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dc.contributor.authorHuang, Chia-Yenen_US
dc.contributor.authorLiao, Kuang-Wenen_US
dc.contributor.authorChou, Chih-Hungen_US
dc.contributor.authorShrestha, Sirjanaen_US
dc.contributor.authorYang, Chi-Dungen_US
dc.contributor.authorChiew, Men-Yeeen_US
dc.contributor.authorHuang, Hsin-Tzuen_US
dc.contributor.authorHong, Hsiao-Chinen_US
dc.contributor.authorHuang, Shih-Hungen_US
dc.contributor.authorChang, Tzu-Haoen_US
dc.contributor.authorHuang, Hsien-Daen_US
dc.date.accessioned2020-03-02T03:23:21Z-
dc.date.available2020-03-02T03:23:21Z-
dc.date.issued2020-01-21en_US
dc.identifier.issn2234-943Xen_US
dc.identifier.urihttp://dx.doi.org/10.3389/fonc.2019.01508en_US
dc.identifier.urihttp://hdl.handle.net/11536/153694-
dc.description.abstractIntroduction: In the United States and Europe, endometrial endometrioid carcinoma (EEC) is the most prevalent gynecologic malignancy. Lymph node metastasis (LNM) is the key determinant of the prognosis and treatment of EEC. A biomarker that predicts LNM in patients with EEC would be beneficial, enabling individualized treatment. Current preoperative assessment of LNM in EEC is not sufficiently accurate to predict LNM and prevent overtreatment. This pilot study established a biomarker signature for the prediction of LNM in early stage EEC. Methods: We performed RNA sequencing in 24 clinically early stage (T1) EEC tumors (lymph nodes positive and negative in 6 and 18, respectively) from Cathay General Hospital and analyzed the RNA sequencing data of 289 patients with EEC from The Cancer Genome Atlas (lymph node positive and negative in 33 and 256, respectively). We analyzed clinical data including tumor grade, depth of tumor invasion, and age to construct a sequencing-based prediction model using machine learning. For validation, we used another independent cohort of early stage EEC samples (n = 72) and performed quantitative real-time polymerase chain reaction (qRT-PCR). Finally, a PCR-based prediction model and risk score formula were established. Results: Eight genes (ASRGL1, ESR1, EYA2, MSX1, RHEX, SCGB2A1, SOX17, and STX18) plus one clinical parameter (depth of myometrial invasion) were identified for use in a sequencing-based prediction model. After qRT-PCR validation, five genes (ASRGL1, RHEX, SCGB2A1, SOX17, and STX18) were identified as predictive biomarkers. Receiver operating characteristic curve analysis revealed that these five genes can predict LNM. Combined use of these five genes resulted in higher diagnostic accuracy than use of any single gene, with an area under the curve of 0.898, sensitivity of 88.9%, and specificity of 84.1%. The accuracy, negative, and positive predictive values were 84.7, 98.1, and 44.4%, respectively. Conclusion: We developed a five-gene biomarker panel associated with LNM in early stage EEC. These five genes may represent novel targets for further mechanistic study. Our results, after corroboration by a prospective study, may have useful clinical implications and prevent unnecessary elective lymph node dissection while not adversely affecting the outcome of treatment for early stage EEC.en_US
dc.language.isoen_USen_US
dc.subjectendometrial canceren_US
dc.subjectlymph node metastasisen_US
dc.subjectRNA sequencingen_US
dc.subjectTCGAen_US
dc.subjectprediction modelen_US
dc.titlePilot Study to Establish a Novel Five-Gene Biomarker Panel for Predicting Lymph Node Metastasis in Patients With Early Stage Endometrial Canceren_US
dc.typeArticleen_US
dc.identifier.doi10.3389/fonc.2019.01508en_US
dc.identifier.journalFRONTIERS IN ONCOLOGYen_US
dc.citation.volume9en_US
dc.citation.spage0en_US
dc.citation.epage0en_US
dc.contributor.department交大名義發表zh_TW
dc.contributor.department生物科技學系zh_TW
dc.contributor.department生物資訊及系統生物研究所zh_TW
dc.contributor.department分子醫學與生物工程研究所zh_TW
dc.contributor.departmentNational Chiao Tung Universityen_US
dc.contributor.departmentDepartment of Biological Science and Technologyen_US
dc.contributor.departmentInstitude of Bioinformatics and Systems Biologyen_US
dc.contributor.departmentInstitute of Molecular Medicine and Bioengineeringen_US
dc.identifier.wosnumberWOS:000511188300001en_US
dc.citation.woscount0en_US
Appears in Collections:Articles