Identification of DEGs in bladder cancers
In the present study, we downloaded two gene expression profiles (GSE3167 and GSE7476) from the GEO database. GSE3167 contained 41 tumor cases and 13 normal cases, and GSE7476 included 9 tumor cases and 3 normal cases respectively (Table 1). Based on GEO2R online tools, P < 0.05 and |logFC|>1, 1520 and 2098 DEGs from GSE3167 and GSE7476 respectively were screened(Fig. 1A-B). Then, venn diagram online tool was performed to select the commonly DEGs in all two datasets (Fig. 1C-D). Finally, a total of 251 genes were identified, including 173 genes were significantly upregulated and 78 genes were significantly downregulated (Supplemental_Table_S3).
GO and KEGG Pathway Analysis in bladder cancers
DAVID software was utilized to analyze the gene ontology and KEGG pathway analysis among all 251 common changed DEGs. The results of GO analysis were divided into three groups: biological process (BP), molecular function (MF) and cellular component (CC). In the BP group, up-regulated DEGs were mainly found in response to drug, positive regulation of transcription, DNA-templated, cell division, cell proliferation, and down-regulated DEGs in muscle contraction, positive regulation of GTPase activity, extracellular matrix organization. Additionally, in the CC group, up-regulated DEGs were mainly associated with cytoplasm, nucleus extracellular exosome, down-regulated DEGs mainly with cytoplasm, extracellular exosome, extracellular space. As for the MF group, the results show that the up-regulated DEGs were mainly enriched in protein binding, ATP binding, poly(A) RNA binding, and down-regulated DEGs in actin-binding, heparin binding, oxidoreductase activity (Fig. 2A-B,Supplemental_Table_S4).
KEGG pathway analysis was also through DAVID online. The results show that the up-regulated differentially expressed genes were significantly gathered in Pathways in cancer, Cell cycle, Biosynthesis of antibiotics, p53 signaling pathway, Oocyte meiosis, while down-regulated DEGs in Vascular smooth muscle contraction, Proteoglycans in cancer, Regulation of actin cytoskeleton, Dilated cardiomyopathy, cGMP-PKG signaling pathway and Melanoma (Fig. 2C-D,Supplemental_Table_S5-6).
PPI network construction and significant module selection
To further understand the relationship among identified DEGs, STRING online tool and Cytoscape software were used. We filtered 210 genes of the 251 commonly altered DEGs into the DEG PPI network (as presented in Fig. 3A), which possessed 210 genes and 1075 edges Among the 210 genes, there were 144 upregulated genes and 66 downregulated genes.
Then Cytotype MCODE (Molecular Complex Detection) plug-in was used to detect significant modules in the PPI network. The results showed that 19 nodes were identified from the PPI network which were all up-regulated genes (Fig. 3B).
Core genes expression between cancer and normal bladder tissues
GEPIA, the website-based GTEx and TCGA database, was utilized to verify the expression level of the 19 core genes between cancerous and normal people. We found that the expression trends were consistent with the two GEO datasets, and 18 of 19 genes were statistically significantly upregulated in bladder cancer tissue compared with normal bladder tissue through analysing RNA-Seq profiles of 28 normal and 404 cancer samples from the GTEx and TCGA database (Fig. 4).
Prognostic value analysis of core genes
Survival datas of 18 core genes were analyzed on the Kaplan Meier plotter prognostic analysis platform (http://kmplot.com/analysis). According to the results in prognostic analysis platform, we found that 11 of 18 genes were associated with the prognosis. However, the high expression group for SMC4, TYMS, CCNB1, CKS1B, NUSAP1 and KPNA2 had significantly unfavorable OS than those in the low expression group while the other five high expression groups contribute to favorable outcomes, which is conflicted with their high expression level in the tumor (Fig. 5).
Frequency and type of hub genes alterations in BLCA
Next, we use the cBioportal database to verify the mutation types and frequencies of six genes in the above steps. As shown in Fig. 6 and Table 2, the mutation frequencies of SMC4, CCNB1, CKS1B, NUSAP1, KPNA2 are 18.45% 7.28% 5.58% 4.61% 9.47% and 14.32% respectively. There is no doubt that gene amplification is the largest type of mutation.
Subgroups analysis of patients with bladder carcinoma
We further used the UALCAN database based on TCGA database to evaluate the transcriptional level of SMC4, and showed that the expression of SMC4 mRNA in bladder cancer tissues was significantly higher than that in normal tissues(Fig. 7). Further subgroup analysis of multiple clinicopathological features of 408 bladder cancer samples in TCGA, in the subgroup analysis of the two clinical characteristics of disease staging and lymph node metastasis, the transcriptional levels of six hub genes in bladder cancer patients were significantly higher than those in healthy people (Fig. 8).
Quantitative real-time PCR validation
After comparing the expression level of six genes between the tumor and normal tissue, we found that the expression levels of SMC4, CCNB1, and CKS1B were upregulated in tumor tissues in 9 pairs of patients(Fig. 9G-I ). Different from the results in the database, our results showed that there were no significant change of TYMS, NUSAP1 and KPNA2 in BLCA tissues compared to the normal samples (Fig. 9J-L ).