Jinhui Liu1, Pinping Jiang1, Xucheng Chen2, Yujie Shen3, Guoliang Cui4, Ziyan Ma5, Shaojie Zhao6, Yan Zhang7. 1. Department of Gynecology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China. 2. College of Pharmacy, Nanjing Medical University, Nanjing, China. 3. Department of Otorhinolaryngology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China. 4. Department of Traditional Chinese Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China. 5. University of New South Wales, Sydney, Australia. 6. Department of Gynecology and Obstetrics, Wuxi Maternal and Child Health Hospital Affiliated to Nanjing Medical University, No. 48, Huaishu Road, Wuxi, 214000, Jiangsu, China. zsjie2005@163.com. 7. Department of Gynecology and Obstetrics, Wuxi Maternal and Child Health Hospital Affiliated to Nanjing Medical University, No. 48, Huaishu Road, Wuxi, 214000, Jiangsu, China. fuyou2007@126.com.
Abstract
BACKGROUND: Endometrial cancer (EC) is one of the most common gynecological malignancies worldwide. However, the molecular mechanisms and the prognostic prediction for EC patients remain unclear. METHODS: In the current study, we performed an in-depth analysis of over 500 patients which were obtained from the Cancer Genome Atlas (TCGA) database. The bioinformatics analysis included gene set enrichment analysis (GSEA) and Cox and lasso regression analyses to ensure overall survival (OS)-related genes, moreover, to construct a prognostic model and a nomogram for EC patients. RESULTS: GSEA identified 4 gene sets significantly associated with EC, which are DNA repair, unfolded protein response, reactive oxygen species pathway and UV response up. Twenty-five OS-related DNA repair genes were screened out, after that, a 9-mRNA signature was constructed to measure the risk scores of patients with different outcomes. In addition, a nomogram contained the 9-mRNA model and clinical parameters was also presented to assess the prognosis. Patients with low risk were more likely to have sensitivity to paclitaxel, vinblastine, rapamycin, metformin, imatinib, Akt inhibitor and lapatinib. CONCLUSIONS: The identified highly enriched gene sets may offer a novel insight into the tumorigenesis and treatment of EC. Additionally, the constructed 9-mRNA model and the nomogram have prominent clinical implications for prognosis evaluation and specific therapy guidance for EC patients.
BACKGROUND:Endometrial cancer (EC) is one of the most common gynecological malignancies worldwide. However, the molecular mechanisms and the prognostic prediction for ECpatients remain unclear. METHODS: In the current study, we performed an in-depth analysis of over 500 patients which were obtained from the Cancer Genome Atlas (TCGA) database. The bioinformatics analysis included gene set enrichment analysis (GSEA) and Cox and lasso regression analyses to ensure overall survival (OS)-related genes, moreover, to construct a prognostic model and a nomogram for ECpatients. RESULTS:GSEA identified 4 gene sets significantly associated with EC, which are DNA repair, unfolded protein response, reactive oxygen species pathway and UV response up. Twenty-five OS-related DNA repair genes were screened out, after that, a 9-mRNA signature was constructed to measure the risk scores of patients with different outcomes. In addition, a nomogram contained the 9-mRNA model and clinical parameters was also presented to assess the prognosis. Patients with low risk were more likely to have sensitivity to paclitaxel, vinblastine, rapamycin, metformin, imatinib, Akt inhibitor and lapatinib. CONCLUSIONS: The identified highly enriched gene sets may offer a novel insight into the tumorigenesis and treatment of EC. Additionally, the constructed 9-mRNA model and the nomogram have prominent clinical implications for prognosis evaluation and specific therapy guidance for ECpatients.
Entities:
Keywords:
Bioinformatics analysis; DNA repair; Endometrial cancer; Prognostic model
Authors: Vijayalakshmi Kari; Wael Yassin Mansour; Sanjay Kumar Raul; Simon J Baumgart; Andreas Mund; Marian Grade; Hüseyin Sirma; Ronald Simon; Hans Will; Matthias Dobbelstein; Ekkehard Dikomey; Steven A Johnsen Journal: EMBO Rep Date: 2016-09-05 Impact factor: 8.807
Authors: Yiai Tong; Diana Merino; Birgit Nimmervoll; Kirti Gupta; Yong-Dong Wang; David Finkelstein; James Dalton; David W Ellison; Xiaotu Ma; Jinghui Zhang; David Malkin; Richard J Gilbertson Journal: Cancer Cell Date: 2015-05-11 Impact factor: 31.743
Authors: Supipi Duffy; Hok Khim Fam; Yi Kan Wang; Erin B Styles; Jung-Hyun Kim; J Sidney Ang; Tejomayee Singh; Vladimir Larionov; Sohrab P Shah; Brenda Andrews; Cornelius F Boerkoel; Philip Hieter Journal: Proc Natl Acad Sci U S A Date: 2016-08-22 Impact factor: 11.205
Authors: Ji-Yeon Yang; Henrica M J Werner; Jie Li; Shannon N Westin; Yiling Lu; Mari K Halle; Jone Trovik; Helga B Salvesen; Gordon B Mills; Han Liang Journal: Clin Cancer Res Date: 2015-07-29 Impact factor: 12.531
Authors: Sun Young Kim; Ji Yeon Baek; Jae Hwan Oh; Sung Chan Park; Dae Kyung Sohn; Min Ju Kim; Hee Jin Chang; Sun-Young Kong; Dae Yong Kim Journal: Radiat Oncol Date: 2017-03-27 Impact factor: 3.481