{"title":"Unveiling EFNB2 as a Key Player in Sorafenib Resistance: Insights from Bioinformatics Analysis and Functional Validation in Hepatocellular Carcinoma.","authors":"Junli Pan, Quanxi Li, Junli Zhu","doi":"10.1007/s10528-024-10903-5","DOIUrl":null,"url":null,"abstract":"<p><p>Sorafenib resistance has become a big hurdle for treating advanced HCC; thus, identifying novel targets to overcome sorafenib resistance is of great importance. Thanks to the massive progress in the sequencing and data analysis, high-throughput screening of novel targets in HCC development has been extensively used in recent years. In present study, we harnessed the public dataset and aimed to identify novel targets related to sorafenib resistance in HCC via bioinformatics analysis and in vitro validation. This study examined three GEO datasets (GSE140202, GSE143233, GSE182593) and identified 20 common DEGs. Functional enrichment analysis suggested these DEGs might play a role in regulating drug resistance pathways. PPI network analysis pinpointed 14 hub genes, with EFNB2 showing high connectivity to other genes. Subsequent in vitro experiments demonstrated that EFNB2 was up-regulated in sorafenib-resistant HCC cells. EFNB2 suppression sensitized HepG2 and Huh7 sorafenib-resistant cells. Furthermore, EFNB2 knockdown increased caspase-3/-7 activities and hindered EMT in sorafenib-resistant HCC cells. Conversely, EFNB2 overexpression promoted sorafenib resistance, decreased caspase-3/-7 activity, and enhanced EMT in HCC cells. Overall, this study identified 14 promising genes potentially linked to sorafenib resistance in HCC, with EFNB2 emerging as a potential contributor to this resistance mechanism.</p>","PeriodicalId":482,"journal":{"name":"Biochemical Genetics","volume":" ","pages":""},"PeriodicalIF":2.1000,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biochemical Genetics","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1007/s10528-024-10903-5","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"BIOCHEMISTRY & MOLECULAR BIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Sorafenib resistance has become a big hurdle for treating advanced HCC; thus, identifying novel targets to overcome sorafenib resistance is of great importance. Thanks to the massive progress in the sequencing and data analysis, high-throughput screening of novel targets in HCC development has been extensively used in recent years. In present study, we harnessed the public dataset and aimed to identify novel targets related to sorafenib resistance in HCC via bioinformatics analysis and in vitro validation. This study examined three GEO datasets (GSE140202, GSE143233, GSE182593) and identified 20 common DEGs. Functional enrichment analysis suggested these DEGs might play a role in regulating drug resistance pathways. PPI network analysis pinpointed 14 hub genes, with EFNB2 showing high connectivity to other genes. Subsequent in vitro experiments demonstrated that EFNB2 was up-regulated in sorafenib-resistant HCC cells. EFNB2 suppression sensitized HepG2 and Huh7 sorafenib-resistant cells. Furthermore, EFNB2 knockdown increased caspase-3/-7 activities and hindered EMT in sorafenib-resistant HCC cells. Conversely, EFNB2 overexpression promoted sorafenib resistance, decreased caspase-3/-7 activity, and enhanced EMT in HCC cells. Overall, this study identified 14 promising genes potentially linked to sorafenib resistance in HCC, with EFNB2 emerging as a potential contributor to this resistance mechanism.
期刊介绍:
Biochemical Genetics welcomes original manuscripts that address and test clear scientific hypotheses, are directed to a broad scientific audience, and clearly contribute to the advancement of the field through the use of sound sampling or experimental design, reliable analytical methodologies and robust statistical analyses.
Although studies focusing on particular regions and target organisms are welcome, it is not the journal’s goal to publish essentially descriptive studies that provide results with narrow applicability, or are based on very small samples or pseudoreplication.
Rather, Biochemical Genetics welcomes review articles that go beyond summarizing previous publications and create added value through the systematic analysis and critique of the current state of knowledge or by conducting meta-analyses.
Methodological articles are also within the scope of Biological Genetics, particularly when new laboratory techniques or computational approaches are fully described and thoroughly compared with the existing benchmark methods.
Biochemical Genetics welcomes articles on the following topics: Genomics; Proteomics; Population genetics; Phylogenetics; Metagenomics; Microbial genetics; Genetics and evolution of wild and cultivated plants; Animal genetics and evolution; Human genetics and evolution; Genetic disorders; Genetic markers of diseases; Gene technology and therapy; Experimental and analytical methods; Statistical and computational methods.