Jia-Ning Zhang, Xi-Rui Zhou, Zi-Lu Yi, Xin-Yu Tian, Hong Liu
{"title":"基于糖基化相关基因机器学习的乳腺癌预后新模型构建及药物敏感性探索。","authors":"Jia-Ning Zhang, Xi-Rui Zhou, Zi-Lu Yi, Xin-Yu Tian, Hong Liu","doi":"10.1016/j.clbc.2025.05.004","DOIUrl":null,"url":null,"abstract":"<p><strong>Aims: </strong>Breast cancer has become the number 1 killer threatening women's health. In recent years, glycosylation modification has played an increasingly important role in tumor progression. The aim of this study was to explore the key genes that may be involved in glycosylation modification, establish prognostic models, and further explore their biological functions.</p><p><strong>Methods: </strong>Using data from TCGA and GEO databases, differentially expressed genes (DEGs) were identified. Subsequently, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted to characterize the functions of the DEGs. LASSO regression analysis was performed to narrow down hub genes. Additionally, single-cell analysis, protein-protein interaction (PPI) network analysis, immune correlation analysis, drug sensitivity analysis, and molecular docking were carried out to investigate the functions of these hub genes.</p><p><strong>Results: </strong>Initially, we identified 110 differentially expressed prognostic genes, among which 89 were potentially associated with glycosylation modification. Enrichment analysis revealed their involvement in oxytocin signaling, chemical carcinogen-DNA adduct formation, and C-type lectin receptor pathways. LASSO regression (Least Absolute Shrinkage and Selection Operator) analysis further refined the selection to 24 hub genes, which exhibited specific genetic interactions. Notably, the expression levels of these genes showed significant associations with various immune cells. Drug sensitivity analysis of the hub genes highlighted methotrexate as a potential therapeutic candidate. Finally, molecular docking demonstrated strong binding affinities between the target receptors and ligands.</p><p><strong>Conclusions: </strong>In conclusion, we screened glycosylation-related Hub genes, constructed prognostic models, explored their biological functions, and proposed new insights for diagnosing and treating breast cancer.</p>","PeriodicalId":10197,"journal":{"name":"Clinical breast cancer","volume":" ","pages":""},"PeriodicalIF":2.9000,"publicationDate":"2025-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The Construction of a New Prognostic Model of Breast Cancer and the Exploration of Drug Sensitivity Based on Machine Learning for Glycosylation-Related Genes.\",\"authors\":\"Jia-Ning Zhang, Xi-Rui Zhou, Zi-Lu Yi, Xin-Yu Tian, Hong Liu\",\"doi\":\"10.1016/j.clbc.2025.05.004\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Aims: </strong>Breast cancer has become the number 1 killer threatening women's health. In recent years, glycosylation modification has played an increasingly important role in tumor progression. The aim of this study was to explore the key genes that may be involved in glycosylation modification, establish prognostic models, and further explore their biological functions.</p><p><strong>Methods: </strong>Using data from TCGA and GEO databases, differentially expressed genes (DEGs) were identified. Subsequently, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted to characterize the functions of the DEGs. LASSO regression analysis was performed to narrow down hub genes. Additionally, single-cell analysis, protein-protein interaction (PPI) network analysis, immune correlation analysis, drug sensitivity analysis, and molecular docking were carried out to investigate the functions of these hub genes.</p><p><strong>Results: </strong>Initially, we identified 110 differentially expressed prognostic genes, among which 89 were potentially associated with glycosylation modification. Enrichment analysis revealed their involvement in oxytocin signaling, chemical carcinogen-DNA adduct formation, and C-type lectin receptor pathways. LASSO regression (Least Absolute Shrinkage and Selection Operator) analysis further refined the selection to 24 hub genes, which exhibited specific genetic interactions. Notably, the expression levels of these genes showed significant associations with various immune cells. Drug sensitivity analysis of the hub genes highlighted methotrexate as a potential therapeutic candidate. Finally, molecular docking demonstrated strong binding affinities between the target receptors and ligands.</p><p><strong>Conclusions: </strong>In conclusion, we screened glycosylation-related Hub genes, constructed prognostic models, explored their biological functions, and proposed new insights for diagnosing and treating breast cancer.</p>\",\"PeriodicalId\":10197,\"journal\":{\"name\":\"Clinical breast cancer\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":2.9000,\"publicationDate\":\"2025-05-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Clinical breast cancer\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1016/j.clbc.2025.05.004\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ONCOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Clinical breast cancer","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1016/j.clbc.2025.05.004","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ONCOLOGY","Score":null,"Total":0}
The Construction of a New Prognostic Model of Breast Cancer and the Exploration of Drug Sensitivity Based on Machine Learning for Glycosylation-Related Genes.
Aims: Breast cancer has become the number 1 killer threatening women's health. In recent years, glycosylation modification has played an increasingly important role in tumor progression. The aim of this study was to explore the key genes that may be involved in glycosylation modification, establish prognostic models, and further explore their biological functions.
Methods: Using data from TCGA and GEO databases, differentially expressed genes (DEGs) were identified. Subsequently, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted to characterize the functions of the DEGs. LASSO regression analysis was performed to narrow down hub genes. Additionally, single-cell analysis, protein-protein interaction (PPI) network analysis, immune correlation analysis, drug sensitivity analysis, and molecular docking were carried out to investigate the functions of these hub genes.
Results: Initially, we identified 110 differentially expressed prognostic genes, among which 89 were potentially associated with glycosylation modification. Enrichment analysis revealed their involvement in oxytocin signaling, chemical carcinogen-DNA adduct formation, and C-type lectin receptor pathways. LASSO regression (Least Absolute Shrinkage and Selection Operator) analysis further refined the selection to 24 hub genes, which exhibited specific genetic interactions. Notably, the expression levels of these genes showed significant associations with various immune cells. Drug sensitivity analysis of the hub genes highlighted methotrexate as a potential therapeutic candidate. Finally, molecular docking demonstrated strong binding affinities between the target receptors and ligands.
Conclusions: In conclusion, we screened glycosylation-related Hub genes, constructed prognostic models, explored their biological functions, and proposed new insights for diagnosing and treating breast cancer.
期刊介绍：
Clinical Breast Cancer is a peer-reviewed bimonthly journal that publishes original articles describing various aspects of clinical and translational research of breast cancer. Clinical Breast Cancer is devoted to articles on detection, diagnosis, prevention, and treatment of breast cancer. The main emphasis is on recent scientific developments in all areas related to breast cancer. Specific areas of interest include clinical research reports from various therapeutic modalities, cancer genetics, drug sensitivity and resistance, novel imaging, tumor genomics, biomarkers, and chemoprevention strategies.
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