Evidence map›Paper›PMID 32765681›Full record

ArticleExperimental and therapeutic medicine2020

Overexpression of MUC16 predicts favourable prognosis in MUC16-mutant cervical cancer related to immune response.

Hao Wang, Chao Yan, Hong Ye

Open access · diamondAbstract read
In one paragraph

Article in Experimental and therapeutic medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
0.7field-weighted citation impact, top 33% 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

7 citing papers in PubMed, 11 citations in OpenAlex.

  1. Review
  2. Article
  3. Review
  4. Article
  5. [MUC16: The Novel Target for Tumor Therapy].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2022
    Review
  6. Article
  7. 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

3 authors at 1 institution in 1 country.

Hao WangDepartment of Gynaecology and Obstetrics, The First College of Clinical Medical Science, China Three Gorges University, Yichang, Hubei 443000, P.R. China.
Chao YanDepartment of Orthopaedics, The People's Hospital of China Three Gorges University, Yichang, Hubei 443000, P.R. China.
Hong YeDepartment of Gynaecology and Obstetrics, The First College of Clinical Medical Science, China Three Gorges University, Yichang, Hubei 443000, P.R. China.
China Three Gorges University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cervical cancer (CC) is the fourth ranking gynaecologic tumour in women worldwide, with respect to both incidence and mortality. MUC16 is one of the most frequently mutated genes, which functions as a tumour marker in CC. In the present study, mutation, clinical and RNA-Seq data collected from The Cancer Genome Atlas database were used to investigate the association between MUC16 mutation, immune response and clinical prognosis in CC. mRNA expression levels from the TCGA datasets and the results from the present study demonstrated that MUC16 was overexpressed in CC samples; however, there was no difference between mutant and wild-type CC samples. Furthermore, the results indicated that patients with MUC16-mutant overexpression had a prolonged survival time. In addition, overexpression of MUC16 was associated with immune responses in the microenvironment of MUC16-mutant CC. Immune responses were upregulated in patients with early-stage MUC16-mutant. The results from the present study provided novel biomarkers for potential immunotherapy approaches for CC.

Indexed as

cervical cancercervical squamous cell carcinomaimmune responseMUC16mutation

Identifiers

PMID32765681
PMCPMC7388571
OpenAlexW3033054761

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.