Comprehensive Profiling and Therapeutic Insights into Differentially Expressed Genes in Hepatocellular Carcinoma

Abstract

Background: Drug repurposing is a strategy that complements the conventional approach of developing new drugs. Hepatocellular carcinoma (HCC) is a highly prevalent type of liver cancer, necessitating an in-depth understanding of the underlying molecular alterations for improved treatment. Methods: We searched for a vast array of microarray experiments in addition to RNA-seq data. Through rigorous filtering processes, we have identified highly representative differentially expressed genes (DEGs) between tumor and non-tumor liver tissues and identified a distinct class of possible new candidate drugs. Results: Functional enrichment analysis revealed distinct biological processes associated with metal ions, including zinc, cadmium, and copper, potentially implicating chronic metal ion exposure in tumorigenesis. Conversely, up-regulated genes are associated with mitotic events and kinase activities, aligning with the relevance of kinases in HCC. To unravel the regulatory networks governing these DEGs, we employed topological analysis methods, identifying 25 hub genes and their regulatory transcription factors. In the pursuit of potential therapeutic options, we explored drug repurposing strategies based on computational approaches, analyzing their potential to reverse the expression patterns of key genes, including AURKA, CCNB1, CDK1, RRM2, and TOP2A. Potential therapeutic chemicals are alvocidib, AT-7519, kenpaullone, PHA-793887, JNJ-7706621, danusertibe, doxorubicin and analogues, mitoxantrone, podofilox, teniposide, and amonafide. Conclusion: This multi-omic study offers a comprehensive view of DEGs in HCC, shedding light on potential therapeutic targets and drug repurposing opportunities.

Keywords: bioinformatics; drug repurposing; druggable genes; liver cancer; reverse expression.

Figure 1

Distribution of DEGs. Volcano plots showing the distribution of DEGs found in (A) healthy and (B) adjacent tissue versus tumor tissues. Gray dots: Genes that do not fit in the FC cutoff and have p-value > 0.05; Blue dots: Genes that do not fit in the FC cutoff and have p-value < 0.05. Green dots: Genes that fit in the FC cutoff and have p-value > 0.05; Red dots: Genes that fit in the FC cutoff and have p-value < 0.05. (C) Venn diagram evidencing the number of shared DEGs between the two conditions.

Figure 2

The top 5 GO terms enriched for DEGs that are (A) down-regulated and (B) up-regulated in HCC.

Figure 3

The top 5 pathways enriched for DEGs that are (A) down-regulated and (B) up-regulated in HCC.

Figure 4

Transcription factors and Hub genes. (A) Sankey diagram providing insights into the relationships between TFs and genes that compose the HCC signature. (B) Expression kinases network, retrieved and adapted from https://maayanlab.cloud/X2K/ (accessed on 15 June 2023). Red nodes represent the top transcription factors predicted to regulate the expression of the Hub genes; gray nodes represent intermediate proteins that physically interact with the enriched TFs and connect them. Blue nodes represent the top predicted protein kinases known to phosphorylate downstream proteins.

Figure 5

OCTAD results, showing the (A) top 50 ranked drugs experimented against HCC cell lines and (B) the main mechanism of action (moa) described by candidate drugs.

Figure 6

Drug and target-gene interactions: (A) different sources where the interactions were retrieved; (B) drugs shared in two distinct databases; and (C) druggable genes and their respective compounds. FDA: Food and Drug Administration; TEND: Trends in the exploitation of novel drug targets; TALC: Targeted Agents in Lung Cancer; CIViC: Clinical Interpretation of Variants in Cancer; NCI: NCI Cancer Gene Index; JAX-CKB: The Jackson Laboratory Clinical Knowledgebase; DTC: Drug Target Commons. Blue arrows mean that the expression pattern may be reversed after treatment.

Figure 7

Essentiality of the five druggable genes for liver tumor cell survival: (A) association between GSVA score and activity of cancer-related pathways in HCC; * p < 0.05; # FDR < 0.05. (B) Efficacy (e) and selectivity (s) of genes across all cancer cell lines available at the Cancer Dependency Map (DepMap); the blue dots represent 15.847 genes evaluated. Efficacy refers to the cellular growth reduction caused by the loss of function of a specific gene.