ArticleCurrent medical science2019
Identification and Interaction Analysis of Key Genes and MicroRNAs in Systemic Sclerosis by Bioinformatics Approaches.
Article in Current medical science, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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.
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.
Who cites it
7 citing papers in PubMed, 12 citations in OpenAlex.
- Article
- Virus-Induced MicroRNA Modulation and Systemic Sclerosis Disease.Biomedicines · 2024Review
- The Roles of Noncoding RNAs in Systemic Sclerosis.Frontiers in immunology · 2022Review
- Review
- Pathogenic Roles of Autoantibodies and Aberrant Epigenetic Regulation of Immune and Connective Tissue Cells in the Tissue Fibrosis of Patients with Systemic Sclerosis.International journal of molecular sciences · 2020Review
- Prediction of severity and subtype of fibrosing disease using model informed by inflammation and extracellular matrix gene index.PloS one · 2020Article
- Screening and identification of biomarkers for systemic sclerosis via microarray technology.International journal of molecular medicine · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Systemic sclerosis (SSc) is a highly heterogeneous autoimmune disease with a high mortality rate. However, the cellular and molecular mechanisms of SSc remain unclear. Here, we identified the key hub genes and microRNAs (miRNAs) that modulate the occurrence and development of SSc. We downloaded the microarray dataset GSE95065 from the Gene Expression Omnibus (GEO) database and then analyzed the data by using GEO2R. The Database for Annotation, Visualization and Integrated Discovery (DAVID) was used for functional pathway enrichment analyses of differentially expressed genes (DEGs), and Cytoscape software was used to generate the protein-protein interaction (PPI) network. In addition, OmicsNet was used to predict the miRNAs for the hub genes of SSc. As a result, 783 DEGs were identified, of which 770 genes (142 up-regulated genes and 628 down-regulated genes) were matched to the genes in SSc skin samples. Gene Ontology (GO) analyses by DAVID indicated that the up-regulated genes were mainly involved in immune response, and the down-regulated genes were greatly enriched in glycinergic synaptic transmission. In the PPI network, 22 nodes were selected as key genes, including several members of the chemokine family. Furthermore, after uploading these key genes to the OmicsNet tool, we found that hsa-miR-26b-5p might target CXCL9 and CXCL13. Moreover, we demonstrated that the hsa-miR-26b-5p inhibitor might inhibit fibrosis in TGF-β-activated fibroblasts, which would be a promising target for SSc therapy.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.