{"title":"基于生物信息学分析和实验验证的乳腺癌关键生物标志物鉴定。","authors":"Yu Huan, Ping She, Xushan Cai, Jiehua Qi, Chunli Zhang","doi":"10.1186/s43046-025-00260-8","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Breast cancer (BC) is a malignant tumor characterized by a high incidence rate and is the leading cause of cancer-related deaths among women worldwide. This study aims to identify key genes and potential prognostic biomarkers using a bioinformatics approach.</p><p><strong>Methods: </strong>Three microarray datasets, GSE86374, GSE120129, and GSE29044, were downloaded from the GEO database. GEO2R and Venn diagram software were employed to identify differentially expressed genes (DEGs), while DAVID was utilized for functional enrichment analysis. Subsequently, STRING and Cytoscape were used to construct the protein-protein interaction (PPI) network among the DEGs. UALCAN, GEPIA, and the Kaplan-Meier plotter were employed for prognostic analysis. Following this, the correlations and alterations of key genes were examined using cBioPortal. Finally, immunohistochemistry (IHC) was performed to validate the expression levels of the key genes.</p><p><strong>Results: </strong>A total of 323 differentially expressed genes (DEGs) were identified. From the protein-protein interaction (PPI) network, 37 hub genes were selected. Validation using UALCAN, GEPIA, and Kaplan-Meier plotters revealed that three key genes-RACGAP1, SPAG5, and KIF20A-were significantly overexpressed and associated with poor prognosis in breast cancer (BC), as well as advanced tumor staging. The correlations and alterations of these key genes, as demonstrated on cBioPortal, indicated that their alterations co-occurred. Experimental verification through immunohistochemistry (IHC) confirmed that the proteins of these key genes were highly expressed in tumor tissues.</p><p><strong>Conclusions: </strong>The key genes identified in this study can enhance our understanding of the molecular mechanisms underlying breast cancer (BC). Additionally, these genes may serve as potential sensitive biomarkers for patients with BC.</p>","PeriodicalId":17301,"journal":{"name":"Journal of the Egyptian National Cancer Institute","volume":"37 1","pages":"5"},"PeriodicalIF":2.1000,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Identification of key biomarkers in breast cancer based on bioinformatics analysis and experimental verification.\",\"authors\":\"Yu Huan, Ping She, Xushan Cai, Jiehua Qi, Chunli Zhang\",\"doi\":\"10.1186/s43046-025-00260-8\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Breast cancer (BC) is a malignant tumor characterized by a high incidence rate and is the leading cause of cancer-related deaths among women worldwide. This study aims to identify key genes and potential prognostic biomarkers using a bioinformatics approach.</p><p><strong>Methods: </strong>Three microarray datasets, GSE86374, GSE120129, and GSE29044, were downloaded from the GEO database. GEO2R and Venn diagram software were employed to identify differentially expressed genes (DEGs), while DAVID was utilized for functional enrichment analysis. Subsequently, STRING and Cytoscape were used to construct the protein-protein interaction (PPI) network among the DEGs. UALCAN, GEPIA, and the Kaplan-Meier plotter were employed for prognostic analysis. Following this, the correlations and alterations of key genes were examined using cBioPortal. Finally, immunohistochemistry (IHC) was performed to validate the expression levels of the key genes.</p><p><strong>Results: </strong>A total of 323 differentially expressed genes (DEGs) were identified. From the protein-protein interaction (PPI) network, 37 hub genes were selected. Validation using UALCAN, GEPIA, and Kaplan-Meier plotters revealed that three key genes-RACGAP1, SPAG5, and KIF20A-were significantly overexpressed and associated with poor prognosis in breast cancer (BC), as well as advanced tumor staging. The correlations and alterations of these key genes, as demonstrated on cBioPortal, indicated that their alterations co-occurred. Experimental verification through immunohistochemistry (IHC) confirmed that the proteins of these key genes were highly expressed in tumor tissues.</p><p><strong>Conclusions: </strong>The key genes identified in this study can enhance our understanding of the molecular mechanisms underlying breast cancer (BC). Additionally, these genes may serve as potential sensitive biomarkers for patients with BC.</p>\",\"PeriodicalId\":17301,\"journal\":{\"name\":\"Journal of the Egyptian National Cancer Institute\",\"volume\":\"37 1\",\"pages\":\"5\"},\"PeriodicalIF\":2.1000,\"publicationDate\":\"2025-02-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of the Egyptian National Cancer Institute\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1186/s43046-025-00260-8\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ONCOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of the Egyptian National Cancer Institute","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1186/s43046-025-00260-8","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ONCOLOGY","Score":null,"Total":0}
Identification of key biomarkers in breast cancer based on bioinformatics analysis and experimental verification.
Background: Breast cancer (BC) is a malignant tumor characterized by a high incidence rate and is the leading cause of cancer-related deaths among women worldwide. This study aims to identify key genes and potential prognostic biomarkers using a bioinformatics approach.
Methods: Three microarray datasets, GSE86374, GSE120129, and GSE29044, were downloaded from the GEO database. GEO2R and Venn diagram software were employed to identify differentially expressed genes (DEGs), while DAVID was utilized for functional enrichment analysis. Subsequently, STRING and Cytoscape were used to construct the protein-protein interaction (PPI) network among the DEGs. UALCAN, GEPIA, and the Kaplan-Meier plotter were employed for prognostic analysis. Following this, the correlations and alterations of key genes were examined using cBioPortal. Finally, immunohistochemistry (IHC) was performed to validate the expression levels of the key genes.
Results: A total of 323 differentially expressed genes (DEGs) were identified. From the protein-protein interaction (PPI) network, 37 hub genes were selected. Validation using UALCAN, GEPIA, and Kaplan-Meier plotters revealed that three key genes-RACGAP1, SPAG5, and KIF20A-were significantly overexpressed and associated with poor prognosis in breast cancer (BC), as well as advanced tumor staging. The correlations and alterations of these key genes, as demonstrated on cBioPortal, indicated that their alterations co-occurred. Experimental verification through immunohistochemistry (IHC) confirmed that the proteins of these key genes were highly expressed in tumor tissues.
Conclusions: The key genes identified in this study can enhance our understanding of the molecular mechanisms underlying breast cancer (BC). Additionally, these genes may serve as potential sensitive biomarkers for patients with BC.
期刊介绍：
As the official publication of the National Cancer Institute, Cairo University, the Journal of the Egyptian National Cancer Institute (JENCI) is an open access peer-reviewed journal that publishes on the latest innovations in oncology and thereby, providing academics and clinicians a leading research platform. JENCI welcomes submissions pertaining to all fields of basic, applied and clinical cancer research. Main topics of interest include: local and systemic anticancer therapy (with specific interest on applied cancer research from developing countries); experimental oncology; early cancer detection; randomized trials (including negatives ones); and key emerging fields of personalized medicine, such as molecular pathology, bioinformatics, and biotechnologies.
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