany NY), 11 (24): 11905-11921.
27. Lu M, Huang X, Chen Y, et al (2018). Aberrant KIF20A expression might independently predict poor overall survival and recurrence‐free survival of hepatocellular carcinoma. IUBMB Life, 70 (4): 328-35.
28. Huang Y, Chen X, Wang L, et al (2021). Centromere protein F (CENPF) serves as a potential prognostic biomarker and target for human hepatocellular carcinoma. J Cancer, 12 (10): 2933-2951.
29. Shao P, Qu W-K, Wang C-Y, et al (2017). MicroRNA-205-5p regulates the chemotherapeutic resistance of hepatocellular carcinoma cells by targeting PTEN/JNK/ANXA3 pathway. Am J Transl Res, 9 (9): 4300-4307.
30. Li S, Peng F, Ning Y, et al (2020). SNHG16 as the miRNA let‐7b‐5p sponge facilitates the G2/M and epithelial‐mesenchymal transition by regulating CDC25B and HMGA2 expression in hepatocellular carcinoma. J Cell Biochem, 121 (3): 2543-58.
31. Chai N, Xie H-h, Yin JP, et al (2018). FOXM1 promotes proliferation in human hepatocellular carcinoma cells by transcriptional activation of CCNB1. Biochem Biophys Res Commun, 500 (4): 924-9.
32. Wang J, Tian Y, Chen H, et al (2018). Key signaling pathways, genes and transcription factors associated with hepatocellular carcinoma. Mol Med Rep, 17 (6): 8153-60.
33. Zheng Q, Fu Q, Xu J, et al (2021). Transcription factor E2F4 is an indicator of poor prognosis and is related to immune infiltration in hepatocellular carcinoma. J Cancer, 12 (6): 1792-1803.
2. Khalaf AM, Fuentes D, Morshid AI, et al (2018). Role of Wnt/β-catenin signaling in hepatocellular carcinoma, pathogenesis, and clinical significance. J Hepatocell Carcinoma, 5:61-73.
3. Hanauer DA, Rhodes DR, Sinha-Kumar C, Chinnaiyan AM (2007). Bioinformatics approaches in the study of cancer. Curr Mol Med, 7 (1): 133-41.
4. Banwait JK, Bastola DR (2015). Contribution of bioinformatics prediction in microRNA-based cancer therapeutics. Adv Drug Deliv Rev, 81: 94-103.
5. Xu L, Tong T, Wang Z, et al (2020). Identification of hub genes and analysis of prognostic values in hepatocellular carcinoma by bioinformatics analysis. Am J Med Sci, 359 (4): 226-34.
6. Wang JH, Zhao LF, Wang HF, et al (2019). GenCLiP 3: mining human genes' functions and regulatory networks from PubMed based on co-occurrences and natural language processing. Bioinformatics, btz807.
7. Szklarczyk D, Gable AL, Nastou KC, et al (2021). The STRING database in 2021: customizable protein–protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res, 49 (D1): D605-D12.
8. Shannon P, Markiel A, Ozier O, et al (2003). Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res, 13 (11): 2498-504.
9. Bader GD, Hogue CW (2003). An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics, 4:2.
10. Chin C-H, Chen S-H, Wu H-H, et al (2014). cytoHubba: identifying hub objects and sub-networks from complex interactome. BMC Syst Biol, 8 Suppl 4(Suppl 4):S11.
11. Tang Z, Kang B, Li C, et al (2019). GEPIA2: an enhanced web server for large-scale expression profiling and interactive analysis. Nucleic Acids Res, 47 (W1): W556-W560.
12. Lánczky A, Győrffy B (2021). Web-based survival analysis tool tailored for medical research (KMplot): development and implementation. J Med Internet Res, 23 (7): e27633.
13. Kuleshov MV, Jones MR, Rouillard AD, et al (2016). Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res, 44 (W1): W90-W7.
14. Jin J, Xu H, Li W, et al (2020). LINC00346 acts as a competing endogenous RNA regulating development of hepatocellular carcinoma via modulating CDK1/CCNB1 axis. Front Bioeng Biotechnol, 8: 54.
15. Zou Y, Ruan S, Jin L, et al (2020). CDK1, CCNB1, and CCNB2 are prognostic biomarkers and correlated with immune infiltration in hepatocellular carcinoma. Med Sci Monit, 26: e925289.
16. Zhang H, Liu R, Sun L, et al (2021). Comprehensive analysis of gene expression changes and validation in hepatocellular carcinoma. Onco Targets Ther, 14: 1021-1031.
17. Zhang H, Yang X, Zhu L, et al (2021). ASPM promotes hepatocellular carcinoma progression by activating Wnt/β‐catenin signaling through antagonizing autophagy‐mediated Dvl2 degradation. FEBS Open Bio, 11 (10): 2784-99.
18. Guo Z, Zhu Z (2021). NCAPG is a prognostic biomarker associated with vascular invasion in hepatocellular carcinoma. Eur Rev Med Pharmacol Sci, 25(23):7238-51.
19. Gong C, Ai J, Fan Y, et al (2019). NCAPG promotes the proliferation of hepatocellular carcinoma through PI3K/AKT signaling. Onco Targets Ther, 12:8537-8552.
20. Ai J, Gong C, Wu J, et al (2019). MicroRNA‑181c suppresses growth and metastasis of hepatocellular carcinoma by modulating NCAPG. Cancer Manag Res, 11: 3455-3467.
21. Xu R, Lin L, Zhang B, et al (2021). Identification of prognostic markers for hepatocellular carcinoma based on the epithelial-mesenchymal transition-related gene BIRC5. BMC Cancer, 21 (1): 687.
22. Chen X, Duan N, Zhang C, et al (2016). Survivin and tumorigenesis: molecular mechanisms and therapeutic strategies. J Cancer, 7 (3): 314-23.
23. Wu R, Tang W, Qiu K, et al (2021). An Integrative Pan-Cancer Analysis of the Prognostic and Immunological Role of Casein Kinase 2 Alpha Protein 1 (CSNK2A1) in Human Cancers: A Study Based on Bioinformatics and Immunohistochemical Analysis. Int J Gen Med, 14: 6215-6232.
24. Lee B, Ha SY, Song DH, et al (2014). High expression of ribonucleotide reductase subunit M2 correlates with poor prognosis of hepatocellular carcinoma. Gut Liver, 8 (6): 662-8.
25. He B, Yin J, Gong S, et al (2017). Bioinformatics analysis of key genes and pathways for hepatocellular carcinoma transformed from cirrhosis. Medicine (Baltimore), 96 (25): e6938.
26. Xiong M, Zhuang K, Luo Y, et al (2019). KIF20A promotes cellular malignant behavior and enhances resistance to chemotherapy in colorectal cancer through regulation of the JAK/STAT3 signaling pathway. Aging (Albany NY), 11 (24): 11905-11921.
27. Lu M, Huang X, Chen Y, et al (2018). Aberrant KIF20A expression might independently predict poor overall survival and recurrence‐free survival of hepatocellular carcinoma. IUBMB Life, 70 (4): 328-35.
28. Huang Y, Chen X, Wang L, et al (2021). Centromere protein F (CENPF) serves as a potential prognostic biomarker and target for human hepatocellular carcinoma. J Cancer, 12 (10): 2933-2951.
29. Shao P, Qu W-K, Wang C-Y, et al (2017). MicroRNA-205-5p regulates the chemotherapeutic resistance of hepatocellular carcinoma cells by targeting PTEN/JNK/ANXA3 pathway. Am J Transl Res, 9 (9): 4300-4307.
30. Li S, Peng F, Ning Y, et al (2020). SNHG16 as the miRNA let‐7b‐5p sponge facilitates the G2/M and epithelial‐mesenchymal transition by regulating CDC25B and HMGA2 expression in hepatocellular carcinoma. J Cell Biochem, 121 (3): 2543-58.
31. Chai N, Xie H-h, Yin JP, et al (2018). FOXM1 promotes proliferation in human hepatocellular carcinoma cells by transcriptional activation of CCNB1. Biochem Biophys Res Commun, 500 (4): 924-9.
32. Wang J, Tian Y, Chen H, et al (2018). Key signaling pathways, genes and transcription factors associated with hepatocellular carcinoma. Mol Med Rep, 17 (6): 8153-60.
33. Zheng Q, Fu Q, Xu J, et al (2021). Transcription factor E2F4 is an indicator of poor prognosis and is related to immune infiltration in hepatocellular carcinoma. J Cancer, 12 (6): 1792-1803.
| Files | ||
| Issue | Vol 53 No 6 (2024) | |
| Section | Original Article(s) | |
| Keywords | ||
| Hepatocellular carcinoma Genes Molecular pathway Systems biology | ||
| Rights and permissions | |
|
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
How to Cite
1.
Shourideh A, Maddah R, Shateri Amiri B, Basharat Z, Shadpirouz M. A Bioinformatics-Based Approach to Discover Novel Biomarkers in Hepatocellular Carcinoma. Iran J Public Health. 2024;53(6):1332-1342.
