ArticleMolecular and clinical oncology2020
Network-based identification of signature genes KLF6 and SPOCK1 associated with oral submucous fibrosis.
Article in Molecular and clinical oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 15 citations in OpenAlex.
- Structure and Function of the Extracellular Matrix in Normal and Pathological Conditions: Looking at the Bicuspid Aortic Valve.International journal of molecular sciences · 2025Review
- DD-CC-II: Data Driven Cell-Cell Interaction Inference and Its Application to COVID-19.International journal of molecular sciences · 2025Article
- Novel transcripts of EMT driving the malignant transformation of oral submucous fibrosis.Scientific reports · 2025Article
- Exploring the role of miR-200 family in regulating CX3CR1 and CXCR1 in lung adenocarcinoma tumor microenvironment: implications for therapeutic intervention.Scientific reports · 2023Article
- Article
- Inhibition of miR-497 Attenuates Oral Submucous Fibrosis by Inhibiting Myofibroblast Transdifferentiation in Buccal Mucosal Fibroblasts.Oral health & preventive dentistry · 2022Article
- Survival-Based Biomarker Module Identification Associated with Oral Squamous Cell Carcinoma (OSCC).Biology · 2021Article
- Unravelling host-pathogen interactions: ceRNA network in SARS-CoV-2 infection (COVID-19).Gene · 2020Article
- Circulating miRNA-181b-5p, miRNA-223-3p, miRNA-210-3p, let 7i-5p, miRNA-21-5p and miRNA-29a-3p in patients with localized scleroderma as potential biomarkers.Scientific reports · 2020Article
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The molecular mechanism of oral submucous fibrosis (OSF) is yet to be fully elucidated. The identification of reliable signature genes to screen patients with a high risk of OSF and to provide oral cancer surveillance is therefore required. The present study produced a filtering criterion based on network characteristics and principal component analysis, and identified the genes that were involved in OSF prognosis. Two gene expression datasets were analyzed using meta-analysis, the results of which revealed 1,176 biologically significant genes. A co-expression network was subsequently constructed and weighted gene modules were detected. The pathway and functional enrichment analyses of the present study allowed for the identification of modules 1 and 2, and their respective genes, SPARC (osteonectin), cwcv and kazal like domain proteoglycan 1 (SPOCK1) and kruppel like factor 6 (KLF6), which were involved in the occurrence of OSF. The results revealed that both genes had a prominent role in epithelial to mesenchymal transition during OSF progression. The genes identified in the present study require further exploration and validation within clinical settings to determine their roles in OSF.
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