Evidence map›Paper›PMID 33388093›Full record

ArticleHereditas2021

IFI27 may predict and evaluate the severity of respiratory syncytial virus infection in preterm infants.

Junyan Gao, Xueping Zhu, Mingfu Wu, Lijun Jiang, Fudong Wang, Shan He

Open access · goldAbstract read
In one paragraph

Article in Hereditas, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed
3.2field-weighted citation impact, top 8% 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

25 citing papers in PubMed, 32 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

6 authors at 2 institutions in 1 country.

Junyan GaoDepartment of Pediatrics, Affiliated Hospital of Yangzhou University, NO.368 Hanjiang Middle Road, Yangzhou, 225000, Jiangsu, China.
Xueping ZhuDepartment of Neonatology, Children's Hospital of Soochow University, NO.92 Zhongnan Street, Industrial Park, Suzhou, 215025, Jiangsu, China.
Mingfu WuDepartment of Pediatrics, Affiliated Hospital of Yangzhou University, NO.368 Hanjiang Middle Road, Yangzhou, 225000, Jiangsu, China.
Lijun JiangDepartment of Pediatrics, Affiliated Hospital of Yangzhou University, NO.368 Hanjiang Middle Road, Yangzhou, 225000, Jiangsu, China.
Fudong WangDepartment of Pediatrics, Affiliated Hospital of Yangzhou University, NO.368 Hanjiang Middle Road, Yangzhou, 225000, Jiangsu, China.
Shan HeDepartment of Neonatology, Children's Hospital of Soochow University, NO.92 Zhongnan Street, Industrial Park, Suzhou, 215025, Jiangsu, China. 543939290@qq.com.
Yangzhou University · CNSoochow University · CN

Funding

Jiangsu Provincial Maternal and Child Health Key Talents Project FRC201731National Natural Science Foundation of China 81771626National Natural Science Foundation of China 81971423
6 · The paper itself

Abstract

backgroundPreterm infants are a special population that vulnerable to respiratory syncytial virus (RSV) infection and the lower respiratory tract infections (LRTIs) caused by RSV could be severe and even life-threating. The purpose of the present study was to identify candidate genes of preterm infants who are susceptible to RSV infection and provide a new insight into the pathogenesis of RSV infection.

methodsThree datasets (GSE77087, GSE69606 and GSE41374) containing 183 blood samples of RSV infected patients and 33 blood samples of healthy controls from Gene Expression Omnibus (GEO) database were downloaded and the differentially expressed genes (DEGs) were screened out. The function and pathway enrichments were analyzed through Database for Annotation, Visualization and Integrated Discovery (DAVID) website. The protein-protein interaction (PPI) network for DEGs was constructed through Search Tool for the Retrieval of Interacting Genes (STRING). The module analysis was performed by Cytoscape software and hub genes were identified. Clinical verification was employed to verify the expression level of top five hub genes among 72 infants including 50 RSV infected patients and 22 non-RSV-infected patients hospitalized in our center. Further, the RSV infected infants with high-expression IFI27 and those with low-expression IFI27 were compared (defined as higher or lower than the median mRNA level). Finally, the gene set enrichment analysis (GSEA) focusing on IFI27 was carried out.

resultsTotally, 4028 DEGs were screened out and among which, 131 most significant DEGs were selected. Subsequently, 13 hub genes were identified, and function and pathway enrichments of hub genes mainly were: response to virus, defense response to virus, regulation of viral genome replication and regulation of viral life cycle. Furthermore, IFI27 was confirmed to be the most significantly expressed in clinical verification. Gene sets associated with calcium signaling pathway, arachidonic acid metabolism, extracellular matrix receptor interaction and so on were significantly enriched when IFI27 was highly expressed. Moreover, high-expression IFI27 was associated with more severe cases (p = 0.041), more requirements of mechanical ventilation (p = 0.034), more frequent hospitalization (p < 0.001) and longer cumulative hospital stay (p = 0.012).

conclusionIFI27 might serve to predict RSV infection and evaluate the severity of RSV infection in preterm infants.

Indexed as

Infant, PrematureCase-Control StudiesGenetic Predisposition to DiseaseHumansInfant, NewbornLength of StayMembrane ProteinsProtein Interaction MapsRespiration, ArtificialRespiratory Syncytial Virus InfectionsIFI27 protein, humanMembrane ProteinsDifferentially expressed genesInterferon alpha inducible protein 27Pathway enrichmentPreterm infantsProtein-protein interactionRespiratory syncytial virus

Identifiers

PMID33388093
PMCPMC7778825
OpenAlexW3117277763

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.