Evidence map›Paper›PMID 36553003›Full record

ReviewDiagnostics (Basel, Switzerland)2022

N6-methyladenosine Modification of Noncoding RNAs: Mechanisms and Clinical Applications in Cancer.

Mingyang Ma, Tong Ye, Jiewei Wang, Haiying Zhao, Shutian Zhang, Peng Li, Guiping Zhao

Open access · goldAbstract readReview
In one paragraph

Review in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.3field-weighted citation impact, top 47% 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

2 citing papers in PubMed, 3 citations in OpenAlex.

  1. Article
  2. 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

7 authors at 2 institutions in 1 country.

Mingyang MaDepartment of Gastroenterology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China.
Tong YeDepartment of Gastroenterology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China.
Jiewei WangDepartment of Gastroenterology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China.
Haiying ZhaoDepartment of Gastroenterology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China.
Shutian ZhangDepartment of Gastroenterology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China.
Peng LiDepartment of Gastroenterology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China.
Guiping ZhaoDepartment of Gastroenterology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China.
Beijing Friendship Hospital · CNCapital Medical University · CN

Funding

Beijing Hospitals Authority "Dengfeng" talent training plan DFL20220101National Natural Science Foundation of China 82070575the Natural Cultivation Foundation of Capital Medical University PYZ21049
6 · The paper itself

Abstract

N6-methyladenosine (m6A) modification remains the most pivotal epigenetic modification on RNA. As we know, m6A not only affects physiological processes but is also involved in carcinoma. Noncoding RNAs play an indispensable role in the occurrence and development of carcinoma. However, a large amount of research is focused on mRNA currently. Insufficient research has been done on the relationship between noncoding RNA (ncRNA) methylation and cancer. Therefore, this review aims to introduce the theoretical knowledge of m6A modification in noncoding RNA, discuss its function in tumorigenesis and progression, and ultimately summarize its potential clinical applications.

Indexed as

cancerN6-methyladenosinenoncoding RNA

Identifiers

PMID36553003
PMCPMC9776883
OpenAlexW4310382708

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.