Evidence map›Paper›PMID 34336826›Full record

ArticleFrontiers in cell and developmental biology2021

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

Yunqin Chen, Zhen Wang, Hong Li, Yixue Li

Open access · goldAbstract read
In one paragraph

Article in Frontiers in cell and developmental biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.2field-weighted citation impact, top 57% of its field
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 2 citations in OpenAlex.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors at 3 institutions in 1 country.

Yunqin ChenSchool of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.
Zhen WangBio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.
Hong LiBio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.
Yixue LiSchool of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.
University of Chinese Academy of Sciences · CNShanghai Institute of Nutrition and Health · CNShanghai Jiao Tong University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundNasopharyngeal 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.

methodsIn 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.

resultsWe 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.

conclusionWe 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.

Indexed as

machine learning methodmiRNA signaturenasopharyngeal carcinomaprecision treatmentprognosis biomarker

Identifiers

PMID34336826
PMCPMC8322954
OpenAlexW3190497474

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.