Evidence map›Paper›PMID 34913774›Full record

SynthesisAnnals of medicine2021

High diagnostic value of miRNAs for NSCLC: quantitative analysis for both single and combined miRNAs in lung cancer.

Minhan Yi, Zexi Liao, Langmei Deng, Li Xu, Yun Tan, Kun Liu, Ziliang Chen, Yuan Zhang

Abstract readMeta-Analysis
In one paragraph

Synthesis in Annals of medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
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

9 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. Article
  5. Article
  6. Review
  7. Article
  8. Article
  9. miRNAs in Cancer (Review of Literature).International journal of molecular sciences · 2022
    Review
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

8 authors.

Minhan YiDepartment of Respiratory Medicine, Xiangya Hospital, Central South University, Changsha, Hunan, China.
Zexi LiaoDepartment of Respiratory Medicine, Xiangya Hospital, Central South University, Changsha, Hunan, China.
Langmei DengDepartment of Respiratory Medicine, Xiangya Hospital, Central South University, Changsha, Hunan, China.
Li XuDepartment of Respiratory Medicine, Xiangya Hospital, Central South University, Changsha, Hunan, China.
Yun TanSchool of Life Sciences, Central South University, Changsha, Hunan, China.
Kun LiuSchool of Life Sciences, Central South University, Changsha, Hunan, China.
Ziliang ChenSchool of Computer Science and Engineering, Central South University, Changsha, Hunan, China.
Yuan ZhangDepartment of Respiratory Medicine, Xiangya Hospital, Central South University, Changsha, Hunan, China.ORCID 0000-0003-0577-7129

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMicroRNAs (miRNAs) are good candidates as biomarkers for Lung cancer (LC). The aim of this article is to figure out the diagnostic value of both single and combined miRNAs in LC.

methodsNormative meta-analysis was conducted based on PRISMA. We assessed the diagnostic value by calculating the combined sensitivity (Sen), specificity (Spe), positive likelihood ratio (PLR), negative likelihood ratio (NLR) and diagnostic odds ratio (DOR) and the area under the curve (AUC) of single and combined miRNAs for LC and specific subgroups.

resultsA total of 80 qualified studies with a total of 8971 patients and 10758 controls were included. In non-small cell lung carcinoma (NSCLC), we involved 20 single-miRNAs and found their Sen, Spe and AUC ranged from 0.52-0.81, 0.66-0.88, and 0.68-0.90, respectively, specially, miR-19 with the maximum Sen, miR-20 and miR-10 with the highest Spe as well as miR-17 with the maximum AUC. Additionally, we detected miR-21 with the maximum Sen of 0.74 [95%CI: 0.62-0.83], miR-146 with the maximum Spe and AUC of 0.93 [95%CI: 0.79-0.98] and 0.89 [95%CI: 0.86-0.92] for early-stage NSCLC. We also identified the diagnostic power of available panel (miR-210, miR-31 and miR-21) for NSCLC with satisfying Sen, Spe and AUC of 0.82 [95%CI: 0.78-0.84], 0.87 [95%CI: 0.84-0.89] and 0.91 [95%CI: 0.88-0.93], and furtherly constructed 2 models for better diagnosis.

conclusionsWe identified several single miRNAs and combined groups with high diagnostic power for NSCLC through pooled quantitative analysis, which shows that specific miRNAs are good biomarker candidates for NSCLC and further researches needed.

Indexed as

Carcinoma, Non-Small-Cell LungLung NeoplasmsMicroRNAsArea Under CurveBiomarkers, TumorHumansBiomarkers, TumorMicroRNAsdiagnostic biomarkerearly diagnosisLung cancermicroRNANSCLC

Identifiers

PMID34913774
PMCPMC8740622

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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.