Evidence map›Paper›PMID 35223539›Full record

ArticleFrontiers in cellular and infection microbiology2022

Differential Gene Sets Profiling in Gram-Negative and Gram-Positive Sepsis.

Qingliang Wang, Xiaojie Li, Wenting Tang, Xiaoling Guan, Zhiyong Xiong, Yong Zhu, Jiao Gong, Bo Hu

Open access · goldAbstract read
In one paragraph

Article in Frontiers in cellular and infection microbiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
0.6field-weighted citation impact, top 37% 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

7 citing papers in PubMed, 8 citations in OpenAlex.

  1. Article
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  7. 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
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.

Qingliang WangDepartment of General Surgery, Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
Xiaojie LiDepartment of Laboratory Medicine, Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
Wenting TangDepartment of Molecular Diagnostics, Sun Yat-sen University Cancer Center, Guangzhou, China.
Xiaoling GuanDepartment of Laboratory Medicine, Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
Zhiyong XiongDepartment of General Surgery, Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
Yong ZhuDepartment of General Surgery, The Fourth Affiliated Hospital of Anhui Medical University, Hefei, China.
Jiao GongDepartment of Laboratory Medicine, Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
Bo HuDepartment of Laboratory Medicine, Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
Sun Yat-sen University · CNAnhui Medical University · CNSun Yat-sen University Cancer Center · CNThird Affiliated Hospital of Sun Yat-sen University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe host response to bacterial sepsis is reported to be nonspecific regardless of the causative pathogen. However, newer paradigms indicated that the host response of Gram-negative sepsis may be different from Gram-positive sepsis, and the difference has not been clearly clarified. The current study aimed to explore the difference by identifying the differential gene sets using the genome-wide technique.

methodsThe training dataset GSE6535 and the validation dataset GSE13015 were used for bioinformatics analysis. The distinct gene sets of sepsis with different infections were screened using gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA). The intersection gene sets based on the two algorithms were confirmed through Venn analysis. Finally, the common gene sets between GSE6535 and GSE13015 were determined by GSEA.

resultsTwo immunological gene sets in GSE6535 were identified based on GSVA, which could be used to discriminate sepsis caused by Gram-positive, Gram-negative, or mixed infection. A total of 19 gene sets were obtained in GSE6535 through Venn analysis based on GSVA and GSEA, which revealed the heterogeneity of Gram-negative and Gram-positive sepsis at the molecular level. The result was also verified by analysis of the validation set GSE13015, and 40 common differential gene sets were identified between dataset GSE13015 and dataset GSE6535 by GSEA.

conclusionsThe identified differential gene sets indicated that host response may differ dramatically depending on the inciting organism. The findings offer new insight to investigate the pathophysiology of bacterial sepsis.

Indexed as

Gram-Negative Bacterial InfectionsGram-Positive Bacterial InfectionsSepsisComputational BiologyGene Expression ProfilingHumansgene setsGram-negativeGram-positivemicroarray analysissepsis

Identifiers

PMID35223539
PMCPMC8863667
OpenAlexW4225781871

What OpenQuestion holds

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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.