
==== Front
JCO Precis Oncol
JCO Precis Oncol
po
PO
JCO Precision Oncology
2473-4284
Wolters Kluwer Health

39151107
PO.23.00661
10.1200/PO.23.00661
00193
Original Reports
Biomarkers
Identification and Validation of Prognostic Model for Tumor Microenvironment-Associated Genes in Bladder Cancer Based on Single-Cell RNA Sequencing Data Sets
Safder Imran PhD 1
https://orcid.org/0000-0001-6869-3497
Valentine Henkel PhD 2
Uzzo Nicole BS 2
Sfakianos John MD 3
Uzzo Robert MD, MBA 2
https://orcid.org/0000-0002-8775-503X
Gupta Shilpa MD 4
https://orcid.org/0000-0001-6225-7555
Brown Jason MD, PhD 1 5
https://orcid.org/0000-0002-8310-1063
Ranti Daniel MD 3
https://orcid.org/0000-0002-7618-0744
Plimack Elizabeth MD 2
Haber George MD, PhD 4
Weight Christopher MD 4
Kutikov Alexander MD 2
https://orcid.org/0000-0003-3611-9532
Abbosh Philip MD, PhD 2 6
https://orcid.org/0000-0001-7129-6230
Bukavina Laura MD, MPH 1 4
1 Case Western Reserve School of Medicine, Cleveland, OH
2 Fox Chase Cancer Center, Philadelphia, PA
3 Mount Sinai Medical Center, New York, NY
4 Cleveland Clinic Foundation, Cleveland, OH
5 University Hospitals Cleveland Medical Center, Cleveland, OH
6 Albert Einstein Medical Center, Philadelphia, PA
Laura Bukavina, MD, MPH; e-mail: BUKAVIL2@ccf.org.
2024
16 8 2024
16 8 2024
8 e230066128 11 2023
1 3 2024
26 4 2024
© 2024 by American Society of Clinical Oncology
2024
American Society of Clinical Oncology
https://creativecommons.org/licenses/by-nc-nd/4.0/ Creative Commons Attribution Non-Commercial No Derivatives 4.0 License: https://creativecommons.org/licenses/by-nc-nd/4.0/

PURPOSE

The purpose of this study was to elucidate the relationship between the tumor microenvironment (TME) and cellular diversity in bladder cancer (BLCA) progression, leveraging single-cell RNA sequencing (scRNA-seq) data to identify potential prognostic biomarkers and construct a prognostic model for BLCA.

METHODS

We analyzed scRNA-seq data of normal and tumor bladder cells from the Gene Expression Omnibus (GEO) database to uncover crucial markers within the bladder TME. The study compared gene expression in normal versus tumor bladder cells, identifying differentially expressed genes. These genes were subsequently assessed for their prognostic significance using patient follow-up data from The Cancer Genome Atlas. Prognostic models were constructed using Least Absolute Shrinkage and Selection Operator and multivariate Cox regression analyses, focusing on eight genes of interest. The predictive performance of the model was also tested against additional GEO data sets (GSE31684, GSE13507, and GSE32894).

RESULTS

The prognostic model demonstrated reliable prediction of patient outcomes. Validation through gene set enrichment analysis and immune cell infiltration assessment supported the model's efficacy. The results from both the univariate and multivariate analyses suggest that the risk score is an independent prognostic factor with a hazard ratio of 2.97 (95% CI, 2.28 to 3.9, P < .001). In the validation cohort, the AUC at 1, 2, and 3 years is 0.74, 0.74, and 0.72, respectively.

CONCLUSION

Our findings proposed biomarkers with prognostic potential, laying the groundwork for future in vitro validation and therapeutic exploration. This contributes to a deeper understanding of the genes associated with bladder TME and may improve prognostic precision in BLCA management.

OPEN-ACCESSTRUE
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pmcINTRODUCTION

While the incidence and mortality rates of bladder cancer (BLCA) are decreasing globally, it remains a significant health challenge with a notable prevalence in Western countries.1,2 Like many other malignancies,3-5 BLCA treatment has evolved beyond traditional methods like chemotherapy and surgery. Emerging approaches such as molecularly targeted therapy and immunotherapy are becoming increasingly important, thus now more than ever, understanding the molecular underpinnings of BLCA is crucial for enhancing therapeutic effectiveness. Molecular markers are instrumental in discerning BLCA subtypes,6 and with advancements in high-throughput sequencing, focusing on tumor heterogeneity is propelling precision medicine forward.7

CONTEXT

Key Objective

The study aimed to evaluate the relationship between the tumor microenvironment (TME) using single-cell RNA sequencing data to identify potential prognostic biomarkers and construct a prognostic model in bladder cancer.

Knowledge Generated

The prognostic model demonstrated reliable prediction of patient outcomes, with validation confirming the model's efficacy through gene set enrichment analysis and immune cell infiltration assessment. The risk score based on TME markers was identified as an independent prognostic factor.

Relevance

These findings contribute to a deeper understanding of genes associated with the bladder TME itself. This lays the groundwork for future in vitro validation and therapeutic exploration.

The tumor microenvironment (TME) is now acknowledged to include not just genomic features but also a complex array of stromal cells, significant angiogenesis, and immune cell infiltration.8,9 Investigating the key genes associated with TME is vital for diagnosing and treating BLCA effectively, particularly within treatment resistance.

Single-cell RNA sequencing (scRNA-seq) is a powerful tool in this context. It provides detailed RNA transcript profiles at the single-cell level, offering advantages over traditional tumor tissue sequencing by highlighting individual cell type differences.10,11 ScRNA-seq excels in studying tumor cell lineage diversity and predicting interactions between cancer cells and the microenvironment.12,13 In BLCA research, scRNA-seq has illuminated the transcriptome landscape at various disease stages and unveiled the spatial heterogeneity of microenvironment-related cells.14,15 However, integrating scRNA-seq data with multiomics data sets to derive clinical implications remains challenging.16

In this study, we harnessed single-cell transcriptome data to find key markers in bladder TME associated with prognosis and constructed a risk-scoring model. We hypothesize that this heterogeneity plays an important role in shaping gene expression pattern in the bladder TME which can be effectively characterized using scRNA-seq. To explore this gene expression pattern, we compared normal and tumor tissues using scRNA data, and we expect to identify key differentially expressed genes (DEGs) in bladder TME which also have prognostic significance and established a gene signature used for creating a risk-scoring prognostic model. These findings may offer new insights into prognostic biomarkers for BLCA research.

METHODS

Single-Cell RNA-Seq Data Set and Preprocessing

We obtained scRNA sequencing data for normal bladder cells from the Gene Expression Omnibus (GEO) database (data set GSE129845), complemented by tumor cell data courtesy of Dr Sfakianos' lab. These raw data underwent preprocessing via the CellRanger software, resulting in normalized, aggregated data across samples, subsequently converted into a unified molecular identifier matrix. Further information on the methodology employed for the scRNA sequencing data analysis can be found in the Data Supplement.

Construction of Prognostic Model Based on DEGs From scRNA-Seq Data

We identified DEGs and employed them as candidate genes to investigate their association with BLCA prognosis. To achieve this, we used step-wise Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression and multivariate Cox regression to identify survival-related DEGs and to develop a prognostic model. The integration of survival event data and overall survival (OS) time with the Lasso Cox regression model facilitated the identification of genes significantly associated with BLCA prognosis.

For initial dimension reduction of the high-dimensional data, LASSO Cox regression (using R packages: Glmnet, survival) was applied. The optimal lambda (λ) value was determined based on the minimum criteria of the penalized maximum likelihood estimator, using a default 10-fold cross-validation with the cv.glmnet function. The gene signatures identified through Lasso were deemed survival-related DEGs or key genes. Subsequently, multivariate Cox regression analysis was conducted on these key genes to select those for model construction. The details regarding the prognostic model and risk score calculation are provided in the Data Supplement.

The Cancer Genome Atlas and GEO Data Sets

To download the The Cancer Genome Atlas (TCGA)-BLCA data, we used the TCGAbiolinks (version 2.28.0) R package and retrieved the normalized expression RNA-seq data for bladder patients from the TCGA-BLCA database. Relevant clinical data (sex, age, OS time, and OS status) for TCGA-BLCA was also obtained from TCGA portal (Data Supplement 4). After excluding samples with incomplete clinical information, 419 patients were selected for further study. We also sourced normalized gene expression and clinical data from three GEO data sets (GSE31684, GSE13507, and GSE32894), which served as validation cohorts for our prognostic model.

Kaplan-Meier Survival Analysis and Receiver Operating Characteristic Curve

The prognostic model was evaluated based on OS between the high- and low-risk groups in TCGA. R packages survival and survminer were used to conduct Kaplan-Meier (KM) survival analysis and log-rank test in TCGA and GEO cohorts (GSE31684, GSE13507, and GSE32894). Receiver operating characteristic (ROC) curves for 1-, 2-, and 3-year intervals were plotted to assess predictive accuracy, and the AUC values were calculated using the survivalROC R package.

Gene Set Enrichment Analysis

Gene set enrichment analysis (GSEA) was conducted to discern significant biological pathways between high- and low-risk groups within the TCGA cohort, using the software provided by the Broad Institute. We used hallmark gene sets, and the statistical significance of the GSEA results was determined with a false discovery rate (FDR) of 0.25, as recommended by the Broad Institute.

Model Validation and Analysis

To validate the independent predictive power of our risk score, we implemented both univariate and multivariate Cox regression analyses on the patient data from TCGA-BLCA, treating a P value at or below .05 as significant. This approach enabled us to assess the performance of the risk score relative to other variables such as age, sex, TNM staging, and smoking status. We investigated the survival disparities across different risk categories via KM analysis. The prognostic accuracy of our model was determined by generating ROC curves and calculating the AUC for the 1-, 2-, and 3-year marks. Additionally, through GSEA, we identified key biological pathways that differed significantly between the high-risk and low-risk groups within the TCGA cohort.

Estimation of Immune Cell-Type Fractions

The xCell algorithm was used to convert the TCGA's transcriptomic data into immune cell profiles to evaluate immune cell changes in high- and low-risk groups in TCGA data. This step translated gene expression data into a profile of immune cell infiltration, which is a critical component of the TME influencing patient outcomes.

RESULTS

Single-Cell Sequencing and Patient Group's Identification

Using the Seurat pipeline for scRNA-seq data, we obtained a merged data set of normal and tumor cells, consisting of 21,886 cells (Fig 1). The initial quality control steps, as described in the methods section, excluded low-quality cells and doublets based on gene expression thresholds and mitochondrial gene content, resulting in a refined set of 20,091 cells for downstream analysis. Figure 1B (on the right) illustrates that all sample clusters are well-integrated after the potential batch effects were addressed in the integration step.

FIG 1. Comprehensive analysis of scRNA sequencing in BLCA. (A) Schematic representation of the study workflow highlighting the process from single-cell data set acquisition through quality control and analysis to the integration of normal and tumor cells and subsequent validation using TCGA data, leading to the construction of a prognostic model. (B) UMAP visualization depicting the distribution of scRNA-seq data from both normal and tumor samples, (left) before and (right) after adjustment for batch effects, showing the grouping of cell populations. BLCA, bladder cancer; GSEA, gene set enrichment analysis; PCA, principal component analysis; ROC, receiver operating characteristic curve; scRNA, single-cell RNA; TCGA, The Cancer Genome Atlas; tSNE, t-distributed stochastic neighbor embedding; UMAP, Uniform Manifold Approximation and Projection.

Identification of DEGs Between Normal and Tumor Cells

Following the criteria outlined in the methods section for identifying DEGs, we pinpointed 403 DEGs between normal and tumor cells, as detailed in the Data Supplement.

Construction and Validation of the Prognostic Risk Model

We applied LASSO Cox regression to the 403 DEGs derived from the scRNA-seq data to assess their correlation with BLCA prognosis using the TCGA-BLCA data set (Data Supplement). The LASSO method estimated 25 genes with the most significant prognostic impact on OS. Subsequent multivariate Cox regression pinpointed eight genes correlated with survival; these key genes included CD74 (coef = –0.16), AMIGO2 (coef = –0.12), IGF2 (coef = 0.08), EVPL (coef = -0.13), TM4SF1 (coef = 0.14), MRFAP1L1 (coef = -0.25), P4HB (coef = 0.59), and DDX39B (coef = -0.41), as illustrated in Figure 2. The selected key genes, as listed in Table 1, were used to construct the prognostic model, with further details provided in the Data Supplement. Patients in the TCGA-BLCA data set were divided into high- or low-risk groups according to the median risk score. Similarly, patients from the GEO data sets were classified into respective risk categories on the basis of the model's coefficient value and the expression of genes within the data sets (refer to the Data Supplement for additional information).

FIG 2. Forest plot representing the HRs of key genes in bladder cancer prognosis. Multivariate Cox regression analysis shows the association of specific genes with patient outcomes. Genes like CD74, AMIGO2, IGF2, EVPL, TM4SF1, MRFAP1L1, P4HB, and DDX39B are highlighted, with HRs and 95% CIs. Statistically significant values (P < .05) are indicated with an asterisk. *Statistically significant at the P < .05 level. **Statistically significant at the P < .01 level. AIC, Akaike Information Criterion; HR, hazard ratio.

TABLE 1. Multivariate Cox Regression Analysis of Key Genes

Gene name	coef	exp(coef)	se(coef)	z	P	
CD74	–0.163872	0.848851	0.058479	–2.802	.00508	
AMIGO2	–0.122205	0.884967	0.050000	–2.444	.01452	
IGF2	0.079144	1.082360	0.027695	2.858	.00427	
EVPL	–0.126985	0.880747	0.062584	–2.029	.04246	
TM4SF1	0.136121	1.145820	0.060705	2.242	.02494	
MRFAP1L1	–0.246343	0.781654	0.124436	–1.980	.04774	
P4HB	0.592153	1.807876	0.183443	3.228	.00125	
DDX39B	–0.410436	0.663361	0.196844	–2.085	.03706	

We compared the OS of patients in high- and low-risk groups using KM survival analysis within the TCGA (Fig 3A) and across three GEO cohorts (Figs 3B-3D) to discern prognostic differences. Patients in the high-risk category exhibited poorer OS rates in the TCGA cohort (P < .0001). Similarly, the GEO cohorts showed that high-risk groups had lower survival rates: GSE13507 (P = .00092), GSE31684 (P = .0057), and GSE32894 (P = .00055) when compared with low-risk groups.

FIG 3. Kaplan-Meier survival curves illustrating the prognostic efficacy of the risk model in BLCA. (A) Construction of the prognostic risk model with KM survival analysis for high- and low-risk groups using TCGA-BLCA data set, showing a statistically significant separation (P < .0001). (B) Model validation and survival analysis on an external GEO data set, again demonstrating significant stratification between high- and low-risk groups (P value indicated). (C and D) Further validation of the prognostic risk model in additional independent cohorts, with each subplot showing the KM curves and their respective P values, confirming the model's consistent prognostic capability across data sets. BLCA, bladder cancer; GEO, Gene Expression Omnibus; KM, Kaplan-Meier; TCGA, The Cancer Genome Atlas.

Time-dependent ROC analysis was conducted to evaluate the accuracy and sensitivity of the prognostic risk score. The AUC values for 1, 2, and 3 years in the TCGA data were 0.74, 0.74, and 0.73, respectively, as depicted in Figure 4. The AUC values for the GEO data sets over 1, 2, and 3 years also exceeded 0.65, which indicates that the risk scores accurately predicted patient survival rates (as shown in Data Supplement, Fig S1). The prognostic model demonstrated robust performance in both the TCGA and GEO cohorts.

FIG 4. ROC curves for 1, 2, and 3-year survival predictions. The ROC curves evaluate the prognostic model's performance at 1-year (AUC = 0.744), 2-year (AUC = 0.7437), and 3-year (AUC = 0.725) time points using TCGA-BLCA data set. The AUC values indicate the model's accuracy in predicting survival at each respective interval. BLCA, bladder cancer; ROC, receiver operating characteristic; TCGA, The Cancer Genome Atlas.

Univariate and Multivariate Cox Regression Analyses

Clinical factors along with the risk score of the TCGA cohort were subjected to univariate and multivariate Cox regression analyses, the results of which are displayed in Figure 5 and detailed in the Data Supplement (Table S1). The findings from both univariate and multivariate analyses indicate that the risk score is an independent prognostic factor, with a hazard ratio of 2.97 (95% CI, 2.28 to 3.90, P < .001). This risk score demonstrated statistical significance in the multivariate Cox regression analysis (as shown in Fig 5).

FIG 5. Multivariate cox regression analysis depicting impact of clinical variables and smoking status on survival. This forest plot displays the HRs for age, sex, tumor stage (T), node involvement (N), metastasis (M), and smoking status, with their respective CIs and P values revealing that the ability of risk score to predict prognosis is better than the other factors. **Statistically significant at the P < .01 level. ***Statistically significant at the P < .001 level. AIC, Akaike Information Criterion; HR, hazard ratio.

GSEA

GSEA was used to evaluate TCGA-BLCA cohort, elucidating the distinct molecular profiles between the high- and low-risk groups. Consistent with previous BLCA studies, the high-risk group was characterized by predominant pathways such as epithelial-mesenchymal transition (EMT), apical junction, angiogenesis, and a variety of signaling pathways including hedgehog, KRAS, complement, notch, MTORC1, and apoptosis (refer to the Data Supplement, Fig S2). Significantly enriched pathways in the high-risk group, which met the criteria of a normalized enrichment score >1, a FDR below 0.25, and a nominal P-value below 0.05, included notably myogenesis and EMT.

Immune Cell Infiltration

Given the significantly poorer survival status of the high-risk group compared with the low-risk group, we investigated potential differences in immune cell infiltration between the two. We employed the xCell tool to deduce the levels of immune cell infiltration. The Data Supplement (Fig S3) features a box plot that highlights the disparities in immune cell populations across the risk groups. Our analysis revealed that high-risk patients displayed differentially expressed levels of tumor-associated immune cells within the TME, specifically B cells, CD4+ T cells, CD8+ T cells, M2 macrophages, Th2 cells, and regulatory T cells (Tregs). This finding implies that the immune landscape in patients with high-risk BLCA may foster a milieu conducive to tumor progression, which could negatively affect patient survival outcomes, as detailed in the Data Supplement (Fig S3).

The Data Supplement (Fig S4) shows the correlations between the expression of prognostic genes (CD74, AMIGO2, IGF2, and EVPL) and immune cell infiltration. Notably, CD74 expression was strongly correlated with infiltration levels of dendritic cells (r = 0.65, P < .0001) and M1 macrophages (r = 0.71, P < .0001), as well as with B cells (r = 0.46, P < .0001) and CD4+ T cells (r = 0.46, P < .0001). Unlike the interaction with CD74 and immune cells, the association between these cell types and other prognostic genes was far less pronounced (Data Supplement, Fig S4). Additional correlations between immune cell components and genes of interest can be seen in the Data Supplement (Figs S5-S15).

DISCUSSION

In this investigation, we leveraged the power of scRNA sequencing data to dissect the cellular heterogeneity within BLCA tissues, identified DEGs in bladder TME, and examined their prognostic associations. Given the influence of TME heterogeneity on the overall gene expression pattern, our objective was to identify and characterize the specific gene expression signatures of the bladder TME at the single-cell level. Our ultimate aim was to develop a TME-associated prognostic model to predict the OS of patients with BLCA. We incorporated these DEGs into Lasso Cox regression and multivariate Cox regression models, alongside TCGA bulk RNA-seq and OS data, to identify biomarkers significantly linked to disease prognosis and subsequently constructed a risk score for the prognostic model. Unlike previous studies that have relied on bulk RNA-seq data—which can overlook cellular differences within the tissue and often mask heterogeneity—our study's novelty lies in the integration of scRNA-seq with bulk RNA-seq data.

The primary outcome of our study is the development of an eight gene prognostic model, constructed using DEGs identified from scRNA bladder TME. This model was established with the TCGA as the training data set and validated using three GEO cohort data sets. Majority of the genes within our model are recognized by existing literature in BLCA as being associated with the disease's prognosis. Specifically, six of eight genes from our gene signature, namely CD74, AMIGO2, IGF2, TM4SF1, P4HB, and DDX39B, have been previously implicated in the prognosis of BLCA.17-21 Notably, to the best of our knowledge, the EVPL and MRFAP1L1 genes have not been studied within the context of BLCA. However, their implication in other types of tumors suggests a potential prognostic role that merits further exploration. Knockdown of EVPL gene has been shown to increase chemoresistance in esophageal cancer cell lines. Further transcriptomic analyses have indicated that these genes may regulate the expression of interleukins and their receptors by inhibiting the NF-κB and TNF signaling pathways in radioresistant esophageal squamous cell carcinoma cells, thereby dampening the immune response.22

CD74, previously established for its role in proliferation and invasion, particularly in muscle-invasive BLCA, has been observed to correlate with higher-grade urothelial carcinoma in vitro and in vivo.23 Targeted CD74 knockdown experiments have demonstrated reduced proliferation and invasion in cell lines, indicating its therapeutic target potential.17 As a receptor for macrophage migration inhibitory factors MIF1 and MIF2, CD74 is part of a signaling cascade integral to BLCA's pathophysiology. Intriguingly, the knockout of MIF1 leads to stage arrest in the N-Butyl-N(4-hydroxybutyl) nitrosamine (BBN) BLCA model, and the upregulation of small molecules via D-dopachrome tautomerase, a functional homologue of MIF1 and MIF2 that can bind to the CD74 cell receptor, has been shown to mediate part of the protumorigenic effects.23 While CD74 expression is low in normal epithelial cells, it is highly expressed in a variety of tumors cells. Recent studies into CD74 mechanism of action have highlighted its role in CD44 regulation and generation and maintenance of cancer stem cells through CD44. 24 The significance of CD74 in multiple cancer types has been validated, with emerging therapies such as novel anti-CD74 antibodies and CD74-drug conjugates currently under development and testing.25

AMIGO2 expression has been identified as a significant oncogenic and prognostic marker across various cancers, including BLCA.26 It is notable for its role in promoting prostate cancer by modulating EMT-related biomarkers—a pathway significantly enriched in our study's high-risk group—and underscores its potential as a biomarker and the need for further investigation into its specific role in the progression of BLCA.27 SOX2-mediated stimulation of IGF2 expression subsequently activates the AKT signaling pathway and has been shown to be associated with aggressive BLCA phenotype and poor prognosis. Knockdown studies involving IGF2 and its receptor, IGF1R, have demonstrated inhibited growth in BLCA cells, proposing the SOX2–IGF2/IGF1R axis as a possible therapeutic target.19

TM4SF1, a member of the transmembrane 4 superfamily, is known to orchestrate complex interactions with an array of biological molecules including integrins, membrane-bound receptors, and proteins belonging to the immunoglobulin superfamily. These interactions culminate in the formation of intricate tetraspanin-enriched microdomains. Specifically, TM4SF1 has been implicated in the orchestration of EMT and the modulation of vascular endothelial growth factor (VEGF)-mediated angiogenesis, in part through synergistic interactions with integrin complexes.28 This parallels the function of AMIGO2, particularly within the context of our study's high-risk cohort, where its involvement in EMT-related activities and angiogenesis reflects the broader influence of the TME and EMT in fostering metastatic competency.

P4HB, identified as a prospective therapeutic target, has been observed to attenuate apoptosis levels triggered by endoplasmic reticulum (ER) stress, an adaptive mechanism advantageous for tumor persistence. The inhibition of P4HB has been demonstrated to enhance the sensitivity of cancer cells to chemotherapeutic agents and radiation by disrupting the unfolded protein response pathway, activated in response to ER stress.29

DDX39B, also known as UAP56 or BAT1, belongs to the DEAD-box helicase family and plays a crucial role in binding mRNA and facilitating its export.30 With evidence linking alterations in RNA expression or processing to the initiation and progression of cancer, DDX39B emerges as a key regulator in the assembly of the spliceosome and various facets of RNA metabolism.31 Inhibition of nuclear translocation of circRNA through DDX39B targeted mechanism has been shown to inhibit critical signaling pathway activation to suppress BLCA metastasis.32

The enrichment analysis of the differences between high- and low-risk groups through GSEA (Data Supplement, Fig S2) revealed that the high-risk group exhibited pathways related to the TME, particularly functions related to EMT and myogenesis. The EMT is a crucial biological process implicated in the progression of cancer, characterized by the loss of cell adhesion and polarity in epithelial cells, which is pivotal for cancer cell invasion and metastasis.33,34 These phenomena have been closely associated with the progression of various cancers, including BLCA.35

To investigate the connection between our risk score and the TME, we analyzed the presence of immune cells in high- and low-risk groups. We found that the high-risk group exhibited lower levels of B cells, CD4+ T cells, and CD8+ T cells, and higher levels of monocytes and M2 macrophages, indicative of an immunosuppressive TME. Tumor-associated macrophages, often in an M2-like polarization, are known to facilitate EMT, a process vital to cancer progression marked by reduced cell adhesion and increased cell invasion and metastasis.36 In this context, we highlight the significance of three genes: AMIGO2, P4HB, and TM4SF1, and their links to EMT and M2 macrophage polarization warranting further exploration in cancer progression. For instance, AMIGO2 has been shown to activate M2 macrophage polarization via the AKT pathway, which may similarly affect EMT within the bladder TME.36 Additionally, the newly identified circRNA circP4HB, derived from P4HB, has been implicated in promoting M2 polarization by interacting with PKM2, which is a key player in lung adenocarcinoma.

While TM4SF1's direct association with M2 polarization remains unclear, its established role in cell survival through AKT signaling pathways suggests a potential influence on M2 polarization akin to AMIGO2. These findings underscore the necessity for further research into how these genes may drive EMT through interactions with M2 macrophages in the TME.36-38

In conclusion, our comprehensive analysis of published scRNA-seq, TCGA, and GEO data sets has culminated in the establishment and validation of a TME-associated risk score for BLCA. The model showed strong correlation with enrichment pathway and levels of immune infiltration. Our findings propose biomarkers with prognostic potential, laying the groundwork for future in vitro validation and therapeutic exploration.

AUTHOR CONTRIBUTIONS

Conception and design: Robert Uzzo, Philip Abbosh, Laura Bukavina

Financial support: Philip Abbosh

Provision of study materials or patients: Shilpa Gupta, Philip Abbosh

Collection and assembly of data: Henkel Valentine, Nicole Uzzo, Daniel Ranti, Laura Bukavina

Data analysis and interpretation: Imran Safder, John Sfakianos, Shilpa Gupta, Jason Brown, Daniel Ranti, Elizabeth Plimack, George Haber, Christopher Weight, Alexander Kutikov, Laura Bukavina

Manuscript writing: All authors

Final approval of manuscript: All authors

Accountable for all aspects of the work: All authors

AUTHORS' DISCLOSURES OF POTENTIAL CONFLICTS OF INTEREST

The following represents disclosure information provided by authors of this manuscript. All relationships are considered compensated unless otherwise noted. Relationships are self-held unless noted. I = Immediate Family Member, Inst = My Institution. Relationships may not relate to the subject matter of this manuscript. For more information about ASCO's conflict of interest policy, please refer to www.asco.org/rwc or ascopubs.org/po/author-center.

Open Payments is a public database containing information reported by companies about payments made to US-licensed physicians (Open Payments).

Nicole Uzzo

Employment: Fox Chase Cancer Center

John Sfakianos

Consulting or Advisory Role: Merck, Asieris Pharmaceuticals, pacific edge

Speakers' Bureau: Natera

Robert Uzzo

Consulting or Advisory Role: Pfizer, Merck

Shilpa Gupta

Stock and Other Ownership Interests: Moderna Therapeutics, BioNTech SE, Nektar

Consulting or Advisory Role: Gilead Sciences, EMD Serono, Pfizer, Merck, Foundation Medicine, Seagen, Bayer, Bristol Myers Squibb/Medarex, Natera, Astellas Pharma

Speakers' Bureau: Bristol Myers Squibb, Janssen Oncology, Gilead Sciences, Seagen

Research Funding: Bristol Myers Squibb Foundation (Inst), Merck (Inst), Roche/Genentech (Inst), EMD Serono (Inst), QED Therapeutics (Inst), Seagen (Inst), Moderna Therapeutics (Inst), Exelixis (Inst), Gilead Sciences (Inst), Novartis (Inst)

Travel, Accommodations, Expenses: Astellas Pharma

Jason Brown

Honoraria: AstraZeneca

Consulting or Advisory Role: EMD Serono, Pfizer, Johnson & Johnson/Janssen

Speakers' Bureau: EMD Serono

Research Funding: Seagen (Inst), Merck (Inst), Novita Pharmaceuticals (Inst), Bicycle Therapeutics (Inst), Pfizer (Inst)

Elizabeth Plimack

Consulting or Advisory Role: AstraZeneca, Bristol Myers Squibb/Medarex, EMD Serono, Exelixis, IMV, Merck, Signatera, Pfizer, Regeneron, Seagen, Eisai, Synthekine, Flagship Pioneering

Research Funding: Bristol Myers Squibb (Inst), Merck Sharp & Dohme (Inst), Astellas Pharma (Inst), Genentech/Roche (Inst)

Open Payments Link: https://openpaymentsdata.cms.gov/physician/66377

Christopher Weight

Research Funding: Cleveland Diagnostics (Inst), Johnson and Johnson (Inst), Cisco Systems (Inst), Cisco Systems (Inst)

Alexander Kutikov

Stock and Other Ownership Interests: Visible Health

Travel, Accommodations, Expenses: Pfizer

Philip Abbosh

Stock and Other Ownership Interests: Abyost PHarmaceuticals

Consulting or Advisory Role: ArTara Therapeutics

Research Funding: Adaptive Biotechnologies, natera, Janssen Oncology (Inst)

Patents, Royalties, Other Intellectual Property: urine biomarkers patent application, Intravesical imidazolium compounds, novel kidney cancer vaccine (Inst)

Laura Bukavina

Employment: Charite, ImmunityBio

Leadership: Urology times, European Urology

Research Funding: BCAN, American Urological Association

No other potential conflicts of interest were reported.
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