Shanshan Qin1, Yingchun Gao1, Yijun Yang1, Lei Zhang1, Ting Zhang1, Juanpeng Yu1, Can Shi2. 1. Department of Obstetrics and Gynecology, The Affiliated Huaian No. 1 People's Hospital of Nanjing Medical University, Huai'an, China. 2. Department of Obstetrics and Gynecology, The Affiliated Huaian No. 1 People's Hospital of Nanjing Medical University, Huai'an, China, shican2015@126.com.
Abstract
AIM: Recurrence being a major challenge for the treatment of cervical cancer, we aimed at identifying novel molecular markers of cervical cancer to improve recurrence prediction. METHODS: Cervical cancer samples were obtained from the Cancer Genome Atlas. Prognosis-associated long non-coding RNAs (lncRNAs), microRNAs (miRNAs), and mRNAs between recurrent and nonrecurrent samples were acquired using expression analysis. Regulatory relationships among these prognosis-associated RNAs were predicted and used to construct a competing endogenous RNA (ceRNA) network. Key prognostic lncRNAs, miRNAs, and mRNAs were identified using the ceRNA network, followed by the Kaplan-Meier survival analysis to reveal the influence of these key prognostic RNAs on prognosis. RESULTS: In total, 15 lncRNAs, 10 miRNAs, and 348 mRNAs were identified as significant prognosis-associated molecules. The cervical cancer-related ceRNA network contained 13 prognosis-associated lncRNAs, 5 prognosis-associated miRNAs, and 120 prognosis-associated mRNAs. Key prognostic lncRNAs, miRNAs, and mRNAs were further identified using the ceRNA network. The key prognostic lncRNAs included H19 and HOTAIR, those for miRNAs included hsa-miR-133b, hsa-miR-138, and hsa-miR-301b, and those for mRNAs included Wnt family member 2, fibroblast growth factor 7, fibronectin 1, synaptic vesicle glycoprotein 2A, and bone morphogenetic protein 7. CONCLUSION: The key prognostic lncRNAs, miRNAs, and mRNAs may serve as potential molecular markers for recurrence prediction and may contribute to the treatment of cervical cancer.
AIM: Recurrence being a major challenge for the treatment of cervical cancer, we aimed at identifying novel molecular markers of cervical cancer to improve recurrence prediction. METHODS:Cervical cancer samples were obtained from the Cancer Genome Atlas. Prognosis-associated long non-coding RNAs (lncRNAs), microRNAs (miRNAs), and mRNAs between recurrent and nonrecurrent samples were acquired using expression analysis. Regulatory relationships among these prognosis-associated RNAs were predicted and used to construct a competing endogenous RNA (ceRNA) network. Key prognostic lncRNAs, miRNAs, and mRNAs were identified using the ceRNA network, followed by the Kaplan-Meier survival analysis to reveal the influence of these key prognostic RNAs on prognosis. RESULTS: In total, 15 lncRNAs, 10 miRNAs, and 348 mRNAs were identified as significant prognosis-associated molecules. The cervical cancer-related ceRNA network contained 13 prognosis-associated lncRNAs, 5 prognosis-associated miRNAs, and 120 prognosis-associated mRNAs. Key prognostic lncRNAs, miRNAs, and mRNAs were further identified using the ceRNA network. The key prognostic lncRNAs included H19 and HOTAIR, those for miRNAs included hsa-miR-133b, hsa-miR-138, and hsa-miR-301b, and those for mRNAs included Wnt family member 2, fibroblast growth factor 7, fibronectin 1, synaptic vesicle glycoprotein 2A, and bone morphogenetic protein 7. CONCLUSION: The key prognostic lncRNAs, miRNAs, and mRNAs may serve as potential molecular markers for recurrence prediction and may contribute to the treatment of cervical cancer.