Data mining reveals novel gene drivers of lenvatinib resistance in hepatocellular carcinoma

Introduction and objectives: Liver Cancer is the sixth most common malignancy and the third leading cause of cancer-related deaths globally. Hepatocellular carcinoma (HCC) is the most prevalent type, accounting for nearly 90 % of all liver Cancer cases. The first-line systemic therapy for advanced HCC includes lenvatinib, an oral multi-kinase tyrosine inhibitor. However, many HCC patients exhibit resistance to lenvatinib, leading to treatment failure. Recent studies suggest that lenvatinib resistance is multi-factorial.

Materials and methods: Four public RNA-seq datasets were retrieved from Gene Expression Omnibus (GEO) database and further analyzed to identify novel gene drivers of lenvatinib resistance. Bioinformatics analyses were performed in differentially expressed genes. In vitro validation was conducted in HCC cell lines after acute lenvatinib treatment.

Results: After applying several filtering conditions, Gene Ontology (GO) and pathway enrichment analyses using Kyoto Encyclopaedia of Genes and Genome (KEGG) databases to identify significantly enriched pathways, a total of five genes emerged as good novel candidate genes which are likely to be associated with lenvatinib resistance: SEZ6L2, SECTM1, FBLN7, IFI6, and NPC1L1. The association of these five genes with patient's prognosis was based on TCGA database. Our validation using Huh7 and Hep3B HCC cells treated with lenvatinib showed increased consistent mRNA expressions of SECTM1 and IFI6.

Conclusions: This study showed the relevance of finding new genes associated with lenvatinib resistance.

Data mining; Drug resistance; Hepatocellular carcinoma; Lenvatinib resistance.