Evidence map›Paper›PMID 34568051›Full record

ArticleFrontiers in oncology2021

A Five-MicroRNA Signature Predicts the Prognosis in Nasopharyngeal Carcinoma.

Shixiong Wu, Cen Zhang, Jing Xie, Shuang Li, Shuo Huang

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in oncology, 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 56% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 3 citations in OpenAlex.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 2 institutions in 1 country.

Shixiong WuDepartment of Otolaryngology, Zhongnan Hospital of Wuhan University, Wuhan, China.
Cen ZhangDepartment of Otolaryngology, Zhongnan Hospital of Wuhan University, Wuhan, China.
Jing XieDepartment of Otolaryngology, Zhongnan Hospital of Wuhan University, Wuhan, China.
Shuang LiDepartment of Otolaryngology, Zhongnan Hospital of Wuhan University, Wuhan, China.
Shuo HuangDepartment of Otolaryngology, Zhongnan Hospital of Wuhan University, Wuhan, China.
Wuhan University · CNZhongnan Hospital of Wuhan University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThere is no effective prognostic signature that could predict the prognosis of nasopharyngeal carcinoma (NPC).

methodsWe constructed a prognostic signature based on five microRNAs using random forest and Least Absolute Shrinkage And Selection Operator (LASSO) algorithm on the GSE32960 cohort (

resultsWe calculated the risk score based on the LASSO algorithm and divided the patients into high- and low-risk groups according to the calculated optimal cutoff value. The patients in the high-risk group tended to have a worse prognosis outcome and chemotherapy response. The time-dependent receiver operating characteristic curve showed that the 1-year overall survival rate of the five-microRNA signature had an area under the curve of more than 0.83. A functional annotation analysis of the five-microRNA signature showed that the patients in the high-risk group were usually accompanied by activation of DNA repair and MYC-target pathways, while the patients in the low-risk group had higher immune-related pathway signals.

conclusionsWe constructed a five-microRNA prognostic signature, which could accurately predict the prognosis of nasopharyngeal carcinoma, and constructed a nomogram that could conveniently predict the overall survival of patients.

Indexed as

chemotherapy responsemicroRNAnasopharyngeal carcinomapredictsprognosis

Identifiers

PMID34568051
PMCPMC8459682
OpenAlexW3199113884

What OpenQuestion holds

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Registered trials

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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.