{"title":"应用生物信息学分析鉴定帕博西尼耐药乳腺癌关键基因及其功能","authors":"Guangyu Gao, Xinya Shi, Zhen Yao, Jiaofeng Shen, Liqin Shen","doi":"10.1097/IJ9.0000000000000084","DOIUrl":null,"url":null,"abstract":"Background: Palbociclib resistance is a significant problem in breast carcinoma, and its underlying molecular mechanisms remain poorly understood. This study aims to elucidate the molecular mechanisms of palbociclib resistance and to identify the key genes and pathways mediating progesterone resistance in breast cancer (BC). Methods: Gene dataset GSE117743 was downloaded from the Gene Expression Omnibus (GEO) database, which included 3 palbociclib-resistant and 3 palbociclib-sensitive BC cell lines. Then, we calculated the differentially expressed genes (DEGs) by using R software. Gene ontology and Enriched pathway analysis of genes we identified were analyzed by using the Database for Database of Annotation Visualization and Integrated Discovery (DAVID) and R software. The protein-protein interaction network was performed according to Metascape, String, and Cytoscape software. Results: In total, 447 DEGs were selected, which consisted of 67 upregulated and 380 downregulated genes. According to gene ontology annotation, DEGs were associated with cytoplasm, signal transduction, and protein binding. The research of the Kyoto Encyclopedia of Genes and Genomes (KEGG) demonstrated that genes enriched in certain tumor pathways, including IL-17 signaling pathways and Herpes simplex infection signaling pathways. Also, certain hub genes were highlighted after constructed and analyzed the protein-protein interaction network, including α-2A adrenergic receptor, cytochrome P450 subfamily IIR polypeptide, Cystathionine β-synthase, nucleotide-binding oligomerization domain-containing, erythropoietin-producing hepatocellular receptor A2 and adrenomedullin, which may be related with BC prognosis. A total of 4 of 6 hub genes had a significant relationship with the overall survival (P<0.05). Conclusions: Using microarray and bioinformatics analyses, we identified DEGs and determined a comprehensive gene network of progesterone resistance. We offered several possible mechanisms of progesterone resistance and identified therapeutic and prognostic targets of palbociclib resistance in BC.","PeriodicalId":42930,"journal":{"name":"International Journal of Surgery-Oncology","volume":"23 1","pages":"e84 - e84"},"PeriodicalIF":0.3000,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Identification of key genes and their functions in palbociclib-resistant breast carcinoma by using bioinformatics analysis\",\"authors\":\"Guangyu Gao, Xinya Shi, Zhen Yao, Jiaofeng Shen, Liqin Shen\",\"doi\":\"10.1097/IJ9.0000000000000084\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Background: Palbociclib resistance is a significant problem in breast carcinoma, and its underlying molecular mechanisms remain poorly understood. This study aims to elucidate the molecular mechanisms of palbociclib resistance and to identify the key genes and pathways mediating progesterone resistance in breast cancer (BC). Methods: Gene dataset GSE117743 was downloaded from the Gene Expression Omnibus (GEO) database, which included 3 palbociclib-resistant and 3 palbociclib-sensitive BC cell lines. Then, we calculated the differentially expressed genes (DEGs) by using R software. Gene ontology and Enriched pathway analysis of genes we identified were analyzed by using the Database for Database of Annotation Visualization and Integrated Discovery (DAVID) and R software. The protein-protein interaction network was performed according to Metascape, String, and Cytoscape software. Results: In total, 447 DEGs were selected, which consisted of 67 upregulated and 380 downregulated genes. According to gene ontology annotation, DEGs were associated with cytoplasm, signal transduction, and protein binding. The research of the Kyoto Encyclopedia of Genes and Genomes (KEGG) demonstrated that genes enriched in certain tumor pathways, including IL-17 signaling pathways and Herpes simplex infection signaling pathways. Also, certain hub genes were highlighted after constructed and analyzed the protein-protein interaction network, including α-2A adrenergic receptor, cytochrome P450 subfamily IIR polypeptide, Cystathionine β-synthase, nucleotide-binding oligomerization domain-containing, erythropoietin-producing hepatocellular receptor A2 and adrenomedullin, which may be related with BC prognosis. A total of 4 of 6 hub genes had a significant relationship with the overall survival (P<0.05). Conclusions: Using microarray and bioinformatics analyses, we identified DEGs and determined a comprehensive gene network of progesterone resistance. We offered several possible mechanisms of progesterone resistance and identified therapeutic and prognostic targets of palbociclib resistance in BC.\",\"PeriodicalId\":42930,\"journal\":{\"name\":\"International Journal of Surgery-Oncology\",\"volume\":\"23 1\",\"pages\":\"e84 - e84\"},\"PeriodicalIF\":0.3000,\"publicationDate\":\"2020-02-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Surgery-Oncology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1097/IJ9.0000000000000084\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ONCOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Surgery-Oncology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1097/IJ9.0000000000000084","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ONCOLOGY","Score":null,"Total":0}
引用次数: 0
摘要
背景:帕博西尼耐药是乳腺癌中的一个重要问题,其潜在的分子机制尚不清楚。本研究旨在阐明帕博西尼耐药的分子机制,确定乳腺癌(BC)中介导孕酮耐药的关键基因和途径。方法:从Gene Expression Omnibus (GEO)数据库下载基因数据集GSE117743,其中包括3株palbociclib耐药和3株palbociclib敏感的BC细胞株。然后利用R软件计算差异表达基因(DEGs)。利用DAVID (Database for Database of Annotation Visualization and Integrated Discovery)和R软件对所鉴定基因的基因本体和富集通路进行分析。蛋白-蛋白相互作用网络根据metscape, String和Cytoscape软件进行。结果:共筛选到447个基因,其中上调基因67个,下调基因380个。根据基因本体注释,deg与细胞质、信号转导和蛋白质结合有关。京都基因与基因组百科全书(KEGG)的研究表明,基因在某些肿瘤通路中富集,包括IL-17信号通路和单纯疱疹感染信号通路。构建并分析了蛋白-蛋白相互作用网络后,突出了α-2A肾上腺素能受体、细胞色素P450亚家族IIR多肽、胱硫氨酸β-合成酶、核苷酸结合寡聚结构域、促红细胞生成素肝细胞受体A2、肾上腺髓质素等可能与BC预后相关的枢纽基因。6个枢纽基因中有4个与总生存率有显著相关(P<0.05)。结论:利用微阵列和生物信息学分析,我们鉴定了deg,并确定了黄体酮耐药的综合基因网络。我们提出了几种可能的孕酮耐药机制,并确定了BC患者帕博西尼耐药的治疗和预后靶点。
Identification of key genes and their functions in palbociclib-resistant breast carcinoma by using bioinformatics analysis
Background: Palbociclib resistance is a significant problem in breast carcinoma, and its underlying molecular mechanisms remain poorly understood. This study aims to elucidate the molecular mechanisms of palbociclib resistance and to identify the key genes and pathways mediating progesterone resistance in breast cancer (BC). Methods: Gene dataset GSE117743 was downloaded from the Gene Expression Omnibus (GEO) database, which included 3 palbociclib-resistant and 3 palbociclib-sensitive BC cell lines. Then, we calculated the differentially expressed genes (DEGs) by using R software. Gene ontology and Enriched pathway analysis of genes we identified were analyzed by using the Database for Database of Annotation Visualization and Integrated Discovery (DAVID) and R software. The protein-protein interaction network was performed according to Metascape, String, and Cytoscape software. Results: In total, 447 DEGs were selected, which consisted of 67 upregulated and 380 downregulated genes. According to gene ontology annotation, DEGs were associated with cytoplasm, signal transduction, and protein binding. The research of the Kyoto Encyclopedia of Genes and Genomes (KEGG) demonstrated that genes enriched in certain tumor pathways, including IL-17 signaling pathways and Herpes simplex infection signaling pathways. Also, certain hub genes were highlighted after constructed and analyzed the protein-protein interaction network, including α-2A adrenergic receptor, cytochrome P450 subfamily IIR polypeptide, Cystathionine β-synthase, nucleotide-binding oligomerization domain-containing, erythropoietin-producing hepatocellular receptor A2 and adrenomedullin, which may be related with BC prognosis. A total of 4 of 6 hub genes had a significant relationship with the overall survival (P<0.05). Conclusions: Using microarray and bioinformatics analyses, we identified DEGs and determined a comprehensive gene network of progesterone resistance. We offered several possible mechanisms of progesterone resistance and identified therapeutic and prognostic targets of palbociclib resistance in BC.