{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["13"],"submitter":["Yu Y"],"funding":["National Natural Science Foundation of China","Shanghai Municipal Health Bureau"],"pubmed_abstract":["Early diagnosis is the key to improving the prognosis of breast cancer (BC) patients; however, there are currently no circulating biomarkers that demonstrate good sensitivity and specificity. This study applied circular RNA (circRNA) microarray analysis, screening, and verification in BC plasma samples to identify three tumor-derived differentially expressed circRNAs: hsa_circ_0000091, hsa_circ_0067772, and hsa_circ_0000512. We constructed a diagnostic model using logistic regression analysis in the training set and established an optimal diagnostic model based on the three circRNAs, which showed sensitivity, specificity, and area under the curve (AUC) values of .971, .902, and .974, respectively. We then verified the diagnostic model in the test set which showed satisfactory stability for"],"journal":["Frontiers in pharmacology"],"pagination":["811856"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8886293"],"repository":["biostudies-literature"],"pubmed_title":["Tumor-Derived circRNAs as Circulating Biomarkers for Breast Cancer."],"pmcid":["PMC8886293"],"pubmed_authors":["Ji C","Hua K","Chen M","Wang X","Zheng W","Fang L","Deng X","Yu Y"],"additional_accession":[]},"is_claimable":false,"name":"Tumor-Derived circRNAs as Circulating Biomarkers for Breast Cancer.","description":"Early diagnosis is the key to improving the prognosis of breast cancer (BC) patients; however, there are currently no circulating biomarkers that demonstrate good sensitivity and specificity. This study applied circular RNA (circRNA) microarray analysis, screening, and verification in BC plasma samples to identify three tumor-derived differentially expressed circRNAs: hsa_circ_0000091, hsa_circ_0067772, and hsa_circ_0000512. We constructed a diagnostic model using logistic regression analysis in the training set and established an optimal diagnostic model based on the three circRNAs, which showed sensitivity, specificity, and area under the curve (AUC) values of .971, .902, and .974, respectively. We then verified the diagnostic model in the test set which showed satisfactory stability for","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022","modification":"2025-04-04T08:57:23.005Z","creation":"2025-04-04T08:57:23.005Z"},"accession":"S-EPMC8886293","cross_references":{"pubmed":["35242035"],"doi":["10.3389/fphar.2022.811856"]}}