Classification Of Bladder Cancer Research Articles

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

To classify bladder cancer based on immune cell infiltration score and to construct a prognosis assessment model of patients with bladder cancer. The transcriptome data and clinical data of breast cancer patients were obtained from the The Cancer Genome Atlas (TCGA) database. Single sample gene set enrichment analysis was used to calculate the infiltration scores of 16 immune cells. The classification of breast cancer patients was achieved by unsupervised clustering, and the sensitivity of patients with different types to immunotherapy and chemotherapy was analyzed. The key modules significantly related to the infiltration of key immune cells were identified by weighted correlation network analysis (WGCNA), and the key genes in the modules were identified. A risk scoring model and a nomogram for prognosis assessment of bladder cancer patients were constructed and verified. B cells, mast cells, neutrophils, T helper cells and tumor infiltrating lymphocytes were determined to be the key immune cells of bladder cancer. The patients were clustered into two groups (Cluster 1 ´ and Custer 2) based on immune cell infiltration scores. Compared with patients with Cluster 1 ´, patients with Cluster 2 were more likely to benefit from immunotherapy (P<0.05), and patients with Cluster 2 were more sensitive to Enbeaten, Docetaxel, Cyclopamine, and Akadixin (P<0.05). 35 genes related to key immune cells were screened out by WGCNA and 4 genes (GPR171, HOXB3, HOXB5 and HOXB6) related to the prognosis of bladder cancer were further screened by LASSO Cox regression. The areas under the ROC curve (AUC) of the bladder cancer prognosis risk scoring model based on these 4 genes to predict the 1-, 3- and 5-year survival of patients were 0.735, 0.765 and 0.799, respectively. The nomogram constructed by combining risk score and clinical parameters has high accuracy in predicting the 1-, 3-, and 5-year overall survival of bladder cancer patients. According to the immune cell infiltration score, bladder cancer patients can be classified. Furthermore, bladder cancer prognosis risk scoring model and nomogram based on key immune cell-related genes have high accuracy in predicting the prognosis of bladder cancer patients.

Development of a stemness-related prognostic index to provide therapeutic strategies for bladder cancer

Bladder cancer (BC) is a heterogeneous disease with varying clinical outcomes. Recent evidence suggests that cancer progression involves the acquisition of stem-like signatures, and assessing stemness indices help uncover patterns of intra-tumor molecular heterogeneity. We used the one-class logistic regression algorithm to compute the mRNAsi for each sample in BLCA cohort. We subsequently classified BC patients into two subtypes based on 189 mRNAsi-related genes, using the unsupervised consensus clustering. Then, we identified nine hub genes to construct a stemness-related prognostic index (SRPI) using Cox regression, LASSO regression and Random Forest methods. We further validated SRPI using two independent datasets. Afterwards, we examined the molecular and immune characterized of SRPI. Finally, we conducted multiply drug screening and experimental approaches to identify and confirm the most proper agents for patients with high SRPI. Based on the mRNAsi-related genes, BC patients were classified into two stemness subtypes with distinct prognosis, functional annotations, genomic variations and immune profiles. Using the SRPI, we identified a specific subgroup of BC patients with high SRPI, who had a poor response to immunotherapy, and were less sensitive to commonly used chemotherapeutic agents, FGFR inhibitors, and EGFR inhibitors. We further identified that dasatinib was the most promising therapeutic agent for this subgroup of patients. This study provides further insights into the stemness classification of BC, and demonstrates that SRPI is a promising tool for predicting prognosis and therapeutic opportunities for BC patients.

MM-SFENet: multi-scale multi-task localization and classification of bladder cancer in MRI with spatial feature encoder network.

Objective. Bladder cancer is a common malignant urinary carcinoma, with muscle-invasive and non-muscle-invasive as its two major subtypes. This paper aims to achieve automated bladder cancer invasiveness localization and classification based on MRI.Approach. Different from previous efforts that segment bladder wall and tumor, we propose a novel end-to-end multi-scale multi-task spatial feature encoder network (MM-SFENet) for locating and classifying bladder cancer, according to the classification criteria of the spatial relationship between the tumor and bladder wall. First, we built a backbone with residual blocks to distinguish bladder wall and tumor; then, a spatial feature encoder is designed to encode the multi-level features of the backbone to learn the criteria.Main Results. We substitute Smooth-L1 Loss with IoU Loss for multi-task learning, to improve the accuracy of the classification task. By learning two datasets collected from bladder cancer patients at the hospital, the mAP, IoU, Acc, Sen and Spec are used as the evaluation metrics. The experimental result could reach 93.34%, 83.16%, 85.65%, 81.51%, 89.23% on test set1 and 80.21%, 75.43%, 79.52%, 71.87%, 77.86% on test set2.Significance. The experimental result demonstrates the effectiveness of the proposed MM-SFENet on the localization and classification of bladder cancer. It may provide an effective supplementary diagnosis method for bladder cancer staging.

What is a Bladder Cancer Molecular Subtype?

BACKGROUND: Several molecular classification systems for bladder cancer have been proposed, but due to differences on how to define molecular subtypes, controversies and misunderstandings have arisen. OBJECTIVE: To discuss different aspects of the molecular classification of bladder cancer and to point to the consequences of using different conceptual approaches. To question some underlying assumptions when defining molecular subtypes. METHODS: To critically reflect on some of the principles and methods used when defining molecular subtypes. RESULTS: Depending on underlying assumptions and aims for the definitions of subtypes, different types of molecular subtypes will be arrived at. CONCLUSION: The underlying assumptions and their consequences must be better clarified when defining molecular subtypes.

The classification of the bladder cancer based on Vision Transformers (ViT)

Bladder cancer is a prevalent malignancy with diverse subtypes, including invasive and non-invasive tissue. Accurate classification of these subtypes is crucial for personalized treatment and prognosis. In this paper, we present a comprehensive study on the classification of bladder cancer into into three classes, two of them are the malignant set as non invasive type and invasive type and one set is the normal bladder mucosa to be used as stander measurement for computer deep learning. We utilized a dataset containing histopathological images of bladder tissue samples, split into a training set (70%), a validation set (15%), and a test set (15%). Four different deep-learning architectures were evaluated for their performance in classifying bladder cancer, EfficientNetB2, InceptionResNetV2, InceptionV3, and ResNet50V2. Additionally, we explored the potential of Vision Transformers with two different configurations, ViT_B32 and ViT_B16, for this classification task. Our experimental results revealed significant variations in the models’ accuracies for classifying bladder cancer. The highest accuracy was achieved using the InceptionResNetV2 model, with an impressive accuracy of 98.73%. Vision Transformers also showed promising results, with ViT_B32 achieving an accuracy of 99.49%, and ViT_B16 achieving an accuracy of 99.23%. EfficientNetB2 and ResNet50V2 also exhibited competitive performances, achieving accuracies of 95.43% and 93%, respectively. In conclusion, our study demonstrates that deep learning models, particularly Vision Transformers (ViT_B32 and ViT_B16), can effectively classify bladder cancer into its three classes with high accuracy. These findings have potential implications for aiding clinical decision-making and improving patient outcomes in the field of oncology.

Proteogenomic Characterization of Bladder Cancer Reveals Sensitivity to Apoptosis Induced by Tumor Necrosis Factor–related Apoptosis-inducing Ligand in FGFR3-mutated Tumors

BackgroundMolecular understanding of muscle-invasive (MIBC) and non–muscle-invasive (NMIBC) bladder cancer is currently based primarily on transcriptomic and genomic analyses. ObjectiveTo conduct proteogenomic analyses to gain insights into bladder cancer (BC) heterogeneity and identify underlying processes specific to tumor subgroups and therapeutic outcomes. Design, setting, and participantsProteomic data were obtained for 40 MIBC and 23 NMIBC cases for which transcriptomic and genomic data were already available. Four BC-derived cell lines harboring FGFR3 alterations were tested with interventions. InterventionRecombinant tumor necrosis factor-related apoptosis-inducing ligand (TRAIL), second mitochondrial–derived activator of caspases mimetic (birinapant), pan-FGFR inhibitor (erdafitinib), and FGFR3 knockdown. Outcome measurements and statistical analysisProteomic groups from unsupervised analyses (uPGs) were characterized using clinicopathological, proteomic, genomic, transcriptomic, and pathway enrichment analyses. Additional enrichment analyses were performed for FGFR3-mutated tumors. Treatment effects on cell viability for FGFR3-altered cell lines were evaluated. Synergistic treatment effects were evaluated using the zero interaction potency model. Results and limitationsFive uPGs, covering both NMIBC and MIBC, were identified and bore coarse-grained similarity to transcriptomic subtypes underlying common features of these different entities; uPG-E was associated with the Ta pathway and enriched in FGFR3 mutations. Our analyses also highlighted enrichment of proteins involved in apoptosis in FGFR3-mutated tumors, not captured through transcriptomics. Genetic and pharmacological inhibition demonstrated that FGFR3 activation regulates TRAIL receptor expression and sensitizes cells to TRAIL-mediated apoptosis, further increased by combination with birinapant. ConclusionsThis proteogenomic study provides a comprehensive resource for investigating NMIBC and MIBC heterogeneity and highlights the potential of TRAIL-induced apoptosis as a treatment option for FGFR3-mutated bladder tumors, warranting a clinical investigation. Patient summaryWe integrated proteomics, genomics, and transcriptomics to refine molecular classification of bladder cancer, which, combined with clinical and pathological classification, should lead to more appropriate management of patients. Moreover, we identified new biological processes altered in FGFR3-mutated tumors and showed that inducing apoptosis represents a new potential therapeutic option.

Risk Classification of Bladder Cancer by Gene Expression and Molecular Subtype.

This study evaluated a panel including the molecular taxonomy subtype and the expression of 27 genes as a diagnostic tool to stratify bladder cancer patients at risk of aggressive behavior, using a well-characterized series of non-muscle invasive bladder cancer (NMIBC) as well as muscle-invasive bladder cancer (MIBC). The study was conducted using the novel NanoString nCounter gene expression analysis. This technology allowed us to identify the molecular subtype and to analyze the gene expression of 27 bladder-cancer-related genes selected through a recent literature search. The differential gene expression was correlated with clinicopathological variables, such as the molecular subtypes (luminal, basal, null/double negative), histological subtype (conventional urothelial carcinoma, or carcinoma with variant histology), clinical subtype (NMIBC and MIBC), tumor stage category (Ta, T1, and T2-4), tumor grade, PD-L1 expression (high vs. low expression), and clinical risk categories (low, intermediate, high and very high). The multivariate analysis of the 19 genes significant for cancer-specific survival in our cohort study series identified TP53 (p = 0.0001), CCND1 (p = 0.0001), MKI67 (p < 0.0001), and molecular subtype (p = 0.005) as independent predictors. A scoring system based on the molecular subtype and the gene expression signature of TP53, CCND1, or MKI67 was used for risk assessment. A score ranging from 0 (best prognosis) to 7 (worst prognosis) was obtained and used to stratify our patients into two (low [score 0-2] vs. high [score 3-7], model A) or three (low [score 0-2] vs. intermediate [score 3-4] vs. high [score 5-7], model B) risk categories with different survival characteristics. Mean cancer-specific survival was longer (122 + 2.7 months) in low-risk than intermediate-risk (79.4 + 9.4 months) or high-risk (6.2 + 0.9 months) categories (p < 0.0001; model A); and was longer (122 + 2.7 months) in low-risk than high-risk (58 + 8.3 months) (p < 0.0001; model B). In conclusion, the molecular risk assessment model, as reported here, might be used better to select the appropriate management for patients with bladder cancer.

Recent Advances in the Classification of Bladder Cancer – Updates from the 5th Edition of the World Health Organization Classification of the Urinary and Male Genital Tumors

BACKGROUND: The World Health Organization Classification (WHO) of Urinary and Male Genital Tumors has recently been updated to its 5th edition. The new edition presents a comprehensive approach to the classification of urinary and male genital tumors with an incorporation of morphologic, clinical, and genomic data. OBJECTIVE: This review aims to update the new classification of bladder cancer in the 5th edition and to highlight important changes in nomenclatures, diagnostic criteria, and molecular characterization, as compared to the 4th edition. METHODS: The pathologic classification of bladder cancer in the 5th edition of WHO Classification of Urinary and Male Genital Tumours was compared to that in the 4th edition. PubMed was searched using key words, including bladder cancer, WHO 1973, WHO 1998, WHO 2004, WHO 2016, histology, pathology, genomics, and molecular classification in the time frame from 1973 to August of 2022. Other relevant papers were also consulted, resulting in the selection of 81 papers as references. RESULTS: The binary grading of papillary urothelial carcinoma (UC) is practical, but it may be oversimplified and contribute to “grade migration” in recent years. An arbitrary cutoff (5%) has been proposed for bladder cancers with mixed grades. The diagnosis of papillary urothelial neoplasm with low malignant potential has been dramatically reduced in recent years because of overlapping morphology and treatment with low-grade papillary UC. An inverted growth pattern should be distinguished from true (or destructive) stromal invasion in papillary UC. Several methods have been proposed for pT1 tumor substaging, but it is often challenging to substage pT1 tumors in small biopsy specimens. Bladder UC shows a high tendency for divergent differentiation, leading to several distinct histologic subtypes associated with an aggressive clinical behavior. Molecular classification based on the genomic analysis may be a useful tool in the stratification of patients for optimal treatment. CONCLUSIONS: The 5th edition of WHO Classification of Urinary and Male Genital Tumours has made several significant changes in the classification of bladder cancer. It is important to be aware of these changes and to incorporate them into routine clinical practice.

A new biomarker panel of ultraconserved long non-coding RNAs for bladder cancer prognosis by a machine learning based methodology

BackgroundRecent studies have indicated that a special class of long non-coding RNAs (lncRNAs), namely Transcribed-Ultraconservative Regions are transcribed from specific DNA regions (T-UCRs), 100% conserved in human, mouse, and rat genomes. This is noticeable, as lncRNAs are usually poorly conserved. Despite their peculiarities, T-UCRs remain very understudied in many diseases, including cancer and, yet, it is known that dysregulation of T-UCRs is associated with cancer as well as with human neurological, cardiovascular, and developmental pathologies. We have recently reported the T-UCR uc.8+ as a potential prognostic biomarker in bladder cancer.ResultsThe aim of this work is to develop a methodology, based on machine learning techniques, for the selection of a predictive signature panel for bladder cancer onset. To this end, we analyzed the expression profiles of T-UCRs from surgically removed normal and bladder cancer tissues, by using custom expression microarray. Bladder tissue samples from 24 bladder cancer patients (12 Low Grade and 12 High Grade), with complete clinical data, and 17 control samples from normal bladder epithelium were analysed. After the selection of preferentially expressed and statistically significant T-UCRs, we adopted an ensemble of statistical and machine learning based approaches (i.e., logistic regression, Random Forest, XGBoost and LASSO) for ranking the most important diagnostic molecules. We identified a signature panel of 13 selected T-UCRs with altered expression profiles in cancer, able to efficiently discriminate between normal and bladder cancer patient samples. Also, using this signature panel, we classified bladder cancer patients in four groups, each characterized by a different survival extent. As expected, the group including only Low Grade bladder cancer patients had greater overall survival than patients with the majority of High Grade bladder cancer. However, a specific signature of deregulated T-UCRs identifies sub-types of bladder cancer patients with different prognosis regardless of the bladder cancer Grade.ConclusionsHere we present the results for the classification of bladder cancer (Low and High Grade) patient samples and normal bladder epithelium controls by using a machine learning application. The T-UCR’s panel can be used for learning an eXplainable Artificial Intelligent model and develop a robust decision support system for bladder cancer early diagnosis providing urinary T-UCRs data of new patients. The use of this system instead of the current methodology will result in a non-invasive approach, reducing uncomfortable procedures (such as cystoscopy) for the patients. Overall, these results raise the possibility of new automatic systems, which could help the RNA-based prognosis and/or the cancer therapy in bladder cancer patients, and demonstrate the successful application of Artificial Intelligence to the definition of an independent prognostic biomarker panel.

The Lund taxonomy for bladder cancer classification - from gene expression clustering to cancer cell molecular phenotypes, and back again.

Treatment of bladder cancer patients depends on precise diagnosis. Molecular subtyping by gene expression profiling may contribute substantially to subclassification of bladder cancer. Several classification systems have been proposed. Most of these base their classification on whole biopsy features, and molecular subtypes are therefore often defined by a combination of features from the cancer cells as well as infiltrating noncancer cells. This makes the link to what is seen at the cancer cell level unclear. The aim of the Lund taxonomy (LundTax) has been to align gene expression-level classification with immunohistochemical classification to identify cancer cell phenotypes independent of infiltration and proliferation. A systematic approach was used in which gene expression clusters were validated and adjusted by immunohistochemistry using markers expressed only by the cancer cells. This review provides a rationale for defining molecular subtypes and a step-by-step description of the development of the LundTax with motivations for each modification and extension. As the cancer cell phenotype defined by gene expression profiling corresponds with the immunohistochemistry of cancer cells, the LundTax represents a harmonization of the gene expression and immunohistochemical levels. Furthermore, the classification system is independent of pathological stage and is, thus, applicable to all urothelial carcinomas. A unified classification system relevant for both the molecular biologist and pathologist will facilitate systematization of current treatment practices, as well as the development of new treatments. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Notch signaling pathway-based classification of bladder cancer in relation to tumor immune infiltration.

The role of the Notch signaling pathway in the development of various tumors has received increasing attention, but the relationship between the Notch signaling pathway and the prognosis of bladder cancer has rarely been studied. The aim of this study was to investigate the function and risk evaluation value of Notch signaling pathway-related genes (NRGs) in bladder cancer. The list of genes related to the Notch signaling pathway was obtained from the molecular signature database. The bladder cancer dataset was obtained from The Cancer Genome Atlas (TCGA) database. Cox regression analysis and Lasso regression analysis were used to construct the characteristics for predicting the overall survival of patients with bladder cancer. The CIBERSORT algorithm was used to evaluate the infiltration of peripheral immune cells in different risk subgroups. NRG expression was remarkably dysregulated in bladder cancer. Next, bladder cancer was classified into two subtypes (C1 and C2) based on NRG expression levels. The two subtypes had a significant difference in prognosis and were closely related to clinical characteristics. Further analysis showed that immune cell infiltration and immune scores were also significantly different between the two subtypes. Notch signaling pathway-based bladder cancer typing has different prognoses and may be related to tumor immunity. NRGs can be identified for risk evaluation and help improve clinical decision-making.

UROSCAN and UROSCANSEQ: a large-scale multicenter effort towards translation of molecular bladder cancer subtypes into clinical practice – from biobank to RNA-sequencing in real time

Background Bladder cancer is molecularly one of the most heterogenous malignancies characterized by equally heterogenous clinical outcomes. Standard morphological assessment with pathology and added immunohistochemical analyses is unable to fully address the heterogeneity, but up to now treatment decisions have been made based on such information only. Bladder cancer molecular subtypes will likely provide means for a more personalized bladder cancer care. Methods To facilitate further development of bladder cancer molecular subtypes and clinical translation, the UROSCAN-biobank was initiated in 2013 to achieve systematic biobanking of preoperative blood and fresh frozen tumor tissue in a population-based setting. In a second phase, we established in 2018 a parallel logistic pipeline for molecular profiling by RNA-sequencing, to develop and validate clinical implementation of molecular subtyping and actionable molecular target identification in real-time. Results Until June 2021, 1825 individuals were included in the UROSCAN-biobank, of which 1650 (90%) had primary bladder cancer, 127 (7%) recurrent tumors, and 48 (3%) unknown tumor status. In 159 patients, multiple tumors were sampled, and metachronous tumors were collected in 83 patients. Between 2016 and 2020 the UROSCAN-biobanking included 1122/2999 (37%) of all primary bladder cancer patients in the Southern Healthcare Region. Until June 2021, the corresponding numbers subjected to RNA-sequencing and molecular subtyping was 605 (UROSCANSEQ), of which 52 (9%) samples were not sequenced due to inadequate RNA-quality (n = 47) or technical failure/lost sample (n = 5). Conclusions The UROSCAN-biobanking and UROSCANSEQ-infrastructure for molecular subtyping by real-time RNA-sequencing represents, to our knowledge, the largest effort of evaluating population-wide molecular classification of bladder cancer.

Identification and analysis of microRNA editing events in recurrent bladder cancer based on RNA sequencing: MicroRNA editing level is a potential novel biomarker.

Background: With the continued advancement of RNA-seq (RNA-sequencing), microRNA (miRNA) editing events have been demonstrated to play an important role in different malignancies. However, there is yet no description of the miRNA editing events in recurrent bladder cancer. Objective: To identify and compare miRNA editing events in primary and recurrent bladder cancer, as well as to investigate the potential molecular mechanism and its impact on patient prognosis. Methods: We examined the mRNA and miRNA transcriptomes of 12 recurrent bladder cancer cases and 13 primary bladder cancer cases. The differentially expressed mRNA sequences were analyzed. Furthermore, we identified the differentially expressed genes (DEGs) in recurrent bladder cancer. The Gene Ontology (GO) functional enrichment analyses on DEGs and gene set enrichment analysis were performed. The consensus molecular subtype (CMS) classification of bladder cancer was identified using the Consensus MIBC package in R (4.1.0); miRNA sequences were then further subjected to differentially expressed analysis and pathway enrichment analysis. MiRNA editing events were identified using miRge3.0. miRDB and TargetScanHuman were used to predict the downstream targets of specific differentially edited or expressed miRNAs. The expression levels of miR-154-5p and ADAR were validated by RT-qPCR. Finally, survival and co-expression studies were performed on the TCGA-BLCA cohort. Results: First, the mRNA expression levels in recurrent bladder cancer changed significantly, supporting progression via related molecular signal pathways. Second, significantly altered miRNAs in recurrent bladder cancer were identified, with miR-154-5p showing the highest level of editing in recurrent bladder cancer and may up-regulate the expression levels of downstream targets HS3ST3A1, AQP9, MYLK, and RAB23. The survival analysis results of TCGA data revealed that highly expressed HS3ST3A1 and RAB23 exhibited poor prognosis. In addition, miR-154 editing events were found to be significant to CMS classification. Conclusion: MiRNA editing in recurrent bladder cancer was detected and linked with poor patient prognosis, providing a reference for further uncovering the intricate molecular mechanism in recurrent bladder cancer. Therefore, inhibiting A-to-I editing of miRNA may be a viable target for bladder cancer treatment, allowing current treatment choices to be expanded and individualized.

Are We Ready to Implement Molecular Subtyping of Bladder Cancer in Clinical Practice? Part 2: Subtypes and Divergent Differentiation.

Following several attempts to achieve a molecular stratification of bladder cancer (BC) over the last decade, a “consensus” classification has been recently developed to provide a common base for the molecular classification of bladder cancer (BC), encompassing a six-cluster scheme with distinct prognostic and predictive characteristics. In order to implement molecular subtyping (MS) as a risk stratification tool in routine practice, immunohistochemistry (IHC) has been explored as a readily accessible, relatively inexpensive, standardized surrogate method, achieving promising results in different clinical settings. The second part of this review deals with the pathological and clinical features of the molecular clusters, both in conventional and divergent urothelial carcinoma, with a focus on the role of IHC-based subtyping.

Intratumoral Switch of Molecular Phenotype and Overall Survival in Muscle Invasive Bladder Cancer.

Simple SummaryThe Cancer Genome Atlas (TCGA) and more recent genome profiling recently revealed major intrinsic molecular subtypes in urothelial carcinoma (UC). Here we propose a fast and standardized immunophenotypical classification score (Piescore) that may discriminate between luminal, basal, or neu-like UC as a surrogate of molecular profile, and we describe, for the first time, an intratumoral phenotypical switch in tissue protein expression, from non-muscle to muscle-invasive progression. Our data show that a change from a luminal to a neu-like phenotype could worsen overall survival compared with a transition to a basal phenotype.In recent years, immunohistochemical protein expression was studied as a surrogate to the molecular classification of bladder cancer, although no tissue biomarkers are available for clinical use to predict survival or the response to neoadjuvant chemotherapy (CT) in UC, as the literature produced conflicting results. This retrospective study included TURB specimens harboring foci of HG pT2 muscle-invasive bladder carcinoma (MIBC) from 251 patients who subsequently underwent radical cystectomy. We performed immunohistochemical analysis on tumor samples, for relevant gene-expression-based markers for basal type (CD44, CK5/6) and luminal type (CK20 and pPARγ). Piescore, investigated in both non-muscle-invasive (NMI) and muscle-invasive (MI) components of the tumor, divided basal and luminal UC-types when at least three of the four markers were consistent with a specific phenotype, mixed types if one/two luminal and basal markers were present simultaneously, and neu-like types when all four markers investigated were negative. Eighteen selected cases were also investigated with RT-PCR to validate, and to increase the specificity of, the immunohistochemical results. We observe an immunophenotypical difference in the NMI and MI components in 96/251 UC patients (38.25%): half of tumors (44/96 cases) have a transition to basal, 36.46% (35/96 cases) to neu-like, 12.5% (12/96 cases) to mixed, and 5.2% (5/96 cases) to luminal phenotypes. Mixed tumors in the NMI component are more likely to change phenotype than other groups, particularly compared with basal tumors, which demonstrate greater stability (only 8/96 cases, p < 0.00001). The transition of luminal tumors to basal display a better OS compared with the transition toward neu-like tumors (p = 0.027). Overall, the phenotypical switch does not affect lymphovascular invasion, pT, DFS, or OS compared with non-switched cases. In the MI component, the presence of CD44 expression, irrespective of score-related phenotype, shows a protective effect in papillary-type UC (OS p = 0.008, HR 0.453, PFS p = 0.07, HR 0.599), and in UC naïve for CT (p = 0.0479). Piescore immunophenotyping reveals an intratumoral phenotypical transition between the NMI and MI components of the same tumor. The molecular change is a common event in the mixed and luminal categories, but not in basal tumors, which show better phenotypical stability. This phenomenon could partially explain the sensitivity of a subset of luminal UC to chemotherapy: good responders could be “non-real” luminal UC, which acquire nasal markers, such as CD44.

A single-sample mRNA molecular classification of bladder cancer predicting prognosis and response to immunotherapy.

BackgroundAs an immunogenic cancer, crosstalk between cancer cells and immune cells has been gradually recognized in bladder cancer (BC). Several studies have emphasized the clinical significance of the molecular stratification of BC without highlighting the role of the immune microenvironment. Although immunotherapy acted as a prospective treatment, more precise molecular stratification should be established to select those sensitive to immunotherapy.MethodsTo select specific immune genes forming subtypes indicating disparate prognoses, we performed bioinformatic analysis using BC transcriptomic profiles from six published datasets, with 408 BC samples in The Cancer Genome Atlas (TCGA) database and 295 individuals in International Cancer Genome Consortium (ICGC) database. Survival analyses were conducted using Kaplan-Meier curves, while Kruskal-Wallis tests were applied to test the differences among groups. Except for unsupervised clustering based on the differential expression of genes, we additionally performed binomial logistic regression, focusing on the mRNA level of a single sample.ResultsUnsupervised clustering showed that 4 clusters captured the best segmentation. After validation with survival data and simplification using binomial logistic regression, we found that cluster B and cluster D showed worse survival outcomes (P=0.012). Considering the similar survival outcomes of these two clusters, we recombined and performed another survival analysis, which also showed significant survival differences (P=0.0041). Bonding with clinical data, a greater proportion of risk factors were assigned to the worse prognosis subtype, especially showing higher grades in the subtype (P<0.001). In addition, immune cell infiltration, single nucleotide polymorphism (SNP) and copy number variation (CNV) all showed differences between clusters, indicating changes in the immune microenvironment and mutation burden. Through phenotypical analysis, we found metabolism and proliferation phenotypes associated with the immune clusters and mutually exclusive in BC, of which proliferation contributed to worse outcomes. Using the tumor immune dysfunction and exclusion (TIDE) score, a worse immunotherapy benefit was predicted in clusters B&D, defined as the worse prognosis subtype.ConclusionsWith this novel clustering criterion based on immune-related genes, we provide a better understanding of the immune microenvironment, further guiding the use of immunotherapy.

In vivo Raman spectroscopy for bladder cancer detection using a superficial Raman probe compared to a nonsuperficial Raman probe.

Raman spectroscopy is promising as a noninvasive tool for cancer diagnosis. A superficial Raman probe might improve the classification of bladder cancer, because information is gained solely from the diseased tissue and irrelevant information from deeper layers is omitted. We compared Raman measurements of a superficial to a nonsuperficial probe, in bladder cancer diagnosis. Two-hundred sixteen Raman measurements and biopsies were taken in vivo from at least one suspicious and one unsuspicious bladder location in 104 patients. A Raman classification model was constructed based on histopathology, using a principal-component fed linear-discriminant-analysis and leave-one-person-out cross-validation. The diagnostic ability measured in area under the receiver operating characteristics curve was 0.95 and 0.80, the sensitivity was 90% and 85% and the specificity was 87% and 88% for the superficial and the nonsuperficial probe, respectively. We found inflammation to be a confounder and additionally we found a gradual transition from benign to low-grade to high-grade urothelial carcinoma. Raman spectroscopy provides additional information to histopathology and the diagnostic value using a superficial probe.

A1069 - Transcriptomic profiling of upper tract urothelial carcinoma: Utility of the consensus molecular classification of bladder cancer, differential immune signatures and molecular heterogeneity

A1069 - Transcriptomic profiling of upper tract urothelial carcinoma: Utility of the consensus molecular classification of bladder cancer, differential immune signatures and molecular heterogeneity

BLADDER CANCER: DIFFERENTIATION OF TYPES, AGE, SEX DISTRIBUTION AND ASSOCIATED VARIANTS WITH GRADATION

 Background:  Urinary bladder cancer (UBC) is the ninth most common cancer in the world, and the third most common cancer among men in West Asian countries, including Arab countries. Despite the increasing prevalence of UBC in developing countries, many places, including Yemen, do not have representative studies showing the true impact of these tumors on the population.&#x0D; Aims: To describe the different types of bladder cancers, and their distributions with age, and sex and to correlated different types with gradation, invasion muscles; and schistosomiasis in the last ten years in a single Yemini institute.&#x0D; Materials and methods:  An observational descriptive study was performed on UBC patients who were subsequently diagnosed selectively by histopathological study in the Department of Pathology at the National Center for Public Health Laboratories (NCPHL) Sana'a, Yemen, over a period of about 10 years from January 1, 2012 to October 31, 2021. The study variables were cancer histological type, sex, grades and age. Types, grading and histological diagnoses were formed in line with the World Health Organization classification of bladder cancer.&#x0D; Results: Most of the cases were in the age group 60-69 years (32%), followed by 70 years (22.3%), and 50-59 years (20%).  The most common UBCs were urothelial neoplasms (UNs) (71.5%), followed by squamous neoplasms (SNs) (24.4%) while other types were less frequent. There was a cytologically high grade with a significance rate (64.2%) with UNs. There was a significance rate of:  schistosomiasis (43.8%) with SNs as OR=19.5 (p &lt; 0.001), and invasion of muscle fibers  (66.4%) with SNs as OR=3.3,  UNs (37.1%) as OR=11.2 (p &lt; 0.001). Also there was a significance rate of GII grade (46.1%) with SNs as OR=64.1 (p&lt;0.001) and GNs (33.3%) as  OR=3.8 (p =0.02).  &#x0D; Conclusion:  The present study verification data congruence with those in the international literature and reports of neighboring countries, with some minor differences. This study documents a high incidence of urothelial neoplasms, with a male predominance and a peak incidence in the sixth decade of life. Imminent studies are needed to identify risk factors that increase cystitis in more detail and to study genetic susceptibility to inflammation and inflammatory markers before cancer is diagnosed.&#x0D;              &#x0D; Peer Review History: &#x0D; Received: 1 November 2021; Revised: 17 December; Accepted: 31 December, Available online: 15 January 2022&#x0D; Academic Editor: Dr. Nuray Arı, Ankara University, Turkiye, ari@ankara.edu.tr&#x0D; UJPR follows the most transparent and toughest ‘Advanced OPEN peer review’ system. The identity of the authors and, reviewers will be known to each other. This transparent process will help to eradicate any possible malicious/purposeful interference by any person (publishing staff, reviewer, editor, author, etc) during peer review. As a result of this unique system, all reviewers will get their due recognition and respect, once their names are published in the papers. We expect that, by publishing peer review reports with published papers, will be helpful to many authors for drafting their article according to the specifications. Auhors will remove any error of their article and they will improve their article(s) according to the previous reports displayed with published article(s). The main purpose of it is ‘to improve the quality of a candidate manuscript’. Our reviewers check the ‘strength and weakness of a manuscript honestly’. There will increase in the perfection, and transparency. &#x0D; Received file:                Reviewer's Comments:&#x0D; Average Peer review marks at initial stage: 6.0/10&#x0D; Average Peer review marks at publication stage: 7.0/10&#x0D; Reviewers:&#x0D; Dr. Bilge Ahsen KARA, Ankara Gazi Mustafa Kemal Hospital, Turkey, ahsndkyc@gmail.com&#x0D; Dr. Eyassu Mathewos, School of public health, college of health sciences and medicine, Wolaita Soddo university- P.O. Box 158, Wolaita Soddo, Ethiopia.   &#x0D; Md. Monirul Islam, Department of Pharmacy, Faculty of Health Sciences, Northern University Bangladesh, Dhaka, Bangladesh.  monirul.islam@nub.ac.bd&#x0D; Dr. Rawaa Souhil Al-Kayali, Aleppo University, Syria, rawah67@hotmail.com&#x0D; Similar Articles:&#x0D; EVALUATION OF CURRENT SCENARIO OF CANCER DISEASE AT CHATTAGRAM IN BANGLADESH &#x0D; ACCESS TO MEDICINES STRATEGIES OF THE NATIONAL CANCER CONTROL PROGRAMME IN CAMEROON

The effect of CT high-resolution imaging diagnosis based on deep residual network on the pathology of bladder cancer classification and staging

Background and objectiveTo study the high-resolution CT image based on deep residual network to efficiently and accurately predict the staging diagnosis of bladder tumors. MethodsThe image was processed with super-resolution to restore the missing details of the image. The CT data of 75 bladder patients who were treated in our hospital from June to December 2013 were collected. And obtain the patient's classification and staging information through pathology, which is used to establish a model of ResNet structure combined with non-Local attention mechanism. The clinical data of 76 patients with bladder disease admitted to our hospital from May 2018 to August 2021 were randomly selected, and the imaging and accuracy of CT diagnosis were retrospectively analyzed. Results52 cases were diagnosed <T1 stage, 16 cases belonged to T2 stage, 2 cases T3 stage, and 2 cases T4 stage. The sensitivity rate of experimental diagnosis was 94.74%, which was not significantly different from the sensitivity rate of preoperative pathological diagnosis. ConclusionCT based on deep residual network has high application value in the diagnosis and staging of bladder cancer, can effectively improve the diagnostic accuracy, and is worthy of clinical application.