Evidence map›Paper›PMID 34764682›Full record

ArticleInternational journal of general medicine2021

Identification of Candidate Genes Related to Synovial Macrophages in Rheumatoid Arthritis by Bioinformatics Analysis.

Jia Xu, Ming-Ying Zhang, Wei Jiao, Cong-Qi Hu, Dan-Bin Wu, Jia-Hui Yu, Guang-Xing Chen

Open access · goldAbstract read
In one paragraph

Article in International journal of general medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 1 pooled it
1.7field-weighted citation impact, top 15% 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, 1 synthesis or guideline pooled it, 14 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

7 authors at 2 institutions in 1 country.

Jia XuFirst Clinical Medical School, Guangzhou University of Chinese Medicine, Guangzhou, 510405, Guangdong, People's Republic of China.
Ming-Ying ZhangDepartment of Rheumatology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510405, Guangdong, People's Republic of China.
Wei JiaoFirst Clinical Medical School, Guangzhou University of Chinese Medicine, Guangzhou, 510405, Guangdong, People's Republic of China.
Cong-Qi HuFirst Clinical Medical School, Guangzhou University of Chinese Medicine, Guangzhou, 510405, Guangdong, People's Republic of China.
Dan-Bin WuDepartment of Rheumatology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510405, Guangdong, People's Republic of China.
Jia-Hui YuFirst Clinical Medical School, Guangzhou University of Chinese Medicine, Guangzhou, 510405, Guangdong, People's Republic of China.
Guang-Xing ChenDepartment of Rheumatology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510405, Guangdong, People's Republic of China.
Guangzhou University of Chinese Medicine · CNFirst Affiliated Hospital of Guangzhou University of Chinese Medicine · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveRheumatoid arthritis (RA) is one of the most prevalent inflammatory arthritis worldwide. However, the genes and pathways associated with macrophages from synovial fluids in RA patients still remain unclear. This study aims to screen and verify differentially expressed genes (DEGs) related to identifying candidate genes related to synovial macrophages in rheumatoid arthritis by bioinformatics analysis.

methodsWe searched the Gene Expression Omnibus (GEO) database, and GSE97779 and GSE10500 with synovial macrophages expression profiling from multiple RA microarray dataset were selected to conduct a systematic analysis. GSE97779 included nine macrophage samples from synovial fluids of RA patients and five macrophage samples from primary human blood of HC. GSE10500 included five macrophage samples from synovial fluids of RA patients and three macrophage samples from primary human blood of HC. Functional annotation of DEGs was performed, including Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Protein-protein interaction (PPI) network of DEGs was established using the STRING database. CytoHubba was used to identify hub genes. MCODE was used to determine gene clusters in the interactive network.

resultsThere were 2638 DEGs (1425 upregulated genes and 1213 downregulated ones) and 889 DEGs (438 upregulated genes and 451 downregulated ones) selected from GSE97779 and GSE10500, respectively. Venn diagrams showed that 173 genes were upregulated and 106 downregulated in both two datasets. The top 10 hub genes, including FN1, VEGFA, HGF, SERPINA1, MMP9, PPBP, CD44, FPR2, IGF1, and ITGAM, were identified using the PPI network.

conclusionThis study provides new insights for the potential biomarkers and the relevant molecular mechanisms in RA patients. Our findings suggest that the 10 candidate genes might be used in diagnosis, prognosis, and therapy of RA in the future. However, further studies are required to confirm the expression of these genes in synovial macrophages in RA and control specimen.

Indexed as

bioinformatics analysiskey genespathwaysrheumatoid arthritissynovitis macrophages

Identifiers

PMID34764682
PMCPMC8575484
OpenAlexW3208059763

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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.