Evidence map›Paper›PMID 26459786›Full record

ArticleMolecular medicine reports2015

Gene expression profile analysis of ventilator-associated pneumonia.

Xiaoli Xu, Bo Yuan, Quan Liang, Huimin Huang, Xiangyi Yin, Xiaoyue Sheng, Niuyan Nie, Hongmei Fang

Open access · hybridAbstract read
In one paragraph

Article in Molecular medicine reports, 2015. 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
0.2field-weighted citation impact, top 41% 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

4 citing papers in PubMed, 9 citations in OpenAlex.

  1. Article
  2. Article
  3. Review
  4. Combined Use of Three Machine Learning Modeling Methods to Develop a Ten-Gene Signature for the Diagnosis of Ventilator-Associated Pneumonia.Medical science monitor : international medical journal of experimental and clinical research · 2020
    Article
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 3 institutions in 1 country.

Xiaoli XuDepartment of Infection Management, Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu 210002, P.R. China.
Bo YuanDepartment of Medical Administration, Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu 210002, P.R. China.
Quan LiangDepartment of Medical Administration, Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu 210002, P.R. China.
Huimin HuangDepartment of Infection Management, Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu 210002, P.R. China.
Xiangyi YinDepartment of Infection Management, Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu 210002, P.R. China.
Xiaoyue ShengDepartment of Infection Management, Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu 210002, P.R. China.
Niuyan NieDepartment of Infection Management, Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu 210002, P.R. China.
Hongmei FangDepartment of Infection Management, Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu 210002, P.R. China.
Nanjing University · CNNanjing General Hospital of Nanjing Military Command · CNNanjing Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Based on the gene expression profile of patients with ventilator-associated pneumonia (VAP) and patients not affected by the disease, the present study aimed to enhance the current understanding of VAP development using bioinformatics methods. The expression profile GSE30385 was downloaded from the Gene Expression Omnibus database. The Linear Models for Microarray Data package in R language was used to screen and identify differentially expressed genes (DEGs), which were grouped as up‑ and down‑regulated genes. The up‑ and downregulated genes were functionally enriched using the Database for Annotation, Visualization and Integrated Discovery system and then annotated according to TRANSFAC, Tumor Suppressor Gene and Tumor Associated Gene databases. Subsequently, the protein‑protein interaction (PPI) network was constructed, followed by module analysis using CFinder software. A total of 69 DEGs, including 33 up‑ and 36 downregulated genes were screened out in patients with VAP. Upregulated genes were mainly enriched in functions and pathways associated with the immune response (including the genes ELANE and LTF) and the mitogen-activated protein kinase (MAPK) signaling pathway (including MAPK14). The PPI network comprised 64 PPI pairs and 44 nodes. The top two modules were enriched in different pathways, including the MAPK signaling pathway. Genes including ELANE, LTF and MAPK14 may have important roles in the development of VAP via altering the immune response and the MAPK signaling pathway.

Indexed as

TranscriptomeDatabases, GeneticGene Expression ProfilingGene Regulatory NetworksGenomicsHumansMicroarray AnalysisPneumonia, Ventilator-AssociatedProtein Interaction Maps

Identifiers

PMID26459786
PMCPMC4626132
OpenAlexW1946179887

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.