Evidence map›Paper›PMID 31545397›Full record

ArticleInternational journal of molecular medicine2019

Screening and identification of biomarkers for systemic sclerosis via microarray technology.

Chen Xu, Ling-Bing Meng, Yu-Chen Duan, Yong-Jing Cheng, Chun-Mei Zhang, Xing Zhou, Ci-Bo Huang

Open access · hybridAbstract read
In one paragraph

Article in International journal of molecular medicine, 2019. 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
1.2field-weighted citation impact, top 21% 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, 15 citations in OpenAlex.

  1. [Pathogenesis and mechanism of serine protease 23 in skin fibrosis of systemic sclerosis].Beijing da xue xue bao. Yi xue ban = Journal of Peking University. Health sciences · 2025
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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 1 institution in 1 country.

Chen XuDepartment of Rheumatology, Beijing Hospital, National Center of Gerontology, Beijing 100730, P.R. China.
Ling-Bing MengDepartment of Neurology, Beijing Hospital, National Center of Gerontology, Beijing 100730, P.R. China.
Yu-Chen DuanDepartment of Rheumatology, Beijing Hospital, National Center of Gerontology, Beijing 100730, P.R. China.
Yong-Jing ChengDepartment of Rheumatology, Beijing Hospital, National Center of Gerontology, Beijing 100730, P.R. China.
Chun-Mei ZhangDepartment of Rheumatology, Beijing Hospital, National Center of Gerontology, Beijing 100730, P.R. China.
Xing ZhouDepartment of Rheumatology, Beijing Hospital, National Center of Gerontology, Beijing 100730, P.R. China.
Ci-Bo HuangDepartment of Rheumatology, Beijing Hospital, National Center of Gerontology, Beijing 100730, P.R. China.
Beijing Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Systemic sclerosis (SSc) is a complex autoimmune disease. The pathogenesis of SSc is currently unclear, although like other rheumatic diseases its pathogenesis is complicated. However, the ongoing development of bioinformatics technology has enabled new approaches to research this disease using microarray technology to screen and identify differentially expressed genes (DEGs) in the skin of patients with SSc compared with individuals with healthy skin. Publicly available data were downloaded from the Gene Expression Omnibus (GEO) database and intra‑group data repeatability tests were conducted using Pearson's correlation test and principal component analysis. DEGs were identified using an online tool, GEO2R. Functional annotation of DEGs was performed using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Finally, the construction and analysis of the protein‑protein interaction (PPI) network and identification and analysis of hub genes was carried out. A total of 106 DEGs were detected by the screening of SSc and healthy skin samples. A total of 10 genes [interleukin‑6, bone morphogenetic protein 4, calumenin (CALU), clusterin, cysteine rich angiogenic inducer 61, serine protease 23, secretogranin II, suppressor of cytokine signaling 3, Toll‑like receptor 4 (TLR4), tenascin C] were identified as hub genes with degrees ≥10, and which could sensitively and specifically predict SSc based on receiver operator characteristic curve analysis. GO and KEGG analysis showed that variations in hub genes were mainly enriched in positive regulation of nitric oxide biosynthetic processes, negative regulation of apoptotic processes, extracellular regions, extracellular spaces, cytokine activity, chemo‑attractant activity, and the phosphoinositide 3 kinase‑protein kinase B signaling pathway. In summary, bioinformatics techniques proved useful for the screening and identification of biomarkers of disease. A total of 106 DEGs and 10 hub genes were linked to SSc, in particular the TLR4 and CALU genes.

Indexed as

BiomarkersComputational BiologyGene Regulatory NetworksHumansMicroarray AnalysisProtein Interaction MapsScleroderma, SystemicSignal TransductionBiomarkers

Identifiers

PMID31545397
PMCPMC6777682
OpenAlexW2972100253

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.