Evidence map›Paper›PMID 34956905›Full record

ArticleFrontiers in oncology2021

Aurora Kinase A as a Diagnostic and Prognostic Marker of Malignant Mesothelioma.

Zhenying Guo, Li Shen, Ningning Li, Xiaoxiao Wu, Canming Wang, Zheng Gu, Zhongjian Chen, Junping Liu, Weimin Mao, Yuchen Han

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.7field-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

5 citing papers in PubMed, 9 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

10 authors at 6 institutions in 1 country.

Zhenying GuoDepartment of Pathology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China.
Li ShenOffice of Education, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Ningning LiDepartment of Pathology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China.
Xiaoxiao WuDepartment of Pathology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China.
Canming WangDepartment of Pathology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China.
Zheng GuDepartment of Clinical Medicine Engineering, Second Affiliated Hospital of Zhejiang University, School of Medicine, Hangzhou, China.
Zhongjian ChenInstitute of Basic Medicine and Cancer, Chinese Academy of Sciences, Hangzhou, China.
Junping LiuInstitute of Basic Medicine and Cancer, Chinese Academy of Sciences, Hangzhou, China.
Weimin MaoInstitute of Basic Medicine and Cancer, Chinese Academy of Sciences, Hangzhou, China.
Yuchen HanDepartment of Pathology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China.
University of Chinese Academy of Sciences · CNZhejiang Cancer Hospital · CNSecond Affiliated Hospital of Zhejiang University · CNShanghai Chest Hospital · CNShanghai Jiao Tong University · CNZhejiang University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMalignant mesothelioma (MM) is a highly aggressive cancer with a poor prognosis. Despite the use of several well-known markers, the diagnosis of MM is still challenging in some cases. we applied bioinformatics to identify key genes and screen for diagnostic and prognostic markers of MM.

methodsThe expression profiles of GSE2549 and GSE112154 microarray datasets from the Gene Expression Omnibus database contained 87 cases of MM tissue and 8 cases of normal mesothelial tissue in total. The GEO2R tool was used to detect differentially expressed genes (DEGs). Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of DEGs were performed using DAVID Bioinformatics Resources. The DEGs protein-protein interaction networks were constructed from the STRING database. Cytoscape was used to identify significant modules and hub genes. The GEPIA database was used to explore relationships between hub genes and prognosis of MM. Immunohistochemistry was used to analyze protein expression in tissue microarrays with 47 Chinese MM tissues. Statistical analyses diagnostic and prognostic values.

results346 DEGs were identified: 111 genes upregulated, and 235 downregulated. GO analysis showed that the primary biological processes of these DEGs were cell adhesion, leukocyte migration, and angiogenesis. The main cellular components included the extracellular space, extracellular exosome, and extracellular region. The molecular functions were integrin binding, heparin binding, and calcium ion binding. KEGG pathway analysis showed that DEGs are primarily involved in PPAR signaling pathway, extracellular matrix-receptor interactions, and regulation of lipolysis in adipocytes. Survival analysis showed that seven genes-

conclusionAurora-A could be an optimal diagnostic biomarker and a potential prognostic marker for MM.

Indexed as

Aurora kinase Abioinformaticsbiomarkersmalignant mesotheliomatissue microarray

Identifiers

PMID34956905
PMCPMC8692759
OpenAlexW4200603062

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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.