ArticleJournal of translational medicine2022
Integrated weighted gene coexpression network analysis identifies Frizzled 2 (FZD2) as a key gene in invasive malignant pleomorphic adenoma.
Article in Journal of translational medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 12 citations in OpenAlex.
- Binaphthoquinone attenuates myofibroblast transition of human ventricular cardiac fibroblastsThe Korean journal of physiology & pharmacology : official journal of the Korean Physiological Society and the Korean Society of Pharmacology · 2026Article
- Pan-cancer profiling of FZD2 as a prognostic biomarker: integrative multi-omics analysis with experimental validation and functional characterization in gastric cancer.Frontiers in pharmacology · 2025Article
- Function-Genes and Disease-Genes Prediction Based on Network Embedding and One-Class Classification.Interdisciplinary sciences, computational life sciences · 2024Article
- Pan-cancer Analysis Reveals m6A Variation and Cell-specific Regulatory Network in Different Cancer Types.Genomics, proteomics & bioinformatics · 2024Article
- Article
- Identification of key modules and driving genes in nonalcoholic fatty liver disease by weighted gene co-expression network analysis.BMC genomics · 2023Article
- Extracellular matrix remodelling and stiffening contributes to tumorigenesis of salivary carcinoma ex pleomorphic adenoma--A study based on patient-derived organoids.Cell & bioscience · 2023Article
- Comparing gradient boosting and neural networks in the prediction of intersecting genes in gingival epithelial immunity.Journal of Indian Society of PeriodontologyArticle
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Authors and funding
7 authors at 4 institutions in 1 country.
Funding
Abstract
backgroundInvasive malignant pleomorphic adenoma (IMPA) is a highly malignant neoplasm of the oral salivary glands with a poor prognosis and a considerable risk of recurrence. Many disease-causing genes of IMPA have been identified in recent decades (e.g., P53, PCNA and HMGA2), but many of these genes remain to be explored. Weighted gene coexpression network analysis (WGCNA) is a newly emerged algorithm that can cluster genes and form modules based on similar gene expression patterns. This study constructed a gene coexpression network of IMPA via WGCNA and then carried out multifaceted analysis to identify novel disease-causing genes.
methodsRNA sequencing (RNA-seq) was performed for 10 pairs of IMPA and normal tissues to acquire the gene expression profiles. Differentially expressed genes (DEGs) were screened out with the cutoff criteria of |log
resultsIn this research, a total of 1970 DEGs were screened out in IMPA tissues, including 1056 upregulated DEGs and 914 downregulated DEGs. Functional enrichment analysis was performed for identified DEGs and revealed an enrichment of tumor-associated GO terms and KEGG pathways. We used WGCNA to identify gene module most relevant with the histological grade of IMPA. The gene FZD2 was then recognized as the hub gene of the selected module with the highest module membership (MM) value and intramodule connectivity in protein-protein interaction (PPI) network. According to immunohistochemistry (IHC) staining, the expression level of FZD2 was higher in low-grade IMPA than in high-grade IMPA.
conclusionFZD2 shows an expression dynamic that is negatively correlated with the clinical malignancy of IMPA and it plays a central role in the transcription network of IMPA. Thus, FZD2 serves as a promising histological indicator for the precise prediction of IMPA histological stages.
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