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Integrative computational modeling to unravel novel potential biomarkers in hepatocellular carcinoma
ID
Walakira, Andrew
(
Author
),
ID
Skubic, Cene
(
Author
),
ID
Nadižar, Nejc
(
Author
),
ID
Rozman, Damjana
(
Author
),
ID
Režen, Tadeja
(
Author
),
ID
Mraz, Miha
(
Author
),
ID
Moškon, Miha
(
Author
)
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MD5: C21BBBF209C7298E0F7C677BC59C2DB5
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https://www.sciencedirect.com/science/article/pii/S0010482523004225
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Abstract
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
Views:
738
Downloads:
61
Metadata:
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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
Projects
Funder:
EC - European Commission
Funding programme:
H2020
Project number:
860895
Name:
Translational SYStemics: Personalised Medicine at the Interface of Translational Research and Systems Medicine
Acronym:
TranSYS
Funder:
ARRS - Slovenian Research Agency
Project number:
P2-0359
Name:
Vseprisotno računalništvo
Funder:
ARRS - Slovenian Research Agency
Project number:
P1-0390
Name:
Funkcijska genomika in biotehnologija za zdravje
Funder:
ARRS - Slovenian Research Agency
Project number:
I0-0022
Name:
Mreža raziskovalnih infrastrukturnih centrov Univerze v Ljubljani (MRIC UL)
Funder:
EC - European Commission
Funding programme:
European Regional Development Fund
Acronym:
ELIXIR-SI RI-SI-2
Funder:
Other - Other funder or multiple funders
Funding programme:
Republic of Slovenia, Ministry of Education, Science and Sport
Acronym:
ELIXIR-SI RI-SI-2
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