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. 2021 Feb 3:9:e10817.
doi: 10.7717/peerj.10817. eCollection 2021.

Evaluation of FGFR1 as a diagnostic biomarker for ovarian cancer using TCGA and GEO datasets

Huiting Xiao #  1 , Kun Wang #  2 , Dan Li  1 , Ke Wang  1 , Min Yu  1
Affiliations

Evaluation of FGFR1 as a diagnostic biomarker for ovarian cancer using TCGA and GEO datasets

Huiting Xiao et al. PeerJ. .

Abstract

Background: Malignant ovarian cancer is associated with the highest mortality of all gynecological tumors. Designing therapeutic targets that are specific to OC tissue is important for optimizing OC therapies. This study aims to identify different expression patterns of genes related to FGFR1 and the usefulness of FGFR1 as diagnostic biomarker for OC.

Methods: We collected data from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. In the TCGA cohort we analyzed clinical information according to patient characteristics, including age, stage, grade, longest dimension of the tumor and the presence of a residual tumor. GEO data served as a validation set. We obtained data on differentially expressed genes (DEGs) from the two microarray datasets. We then used gene set enrichment analysis (GSEA) to analyze the DEG data in order to identify enriched pathways related to FGFR1.

Results: Differential expression analysis revealed that FGFR1 was significantly downregulated in OC specimens. 303 patients were included in the TCGA cohort. The GEO dataset confirmed these findings using information on 75 Asian patients. The GSE105437 and GSE12470 database highlighted the significant diagnostic value of FGFR1 in identifying OC (AUC = 1, p = 0.0009 and AUC = 0.8256, p = 0.0015 respectively).

Conclusions: Our study examined existing TCGA and GEO datasets for novel factors associated with OC and identified FGFR1 as a potential diagnostic factor. Further investigation is warranted to characterize the role played by FGFR1 in OC.

Keywords: FGFR1; Ovarian cancer; RNA-sequencing; Targeted treatment.

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Conflict of interest statement

The authors declare there are no competing interests.

Figures

Figure 1
Figure 1. Flow chart of study selection for GEO and TCGA based data.
Figure 2
Figure 2. Expression date in OC tissue from the GEO dataset.
(A) differential expression of FGFR1 in GSE105437; (B) differential expression of FGFR1 in GSE12470; (C) differential expression of FGFR1 in GSE12470 with subgroup analysis; (D) differential expression of EGFR in GSE105437; (E) differential expression of FGFR2 in GSE12470; E differential expression of FGFR3 in GSE12470.
Figure 3
Figure 3. ROC analysis of GSE105437 and GSE12470 of FGFR1 for the diagnosis of OC.
(A) GSE105437: The area under the ROC curve (AUC) 1.000, p = 0.0009; (B) GSE12470: AUC 0.8256, p = 0.0015.
Figure 4
Figure 4. Meta-analysis of the combined SMD for FGFR1 expression between OC and normal groups in the GEO database using the fixed effects models.
(A) Forest plot; (B) funnel plot.
Figure 5
Figure 5. Differential expression of data between two sets of samples.
(A) GSE105437 (B) GSE12470. Red points represent upregulated genes (i.e., —FC— > 2.0 and a corrected P-value of < 0.05). Green points represent downregulated genes(i.e., —FC— > 2.0 and a corrected P-value of < 0.05). Black points represent genes with no significant difference in expression. FC is the fold change.
Figure 6
Figure 6. Enrichment plots from gene set enrichment analysis (GSEA).
Common pathways in the GSE105437 (A) and GSE12470 (B) datasets according to GSEA, specific pathways involved FGFR1 GSE105437 datasets according to GSEA (C). ES, enrichment score; NES, normalized ES; NOM p-val, normalized p-value.
Figure 7
Figure 7. Kaplan–Meier estimates of OC overall survival of by residual tumor, stage and expression of FGFR1.
(A) Residual tumor, p = 0.001; (B) Stage, p = 0.148; (C) Expression of FGFR1, p = 0.233.
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