Evidence map›Paper›PMID 33604163›Full record

ArticlePeerJ2021

High expression of MCM10 is predictive of poor outcomes in lung adenocarcinoma.

Mingrui Shao, Shize Yang, Siyuan Dong

Open access · goldAbstract read
In one paragraph

Article in PeerJ, 2021. 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
0.5field-weighted citation impact, top 39% 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

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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, 6 citations in OpenAlex.

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4 · The record

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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.

Mingrui ShaoDepartment of Thoracic Surgery, The first hospital of China Medical University, Shenyang, Liaoning, China.
Shize YangDepartment of Thoracic Surgery, The first hospital of China Medical University, Shenyang, Liaoning, China.
Siyuan DongDepartment of Thoracic Surgery, The first hospital of China Medical University, Shenyang, Liaoning, China.
First Hospital of China Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundsLung adenocarcinoma is a complex disease that results in over 1.8 million deaths a year. Recent advancements in treating and managing lung adenocarcinoma have led to modest decreases in associated mortality rates, owing in part to the multifactorial etiology of the disease. Novel prognostic biomarkers are needed to accurately stage the disease and act as the basis of adjuvant treatments. MATERIAL AND

methodsThe microarray datasets GSE75037, GSE31210 and GSE32863 were downloaded from the Gene Expression Omnibus (GEO) database to identify prognostic biomarkers for lung adenocarcinoma and therapy. The differentially expressed genes (DEGs) were identified by GEO2R. Functional and pathway enrichment analysis were performed by Kyoto Encyclopedia of Genes and Genomes and Gene Ontology (GO). Validation was performed based on 72 pairs of lung adenocarcinoma and adjacent normal lung tissues.

resultsResults showed that the DEGs were mainly focused on cell cycle and DNA replication initiation. Forty-one hub genes were identified and further analyzed by CytoScape. Here, we provide evidence which suggests MCM10 is a potential target with prognostic, diagnostic and therapeutic value. We base this on an integrated approach of comprehensive bioinformatics analysis and in vitro validation using the A549 lung adenocarcinoma cell line. We show that MCM10 overexpression correlates with a poor prognosis, while silencing of this gene decreases aberrant growth by 2-fold. Finally, evaluation of 72 clinical biopsy samples suggests that overexpression of MCM10 in the lung adenocarcinoma highly correlates with larger tumor size. Together, this work suggests that MCM10 may be a clinically relevant gene with both predictive and therapeutic value in lung adenocarcinoma.

Indexed as

BiomarkerLung adenocarcinomaMCM10Prognosis

Identifiers

PMID33604163
PMCPMC7866887
OpenAlexW3128594721

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.