Pilot Study to Establish a Novel Five-Gene Biomarker Panel for Predicting Lymph Node Metastasis in Patients With Early Stage Endometrial Cancer

Frontiers in Oncology

卷:9

Tzu-Hao Chang²  Sirjana Shrestha⁴  Hsin-Tzu Huang⁴  Men-Yee Chiew⁴  Shih-Hung Huang⁶  Chih-Hung Chou⁷  Kuang-Wen Liao⁸  Chia-Yen Huang¹⁰  Hsien-Da Huang¹¹  Hsiao-Chin Hong¹¹  Chi-Dung Yang¹¹ 

[1] 0International Center for Health Information Technology, Taipei Medical University, Taipei, Taiwan;

[2] 1Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan;

[3] Center for Intelligent Drug Systems and Smart Bio-devices (IDS2B), National Chiao Tung University, Hsinchu, Taiwan;

[4] Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, Taiwan;

[5] Department of Obstetrics and Gynecology, Gynecologic Cancer Center, Cathay General Hospital, Taipei, Taiwan;

[6] Department of Pathology, Cathay General Hospital, Taipei, Taiwan;

[7] Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, Taiwan;

[8] Institute of Molecular Medicine and Bioengineering, National Chiao Tung University, Hsinchu, Taiwan;

[9] School of Life and Health Sciences, Chinese University of Hong Kong, Shenzhen, China;

[10] School of Medicine, Fu Jen Catholic University, New Taipei City, Taiwan;

[11] Warshel Institute for Computational Biology, Chinese University of Hong Kong, Shenzhen, China;

关键词: endometrial cancer;    lymph node metastasis;    RNA sequencing;    TCGA;    prediction model;   

DOI  :  10.3389/fonc.2019.01508

来源: DOAJ

【 摘 要 】

Introduction: 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.

【 授权许可】

Unknown