Pharmaceutical targets of curcumin in liver cancer treatment and molecular mechanisms: a bioinformatics analysis and computer-aided study
The most common type of primary liver cancer, hepatocellular carcinoma (HCC, or LIHC), is one of the leading causes of cancer-associated death worldwide. Patients with HCC have a short 5-year survival rate. Identifying useful biomarkers and developing effective drugs can improve early diagnosis and treatment efficacy to increase the survival rate of LIHC patients. However, the process of screening effective biomarkers and drugs for treatment is time- and cost-consuming. Repurposing naturally derived bioactive products is a promising strategy to address this issue. Herein, we took advantage of the bioinformatics analysis and computational-aided processes to explore the therapeutic potential of bioactive compound curcumin against LIHC. The functional enrichment and signaling pathway enrichment of curcumin-targeted proteins were analyzed, and 10 key hub genes were identified as potential targets. By further analyzing the mRNA expression levels and their association with the clinical prognostic outcomes of LIHC patients, we identified that four key genes ( AKT1, GSK3B, MMP9 , and SERPINE1 ) were therapeutic targets of curcumin in LIHC treatment. The binding affinity and molecular mechanisms of curcumin and target proteins were examined using computational-based approaches. The results in this study shed light on how curcumin functions as a treatment or template drug against LIHC.
Authors
- Chunye Zhang (ORCID: https://orcid.org/0000-0003-2567-029X)
- Ming Yang (ORCID: https://orcid.org/0000-0002-1617-9960)
- Shuai Liu (ORCID: https://orcid.org/0000-0002-0699-2296)
- Yuxiang Sui (ORCID: https://orcid.org/0009-0002-9305-9310)
Institutions
- Shanxi Normal University (CN)
- First Affiliated Hospital Zhejiang University (CN)
- UConn Health (US)
- University of Missouri (US)
Publication Details
- Journal
- In Silico Research in Biomedicine
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1016/j.insi.2025.100045
- Primary Topic
- Computational Drug Discovery Methods
- Type
- article
- Field-Weighted Citation Impact
- 0.00