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BACKGROUND The aim of this work was to systematically compare the differences between sorafenib and lenvatinib for patients with hepatocellular carcinoma (HCC) from genetic and clinical perspectives. MATERIAL AND METHODS The mRNA and miRNA sequencing information of patients with HCC treated with either sorafenib or lenvatinib was analyzed using differential expression and a protein-protein interaction assay. The clinical manifestations and adverse events of the 2 drugs were also investigated. RESULTS Compared with patients with HCC treated with sorafenib, patients treated with lenvatinib developed 8 differentially expressed genes (DEGs, FGF4, FGF23, UNC13C, RIMBP2, STXBP5L, PHOX2B, NEUROD4, and POU4F2) and 3 miRNAs (DEMs, has-miR-548ah, has-miR-888, and has-miR-196a-1), of which hsa-miR-548 regulated 4 target genes, the largest number among the 3 miRNAs. The functions of these DEMs and DEGs were verified by external experiments in the HCC cell line Hep3B2.1-7. We further investigated the adverse events of the drugs for patients with advanced HCC in clinical treatment. The patients in the sorafenib group developed less frequent symptoms of hypertension and diarrhea. Also, the frequency of hand-foot skin reactions in patients treated with lenvatinib was lower than that of patients treated with sorafenib (P<0.05). There were no significant differences in nausea, fatigue, frequent urination, and dizziness (P>0.05). CONCLUSIONS In a time of increasing interest in chemotherapy drug treatments for patients with HCC, this study provided a better understanding of the clinical evaluations of sorafenib and lenvatinib.
Pubmed ID: 35473892
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Software platform for complex network analysis and visualization. Used for visualization of molecular interaction networks and biological pathways and integrating these networks with annotations, gene expression profiles and other state data.
View all literature mentionsDatabase of known and predicted protein interactions. The interactions include direct (physical) and indirect (functional) associations and are derived from four sources: Genomic Context, High-throughput experiments, (Conserved) Coexpression, and previous knowledge. STRING quantitatively integrates interaction data from these sources for a large number of organisms, and transfers information between these organisms where applicable. The database currently covers 5''214''234 proteins from 1133 organisms. (2013)
View all literature mentionsTHIS RESOURCE IS NO LONGER IN SERVICE, documented on February 1st, 2022. Software application for genetic analysis of classical biometric traits like blood pressure or height that are caused by a combination of polygenic inheritance and complex environmental forces. (entry from Genetic Analysis Software)
View all literature mentionsAn online database for miRNA target prediction and functional annotations.
View all literature mentionsBioconductor software package for Empirical analysis of Digital Gene Expression data in R. Used for differential expression analysis of RNA-seq and digital gene expression data with biological replication.
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