Evidence map›Paper›PMID 35117288›Full record

ArticleTranslational cancer research2020

The rate of miR-146a rs2910164 mutations in patients with lung cancer: a meta-analytic review.

Jun Zhang, Weipeng Shao, Zhenrong Zhang, Deruo Liu

Open access · diamondAbstract read
In one paragraph

Article in Translational cancer research, 2020. 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
0.2field-weighted citation impact, top 52% 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.

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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

4 authors at 2 institutions in 1 country.

Jun ZhangDepartment of Thoracic Surgery, Peking University China-Japan Friendship School of Clinical Medicine, Beijing, China.
Weipeng ShaoDepartment of Thoracic Surgery, Peking University China-Japan Friendship School of Clinical Medicine, Beijing, China.
Zhenrong ZhangDepartment of Thoracic Surgery, China-Japan Friendship Hospital, Beijing, China.
Deruo LiuDepartment of Thoracic Surgery, Peking University China-Japan Friendship School of Clinical Medicine, Beijing, China.
China-Japan Friendship Hospital · CNPeking University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWorldwide, lung cancer has become the most common cancer type and lung cancer gradually becomes the leading cause of cancer death. MiR-146a

methodsWe searched the relevant published literature from January 1, 2009, to December 31, 2019, in English or Chinese, using the PubMed, Medline, Embase, China Biology Medicine disc, China National Knowledge Infrastructure, and WanFang databases. Two reviewers extracted data from eligible studies in duplicate with a standard data collection form and reached a consensus on each item. All analyses were performed using the Review Manager.

resultsIn conclusion, the frequency of miR-146a GC genotype mutation in normal people and patients with lung cancer is 44% and 47% respectively and the rate of miR-146a CC genotype mutation in the normal people and patients with lung cancer is 17% and 22% respectively. What's more, the frequency of miR-146a C allele mutation in normal people and patients with lung cancer is 40% and 46% respectively.

conclusionsIn conclusion, we can find that the mutation rate of the lung cancer group is higher than the normal people. In the lung cancer group, we can find that the rate of mutation is highest in Italians and lowest in Indians, both in genotypes and in allele C, in which there are significant differences. Chinese and Koreans have similar mutation rates.

Indexed as

cancermeta-analysisMiR-146apolymorphismrate

Identifiers

PMID35117288
PMCPMC8797716
OpenAlexW3103922794

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.