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BY 4.0 license Open Access Published by De Gruyter April 27, 2020

Bioinformatics analysis of FOLR1 expression, functional enrichment, related signaling pathways and relationship with prognosis in ovarian cancer

  • Yan Wang , Xiao Li and Pengpeng Qu EMAIL logo
From the journal Pteridines

Abstract

Objective To investigate folate-receptor 1 (FOLR1) expression in ovarian cancer and its association with patient prognosis.

Methods TCGA and Oncomine databases were used to collect data about FOLR1 mRNA expression in multiple carcinomas. FOLR1 mRNA expression levels in ovarian cancer samples and corresponding adjacent normal ovary tissue were compared. A protein-protein interaction (PPI) network was constructed using the STRING database of FOLR1 and relevant genes. The overall survival (OS) and progression free survival (PFS) rates of ovarian cancer patients in high- and low- FOLR1 expression groups were compared by log-rank test. Sixty-six ovarian epithelial carcinoma samples were included in the study, and tumor specimens of the 66 cases were tested for FOLR1 protein expression by an immunohistochemistry assay.

Results FOLR1 mRNA was significantly elevated in ovarian cancer compared to other carcinomas. FOLR1 mRNA expression levels were significantly higher in tumor tissues than in the corresponding normal tissues (P<0.05) of ovarian cancer patients. The PPI network indicated that the local clustering coefficient was 0.898, indicating that the PPI network was enriched significantly (P<0.05). The median PFS values were 22.39 and 19.00 months for lowand high-FOLR1 expression groups, respectively, with significant statistical difference between the two (HR=1.26, 95%CI:1.09-1.45, P<0.05). FOLR1 protein expression was correlated with tumor differentiation (P<0.05) in ovarian cancer patients. However, its levels were not correlated with patient age, tumor diameter, lymph node metastasis or FIGO stage (P>0.05).

Conclusion FOLR1 is upregulated in epithelial ovarian cancer, and its expression is correlated with patients’ progression free survival, making it a valuable biomarker for prognosis.


#Yan Wang and Xiao Li contribute equally to this work


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Received: 2020-01-02
Accepted: 2020-02-19
Published Online: 2020-04-27

© 2020 Yan Wang et al., published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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