4.7 Article

Integrative Analysis Identified a 6-miRNA Prognostic Signature in Nasopharyngeal Carcinoma

期刊

出版社

FRONTIERS MEDIA SA
DOI: 10.3389/fcell.2021.661105

关键词

nasopharyngeal carcinoma; miRNA signature; prognosis biomarker; machine learning method; precision treatment

资金

  1. National Key R&D Program of China [2019YFC1315804, 2017YFA0505500, 2016YFC0901704]
  2. National Natural Science Foundation of China [31771472]
  3. Chinese Academy of Sciences [ZDBS-SSW-DQC-02, KFJ-STS-QYZD-126]
  4. SA-SIBS Scholarship Program, Shanghai Municipal Science and Technology Major Project [2018SHZDZX01]
  5. CAS Youth Innovation Promotion Association [2018307]
  6. LCNBI
  7. Zjlab

向作者/读者索取更多资源

This study identified a 6-miRNA prognostic signature in nasopharyngeal carcinoma, which showed good prediction capability in terms of overall survival, disease-free survival, and metastasis-free survival. The 6-miRNA risk score may be useful for clinicians in treatment strategies and predicting patient outcomes.
Background Nasopharyngeal carcinoma (NPC) is an Epstein-Barr virus-associated epithelial malignancy, which is rare in America but endemic in China. The current clinical gold TNM-based standard for prognosis may not be enough. Although some studies have reported that some miRNAs have a prognostic power in NPC, there is a scarcity of independent validation for these miRNAs. Methods In this work, we firstly conducted a literature review of all miRNA profiling datasets with survival information, then integrated miRNA expression data across different profiling platforms and built prognostic models using machine learning methods. The Kaplan-Meier method and log-rank tests were applied to estimate correlations of the miRNA signature with survival, and the area under the time-dependent ROC curve (AUC) and concordance index (C-index) were used to assess the predictive power of prognostic models. We also investigated the biological roles of the prognostic miRNAs through identifying their regulated genes and association with immune infiltration. Results We constructed a prognostic model based on 6-miRNA signature (ebv-miR-BART12, ebv-miR-BART15, miR-29c-3p, miR-30e-5p, hsa-miR-375-3p, has-miR-93-5p) using the elastic net penalized Cox regression model. The AUCs of our model predicting 1-, 3-, and 5-year overall survival rates were 0.90, 0.73, and 0.70 for the external validation dataset and showed better prognostic power than models using previously reported miRNA signatures. The 6-miRNA risk score was an independent prognostic predictor for overall survival (OS), disease-free survival (DFS), and metastasis-free survival (MFS). It could stratify patients into low-risk and high-risk groups; patients in the low-risk group treated with concurrent chemotherapy had a better survival. On the basis that the 6-miRNA risk score improved the current clinical gold standard for prognosis, we built a nomogram integrating both clinical characterizations and the risk score to predict 3-, 5-, and 10-year overall survival. Functional analysis suggested that the six miRNAs mainly play roles in virus infection pathways and oncogenic signaling pathways and associated with B-cell expression. Conclusion We identified a 6-miRNA prognostic signature in nasopharyngeal carcinoma across miRNA profiling platforms and patient geographical difference, which showed good prediction capability in terms of OS, DFS, and MFS. The 6-miRNA risk score might be helpful for clinicians to make treatment strategies and predict patient outcomes.

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