Evidence map›Paper›PMID 34997902›Full record

ArticleMolecular biotechnology2022

The Regulation of let-7c-5p on the Biological Characteristics of Lung Adenocarcinoma Cells by Targeting AURKB.

Lisi Wang, Xiaolong Xiao, Hong Du

Abstract read
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In one paragraph

Article in Molecular biotechnology, 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 46% 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, 4 citations in OpenAlex.

  1. Article
  2. 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.

Lisi WangDepartment of Respiratory and Critical Care Medicine, Chongqing People's Hospital, Chongqing, 400013, People's Republic of China.
Xiaolong XiaoShanghai Engineering Research Center of Pharmaceutical Translation, Shanghai, 200231, People's Republic of China.
Hong DuQuality Control Department of Medical, Chongqing People's Hospital, 118 Xingguang Avenue, Liangjiang New Area, Chongqing, 401121, People's Republic of China. dujuanddddjjjj@163.com.ORCID http://orcid.org/0000-0002-4958-9608
First People's Hospital of Chongqing · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

To study the modulatory mechanism of let-7c-5p on the biological characteristics of lung adenocarcinoma (LUAD) cells by targeting AURKB. Differentially expressed genes (DEGs) were screened by bioinformatics analysis. CCK-8, colony formation, scratch healing, Transwell, and flow cytometry assays were employed to test biological functions of LUAD cells. Western Blot was undertaken to assay the protein level of AURKB, and qRT-PCR was undertaken to test AURKB mRNA and let-7c-5p expression. Dual-luciferase reporter gene method was applied to detect the interaction between AURKB and let-7c-5p. Let-7c-5p was much likely to target AURKB expression. Let-7c-5p was poorly expressed in LUAD cells and suppressed AURKB. Silencing AURKB or overexpressing let-7c-5p both could suppress proliferation, migration, and invasion and stimulate apoptosis, while overexpressing the two simultaneously could reverse such effect. Forced expression of let-7c-5p inhibited proliferation, migration, and invasion and accelerated apoptosis of LUAD cells by inhibiting AURKB, which may provide a new way to understand the malignant progression of LUAD.

Indexed as

Adenocarcinoma of LungLung NeoplasmsMicroRNAsAurora Kinase BCell MovementCell ProliferationGene Expression Regulation, NeoplasticHumansAURKB protein, humanAurora Kinase BMicroRNAsAURKBLet-7c-5pLung adenocarcinomaMalignant progression

Identifiers

PMID34997902
OpenAlexW4206442613

What OpenQuestion holds

Textmetadata
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.