Evidence map›Paper›PMID 30720085›Full record

ArticleMolecular medicine reports2019

Investigating age‑induced differentially expressed genes and potential molecular mechanisms in osteosarcoma based on integrated bioinformatics analysis.

Jian-Sheng Wang, Ming-Yue Duan, Yong-Sheng Zhong, Xue-Dong Li, Shi-Xin Du, Peng Xie, Gui-Zhou Zheng, Jing-Ming Han

Open access · hybridAbstract read
In one paragraph

Article in Molecular medicine reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
0.8field-weighted citation impact, top 35% 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

11 citing papers in PubMed, 14 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

8 authors at 4 institutions in 1 country.

Jian-Sheng WangDepartment of Orthopedics Ward II, Shenzhen Children's Hospital, Shenzhen, Guangdong 518038, P.R. China.
Ming-Yue DuanShanxi Institute of Pediatric Diseases, Xi'an Children's Hospital, Xi'an, Shanxi 710043, P.R. China.
Yong-Sheng ZhongDepartment of Neurosurgery, The First Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong 515041, P.R. China.
Xue-Dong LiDepartment of Orthopedics, The Third Affiliated Hospital of Shenzhen University Health Science Center, Shenzhen, Guangdong 518000, P.R. China.
Shi-Xin DuDepartment of Orthopedics, The Third Affiliated Hospital of Shenzhen University Health Science Center, Shenzhen, Guangdong 518000, P.R. China.
Peng XieDepartment of Orthopedics, The Third Affiliated Hospital of Shenzhen University Health Science Center, Shenzhen, Guangdong 518000, P.R. China.
Gui-Zhou ZhengDepartment of Orthopedics, The Third Affiliated Hospital of Shenzhen University Health Science Center, Shenzhen, Guangdong 518000, P.R. China.
Jing-Ming HanDepartment of Orthopedics Ward II, Shenzhen Children's Hospital, Shenzhen, Guangdong 518038, P.R. China.
Shenzhen University Health Science Center · CNShenzhen Children's Hospital · CNShantou University · CNXi’an Children’s Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Osteosarcoma (OS) is the most common primary bone malignancy. It predominantly occurs in adolescents, but can develop at any age. The age at diagnosis is a prognostic factor of OS, but the molecular basis of this remains unknown. The current study aimed to identify age‑induced differentially expressed genes (DEGs) and potential molecular mechanisms that contribute to the different outcomes of patients with OS. Microarray data (GSE39058 and GSE39040) obtained from the Gene Expression Omnibus database and used to analyze age‑induced DEGs to reveal molecular mechanism of OS among different age groups (<20 and >20 years old). Differentially expressed mRNAs (DEMs) were divided into up and downregulated DEMs (according to the expression fold change), then Gene Ontology function enrichment and Kyoto Encyclopedia of Genes and Genomes pathway analysis were performed. Furthermore, the interactions among proteins encoded by DEMs were integrated with prediction for microRNA‑mRNA interactions to construct a regulatory network. The key subnetwork was extracted and Kaplan‑Meier survival analysis for a key microRNA was performed. DEMs within the subnetwork were predominantly involved in 'ubiquitin protein ligase binding', 'response to growth factor', 'regulation of type I interferon production', 'response to decreased oxygen levels', 'voltage‑gated potassium channel complex', 'synapse part', 'regulation of stem cell proliferation'. In summary, integrated bioinformatics was applied to analyze the potential molecular mechanisms leading to different outcomes of patients with OS among different age groups. The hub genes within the key subnetwork may have crucial roles in the different outcomes associated with age and require further analysis.

Indexed as

Computational BiologyGene Expression Regulation, NeoplasticBiomarkers, TumorBone NeoplasmsGene Expression ProfilingGene Regulatory NetworksHumansKaplan-Meier EstimateOsteosarcomaPrognosisProtein Interaction MappingProtein Interaction MapsBiomarkers, Tumor

Identifiers

PMID30720085
PMCPMC6423644
OpenAlexW2914479485

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.