Evidence map›Paper›PMID 28714002›Full record

ArticleMolecular medicine reports2017

Identification of key genes in Gram‑positive and Gram‑negative sepsis using stochastic perturbation.

Zhenliang Li, Ying Zhang, Yaling Liu, Yanchun Liu, Youyi Li

Open access · hybridAbstract read
In one paragraph

Article in Molecular medicine reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
0.2field-weighted citation impact, top 46% 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

9 citing papers in PubMed, 12 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Risk Factors and Outcome of Sepsis in Traumatic Patients and Pathogen Detection Using Metagenomic Next-Generation Sequencing.The Canadian journal of infectious diseases & medical microbiology = Journal canadien des maladies infectieuses et de la microbiologie medicale · 2022
    Article
  6. Differential Gene Sets Profiling in Gram-Negative and Gram-Positive Sepsis.Frontiers in cellular and infection microbiology · 2022
    Article
  7. Article
  8. Article
  9. 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

5 authors at 2 institutions in 1 country.

Zhenliang LiIntensive Care Unit, Pinggu Hospital Affiliated to Capital Medical University, Beijing 101200, P.R. China.
Ying ZhangDepartment of Infection Diseases, Beijing Pinggu Hospital of Traditional Chinese Medicine, Beijing 101200, P.R. China.
Yaling LiuIntensive Care Unit, Pinggu Hospital Affiliated to Capital Medical University, Beijing 101200, P.R. China.
Yanchun LiuIntensive Care Unit, Pinggu Hospital Affiliated to Capital Medical University, Beijing 101200, P.R. China.
Youyi LiIntensive Care Unit, Pinggu Hospital Affiliated to Capital Medical University, Beijing 101200, P.R. China.
Capital Medical University · CNBeijing Hospital of Traditional Chinese Medicine · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Sepsis is an inflammatory response to pathogens (such as Gram‑positive and Gram‑negative bacteria), which has high morbidity and mortality in critically ill patients. The present study aimed to identify the key genes in Gram‑positive and Gram‑negative sepsis. GSE6535 was downloaded from Gene Expression Omnibus, containing 17 control samples, 18 Gram‑positive samples and 25 Gram‑negative samples. Subsequently, the limma package in R was used to screen the differentially expressed genes (DEGs). Hierarchical clustering was conducted for the specific DEGs in Gram‑negative and Gram‑negative samples using cluster software and the TreeView software. To analyze the correlation of samples at the gene level, a similarity network was constructed using Cytoscape software. Functional and pathway enrichment analyses were conducted for the DEGs using DAVID. Finally, stochastic perturbation was used to determine the significantly differential functions between Gram‑positive and Gram‑negative samples. A total of 340 and 485 DEGs were obtained in Gram‑positive and Gram‑negative samples, respectively. Hierarchical clustering revealed that there were significant differences between control and sepsis samples. In Gram‑positive and Gram‑negative samples, myeloid cell leukemia sequence 1 was associated with apoptosis and programmed cell death. Additionally, NADH:ubiquinone oxidoreductase subunit S4 was associated with mitochondrial respiratory chain complex I assembly. Stochastic perturbation analysis revealed that NADH:ubiquinone oxidoreductase subunit B2 (NDUFB2), NDUFB8 and ubiquinol‑cytochrome c reductase hinge protein (UQCRH) were associated with cellular respiration in Gram‑negative samples, whereas large tumor suppressor kinase 2 (LATS2) was associated with G1/S transition of the mitotic cell cycle in Gram‑positive samples. NDUFB2, NDUFB8 and UQCRH may be biomarkers for Gram‑negative sepsis, whereas LATS2 may be a biomarker for Gram‑positive sepsis. These findings may promote the therapies of sepsis caused by Gram‑positive and Gram‑negative bacteria.

Indexed as

Cluster AnalysisDown-RegulationGene Expression ProfilingGene OntologyGene Regulatory NetworksGram-Negative BacteriaGram-Positive BacteriaHumansSepsisStochastic ProcessesUp-Regulation

Identifiers

PMID28714002
PMCPMC5548058
OpenAlexW2735807566

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.