Introduction

Bladder cancer (BLCA) is the most common malignancy of the urinary system, with about 549,000 new cases and 200,000 death worldwide in 20181,2. BLCA is characterized by a high rate of recurrence and progression, which impose a considerable economic burden on the healthcare system and have substantial effects on the quality of life and overall outcome of BLCA patients. Although novel treatments for BLCA have been proposed, such as immunotherapy of PD-1/PD-L13, the 5-year survival rate for advanced/metastatic BLCA is only 15%, and the median overall survival is less than 15 months4. Early diagnosis and recurrence monitoring of BLCA would be valuable for a better prognosis. However, the predictive accuracy of commonly used predictors, including patients’ general condition, histological grade, and pathological stage, is insufficient to identify the patients who need invasive treatment5. Recently, the biomarker-based signature is regarded as a promising tool to predict the prognosis of BLCA patients and assist clinical decisions6.

Autophagy is a highly conserved cellular self-degradative process. Some cancers could use autophagy-mediated recycling to maintain mitochondrial function and energy homeostasis to meet their elevated metabolic demand for growth and proliferation7. Therefore, autophagy-based prognostic signature has been widely investigated, and autophagy inhibition has been proposed as a novel cancer therapy strategy8. As reported, autophagy-related genes (ARGs) can effectively select high-risk colorectal cancer patients who require more aggressive therapeutic interventions9. Liu et al. constructed a 22-ARGs-based signature that could independently predict the overall survival (OS) in TCGA lung adenocarcinoma10. ARGs-based signature's predictive value has also been validated in glioblastoma11 and breast cancer12. However, the clinical relevance and prognostic significance of ARGs-based signature in BLCA remain unknown.

In present study, we aimed to develop a reliable prognostic model for BLCA patients using multiple ARGs and investigate its clinical implications. Furthermore, we evaluated the correlation between present prognostic model and tumor microenvironment (TME) and explored the underlying mechanism. Finally, to facilitate clinical utility of the model mentioned above, a systematic prognostic signature was constructed by integrating clinical predictors and ARGs-based molecular biomarkers.

Materials and methods

Data source and preprocessing

Transcriptomic data (RNA-Seq FPKM) and clinical information were downloaded from The Cancer Genome Atlas (TCGA) portal (https://portal.gdc.cancer.gov/), including 413 BLCA data and 19 non-tumor data. After integrating those data through ID numbers, the gene measured with multi-probes were replaced with their average via limma package (http://www.bioconductor.org/packages/release/bioc/html/limma.html)13. Then, 371 patients with follow-up time > 90 d and complete data were selected for further analyses. All data were processed and analyzed with R software 3.6.0 (https://www.r-project.org/), and the flow diagram of present study was shown in Fig. 1.

Figure 1
figure 1

Flow diagram of the present study.

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Differential expression analysis and ARGs identification

DEGs between tumor tissues and normal tissues were analyzed through Wilcoxon test. p-value was adjusted with FDR, and filter criteria was FDR < 0.05 and|log2 fold-change [FC]|> 1. DEARGs were identified by matching the DEGs with the latest list of ARGs obtained from the Human Autophagy Database (HADb) website (http://www.autophagy.lu/index.html). Univariate Cox regression analysis was used to identify possible PDEARGs (P < 0.05).

Prognostic signature construction and validation

Survival R package (https://cran.r-project.org/web/packages/survival/index.html) was adopted to construct a PDEARGs-based prognostic model. To avoid overfitting, PDEARGs that correlated highly with other genes were deleted. Then, Cox proportional hazards regression was used to build a prognostic risk model, and the regression coefficients were used to weight the expression value of selected PDEARGs. The risk score of each patient was calculated using the following formula14:

$${\text{Risk}}\;{\text{score}} = \mathop \sum \limits_{i = 1}^{n} \;{\text{coefficient}} \left( {{\text{gene}}\;{\text{i}}} \right)*{\text{Expression}}\;{\text{value}}\;{\text{of}}\left( {{\text{gene}}\;{\text{i}}} \right)$$

Individuals were separated into high-risk or low-risk groups according to the median risk score. Subsequently, survival analysis and receiver operating characteristic (ROC) analysis were performed as reported6. The expression level of the five selected PDEARGs between two risk groups was analyzed with R software 3.6.0.

Quantitative real-time PCR (qRT-PCR) analysis

The expression level of five selected PDEARGs between tumor and adjacent tissues were further detected through qRT-PCR. Briefly, the BLCA samples used for present assay were collected from three BLCA patients who underwent radical cystectomy at Xijing Hospital, and corresponding paracancerous tissue were used as control. The clinical characterization of selected patients was shown in Supplementary Table 1. Then, TRIzol™ Reagent (Cat#15596018, Invitrogen, USA) was used to isolate the total RNA according to the manufacturer’s protocol. The RevertAid First Strand cDNA Synthesis Kit (Cat#RR036A, TAKARA, Japan) was used to synthesize cDNA from 500 ng of total RNA. qRT-PCR was carried out by using TB Green Premix Ex Taq II Kit (Cat#RR820A, TAKARA, Japan) on a BioRad CFX96 system. GAPDH served as an endogenous control. The sequences of the primers are listed in Supporting Data 1. All experiments reported in this study were conducted according to an experimental protocol approved by the Research Ethics Committee of the Fourth Military Medical University. All methods were performed in accordance with the relevant guidelines and regulations, and all of the participating patients gave their informed written consent.

Independent prognostic factors analysis

Univariate Cox regression analysis was performed to identify factors affecting the OS of BLCA patients. Then, multivariate Cox regression analysis was used to evaluate whether the risk score generated from the present prognostic model could be used as an independent prognostic factor. P < 0.05 was considered statistically significant.

Clinical utility of prognostic signature

The relationship between risk factors of the present model and certain clinical variables (i.e., age, gender, histological grade, pathological stage, and tumor-node-metastasis (TNM) status) was analyzed with t-test. The box plot was prepared with beeswarm R package (https://cran.r-project.org/web/packages/beeswarm/index.html), and the impact of PDEARGs on BLCA prognosis was analyzed through survival R package (https://cran.r-project.org/web/packages/survivalAnalysis/index.html).

Correlation between risk score and TME

Tumor purity, infiltrating stromal and immune cells of TCGA-BLCA were assessed through ESTIMATE R package (https://r-forge.r-project.org/projects/estimate/) as previously reported15. The relative fraction of 22 TIICs types in each sample was quantified by the CIBERSORT method and LM22 signature matrix (https://rdrr.io/github/singha53/amritr/src/R/supportFunc_cibersort.R)16,17. The algorithm ran at 100 permutations with a threshold of P < 0.05 to select eligible patients for further analysis18. The correlation between risk score and TME was analyzed with the Pearson correlation coefficient test, and the impact of TME on clinical variables was evaluated as well.

Enrichment analysis of PDEARGs

GO function enrichment of the five selected PDEARGs was performed via clusterProfiler and enrichplot R package (http://master.bioconductor.org/). FDR < 0.05 was considered statistically significant. Then, the top 10 terms involved with different GO functions were plotted. To better explain the relationship between PDEARGs and GO function, a chord plot of representative terms was constructed with Goplot R package (http://wencke.github.io/).

Construction and validation of PDEARGs-based systematic prognostic signature

A systematic prognostic signature was constructed through a similar method mentioned above by integrating seven clinical variables (i.e., age, gender, histological grade, pathological stage, and TNM status) with the signature discussed above. The variables highly correlated with others were deleted to avoid overfitting, and the regression coefficients were used to weight selected variables. The median of the systematic risk was used to separate the patients into two risk groups, and survival probability was analyzed using R software 3.6.0. Predictive accuracy of the novel systematic signature was evaluated with survivalROC R package (https://cran.r-project.org/web/packages/survivalROC/index.html). Finally, the systematic signature was visualized through a nomogram constructed by rms R package (https://cran.r-project.org/web/packages/rms/index.html).

External validation of both prognostic signature

GSE13507 and GSE31684 cohorts with gene expression data and clinical information were downloaded from gene expression omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/). Patients with follow-up time < 90 d or other incomplete data were removed. Then, the risk score and systematic risk of 143 patients were calculated. The efficacy of present signatures was validated in external datasets with survival analysis. The prognostic value of selected PDEARGs was estimated as well.

Results

Identification of BLCA specific PDEARGs

We obtained 3126 differentially expressed genes (DEGs) based on the TCGA-BLCA dataset, among which 1223 genes were downregulated, and 1903 genes were upregulated in tumor tissues compared with normal tissues (false discovery rate (FDR) < 0.05, |log2 FC|> 1; Fig. 2A). Then, 34 BLCA-specific differentially expressed ARGs (DEARGs) were identified (Fig. 2B), and five prognostic DEARGs (PDEARGs) were found to be significantly associated with the OS of BLCA patients (P < 0.05; Fig. 2C). Among the five PDEARGs, APOL1 (apolipoprotein L1) with hazard ratio ≤ one was regarded as a protective gene, while the other four genes [DIRAS3 (DIRAS family, GTP-binding RAS-like 3), NAMPT (Nicotinamide phosphoribosyltransferase), P4HB (Proly 4-hydroxylase beta polypeptide), and SPHK1 (Sphingosine kinase-1)] were identified as high-risk genes predicting a poor prognosis of BLCA.

Figure 2
figure 2

Identification of BLCA specific DEGs, DEARGs, and PDEARGs. (A) Heat map and volcano plot of DEGs. (B) Heat map and volcano plot of DEARGs. The green to red spectrum indicates low to high gene expression in the heat map; the red, green and black dots represent upregulated, downregulated and unchanged genes in the volcano plot, respectively. (C) Forest graph of PDEARGs. The red and green dots represent PDEARGs with a hazard ratio > 1 and ≤ 1, respectively.

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Construction of PDEARGs-based prognostic signature

The regression coefficients were employed to construct a five PDEARGs-based prognostic risk model (Table 1). Subsequently, the risk score for each patient was calculated with the following computational formula:

$$\begin{aligned} {\text{Risk}}\;{\text{score}} & = \left( { - 0.0019 \times {\text{expression}}\;{\text{of}}\;APOL1} \right) + \left( {0.0775 \times {\text{expression}}\;{\text{of}}\;DIRAS3} \right) \, \\ & \quad + \left( {0.0127 \times {\text{expression}}\;{\text{of}}\;NAMPT} \right) + \left( {0.0030 \times {\text{expression}}\;{\text{of}}\;P4HB} \right) \\ & \quad + \left( {0.0120 \times {\text{expression}}\;{\text{of}}\;SPHK1} \right). \\ \end{aligned}$$
Table 1 Construction of five PDEARGs-based prognostic risk model.
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Individuals were sorted into a high-risk group (n = 185) and a low-risk group (n = 186) by the median risk score of 1.02. Survival analysis indicated that the prognosis was poorer in the high-risk group than in the low-risk group (P < 0.001; Fig. 3A). Precisely, the 5-year OS rate in the high-risk group was 33.2%, while it was 59.5% in the low-risk group. Then, we analyzed the distribution of each patient's risk score and survival status, and a large amount of death existed in the high-risk group (Fig. 3B). As well, a heat map and a series of box plots were generated to depict the expression level of the five selected PDEARGs, among which the protective gene (APOL1) was downregulated, and the other four risk genes were upregulated in patients with high-risk score (Fig. 3B, D). The area under the curve (AUC) was 0.724, which was much higher than other clinical parameters, suggesting that the present model was more accurate in predicting BLCA patients' OS (Fig. 3C).

Figure 3
figure 3

Prognostic value of present risk model. (A) Survival analysis between high-risk and low-risk groups. The 95% confidence interval was shown as a light-colored background around the Kaplan–Meier curve. (B) Risk plot encompassing the distribution of risk score, survival status, and risk genes expression of each patient. (C) ROC curve analysis of different variables. female = 1, male = 0. (D) Expression level of the five selected PDEARGs between low-risk and high-risk group. (E) Expression level of the five selected PDEARGs between tumor and adjacent normal tissue (n = 3). Data are presented as the means ± SD from 3 independent experiments.

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To validate the RNA-seq data, we analyzed the expression level of five selected PDEARGs using qRT-PCR. As shown in Fig. 3E and Supplementary Table 2, the protective gene (APOL1) was downregulated, while the other four risk genes were upregulated in tumor, compared with the adjacent normal tissue, which were consistent with the RNA-seq data above.

Independent prognostic value of present signature

As shown in Fig. 4A, the variables of pathological stage, tumor-node-metastasis (TNM) status, and risk score were associated with the prognosis of BLCA patients (P < 0.05). Multivariate analysis showed that the risk score was an independent prognostic factor for OS (P < 0.01; Fig. 4B). The hazard ratio for the risk score was 1.695, indicating a high-risk score would predict a bad prognosis. The commonly used clinical variables, such as age, gender, pathological stage, and TNM status, were insufficient to serve as independent prognostic predictors (P > 0.05).

Figure 4
figure 4

Univariate and multivariate Cox regression analysis. (A) Univariate Cox regression analysis to identify prognosis associated factors. (B) Multivariate Cox regression analysis to assess independent prognostic factors. The red and green dots represent variables with hazard ratio > 1 and ≤ 1, respectively.

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Clinical utility of present signature

The relationship between present model and clinical variables was analyzed (Table 2). High-expression of APOL1 was associated with decreased pathological stage, N status, and M status (P < 0.05; Fig. 5B–D). Contrarily, as the value of risk score or the expression of the other four risk genes (DIRAS3, NAMPT, P4HB, and SPHK1) increased, the histological grade, pathological stage, T status, or N status of BLCA patients increased (P < 0.05; Fig. 5E–U). Furthermore, low-expression of APOL1 and increased risk score were observed in patients with age > 65 (P < 0.05; Fig. 5A–Q). Survival analysis showed that high expression of the protective gene APOL1 indicated a good prognosis (P < 0.01; Fig. 6A), while increased expression of risk gene P4HB resulted in a poor prognosis (P < 0.05; Fig. 6D). The other three risk genes (DIRAS3, NAMPT, and SPHK1) had no significant effect on survival outcome (P > 0.05; Fig. 6B, C, and E).

Table 2 Clinical utility of prognostic model related factors [t-value (P value)].
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Figure 5
figure 5

Relationship between prognostic model related factors and clinical variables. (A–D) APOL1 and clinical variables. (E–G) DIRAS3 and clinical variables. (H–J) NAMPT and clinical variables. (K–M) P4HB and clinical variables. (N–P) SPHK1 and clinical variables. (Q–U) Risk score and clinical variables. Box plots with P < 0.05 are shown.

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Figure 6
figure 6

Survival analysis of prognostic model related genes. Kaplan–Meier curve of patients with different expression levels of APOL1 (A), DIRAS3 (B), NAMPT (C), P4HB (D), SPHK1 (E). The median expression level was used as the cut-off value. Red line and blue line represent high expression and low expression group, respectively.

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Correlation between risk score and TME

As shown in Fig. 7A–D, with the increase of risk score generated by present prognostic signature, tumor-infiltrating stromal cells (i.e., stromal score) and estimate score (sum of stromal score and immune score) significantly increased, meanwhile tumor purity significantly decreased (P < 0.05). Additionally, 22 types of tumor-infiltrating immune cells (TIICs) in TCGA-BLCA were analyzed with CIBERSORT, and the content of neutrophils, macrophages M0 and M2 increased with risk score (P < 0.05; Fig. 7E–H). Furthermore, a high proportion of macrophages M0 could lead to a poor prognosis, accompanied by increased pathological stage, N status, and M status of BLCA patients (P < 0.05; Fig. 7I–L), indicating that tumor-associated macrophage (TAM) might involve with PDEARGs mediated biological process.

Figure 7
figure 7

The correlation between risk score and TME. (A) Risk score and stromal score. (B) Risk score and immune score. (C) Risk score and ESTIMATE score. (D) Risk score and tumor purity. (E) Risk score and neutrophils. (F) Risk score and macrophage M0. (G) Risk score and macrophage M1. (H) Risk score and macrophage M2. (I–L) Macrophage M0 and clinical variables.

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GO enrichment analysis

To further explore the relationship between PDEARGs and TAM, Gene Ontology (GO) enrichment analysis was employed. The five selected PDEARGs were associated with 52 biological processes (BPs), six molecular functions (MFs), and nine cellular components (CCs), among which immunologic process-related GO terms were significantly enriched, such as the activation of microglial cell, leukocyte, and macrophage as well as inflammatory response. Besides, oxidative stress and relevant apoptotic signal pathway were significantly enriched (FDR < 0.05; Fig. 8A). Therefore, TAM might be regulated by PDEARGs. To better explain the relationship between PDEARGs and GO terms, a chord plot was constructed, and three essential genes (SPHK1, P4HB, and NAMPT) that might participate in macrophage regulation were found (Fig. 8B).

Figure 8
figure 8

GO enrichment analysis of PDEARGs. (A) Bar plot of enriched GO terms. (B) Chord plot of enriched GO terms. FDR < 0.05 was considered statistically significant.

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Construction of PDEARGs-based systematic signature

To promote clinical application and predictive accuracy of the model mentioned above, we constructed a systematic prognostic signature by integrating seven clinical variables and the five PDEARGs-based signatures. The clinical variables (i.e., age, histological grade, and TNM status) correlated highly with the aforementioned risk score were deleted to avoid overfitting. Then, the regression coefficients were used to weight selected variables, and the systematic risk of each patient was calculated with the following computational formula (Table 3):

$$\begin{aligned} {\text{Systematic}}\;{\text{risk}} & = \left( { - 0.5755 \times {\text{gender}}} \right) + \left( {0.6253 \times {\text{pathological}}\;{\text{stage}}} \right) \\ & \quad + \left( {0.5276 \times {\text{risk}}\;{\text{score}}\;{\text{generated}}\;{\text{from}}\;{\text{aforementioned}}\;{\text{model}}} \right);\;{\text{gender:female}} = 1,\;{\text{male}} = 0. \\ \end{aligned}$$
Table 3 The construction of PDEARGs-based systematic prognostic signature.
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The median of the systematic risk was 0.96, and with the increase of systemic risk, a bad prognosis of BLCA patients was observed (P < 0.001; Fig. 9A, C). Precisely, the three-year and 5-year OS rate was 49.0% and 37.6% in the high-risk group, while it was 77.6% and 69.0% in the low-risk group, respectively. A nomogram was constructed accordingly, and the AUC value for systematic prognostic signature was 0.791, which was more accurate than the pure PDEARGs-based model (Fig. 9B, D).

Figure 9
figure 9

The construction and validation of PDEARGs-based systematic prognostic signature. (A) Survival analysis between two groups with different systematic risk. The 95% confidence interval was shown as a light-colored background around the Kaplan–Meier curve. (B) Nomogram of the systematic prognostic signature. (C) Risk plot encompassing the distribution of systematic risk and survival status of each patient. (D) ROC curve analysis of systematic risk.

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External validation of both signatures

Both PDEARGs-based prognostic signature and systematic signature were validated in external datasets. As shown in Fig. 10A and B, a significantly different survival outcome was observed between the two risk groups (P < 0.005, log-rank test). The OS rate at 5-year was 15.49% and 37.50% for PDEARGs-based high-risk and low-risk groups. The corresponding rate for systematic signature was 20.83% and 32.39%, indicating that both signatures could precisely predict BLCA prognosis regardless of internal and external cohorts. Besides, the prognostic value of five selected PDEARGs was estimated through survival analysis. Except for APOL1, the other four risk genes could effectively separate BLCA patients with different survival outcomes (P < 0.05, Fig. 10C). Though these results were not exactly consistent with Fig. 6, the general trend of survival curve between the high expressive and low expressive groups was similar.

Figure 10
figure 10

External validation of both signatures. (A) Validation of PDEARGs-based signature. (B) Validation of systematic signature. The 95% confidence interval was shown as a light-colored background around the Kaplan–Meier curve. (C) Survival analysis of five selected PDEARGs in external cohorts. The median expression level was used as the cut-off value.

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Discussion

BLCA is a frequent malignant disease with rising incidence and high recurrence rates19. Although new diagnostic and therapeutic strategies have been carried out over the past several decades, the clinical outcome of BLCA patients remains unsatisfied4,20. Hence, there is an urgent need to develop better diagnostic methods to accurately identify asymptomatic and recurrent individuals at an early stage and propose novel treatment targets to improve prognosis. Previous studies have revealed that autophagy plays an essential role in TME regulation, resulting in tumor cell migration and invasion, tumor stem cell maintenance, and therapy resistance21,22. Therefore, ARGs may be an ideal biomarker or indicator to predict the progression and prognosis of BLCA.

Recently, many malignant tumor-related studies have shown that ARGs-based signature or multigene expression patterns can favorably predict cancer prognosis9,10,12. For example, P4HB retrieved from HADb website has been identified as a prognostic biomarker for BLCA, which could dramatically inhibit the invasion and proliferation of bladder cancer cells, providing novel insights for autophagy-mediated tumor progression23. However, whether multigene-based prognostic signature can predict the outcome of BLCA patients aroused our interest. In present study, we identified five optimal PDEARGs based on a comprehensive analysis, among which APOL1 was a protective gene, and the other four PDEARGs were risk genes. Then, we constructed a reliable PDEARGs-based prognostic model that could stratify TCGA-BLCA patients into two risk groups with statistically different survival outcomes. The risk score generated from the present signature could serve as an independent prognostic factor to predict patients’ OS, and the predictive accuracy (AUC = 0.724) was much better than other clinical parameters. As reported, AUC > 0.60 was regarded as acceptable for predictions24; thus, the present PDEARGs-based signature can precisely predict the prognosis of BLCA patients.

Subsequently, we analyzed the expression of the five PDEARGs in two risk groups and evaluated the relationship between present signature and certain clinical variables. Firstly, the expression of the protective gene APOL1 was downregulated in the high-risk group, and the other four risk genes were upregulated in the patient with an increased risk score. Consistently, the RNA level of five PDEARGs detected through qRT-PCR obtained similar results as RNA-Seq data, indicating its potential for clinical transformation. However, we didn’t include a large sample in the present proof of concept study, which need further confirmation with prospective clinical study. Besides, APOL1 expression significantly decreased with the increase of pathological stage, N and M status. The high expression of risk genes was accompanied by bad histological grade, pathological stage, TNM status, and prognosis. Based on the above, the present signature constructed with low-expression of protective gene and high-expression of risk genes could accurately predict BLCA patients' progression, including histological grade, pathological stage, TNM status, and survival outcome. What’s more, BLCA incidence increased with age25, and we found that APOL1 level was down-regulated and risk score increased in elderly patients (age > 65), which further confirmed the reliability and clinical consistency of present model.

TME, which comprises recruited stromal cells and TIICs, has emerged as an important player in tumor progression, with the potential to be used in future treatment and diagnosis26. In present study, the TME of BLCA patients was analyzed with the ESTIMATE and CIBERSORT R package. With the increase of risk score generated from PDEARGs-based signature, tumor purity significantly decreased, and the proportion of stromal cells, neutrophils, and macrophage (M0 and M2 phenotypes) in tumor tissues significantly increased, which was correlated with bad pathological stage, detrimental TNM status, and poor prognosis. As reported, tumor-associated stromal cells can synthesize and secrete many pro-tumorigenic factors to promote cancer initiation, angiogenesis, invasion, and metastasis27. Additionally, emerging evidence indicates that elevated neutrophils are associated with detrimental outcomes in several solid tumors, and new strategies to decrease their presence and activity have shown efficacy in preclinical models28,29. TAM is another prominent component of TME, and the accumulation of M0 and M2 macrophages in tumors is most strongly associated with poor clinical outcomes18. Therefore, the ability to reflect the tumor-promoting microenvironment may explain why the present PDEARGs-based signature could precisely predict the prognosis of BLCA patients.

To explore the underlying mechanism by which the present prognostic model effectively stratified BLCA patients, GO enrichment analysis of the five PDEARGs was performed. As a result, several immune-related GO terms were significantly enriched, such as macrophage activation, inflammatory response, cellular response to decreased oxygen levels, and related apoptotic signaling pathway. It has been reported that ARGs are tightly related to hypoxia and hypoxia-induced metabolic reprogramming, among which P4HB is a crucial molecule that has been extensively studied7,30. The expression of P4HB significantly increased in several solid tumors, including bladder cancer31, kidney cancer32 and prostate cancer30, which is consistent with the present study. Besides, hypoxia and oxygen stress-induced autophagy and apoptosis could be regulated by ARGs such as SPHK1, P4HB, and NAMPT33. During the process mentioned above, inflammatory response would be initiated, and TME could be changed to recruit and activate macrophages. NAMPT is regarded as a pleiotropic modulator governing monocyte/macrophage differentiation, polarization, and migration34. Therefore, we speculate that the process of “PDEARGs → autophagy → TME change → TAM recruitment and polarization → tumor progression” would provide insights for present prognostic signature and propose potential treatment targets for BLCA patients.

Though the present model exhibits a promising value in predicting BLCA patients' prognosis, we intend to integrate clinical variables and risk scores generated from the model above to construct a systematic signature with promoted clinical utility and predictive accuracy. Notably, the novel systematic signature could more precisely predict the prognosis of TCGA-BLCA patients (AUC = 0.791). As reported, AUC > 0.75 was deemed to have an excellent predictive value24. The reliability of both PDEARGs-based signatures was also validated through external cohorts downloaded from the GEO database. A nomogram of the systematic signature was then prepared for clinicians to identify BLCA patients who need invasive therapy.

In conclusion, we constructed a valuable PDEARGs-based prognostic model that can precisely predict the prognosis of BLCA patients. This model's risk score can serve as an independent prognostic factor and can effectively reflect the tumor-promoting microenvironment. GO enrichment analysis revealed the underlying mechanism, which may provide insights for the present model and propose potential treatment targets for BLCA patients. Additionally, a systematic signature integrating clinical variables and the aforementioned five PDEARGs-based model was constructed for clinical application. Inevitably, large-scale, multi-center studies are necessary to confirm the clinical benefit of our results. In vitro or in vivo experiments need to be performed to provide more evidence for PDEARGs-based regulatory mechanism.