{"title":"乳腺组织差异表达基因的研究","authors":"Mengzhen Jiang, Chao Wei, Guannan Chen","doi":"10.1145/3351917.3351983","DOIUrl":null,"url":null,"abstract":"Nowadays, breast cancer has been considered as one of the most common malignant tumors among female diseases, and its incidence is increasing year by year. Genomic and transcriptome changes that affect cellular processes, such as cell proliferation, differentiation, apoptosis, and invasion, are commonly associated with the development of breast cancer. Epidemiological evidence has shown that the risk of breast cancer susceptibility varies among ethnic groups. In this paper, differentially expressed genes between human breast tumors and normal tissues are identified by studying Chinese, Malay and Indian ethnic groups and their paired tissues. The GO and KEGG methods were used for annotation and analysis of genes, and protein interaction networks were used to analyze protein interactions. The results of the study showed that there were 33 genes with significant differences in expression. The differentially expressed genes were CD36, LEP, LPL, ADIPOQ, PCK1, ACASL1, PPARG, PLIN1, ACACB and PPARG. In the protein interaction network, the interaction between AKR1C3 and AKR1C1 is the strongest. The study of differentially expressed genes and the search for proteins with the strongest interactions will contribute to the study of the molecular mechanism of the disease and the discovery of new drug targets, so as to improve the future treatment of breast cancer patients.","PeriodicalId":367885,"journal":{"name":"Proceedings of the 2019 4th International Conference on Automation, Control and Robotics Engineering","volume":"15 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Study on Differentially Expressed Genes in Breast Tissue\",\"authors\":\"Mengzhen Jiang, Chao Wei, Guannan Chen\",\"doi\":\"10.1145/3351917.3351983\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Nowadays, breast cancer has been considered as one of the most common malignant tumors among female diseases, and its incidence is increasing year by year. Genomic and transcriptome changes that affect cellular processes, such as cell proliferation, differentiation, apoptosis, and invasion, are commonly associated with the development of breast cancer. Epidemiological evidence has shown that the risk of breast cancer susceptibility varies among ethnic groups. In this paper, differentially expressed genes between human breast tumors and normal tissues are identified by studying Chinese, Malay and Indian ethnic groups and their paired tissues. The GO and KEGG methods were used for annotation and analysis of genes, and protein interaction networks were used to analyze protein interactions. The results of the study showed that there were 33 genes with significant differences in expression. The differentially expressed genes were CD36, LEP, LPL, ADIPOQ, PCK1, ACASL1, PPARG, PLIN1, ACACB and PPARG. In the protein interaction network, the interaction between AKR1C3 and AKR1C1 is the strongest. The study of differentially expressed genes and the search for proteins with the strongest interactions will contribute to the study of the molecular mechanism of the disease and the discovery of new drug targets, so as to improve the future treatment of breast cancer patients.\",\"PeriodicalId\":367885,\"journal\":{\"name\":\"Proceedings of the 2019 4th International Conference on Automation, Control and Robotics Engineering\",\"volume\":\"15 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-07-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2019 4th International Conference on Automation, Control and Robotics Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3351917.3351983\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2019 4th International Conference on Automation, Control and Robotics Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3351917.3351983","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Study on Differentially Expressed Genes in Breast Tissue
Nowadays, breast cancer has been considered as one of the most common malignant tumors among female diseases, and its incidence is increasing year by year. Genomic and transcriptome changes that affect cellular processes, such as cell proliferation, differentiation, apoptosis, and invasion, are commonly associated with the development of breast cancer. Epidemiological evidence has shown that the risk of breast cancer susceptibility varies among ethnic groups. In this paper, differentially expressed genes between human breast tumors and normal tissues are identified by studying Chinese, Malay and Indian ethnic groups and their paired tissues. The GO and KEGG methods were used for annotation and analysis of genes, and protein interaction networks were used to analyze protein interactions. The results of the study showed that there were 33 genes with significant differences in expression. The differentially expressed genes were CD36, LEP, LPL, ADIPOQ, PCK1, ACASL1, PPARG, PLIN1, ACACB and PPARG. In the protein interaction network, the interaction between AKR1C3 and AKR1C1 is the strongest. The study of differentially expressed genes and the search for proteins with the strongest interactions will contribute to the study of the molecular mechanism of the disease and the discovery of new drug targets, so as to improve the future treatment of breast cancer patients.