Integrative computational modeling to unravel novel potential biomarkers in hepatocellular carcinoma

Walakira, Andrew (author), Skubic, Cene (author), Nadižar, Nejc (author), Rozman, Damjana (author), Režen, Tadeja (author), Mraz, Miha (author), Moškon, Miha (author)

Hepatocellular carcinoma (HCC) is a major health problem around the world. The management of this disease is complicated by the lack of noninvasive diagnostic tools and the few treatment options available. Better clinical outcomes can be achieved if HCC is detected early, but unfortunately, clinical signs appear when the disease is in its late stages. We aim to identify novel genes that can be targeted for the diagnosis and therapy of HCC. We performed a meta-analysis of transcriptomics data to identify differentially expressed genes and applied network analysis to identify hub genes. Fatty acid metabolism, complement and coagulation cascade, chemical carcinogenesis and retinol metabolism were identified as key pathways in HCC. Furthermore, we integrated transcriptomics data into a reference human genome-scale metabolic model to identify key reactions and subsystems relevant in HCC. We conclude that fatty acid activation, purine metabolism, vitamin D, and E metabolism are key processes in the development of HCC and therefore need to be further explored for the development of new therapies. We provide the first evidence that GABRP, HBG1 and DAK (TKFC) genes are important in HCC in humans and warrant further studies.

• Language: English
• Keywords: hepatocellular carcinoma, meta-analysis, genome-scale metabolic modeling, network analysis, enriched pathways, enriched reactions, enriched subsystems
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: MF - Faculty of Medicine; FRI - Faculty of Computer and Information Science
• Publication status: Published
• Publication version: Version of Record
• Year: 2023
• Number of pages: 12 str.
• Numbering: Vol. 159, art. 106957
• PID: 20.500.12556/RUL-148294
• UDC: 616-006
• ISSN on article: 1879-0534
• DOI: 10.1016/j.compbiomed.2023.106957
• COBISS.SI-ID: 152497155
• Publication date in RUL: 10.08.2023

WALAKIRA, Andrew, SKUBIC, Cene, NADIŽAR, Nejc, ROZMAN, Damjana, REŽEN, Tadeja, MRAZ, Miha and MOŠKON, Miha, 2023, Integrative computational modeling to unravel novel potential biomarkers in hepatocellular carcinoma. Computers in biology and medicine [online]. 2023. Vol. 159,  106957. [Accessed 8 April 2025]. DOI 10.1016/j.compbiomed.2023.106957. Retrieved from: https://repozitorij.uni-lj.si/IzpisGradiva.php?lang=eng&id=148294

Record is a part of a journal

• Title: Computers in biology and medicine
• Shortened title: Comput. biol. & med.
• Publisher: Elsevier
• ISSN: 1879-0534
• COBISS.SI-ID: 518726681

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

Secondary language

• Language: Slovenian
• Keywords: hepatocelularni karcinom, metaanaliza, presnovno modeliranje na ravni genoma