Evidence map›Paper›PMID 36011391›Full record

ArticleGenes2022

Gene-microRNA Network Analysis Identified Seven Hub Genes in Association with Progression and Prognosis in Non-Small Cell Lung Cancer.

Zhiyuan Yang, Hongqi Wang, Zixin Zhao, Yunlong Jin, Zhengnan Zhang, Jiayi Tan, Fuyan Hu

Open access · goldAbstract read
In one paragraph

Article in Genes, 2022. 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
0.7field-weighted citation impact, top 36% 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, 7 citations in OpenAlex.

  1. [Predictive Value of A miRNA Signature for Distant Metastasis in Lung Cancer].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2024
    Article
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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

7 authors at 2 institutions in 1 country.

Zhiyuan YangSchool of Artificial Intelligence, Hangzhou Dianzi University, Hangzhou 310018, China.
Hongqi WangSchool of Artificial Intelligence, Hangzhou Dianzi University, Hangzhou 310018, China.
Zixin ZhaoSchool of Artificial Intelligence, Hangzhou Dianzi University, Hangzhou 310018, China.
Yunlong JinSchool of Artificial Intelligence, Hangzhou Dianzi University, Hangzhou 310018, China.
Zhengnan ZhangSchool of Artificial Intelligence, Hangzhou Dianzi University, Hangzhou 310018, China.
Jiayi TanSchool of Artificial Intelligence, Hangzhou Dianzi University, Hangzhou 310018, China.
Fuyan HuDepartment of Statistics, Faculty of Science, Wuhan University of Technology, 122 Luoshi Road, Wuhan 430070, China.ORCID 0000-0003-1817-7982
Hangzhou Dianzi University · CNWuhan University of Technology · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionLung cancer is the leading cause of cancer deaths in the world and is usually divided into non-small cell lung cancer (NSCLC) and small cell lung cancer. NSCLC is dominant and accounts for 85% of the total cases. Currently, the therapeutic method of NSCLC is not so satisfactory, and thus identification of new biomarkers is critical for new clinical therapy for this disease.

methodsDatasets of miRNA and gene expression were obtained from the NCBI database. The differentially expressed genes (DEGs) and miRNAs (DEMs) were analyzed by GEO2R tools. The DEG-DEM interaction was built via miRNA-targeted genes by miRWalk. Several hub genes were selected via network topological analysis in Cytoscape.

resultsA set of 276 genes were found to be significantly differentially expressed in the three datasets. Functional enrichment by the DAVID tool showed that these 276 DEGs were significantly enriched in the term "cancer", with a statistic

Indexed as

Carcinoma, Non-Small-Cell LungLung NeoplasmsMicroRNAsComputational BiologyGene Expression ProfilingGene Expression Regulation, NeoplasticHumansPrognosisMicroRNAsbiomarkermicroRNA–gene interaction networknon-small cell lung cancer

Identifiers

PMID36011391
PMCPMC9407881
OpenAlexW4293780330

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.