Differential Gene Expression and Pathway Analysis in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the most common primary malignancy of the liver and is one of the leading causes of cancer-related mortality (death) worldwide. Its incidence is particularly high in areas where hepatitis B and C virus infections are endemic, as well as in patients with chronic liver disease and cirrhosis. HCC is often diagnosed at an advanced stage because of the lack of specific early clinical symptoms, resulting in limited treatment options and poor prognosis. Recent advances in genomics, transcriptomics and bioinformatics have provided insight into the molecular mechanisms of HCC initiation and progression. Analysis of differential gene expression and functional enrichment and pathways analysis have helped identify key regulatory genes, hub genes and dysregulated signalling pathways associated with tumour development. In this report, we discuss the gene expression profiles of HCC by means of high-throughput data analysis, particularly on the identification of possible biomarkers for early diagnosis and novel therapeutic targets. We also discuss integration of bioinformatics tools like STRING, Cytoscape and DAVID for construction of interaction networks, functional annotation of genes and visualization of enriched pathways. The findings highlight the necessity of molecular profiling for the understanding of the complexity of HCC and the progress of personalized medicine. Further studies are needed to translate these molecular insights into effective diagnostic, prognostic and therapeutic strategies with improved clinical outcomes in HCC patients.

Hepatocellular Carcinoma, Hub Gene, Primary Liver Tumor, DEGs, HCC.

Llovet, J. M., Kelley, R. K., Villanueva, A., et al. (2021). Hepatocellular carcinoma. Nature Reviews Disease Primers, 7(1), 6.

Sung, H., Ferlay, J., Siegel, R. L., et al. (2021). Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide. CA: A Cancer Journal for Clinicians, 71(3), 209–249.

Taher, M., Asri, M. A. I. M., Rahman, N. D. A., Zamzuri, N. E., Mohammad Farizal, N. S., Khotib, J., Susanti, D., Hasdan, N. A., Haris, M. S., & Rakhmawati, R. (2026). Serum biomarkers as personalised medicine for diagnostic and therapeutic approaches of hepatocellular carcinoma. Journal of the Egyptian National Cancer Institute, 38(1), 7. https://doi.org/10.1186/s43046-026-00342-1

Wang, Z., Gerstein, M., & Snyder, M. (2009). RNA-Seq: A revolutionary tool for transcriptomics. Nature Reviews Genetics, 10(1), 57–63.

Weinstein, J. N., Collisson, E. A., Mills, G. B., et al. (2013). The Cancer Genome Atlas Pan-Cancer analysis project. Nature Genetics, 45(10), 1113–1120.

Tomczak, K., Czerwińska, P., & Wiznerowicz, M. (2015). The Cancer Genome Atlas (TCGA): An immeasurable source of knowledge. Contemporary Oncology, 19(1A), A68–A77.

Agarwal, R., Narayan, J., Bhattacharyya, A., Saraswat, M., & Tomar, A. K. (2017). Gene expression profiling, pathway analysis and subtype classification reveal molecular heterogeneity in hepatocellular carcinoma and suggest subtype specific therapeutic targets. Cancer Genetics, 216–217, 37–51.

Yamazoe, T., Kakazu, E., Matsuda, M., et al. (2026). Genomic and transcriptomic profiling of hepatocellular carcinoma in patients with Fontan-associated liver disease. Hepatology. doi: 10.1097/HEP.0000000000001693

Li, P., Guo, A., Zhao, M., & Chen, G. (2026). Comparative transcriptomics and computational drug discovery identify ASPM as a key oncogenic driver and therapeutic target in hepatocellular carcinoma. Frontiers in Bioinformatics, 6, Article 1795889.doi: 10.3389/fbinf.2026.1795889

Allison, D. B., Cui, X., Page, G. P., & Sabripour, M. (2006). Microarray data analysis: From disarray to consolidation and consensus. Nature Reviews Genetics, 7(1), 55–65.

Wurmbach, E., Chen, Y. B., Khitrov, G., et al. (2007). Genome-wide molecular profiles of HCV-induced dysplasia and hepatocellular carcinoma. Hepatology, 45(4), 938–947.

Zhang, C., Peng, L., Zhang, Y., Liu, Z., Li, W., Chen, S., & Li, G. (2017). The identification of key genes and pathways in hepatocellular carcinoma by bioinformatics analysis of high-throughput data. Medical Oncology, 34(6), 101.

Oliveros, JC. (2007-2015) Venny. An interactive tool for comparing lists with Venn's diagrams. https://bioinfogp.cnb.csic.es/tools/venny/index.html

Huang da W, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009;4(1):44-57. doi: 10.1038/nprot.2008.211. PMID: 19131956

Sherman BT, Hao M, Qiu J, Jiao X, Baseler MW, Lane HC, Imamichi T, Chang W. DAVID: a web server for functional enrichment analysis and functional annotation of gene lists (2021 update). Nucleic Acids Res. 2022 Jul 5;50(W1): W216-W221. doi: 10.1093/nar/gkac194. PMID: 35325185; PMCID: PMC9252805.

Szklarczyk, D., Kirsch, R., Koutrouli, M., Nastou, K., Mehryary, F., Hachilif, R., Annika, G. L., Fang, T., Doncheva, N. T., Pyysalo, S., Bork, P., Jensen, L. J., & von Mering, C. (2023). The STRING database in 2023: Protein–protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Research, 51(D1), D638–D646.

Cline, M. S., Smoot, M., Cerami, E., Kuchinsky, A., Landys, N., Workman, C., Christmas, R., Avila-Campilo, I., Creech, M., Gross, B., Hanspers, K., Isserlin, R., Kelley, R., Killcoyne, S., Lotia, S., Maere, S., Morris, J., Ono, K., Pavlovic, V., ... Bader, G. D. (2007). Integration of biological networks and gene expression data using Cytoscape. Nature Protocols, 2(10), 2366–2382.

Shao, R., Hamel, K., Petersen, L., et al. (2009). YKL-40, a secreted glycoprotein, promotes tumor angiogenesis. Oncogene, 28(50), 4456–4468. doi: https://doi.org/10.1038/onc.2009.292

Libreros, S., Garcia-Areas, R., & Iragavarapu-Charyulu, V. (2013). CHI3L1 plays a role in cancer progression. International Journal of Cancer, 133(6), 1286–1295. doi: https://doi.org/10.1002/ijc.28138

Reitz, K., Yao, P., & Emmons, G. T. (2015). ABHD family proteins and lipid metabolism. Biochimica et Biophysica Acta, 1851(6), 626–635. doi: https://doi.org/10.1016/j.bbalip.2014.09.01

Dvinge, H., Kim, E., Abdel-Wahab, O., & Bradley, R. K. (2016). RNA splicing factors as oncoproteins and tumour suppressors. Nature Reviews Cancer, 16(7), 413–430. doi: https://doi.org/10.1038/nrc.2016.51

Yoshida, K., & Ogawa, S. (2020). Splicing factor mutations and cancer. Wiley Interdisciplinary Reviews: RNA, 11(6), e1573. doi: https://doi.org/10.1002/wrna.1573

Behrouzifar, S. (2023). Identifying downregulated hub genes and key pathways in HBV-related hepatocellular carcinoma using systems biology approach. doi: https://doi.org/10.48550/arXiv.2306.16173

Wang, H., Xu, F., Lu, L., et al. (2022). The diagnostic and prognostic significance of SNRPD1 aberrantly high expression in hepatocellular carcinoma. Journal of Cancer, 13(1), 184–201. doi: https://doi.org/10.7150/jca.65225

Hershberger, C. E., Daniels, N. J., Patterson, W. M., & Rotroff, D. M. (2025). The alternative splicing landscape of hepatocellular carcinoma and its potential for HCC detection. Hepatology Communications, 10(1), e0838. doi: 10.1097/HC9.0000000000000838

Callegari, E., Elamin, B. K., Sabbioni, S., Gramantieri, L., & Negrini, M. (2013). Role of microRNAs in hepatocellular carcinoma. Journal of Cellular Physiology, 228(4), 625–634. doi: https://doi.org/10.1002/jcp.24224

Oura, K., Morishita, A., Masaki, T. (2019). Molecular and functional roles of microRNAs in hepatocellular carcinoma. International Journal of Molecular Sciences, 20(24), 6329. doi: https://doi.org/10.3390/ijms20246329