Evidence map›Paper›PMID 35693013›Full record

ArticleFrontiers in neurology2022

miR-17-5p/

Quanfang Chen, Qingyun Pan, Han Gao, Yingju Wang, Xiaoning Zhong

Open access · goldAbstract read
In one paragraph

Article in Frontiers in neurology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
1.9field-weighted citation impact, top 13% 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

15 citing papers in PubMed, 21 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. Review
  5. Review
  6. Article
  7. Article
  8. Review
  9. Article
  10. Review
  11. Review
  12. Article
  13. Review
  14. Article
  15. 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

5 authors at 2 institutions in 1 country.

Quanfang ChenDepartment of Respiratory Medicine, First Affiliated Hospital of Guangxi Medical University, Nanning, China.
Qingyun PanDepartment of Respiratory Medicine, First Affiliated Hospital of Guangxi Medical University, Nanning, China.
Han GaoDepartment of Respiratory Medicine, First Affiliated Hospital of Guangxi Medical University, Nanning, China.
Yingju WangDepartment of Respiratory Medicine, First Affiliated Hospital of Guangxi Medical University, Nanning, China.
Xiaoning ZhongDepartment of Respiratory Medicine, First Affiliated Hospital of Guangxi Medical University, Nanning, China.
Guangxi Medical University · CNFirst Affiliated Hospital of GuangXi Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: Present study aims to identify the essential mRNAs responsible for the development of brain neurovascular-related metastases (BNM) among lung adenocarcinoma (LUAD) patients. Further, we attempted to predict brain metastases more accurately and prevent their development in LUAD patients. Methods: Transcriptome data analysis was used to identify differentially expressed mRNAs (DEMs) associated with brain metastasis, and thereby the ferroptosis index (FPI) is calculated using a computational model. Meanwhile, the DEmRNAs linked with FPI, and brain metastasis were derived by the intersection of these two groups of DEMs. We also constructed a ceRNA network containing these DEmRNAs, identifying the Results: Here we report the development of a ceRNA network based on BNM-associated DEMs and FPI-associated DEmRNAs which includes three core miRNAs (hsa-miR-338-3p, hsa-miR-429, and hsa-miR-17-5p), three mRNAs ( Conclusions: The expression of both

Indexed as

biomarkerbrain metastasisferroptosisHCP5HOXA7hsa-miR-17-5plung adenocarcinomaneurovascular

Identifiers

PMID35693013
PMCPMC9174431
OpenAlexW4281489838

What OpenQuestion 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 OpenQuestion graph.