Evidence map›Paper›PMID 35082520›Full record

ArticleInternational journal of general medicine2022

CD63 and C3AR1: The Potential Molecular Targets in the Progression of Septic Shock.

Ning Yu, Xuefang Liu, Dandan Shi, Long Bai, Tianfu Niu, Ya Liu

Open access · goldAbstract read
In one paragraph

Article in International journal of general medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.5field-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

3 citing papers in PubMed, 7 citations in OpenAlex.

  1. Article
  2. Article
  3. Review
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.

Ning YuDepartment of Anaesthesiology and Intensive Care, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei Province, 050004, People's Republic of China.
Xuefang LiuDepartment of Anaesthesiology and Intensive Care, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei Province, 050004, People's Republic of China.
Dandan ShiDepartment of Anaesthesiology and Intensive Care, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei Province, 050004, People's Republic of China.
Long BaiDepartment of Anaesthesiology and Intensive Care, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei Province, 050004, People's Republic of China.
Tianfu NiuDepartment of Anaesthesiology and Intensive Care, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei Province, 050004, People's Republic of China.
Ya LiuDepartment of Anaesthesiology and Intensive Care, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei Province, 050004, People's Republic of China.
Second Hospital of Hebei Medical University · CNHebei Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe molecular mechanism of septic shock is unknown. We studied the pathogenesis of septic shock and provide a novel strategy for treating and improving the prognosis of septic shock.

methodsGluten-Sensitive Enteropathy (GSE) 131761, GSE119217, GSE26378 datasets were downloaded from the Gene Expression Omnibus (GEO) database. The three datasets included 204 septic shock samples and 48 normal samples. The R packages "affy" and "limma" were employed to identify the differently expressed genes (DEGs) between septic shock and normal samples. Weighted gene co-expression network analysis (WGCNA) was performed to search for modules that play an important role in septic shock. Functional annotation of DEGs and construction and analysis of hub genes were used to explore the pathomechanism of septic shock. The receiver operating characteristic (ROC) curves were obtained using MedCalc software. The drug molecules that could regulate hub genes associated with septic shock were searched for in the CMap database. An animal model of septic shock was constructed to analyze the role of these hub genes.

resultsThe merged series contained 321 up-regulated and 255 down-regulated genes. WGCNA showed the brown module had the highest correlation with the status of septic shock. GO and KEGG enrichment analysis results of the brown module genes showed they were mainly enriched in "leukocyte differentiation", "Ras-proximate-1 (Rap1) signaling pathway", and "cytokine-cytokine receptor interaction". Through construction and analysis of a protein-protein interaction (PPI) network, cluster of differentiation 63 (CD63) and complement component 3a receptor 1 (C3AR1) were identified as hub genes of septic shock. The area under curve (AUC) of C3AR1 for the septic shock is 0.772 (P<0.001), and the AUC of CD63 for the septic shock is 0.871 (P<0.001). Small molecule drugs were filtered by the number of instances (n>3) and P-values <0.05, including "monensin", "verteporfin", "ikarugamycin", "tetrahydroalstonine", "cefamandole", "etoposide". In the animal model, the relative expression levels of interleukin-6 (IL-6), Tumor Necrosis Factor-α (TNF-α), and lactic acid were significantly higher in the septic shock group compared with the control group. Results of Real Time Quantitative PCR (RT-qPCR) and enzyme-linked immunosorbent assay (ELISA) analysis for CD63 and C3AR1 showed that their relative expression levels were significantly lower in the septic shock group compared with the control group (P<0.05).

conclusionCD63 and C3AR1 are significant hub genes of septic shock and may represent potential molecular targets for future studies of septic shock.

Indexed as

animal modelC3AR1CD63hub genesseptic shock

Identifiers

PMID35082520
PMCPMC8784317
OpenAlexW4205227487

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.