Identification of autophagy-related genes signature predicts chemotherapeutic and immunotherapeutic efficiency in bladder cancer (BLCA)

Homogeneous Polyporus polysaccharide exerts anti-bladder cancer effects via autophagy induction

CONTEXT

Polyporus polysaccharide (PPS), the leading bioactive ingredient extracted from Polyporus umbellatus (Pers.) Fr. (Polyporaceae), has been demonstrated to exert anti-bladder cancer and immunomodulatory functions in macrophages.

OBJECTIVE

To explore the effects of homogeneous Polyporus polysaccharide (HPP) on the proliferation and autophagy of bladder cancer cells co-cultured with macrophages.

MATERIALS AND METHODS

MB49 bladder cancer cells and RAW264.7 macrophages were co-cultured with or without HPP intervention (50, 100, or 200 μg/mL) for 24 h. The cell counting kit-8 (CCK-8) assay and 5-ethynyl-2″-deoxyuridine (EdU) staining evaluated MB49 cell proliferation. Monodansylcadaverine (MDC) staining and transmission electron microscopy (TEM) observed autophagosomes. Western blotting detected the expression levels of autophagy-related proteins and PI3K/Akt/mTOR pathway proteins.

RESULTS

HPP inhibited the proliferation of MB49 cells co-cultured with RAW264.7 cells but not MB49 cells alone. HPP altered the expression of autophagy-related proteins and promoted the formation of autophagosomes in MB49 cells in the co-culture system. Autophagy inhibitors 3-methyladenine (3-MA) and chloroquine (CQ) not only antagonized HPP-induced autophagy but also attenuated the inhibitory effects of HPP on MB49 cell proliferation in the co-culture system. HPP or RAW264.7 alone was not sufficient to induce autophagy in MB49 cells. In addition, HPP suppressed the protein expression of the PI3K/Akt/mTOR pathway in MB49 cells in the co-culture system.

DISCUSSION AND CONCLUSIONS

HPP induced bladder cancer cell autophagy by regulating macrophages in the co-culture system, resulting in the inhibition of cancer cell proliferation. The PI3K/Akt/mTOR pathway was involved in HPP-induced autophagy in the co-culture system.

The role of autophagy dysregulation in low and high-grade nonmuscle invasive bladder cancer: A survival analysis and clinicopathological association

INTRODUCTION

Bladder cancer disproportionately affects men and often presents as nonmuscle-invasive bladder cancer (NMIBC). Despite initial treatments, the recurrence and progression of NMIBC are linked to autophagy. This study investigates the expression of autophagy genes (mTOR, ULK1, Beclin1, and LC3) in low and high-grade NMIBC, providing insights into potential prognostic markers and therapeutic targets.

MATERIAL AND METHODS

A total of 115 tissue samples (n = 85 NMIBC (pTa, pT1, and CIS) and n = 30 control from BPH patients) were collected. The expression level of autophagy genes (mTOR, ULK1, Beclin1, and LC3) and their proteins were assessed in low and high-grade NMIBC, along with control tissue samples using quantitative real-time polymerase chain reaction and western blotting. Association with clinicopathological characteristics and autophagy gene expression was analyzed by multivariate and univariate survival analysis using SPSS.

RESULT

In high-grade NMIBC, ULK1, P = 0.0150, Beclin1, P = 0.0041, and LC3, P = 0.0014, were substantially downregulated, whereas mTOR, P = 0.0006, was significantly upregulated. The KM plots show significant survival outcomes with autophagy genes. The clinicopathological characters, high grade (P = 0.019), tumor stage (CIS P = 0.039, pT1 P = 0.018, P = 0.045), male (P = 0.010), lymphovascular invasion (P = 0.028) and autophagy genes (ULK1 P = 0.002, beclin1 (P = 0.010, P = 0.022) were associated as risk factors for survival outcome in NMIBC patients.

CONCLUSION

The upregulated mTOR, downregulated ULK1, and beclin1 expression is linked to a high-grade, CIS and pT1 stage, resulting in poor recurrence-free survival and progression-free survival and highlights the prognostic significance of autophagy gene in nonmuscle-invasive bladder cancer.

Improve clinical feature-based bladder cancer survival prediction models through integration with gene expression profiles and machine learning techniques

Background

Bladder cancer (BCa), one of the most common cancers worldwide, is characterized by high rates of recurrence, progression, and mortality. Machine learning algorithms offer promising advancements in enhancing predictive models. This study aims to develop robust machine learning models for predicting BCa survival using clinical and gene expression data.

Methods

Clinical data from BCa patients were obtained from the Surveillance, Epidemiology, and End Results database. Cox proportional hazards regression models assessed the association between clinical variables and overall survival. Machine learning algorithms, including logistic regression, random forest, XGBoost, decision tree, and LightGBM, were employed to predict survival at 1, 3, and 5 years. The TAGO database, combined with the data from The Cancer Genome Atlas and four databases from the Gene Expression Omnibus, which have available genomic data and clinical data, were selected. Gene expression data were transformed into gene sets data, and the performance of models based on clinical data and gene sets data and their combination were compared. Furthermore, the impact of model-derived scores on overall survival was evaluated.

Results

Among 138,741 BCa patients with available clinical data, key independent predictors of survival included age, race, marital status, surgery, chemotherapy, radiation, and TNM stages. Clinical data machine learning (CML) models used these clinical predictors to achieve AUC values of 0.860, 0.821, and 0.804 in the testing sets for predicting survival at 1, 3, and 5 years, respectively. In the TAGO database, which has 863 patients with clinical and genomic data, the integrated clinical and gene expression machine learning model (IML) outperformed the CML and gene expression machine learning (GML) models in survival prediction. Patients with higher IML and GML model scores exhibited poorer survival outcomes.

Conclusions

This study successfully identifies key clinical and genomic predictors, a significant step forward in BCa research. The development of predictive models for BCa survival underscores the potential of integrated data approaches in improving BCa management and treatment strategies.

Autophagy flux in bladder cancer: Cell death crosstalk, drug and nanotherapeutics

Autophagy, a self-digestion mechanism, has emerged as a promising target in the realm of cancer therapy, particularly in bladder cancer (BCa), a urological malignancy characterized by dysregulated biological processes contributing to its progression. This highly conserved catabolic mechanism exhibits aberrant activation in pathological events, prominently featured in human cancers. The nuanced role of autophagy in cancer has been unveiled as a double-edged sword, capable of functioning as both a pro-survival and pro-death mechanism in a context-dependent manner. In BCa, dysregulation of autophagy intertwines with cell death mechanisms, wherein pro-survival autophagy impedes apoptosis and ferroptosis, while pro-death autophagy diminishes tumor cell survival. The impact of autophagy on BCa progression is multifaceted, influencing metastasis rates and engaging with the epithelial-mesenchymal transition (EMT) mechanism. Pharmacological modulation of autophagy emerges as a viable strategy to impede BCa progression and augment cell death. Notably, the introduction of nanoparticles for targeted autophagy regulation holds promise as an innovative approach in BCa suppression. This review underscores the intricate interplay of autophagy with cell death pathways and its therapeutic implications in the nuanced landscape of bladder cancer.

Classification of bladder cancer based on immune cell infiltration and construction of a risk prediction model for prognosis

OBJECTIVES

To classify bladder cancer based on immune cell infiltration score and to construct a prognosis assessment model of patients with bladder cancer.

METHODS

The transcriptome data and clinical data of breast cancer patients were obtained from the The Cancer Genome Atlas (TCGA) database. Single sample gene set enrichment analysis was used to calculate the infiltration scores of 16 immune cells. The classification of breast cancer patients was achieved by unsupervised clustering, and the sensitivity of patients with different types to immunotherapy and chemotherapy was analyzed. The key modules significantly related to the infiltration of key immune cells were identified by weighted correlation network analysis (WGCNA), and the key genes in the modules were identified. A risk scoring model and a nomogram for prognosis assessment of bladder cancer patients were constructed and verified.

RESULTS

B cells, mast cells, neutrophils, T helper cells and tumor infiltrating lymphocytes were determined to be the key immune cells of bladder cancer. The patients were clustered into two groups (Cluster 1 ´ and Custer 2) based on immune cell infiltration scores. Compared with patients with Cluster 1 ´, patients with Cluster 2 were more likely to benefit from immunotherapy (P<0.05), and patients with Cluster 2 were more sensitive to Enbeaten, Docetaxel, Cyclopamine, and Akadixin (P<0.05). 35 genes related to key immune cells were screened out by WGCNA and 4 genes (GPR171, HOXB3, HOXB5 and HOXB6) related to the prognosis of bladder cancer were further screened by LASSO Cox regression. The areas under the ROC curve (AUC) of the bladder cancer prognosis risk scoring model based on these 4 genes to predict the 1-, 3- and 5-year survival of patients were 0.735, 0.765 and 0.799, respectively. The nomogram constructed by combining risk score and clinical parameters has high accuracy in predicting the 1-, 3-, and 5-year overall survival of bladder cancer patients.

CONCLUSIONS

According to the immune cell infiltration score, bladder cancer patients can be classified. Furthermore, bladder cancer prognosis risk scoring model and nomogram based on key immune cell-related genes have high accuracy in predicting the prognosis of bladder cancer patients.

Identification and validation of molecular subtypes' characteristics in bladder urothelial carcinoma based on autophagy-dependent ferroptosis

Background

Nowadays, more evidences indicated that autophagy-dependent ferroptosis regulatory molecules (ADFRMs) may be closely related to various tumors. In current study, we intended to establish a prognostic ADFRMs signature and investigated its potential roles in bladder urothelial carcinoma (BLCA).

Methods

Two distinct clusters were determined by consensus clustering with expression of 119 identified ADFRMs in BLCA. The tumor microenvironment was investigated through "CIBERSORT" algorithm, and enrichment analyses were utilized to seek molecular characteristics of differentially expressed genes (DEGs) between clusters. Moreover, a 2-ADFRMs prognostic signature including TRIB3 and WIPI1 was identified in TCGA cohort and further evaluated in the GSE13507 cohort. The qRT-PCR was conducted to examine the expression of prognostic genes. Further, the risk score was gained through calculating the level of TRIB3 and WIPI1 expression through the coefficient. The correlations between risk score with clinicopathologica features, tumor microenvironment, and drug sensitivity were explored.

Results

Patients in TCGA-BLCA were grouped into two clusters with different expression patterns of ADFRMs. And the overall survival, tumor microenvironment and biological functions were significant different between two clusters. Moreover, a 2-ADFRMs model was constructed, and patients were separated into a low-risk and high-risk group. Survival analysis indicated patients with low risk promised a good prognosis, suggesting the risk score determined with ADFRMs signature exhibited an acceptable capacity for survival prediction in BLCA. Correlation analysis demonstrated risk score had close ties with age, stage, and tumor microenvironment. In vivo, the expression of prognostic genes was identified to be up-regulated in BLCA cell line T24.

Conclusion

The constructed 2-ADFRMs signature was a promising model to predict prognosis and correlated with tumor microenvironment, which had latent clinical value in the intervention for BLCA.

Construction of a prediction model for prognosis of bladder cancer based on the expression of ion channel-related genes

OBJECTIVES

To construct a prediction model for the prognosis of bladder cancer patients based on the expression of ion channel-related genes (ICRGs).

METHODS

ICRGs were obtained from the existing researches. The clinical information and the expression of ICRGs mRNA in breast cancer patients were obtained from the Cancer Genome Atlas database. Cox regression analysis, minimum absolute shrinkage and selection operator regression analysis were used to screen breast cancer prognosis related genes, which were verified by immunohistochemistry and qRT-PCR. The risk scoring equation for predicting the prognosis of patients with bladder cancer was constructed, and the patients were divided into high-risk group and low-risk group according to the median risk score. Immune cell infiltration was compared between the two groups. Kaplan-Meier survival curve and receiver operating characteristic (ROC) curve were used to evaluate the accuracy and clinical application value of the risk scoring equation. The factors related to the prognosis of bladder cancer patients were analyzed by univariate and multivariate Cox regression, and a nomogram for predicting the prognosis of bladder cancer patients was constructed.

RESULTS

By comparing the expression levels of ICRGs in bladder cancer tissues and normal bladder tissues, 73 differentially expressed ICRGs were dentified, of which 11 were related to the prognosis of bladder cancer patients. Kaplan-Meier survival curve suggested that the risk score based on these 11 genes was negatively correlated with the prognosis of patients. The area under the ROC curve of the risk score for predicting the prognosis of patients at 1, 3 and 5 year was 0.634, 0.665 and 0.712, respectively. Stratified analysis showed that the ICRGs-based risk score performed well in predicting the prognosis of patients with American Joint Committee on Cancer (AJCC) stage Ⅲ-Ⅳ bladder cancer (P<0.05), while it had a poor value in predicting the prognosis of patients with AJCC stage Ⅰ-Ⅱ (P>0.05). There were significant differences in the infiltration of plasma cells, activated natural killer cells, resting mast cells and M2 macrophages between the high-risk group and the low-risk group. Cox regression analysis showed that risk score, smoking, age and AJCC stage were independently associated with the prognosis of patients with bladder cancer (P<0.05). The nomogram constructed by combining risk score and clinical parameters has high accuracy in predicting the 1, 3 and 5 year overall survival rate of bladder cancer patients.

CONCLUSIONS

The study shows the potential value of ICRGs in the prognostic risk assessment of bladder cancer patients. The constructed prognostic nomogram based on ICRGs risk score has high accuracy in predicting the prognosis of bladder cancer patients.

Cystatin B increases autophagic flux by sustaining proteolytic activity of cathepsin B and fuels glycolysis in pancreatic cancer: CSTB orchestrates autophagy and glycolysis in PDAC

BACKGROUND

Both autophagy and glycolysis are essential for pancreatic ductal adenocarcinoma (PDAC) survival due to desmoplasia. We investigated whether targeting a hub gene which participates in both processes could be an efficient strategy for PDAC treatment.

METHODS

The expression pattern of glycolysis signatures (GS) and autophagy signatures (AS) and their correlation with cystatin B (CSTB) in PDAC were analysed. It was discovered how CSTB affected the growth, glycolysis, and autophagy of PDAC cells. We assessed competitive binding to cathepsin B (CTSB) between CSTB and cystatin C (CSTC) via immunoprecipitation (IP) and immunofluorescence (IF). Chromatin immunoprecipitation quantitative polymerase chain reaction (ChIP-qPCR) and luciferase reporter gene assays were used to unveil the mechanism underlying CSTB upregulation. The expression pattern of CSTB was examined in clinical samples and KrasG12D/+, Trp53R172H/+, Pdx1-Cre (KPC) mice.

RESULTS

GS and AS were enriched and closely associated in PDAC tissues. CSTB increased autophagic flux and provided substrates for glycolysis. CSTB knockdown attenuated the proliferation of PDAC cells and patient-derived xenografts. The liquid chromatography-tandem mass spectrometry assay indicated CSTB interacted with CTSB and contributed to the proteolytic activity of CTSB in lysosomes. IF and IP assays demonstrated that CSTB competed with CSTC to bind to CTSB. Mutation of the key sites of CSTB abolished the interaction between CSTB and CTSB. CSTB was highly expressed in PDAC due to H3K27acetylation and SP1 expression. High expression of CSTB in PDAC was observed in tissue microarray and patients' serum samples.

CONCLUSIONS

Our work demonstrated the tumorigenic roles of autophagy and glycolysis in PDAC. CSTB is a key role in orchestrating these processes to ensure energy supply of PDAC cells.

Identification of a New Prediction Model for Bladder Cancer Related to Immune Functions and Chemotherapy Using Gene Sets of Biological Processes

Background. Biological processes serve crucial functions in the initiation and development of cancer. Therefore, we constructed and validated a model for bladder cancer (BLCA) with good predictive power for immunity, prognosis, and therapy. Methods. Using the expression of the gene sets based on biological processes, BLCA patients were divided into three clusters by consensus cluster analysis. By performing LASSO regression analysis twice, key genes were selected, and the biological processes-related genes’ (BPRG) score was calculated. Differences in immune infiltration, tumor microenvironment, tumor mutation burden, immunotherapy, and sensitivity towards chemotherapy were analyzed between two groups divided by BPRG score. Results. Good accuracy was observed for the three clusters. They showed different prognoses and levels of immune cell infiltration. The selected key genes were mainly enriched in immune-related pathways. The high-BPRG score group was related to poor prognosis, higher immune cell infiltration, interstitial scores, and increased tumor mutation. Moreover, the effects of immunotherapy were good, and those of chemotherapy were poor. Conclusion. Overall, key genes may be involved in various complex immune regulation processes. Therefore, the quantification and verification of the BPRG score are expected to facilitate the understanding of the immunosuppressive microenvironment in BLCA and guide the choice of chemotherapeutic drugs and immunotherapeutic regimens and help predict the prognoses of patients with BLCA.

Analysis of prognostic oncogene filaggrin (FLG) wild-type subtype and its implications for immune checkpoint blockade therapy in bladder urothelial carcinoma

BACKGROUND

Bladder urothelial carcinoma (BLCA) is one of the most common urinary tract malignant tumors. Immune checkpoint blockade (ICB) therapy has significantly progressed the treatment of BLCA. This study aimed to investigate the role of specific genetic mutations that may serve as immune biomarkers for ICB therapy in BLCA.

METHODS

Mutation information and expression profiles were acquired from The Cancer Genome Atlas (TCGA) database. Integrated bioinformatics analysis was carried out to explore the subtypes with poor prognosis of BLCA. Functional enrichment analysis was also conducted. The infiltrating immune cells and the prediction of ICB response between different subtypes were explored using the immuCellAI algorithm. Cell Counting Kit-8 (CCK-8) and flow cytometry assays were conducted to explore the effect of filaggrin (FLG) knockdown in BLCA 5637 and T24 cell lines.

RESULTS

An overview of mutation information in BLCA patients was shown. FLG was identified to be strongly associated with the prognosis of BLCA patients and FLG wild-type was associated with poorer outcome. Prognostic FLG wild-type was divided into 2 subtypes (Sub1 and Sub2). Following an investigation of the subtypes, Sub2 of FLG wild-type was found to be associated with poorer outcome in BLCA. The differentially expressed genes (DEGs) between Sub1 and Sub2 were screened out and the DEGs were enriched in malignant tumor proliferation, DNA damage repair, and immune-related pathways. Furthermore, Sub2 of FLG wild-type was associated with infiltrated immune cells, and responded worse to ICB. Sub2 of FLG wild-type may be used as a biomarker to predict the prognosis of BLCA patients receiving ICB. The cellular experiments revealed that knockdown of FLG could suppress BLCA cell proliferation and promote apoptosis.

CONCLUSIONS

FLG is an oncogene that may affect the prognosis of BLCA patients through mutation. Sub2 of FLG wild-type is associated with poor prognosis and can be used to predict ICB response for BLCA treatment. This research provides a new basis and ideas for guiding the clinical application of BLCA immunotherapy.

Identification of a Novel Tumor Microenvironment Prognostic Signature for Bladder Urothelial Carcinoma

Background

The tumor microenvironment (TME) regulates the proliferation and metastasis of solid tumors and the effectiveness of immunotherapy against them. We investigated the prognostic role of TME-related genes based on transcriptomic data of bladder urothelial carcinoma (BLCA) and formulated a prediction model of TME-related signatures.

Methods

Molecular subtypes were identified using the non-negative matrix factorization (NMF) algorithm based on TME-related genes from the TCGA database. TME-related genes with prognostic significance were screened with univariate Cox regression analysis and lasso regression. Nomogram was developed based on risk genes. Receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used for inner and outer validation of the model. Risk scores (RS) of patients were calculated and divided into high-risk group (HRG) and low-risk group (LRG) to compare the differences in clinical characteristics and PD-L1 treatment responsiveness between HRG and LRG.

Results

We identified two molecular subtypes (C1 and C2) according to the NMF algorithm. There were significant differences in overall survival (OS) (p<0.05), progression-free survival (PFS) (p<0.05), and immune cell infiltration between the two subtypes. A total of eight TME-associated genes (CABP4, ZNF432, BLOC1S3, CXCL11, ANO9, OAS1, FBN2, CEMIP) with independent prognostic significance were screened to build prognostic risk models. Age (p<0.001), grade (p<0.001), and RS (p<0.001) were independent predictors of survival in BLCA patients. The developed RS nomogram was able to predict the prognosis of BLCA patients at 1, 3, and 5 years more potentially than the models of other investigators according to ROC and DCA. RS showed significantly higher values (p = 0.047) in patients with stable disease (SD)/progressive disease (PD) compared to patients with complete response (CR)/partial response (PR).

Conclusions

We successfully clustered and constructed predictive models for TME-associated genes and helped guide immunotherapy strategies.

An Immunosenescence-Related Gene Signature to Evaluate the Prognosis, Immunotherapeutic Response, and Cisplatin Sensitivity of Bladder Cancer

Immunosenescence refers to the immune system undergoing a series of degenerative changes with advancing age and is tightly associated with the initiation and progression of cancers. However, the immunosenescence-related genes as critical biomarkers for bladder cancer (BLCA) have not been systematically analyzed. We retrieved the immunosenescence-related genes from the public database and verified their association with hallmarks of immunosenescence based on The Cancer Genome Atlas (TCGA) cohort. Through gene pairing, Lasso, and univariate Cox regression, an 8-gene pair model was constructed to evaluate the overall survival of BLCA, which was then validated in the training cohort (P < 0.001, n = 396), two external validation cohorts (P < 0.05, n = 165; P < 0.001, n = 224), and local samples (P < 0.05, n = 10). We also downloaded the clinical information and gene expression matrices of other 32 different cancers from TCGA. The established model showed significant predictive value for the prognosis in 15 cancers (P < 0.05). The risk model could also serve as a promising predictor for immunotherapeutic response, which has been verified by the TIDE algorithm (P < 0.05), IMvigor210 dataset (P < 0.01, n = 298), and other two datasets correlated with immunotherapy (P < 0.05, n = 56; P = 0.17, n = 27). The TCGA dataset, in vitro cell experiments, and pan-cancer analysis displayed that the gene signature was associated with cisplatin sensitivity (P < 0.05). Overall, we proposed a novel immunosenescence-related gene signature to predict prognosis, immunotherapeutic response, and cisplatin sensitivity of BLCA, which were validated in different independent cohorts, local samples, and pan-cancer analyses.

Characterization of an Autophagy-Immune Related Genes Score Signature and Prognostic Model and its Correlation with Immune Response for Bladder Cancer

Purpose

The study aimed to identify an autophagy-related molecular subtype and characterize a novel defined autophagy-immune related genes score (AI-score) signature and prognosis model in bladder cancer (BLCA) patients using public databases.

Methods

The transcriptome cohorts downloaded from TCGA and GEO database were carried out with genomic analysis and unsupervised methods to obtain autophagy-related molecular subtypes. The single-sample gene-set enrichment analysis (ssGSEA) was utilized to perform immune subtype clustering. We defined a novel autophagy subtype and evaluated the role in TME cell infiltration. Then, the principal-component analysis (PCA) was applied to construct an AI-score signature. Subsequently, two immunotherapeutic cohorts were used to evaluate the predictive value in immunotherapeutic benefits and immune response. Finally, univariate, Lasso and multivariate Cox regression algorithm were used to construct and evaluate an autophagy-immune-related genes prognosis model. Also, qRT-PCR and IHC was applied to validate the expression of the 6 genes in the model.

Results

Three distinct autophagy clusters and immune-related clusters were identified, and a novel autophagy-related molecular subtypes were defined. Furthermore, the roles in TME cell infiltration and clinical traits for the autophagy subtypes were characterized. Meanwhile, we constructed an AI-score signature and demonstrated it could predict genetic mutation, clinicopathological traits, prognosis, and TME stromal activity. We found that it could accurately predict the clinicopathological characteristics and immune response of individual BLCA patients and provide guidance for selecting immunotherapy. Ultimately, we constructed and verified an autophagy-immune-related prognostic model of BLCA patients and established a prognostic nomogram with a good prediction accuracy.

Conclusion

We constructed AI-score signatures and prognosis risk model to characterize their role in clinical features and TME immune cell infiltration. It revealed that the AI-score signature and prognosis model could be a valid predictive tool, which could accurately predict the prognosis of BLCA patients and contribute to choosing effective personalized immunotherapy strategies.

Identification of autophagy-related genes signature predicts chemotherapeutic and immunotherapeutic efficiency in bladder cancer (BLCA)

Autophagy maintains cellular homeostasis by degrading and recycling cytoplasmic components under stress conditions, which is identified to be involved in tumorigenesis and now has been recognized as novel target in cancer treatment. In present study, we gathered total autophagy‐related genes and established an autophagy‐related genes signature (ATGRS) through LASSO cox regression analysis in BLCA. Kaplan‐Meier survival and multivariate cox regression analyses both showed the ATGRS was a robust independent prognostic factor with high accuracy. Subsequently, integrated analyses indicated that ATGRS had a strong correlation with molecular subtypes, clinicopathological characteristics and somatic mutation alteration. Moreover, ATGRS was found to be positively correlated with the infiltration of immune cells in tumour microenvironment (TME) and immune checkpoint expression, indicating the potent role of autophagy by regulating the TME. In addition, ATGRS was proved to be efficient in predicting the clinical benefit of immune checkpoint inhibitors (ICIs) based immunotherapy and chemotherapy in BLCA. Furthermore, we observed abnormal expression levels of autophagy‐related genes and found the different behaviour of ATGRS in pancancer by LASSO cox regression analysis. Therefore, construction of ATGRS in BLCA could help us to interpret the underlying mechanism of autophagy and sheds a light on the clinical application for a combination of autophagy modification with targeted immunotherapy and chemotherapy in BLCA.