br MODEL br ARHGEF as a novel biomarker br IHC
ARHGEF38 as a novel biomarker 5
IHC studies to determine the expression of ARHGEF38 and Ki67 in PCa
IHC was performed to detect the expression of ARHGEF38 and Ki67 (encodes a nuclear protein that is associated with and may be necessary for cellular proliferation), in differ-ential grade tumors (Fig. 3). The results indicated that the combined detection of ARHGEF38 and Ki67 in the PCa tissue samples could easily differentiate the tumors according to their grade. We observed that ARHGEF38 was not expressed in BPH tissues (Fig. 3A). Meanwhile, its expression intensity in HGPCa (Fig. 3D) was significantly higher than in MGPCa (Fig. 3C) and LGPCa (Fig. 3B). Ki67 was weakly expressed in BPH (Fig. 3E), but the expression of Ki67 enhanced with the increase in GS score of the PCa samples (Fig. 3FeH). Consistent with the previous study by Rebecca L1 on the age-based grouping of the patients with invasive cancer in the US population, the protein expression of ARHGEF38 in patients older than 68 years was significantly higher than that in patients below 68 years (Table 3).
Expression of ARHGEF38 and Ki67 in tissues and survival analysis
mRNA expression of ARHGEF38 in PCa was significantly higher than that in BPH (p < 0.0001, Fig. 4A). Similarly, expression of the ARHGEF38 protein in PCa was also significantly higher than that in BPH (Fig. 4B). Quantitative analysis of the IHC results showed that the staining in-tensity was significantly higher in the metastasis group than in the non-metastasis group (p < 0.0001, Fig. 4C). This in-dicates that the ARHGEF38 expression is proportional to the aggressiveness of PCa. The intensity of ARHGEF38 protein
Table 3 Relationship between ARHGEF38 expression andclinicopathological factors.
High Low þ
staining in patients with stage 3 PCa was significantly higher than that in stage 2 patients (p < 0.0001, Fig. 4D). The rate of ki67 positive MCC950 in patients with T3 PCa was
Figure 3 Comparison of ARHGEF38 and Ki67 expression in BPH, LGPCA, MGPCa and HGPCA. (A) ARHGEF38 was not expressed in BPH tissues. Meanwhile, its expression intensity in (D) HGPCa was significantly higher than in (C) MGPCa and (B) LGPCa. (E) Ki67 was weakly expressed in BPH, but the expression of (FeH) Ki67 enhanced with the increase in GS score of the PCa samples.
significantly higher than that in patients with T2 (p < 0.0001, Fig. 4E). According to the survival curves, we observed that the survival rate of patients with high expression of the ARHGEF38 gene was significantly lower than those with low expression in the TCGA PRAD tumor dataset (high Z 250, low Z 249, p Z 0.02, HR Z 5.3) (Fig. 4F). Similarly, the survival rate of patients with higher expression of ki67 is accompanied by poorer prognosis (high Z 250, low Z 249, p Z 0.00061, HR Z 2.09) (Fig. 4G). Our follow-up results for 100 patients with PCa also indi-cated that the prognosis of patients was dependent on the ARHGEF38 expression (high Z 50, low Z 50, p Z 0.0045, HR Z 6.55) (Fig. 4H). These results strongly suggest that ARHGEF38 might be a potential biomarker candidate to predict PCa prognosis.
With the advancement in sequencing technologies over the last decade, gene expression analysis has become far much accessible for oncological research. Various strategies have been applied to identify diagnostic and prognostic markers based on the gene expression profiles from TCGA and GEO
datasets. One approach is to analyze the known gene interaction networks and pathways to predict the progres-sion and survival of cancer patients. Previous studies have shown that the RAS superfamily genes Cdc42, RAC, and RHOA, play an important role in tumorigenesis and metas-tasis, and have been implicated in cancer prognosis.19e22 RHOGEF catalyzes the conversion between GTP and GDP, thereby modulating CDC42, RAS, and RHOA activities involved in the regulation of various cellular processes related to cancer cells proliferation.23,24
In this study, we retrieved the microarray data from GEO and TCGA and identified 243 DEGs through different bioin-formatics methods. Among the identified DEGs, 32 genes were upregulated and 211 were downregulated. These DEGs were found to be the genes involved in biological processes such as muscle movement, cell migration, and actin filament-based process. Hence, we suggest that the DEGs might be involved in tumor metastasis processes. Among the DEGs, CDC42EP3, ABCC9, FGFR2, PRDM5, and ARHGEF38 were found to be co-expressed. In particular, ARHGEF38 was significantly upregulated in PCa samples analyzed using GEO and TCGA PRAD data sets. Analyzing the TCGA dataset indicated that ARHGEF38 was not only overexpressed in PRAD, but also in the five most prevalent