Downregulated exosome-associated gene FGF9 as a novel diagnostic and prognostic target for ovarian cancer and its underlying roles in immune regulation

Abstract

Exosome has been demonstrated to be secreted from cells and seized by targeted cells. Exosome could transmit signals and exert biological functions in cancer progression. Nevertheless, the underlying mechanisms of exosome in ovarian cancer (OC) have not been fully explored. In this study, we wanted to explore whether Fibroblast growth factor 9 (FGF9), as an exosome-associated gene, was importantly essential in OC progression and prognosis. Firstly, comprehensive bioinformatics platforms were applied to find that FGF9 expression was lower in OC tissues compared to normal ovarian tissues. Meanwhile, downregulated FGF9 displayed favorable prognostic values in OC patients. The gene enrichment of biological functions indicated that abnormally expressed FGF9 could be involved in the OC-related immune signatures, such as immunoinhibitors and chemokine receptors. Taken together, these findings could provide a novel insight into the significance of FGF9 in OC progress and supply a new destination of FGF9-related immunotherapy in clinical treatment.

Keywords: FGF9; exosome; immune regulation; ovarian cancer; prognosis.

Figure 1

The co-DEGs between the exosome-associated genes and two OC datasets. The Venn plot showed that two upregulated exosome-correlated genes (CD24 and CP) and one downregulated exosome-correlated gene (FGF9) might play pivotal roles in OC progression.

Figure 2

The prognostic values of CD24, CP and FGF9 in OC. (A–I) The prognostic values of CD24, CP and FGF9 in ovarian cancer patients. Abbreviations: OS: overall survival; PFS: progression-free survival; PPS: post progression survival.

Figure 3

FGF9 was down-regulated in OC patients. (A, B) In the two datasets, the expression level of FGF9 was lower in OC tissues than that in normal ovarian tissues. (C, D) The GEPIA2.0 database and TCGA database have depicted that the expression of FGF9 decreased in OC tissues compared to normal ovarian tissues. (E, F) TNMplot database depicting FGF9 expression was lower in OC tissues compared to normal tissues from gene chip data and RNA-seq data.

Figure 4

The co-expression network of FGF9 in OC. (A) The LinkedOmics platform portraying the crucially associated genes with FGF9 in OC patients. (B, C) Heatmaps showing the top genes that were positively and negatively correlated with FGF9 in OC. (D) Survival heatmaps downloaded from the GEPIA2.0 database displayed that the top genes that were positively and negatively associated with FGF9 in OC. (E, F) GO signaling pathway and KEGG signaling pathway of FGF9 in OC patients.

Figure 5

The relationship between the expression level of FGF9 and immune responses of OC patients. (A) The diagraph showing the relation between FGF9 expression and 24 types of immune cells. The size of the dots represented the values of Spearman r (p < 0.05). (B) The pictures downloaded from TISIDB database showing the relationship between FGF9 and immune infiltration cells, such as activated dendritic cells (aDC), Treg, Th17 cells, NK CD56dim cells (p < 0.05). (C) The Timer database showing the relationship between the expression level of FGF9 and immune infiltration cells. (D, E) The heatmap and scatterplot depicting FGF9 expression was negatively correlated to VSIR or CTLA4.