scholarly journals A Novel TGF-β Risk Score Predicts the Clinical Outcomes and Tumour Microenvironment Phenotypes in Bladder Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
Zhi Liu ◽  
Tiezheng Qi ◽  
Xiaowen Li ◽  
Yiyan Yao ◽  
Belaydi Othmane ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Risk Score ◽  
Tumour Microenvironment ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Risk Scores ◽  
Immune Checkpoints ◽  
Normal Tissues ◽  
Cox Analysis

BackgroundThe TGF-β pathway plays critical roles in numerous malignancies. Nevertheless, its potential role in prognosis prediction and regulating tumour microenvironment (TME) characteristics require further elucidation in bladder cancer (BLCA).MethodsTGF-β-related genes were comprehensively summarized from several databases. The TCGA-BLCA cohort (training cohort) was downloaded from the Cancer Genome Atlas, and the independent validation cohorts were gathered from Xiangya Hospital (Xinagya cohort) and Gene Expression Omnibus. Initially, we identified differentially expressed TGF-β genes (DEGs) between cancer and normal tissues. Subsequently, univariate Cox analysis was applied to identify prognostic DEGs, which were further used to develop the TGF-β risk score by performing LASSO and multivariate Cox analyses. Then, we studied the role of the TGF-β risk score in predicting prognosis and the TME phenotypes. In addition, the role of the TGF-β risk score in guiding precision treatments for BLCA has also been assessed.ResultsWe successfully constructed a TGF-β risk score with an independent prognostic prediction value. A high TGF-β risk score indicated an inflamed TME, which was supported by the positive relationships between the risk score, enrichment scores of anticancer immunity steps, and the infiltration levels of tumour-infiltrating immune cells. In addition, the risk score positively correlated with the expression of several immune checkpoints and the T cell inflamed score. Consistently, the risk score was positively related to the enrichment scores of most immunotherapy-positive pathways. In addition, the sensitivities of six common chemotherapeutic drugs were positively associated with the risk score. Furthermore, higher risk score indicated higher sensitivity to radiotherapy and EGFR-targeted therapy. On the contrary, patients with low-risk scores were more sensitive to targeted therapies, including the blockade of FGFR3 and WNT-β-catenin networks.ConclusionsWe first constructed and validated a TGF-β signature that could predict the prognosis and TME phenotypes for BLCA. More importantly, the TGF-β risk score could aid in individual precision treatment for BLCA.

scholarly journals The expression characteristics and prognostic roles of autophagy-related genes in gastric cancer

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e10814
Author(s):  
Mengya Wang ◽  
Jingjing Jing ◽  
Hao Li ◽  
Jingwei Liu ◽  
Yuan Yuan ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Prognostic Factors ◽  
Differential Expression Analysis ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Normal Tissues ◽  
Genes Expression ◽  
Expression Characteristics ◽  
Cellular Components

Background Autophagy is an evolutionally highly conserved process, accompanied by the dynamic changes of various molecules, which is necessary for the orderly degradation and recycling of cellular components. The aim of the study was to identify the role of autophagy-related (ATG) genes in the occurrence and development of gastric cancer (GC). Methods Data from Oncomine dataset was used for the differential expression analysis between cancer and normal tissues. The association of ATG genes expression with clinicopathologic indicators was evaluated by The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) database. Moreover, using the TCGA datasets, the prognostic role of ATG genes was assessed. A nomogram was further built to assess the independent prognostic factors. Results The expression of autophagy-related genes AMBRA1, ATG4B, ATG7, ATG10, ATG12, ATG16L2, GABARAPL2, GABARAPL1, ULK4 and WIPI2 showed differences between cancer and normal tissues. After verification, ATG14 and ATG4D were significantly associated with TNM stage. ATG9A, ATG2A, and ATG4D were associated with T stage. VMP1 and ATG4A were low-expressed in patients without lymph node metastasis. No gene in autophagy pathway was associated with M stage. Further multivariate analysis suggested that ATG4D and MAP1LC3C were independent prognostic factors for GC. The C-index of nomogram was 0.676 and the 95% CI was 0.628 to 0.724. Conclusion Our study provided a comprehensive illustration of ATG genes expression characteristics in GC. Abnormal expressions of the ubiquitin-like conjugated system in ATG genes plays a key role in the occurrence of GC. ATG8/LC3 sub-system may play an important role in development and clinical outcome of GC. In the future, it is necessary to further elucidate the alterations of specific ATG8/LC3 forms in order to provide insights for the discovery, diagnosis, or targeting for GC.

scholarly journals DNASE1L3 as a Novel Diagnostic and Prognostic Biomarker for Lung Adenocarcinoma Based on Data Mining

2021 ◽  
Vol 12 ◽  
Author(s):  
Jianlin Chen ◽  
Junping Ding ◽  
Wenjie Huang ◽  
Lin Sun ◽  
Jinping Chen ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Prognostic Biomarker ◽  
Function Analysis ◽  
Mrna Level ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Immune Checkpoints ◽  
Diagnostic Efficiency ◽  
Protein Levels ◽  
Normal Tissues

Previous researches have highlighted that low-expressing deoxyribonuclease1-like 3 (DNASE1L3) may play a role as a potential prognostic biomarker in several cancers. However, the diagnosis and prognosis roles of DNASE1L3 gene in lung adenocarcinoma (LUAD) remain largely unknown. This research aimed to explore the diagnosis value, prognostic value, and potential oncogenic roles of DNASE1L3 in LUAD. We performed bioinformatics analysis on LUAD datasets downloaded from TCGA (The Cancer Genome Atlas) and GEO (Gene Expression Omnibus), and jointly analyzed with various online databases. We found that both the mRNA and protein levels of DNASE1L3 in patients with LUAD were noticeably lower than that in normal tissues. Low DNASE1L3 expression was significantly associated with higher pathological stages, T stages, and poor prognosis in LUAD cohorts. Multivariate analysis revealed that DNASE1L3 was an independent factor affecting overall survival (HR = 0.680, p = 0.027). Moreover, decreased DNASE1L3 showed strong diagnostic efficiency for LUAD. Results indicated that the mRNA level of DNASE1L3 was positively correlated with the infiltration of various immune cells, immune checkpoints in LUAD, especially with some m6A methylation regulators. In addition, enrichment function analysis revealed that the co-expressed genes may participate in the process of intercellular signal transduction and transmission. GSEA indicated that DNASE1L3 was positively related to G protein-coupled receptor ligand biding (NES = 1.738; P adjust = 0.044; FDR = 0.033) and G alpha (i) signaling events (NES = 1.635; P adjust = 0.044; FDR = 0.033). Our results demonstrated that decreased DNASE1L3 may serve as a novel diagnostic and prognostic biomarker associating with immune infiltrates in lung adenocarcinoma.

scholarly journals Prognostic model of head and neck squamous cell carcinoma based on 12 ferroptosis-related genes

2021 ◽  
Author(s):  
Sijia Li ◽  
Hongyang Zhang ◽  
Wei Li
Keyword(s):  
Regression Analysis ◽  
Risk Score ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Risk Scores ◽  
Cox Regression Analysis ◽  
Normal Tissues ◽  
Model Based ◽  
Cox Analysis

Abstract Background: The purpose of our study is establishing a model based on ferroptosis-related genes predicting the prognosis of patients with head and neck squamous cell carcinoma (HNSCC).Methods: In our study, transcriptome and clinical data of HNSCC patients were from The Cancer Genome Atlas, ferroptosis-related genes and pathways were from Ferroptosis Signatures Database. Differentially expressed genes (DEGs) were screened by comparing tumor and adjacent normal tissues. Functional enrichment analysis of DEGs, protein-protein interaction network and gene mutation examination were applied. Univariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression were used to identified DEGs. The model was constructed by multivariate Cox regression analysis and verified by Kaplan-Meier analysis. The relationship between risk scores and other clinical features was also analyzed. Univariate and multivariate Cox analysis was used to verified the independence of our model. The model was evaluated by receiver operating characteristic analysis and calculation of the area under the curve (AUC). A nomogram model based on risk score, age, gender and TNM stages was constructed.Results: We analyzed data including 500 tumor tissues and 44 adjacent normal tissues and 259 ferroptosis-related genes, then obtained 73 DEGs. Univariate Cox regression analysis screened out 16 genes related to overall survival, and LASSO analysis fingered out 12 of them with prognostic value. A risk score model based on these 12 genes was constructed by multivariate Cox regression analysis. According to the median risk score, patients were divided into high-risk group and low-risk group. The survival rate of high-risk group was significantly lower than that of low-risk group in Kaplan-Meier curve. Risk scores were related to T and grade. Univariate and multivariate Cox analysis showed our model was an independent prognostic factor. The AUC was 0.669. The nomogram showed high accuracy predicting the prognosis of HNSCC patients.Conclusion: Our model based on 12 ferroptosis-related genes performed excellently in predicting the prognosis of HNSCC patients. Ferroptosis-related genes may be promising biomarkers for HNSCC treatment and prognosis.

scholarly journals Immunogenomic Analyses of the Prognostic Predictive Model for Patients With Renal Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
Tao Feng ◽  
Jiahui Zhao ◽  
Dechao Wei ◽  
Pengju Guo ◽  
Xiaobing Yang ◽  
...  
Keyword(s):  
Cell Carcinoma ◽  
Predictive Model ◽  
Risk Score ◽  
Immune Cell ◽  
The Cancer Genome Atlas ◽  
Risk Scores ◽  
Immune Checkpoints ◽  
Survival Outcomes ◽  
Prognostic Signature ◽  
Therapeutic Benefits

BackgroundRenal cell carcinoma (RCC) is associated with poor prognostic outcomes. The current stratifying system does not predict prognostic outcomes and therapeutic benefits precisely for RCC patients. Here, we aim to construct an immune prognostic predictive model to assist clinician to predict RCC prognosis.MethodsHerein, an immune prognostic signature was developed, and its predictive ability was confirmed in the kidney renal clear cell carcinoma (KIRC) cohorts based on The Cancer Genome Atlas (TCGA) dataset. Several immunogenomic analyses were conducted to investigate the correlations between immune risk scores and immune cell infiltrations, immune checkpoints, cancer genotypes, tumor mutational burden, and responses to chemotherapy and immunotherapy.ResultsThe immune prognostic signature contained 14 immune-associated genes and was found to be an independent prognostic factor for KIRC. Furthermore, the immune risk score was established as a novel marker for predicting the overall survival outcomes for RCC. The risk score was correlated with some significant immunophenotypic factors, including T cell infiltration, antitumor immunity, antitumor response, oncogenic pathways, and immunotherapeutic and chemotherapeutic response.ConclusionsThe immune prognostic, predictive model can be effectively and efficiently used in the prediction of survival outcomes and immunotherapeutic responses of RCC patients.

scholarly journals Identification of an immune-related signature indicating the dedifferentiation of thyroid cells

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Xuemin Wang ◽  
Wen Peng ◽  
Chunyan Li ◽  
Rujia Qin ◽  
Zhaoming Zhong ◽  
...  
Keyword(s):  
Risk Score ◽  
Immune Cells ◽  
Immune Checkpoint ◽  
Tumour Microenvironment ◽  
The Cancer Genome Atlas ◽  
Risk Scores ◽  
Thyroid Cells ◽  
Normal Thyroid ◽  
Thyroid Carcinomas ◽  
Immune Checkpoint Molecules

Abstract Background Immune cells account for a large proportion of the tumour microenvironment in anaplastic thyroid carcinomas (ATCs). However, the expression pattern of immune-related genes (IRGs) in ATCs is unclear. Our study aimed to identify an immune-related signature indicating the dedifferentiation of thyroid cells. Methods We compared the differences in thyroid differentiation score (TDS), infiltration of immune cells and enriched pathways between ATCs and papillary thyroid carcinomas (PTCs) or normal thyroid tissues in the Gene Expression Omnibus database. Univariate and multivariable Cox analyses were used to screen prognosis-associated IRGs in The Cancer Genome Atlas database. After constructing a risk score, we investigated its predictive value for differentiation and survival by applying receiver operating characteristic and Kaplan–Meier curves. We further explored its associations with important immune checkpoint molecules, infiltrating immune cells and response to immunotherapy. Results Compared with PTCs or normal thyroid tissues, ATCs exhibited lower TDS values and higher enrichment of immune cells and activation of the inflammatory response. The quantitative analyses and immunohistochemical staining validated that most ATC cell lines and ATC tissues had higher expression of MMP9 and lower expression of SDC2 than normal thyroid samples and PTC. Higher risk scores indicates dedifferentiation and a worse prognosis. Additionally, the risk score was positively correlated with the immune checkpoint molecules PDL1, CTLA4, IDO1, and HAVCR2 and infiltration of multiple immune cells. Importantly, we found that the samples with higher risk scores tended to have a better response to immunotherapy than those with lower scores. Conclusion Our findings indicate that the risk score may not only contribute to the determination of differentiation and prognosis of thyroid carcinomas but also help the prediction of immune cells infiltration and immunotherapy response.

scholarly journals Prognostic Model of Head and Neck Squamous Cell Carcinoma Based on 12 Ferroptosis-related Genes

2021 ◽  
Author(s):  
Sijia Li ◽  
Hongyang Zhang ◽  
Wei Li
Keyword(s):  
Regression Analysis ◽  
Risk Score ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Risk Scores ◽  
Cox Regression Analysis ◽  
Normal Tissues ◽  
Model Based ◽  
Cox Analysis

Abstract Background: The purpose of our study is establishing a model based on ferroptosis-related genes predicting the prognosis of patients with head and neck squamous cell carcinoma (HNSCC).Methods: In our study, transcriptome and clinical data of HNSCC patients were from The Cancer Genome Atlas, ferroptosis-related genes and pathways were from Ferroptosis Signatures Database. Differentially expressed genes (DEGs) were screened by comparing tumor and adjacent normal tissues. Functional enrichment analysis of DEGs, protein-protein interaction network and gene mutation examination were applied. Univariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression were used to identified DEGs. The model was constructed by multivariate Cox regression analysis and verified by Kaplan-Meier analysis. The relationship between risk scores and other clinical features was also analyzed. Univariate and multivariate Cox analysis was used to verified the independence of our model. The model was evaluated by receiver operating characteristic analysis and calculation of the area under the curve (AUC). A nomogram model based on risk score, age, gender and TNM stages was constructed. Our model was also validated in the Gene Expression Omnibus (GEO) verification set. Results: We analyzed data including 500 tumor tissues and 44 adjacent normal tissues and 259 ferroptosis-related genes, then obtained 73 DEGs. Univariate Cox regression analysis screened out 16 genes related to overall survival, and LASSO analysis fingered out 12 of them with prognostic value. A risk score model based on these 12 genes was constructed by multivariate Cox regression analysis. According to the median risk score, patients were divided into high-risk group and low-risk group. The survival rate of high-risk group was significantly lower than that of low-risk group in Kaplan-Meier curve. Risk scores were related to T and grade. Univariate and multivariate Cox analysis showed our model was an independent prognostic factor. The AUC was 0.669. Those all could prove our model had great predictive ability of HNSCC prognosis and it could be validated in GEO dataset. The nomogram showed high accuracy predicting the prognosis of HNSCC patients.Conclusion: Our model based on 12 ferroptosis-related genes performed excellently in predicting the prognosis of HNSCC patients. Ferroptosis-related genes may be promising biomarkers for HNSCC treatment and prognosis.

scholarly journals Identification of an immune-related signature indicating the dedifferentiation of thyroid cells

2021 ◽  
Author(s):  
Xuemin Wang ◽  
Wen Peng ◽  
Chunyan Li ◽  
Rujia Qin ◽  
Zhaoming Zhong ◽  
...  
Keyword(s):  
Risk Score ◽  
Immune Cells ◽  
Immune Checkpoint ◽  
Tumour Microenvironment ◽  
The Cancer Genome Atlas ◽  
Risk Scores ◽  
Thyroid Cells ◽  
Normal Thyroid ◽  
Thyroid Carcinomas ◽  
Immune Checkpoint Molecules

Abstract Background: Immune cells account for a large proportion of the tumour microenvironment in anaplastic thyroid carcinomas (ATCs). However, the expression pattern of immune-related genes (IRGs) in ATCs is unclear. Our study aimed to identify an immune-related signature indicating the dedifferentiation of thyroid cells.Methods: We compared the differences in thyroid differentiation score (TDS), infiltration of immune cells and enriched pathways between ATCs and papillary thyroid carcinomas (PTCs) or normal thyroid tissues in the Gene Expression Omnibus database. Univariate and multivariable Cox analyses were used to screen prognosis-associated IRGs in The Cancer Genome Atlas database. After constructing a risk score, we investigated its predictive value for differentiation and survival by applying receiver operating characteristic and Kaplan-Meier curves. We further explored its associations with important immune checkpoint molecules, infiltrating immune cells and response to immunotherapy. Results: Compared with PTCs or normal thyroid tissues, ATCs exhibited lower TDS values and higher enrichment of immune cells and activation of the inflammatory response. The quantitative analyses and immunohistochemical staining validated that most ATC cell lines and ATC tissues had higher expression of MMP9 and lower expression of SDC2 than normal thyroid samples and PTC. Higher risk scores indicates dedifferentiation and a worse prognosis. Additionally, the risk score was positively correlated with the immune checkpoint molecules PDL1, CTLA4, IDO1, and HAVCR2 and infiltration of multiple immune cells. Importantly, we found that the samples with higher risk scores tended to have a better response to immunotherapy than those with lower scores. Conclusion: Our findings indicate that the risk score may not only contribute to the determination of differentiation and prognosis of thyroid carcinomas but also help the prediction of immune cells infiltration and immunotherapy response.

scholarly journals A Novel Ferroptosis-Related Prognostic Signature Reveals Macrophage Infiltration and EMT Status in Bladder Cancer

2021 ◽  
Vol 9 ◽  
Author(s):  
Yilin Yan ◽  
Jinming Cai ◽  
Zhengnan Huang ◽  
Xiangqian Cao ◽  
Pengfei Tang ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Risk Score ◽  
Risk Model ◽  
Epithelial Mesenchymal Transition ◽  
Positive Association ◽  
Lipid Peroxides ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Mutation Rates ◽  
Mesenchymal Transition

Bladder cancer (BC) belongs to one of the most common and highly heterogeneous malignancies. Ferroptosis is a newly discovered regulated cell death (RCD), characterized by accumulation of toxic lipid peroxides, and plays a crucial role in tumor progression. Here, we conducted a comprehensive analysis on the transcriptomics data of ferroptosis-related genes in BC based on The Cancer Genome Atlas (TCGA) and three Gene Expression Omnibus (GEO) datasets. In our study, a 6-gene signature was identified based on the potential prognostic ferroptotic regulatory genes. Furthermore, our signature revealed a good independent prognostic ability in BC. Patients with low-risk score exhibited higher FGFR3 mutation rates while high risk score had a positive association with higher RB1 mutation rates. Meanwhile, higher proportions of macrophages were observed in high BC risk group simultaneously with four methods. Unexpectedly, the risk score showed a significant positive correlation with epithelial-mesenchymal transition (EMT) status. Functional assays indicated that CRYAB and SQLE knockdown was associated with attenuated invasion capacity. Our study revealed a ferroptosis-related risk model for predicting prognostic and BC progression. Our results indicate that targeting ferroptosis may be a therapeutic strategy for BC.

scholarly journals Identification and Validation of a Novel Six-lncRNA-Based Prognostic Model for Lung Adenocarcinoma

2022 ◽  
Vol 11 ◽  
Author(s):  
Lingge Yang ◽  
Yuan Wu ◽  
Huan Xu ◽  
Jingnan Zhang ◽  
Xinjie Zheng ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Risk Score ◽  
Prognostic Model ◽  
Roc Curves ◽  
The Cancer Genome Atlas ◽  
Immune Checkpoints ◽  
Lasso Regression ◽  
Predictive Values ◽  
Angiogenic Therapy ◽  
Cox Analysis

ObjectiveThis study was conducted in order to establish a long non-coding RNA (lncRNA)-based model for predicting overall survival (OS) in patients with lung adenocarcinoma (LUAD).MethodsOriginal RNA-seq data of LUAD samples were extracted from The Cancer Genome Atlas (TCGA) database. Univariate Cox survival analysis was performed to select lncRNAs associated with OS. The least absolute shrinkage and selection operator (LASSO) regression analysis and multivariate Cox analysis were performed for building an OS-associated lncRNA prognostic model. Moreover, receiver operating characteristic (ROC) curves were generated to assess predictive values of the hub lncRNAs. Consequently, qRT-PCR was conducted to validate its prognostic value. The potential roles of these lncRNAs in immunotherapy and anti-angiogenic therapy were also investigated.ResultsThe lncRNA-associated risk score of OS (LARSO) was established based on the LASSO coefficient of six individual lncRNAs, including CTD-2124B20.2, CTD-2168K21.1, DEPDC1-AS1, RP1-290I10.3, RP11-454K7.3, and RP11-95M5.1. Kaplan–Meier analysis revealed that LUAD patients with higher LARSO values had a shorter OS. Furthermore, a new risk score (NRS), including LARSO, stage, and N stage, could better predict the prognosis of LUAD patients compared with LARSO alone. Evaluation of the prognostic model in our cohort demonstrated that patients with higher scores had a worse prognosis. In addition, correlation analysis between these six lncRNAs and immune checkpoints or anti-angiogenic targets suggested that LUAD patients with high LARSO might not be sensitive to immunotherapy or anti-angiogenic therapy.ConclusionsThis robust six-lncRNA prognostic signature may be used as a novel and powerful prognostic biomarker for lung adenocarcinoma.

scholarly journals Exploration of Prognostic Biomarkers of Muscle-Invasive Bladder Cancer (MIBC) by Bioinformatics

2021 ◽  
Vol 17 ◽  
pp. 117693432110492
Author(s):  
Xianglai Xu ◽  
Yelin Wang ◽  
Sihong Zhang ◽  
Yanjun Zhu ◽  
Jiajun Wang
Keyword(s):  
Bladder Cancer ◽  
Characteristic Curve ◽  
Prognostic Index ◽  
The Cancer Genome Atlas ◽  
Invasive Bladder Cancer ◽  
Immune Checkpoints ◽  
Venn Diagram ◽  
Muscle Invasive Bladder Cancer ◽  
Muscle Invasive ◽  
Cox Analysis

We aimed to discover prognostic factors of muscle-invasive bladder cancer (MIBC) and investigate their relationship with immune therapies. Online data of MIBC were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus database (GEO) database. Weighted gene co-expression network analysis (WGCNA) and univariate Cox analysis were applied to classify genes into different groups. Venn diagram was used to find the intersection of genes, and prognostic efficacy was proved by Kaplan-Meier analysis. Heatmap was utilized for differential analysis. Riskscore (RS) was calculated according to multivariate Cox analysis and evaluated by receiver operating characteristic curve (ROC). MIBC samples from TCGA and GEO were analyzed by WGCNA and univariate Cox analysis and intersected at 4 genes, CLK4, DEDD2, ENO1, and SYTL1. Higher SYTL1 and DEDD2 expressions were significantly correlated with high tumor grades. Riskscore based on genes showed great prognostic efficiency in predicting overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI) in TCGA dataset ( P < .001). The area under the ROC curve (AUC) of RS reached 0.671 in predicting 1-year survival and 0.653 in 3-year survival. KEGG pathways enrichment filtered 5 enriched pathways. xCell analysis showed increased T cell CD4+ Th2 cell, macrophage, macrophage M1, and macrophage M2 infiltration in high RS samples ( P < .001). In immune checkpoints analysis, PD-L1 expression was significantly higher in patients with high RS. We have, therefore, constructed RS as a convincing prognostic index for MIBC patients and found potential targeted pathways.

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