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

Panagiotis C Agioutantis, Heleni Loutrari, Fragiskos N Kolisis, Panagiotis C Agioutantis, Heleni Loutrari, Fragiskos N Kolisis

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.

Keywords: Gene Ontology; Reactome Pathways; bioinformatics analysis; differentiation; gene-set enrichment; hepatocellular carcinoma; proteomics; transcriptomics.

Conflict of interest statement

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Figures

Figure 1
Figure 1
Gene-based principal component analysis (PCA) and clustering of HCC lines (A) PCA on 500 genes with the largest cross-sample variation. PC1 (x-axis) versus PC2 (y-axis) for 21 HCC lines indicated by blue color. Dashed horizontal and vertical lines mark zero values of PC1 and PC2, respectively. (B) The two optimal k-means clusters based on PC1 scores as identified by the Bayesian information criterion (BIC). All HCC lines were treated as equally weighted.
Figure 2
Figure 2
Specifically Upregulated in Liver (SU_LIVER) clustering of HCC lines and PC1-SU_LIVER correlation. (A) The two optimal k-means clusters based on computed cell line ssGSEA SU_LIVER enrichment scores as identified by the BIC. All HCC lines were treated as equally weighted. (B) PC1 score correlation (Pearson’s) with individual cell-line enrichment scores for the SU_LIVER gene-set. Cell lines included in the “high SU_LIVER score”/“high PC1 score” clusters were identified as liver-like and well-differentiated (green circles), while the ones in the “low SU_LIVER score”/“low PC1 score” clusters were characterized as poorly differentiated (purple circles). Yellow circles indicate ambiguous cell lines.
Figure 3
Figure 3
Heatmap illustrating the hierarchical clustering of cancer cell lines (columns) based on the epithelial-to-mesenchymal transition (EMT) signature of 239 genes (rows). Scaled values indicate relative downregulation (green color) or upregulation (red color) of gene expression. Cell lines are annotated by color, based on the clusters that were predicted by the SU_LIVER enrichment scores shown in Figure 2B.
Figure 4
Figure 4
PCA based on all 214 protein/phosphoprotein reverse-phase protein array (RPPA) expression data. PC1 (x-axis) versus PC2 (y-axis). Cell lines are annotated by color, based on the gene-expression-derived clusters that are predicted by the SU_LIVER enrichment scores shown in Figure 2B.
Figure 5
Figure 5
Volcano plots illustrating differentially expressed genes (DEGs) (A) and differentially expressed proteins (DEPs) (B) in poorly differentiated versus well-differentiated HCC lines. Red and green dots represent up- and downregulated genes/proteins, respectively; grey dots represent non-statistically-significant altered genes/proteins. Horizontal dashed lines indicate a statistical threshold corresponding to an adjusted p-value of < 0.1; x-axis: mRNA log2 fold-change (A) or RPPA log2 fold-change (B), y-axis: p-value in negative log10 scale.
Figure 5
Figure 5
Volcano plots illustrating differentially expressed genes (DEGs) (A) and differentially expressed proteins (DEPs) (B) in poorly differentiated versus well-differentiated HCC lines. Red and green dots represent up- and downregulated genes/proteins, respectively; grey dots represent non-statistically-significant altered genes/proteins. Horizontal dashed lines indicate a statistical threshold corresponding to an adjusted p-value of < 0.1; x-axis: mRNA log2 fold-change (A) or RPPA log2 fold-change (B), y-axis: p-value in negative log10 scale.
Figure 6
Figure 6
Pairwise Pearson correlation between identified DEPs (total proteins) and their corresponding DEGs. x-axis: mRNA log2(fold-change) of DEGs, y-axis: RPPA log2(fold-change) of DEPs. Protein–gene pairs are represented by their corresponding HGNC gene symbol.
Figure 7
Figure 7
Top 30 significantly enriched Gene Ontology (GO) biological process terms, ranked by their hypergeometric corrected p-value in negative log10 scale (x-axis). Gene enrichment is also presented in total gene numbers, right after each GO term.
Figure 8
Figure 8
Significantly enriched Reactome Pathway terms ranked by their hypergeometric corrected p-value in negative log10 scale (x-axis). Gene enrichment is also presented in total gene numbers, right after each Reactome Pathway term.
Figure 9
Figure 9
Top 35 gene-set enrichment terms as identified by camera testing, ranked by their p-value in negative log10 scale (x-axis).
Figure 10
Figure 10
Volcano plot depicting drugs characterized by a statistically significant correlation between area under concentration–response curve (AUC) response measurements and SU_LIVER enrichment scores. Green dots represent drugs that were more effective against well-differentiated cell lines compared to poorly differentiated ones, while red dots mark drugs that were relatively more effective against poorly differentiated HCC lines. Grey dots indicate drugs without a significant correlation between their effect and the differentiation status of cell lines. The horizontal dashed line marks the highest p-value corresponding to an adjusted p-value < 0.3, whereas the two vertical dashed lines mark Spearman’s ρ values equal to –0.5 and 0.5; x-axis: Spearman’s ρ, y-axis: p-value in negative log10 scale.
Figure 11
Figure 11
Heatmap illustrating the hierarchical clustering of HCC tumors (columns) based on the full set of SU_LIVER genes (rows). Scaled values indicate relative downregulation (green color) or upregulation (red color) of gene expression. HCC tumors are annotated by color according to their documented histological grade (G1+G2 versus G3+G4).

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