Cargando…

Network Analysis for the Discovery of Common Oncogenic Biomarkers in Liver Cancer Experimental Models

Hepatocellular carcinoma (HCC) is a malignancy marked by heterogeneity. This study aimed to discover target molecules for potential therapeutic efficacy that may encompass HCC heterogeneity. In silico analysis using published datasets identified 16 proto-oncogenes as potential pharmacological target...

Descripción completa

Detalles Bibliográficos
Autores principales: Cabral, Loraine Kay D., Giraudi, Pablo J., Giannelli, Gianluigi, Dituri, Francesco, Negro, Roberto, Tiribelli, Claudio, Sukowati, Caecilia H. C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9953082/
https://www.ncbi.nlm.nih.gov/pubmed/36830879
http://dx.doi.org/10.3390/biomedicines11020342
_version_ 1784893789767204864
author Cabral, Loraine Kay D.
Giraudi, Pablo J.
Giannelli, Gianluigi
Dituri, Francesco
Negro, Roberto
Tiribelli, Claudio
Sukowati, Caecilia H. C.
author_facet Cabral, Loraine Kay D.
Giraudi, Pablo J.
Giannelli, Gianluigi
Dituri, Francesco
Negro, Roberto
Tiribelli, Claudio
Sukowati, Caecilia H. C.
author_sort Cabral, Loraine Kay D.
collection PubMed
description Hepatocellular carcinoma (HCC) is a malignancy marked by heterogeneity. This study aimed to discover target molecules for potential therapeutic efficacy that may encompass HCC heterogeneity. In silico analysis using published datasets identified 16 proto-oncogenes as potential pharmacological targets. We used an immortalized hepatocyte (IHH) and five HCC cell lines under two subtypes: S1/TGFβ-Wnt-activated (HLE, HLF, and JHH6) and the S2/progenitor subtype (HepG2 and Huh7). Three treatment modalities, 5 µM 5-Azacytidine, 50 µM Sorafenib, and 20 nM PD-L1 gene silencing, were evaluated in vitro. The effect of treatments on the proto-oncogene targets was assessed by gene expression and Western blot analysis. Our results showed that 10/16 targets were upregulated in HCC cells, where cells belonging to the S2/progenitor subtype had more upregulated targets compared to the S1/TGFβ-Wnt-activated subtype (81% vs. 62%, respectively). Among the targets, FGR was consistently down-regulated in the cell lines following the three different treatments. Sorafenib was effective to down-regulate targets in S2/progenitor subtype while PD-L1 silencing was able to decrease targets in all HCC subtypes, suggesting that this treatment strategy may comprise cellular heterogeneity. This study strengthens the relevance of liver cancer cellular heterogeneity in response to cancer therapies.
format Online
Article
Text
id pubmed-9953082
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-99530822023-02-25 Network Analysis for the Discovery of Common Oncogenic Biomarkers in Liver Cancer Experimental Models Cabral, Loraine Kay D. Giraudi, Pablo J. Giannelli, Gianluigi Dituri, Francesco Negro, Roberto Tiribelli, Claudio Sukowati, Caecilia H. C. Biomedicines Article Hepatocellular carcinoma (HCC) is a malignancy marked by heterogeneity. This study aimed to discover target molecules for potential therapeutic efficacy that may encompass HCC heterogeneity. In silico analysis using published datasets identified 16 proto-oncogenes as potential pharmacological targets. We used an immortalized hepatocyte (IHH) and five HCC cell lines under two subtypes: S1/TGFβ-Wnt-activated (HLE, HLF, and JHH6) and the S2/progenitor subtype (HepG2 and Huh7). Three treatment modalities, 5 µM 5-Azacytidine, 50 µM Sorafenib, and 20 nM PD-L1 gene silencing, were evaluated in vitro. The effect of treatments on the proto-oncogene targets was assessed by gene expression and Western blot analysis. Our results showed that 10/16 targets were upregulated in HCC cells, where cells belonging to the S2/progenitor subtype had more upregulated targets compared to the S1/TGFβ-Wnt-activated subtype (81% vs. 62%, respectively). Among the targets, FGR was consistently down-regulated in the cell lines following the three different treatments. Sorafenib was effective to down-regulate targets in S2/progenitor subtype while PD-L1 silencing was able to decrease targets in all HCC subtypes, suggesting that this treatment strategy may comprise cellular heterogeneity. This study strengthens the relevance of liver cancer cellular heterogeneity in response to cancer therapies. MDPI 2023-01-25 /pmc/articles/PMC9953082/ /pubmed/36830879 http://dx.doi.org/10.3390/biomedicines11020342 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Cabral, Loraine Kay D.
Giraudi, Pablo J.
Giannelli, Gianluigi
Dituri, Francesco
Negro, Roberto
Tiribelli, Claudio
Sukowati, Caecilia H. C.
Network Analysis for the Discovery of Common Oncogenic Biomarkers in Liver Cancer Experimental Models
title Network Analysis for the Discovery of Common Oncogenic Biomarkers in Liver Cancer Experimental Models
title_full Network Analysis for the Discovery of Common Oncogenic Biomarkers in Liver Cancer Experimental Models
title_fullStr Network Analysis for the Discovery of Common Oncogenic Biomarkers in Liver Cancer Experimental Models
title_full_unstemmed Network Analysis for the Discovery of Common Oncogenic Biomarkers in Liver Cancer Experimental Models
title_short Network Analysis for the Discovery of Common Oncogenic Biomarkers in Liver Cancer Experimental Models
title_sort network analysis for the discovery of common oncogenic biomarkers in liver cancer experimental models
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9953082/
https://www.ncbi.nlm.nih.gov/pubmed/36830879
http://dx.doi.org/10.3390/biomedicines11020342
work_keys_str_mv AT cabrallorainekayd networkanalysisforthediscoveryofcommononcogenicbiomarkersinlivercancerexperimentalmodels
AT giraudipabloj networkanalysisforthediscoveryofcommononcogenicbiomarkersinlivercancerexperimentalmodels
AT giannelligianluigi networkanalysisforthediscoveryofcommononcogenicbiomarkersinlivercancerexperimentalmodels
AT diturifrancesco networkanalysisforthediscoveryofcommononcogenicbiomarkersinlivercancerexperimentalmodels
AT negroroberto networkanalysisforthediscoveryofcommononcogenicbiomarkersinlivercancerexperimentalmodels
AT tiribelliclaudio networkanalysisforthediscoveryofcommononcogenicbiomarkersinlivercancerexperimentalmodels
AT sukowaticaeciliahc networkanalysisforthediscoveryofcommononcogenicbiomarkersinlivercancerexperimentalmodels