Evidence map›Paper›PMID 34900662›Full record

SynthesisFrontiers in oncology2021

High Moesin Expression Is a Predictor of Poor Prognosis of Breast Cancer: Evidence From a Systematic Review With Meta-Analysis.

Xiaoli Hu, Yang Liu, Zhitong Bing, Qian Ye, Chengcheng Li

Open access · goldAbstract readSystematic Review
In one paragraph

Synthesis 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 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.3field-weighted citation impact, top 40% 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

4 citing papers in PubMed, 4 citations in OpenAlex.

  1. Article
  2. Article
  3. Review
  4. 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 4 institutions in 1 country.

Xiaoli HuDepartment of Medical Physics, Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, China.
Yang LiuDepartment of Medical Physics, Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, China.
Zhitong BingAdvanced Energy Science and Technology Guangdong Laboratory, Huizhou, China.
Qian YeDepartment of Medical Physics, Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, China.
Chengcheng LiThe First School of Clinical Medicine, Lanzhou University, Lanzhou, China.
Ji Hua Laboratory · CNChinese Academy of Sciences · CNLanzhou University · CNUniversity of Chinese Academy of Sciences · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Owing to metastases and drug resistance, the prognosis of breast cancer is still dismal. Therefore, it is necessary to find new prognostic markers to improve the efficacy of breast cancer treatment. Literature shows a controversy between moesin (MSN) expression and prognosis in breast cancer. Here, we aimed to conduct a systematic review and meta-analysis to evaluate the prognostic relationship between MSN and breast cancer. Literature retrieval was conducted in the following databases: PubMed, Web of Science, Embase, and Cochrane. Two reviewers independently performed the screening of studies and data extraction. The Gene Expression Omnibus (GEO) database including both breast cancer gene expression and follow-up datasets was selected to verify literature results. The R software was employed for the meta-analysis. A total of 9 articles with 3,039 patients and 16 datasets with 2,916 patients were ultimately included. Results indicated that there was a significant relationship between MSN and lymph node metastases ( SYSTEMATIC REVIEW REGISTRATION: https://inplasy.com/inplasy-2020-8-0039/.

Indexed as

breast cancerlymph node metastasismeta-analysisMSNprognosis

Identifiers

PMID34900662
PMCPMC8660674
OpenAlexW3214745179

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.