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Loss of OBSCN expression promotes bladder cancer progression but enhances the efficacy of PD-L1 inhibitors

Cell & Bioscience volume 15, Article number: 40 (2025) Cite this article

Abstract

Background

As the objective overall response rate to immune checkpoint inhibitors (ICIs) is less than 30% in late stage or metastatic bladder cancer (BLCA), elucidating the intrinsic mechanisms of immune evasion is of great importance for the discovery of predictive and prognostic biomarkers and the exploration of novel targets for intervention. Recent studies have shown that OBSCN and the cytoskeletal protein it encodes, obscurin, play an important role in tumour progression. However, no studies have reported the role of OBSCN in BLCA.

Methods

RNA sequencing and clinical data were downloaded from multiple public databases including The Cancer Genome Atlas and the Gene Expression Omnibus. Immunohistochemistry (IHC) was performed on tissue microarrays including 80 BLCA patients from Shuguang Hospital. Kaplan-Meier curves with log-rank test, univariate and multivariate COX regression were performed to evaluate the prognostic efficacy of OBSCN expression. In vitro experiments were conducted to determine the role of OBSCN deficiency in promoting BLCA progression. Pan-cancer tumour immune microenvironment (TIME) analysis was performed to explore the potential correlation between OBSCN deficiency and immune evasion.

Results

Pan-cancers and single-cell sequencing analysis revealed that the expression level and proportion of OBSCN was significantly decreased in BLCA cells compared to normal urothelium. Survival curves showed that BLCA patients with low OBSCN expression had a worse prognosis, yet a better clinical response to PD-L1 ICIs. Gene set variation analysis and Gene set enrichment analysis revealed that epithelial-mesenchymal transition (EMT) and immune-related processes were significantly enriched in BLCA samples with low OBSCN expression. In vitro experiments identified that OBSCN-deficient BLCA cells enhanced invasion, migration and EMT. Pan-cancer analysis of TIME revealed that neoantigen, tumor mutation burden, CD8+T cells and immune checkpoints were significantly negatively associated with OBSCN expression. IHC and Western blot assay identified that BLCA samples with low OBSCN expression had more CD8+ T-cell infiltration and higher PD-L1 expression.

Conclusions

This study confirmed that BLCA patients with low OBSCN expression had a worse prognosis but a superior response to ICIs, providing a reference for individualised treatment of BLCA patients.

Introduction

The most recent data indicate that bladder cancer (BLCA) is the fourth most prevalent malignant neoplasm in males, with 20–25% of these cancers exhibiting muscle-invasive and 5% metastasizing to other sites at the time of initial diagnosis [1]. In recent years, immunotherapy has emerged as a promising new treatment option for metastatic BLCA [1, 2]. PD-1/PD-L1 immune checkpoint inhibitors (ICIs) have been approved for maintenance therapy after platinum chemotherapy or as first-line treatment for patients with platinum drug intolerance [1, 2]. Currently, PD-L1 expression, tumor mutation burden (TMB), and microsatellite instability (MSI) have been approved by the U.S. Food and Drug Administration to predict the efficacy of ICIs [1, 3]. Nevertheless, the insufficient prediction accuracy and high measurement technical requirements restrict their extensive clinical application [1, 4]. Further investigation is required to elucidate the mechanism of immune evasion in BLCA and to identify more precise biomarkers to predict the survival and the efficacy of ICIs in BLCA patients.

OBSCN is located on human chromosome 1q42.13 and encodes obscurin, a cytoskeletal protein that plays a scaffold and regulatory role in striated muscle [5]. The majority of contemporary research on the structure, regulation, binding, and pathophysiological functions of OBSCN and obscurin originates in striated muscle [5]. Due to its molecular diversity and presence in multiple locations of muscle cells, obscurin can be employed as a scaffold during myogenesis and can act as a mechanical sensor [5]. Furthermore, it facilitates cell adhesion and regulates calcium homeostasis [5]. Consequently, OBSCN mutations are linked to various forms of skeletal muscle and cardiomyopathy, including rhabdomyolysis, myocardial remodeling, and arrhythmias [6,7,8]. In recent years, an increasing number of studies have demonstrated that OBSCN plays a pivotal role in the development of tumors [5]. In breast and colorectal cancer, OBSCN and TP53 are the only co-mutated genes among more than 13,000 candidate genes [9]. The loss of OBSCN expression can induce cytoskeletal remodeling or promote epithelial-mesenchymal transition (EMT), and promote the growth, migration, and metastasis of pancreatic or breast cancer cells [10, 11]. To date, no studies have reported the role of OBSCN in BLCA.

In this study, we initially conducted a pan-cancer analysis to investigate the mutation and expression of OBSCN in tumors. Subsequently, the study focused on BLCA to determine the predictive role of OBSCN expression on BLCA prognosis, as well as its role in BLCA progression. Finally, pan-cancer analysis was performed to assess the impact of OBSCN expression on the tumor immune microenvironment (TIME). This was followed by a focus on OBSCN expression as a predictor of response to ICIs and the potential mechanism of immune evasion in BLCA.

Materials and methods

Patient cohorts and data resources

In this study, the RNA-sequencing and clinical data of patients were downloaded from The Cancer Genome Atlas (TCGA) (https://portal.gdc.cancer.gov/) and the Gene Expression Omnibus (GEO) (https://www.ncbi.nlm.nih.gov/geo). This study involves eight published datasets, including TCGA-BLCA (n = 404), GSE48075 (n = 73), GSE31684 (n = 93), GSE32894 (n = 224), GSE176307 (n = 90), E-MTAB-4321 (n = 476), IMVigor210 (n = 348), and a single-cell sequencing database, GSE135337 (n = 5). Besides, the prognostic role of obscurin was further verified in a Shuguang cohort (n = 80). We retrospectively recruited 80 patients with BLCA who underwent surgical treatment at the Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine from September 2010 to December 2015. The GTEX database (https://commonfund.nih.gov/GTEx) was also used to download the gene expression data of different organizations and calculate the gene expression difference among different types of cancer after combining with TCGA data. The Cancer Cell Line Encyclopedia (CCLE) database (https://portals.broadinstitute.org/ccle/) was explored to analyze the expression levels of OBSCN among tumor cell lines.

Genetic alteration analysis

cBioPortal (https://www.cbioportal.org/) database was utilized to evaluate the alteration frequency and mutation site of OBSCN in pan-cancers [12]. Besides, SNP-related data of BLCA in TCGA database was downloaded, and top 30 genes with high mutation frequency were selected as display. The differences of mutant genes in BLCA patients with low and high OBSCN expression were compared, and mutation landscape maps were drawn with R package ComplexHeatmap.

Analysis of single-cell RNA-sequencing data

Single-cell data of GSE135337 was used in this analysis. GSE135337 contained seven BLCA samples and one paracancerous sample. Only cells with nFeature_RNA > 1000&percent.mt < 5&nCount_RNA < 100,000 in the expression profile were retained for this analysis. The feature expression levels of 23,011 cells were included for subsequent analysis. Data were standardized and homogenized and combined to reduce technical deviations. In this study, 2000 highly variable genes were selected for PCA dimensionality reduction in order to reduce the dimensions of the data, while retaining the most important variation information. Harmony was further used to analyze dimensionality reduction and batch removal. Based on this optimal number of PCS, this study applied the t-distributed stochastic neighborhood embedding (tSNE) method to further reduce and visualize the data, and obtained 11 subtypes by tSNE. In this study, each subtype was annotated using the R-package SingleR, eleven clusters were annotated into six cell categories, including cancer cells, urothelial cells, smooth muscle cells, monocyte, tissue stem cells, endothelial cells (Fig. 1C). The study also investigated the expression of OBSCN in these types of cells (Fig. 1D-E).

Immunohistochemical detection and interpretation of obscurin protein expression

The expression of obscurin protein was detected by IHC. Rabbit anti-human OBSCN antibody was purchased from Proteintech Company (55281-1-AP). Rabbit anti-human obscurin monoclonal antibody were diluted at 1:100 ratio. CD8A Rabbit antibody (A11856) and PD-L1 Rabbit antibody (A1645) were obtained from Abclonal at a dilution ratio of 1:100. IHC staining EnVision method was used, the specific operation steps were carried out according to the kit instructions, and the primary antibody was replaced by PBS as the negative control. The staining intensity of cytoplasm was evaluated by H-score semi-quantitative method. The average H-score of cancer-containing tissue points was calculated for each case, and the patients were divided into H-score = 0 and H-score > 0 groups. H-score of 0 is negative, meaning that obscurin protein is missing. H-score > 0 is considered positive. Blind interpretation was performed by 2 pathologists in the field of urologic tumors according to the above scoring criteria. Besides, CD8+T cell counts were enumerated as the mean value of 3 randomized high power magnification fields (HPF, 200× magnification) of each sample. The percentage of stained viable tumor cells over total tumor cells (tumor proportion score, TPS) was calculated for PD-L1 staining [13].

Pathways and function enrichment analysis

In this study, gene sets were downloaded from Molecular signatures database (v7.0), and each gene set was comprehensively scored using Gene set variation analysis (GSVA) algorithm to evaluate potential biological functional changes in different samples [14]. Besides, “clusterprofiler” and “enrichplot” kits were used to perform Gene set enrichment analysis (GSEA) analysis and compare the differences in signaling pathways between groups with high or low OBSCN expression [15].

Cell culture and Si-RNA transfection

Human BLCA cell lines UMUC3 and 5637 were obtained from American Type Culture Collection (ATCC). All cells were cultured in RPMI 1640 medium (Gibco) supplemented with 10% fetal bovine serum (FBS, Gibco) and 1% penicillin and streptomycin (Gibco). 3.75 µL of Lipofectamine3000 reagent (Invitrogen) and 250 pmol siRNA (Tsingke) were separately added to 125 µL of serum-free, opti-MEM (Gibco) media, and mixed gently. Finally, the mixed solution was added to the cells cultured in 1000 µL fresh RPMI 1640 medium with 10% FBS after 15 min incubation at room temperature. Subsequent experiments were carried out after 48 h of continuous cell culture. The sense sequences of siRNA are as follows siOBSCN#1: 5’-AGAGGCAGGAGCCAGUGCCACACUGAGCU-3’; siOBSCN#2: 5’-CGGAGGUGAUGUGGUACAAAG-3’.

Western blot assay (WB)

RIPA lysis buffer and a BCA protein quantification kit (Beyotime) were used to extract total protein from the cells and determine protein concentration, respectively. Equal amounts of protein were subjected to sodium dodecyl sulfate polyacrylamide gel electrophoresis and transferred to a membrane. It was incubated with the anti-OBSCN (Cat#A18110, Proteintech, 1:1000) at 4 ◦C overnight after blocking for 1 h at room temperature. The membrane was washed and incubated with horseradish peroxidase-conjugated secondary antibody at room temperature for 1 h. Finally, protein bands were detected using an ECL kit (Beyotime) and protein expression levels were analyzed. Additional antibody information is as follows: anti-β-actin (Cat#AC038, ABclonal, 1:20000), anti-Vimentin (Cat#A19607, ABclonal, 1:1000), anti-Snail (Cat#A11974, ABclonal, 1:1000), anti-N-Cadherin (Cat#A19038, ABclonal, 1:1000), anti-E-Cadherin (Cat#A22333, ABclonal, 1:1000), anti-Slug (Cat#A1057, ABclonal, 1:1000), anti-PD-L1 (Cat#A19135, ABclonal, 1:1000) and HRP Goat Anti-Rabbit IgG (Cat#AS014, ABclonal, 1:20000).

Cell proliferation experiment

Cell counting kit-8 (CCK-8, Beyotime) was used to evaluate cell proliferation. Transfected cells were seeded in 96-well plates (2000 cells/well) for the CCK-8 assay. CCK-8 reagent was added to the plates at 0, 24, 48, and 72 after inoculation, and absorbance at 450 nm was measured using a spectrophotometer.

Wound healing assay

The transfected cells were seeded in a 6-well plate at 100% density, and a straight line was made with the tip of a 200 µL pipette. The fluid in each well was replaced with 2 mL of serum-free RPMI 1640 medium, and the area was photographed at 0 and 24 h. The wound healing ability was measured as the area between the two sides of the wound.

Transwell assay

First, Transwell cell suspensions were prepared in serum-free RPMI 1640 medium. Then, 2 × 104 cells were inoculated with serum-free RPMI 1640 medium in the upper chamber of a transwell with an aperture of 8 mm, coated without matrix gel for cell migration or with matrix gel for the cell invasion assay, and complete medium containing 10% FBS was added to the lower chamber. Finally, the cotton swab was used to remove the upper chamber cells, while other cells were fixed and stained with 0.1% crystal violet after incubated in 37 ◦C incubator for 48 h. The number of migrating or invading cells was counted under a microscope.

Pan-cancer analysis of tumor immune microenvironment

TMB is defined as the total number of somatic gene coding errors, base substitutions, insertions, or deletions detected per million bases. This study defined TMB by calculating the frequency of variation and variance/exon length for each tumor sample by dividing the non-synonymous mutation site by the total length of the protein coding region. The MSI values for each TCGA patient were derived from previously published studies [16]. NetMHCpan v3.0 was used to evaluate Neoantigen (NEO) in each patient [17]. Fmsb, limma and dplyr packages were used to analyze the correlation between the expression of OBSCN and TMB, NEO and MSI in TIME. The potential relationship between OBSCN expression and TIME was explored through the TISIDB website [18]. The correlation between OBSCN and the infiltration level of CD8+T cells was further analyzed using the TIMER2.0 database (http://timer.cistrome.org) [19].

Statistical analysis

All statistical analyses were performed in GraphPad Prism 9 software and R language (version 4.2.2). Chi squared test and logistic regression were utilized to analyze the correlation between OBSCN expression and clinical features in BLCA. Kaplan-Meier with log-rank test and Cox regression analyses were used to evaluate the prognostic value of OBSCN expression. All statistical tests were two-sided, and p < 0.05 was considered statistically significant.

Results

Genetic changes in OBSCN in pan-cancers were revealed through comprehensive analysis

According to the cBioPortal website, OBSCN is mutated in 10% of all cancers (Supplementary Fig. 1A). Further investigation of the genetic variation of OBSCN in different tumor types in the TCGA dataset revealed that BLCA had the 7th highest frequency of genetic variation, mainly mutation, amplification and depth deletion (supplementary Fig. 1A-B). In addition, missense mutations at 1260 amino acids were found to be the dominant type of genetic variation in all TCGA tumor samples (Supplemental Fig. 2A). By using the cBioPortal website to analyze the mutation frequency of OBSCN genes, it was found that several genes, including TTN, TP53, MUC16, PIK3CA, were mutated more frequently in samples with OBSCN mutations (supplementary Fig. 2B-D). Finally, the expression of OBSCN under different gene mutation types and copy number variation was analyzed, and it was found that the expression of OBSCN was significantly reduced when the copy number was lost (supplementary Fig. 2E-F).

Analysis of OBSCN expression in pan-cancers

A comprehensive analysis of OBSCN expression in 33 types of human cancers, conducted using the TCGA combined GTEx dataset, revealed a notable reduction in the expression of OBSCN in the majority of tumors, including BLCA (Fig. 1A). Figure 1B illustrated the expression of OBSCN in different tumor cell lines as observed in the CCLE. Single-cell sequencing analysis further demonstrated that the expression levels and proportions of OBSCN in BLCA cells were significantly decreased in comparison to normal urothelium (Fig. 1C-E).

Fig. 1
figure 1

OBSCN expression in pan-cancer analysis. (A) The expression of OBSCN in tumors and adjacent tissues were revealed in pan-cancer analysis; (B) Pan-cancer analysis of OBSCN expression in tumor cell lines; (C) tSNE degradation and visualization analysis of OBSCN expression in single-cell sequencing database GSE135337; (D–E) Expression levels and proportions of OBSCN among six types of cells in TIME of BLCA annotated with R package SingleR

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Correlation between OBSCN expression and clinical characteristics of BLCA

Subsequently, IHC staining of obscurin was performed on the tissue microarrays (TMAs) of the Shuguang cohort (Fig. 2 A-B). Chi squared and univariate logistic regression analysis both demonstrated that the absence of OBSCN expression was significantly associated with tumor size, pT stage, pN stage, and lymph vascular invasion (Fig. 2 C-F, supplementary Tables 1–2). Further multivariate logistic regression analysis indicated that OBSCN expression was significantly associated with pT stage (T2 vs. T3-4 OR = 0.215, P = 0.009) (Supplementary Table 2). To further identify the correlation between OBSCN expression and clinical parameters, we explored two public databases, TCGA and e-MTAB-4321, which mainly included muscle-invasive bladder cancer (MIBC) and non-muscle invasive bladder cancer (NMIBC), respectively. In TCGA, it was confirmed that OBSCN expression was associated with grade, pT stage, and pN stage (Supplementary Tables 3–4). While in e-MTAB-4321 database, it was revealed that OBSCN expression was associated with age, grade, and pT stage (Supplementary Tables 5–6). Multivariate logistic regression analysis showed that OBSCN expression were significantly correlated with grade and pT stage in both databases (Supplementary Tables 4,6).

The prognosis role of OBSCN expression in BLCA

In view of the close correlation between OBSCN expression and clinical characteristics of BLCA, we investigated its potential prognostic role. It was found that BLCA patients with negative OBSCN expression exhibited a significantly worse overall survival (OS) and recurrence-free survival (Fig. 2G, supplementary Fig. 3). Univariate COX regression analyses showed that maximum tumour size, pT stage, pN stage, pM stage, lympho-vascular invasion, nerve invasion, and OBSCN expression were associated with worse OS (Table 1). In multivariate COX regression analyses, it was demonstrated that pM stage, nerve invasion and OBSCN expression were independent predictors of BLCA prognosis (Table 1). To verify this conclusion, we further investigated BLCA cohorts from multiple public databases, including TCGA, GSE48075, GSE31684, GSE894, E-MTAB-4321. It was also found that BLCA patients with low OBSCN expression exhibited worse OS through Kaplan-meier with log-rank tests (Fig. 2H-L).

Table 1 Univariate and multivariate Cox regression for predicting OS of BLCA in Shuguang cohort
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While in TCGA database, OBSCN expression were correlated with worse OS in univariate COX regression analyses, it acted as an independently predictor of PFS through univariate and multivariate COX regression analyses with E-MTAB-4321 cohort (Supplementary Tables 7–8).

Fig. 2
figure 2

Relationship between OBSCN expression and clinical parameters and OS of BLCA. (A) HE, IHC and immunofluorescence staining of obscurin in low grade BLCA; (B) HE, IHC and immunofluorescence staining of obscurin in high grade BLCA; (C–F) Relationship between OBSCN expression and tumour size, pT stage, pN stage, and lympho-vascular of Shuguang cohort; (G) The relationship between OBSCN expression and OS in the Shuguang cohort; (H) The relationship between OBSCN expression and OS in the TCGA cohort; (I) The relationship between OBSCN expression and OS in the GSE48075 cohort; (J) The relationship between OBSCN expression and OS in the GSE31684 cohort; (K) The relationship between OBSCN expression and OS in the GSE32894 cohort; (L) The relationship between OBSCN expression and OS in the E-MTAB-4321 cohort

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Deficiency of OBSCN expression promoted the progression of BLCA

To understand the role of OBSCN expression deficiency in the progression of BLCA, we firstly established BLCA cell lines UMUC-3 and 5637 with OBSCN knockdown (Fig. 3A-B). In vitro experiments demonstrated that OBSCN deficiency significantly enhanced the invasion and migration, while having no effect on the proliferation ability of BLCA cells (Fig. 3C-E). In order to elucidate the underlying mechanism, we initially scored GSVA for BLCA and subsequently divided the samples into high and low expression groups according to the median value of OBSCN expression. The results demonstrated that Wnt/beta-Catenin signaling pathway was significantly enriched in the high OBSCN expression group, while pathways such as EMT, Kras signaling up were significantly enriched in the group with low OBSCN expression (Fig. 3F). Besides, the complement, IL-2-STAT5 signaling, allograft rejection and inflammatory rejection were also significantly enriched in the samples with low OBSCN expression, indicating that the absence of OBSCN expression may be associated with immune response (Fig. 3F). Furthermore, the chemokine signaling pathway, cytokine-cytokine receptor interaction, and focal adhesion pathways in cancer were also significantly enriched in the OBSCN low expression group through the GSEA analysis (Fig. 3G). This further suggested that the absence of OBSCN expression was closely related to immune activation and cell motility. Subsequent in vitro experiments demonstrated that the expression of E-cadherin in BLCA cells with OBSCN expression deficiency was significantly reduced, while the expression of N-cadherin, Vimentin, Snail, and Slug were significantly increased, indicating that OBSCN expression deficiency enhanced the ability of EMT in BLCA cells (Fig. 3H).

Fig. 3
figure 3

OBSCN deficiency promoted the progression of BLCA. (A) The mRNA expression of OBSCN was stably down-regulated after knockdown; (B) The protein expression of OBSCN was stably down-regulated after knockdown; (C) CCK8 experiment: There was no effect on the proliferative ability of BLCA cells after OBSCN knockdown; (D) Scratch test: The invasion ability of BLCA cells was enhanced after OBSCN knockdown; (E) Transwell experiment: The migration ability of BLCA cells was enhanced after OBSCN knockdown; (F) The results of pathways enrichment were analyzed by GSVA between low and high OBSCN expression of BLCA samples; (G) The results of pathways enrichment were analyzed by GSEA between low and high OBSCN expression of BLCA samples; (H) OBSCN knockdown enhanced the EMT of BLCA cells

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Pan-cancer analysis of the correlation between OBSCN expression and TIME

In light of the potential immunoregulatory impact of OBSCN expression deficiency on the TIME of BLCA, as indicated by the enrichment analysis, we initially conducted a pan-cancer investigation into the correlation between OBSCN expression and NEO, TMB, and MSI, which are emerging biomarkers associated with the efficacy of immunotherapy. The results demonstrated that the expression levels of OBSCN were significantly negatively associated with NEO and TMB in several types of tumors, including BLCA (Fig. 4A-B). In the context of MSI, no significant difference was identified in BLCA (Fig. 4C). To further analyze the relationship between OBSCN expression and BLCA mutations, we utilized the sequencing mutation data from the TCGA database. Our findings indicated that TP53, TTN, and other genes exhibited higher mutation frequencies in BLCA with low OBSCN expression (Supplementary Fig. 4).

Fig. 4
figure 4

Correlation between OBSCN expression and TIME. (A–C). The relationship between OBSCN expression and NEO, TMB, and MSI in pan-cancer analysis; (D). Correlation between OBSCN expression and essential factors of TIME in pan-cancer analysis

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The TIME is primarily comprised of cancer cells, immune cells, tumor-related fibroblasts, extracellular matrix, various growth factors, inflammatory factors, and special physicochemical characteristics [20]. Cancer cell-intrinsic features, including genetic aberrations, signaling pathway deregulation and altered metabolism, play a key role in orchestrating the composition and functional state of the immune landscape and influence the therapeutic response of immunotherapy [20, 21]. Our findings revealed that unlike other types of cancers, the expression of OBSCN was significantly negatively correlated with important parameters in the TIME of BLCA, such as TMEscoreB, TMEscoreA, Pan_F_TBRs, nucleotide_excision_repair, mismatch repair, immune checkpoint, EMT3, EMT2, DNA replication, DNA damage response, CD8+T effector, base excision repair, and antigen processing machinery, indicating the essential role of OBSCN expression deficiency in the immunoregulatory of BLCA (Fig. 4D).

Predictive value of OBSCN expression for the efficacy of ICIs in BLCA

According to the results of the enrichment and TIME analysis, it can be postulated that OBSCN expression may affect the efficacy of PD-L1/PD-1 ICIs. By analyzing the IMVigor210 cohort, we unexpectedly found that BLCA patients with low OBSCN expression exhibited a higher response to PD-1/PD-L1 ICIs and a better OS (Fig. 5A-B). Concurrently, the molecular subtype distribution of BLCA patients with high or low OBSCN expression exhibited notable discrepancies (Fig. 5C). A greater proportion of BLCA samples with low OBSCN expression exhibited genomically unstable and TCGA type II characteristics, and higher CD8+T effector scores, findings that align with previous TIME analysis (Fig. 5C-D). To further substantiate this conclusion, we conducted a verification analysis in the BLCA immunotherapy cohort GSE176307. Similar findings demonstrated that BLCA patients with low OBSCN expression exhibited a superior response to ICIs and a significantly improved OS (Fig. 5E-F).

Fig. 5
figure 5

OBSCN expression predicted the efficacy of PD-L1 ICIs in BLCA. (A). The relationship between OBSCN expression and OS in the IMVigor210 cohort; (B). Correlation between OBSCN expression and clinical response in the IMVigor210 cohort; (C). Correlation between OBSCN expression and molecular subtypes in the IMVigor210 cohort; (D). Correlation between OBSCN expression and CD8+T effector score in IMVigor210 cohort; (E). The relationship between OBSCN expression and OS in the GSE176307 cohort; (A) Correlation between OBSCN expression and clinical response in the GSE176307 cohort

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Potential mechanisms of OBSCN expression deficiency in enhancing the efficacy of ICIs

A preliminary analysis of the TIME and PD-1/PD-L1 therapeutic reactivity in the BLCA patients indicated that OBSCN expression deficiency may induce the BLCA to recruit more CD8+T cells. The possible reason for worse OS in BLCA patients with low OBSCN expression may be the functional exhaustion of CD8+T cells owing to the interaction of PD-L1/PD-1 axis between BLCA and CD8+T cells, which can be reactivated by using PD-1/PD-L1 ICIs. To identify this hypothesis, we initially performed a pan-cancer analysis of the correlation between OBSCN expression and chemokines. The results revealed a significantly negative correlation between OBSCN expression and a variety of chemokines that can recruit CD8+T cells in BLCA, such as CXCL10 and CXCL11 (Fig. 6A). The significant negative correlation between OBSCN and CD8+T cells was further identified through TIMER 2.0 database, which includes multiple immune cell infiltration analysis algorithms, such as TIMER, EPIC, MCP-COUNTER, Cibersort, and Xcell (Fig. 6B). Furthermore, we conducted IHC staining for CD8+T cells and PD-L1 with the Shuguang cohort, which confirmed that theinfiltration of CD8+T cells and expression of PD-L1 in BLCA increased significantly with low OBSCN expression (Fig. 6C-F). WB analysis further identified that PD-L1 expression was significantly enhanced in BLCA cells with OBSCN knockdown (Fig. 6G).

Fig. 6
figure 6

Potential mechanism of OBSCN expression deficiency in the immune evasion of BLCA. (A) Heatmap representing the correlation between OBSCN expression and chemokines in pan-cancer analysis. (B) Heatmap representing the correlation between OBSCN expression and infiltration of CD8+T cells in pan-cancer analysis from TIMER2.0 website. (C) IHC staining of CD8A on the TMAs of Shuguang cohort. (D) IHC analysis of the CD8+T cells between OBSCN low and high group in Shuguang TMAs (n = 80). (E) IHC staining of PD-L1 on the TMAs of Shuguang cohort. (F) IHC analysis of the PD-L1 expression in BLCA between OBSCN low and high group in Shuguang TMAs (n = 80). (G) WB test was used to evaluate the expression of PD-L1 after knocking down OBSCN in BLCA cell lines UMUC3 and 5637

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Discussion

This study revealed that OBSCN expression was deficient in the majority of tumors, including BLCA. Through survival analysis in multiple public databases and our own cohort, it was found that BLCA patients with low OBSCN expression exhibited shorter OS. In vitro cell experiments confirmed that the invasion and migration of BLCA with OBSCN knockdown were significantly enhanced, as well as the ability of EMT. The results of pathway enrichment and TIME analysis, in vitro experiments and self-owned validation cohort demonstrated that OBSCN expression deficiency exhibited significantly increased PD-L1 expression and CD8+ T cell infiltration. Two public immunotherapy cohorts demonstrated that BLCA patients with low OBSCN expression exhibited a superior response to PD-L1 ICIs.

There was mounting evidence that OBSCN was associated with the susceptibility and progression of several types of tumors [9,10,11, 22]. Among more than 13,000 candidate genes, OBSCN and TP53 are the only commonly mutated genes in breast and colorectal cancer [9]. In pancreatic cancer, Tuntithavornwat et al. demonstrated that pancreatic cancer cells with OBSCN expression loss induced cytoskeletal remodeling through downregulation of RhoA activity, thus accelerating cell migration, enhancing motility, and EMT [11]. In vivo studies have demonstrated that loss of OBSCN function was associated with increased tumor growth and metastasis [11]. A comparable phenomenon has been documented in breast cancer [10]. In the context of carcinogenic KRAS signaling, the loss of OBSCN expression in normal breast epithelial cells has been observed to result in a number of adverse effects, including increased cell survival and chemotherapy resistance, cytoskeleton changes, enhanced cell migration and invasion, and promotion of metastasis [10]. Activation of OBSCN-AS1 by CRISPR in triple-negative breast cancer can effectively restore the expression of OBSCN, inhibit the migration, invasion, and metastasis of tumor cells in vitro, and may become a therapeutic target for triple-negative breast cancer [22]. Functional and pathway enrichment analysis in our study demonstrated that OBSCN expression deficiency was significantly enriched in EMT, and the KRAS signaling pathway was up-regulated. In vitro cell experiments further demonstrated that BLCA with OBSCN expression deficiency exhibited enhanced invasion, migration, and EMT.

This study revealed that while BLCA patients with low OBSCN expression exhibited a worse OS, they exhibited a superior response to PD-L1 ICIs. Through pan-cancer TIME analysis and self-contained cohort validation, we found that, in contrast to other cancers, OBSCN expression in BLCA was significantly negatively correlated with multiple immune-related factors, including CD8+T cell infiltration, immune checkpoint expression, EMT score, and TME score. Pan-cancer analysis of the relationship between OBSCN expression and chemokines revealed a significant negative correlation between OBSCN expression and a variety of chemokines, including CXCL10, CXCL11, CCL11, CCL21, and CCL23. The recruitment of CD8+ T cells by chemokines has been demonstrated in multiple studies to occur within the tumor microenvironment [23,24,25,26]. For instance, Kumagai et al. demonstrated that RHOA wild-type gastric cancer cells secrete greater quantities of CXCL10 and CXCL11, thereby recruiting more CD8+T cells [23]. RHOA mutated gastric cancer cells reduced the secretion of CXCL10 and CXCL11, and stimulated the synthesis of fatty acids through the PI3K-AKT-mTOR pathway, which in turn enhances the inhibition of regulatory T cells (Tregs) within the TIME, thereby inducing immunosuppression [23]. Qi et al. demonstrated that CCL11 was the most effective chemokine in improving immunogenicity, promoting specific CD8+T cell infiltration, and inducing tumor rejection [25]. The combination of CCL11 with the E6/E7 antigen as a therapeutic DNA vaccine has the potential to markedly enhance the therapeutic efficacy [25]. In human breast cancer, higher CCL21 expression levels are associated with increased infiltration of CD8+T cells and reduced long-term recurrence rates [27]. In ovarian cancer, CCL23, produced by macrophages, was involved in the formation of an immunosuppressive TIME in ovarian cancer by inducing the depletion of T cell phenotypes [28]. The specific chemokine expression induced by the OBSCN expression deficiency in BLCA warranted further confirmation in vitro and in vivo.

This study is subject to several shortcomings. Firstly, due to the limited number of patients in the self-verified cohort, it is necessary to expand the cohort to verify the predictive value of OBSCN expression loss on the OS and ICIs response of BLCA patients. Secondly, further in vivo experimentation is required to confirm that OBSCN expression deficiency may promote epithelial mesenchymal transformation and metastasis of BLCA. Finally, through pan-cancer bioinformatics analysis and self-contained cohort analysis, we discovered that BLCA with OBSCN expression deficiency can up-regulate the expression of PD-L1 and secrete more chemokines that recruit CD8+ T cells. However, the underlying mechanism remains to be confirmed.

Conclusions

Our study confirmed that BLCA patients with low OBSCN expression had a worse prognosis, yet a superior response to PD-L1 ICIs. This study provided a reference for individualized and precise treatment of BLCA patients.

Data availability

All data generated that are relevant to the results presented in this article are included in this article. The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/supplementary material.

Abbreviations

BLCA:

Bladder cancer

CCLE:

Cancer Cell Line Encyclopedia

EMT:

Epithelial-mesenchymal transition

FDA:

Food and Drug Administration

GEO:

Gene Expression Omnibus

GSEA:

Gene set enrichment analysis

GSVA:

Gene set variation analysis

ICIs:

Immune checkpoint inhibitors

IHC:

Immunohistochemistry

MIBC:

Muscle-invasive bladder cancer

MSI:

Microsatellite instability

NMIBC:

Non-muscle invasive bladder cancer

OS:

Overall survival

TCGA:

The Cancer Genome Atlas

TIME:

Tumor immune microenvironment

TMB:

Tumor mutation burden

tSNE:

t-distributed stochastic neighborhood embedding

WB:

Western blot assay

References

  1. Lopez-Beltran A, Cookson MS, Guercio BJ, Cheng L. Advances in diagnosis and treatment of bladder cancer. BMJ. 2024;384:e076743. https://doi.org/10.1136/bmj-2023-076743.

    Article  CAS  PubMed  Google Scholar 

  2. Dyrskjøt L, Hansel DE, Efstathiou JA, Knowles MA, Galsky MD, Teoh J, Theodorescu D. Bladder cancer. Nat Rev Dis Primers. 2023;9:58. https://doi.org/10.1038/s41572-023-00468-9.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Necchi A, Madison R, Raggi D, Jacob JM, Bratslavsky G, Shapiro O, Elvin JA, Vergilio JA, Killian JK, Ngo N, et al. Comprehensive assessment of Immuno-oncology biomarkers in adenocarcinoma, urothelial carcinoma, and Squamous-cell carcinoma of the bladder. Eur Urol. 2020;77:548–56. https://doi.org/10.1016/j.eururo.2020.01.003.

    Article  CAS  PubMed  Google Scholar 

  4. Bilé-Silva A, Lopez-Beltran A, Blanca A, Lopez-Rios F, Gómez-Gómez E, Cimadamore A, Montironi R, Vau N, Cheng L. Clinical utility of checkpoint inhibitors against metastatic bladder cancer: overcoming challenges to find a way forward. Expert Opin Biol Ther. 2023;23:407–18. https://doi.org/10.1080/14712598.2023.2201371.

    Article  CAS  PubMed  Google Scholar 

  5. Guardia T, Eason M, Kontrogianni-Konstantopoulos A, Obscurin. A multitasking giant in the fight against cancer. Biochim Biophys Acta Rev Cancer. 2021;1876:188567. https://doi.org/10.1016/j.bbcan.2021.188567.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Cabrera-Serrano M, Caccavelli L, Savarese M, Vihola A, Jokela M, Johari M, Capiod T, Madrange M, Bugiardini E, Brady S, et al. Bi-allelic loss-of-function OBSCN variants predispose individuals to severe recurrent rhabdomyolysis. Brain. 2022;145:3985–98. https://doi.org/10.1093/brain/awab484.

    Article  PubMed  Google Scholar 

  7. Grogan A, Coleman A, Joca H, Granzier H, Russel MW, Ward CW, Kontrogianni-Konstantopoulos A. Deletion of obscurin Immunoglobulin domains Ig58/59 leads to age-dependent cardiac remodeling and arrhythmia. Basic Res Cardiol. 2020;115:60. https://doi.org/10.1007/s00395-020-00818-8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Chen P, Xiao Y, Wang Y, Zheng Z, Chen L, Yang X, Li J, Wu W, Zhang S. Intracellular calcium current disorder and disease phenotype in OBSCN mutant iPSC-based cardiomyocytes in arrhythmogenic right ventricular cardiomyopathy. Theranostics. 2020;10:11215–29. https://doi.org/10.7150/thno.45172.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Sjöblom T, Jones S, Wood LD, Parsons DW, Lin J, Barber TD, Mandelker D, Leary RJ, Ptak J, Silliman N, et al. The consensus coding sequences of human breast and colorectal cancers. Science. 2006;314:268–74. https://doi.org/10.1126/science.1133427.

    Article  CAS  PubMed  Google Scholar 

  10. Shriver M, Stroka KM, Vitolo MI, Martin S, Huso DL, Konstantopoulos K, Kontrogianni-Konstantopoulos A. Loss of giant obscurins from breast epithelium promotes epithelial-to-mesenchymal transition, tumorigenicity and metastasis. Oncogene. 2015;34:4248–59. https://doi.org/10.1038/onc.2014.358.

    Article  CAS  PubMed  Google Scholar 

  11. Tuntithavornwat S, Shea DJ, Wong BS, Guardia T, Lee SJ, Yankaskas CL, Zheng L, Kontrogianni-Konstantopoulos A, Konstantopoulos K. Giant obscurin regulates migration and metastasis via RhoA-dependent cytoskeletal remodeling in pancreatic cancer. Cancer Lett. 2022;526:155–67. https://doi.org/10.1016/j.canlet.2021.11.016.

    Article  CAS  PubMed  Google Scholar 

  12. Gao J, Aksoy BA, Dogrusoz U, Dresdner G, Gross B, Sumer SO, Sun Y, Jacobsen A, Sinha R, Larsson E, et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci Signal. 2013;6:pl1. https://doi.org/10.1126/scisignal.2004088.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Wang H, Agulnik J, Kasymjanova G, Wang A, Jiménez P, Cohen V, Small D, Pepe C, Sakr L, Fiset PO, et al. Cytology cell blocks are suitable for immunohistochemical testing for PD-L1 in lung cancer. Ann Oncol. 2018;29:1417–22. https://doi.org/10.1093/annonc/mdy126.

    Article  CAS  PubMed  Google Scholar 

  14. Hänzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinf. 2013;14. https://doi.org/10.1186/1471-2105-14-7.

  15. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 2005;102:15545–50. https://doi.org/10.1073/pnas.0506580102.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Bonneville R, Krook MA, Kautto EA, Miya J, Wing MR, Chen HZ, Reeser JW, Yu L, Roychowdhury S. Landscape of microsatellite instability across 39 cancer types. JCO Precis Oncol. 2017;2017. https://doi.org/10.1200/po.17.00073.

  17. Thorsson V, Gibbs DL, Brown SD, Wolf D, Bortone DS, Ou Yang TH, Porta-Pardo E, Gao GF, Plaisier CL, Eddy JA, et al. The immune landscape of cancer. Immunity. 2018;48:812–e830814. https://doi.org/10.1016/j.immuni.2018.03.023.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Ru B, Wong CN, Tong Y, Zhong JY, Zhong SSW, Wu WC, Chu KC, Wong CY, Lau CY, Chen I, et al. TISIDB: an integrated repository portal for tumor-immune system interactions. Bioinformatics. 2019;35:4200–2. https://doi.org/10.1093/bioinformatics/btz210.

    Article  CAS  PubMed  Google Scholar 

  19. Li T, Fu J, Zeng Z, Cohen D, Li J, Chen Q, Li B, Liu XS. TIMER2.0 for analysis of tumor-infiltrating immune cells. Nucleic Acids Res. 2020;48:W509–14. https://doi.org/10.1093/nar/gkaa407.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Mellman I, Chen DS, Powles T, Turley SJ. The cancer-immunity cycle: indication, genotype, and immunotype. Immunity. 2023;56:2188–205. https://doi.org/10.1016/j.immuni.2023.09.011.

    Article  CAS  PubMed  Google Scholar 

  21. van Weverwijk A, de Visser KE. Mechanisms driving the immunoregulatory function of cancer cells. Nat Rev Cancer. 2023;23:193–215. https://doi.org/10.1038/s41568-022-00544-4.

    Article  CAS  PubMed  Google Scholar 

  22. Guardia T, Zhang Y, Thompson KN, Lee SJ, Martin SS, Konstantopoulos K, Kontrogianni-Konstantopoulos A. OBSCN restoration via OBSCN-AS1 long-noncoding RNA CRISPR-targeting suppresses metastasis in triple-negative breast cancer. Proc Natl Acad Sci U S A. 2023;120:e2215553120. https://doi.org/10.1073/pnas.2215553120.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Kumagai S, Togashi Y, Sakai C, Kawazoe A, Kawazu M, Ueno T, Sato E, Kuwata T, Kinoshita T, Yamamoto M, et al. An oncogenic alteration creates a microenvironment that promotes tumor progression by conferring a metabolic advantage to regulatory T cells. Immunity. 2020;53:187–e203188. https://doi.org/10.1016/j.immuni.2020.06.016.

    Article  CAS  PubMed  Google Scholar 

  24. Limagne E, Nuttin L, Thibaudin M, Jacquin E, Aucagne R, Bon M, Revy S, Barnestein R, Ballot E, Truntzer C, et al. MEK Inhibition overcomes chemoimmunotherapy resistance by inducing CXCL10 in cancer cells. Cancer Cell. 2022;40:136–e152112. https://doi.org/10.1016/j.ccell.2021.12.009.

    Article  CAS  PubMed  Google Scholar 

  25. Qi H, Sun Z, Gao T, Yao Y, Wang Y, Li W, Wang X, Wang X, Liu D, Jiang JD. Genetic fusion of CCL11 to antigens enhances antigenicity in nucleic acid vaccines and eradicates tumor mass through optimizing T-cell response. Mol Cancer. 2024;23:46. https://doi.org/10.1186/s12943-024-01958-4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Ozga AJ, Chow MT, Lopes ME, Servis RL, Di Pilato M, Dehio P, Lian J, Mempel TR, Luster AD. CXCL10 chemokine regulates heterogeneity of the CD8(+) T cell response and viral set point during chronic infection. Immunity. 2022;55:82–e9788. https://doi.org/10.1016/j.immuni.2021.11.002.

    Article  CAS  PubMed  Google Scholar 

  27. Li K, Li T, Feng Z, Huang M, Wei L, Yan Z, Long M, Hu Q, Wang J, Liu S, et al. CD8(+) T cell immunity blocks the metastasis of carcinogen-exposed breast cancer. Sci Adv. 2021;7. https://doi.org/10.1126/sciadv.abd8936.

  28. Kamat K, Krishnan V, Dorigo O. Macrophage-derived CCL23 upregulates expression of T-cell exhaustion markers in ovarian cancer. Br J Cancer. 2022;127:1026–33. https://doi.org/10.1038/s41416-022-01887-3.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

Not applicable.

Funding

This study was funded by grants from National Natural Science Foundation of China (82172920, 82174122), National Natural Science Foundation for Young Scholars of China (82303746), 2024 Shanghai Eastern Elite Talents Program, Top-Tier Project (BJWS2024031), the Health Discipline Leader Program of Shanghai Health Commission(2022XD011), and the Promotion and Optimization Management of Diagnosis and treatment technology in Municipal Hospitals of Shanghai Shenkang Hospital Development Center (SHDC12023113).

Author information

Author notes

  1. Tao Wang, Tuanjie Guo, Juanjuan Sun and Xinyue Zang have contributed equally to this work and share last authorship.

Authors and Affiliations

  1. Department of Urology, Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China

    Tao Wang, Xinyue Zang, Sicong Ma, Xinyu Zhai, Chuanmin Chu, Dongliang Xu & Mingyue Tan

  2. Surgical Institute of Integrative Medicine, Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China

    Tao Wang, Xinyue Zang, Sicong Ma, Xinyu Zhai, Chuanmin Chu, Dongliang Xu & Mingyue Tan

  3. Surgical Institute, Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China

    Tao Wang, Xinyue Zang, Sicong Ma, Xinyu Zhai, Chuanmin Chu, Dongliang Xu & Mingyue Tan

  4. Department of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China

    Tuanjie Guo, Guihua Chen & Xiang Wang

  5. Department of Pathology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China

    Juanjuan Sun

  6. Department of Pathology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China

    Lei Dong & Chaofu Wang

  7. Department of Urology, Shanghai Geriatric Medical Center, Shanghai, China

    Jian Zhang

  8. Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China

    Siteng Chen

Authors
  1. Tao Wang
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Contributions

Conceptualization, D.X., M.T., X.W., and T.W.; methodology, T.W., T.G., J.S. and X.Z.; software, L.D., and J.Z.; validation, T.W., T.G., J.S. and X.Z.; formal analysis, J.S., and C.W.; investigation, S.C. G.C. and S.M.; resources, X.Z. and C.C.; data curation, T.W., and X.W.; writing-original draft preparation, T.W., T.G., J.S. and X.Z.; writing-review and editing, D.X., M.T., X.W., and C.W.; visualization, T.W. and T.G.; supervision, X.W., and D.X.; project administration, M.T.; funding acquisition, T.W., X.W. M.T. and D.X. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Xiang Wang, Dongliang Xu or Mingyue Tan.

Ethics declarations

Ethics approval and consent to participate

This study was approved by the Ethics Committee of Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine. Written informed consent was obtained from each of the patients or their guardians. The study followed the principles of the Helsinki Declaration.

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All authors provide their consent for publication of the manuscript.

Competing interests

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Wang, T., Guo, T., Sun, J. et al. Loss of OBSCN expression promotes bladder cancer progression but enhances the efficacy of PD-L1 inhibitors. Cell Biosci 15, 40 (2025). https://doi.org/10.1186/s13578-025-01379-w

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Keywords

Cell & Bioscience

ISSN: 2045-3701

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