OR OR OR Oncology Research 0965-0407 1555-3906 Tech Science Press USA 45442 10.32604/or.2024.045442 Review Clinical implications of single cell sequencing for bladder cancerClinical implications of single cell sequencing for bladder cancerClinical implications of single cell sequencing for bladder cancer YADOLLAHVANDMIANDOABREZA 1# JALALIZADEHMEHRSA 1# DIONATOFRANCIELE APARECIDA VECHIA 1 BUOSIKEINI 1 LEMEPATRÍCIA A. F. 1 COLLUCIANA S. B. DAL 1 GIACOMELLICRISTIANE F. 1 ASSISALEX DIAS 1 BASHIRICHELKASARINASIM 1 REISLEONARDO OLIVEIRA 1 2Reisleo@unicamp.br UroScience, School of Medical Sciences, University of Campinas, UNICAMP, Campinas, Sao Paulo, 13083-872, Brazil ImmunOncology, Pontifical Catholic University of Campinas, PUC-Campinas, Campinas, Sao Paulo, 13087-571, Brazil Address correspondence to: Leonardo Oliveira Reis, Reisleo@unicamp.br

#These authors contributed equally

 2024 2032024 32 4 597 605 2782023 0812024 © 2024 Yadollahvandmiandoab et al. 2024 Yadollahvandmiandoab et al. This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Bladder cancer (BC) is the 10th most common cancer worldwide, with about 0.5 million reported new cases and about 0.2 million deaths per year. In this scoping review, we summarize the current evidence regarding the clinical implications of single-cell sequencing for bladder cancer based on PRISMA guidelines. We searched PubMed, CENTRAL, Embase, and supplemented with manual searches through the Scopus, and Web of Science for published studies until February 2023. We included original studies that used at least one single-cell technology to study bladder cancer. Forty-one publications were included in the review. Twenty-nine studies showed that this technology can identify cell subtypes in the tumor microenvironment that may predict prognosis or response to immune checkpoint inhibition therapy. Two studies were able to diagnose BC by identifying neoplastic cells through single-cell sequencing urine samples. The remaining studies were mainly a preclinical exploration of tumor microenvironment at single cell level. Single-cell sequencing technology can discriminate heterogeneity in bladder tumor cells and determine the key molecular properties that can lead to the discovery of novel perspectives on cancer management. This nascent tool can advance the early diagnosis, prognosis judgment, and targeted therapy of bladder cancer.

 Bladder cancer Urothelial carcinoma Transitional cell carcinoma Single-cell sequencing Tumor heterogeneity Immunotherapy Scoping review Tumor microenvironment Introduction

Single-cell sequencing technologies were pioneered in the early 90s and gained widespread attention in 2013 when they were named “Nature Methods”. These technologies are superior to traditional bulk sequencing methods due to their ability to map heterogenicity among individual cells of the same tissue. In short, these technologies isolate cells before administrating highly sensitive next-generation sequencing (NGS), which can sequence the very small amount of material available at the single-cell level. There are numerous techniques for isolating cells. The simplest method is separating cells one by one by micropipette under a microscope. The limiting dilution technique involves separating and suspending the cells in liquid before distributing the suspension in multi-well plates at the concentration of 0.5 cells per well. Microfluidic separation is a more advanced high throughput technique with relatively low cost. Single-nucleus separating is sometimes used instead of whole-cell separating to increase the durability of samples [1,2].

RNA is the most commonly sequenced molecule in these technologies (RNAseq), allowing researchers to map gene activity at the time of biopsy in each cell (transcriptional variability). In the field of oncology, this allows for the assessment of the tumor microenvironment (TME) at the single-cell level. Whole-exome sequencing (WES) maps the functional DNA and whole-genome sequencing (WGS) maps the entire DNA. Targeted NGS (tNGS) maps a specific panel of related genes that may be altered in single cells. T cell receptor sequencing (TCR-seq) maps the TCR-related RNA activity of single T cells [2–4]. Recently, NGS has been combined with the detection of other molecules such as proteins to concomitantly map them on individual cells (multi-omics). Chromatin immunoprecipitation (CHIP-seq), REAP-seq (RNA and surface protein), CITE-seq, and QuRIE-seq (RNA and epitopes) are among these methods [5–8].

Bladder cancer (BC) accounts for 3% of all new cancer diagnoses with 0.5 million new cases and 0.2 million deaths per year [9]. It is the 13th leading cause of cancer-related death in the world [10]. The 5-year overall survival rate for BC can be as high as 77% in non-metastatic disease and only 6% in metastatic BC. This cancer is four times more common in men (9.6 per 100,000) than in women (2.4 per 100,000). Tobacco smoking, positive family history, and exposure to chemicals are the main risk factors for this malignancy [11]. The frequency of BC rises with age; more than 90% of diagnoses occur in patients over 55 years of age [11]. Bladder cancer is classified into muscle-invasive bladder cancer (MIBC) and non-muscle-invasive bladder cancer (NMIBC) [9,12]. In addition to surgery, patients may receive radiotherapy, chemotherapy with mitomycin C, epirubicin, thiotepa, gemcitabine, platinum-based agents, doxorubicin, Bacillus Calmette Guerin (BCG) immunotherapy, and Immune Checkpoint Inhibitor (ICI) THERAPY [13]. Despite advances in available therapeutic methods, the clinical outcome of patients with bladder cancer remains unsatisfactory.

In this review, we summarize the clinical implications of single-cell sequencing technologies in bladder cancer.

 Methods

The design and implementation of this scoping review were guided by Arksey et al.’s [14] methodological framework, described by the Joanna Briggs Institute [15]. To organize the information, the PRISMA model [16] was used and we followed the PRISMA-ScR recommendations [17].

Inclusion and exclusion criteria

This review focuses on the clinically significant application of single-cell sequencing technologies in bladder cancer. Inclusion criteria are in vitro or in vivo studies involving single-cell sequencing in bladder cancer published in any language. Exclusion criteria are letters, reviews, conference abstracts, editorials, unrelated articles, replies, unpublished articles, and articles without full-text access.

 Information sources

A comprehensive search of the literature published until February 2023 was conducted using Emtree language and medical subject headings (MeSh), in Pubmed, Embase, and the Cochrane Central Register of Controlled Trials (CENTRAL) databases. A broad search strategy was performed using a combination of the following search terms: ((urothelial cancer OR bladder cancer) AND single-cell sequencing). We supplemented this search with manual searches of the relevant articles identified through the Scopus, and Web of Science. Details of the search strategy are available in Appendix 1.

 Data extraction

We used the Rayyan platform to screen articles and extract data. The software automatically removed duplicates; the remaining exclusions were manually performed by two researchers. Titles and abstracts were separately screened by the first and third authors with a portion of articles double screened for application of inclusion and exclusion criteria. The second author separately screened all articles. Data were extracted by the first three authors.

 Results and Discussion

Our initial search resulted in 247 citations (187 citations from electronic databases and 60 citations from other sources). After removing the duplicates, the first two authors independently reviewed the remaining 180 titles and extracted those that clearly did not meet the inclusion criteria. The first three authors independently reviewed the abstracts of 135 potentially eligible studies. The full text of the remaining 66 studies was obtained and evaluated in accordance with the inclusion criteria. Any disagreement over inclusion was resolved by discussion. Finally, 41 studies were included in the review (Fig. 1).

Process of identification and inclusion of studies—PRISMA flow diagram.

Table 1 summarizes the included articles. The first 19 articles performed single-cell sequencing on samples acquired from patient tumors [18–36]. Five studies performed this technique in vitro or on animal BC models [37–41]. The remaining used public database [42–58]. All studies classified single cells and defined cell clusters contributing to the TME of BC. They identified clinically important cell subtypes by analyzing patient outcomes or responses to treatment.

The summary of the included articles
Main study Studied cells/Tissue Clinical implication Key findings Validation
[18] Gouin 3rd et al. snRNA seq, spatial transcriptomics, and sc resolution spatial proteomic of patient tumors High grade urothelial MIBC Response prediction An epithelial cell subtype with overexpression of Cadherin 12 can predict response to surgery and ICI Validated by analyzing TCGA and IMvigor databases
[19] Ma et al. scRNA seq of patient tumors CAFs Response and outcome prediction A subpopulation of CAF with overexpression of urea transporter SLC14A1 can confer to BC stemness Validated by comparing to a previously published study
[20] Lindskrog et al. snRNA seq and spatial transcriptomic of patient tumors NMIBC Outcome prediction Molecular classification of tumors based on bulk RNA seq has prognostic value Validated by bulk seq and spatial transcriptomics
[21] Fehlings et al. Bulk RNA seq, TCR seq, and CITE-seq of patient blood PBMCs Response prediction Subtypes of T cells are associated with response to ICI therapy Validated by analyzing IMvigor database
[22] Wu et al. scRNA seq of patient tumors Different BC tumors and NAT Potential therapeutic target Treg subtype that highly expresses TIGIT and IL-32 has immunosuppressive properties, targeting TIGIT can suppress metastasis Validated by analyzing TCGA and GTEx public databases
[23] Xu et al. IHC, scRNA seq and flow cytometry of patient tumors TAMs in MIBC Outcome prediction IL-10+ TAMs are associated with immune evasive tumor environment Validated by analyzing TCGA public database
[24] Chen et al. scRNA seq of patient tumors BC and NAT Outcome prediction iCAFs may have prognostic value, tumor cells downregulated MHC-II, identified immune cell subgroups in the TME Validated by analyzing TCGA public database
[25] Oh et al. scRNA seq and TCR seq of patient tumors T cells from BC and NAT Response prediction Subtypes of cytotoxic CD4+ T cells can predict response to ICI Validated by analyzing IMvigor database
[26] Liang et al. scRNA seq of patient tumors UTUC Response and outcome prediction A gene expression score describing macrophage exhaustion can predict response to ICI and has prognostic value Compared results to other cohorts involving bulk RNA seq of urothelial BC and UTUC
[27] Tao et al. Bulk seq and cytometry of patient tumors MIBC Outcome prediction A TP53-related score can predict BC outcome Validated by analyzing GEO public database
[28] You et al. Bulk RNA seq of patient tumors NMIBC and NAT Outcome prediction Expression level of cell cycle regulating gene SKA3 can predict NMIBC progression Validated by analyzing TCGA and GEO public databases
[29] Wang et al. scRNA seq and ATAC-seq of patient tumors Different BC subtypes Outcome prediction Elevated expression of EZH2 gene predicts recurrence, clustered heterogenous cells of TME focusing on stem cells and EMT
[30] Lai et al. scRNA seq of patient tumors Different BC subtypes Clustered heterogenous cells of TME, investigated EMT, matched their findings with histopathology
[31] Li et al. scExome seq of one patient tumor MIBC and NAT Found single ancestral cells for all tumor cells, identified mutated genes Compared to bulk exome seq of a previously published cohort study
[32] Banchereau et al. scRNA seq and TCR seq of IMvigor patients Metastatic urothelial carcinoma and lung cancer Response prediction A subtype of tissue-resident memory T cells (CD103+CD8+) predicts response to ICI Validated by analyzing GEO publicly available database
[33] Yang et al. sc-DNA seq of patient tumors Stem cells and non-stem cells in BC Identified mutated genes associated with self-renewal of stem cells, identified two phylogenic origins of stem cells in BC
[34] Luo et al. scRNA seq of patient tumors Different BC subtypes Clustered heterogenous cells of TME, matched the scRNA-seq findings with the histopathology of each tumor
[35] Warrick et al. scRNA seq of one patient tumor Squamous differentiated urothelial carcinoma Response prediction Found single ancestral cells for all tumor cells, found expression signature for squamous differentiated cells that may predict response to platinum-based chemotherapy
[36] Zhang et al scRNA seq of one patient tumor Squamous cell carcinoma of bladder and NAT Identified gene expression differences between cancer cells and NAT
[37] Tanaka et al. scRNA seq of cell culture 5637 human urothelial carcinoma Response prediction Gene COX7B and its surrogate marker CD63 predict response to platinum-based medication Validated by analyzing TCGA public database and inhouse IHC of patient BC tumors
[38] Sfakianos et al. scRNA seq of human and murine BC models BBN induced murine BC plus xenografts of patient MIBCs in nude mouse Clustered heterogenous cells of TME and identified lineage plasticity origins leading to TME heterogeneity
[39] Kerzeli et al. scRNA seq of mouse BC BBN induced murine BC Potential new treatment Clustered heterogenous cells of TME and introduced CpG-oligodeoxynucleotides as a potential treatment
[40] Esteso et al. scRNA seq and flow cytometry on lymphocytes PBMCs PBMCs exposed to BCG show expansion of IFN-γ producing γδ T-cells with high anti-neoplastic functions
[41] Podojil et al. scRNA seq and mass cytometry of mouse tumors BBN induced murine BC Potential therapeutic target B7-H4, a protein with T cell inhibition properties expressed by luminal tumors can be targeted by immunotherapy Validated by expression analysis of patient tumors
[42] Zhou et al. Analyzed TCGA public database B cells from BC Response and outcome prediction B cell senescence-related gene signature can predict response to ICI and has prognosis value Validated by analyzing GEO and IMvigor databases plus inhouse RT-qPCR and IHC
[43] Liu et al. Analyzed TCGA public database plus scRNA seq patient tumors BC and NAT Response and outcome prediction CD39 gene has prognostic value, inhibiting CD39 increases intratumor natural killer cell invasion and enhances response to cisplatin but not ICI Validated by inhibiting CD39 in different murine models
[44] Chen et al. Analyzed GEO public database Human BC Response and outcome prediction Markers of iCAF can predict variation in immunological TME, prognosis, and response to ICI, an iCAF subtype can inhibit proliferation and migration of carcinoma cells in vitro Validated by analyzing TCGA, GEO, and IMvigor databasesValidated the iCAF subtype properties by co-culturing them with T24 cell lines
[45] Zhao et al. Analyzed GEO, ENA, and TCGA public database Epithelial cells in NMIBC, MIBC, and NAT Outcome prediction Expression level of SLC16A3 gene has prognostic value Validated by inhouse IHC for MCT4 protein in patient tumors
[46] Du et al. Analyzed TCGA public database BC and NAT Response and outcome prediction Calumenin gene is correlated to epithelial-mesenchymal transition, ferroptosis, TP53 mutation, and may predict response to ICI Validated by inhouse qPCR and IHC of patient tumors
[47] Chen et al. Analyzed GEO and TCGA public databases Pan-cancer Response and outcome prediction JunB proto-oncogene can be the hub gene responsible for immunosuppressive TME and may predict response to ICI Validated by various inhouse experiments on patient BC tumors and T24 urothelial carcinoma cell lines
[48] Xiang et al. Analyzed TCGA public database BC and NAT Outcome prediction Ferroptosis-associated lncRNAs are specifically expressed in TME and have prognostic value Validated by inhouse scRNA seq on patient tumors
[49] Wang et al. Analyzed IMvigor 210 and CheckMate275 public databases Metastatic urothelial carcinoma Response and outcome prediction A “protumorigenic inflammation signature” can predict response to ICI and has prognostic value, the cell subtype associated with the signature are myeloid phagocytic cells Validated by inhouse scRNA seq on patient tumors
[50] Li et al. Analyzed GEO, ArrayExpress, TCGA, and GTEx public databases BC and NAT Outcome prediction Enhanced immune cell infiltration in tumors with high expression level of PTTG1 Validated by inhouse microarray and IHC of patient tumors
[51] Jiang et al. Analyzed TCGA public database BC and NAT Outcome prediction Expression level of TAM-related genes has prognostic value Validated by analyzing GEO public database
[52] Yao et al. Analyzed GEO public database MIBC cells with stemness properties Outcome prediction The gene DBI is associated with stemness and its expression is associated with higher grade tumors, acetaminophen can reduce DBI expression Validated by analyzing TCGA public database
[53] Yu et al. Analyzed GEO and TCGA public databases scRNA seq of normal bladder was used to deconvolute MIBC data Outcome prediction Identifying cell subtypes has prognostic value Validated by analyzing TCGA, GEO, and MSKCC public databases
[54] Acharjee et al. Analyzed TCGA public database BC and colon cancer Diagnosis and outcome prediction LGALS4 (Galectin-4) gene can be a prognostic and diagnostic marker for BC Validated by analyzing GEO public database
[55] Das et al. Analyzed TCGA and GEO public databases Pan-cancer Response and outcome prediction A “Cancer-Specific Immune Prognostic Signature” can predict response to ICI Validated BC response to ICI by analyzing IMvigor public database
[56] Hu et al. Analyzed TCGA, SRA, GEO, ArrayExpress, and IMvigor public databases BC Response and outcome prediction TME of different tumors can be classified into four subtypes to predict response to ICI and prognosis
[57] Wang et al. scRNA seq of patient urine Exfoliated cells in urine Diagnosis Hexokinase 2 can be a urine marker for BC
[58] Chen et al. Microfluidic immunoassay of patient urine Exfoliated cells in urine Diagnosis Immunofluorescent staining for oncoproteins CK20 and CD44v6 can have diagnostic value Validated by sc-whole genome seq

Note: All patient tumors are bladder cancer samples from human patients. AIM2, absent in melanoma 2; ATAC-seq, assay for transposase-accessible chromatin using sequencing; BBN, N-butyl-N-(4-hydroxybutyl)-nitrosamine; BC, bladder cancer; DBI, diazepam binding inhibitor; EMT, epithelial-mesenchymal transition; ENA, European nucleotide archive (www.ebi.ac.uk/ena); EZH2, enhancer of zeste 2 polycomb repressive complex 2 Subunit; IL, interleukin; IHC, immunohistochemistry; lncRNAs, long non-coding RNAs; MIBC, muscle invasive bladder cancer; MCT4, monocarboxylate transporter 4 protein coded by SLC16A3 gene; MSKCC, memorial sloan-kettering cancer center; NAT, normal adjacent tissue; PTTG1, pituitary tumor transforming gene 1; PBMC, peripheral blood mononuclear cells; sc, single cell; sn, single nucleus; seq, sequencing; ICI, immune checkpoint inhibition; IMvigor, lung and bladder cancer clinical trials of atezolizumab; ICI (anti-programmed cell death ligand 1); Treg, T regulatory; TIGIT, T-cell immunoreceptor with immunoglobulin and immunoreceptor tyrosine-based inhibitory motif domain; CAF, cancer associated fibroblast; iCAF, inflammatory cancer-associated fibroblasts; SRA, sequence read archive (www.ncbi.nlm.nih.gov/sra); TME, tumor microenvironment; TAM, tumor-associated macrophage; RT-qPCR, real-time quantitative PCR; UTUC, upper tract urothelial carcinoma.

Public databases: 29 articles analyzed public cancer databases as part of their study. Gene Expression Omnibus (GEO) [59] is currently the largest database containing single-cell sequencing data of cancer patients. Other databases such as The Cancer Genome Atlas project (TCGA) [60], ArrayExpress [61], and Genotype Tissue Expression project (GTEx) [62] mainly contain bulk sequencing data. TISCH [63] and The Cancer Single-Cell State Atlas (CancerSEA) [64] are lesser-known public databases of single-cell RNA sequences focusing on TME. The website of CellPress provides a review of highlighted reveals by these pooled public databases [65].

Fourteen studies analyzed these publicly available databases to generate a hypothesis [42–56]. Nine studies validated their hypothesis by in-house experiments [42–50]. Five studies validated their hypothesis by analyzing other databases [51–55].

Bladder cancer tends to be a heterogeneous tumor [66]. Nine studies investigated this heterogeneity by single-cell analysis of BC tumors and normal adjacent tissue (NAT). They classified cells into clusters and showed their ancestral origin [31–36,38,39,56].

The clinical implication of single-cell sequencing is evident in seven studies [18,19,25,32,42,55,56]. These studies show that separating cells of a patient’s tumor and characterizing cell subtypes can improve clinical decision-making by predicting the tumor’s response to different treatments.

Limitations of studies

Current single-cell sequencing techniques are extremely complex and involve multiple steps. The process of separating cells could damage the cell contents. Two studies addressed this issue by performing single-nucleus separation instead of whole-cell separation; the nucleus is less prone to degradation during the separation procedure [18,20].

Analyzing the high-dimensional data generated by single-cell sequencing is extremely complex and suffers from “the curse of dimensionality” [67], i.e., too much information [2,3,68–70]. Most studies addressed this issue by filtering for genes of interest as the first step of their analysis. For example, Zhou et al. [42] gathered a list of senescence-related genes from the website CellAge (http://genomics.senescence.info/cells) and only investigated related data in the TCGA database to generate their B cell senescence-related gene signature (Table 1). Other studies addressed this complexity through the reduction of dimensions (excluding insignificant genes). The most common dimension reduction techniques were clustering using the R package Seurat and visualizing using t-SNE [71].

Public databases such as TCGA and GEO pool data from various sequencing studies without providing sufficient data on heterogeneity among their techniques and patient factors. This is less important when sequencing DNA since DNA is a stable molecule designed by nature to maintain its integrity throughout an animal’s life. However, RNA sequencing is tricky since RNA is a transient molecule, only expressed when the cell requires it and degraded fairly rapidly. At room temperature, mRNA has a half-life of 16 h [72]. It is assumed that all RNA sequencing data pooled in these databases are acquired from analyzing fresh samples using similar techniques. However, a sentence in the article by Banchereau et al. (Table 1) casts doubt on this assumption: “…a bulk RNAseq analysis was performed on formalin-fixed paraffin-embedded tumor tissue”. Banchereau et al. experiments are a major contributor to IMvigor studies used by many articles included in this review [32].

It is important to keep in mind that transcription in cells is highly dependent on environmental factors. Temperature, oxygen level, stress, and even the circadian rhythm (time of the day) can influence transcription. Many commonly used medications such as steroids directly affect transcription [73–76]. As Yao et al. showed (Table 1), the over-the-counter drug acetaminophen influences the expression level of the gene, they found to be associated with BC tumor grade [52].

 Conclusion

Single-cell analysis is a relatively new tool that aids in understanding the complexity of bladder cancer tumor’s microenvironment. Included studies in our scoping review collectively show that identifying certain cell subtypes in these heterogenous tumors may help clinicians in predicting response to expensive or invasive therapies such as immune checkpoint inhibitors or surgery with or without neoadjuvant chemotherapy. However, there are currently no clinical trials showing the benefit of performing this test on the overall well-being of the patients. Future single-cell sequencing studies should include epidemiological data, environmental factors, as well as comprehensive clinical data to improve our understanding of bladder cancer etiology and its heterogenous response to treatment.

 Supplementary Materials Abbreviations BC

Bladder cancer

 CAF

Cancer associated fibroblast

 ICI

Immune checkpoint inhibitor

 scRNA seq

Single cell RNA sequencing

 snRNA seq

Single nucleus rna sequencing

 TAM

Tumor associated macrophages

 TME

Tumor microenvironment

Not applicable.

 Funding Statement

This review did not receive any funding.

 Author Contributions

R.Y.: Literature Search, Methodology, Literature Review, Data Extraction and Manuscript Writing; M.J., F.A.V.D., K.B., P.A.F.L., L.S.B.D.C., C.F.G., A.D.A., and N.B.: Literature Review, Data Extraction and Manuscript Writing; L.O.R.: Guidance, Editing, Final Review and Submission. All authors have read and agreed to the published version of the manuscript.

 Availability of Data and Materials

Not applicable.

 Ethics Approval

Not applicable.

 Conflicts of Interest

The authors declare that they have no conflicts of interest to report regarding the present study.

 Supplementary Materials

The supplementary material is available online at https://doi.org/10.32604/or.2024.045442.

 References Ding, J., Adiconis, X., Simmons, S. K., Kowalczyk, M. S., Hession, C. C. et al. (2020). Systematic comparison of single-cell and single-nucleus RNA-sequencing methods. Nature Biotechnology, 38(6), 737–746; 32341560 Hwang, B., Lee, J. H., Bang, D. (2018). Single-cell RNA sequencing technologies and bioinformatics pipelines. Experimental & Molecular Medicine, 50(8), 1–14. Jovic, D., Liang, X., Zeng, H., Lin, L., Xu, F. et al. (2022). Single-cell RNA sequencing technologies and applications: A brief overview. Clinical and Translational Medicine, 12(3), e694; 35352511 Tang, X., Huang, Y., Lei, J., Luo, H., Zhu, X. (2019). The single-cell sequencing: New developments and medical applications. Cell & Bioscience, 9(1), 53. Kyrochristos, I. D., Ziogas, D. E., Goussia, A., Glantzounis, G. K., Roukos, D. H. (2019). Bulk and single-cell next-generation sequencing: Individualizing treatment for colorectal cancer. Cancers, 11(11), 1809; 31752125 Rivello, F., van Buijtenen, E., Matuła, K., van Buggenum, J. A. G. L., Vink, P. et al. (2021). Single-cell intracellular epitope and transcript detection reveals signal transduction dynamics. Cell Reports Methods, 1(5), 100070; 35474668 Peterson, V. M., Zhang, K. X., Kumar, N., Wong, J., Li, L. et al. (2017). Multiplexed quantification of proteins and transcripts in single cells. Nature Biotechnology, 35(10), 936–939; 28854175 Yuan, M., Chen, L., Deng, M. (2022). Clustering CITE-seq data with a canonical correlation-based deep learning method. Frontiers in Genetics, 13, 977968; 36072672 Chen, C. W. (2022). Comment on “Long noncoding RNA UCA1 promotes glutamine-driven anaplerosis of bladder cancer by interacting with hnRNP I/L to upregulate GPT2 expression” by Chen et al. Translational Oncology, 18, 101372; 35182956 Patelli, G., Zeppellini, A., Spina, F., Righetti, E., Stabile, S. et al. (2022). The evolving panorama of HER2-targeted treatments in metastatic urothelial cancer: A systematic review and future perspectives. Cancer Treatment Reviews, 104, 102351; 35180563 Maiorano, B. A., de Giorgi, U., Ciardiello, D., Schinzari, G., Cisternino, A. et al. (2022). Immune-checkpoint inhibitors in advanced bladder cancer: Seize the day. Biomedicines, 10(2), 411; 35203620 Tran, L., Xiao, J. F., Agarwal, N., Duex, J. E., Theodorescu, D. (2021). Advances in bladder cancer biology and therapy. Nature Reviews. Cancer, 21(2), 104–121; 33268841 Yadollahvandmiandoab, R., Jalalizadeh, M., Buosi, K., Garcia-Perdomo, H. A., Reis, L. O. (2022). Immunogenic cell death role in urothelial cancer therapy. Current Oncology, 29(9), 6700–6713; 36135095 Arksey, H., O’Malley, L. (2005). Scoping studies: Towards a methodological framework. International Journal of Social Research Methodology, 8(1), 19–32. Chapter 11: Scoping reviews (2020). JBI manual for evidence synthesis. JBI. https://jbi-global-wiki.refined.site/space/MANUAL/4687342/Chapter+11%3A+Scoping+reviews (accessed on 02/01/2024). Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., PRISMA Group (2009). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLoS Medicine, 6(7), e1000097; 19621072 Tricco, A. C., Lillie, E., Zarin, W., O’Brien, K. K., Colquhoun, H. et al. (2018). Prisma extension for scoping reviews (Prisma-scr): Checklist and explanation. Annals of Internal Medicine, 169(7), 467–473; 30178033 Gouin, K. H., Ing, N., Plummer, J. T., Rosser, C. J., Ben Cheikh, B. et al. (2021). An N-Cadherin 2 expressing epithelial cell subpopulation predicts response to surgery, chemotherapy and immunotherapy in bladder cancer. Nature Communications, 12(1), 4906; 34385456 Ma, Z., Li, X., Mao, Y., Wei, C., Huang, Z. et al. (2022). Interferon-dependent SLC14A1+ cancer-associated fibroblasts promote cancer stemness via WNT5A in bladder cancer. Cancer Cell, 40(12), 1550–1565.E7; 36459995 Lindskrog, S. V., Schmøkel, S. S., Nordentoft, I., Lamy, P., Knudsen, M. et al. (2023). Single-nucleus and spatially resolved intratumor subtype heterogeneity in bladder cancer. European Urology Open Science, 51, 78–88; 37187723 Fehlings, M., Kim, L., Guan, X., Yuen, K., Tafazzol, A. et al. (2022). Single-cell analysis reveals clonally expanded tumor-associated CD57+ CD8 T cells are enriched in the periphery of patients with metastatic urothelial cancer responding to PD-L1 blockade. Journal for Immunotherapy of Cancer, 10(8), e004759; 35981786 Wu, K., Zeng, J., Shi, X., Xie, J., Li, Y. et al. (2021). Targeting tigit inhibits bladder cancer metastasis through suppressing IL-32. Frontiers in Pharmacology, 12, 801493; 35069212 Xu, Y., Zeng, H., Jin, K., Liu, Z., Zhu, Y. et al. (2022). Immunosuppressive tumor-associated macrophages expressing interlukin-10 conferred poor prognosis and therapeutic vulnerability in patients with muscle-invasive bladder cancer. Journal for Immunotherapy of Cancer, 10(3), e003416; 35338085 Chen, Z., Zhou, L., Liu, L., Hou, Y., Xiong, M. et al. (2020). Single-cell RNA sequencing highlights the role of inflammatory cancer-associated fibroblasts in bladder urothelial carcinoma. Nature Communications, 11(1), 5077; 33033240 Oh, D. Y., Kwek, S. S., Raju, S. S., Li, T., McCarthy, E. et al. (2020). Intratumoral CD4+ T cells mediate anti-tumor cytotoxicity in human bladder cancer. Cell, 181(7), 1612–1625.E13; 32497499 Liang, Y., Tan, Y., Guan, B., Guo, B., Xia, M. et al. (2022). Single-cell atlases link macrophages and CD8+ T-cell subpopulations to disease progression and immunotherapy response in urothelial carcinoma. Theranostics, 12(18), 7745–7759; 36451860 Tao, Y., Li, X., Zhang, Y., He, L., Lu, Q. et al. (2022). TP53-related signature for predicting prognosis and tumor microenvironment characteristics in bladder cancer: A multi-omics study. Frontiers in Genetics, 13, 1057302; 36568387 You, C., Piao, X. M., Kang, K., Kim, Y. J., Kang, K. (2021). Integrative transcriptome profiling reveals ska3 as a novel prognostic marker in non-muscle invasive bladder cancer. Cancers, 13(18), 4673; 34572901 Wang, H., Mei, Y., Luo, C., Huang, Q., Wang, Z. et al. (2021). Single-cell analyses reveal mechanisms of cancer stem cell maintenance and epithelial-mesenchymal transition in recurrent bladder cancer. Clinical Cancer Research, 27(22), 6265–6278; 34526362 Lai, H., Cheng, X., Liu, Q., Luo, W., Liu, M. et al. (2021). Single-cell RNA sequencing reveals the epithelial cell heterogeneity and invasive subpopulation in human bladder cancer. International Journal of Cancer, 149(12), 2099–2115; 34480339 Li, Y., Xu, X., Song, L., Hou, Y., Li, Z. et al. (2012). Single-cell sequencing analysis characterizes common and cell-lineage-specific mutations in a muscle-invasive bladder cancer. GigaScience, 1(1), 12; 23587365 Banchereau, R., Chitre, A. S., Scherl, A., Wu, T. D., Patil, N. S. et al. (2021). Intratumoral CD103+ CD8+ T cells predict response to PD-L1 blockade. Journal for Immunotherapy of Cancer, 9(4), e002231; 33827905 Yang, Z., Li, C., Fan, Z., Liu, H., Zhang, X. et al. (2017). Single-cell sequencing reveals variants in ARID1A, GPRC5A and MLL2 driving self-renewal of human bladder cancer stem cells. European Urology, 71(1), 8–12; 27387124 Luo, Y., Tao, T., Tao, R., Huang, G., Wu, S. (2022). Single-cell transcriptome comparison of bladder cancer reveals its ecosystem. Frontiers in Oncology, 12, 818147; 35265520 Warrick, J. I., Hu, W., Yamashita, H., Walter, V., Shuman, L. et al. (2022). Author Correction: FOXA1 repression drives lineage plasticity and immune heterogeneity in bladder cancers with squamous differentiation. Nature Communications, 13(1), 6575; 36323682 Zhang, X., Zhang, M., Hou, Y., Xu, L., Li, W. et al. (2016). Single-cell analyses of transcriptional heterogeneity in squamous cell carcinoma of urinary bladder. Oncotarget, 7(40), 66069–66076; 27602771 Tanaka, N., Katayama, S., Reddy, A., Nishimura, K., Niwa, N. et al. (2018). Single-cell RNA-seq analysis reveals the platinum resistance gene COX7B and the surrogate marker CD63. Cancer Medicine, 7(12), 6193–6204; 30367559 Sfakianos, J. P., Daza, J., Hu, Y., Anastos, H., Bryant, G. et al. (2020). Epithelial plasticity can generate multi-lineage phenotypes in human and murine bladder cancers. Nature Communications, 11(1), 2540; 32439865 Kerzeli, I. K., Lord, M., Doroszko, M., Elgendy, R., Chourlia, A. et al. (2021). Single-cell RNAseq and longitudinal proteomic analysis of a novel semi-spontaneous urothelial cancer model reveals tumor cell heterogeneity and pretumoral urine protein alterations. PLoS One, 16(7), e0253178; 34232958 Esteso, G., Felgueres, M. J., García-Jiménez, Á.F., Reyburn-Valés, C., Benguría, A. et al. (2023). BCG-activation of leukocytes is sufficient for the generation of donor-independent innate anti-tumor NK and γδ T-cells that can be further expanded in vitro. Oncoimmunology, 12(1), 2160094; 36567803 Podojil, J. R., Glaser, A. P., Baker, D., Courtois, E. T., Fantini, D. et al. (2020). Antibody targeting of B7-H4 enhances the immune response in urothelial carcinoma. Oncoimmunology, 9(1), 1744897; 32363111 Zhou, R., Zhou, J., Muhuitijiang, B., Tan, W. (2022). Construction and experimental validation of a B cell senescence-related gene signature to evaluate prognosis and immunotherapeutic sensitivity in bladder cancer. Functional & Integrative Genomics, 23(1), 3. Liu, L., Hou, Y., Deng, C., Tao, Z., Chen, Z. et al. (2022). Single cell sequencing reveals that CD39 inhibition mediates changes to the tumor microenvironment. Nature Communications, 13(1), 6740; 36347860 Chen, H., Yang, W., Xue, X., Li, Y., Jin, Z. et al. (2022). Integrated analysis revealed an inflammatory cancer-associated fibroblast-based subtypes with promising implications in predicting the prognosis and immunotherapeutic response of bladder cancer patients. International Journal of Molecular Sciences, 23(24), 15970; 36555612 Zhao, Y., Zhao, B., Yan, W. H., Xia, Y., Wang, Z. H. et al. (2021). Integrative analysis identified MCT4 as an independent prognostic factor for bladder cancer. Frontiers in Oncology, 11, 704857; 34513685 Du, Y., Miao, W., Jiang, X., Cao, J., Wang, B. et al. (2021). The epithelial to mesenchymal transition related gene calumenin is an adverse prognostic factor of bladder cancer correlated with tumor microenvironment remodeling, gene mutation, and ferroptosis. Frontiers in Oncology, 11, 683951; 34150647 Chen, H., Chen, G. (2022). Dissecting immunosuppressive cell communication patterns reveals junb proto-oncogene (Junb) shaping a non-inflamed tumor microenvironment. Frontiers in Genetics, 13, 883583; 35812726 Xiang, X., Guo, Y., Chen, Z., Zhang, F., Huang, J. et al. (2022). A prognostic risk prediction model based on ferroptosis-related long non-coding RNAs in bladder cancer: A bulk RNA-seq research and scRNA-seq validation. Medicine, 101(51), e32558; 36595859 Wang, L., Sfakianos, J. P., Beaumont, K. G., Akturk, G., Horowitz, A. et al. (2021). Myeloid cell-associated resistance to pd-1/pd-l1 blockade in urothelial cancer revealed through bulk and single-cell rna sequencing. Clinical Cancer Research, 27(15), 4287–4300; 33837006 Li, J. D., Farah, A. A., Huang, Z. G., Zhai, G. Q., Wang, R. G. et al. (2022). Clinical significance and potential regulatory mechanism of overexpression of pituitary tumor-transforming gene transcription factor in bladder cancer. BMC Cancer, 22(1), 713; 35768832 Jiang, Y., Qu, X., Zhang, M., Zhang, L., Yang, T. et al. (2022). Identification of a six-gene prognostic signature for bladder cancer associated macrophage. Frontiers in Immunology, 13, 930352; 36275756 Yao, J., Liu, Y., Yang, J., Li, M., Li, S. et al. (2022). Single-cell sequencing reveals that DBI is the key gene and potential therapeutic target in quiescent bladder cancer stem cells. Frontiers in Genetics, 13, 904536; 35769986 Yu, L., Hu, R., Peng, G., Ding, Q., Tao, T. et al. (2022). Prognostic significance of lineage diversity in bladder cancer revealed by single-cell sequencing. Frontiers in Genetics, 13, 862634; 35664301 Acharjee, A., Agarwal, P., Nash, K., Bano, S., Rahman, T. et al. (2021). Immune infiltration and prognostic and diagnostic use of LGALS4 in colon adenocarcinoma and bladder urothelial carcinoma. American Journal of Translational Research, 13(10), 11353–11363; 34786063 Das, S., Camphausen, K., Shankavaram, U. (2020). Cancer-specific immune prognostic signature in solid tumors and its relation to immune checkpoint therapies. Cancers, 12(9), 2476; 32882873 Hu, R., Tao, T., Yu, L., Ding, Q., Zhu, G. et al. (2021). Multi-omics characterization of tumor microenvironment heterogeneity and immunotherapy resistance through cell states-based subtyping in bladder cancer. Frontiers in Cell and Developmental Biology, 9, 809588; 35223867 Wang, Z., Chen, J., Yang, L., Cao, M., Yu, Y. et al. (2020). Single-cell sequencing-enabled hexokinase 2 assay for noninvasive bladder cancer diagnosis and screening by detecting rare malignant cells in urine. Analytical Chemistry, 92(24), 16284–16292; 33269906 Chen, A., Fu, G., Xu, Z., Sun, Y., Chen, X. et al. (2018). Detection of urothelial bladder carcinoma via microfluidic immunoassay and single-cell DNA copy-number alteration analysis of captured urinary-exfoliated tumor cells. Cancer Research, 78(14), 4073–4085; 29789419 Edgar, R., Domrachev, M., Lash, A. E. (2002). Gene expression omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Research, 30(1), 207–210; 11752295 https://www.cancer.gov/tcga (accessed on 02/01/2024). Sarkans, U., Gostev, M., Athar, A., Behrangi, E., Melnichuk, O. et al. (2018). The BioStudies database-one stop shop for all data supporting a life sciences study. Nucleic Acids Research, 46(D1), D1266–D1270; 29069414 www.gtexportal.org (accessed on 02/01/2024). Sun, D., Wang, J., Han, Y., Dong, X., Ge, J. et al. (2021). TISCH: A comprehensive web resource enabling interactive single-cell transcriptome visualization of tumor microenvironment. Nucleic Acids Research, 49(D1), D1420–D1430; 33179754 Yuan, H., Yan, M., Zhang, G., Liu, W., Deng, C. et al. (2019). CancerSEA: A cancer single-cell state atlas. Nucleic Acids Research, 47(D1), D900–D908; 30329142 www.cell.com/pb-assets/consortium/pancanceratlas/pancani3/index.html (accessed on 02/01/2024). Minoli, M., Kiener, M., Thalmann, G. N., Kruithof-de Julio, M., Seiler, R. (2020). Evolution of urothelial bladder cancer in the context of molecular classifications. International Journal of Molecular Sciences, 21(16), 5670; 32784716 Altman, N., Krzywinski, M. (2018). The curse(s) of dimensionality. Nature Methods, 15(6), 399–400; 29855577 Hong, R., Koga, Y., Bandyadka, S., Leshchyk, A., Wang, Y. et al. (2022). Comprehensive generation, visualization, and reporting of quality control metrics for single-cell RNA sequencing data. Nature Communications, 13(1), 1688; 35354805 Wu, Y., Tamayo, P., Zhang, K. (2018). Visualizing and interpreting single-cell gene expression datasets with similarity weighted nonnegative embedding. Cell Systems, 7(6), 656–666.E4; 30528274 Kharchenko, P. V. (2021). The triumphs and limitations of computational methods for scRNA-seq. Nature Methods, 18(7), 723–732; 34155396 Becht, E., McInnes, L., Healy, J., Dutertre, C. A., Kwok, I. W. H. et al. (2018). Dimensionality reduction for visualizing single-cell data using UMAP. Nature Biotechnology. Wang, C., Liu, H. (2022). Factors influencing degradation kinetics of mRNAs and half-lives of microRNAs, circRNAs, lncRNAs in blood in vitro using quantitative PCR. Scientific Reports, 12(1), 7259; 35508612 Pai, A. A., Luca, F. (2019). Environmental influences on RNA processing: Biochemical, molecular and genetic regulators of cellular response. Wiley Interdisciplinary Reviews. RNA, 10(1), e1503; 30216698 Lobo, I. (2008). Environmental influences on gene expression. Nature Education, 1(1), 39. Rijo-Ferreira, F., Takahashi, J. S. (2019). Genomics of circadian rhythms in health and disease. Genome Medicine, 11(1), 82; 31847894 Beato, M., Truss, M., Chávez, S. (1996). Control of transcription by steroid hormones. Annals of the New York Academy of Sciences, 784, 93–123; 8651621