Computational Analysis of Transcriptomic and Proteomic Data for Deciphering Molecular Heterogeneity and Drug Responsiveness in Model Human Hepatocellular Carcinoma Cell Lines

Abstract

Hepatocellular carcinoma (HCC) is associated with high mortality due to its inherent heterogeneity, aggressiveness, and limited therapeutic regimes. Herein, we analyzed 21 human HCC cell lines (HCC lines) to explore intertumor molecular diversity and pertinent drug sensitivity. We used an integrative computational approach based on exploratory and single-sample gene-set enrichment analysis of transcriptome and proteome data from the Cancer Cell Line Encyclopedia, followed by correlation analysis of drug-screening data from the Cancer Therapeutics Response Portal with curated gene-set enrichment scores. Acquired results classified HCC lines into two groups, a poorly and a well-differentiated group, displaying lower/higher enrichment scores in a “Specifically Upregulated in Liver” gene-set, respectively. Hierarchical clustering based on a published epithelial–mesenchymal transition gene expression signature further supported this stratification. Between-group comparisons of gene and protein expression unveiled distinctive patterns, whereas downstream functional analysis significantly associated differentially expressed genes with crucial cancer-related biological processes/pathways and revealed concrete driver-gene signatures. Finally, correlation analysis highlighted a diverse effectiveness of specific drugs against poorly compared to well-differentiated HCC lines, possibly applicable in clinical research with patients with analogous characteristics. Overall, this study expanded the knowledge on the molecular profiles, differentiation status, and drug responsiveness of HCC lines, and proposes a cost-effective computational approach to precision anti-HCC therapies.

Transcriptomic evidence for tumor‐specific beneficial or adverse effects of TGFβ pathway inhibition on the prognosis of patients with liver cancer

“…Conversely, genes associated with cancer stem cells (e.g., CD44), TGFb pathway (e.g., SERPINE1), and EMT (e.g., SNAI2) were expressed at a higher level in SNU-449 than in PLC/PRF/5 cell line (Fig. 1C), recapitulating previous reports made at transcriptomic and proteomic levels [27,29]. Supporting a higher proliferative potential of SNU-449 cells, cyclin-dependent kinase inhibitor 2A (CDKN2A) and BIRC3, encoding an inhibitor of apoptosis, were underexpressed while MYC was overexpressed (Fig.…”

“…PLC/PRF/5 and SNU-449 reproduce human HCC with different prognosis Hepatocellular carcinoma cell lines constitute relevant models to investigate signaling pathways altered in liver cancer and to test new drug candidates [14,[25][26][27][28]. However, to what extent these cell lines recapitulate the biology of human HCC tumors has been partly addressed [29]. Here, we evaluated the clinical relevance of PLC/ PRF/5 and SNU-449 cell lines, which were representative of two clusters differing notably in the expression of genes associated with metastasis in a panel of 19 HCC cell lines [16].…”

Ontologies and Knowledge Graphs in Oncology Research

“…Due to their axiom-based structure, ontologies can support reasoning applications, first to confirm consistency in the ontology and data themselves [14] but also to obtain further inferences from the formal definitions that are established by the ontologies [15]. Lastly, annotation of data with ontologies allows for further use in mining and analyzing this data, for example, with enrichment methods or similarity measures [16,17]. Additionally, there has been an increase in the use of ontology-structured data as input for ML methods, particularly in the biomedical domain with, for example, gene function predictions and clinical decision support systems [18,19].…”

“…However, in the end, the most common approach to the use of ontologies in the analysis of biomedical data was the application of GO in Gene Set Enrichment Analysis (GSEA) [16,53,73,. GSEA statistically compares set of genes that share biological characteristics and interprets their expression data in light of on whether they differ across defined phenotypes [160] and as such is commonly used in biomedical research to, for example, establish candidate genes for further studies.…”

Computational Methods for the Analysis of Genomic Data and Biological Processes

“…Computationally based analyses of biological processes are also included in this Special Issue. For example, the work by Agioutantis et al [ 8 ] analysed 21 human hepatocellular carcinoma (HCC) cell lines (HCC lines) to explore intertumoral molecular diversity and pertinent drug sensitivity. This article proposes an integrative computational approach based on an exploratory and single-sample gene-set enrichment analysis of transcriptome and proteome data, and then a correlation analysis of drug-screening data.…”