Identification of immune-related genes that predict prognosis and risk of bladder cancer: bioinformatics analysis of TCGA database

Results

Identification of differentially expressed IRGs

Compared with normal tissues, we identified 4876 differentially expressed genes (DEGs) in BLCA tissues including 3453 upregulated and 1423 downregulated genes (Figure 1A and Supplementary Figure 1A). We extracted 120 upregulated and 140 downregulated IRGs corresponding to those identified in the ImmPort database (Figure 1B and Supplementary Figure 1B). In addition, we performed GO functional enrichment and KEGG pathway analyses in 260 differentially expressed IRGs (DEIRGs). The top ten functional annotations were shown in Supplementary Table 1. The DEIRGs were mostly enriched in cell migration, leukocyte migration, extracellular matrix, receptor complex, receptor ligand activity and cytokine activity (Supplementary Figure 2A–2C). Furthermore, cytokine–cytokine receptor interactions were enriched in the KEGG pathways (Supplementary Figure 2D).

Figure 1. Volcano plot of differentially expressed genes and immune-related genes. (A) Volcano plot of differentially expressed genes between bladder cancer (BLCA) and non-tumor tissues. (B) Volcano plot of differentially expressed immune-related genes between bladder cancer (BLCA) and non-tumor tissues. The green dots represent downregulated genes, the red dots represent upregulated genes, and the black dots represent genes that were not significantly differentially expressed.

Identification of prognosis-associated DEIRGs

To determine possible prognosis-associated DEIRGs, univariate Cox regression analysis of DEIRGs were performed in the present study. After screening, we identified 51 DEIRGs that significantly correlated with the overall survival of BLCA patients (Figure 2). Similar to the results for the DEIRGs, we found that these prognosis-associated DEIRGs were most enriched in cell migration, cell proliferation, extracellular matrix, receptor ligand activity and growth factor activity (Supplementary Table 2 and Figure 3A–3C). Human cytomegalovirus infection and the MAPK signaling pathway were most enriched in the KEGG analysis (Figure 3D).

Figure 2. Prognosis-associated DEIRGs. Forest plot of hazard ratios showing the prognostic values of IRGs.

Figure 3. Gene functional enrichment of prognosis-associated DEIRGs. (A) The top ten most significant biological processes in the gene ontology. (B) The top ten most significant cellular components in the gene ontology. (C) The top ten most significant molecular functions in the gene ontology. (D) The top ten most significant KEGG pathways.

Significant modular analysis based on a PPI network

A PPI network was established utilizing the 51 prognosis-associated DEIRGs, shown in Figure 4A. In the PPI network, 27 hub genes were identified by the MCODE plugin of Cytoscape. When the k-core = 2, four significant module subgroups were obtained and were showed in different colors, and the most important modules, including THBS1, PGF, SPP1, TGFB3, ELN, OXTR, PROK1, AGTR1, TACR1, and EDNRA, were marked in green (Figure 4B–4E). As shown in Figure 4F, functional annotation indicated the 27 hub genes were mostly related in sprouting angiogenesis, positive regulation of leukocyte chemotaxis, regulation of smooth muscle cell proliferation, MHC class I peptide loading complex, response to testosterone, and embryo implantation.

Figure 4. Significant modular analysis and functional enrichment analysis based on the PPI network. (A) Construction of a PPI network using a total of 51 prognosis-associated DEIRGs. (B) The most significant module subgroup of the hub genes, identified by MCODE plug-in, contains ten genes. (C) Module 2 contains ten hub genes. (D) Module 3 contains four hub genes. (E) Module 4 contains three hub genes. (F) Functional enrichment analysis of the 27 hub genes in the PPI network.

Construction of a transcription factor (TF) regulatory network

Next, we investigated the relationships between the DEIRGs in BLCA and the cancer-associated transcription factors (TFs). In total, we identified 77 differentially expressed TFs between BLCA (n = 414) and para-cancer tissues (n = 19) (Figure 5A, Supplementary Figure 3). Subsequently, using correlation scores > 0.4 and p values <0.05 as reference values, combined with 16 TFs and 51 DEIRGs, we constructed a TF regulatory network to illustrate the correlation between TFs and IRGs (Figure 5B).

Figure 5. TF regulatory network. (A) Volcano plot of differentially expressed TFs. The green dots represent downregulated TFs, the red dots represent upregulated TFs, and the black dots represent TFs that were not significantly differentially expressed. (B) Regulatory network of TFs and IRGs; the yellow nodes represent TFs that correlated with the IRGs, the red nodes represent IRGs with hazard ratios < 1 (p < 0.05), the purple nodes represent IRGs with hazard ratios > 1 (p < 0.05) (correlation coefficient > 0.4 and p < 0.05), the green lines indicate negative regulatory relationships, and the red lines indicate positive regulatory relationships.

Construction of a prognostic risk model

Multivariate Cox regression analysis were performed to calculate a risk score for each patient as follows: Risk score = (−0.0067 ×TAP1(expression)) + (0.0004 ×MMP9(expression)) + (0.0616 ×ADIPOQ(expression)) + (0.0359 ×PDGFRA(expression)) + (0.01261 ×AHNAK(expression)) + (0.0295 ×RAC3(expression)) + (0.0066 ×OLR1(expression)) + (0.0263 ×IL34(expression)) + (0.0149 ×NAMPT(expression)) + (0.0172 ×PPY(expression)) + (−0.0720 × SH3BP2(expression)). Based on the risk scores, patients with bladder cancer were divided into high-risk (n = 186) and low-risk (n = 185) groups. Kaplan–Meier analysis revealed that the survival rate significantly favored the low-risk group (p < 0.001) (Figure 6A). The AUC of the ROC curve was 0.745, suggesting moderate effectiveness for the prognostic risk model for monitoring survival (Figure 6B). Figure 6C–6E represented the risk scores, survival status, and heatmap of the ten IRGs between the two groups.

Figure 6. Prognostic risk model of the cohort. (A) Kaplan–Meier curve analysis showed that patients with a high-risk score were correlated with a worse survival outcome (p < 0.05). (B) ROC curve analysis of the prognostic risk model. (C) Risk score distribution of patients in the prognostic risk model in the cohort. (D) Survival status scatter plots for patients in the prognostic risk model. (E) Heatmap showing the distribution of the expression of the 11 immune-related genes in the cohort.

Independent prognostic value of the risk model of the cohort

We then performed univariate and multivariate Cox regression analysis to determine the efficacy of risk score derived from our prognostic risk model as an independent predictor after adjusting for other clinical parameters (Table 1). Univariate analysis revealed that age, clinical stage, tumor stage (T), lymph node (N), and risk score were significantly related to the prognosis of BLCA patients (p < 0.05). Multivariate Cox regression analysis demonstrated that the risk score was independently corelated with the overall survival in the cohort (p < 0.001) (Figure 7).

Table 1. Univariate and multivariate Cox regression analyses of the cohort.

	Univariate analysis			Multivariate analysis
	HR	95%CI	P-value	HR	95%CI	p-value
Age	1.029	1.001-1.057	0.04545	1.022	0.993-1.052	0.13814
Gender	0.616	0.352-1.078	0.08953	0.873	0.466-1.635	0.67126
Stage	1.863	1.293-2.683	0.00084	1.064	0.519-2.181	0.86637
T	1.769	1.193-2.622	0.00453	1.440	0.857-2.418	0.16832
M	2.167	0.779-6.025	0.13822	0.907	0.274-3.000	0.87234
N	1.573	1.196-2.070	0.00121	1.227	0.728-2.068	0.44174
Risk score	1.398	1.265-1.544	4.33e-11	1.327	1.174-1.500	6.28e-06
HR, hazard ratio; CI, confidence interval; T, tumor stage; M, metastasis; N, Lymph nodes.

Figure 7. Univariate and multivariate independent prognostic analysis of the cohort. (A) Univariate Cox regression analysis showed clinical stage, tumor, lymph nodes, and risk score were associated with the prognosis of BLCA patients. (B) Multivariate Cox regression analysis revealed that the risk score was independently associated with OS in the cohort.

External validation of the prognostic risk model in the GSE13507 and GSE32894 cohorts

To validate the reliability of our IRG-based prognostic risk model, we utilized two external validation cohorts, GSE13507 and GSE32894. Similarly, patients in the high-risk group showed poorer survival than in the low-risk group (Figure 8A, 8B). In addition, the results were also shown with a KM curve and a ROC curve (Figure 8C–8F). The cumulative results imply that the IRG-based prognostic risk characteristics based on IRGs can be used as a reliable prognostic model.

Figure 8. Validation of the risk signature in GSE13507 and GSE32894 datasets. (A, B) Heatmap of the 11 IRGs expression distribution, risk score distribution, and survival status between the low-risk group and high-risk group in the validation cohort. (C, D) Kaplan–Meier curve showed shorter survival time in the high-risk group patients. (E, F) ROC curve illustrated the prognostic value of the risk signature.

Validation of the IRGs in the proposed signature by western blot

To further validate the proposed signature at the protein level, we performed western blotting on bladder cancer tissues, adjacent tissues, SV-HUC-1, and T24 cells lines. Comparing the gene levels of RNA-seq from TCGA dataset (data not shown) with the expression levels of WB protein, our results indicated that the protein levels of ADIPOQ, NAMPT, TAP1, OLR1, AHNAK, PDGFRA, IL34, MMP9 and RAC3 were consistent with their RNA expression trends (Figure 9).

Figure 9. Validation of the IRGs by western blot. Validation of the IRGs by western blot on bladder cancer tissues and adjacent tissues, human normal bladder epithelial cells (SV-HUC-1), and a bladder cancer cell line (T24).

Clinical application of the prognostic model

To assess the efficacy of our model in predicting the progression of BLCA, we evaluated the correlations between the risk signatures and clinical parameters including age, sex, pathological grade and clinical stage in the TCGA data cohort (Table 2). As the levels of PPY, NAMPT, ADIPOQ, IL34, RAC3, TAP1, AHNAK, PDGFRA and the risk score increased, the pathological grade of BLCA patients increased (p < 0.01) (Figure 10A–10I). With the increase of the other factors (risk score and PDGFRA and AHNAK levels), the clinical stage and the tumor stage of patients with BLCA also increased (all p < 0.01) (Figure 10J–10O). These results demonstrate that the dysregulation of the expression of the DEIRGs is significantly associated with the development of BLCA.

Table 2. Relationships between prognostic model associated IRGs and clinical variables of patients with BLCA.

Variables	Age (≤65/≥65) t(p)	Gender (Female/Male) t(p)	Pathological grade (High grade/Low grade) t(p)	Clinical stage (3&4/1&2) t(p)	Tumor stage (T3&4/T1&2) t(p)	Lymph nodes (N1&2/N0) t(p)
TAP1	1.132(0.259)	-0.104(0.917)	9.129(3.073e-13)	1.298(0.197)	1.006(0.316)	1.492(0.137)
MMP9	-0.974(0.331)	-0.888(0.376)	1.663(0.097)	-1.16(0.247)	-1.206(0.229)	-1.003(0.318)
ADIPOQ	-2.377(0.018)	1.167(0.246)	3.645(3.115e-04)	-0.339(0.735)	-0.662(0.509)	0.233(0.816)
PDGFRA	0.366(0.714)	1.446(0.150)	4.534(9.997e-05)	-3.645(3.199e-04)	-4.011(7.503e-05)	-1.492(0.137)
AHNAK	0.087(0.931)	0.901(0.369)	6.793(2.305e-07)	-3.435(7.172e-04)	-2.989(0.003)	-2.321(0.021)
OLR1	-0.016(0.987)	-0.369(0.712)	2.12(0.047)	-1.851(0.066)	-2.358(0.019)	-0.388(0.698)
RAC3	-1.8(0.073)	0.886(0.377)	4.227(1.542e-04)	-1.774(0.078)	-1.059(0.291)	-0.581(0.562)
IL34	-0.849(0.397)	0.352(0.725)	5.066(7.503e-07)	-1.974(0.049)	-2.444(0.015)	-2.053(0.042)
NAMPT	-0.698(0.486)	2.224(0.028)	4.5(1.488e-04)	-1.933(0.054)	-1.62(0.106)	0.682(0.495)
PPY	-1.385(0.167)	-0.174(0.862)	4.995(9.603e-07)	-1.524(0.128)	-1.795(0.074)	0.139(0.890)
SH3BP2	0.983(0.327)	-2.401(0.017)	-0.586(0.566)	2.384(0.019)	1.327(0.186)	1.974(0.049)
Risk Score	-1.426(0.155)	1.464(0.146)	5.272(2.433e-07)	-4.304(2.323e-05)	-3.926(1.101e-04)	-1.371(0.172)
t, t value determined using the Student t test; p, p value determined using the Student t test.

Figure 10. Relationships between prognostic-model-associated IRGs and clinical variables in the TCGA cohort. (A) PPY expression and pathological grade. (B) NAMPT expression and pathological grade. (C) ADIPOQ expression and pathological grade. (D) IL34 expression and pathological grade. (E) RAC3 expression and pathological grade. (F) TAP1 expression and pathological grade. (G) AHNAK expression and pathological grade. (H) PDGFRA expression and pathological grade. (I) Risk score and pathological grade. (J) AHNAK expression and clinical stage. (K) PDGFRA expression and clinical stage; (L) Risk score and clinical stage. (M) AHNAK expression and tumor stage. (N) PDGFRA expression and tumor stage. (O) Risk score and tumor stage.

Inferred immune cell fractions of the high- and low-risk groups

To clarify whether our prognostic risk model can reflect the status of tumor immune microenvironment in BLCA patients, we evaluated the correlation between risk scores and immune cell infiltrations estimated by the CIBERSORT algorithm (Figure 11). The correlations between the risk score of the prognostic signature and immune cell infiltration are shown in Figure 12. As the risk score increased, the numbers of neutrophils, M2 macrophages, and CD8⁺ T cells in BLCA tissues increased (Figure 12).

Figure 11. Summary of the 22 immune cell subtypes estimated by the CIBERSORT algorithm. The bar charts exhibit the cell proportions of BLCA patients and various colors represent the 22 immune cells with annotations below the legend.

Figure 12. The correlation between the risk score and immune cell infiltration in the cohort. (A) B cells memory. (B) Dendritic cells activated. (C) Dendritic cells resting. (D) Eosinophils. (E) Macrophages M0. (F) Macrophages M1. (G) Macrophage M2. (H) Mast cell activated. (I) Mast cell resting. (J) Monocytes. (K) Neutrophils. (L) NK cells activated. (M) NK cells resting. (N) Plasma cells. (O) T cells CD4 memory activated. (P) T cells CD4 memory resting. (Q) T cells CD4 naïve. (R) T cells CD8. (S) T cells follicular helper. (T) T cells gamma delta. (U) T cells regulatory (Tregs).

Abbreviations

BLCA: bladder cancer; IRGs: immune-related genes; DEIRGs: differentially expressed immune-related genes; NMIBC: non muscle invasive bladder cancer; MIBC: muscle-invasive bladder cancer; TCGA: The Cancer Genome Atlas; FC: fold-change; TF: transcription factor; AUC: area under the curve; ROC: receiver operating characteristic; KM: Kaplan Meier.

Author Contributions

Conceptualization, Liqiang Guo; Methodology, Shengtian Zhao; Data curation, Zhen Ma; Writing Original Draft Preparation, Qiong Wu; Writing and Editing, Liqiang Guo; Project Administration, Mingzhen Yuan; Funding Acquisition, Shengtian Zhao. All authors have read and agreed to the submitted version of the manuscript.

Conflicts of Interest

The authors declare no conflicts of interest.

Funding

This study was funded by Clinical Research Center of Shandong University (2020SDUCRCC001), Shandong Province Natural Science Fund (ZR2020QH067), the Jinan Clinical Medical Technology Innovation Program (201907063), National Natural Science Funds of China (81670686, 81970578), and Engineering laboratory of urinary organ and functional reconstruction of Shandong Province.

Editorial Note

This corresponding author has a verified history of publications using a personal email address for correspondence

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