Supplementary Material for: Identifying Molecular Markers of Cervical Cancer Based on Competing Endogenous RNA Network Analysis
datasetposted on 09.01.2019 by Qin S., Gao Y., Yang Y., Zhang L., Zhang T., Yu J., Shi C.
Datasets usually provide raw data for analysis. This raw data often comes in spreadsheet form, but can be any collection of data, on which analysis can be performed.
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.