Evidence map›Paper›PMID 37153047›Full record

ArticleOncology letters2023

AURKA, TOP2A and MELK are the key genes identified by WGCNA for the pathogenesis of lung adenocarcinoma.

Yunqing Xu, Sen Wang, Bin Xu, Huiqing Lin, Na Zhan, Jiacai Ren, Wenling Song, Rong Han, Liping Cheng, Man Zhang and 1 more

Open access · diamondAbstract read
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Article in Oncology letters, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.2field-weighted citation impact, top 21% 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

6 citing papers in PubMed, 8 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

11 authors at 3 institutions in 1 country.

Yunqing XuDepartment of Oncology, People's Hospital of Huangpi District, Wuhan, Hubei 430000, P.R. China.
Sen WangDepartment of Forensic Medicine, Guangxi Medical University, Nanning, Guanxi 530021, P.R. China.
Bin XuDepartment of Oncology, People's Hospital of Huangpi District, Wuhan, Hubei 430000, P.R. China.
Huiqing LinDepartment of Thoracic Surgery, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, P.R. China.
Na ZhanDepartment of Pathology, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, P.R. China.
Jiacai RenDepartment of Pathology, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, P.R. China.
Wenling SongDepartment of Oncology, People's Hospital of Huangpi District, Wuhan, Hubei 430000, P.R. China.
Rong HanDepartment of Oncology, People's Hospital of Huangpi District, Wuhan, Hubei 430000, P.R. China.
Liping ChengDepartment of Oncology, People's Hospital of Huangpi District, Wuhan, Hubei 430000, P.R. China.
Man ZhangDepartment of Oncology, People's Hospital of Huangpi District, Wuhan, Hubei 430000, P.R. China.
Xiuyun ZhangDepartment of Pathology, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, P.R. China.
Renmin Hospital of Wuhan University · CNGuangxi Medical University · CNWuhan University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The comprehensive analysis of single or multiple microarray datasets is currently available in Gene Expression Omnibus (GEO) databases, with several studies having identified genes strongly associated with the development of lung adenocarcinoma (LUAD). However, the mechanisms of LUAD development remain largely unknown and has not yet been systematically studied; thus, further studies are required in this field. In the present study, weighted gene co-expression network analysis (WGCNA) was used for the evaluation of key genes with potential high risk of LUAD, and to provide more reliable evidence concerning its pathogenesis. The GSE140797 dataset from the high-throughput GEO database was downloaded and was first analyzed using the Limma package in the R language in order to determine the differentially expressed genes. The dataset was then analyzed using the WGCNA package to analyze the co-expressed genes, and the modular genes with the highest correlation with the clinical phenotype were identified. Subsequently, the pathogenic genes shared in common between the result of the two analyses were imported into the STRING database for protein-protein interaction network analysis. The hub genes were screened out using Cytoscape, and then The Cancer Genome Atlas analysis, receiver operating characteristic analysis and survival analysis were subsequently performed. Finally, the key genes were evaluated using reverse transcription-quantitative PCR and western blot analysis. Bioinformatics analysis of the GSE140797 dataset revealed eight key genes:

Indexed as

key geneslung adenocarcinomaprotein-protein interaction networkweighted gene co-expression network analysis

Identifiers

PMID37153047
PMCPMC10161350
OpenAlexW4366421227

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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.