ArticleMedical science monitor : international medical journal of experimental and clinical research2020
Comprehensive Analysis of Fibroblast Growth Factor Receptor (FGFR) Family Genes in Breast Cancer by Integrating Online Databases and Bioinformatics.
Article in Medical science monitor : international medical journal of experimental and clinical research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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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.
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Who cites it
4 citing papers in PubMed, 6 citations in OpenAlex.
- FGFR‑related phenotypic and functional profile of CAFs in prognostication of breast cancer (Review).International journal of oncology · 2024Review
- Robust Identification of Differential Gene Expression Patterns from Multiple Transcriptomics Datasets for Early Diagnosis, Prognosis, and Therapies for Breast Cancer.Medicina (Kaunas, Lithuania) · 2023Article
- Fibroblast growth factor receptor signalling dysregulation and targeting in breast cancer.Open biology · 2022Review
- Gene Regulation Network of Prognostic Biomarker YAP1 in Human Cancers: An Integrated Bioinformatics Study.Pathology oncology research : POR · 2021Article
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND Fibroblast growth factor receptors (FGFRs) play vital roles in the development and progression of human cancers. This study aimed to comprehensively understand the prognostic performances of FGFR1-4 expression in breast cancer (BC) by mining databases. MATERIAL AND METHODS The levels of FGFR1-4 expression in BC were analyzed by online databases, GEPIA (Gene Expression Profiling Interactive Analysis) and UALCAN. Survival analysis of FGFR1-4 was carried out by Kaplan-Meier plotter. GSE74146 was downloaded from Gene Expression Omnibus (GEO) and analyzed by GEO2R to screen the differentially expressed genes (DEGs) between FGFR2-silenced BC cells and control. Over-presentation for DEGs were done by Enrichr tool. Networks of DEGs were obtained by using Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape software. Hub genes were identified by cytoHubba Cytoscape plugin. RESULTS The online databases showed that FGFR1 was significantly downregulated whereas FGFR3 was upregulated in BC. Kaplan-Meier plotter demonstrated the upregulation of both FGFR1 and FGFR3 indicated favorable relapse free survival (RFS) whereas FGFR4 overexpression predicted unfavorable overall survival (OS) in BC patients. Importantly, our results showed FGFR2 overexpression robustly predicted favorable OS and RFS in BC. Further bioinformatics analysis of GSE74146 suggested FGFR2 mainly participated in regulating degradation and organization of the extracellular matrix and signaling of retinoic acid. Moreover, CXCL8, CD44, MMP9, and BMP7 were identified as crucial FGFR2-related hub genes. CONCLUSIONS Our study comprehensively analyzed the prognostic values of FGFR1-4 expression in BC and proposed FGFR2 might serve as a promising biomarker. However, the underlying mechanisms remain to be elucidated.
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