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Supplementary Material for: Identifying Molecular Markers of Cervical Cancer Based on Competing Endogenous RNA Network Analysis

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posted on 09.01.2019 by Qin S., Gao Y., Yang Y., Zhang L., Zhang T., Yu J., Shi C.
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.

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