Evidence map›Paper›PMID 32934750›Full record

ArticleOncology letters2020

Analysis of diagnostic and prognostic value of lncRNA MEG3 in cervical cancer.

Shuqiong Wan, Huanqiu Zhao

Abstract read
In one paragraph

Article in Oncology letters, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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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

14 citing papers in PubMed.

  1. Review
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  5. Advances in the Regulation of Lipid Metabolism by Non-Coding RNAs.Animals : an open access journal from MDPI · 2025
    Review
  6. Review
  7. Article
  8. Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2023
    Article
  9. Review
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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

2 authors.

Shuqiong WanDepartment of Obstetrics and Gynecology, Huangshi Central Hospital, Huangshi, Hubei 435000, P.R. China.
Huanqiu ZhaoDepartment of Gynecology, Huangshi Maternity and Children's Health Hospital, Huangshi, Hubei 435000, P.R. China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The present study aimed to explore the diagnostic and prognostic value of lncRNA maternally expressed 3 (MEG3) in cervical cancer. Eighty-four patients with cervical cancer from February 2013 to March 2014 were enrolled in the observation group (OG), and another 58 female subjects who underwent physical examination at Huangshi Central Hospital were enrolled as the control group (CG). The serum MEG3 expression of patients in the two groups was detected by RT-qPCR, and the ability of MEG3 to aid in the diagnosis of cervical cancer, lymph node metastasis and FIGO staging, as well as to predict mortality was evaluated by ROC curve. In addition, the patients in the OG were divided into high- and low-expression groups according to the median value of MEG3. Kaplan Meier was employed to analyze the survival status, and Cox regression to analyze the independent prognostic factors of cervical cancer patients. The results of the present study revealed that the serum MEG3 expression in the OG was significantly lower than that of the CG (P<0.05). The area under the curve (AUC) of MEG3 in diagnosing cervical cancer was 0.844, the AUC in predicting mortality was 0.858, while that in diagnosing lymph node transfer was 0.707, and that in diagnosing FIGO staging was 0.791. The 5-year survival rate of the high-expression group was higher than that of the low-expression group (P=0.020). Multivariate analysis indicated that MEG3 (HR, 0.173; 95 CI%, 0.028-0.919), lymph node metastasis (HR, 2.259; 95 CI%, 1.004-5.025) and FIGO staging (HR, 0.008; 95 CI%, 1.453-6.248) were independent prognostic factors for cervical cancer patients. Collectively, lncRNA MEG3 may be a diagnostic marker and prognostic indicator for cervical cancer, and has a certain diagnostic value for lymph node metastasis and FIGO staging. Lymph node metastasis, FIGO stage III and IV, and low MEG3 levels were revealed to be independent prognostic factors for cervical cancer patients.

Indexed as

cervical cancerdiagnosislncRNA MEG3prognosis

Identifiers

PMID32934750
PMCPMC7471741

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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.