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pmid:32381997

Comprehensive Analysis of Fibroblast Growth Factor Receptor (FGFR) Family Genes in Breast Cancer by Integrating Online Databases and Bioinformatics.

Zhaoping Zhou · Baojin Wu · Xinjie Tang · Ronghu Ke · Qiang Zou

Medical science monitor : international medical journal of experimental and clinical research · 2020 doi:10.12659/MSM.923517

Paper

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Experiment

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Data

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Comprehensive Analysis of Fibroblast Growth Factor Receptor (FGFR) Family Genes in Breast Cancer by Integrating Online Databases and Bioinformatics.
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10.12659/MSM.923517
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Zhaoping Zhou
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Baojin Wu
[2]
Xinjie Tang
[3]
Ronghu Ke
[4]
Qiang Zou
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Medical science monitor : international medical journal of experimental and clinical research
year
2020
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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To explore the associations between the levels of FGFR1–4 genes and patients’ clinical outcomes, we performed prognostic analysis within the Kaplan-Meier database (www.kmplot.com).
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To explore the associations between the levels of FGFR1–4 genes and patients’ clinical outcomes, we performed prognostic analysis within the Kaplan-Meier database (www.kmplot.com). Kaplan-Meier is an online public database to draw survival plots, which can be used to study the effects of gene expression on the clinical outcome in BC patients [19]. We used the database to assess the relationship between the levels of FGFR1–4 mRNA and patients’ survival.
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To explore the associations between the levels of FGFR1–4 genes and patients’ clinical outcomes, we performed prognostic analysis within the Kaplan-Meier database (www.kmplot.com). Kaplan-Meier is an online public database to draw survival plots, which can be used to study the effects of gene expression on the clinical outcome in BC patients [19]. We used the database to assess the relationship between the levels of FGFR1–4 mRNA and patients’ survival.
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Complete file
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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. 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