High SKA3 expression in bladder cancer

To investigate the distribution of SKA3 expression in different tumors, SKA3 expression in each cancer type is compared to that of normal tissue samples (reference control) in TIMER database. The discrepancy in SKA3 expression levels between tumors and corresponding normal tissues is depicted in Fig. 1A. SKA3 levels were obviously higher in BLCA, BRCA, CESC, CHOL, COAD, ESCA, GBM, HNSC, KIRC, KIRP, LIHC, LUAD, LUSC, PAAD, PCPG, PRAD, READ, STAD, THCA, and UCEC (Supplementary Table 1) than in their respective normal tissues. Interestingly, none of tumors expressed at a lower level than their normal tissues counterparts.

Fig. 1
figure 1

Pan-cancer SKA3 expression analysis. A SKA3 expression level in tumor and normal tissues in TCGA pan-cancer data using TIMER. B The comparison of RNA-seq expression levels between bladder cancer tissue (n = 404) and normal control tissue (n = 28), *indicates a significant difference (p < 0.05). C SKA3 protein level in normal urinary bladder tissues (quantity: none; intensity: negative; staining: not detected; antibody HPA03972). D SKA3 protein level in bladder urothelial carcinoma (quantity: 75%-25%; intensity: weak; staining: low; antibody: HPA03972). E SKA3 expression based on molecular subtypes on BLCA. F SKA3 expression based on TP53 mutation status of BLCA. *P < 0.05, **P < 0.01, ***P < 0.001

In addition, RNA-seq of SKA3 from TCGA bladder cancer dataset and GTEx dataset revealed that SKA3 expression was obviously higher in bladder cancer tissue (n = 404) than in normal bladder samples (n = 28) (Fig. 1B). Additionally, we confirmed SKA3 expression in bladder cancer using immunohistochemical staining in HPA database. SKA3 protein expression was enhanced in bladder urothelial carcinoma tissue than normal bladder urothelial tissue in Figs. 1C and D. In contrast to normal bladder tissue, the results confirmed that SKA3 was overexpressed in bladder cancer tissues. We further analyzed SKA3 expression in various molecular subtypes and TP53 mutation using TCGA bladder cancer dataset. The results proclaimed that SKA3 expression was much higher in neuronal type than in other four subtypes, and SKA3 expression was also higher in TP53 mutant type than in TP53 nonmutant type (Figs. 1E and F).

Correlation between cancer patient prognosis and SKA3 expression level

Then, we explored the prognostic value of SKA3 in various kinds of carcinomas using two powerful databases. In PrognoScan, we investigated the association between SKA3 expression level and the prognosis of multiple cancers. Notably, SKA3 expression was significantly associated with seven kinds of cancers, including bladder, lung, ovarian, colorectal, breast, brain, and skin cancers (Fig. 2). In particular, SKA3 developed a pernicious effect on six kinds of these cancers, including bladder (OS: Cox P = 0.016, HR = 1.51, gross number = 165; DSS: Cox P = 0.00004, HR = 2.41, gross number = 165), lung [OS: Cox P = 0.00019, HR = 1.53, gross number = 117; RFS (relapse-free survival): Cox P = 0.000006, HR = 3.18, gross number = 204], ovarian (OS: Cox P = 0.03, HR = 1.61, gross number = 110), breast (DSS: Cox P = 0.0087, HR = 2.04, gross number = 236),brain(OS: Cox P = 0.005, HR = 2.07, gross number = 77), and skin cancers (OS: Cox P = 0.00084, HR = 4.48, gross number = 38). Meanwhile, SKA3 developed a conservatory effect in colorectal cancer (DFS: Cox P = 0.0385, HR = 0.60, gross number = 226).

Fig. 2
figure 2

Kaplan Meier survival curve assessed the relationship between SKA3 expression level and prognosis in Prognoscan. A, B OS (overall survival) and DSS (disease-specific survival) in cohort GSE13507 of bladder cancer. C OS in cohort GSE13213 of lung cancer. D RFS (relapse-free survival) in cohort GSE31210 of lung cancer. E OS in cohort GSE31210 of ovarian cancer. F DFS (disease-free survival) in cohort GSE14333 of colorectal cancer. G DSS in cohort GSE3499-GPL97 of breast cancer. H OS in cohort GSE4271-GPL97 of brain cancer cohort. I OS in cohort GSE19234 of skin cancer

Meanwhile, Kaplan–Meier curve analysis was executed to investigate SKA3-related survival using Kaplan–Meier plotter and the Cancer Genome Atlas (TCGA) datasets because PrognoScan information is primarily extracted from Gene Expression Omnibus (GEO) database. Notably, we unexpectedly found SKA3 as a pernicious prognostic element in KIRP (OS: HR = 2.6, log-rank P = 0.0024; DFS, HR = 3, log-rank P = 0.00024) (Figs. 3A and B). For LUAD, SKA3 was identified to play a detrimental factor (OS: HR = 1.9, log-rank P = 2.1e-05). However, SKA3 had no obvious significance in LUAD (DFS: HR = 1.3, log-rank P = 0.1) (Figs. 3C and D). The findings for breast, colorectal, and ovarian cancers were inconsistent with those on PrognoScan, as high SKA3 expression levels had no discernible effect on the prognosis of three cancers, including BRCA (OS: HR = 1.2, log-rank P = 0.23) (Fig. 3F), COAD (DFS: HR = 1.1, log-rank P = 0.61) (Fig. 3G), and OVC (OS: HR = 0.95, log-rank P = 0.67) (Fig. 3H). Specifically, only BLCA (DFS: HR = 1.8, log-rank P = 0.032) (Fig. 3E) results were consistent with previous predictions using PrognoScan. Consequently, high SKA3 expression is an independent risk factor for bladder cancer patients, rather than a protective factor.

Fig. 3
figure 3

Kaplan–Meier survival curves evaluated the connection between SKA3 expression and prognosis in various cancer kinds using Kaplan–Meier plotter. A, B OS and DFS survival curves of KIRP. A, B OS and DFS survival curves of LUAD. E DFS survival curves of BLCA. F OS survival curves of BRCA. G DFS survival curves of COAD. H OS survival curves of OVC

Gene ontology and KEGG enrichment analysis

To predict SKA3 function, TCGA-BLCA samples were divided into two groups according to their SKA3 expression quartile. Differentially expressed gene analysis was performed to determine 847 differentially expressed genes (DEGs) (|log2FC|> 1, Benjamin Hochberg adjusted P < 0.01). All genes were also displayed in a volcano plot to illustrate their distribution (Fig. 4A). To explore the latent functions associated with 847 genes, they were divided into upregulated group and downregulated group, and the GO enrichment analysis was conducted using biological process, molecular function, and cellular component in bubble plots (Fig. 4B and D). The results revealed that SKA3 was primarily linked to, nuclear division,organelle fission,mitotic nuclear division,mitotic sister chromatid segregation in upregulated group; and linked to, extracellular matrix organization, extracellular structure organization, humoral immune response,cell chemotaxis in downregulated group. Following that, KEGG pathway analysis revealed that the upregulated group was enriched and interacted with in cell cycle,DNA replication,oocyte meiosis,and p53 signaling pathway (Fig. 4C); and the downregulated group was enriched and interacted with in Cytokine − cytokine receptor interaction, Viral protein interaction with cytokine and cytokine receptor, Cell adhesion molecules, and Chemokine signaling pathway (Fig. 4E). These studies confirm that SKA3 is linked to a few malignancy-related pathways that lead to bladder cancer.

Fig. 4
figure 4

Function and pathway enrichment analyses for SKA3 in bladder cancer. A A volcano plot of all DEGs from indicated TCGA microarray data. Green dots represent significantly downregulated genes, and red dots represent significantly upregulated genes. B Gene ontology analysis of upregulated group, containing biological processes (BP), molecular function (MF), and cell component (CC). C Kyoto encyclopedia of genes and genomes analyzes pathways of upregulated group. D Gene ontology analysis of downregulated group. E Kyoto encyclopedia of genes and genomes analyzes pathways of downregulated group

Confirmation of SKA3 Expression by Immunohistochemistry

To investigate the expression of SKA3 in baldder cancer, SKA3 protein levels were examined using immunohistochemistry in 25 cases of bladder cancer tissues and 17 cases of normal bladder tissues. We found negative SKA3 immunostaining in normal urothelial tissues. Increased cytoplasm SKA3 staining was found in 21 of 25 cases of bladder cancer (Fig. 5). An independent sample t-test of SPSS 26.0 was used to compare the IRS between the two groups, and the results indicated that the protein expression of SKA3 in bladder cancer tissues was significantly higher than that in noncancerous tissues (P < 0.05).

Fig. 5
figure 5

Results of SKA3 immunohistochemistry. A The weak positive expression of SKA3 in bladder cancer tissues. B The moderate strong positive expression of SKA3 in bladder cancer tissues. C The strong positive expression of SKA3 in bladder cancer tissues

Correlation between immune cell infiltration and SKA3

Immune cell infiltration surrounding tumors was confirmed to be intimately associated with the clinical outcome of cancer patients [21, 22]. Consequently, we defined if SKA3 expression was connected to immune infiltration level by examining their correlation in bladder cancer using TIMER. This analysis revealed that SKA3 expression level in BLCA was positively correlated with the infiltration levels of CD8 + T cells, macrophages, neutrophils, and dendritic cells. Besides, it had a negative association with CD4+ T cell infiltration levels (Fig. 6A). The preceding investigation indicated that SKA3 influences patient prognosis via its interaction with immune cell infiltration in bladder cancer. We then investigated the connection between SKA3 and immune checkpoint genes in TIMER database. SKA3 was closely correlated with immune checkpoint genes in most tumors, including bladder cancer (Fig. 6B).

Fig. 6
figure 6

Correlation between SKA3 expression level with immune infiltration. A Correlation between SKA3 expression level and infiltration level of each immune cell in bladder cancer in TIMER. B Correlation between SKA3 and immune checkpoint genes are shown in a heatmap. *P < 0.05, **P < 0.01, ***P < 0.001

SKA3 expression was related to M2 macrophage and Th2 cell infiltration and polarization

The immune infiltration state in tumor microenvironment (TME) can affect patient prognosis, and previous results indicate that SKA3 overexpression was connected to deleterious prognosis in bladder cancer patients. This implies that prominent SKA3 expression promotes the proliferation and metastasis of bladder tumors, which may be tightly associated with immunosuppressive tumors. Accordingly, we further explored whether SKA3 expression was linked to immune infiltration levels of macrophages and CD4+ T cells. SKA3 expression was found to be positively associated with macrophage infiltration level using four algorithms, including EPIC (R = 0.198, P = 1.28E-04) (Fig. 7A), TIMER (R = 0.121, P = 2.00E-02) (Fig. 7B), XCELL (R = 0.109, P = 3.68E-02) (Fig. 7C), and MCPCOUNTER (R = 0.174, P = 7.92E-04) (Fig. 7D). Besides, SKA3 expression was positively linked to CD4 + memory-activated T cells infiltration level in two different algorithms, including CIBERSOFT (R = 0.177, P = 6.28E-04) (Fig. 7E) and CIBERSOFT-ABS (R = 0.176, P = 6.95E-04) (Fig. 7F). For CD4+ T cell polarization, SKA3 expression was positively linked to Th2 cells polarization (R = 0.639, P = 1.48E-43) (Fig. 7G). For macrophage polarization, SKA3 expression was positively correlated with M2 polarization (R = 0.119, P = 2.19E-02) (Fig. 7H). In addition, SKA3 expression in bladder cancer was significantly correlated with M2 macrophage markers, including MRC1 (Fig. 8A) and CD163 (Fig. 8B), and Th2 cell markers, including CCR3 (Fig. 8C) and IL-4 (Fig. 8D). The above findings support that high SKA3 expression is connected with immunosuppressive microenvironment of bladder cancer via infiltration and polarization of M2 macrophages and Th2 cells. M2 macrophages are derived from M1 activation by factors such as IL-4 and IL-13 and have the potential to suppress immune responses, promote angiogenesis, tissue repair and promote tumor growth. Tumor tissues mostly secrete Th2-like cytokines and the body is in a state of Th2 cell dominance as one of the mechanisms of tumor immune escape.

Fig. 7
figure 7

Connection among SKA3 expression with macrophage and CD4 + T cell infiltration and polarization. AD Correlation between SKA3 expression and macrophage infiltration levels using four different algorithms: EPIC, TIMER, XCELL, and MCPCOUNTER. E, F Correlation between SKA3 expression and CD4 + memory activated T cells infiltration levels using two different algorithms: CIBERSOFT and CIBERSOFT-ABS. G Correlation between SKA3 expression and CD4 + Th2 cell infiltration levels using algorithms of XCELL. H Correlation between SKA3 expression and M2 macrophage infiltration levels using CIBERSOFT-ABS algorithms

Fig. 8
figure 8

Correlation between SKA3 expression and gene markers of M2 macrophages and TH2 cells. A Correlation among SKA3 expression with MRC1 in bladder cancer. B Correlation among SKA3 expression with CD163 in bladder cancer. C Correlation among SKA3 expression with CCR3 in bladder cancer. D Correlation among SKA3 expression with IL-4 in bladder cancer

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