Pan-cancer analysis and verification revealed the clinical significance of SLC9A3R1 in bladder cancer cohort

Pan-cancer analysis and verification revealed the clinical significance of SLC9A3R1 in bladder cancer cohort | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Pan-cancer analysis and verification revealed the clinical significance of SLC9A3R1 in bladder cancer cohort Yimin Wang, Xiaoxiang Wu, Chong Shu, Xin Chen, Tong Bao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9211687/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Emerging evidence indicates that SLC9A3R1 participates in oncogenesis, yet its prognostic relevance and immune-regulatory circuitry remain largely undefined. Methods A harmonized pan-cancer transcriptomic compendium was retrieved from public repositories, and the clinical cohort was employed for bladder-cancer validation of expression patterns and biological function. Cox regression models were constructed to quantify the prognostic impact of SLC9A3R1, while immunohistochemistry on paired tumor and adjacent urothelium was performed to corroborate protein abundance and clinicopathological associations. Oncogenic and immunological roles were subsequently interrogated using R v4.2.1 and associated bioinformatics packages. Results Pan-cancer profiling demonstrated widespread SLC9A3R1 dysregulation that correlated with patient outcome across malignancies. Moreover, its expression aligned with genomic-heterogeneity indices and stemness scores in multiple tumor entities. Immunohistochemistry confirmed elevated SLC9A3R1 protein in bladder tumors, and, within the same cohort, transcript abundance paralleled tumor-associated fibroblast distribution.SLC9A3R1 up-regulation sustains stem-like traits, migration, chemoresistance and immune escape, driving bladder-cancer aggressiveness. Conclusion SLC9A3R1 constitutes a multi-dimensional prognostic biomarker that integrates tumor progression, immune contexture and patient survival, thereby offering a rational target for precision oncology. SLC9A3R1 Pan-cancer analysis Prognostic biomarker Immune cell Bladder cancer Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Bladder cancer (BLCA) is a malignant tumor of the urinary system with high morbidity and mortality, and is reported to be the tenth most common cancer[ 1 – 3 ]with more than 430,000 new cases each year. Advances in conventional surgery, chemotherapy and radiotherapy, as well as the advent of targeted therapies and immunotherapies, have brought new hope to cancer patients[ 4 , 5 ]. However, despite these advances, significant challenges remain. Despite these advances, formidable obstacles persist: precision agents remain constrained by inter-tumor hetero[ 3 ]geneity and inter-individual variability[ 6 , 7 ], and the paucity of effective regimens for invasive or metastatic urothelial carcinoma represents a pressing clinical unmet need. Consequently, the identification of innovative molecular targets capable of restraining local invasion and systemic dissemination of bladder cancer is urgently warranted. SLC9A3R1 (solute carrier family 9 member A3 regulator 1), located at 17q25.1 and comprising six exons, encodes a scaffolding protein with dual PDZ domains and a C-terminal ERM-binding motif[ 8 , 9 ]. This multifunctional adaptor modulates ion-channel localization, receptor trafficking and signal-transduction cascades, and emerging evidence implicates its dysregulation in malignant progression[ 9 – 11 ]. In this study, we combined pan-cancer analysis with experimental validation of BLCA to comprehensively explore the oncogenic and immunologic roles of SLC9A3R1. Collectively, our findings establish SLC9A3R1 as a pivotal determinant of tumor aggressiveness and immune evasion in urothelial carcinoma, thereby providing a rational framework for future targeted therapeutic development. 2. Materials and methods 2.1. Data collection, processing, and analysis of variance and prognosis As similar to previous studies[ 12 , 13 ], we downloaded an integrated pan-cancer transcriptome and clinical cohort from the UCSC Xena repository. To contextualise tumor-specific expression, we appended matched normal-tissue data for SLC9A3R1 retrieved from the GTEx portal ( https://gtexportal.org ). In addition, we obtained prognostic information for 34 tumor types, including progression-free interval (PFI), disease-specific survival (DSS), overall survival (OS), and disease-free interval (DFI) [ 14 ]. We conducted prognostic analyses utilizing the Cox proportional hazards regression model, which was executed with the "survival" package. Statistical significance was evaluated using the log-rank test[ 15 ]. 2.2. Genomic heterogeneity and tumor stemness metrics Single-nucleotide-variant (SNV) profiles for each cancer type were retrieved from the Genomic Data Commons (GDC) repository, and tumor mutational burden (TMB) was subsequently estimated with the R package "maftools" [ 14 ]. The association between microsatellite-instability (MSI) scores and SLC9A3R1 transcript levels was evaluated following the analytical framework reported earlier. Moreover, stemness indices (DNAss and RNAss) together with matched gene-expression matrices were compiled from prior work to quantify the relationship between DNA-based and RNA-based stemness signatures across tumor lineages[ 16 , 17 ]. 2.3. Immune landscapes and RNA-modification signatures Transcript-level quantifications for 150 immunomodulators were compiled across TCGA tumor types, and their pairwise relationships with SLC9A3R1 were systematically interrogated with Spearman correlation. In parallel, the same analytical workflow was applied to 60 immune-checkpoint genes[ 18 ]. Furthermore, expression matrices for 44 RNA-modifying enzymes were extracted, and their co-expression patterns with SLC9A3R1 were evaluated across malignancies[ 12 ]. 2.4. Somatic mutation landscape of SLC9A3R1 Somatic mutation calls across the entire TCGA cohort were consolidated, and the R package "maftools" was employed to annotate affected protein domains. Cancer-specific mutation frequencies of SLC9A3R1 were subsequently extracted from the cBioPortal resource. 2.5. Functional enrichment profiling To dissect the biological roles and signaling cascades linked to SLC9A3R1-associated genes, we conducted Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) using the "clusterProfiler" R package[ 19 ]. Briefly, SLC9A3R1-correlated genes (|r| ≥ 0.3 and FDR < 0.05) were first identified across all tumor types via weighted gene co-expression network analysis (WGCNA). These gene sets were then subjected to over-representation tests against the org.Hs.eg.db (v3.17) background for GO and KEGG terms, with Benjamini–Hochberg correction applied. Visualization of enriched terms was performed with the enrichplot and ggplot2 packages, focusing on the top ten pathways ranked by normalized enrichment score (NES). 2.6. Immunological value analysis Using the CIBERSORT deconvolution algorithm, we estimated the relative abundance of 22 tumor-infiltrating immune cell subsets in the TCGA-BLCA dataset and subsequently correlated these estimates with SLC9A3R1 transcript levels in urothelial carcinoma patients [ 20 ]. To ensure robustness, proportions were inferred from log2-transformed TPM matrices with 1,000 permutations and a CIBERSORT p-value < 0.05 set as the quality threshold. Additionally, single-sample gene-set enrichment analysis (ssGSEA) was executed with the GSVA R package (v1.44.0) to generate immune-signature scores across individual tumors. Resulting enrichment scores were z-standardized per cancer type, and Spearman correlations with SLC9A3R1 expression were calculated; statistical significance was declared at FDR < 0.05. Immune cell infiltration patterns were visualized with the ggpubr and ComplexHeatmap packages. 2.7. Immunohistochemistry (IHC) Analysis A commercially available bladder cancer tissue microarray (Cat. ZL-BlaU961; Shanghai Zhuoli Biotechnology Co., Ltd., China) was used to evaluate SLC9A3R1 expression. The TMA contained 36 primary bladder tumor tissues and 10 matched adjacent non-tumor bladder tissues. IHC staining was performed using a standard immunoperoxidase protocol. Following deparaffinization, rehydration and heat-induced epitope retrieval, endogenous peroxidase activity was quenched with 3% H₂O₂. Sections were then incubated overnight at 4°C with rabbit anti-SLC9A3R1 polyclonal antibody (Affinity, USA). Immune complexes were visualized with an HRP-polymer detection system (ZSGB-BIO, China) and 3,3'-diaminobenzidine chromogen according to the manufacturer's protocol. Staining intensity (0–3). Cases were dichotomized into low (score ≤ 3) and high (score > 3) SLC9A3R1 expression groups for subsequent statistical analyses. 2.8. Cell culture and transient transfection Human bladder-cancer cell T24 and UM-UC-3 were cultured under standard conditions in a humidified incubator containing 5% CO 2 at 37°C. To investigate the biological role of SLC9A3R1 in bladder cancer, cells were transiently transfected with three independent small interfering RNAs targeting SLC9A3R1 (si-SLC9A3R1-1, si-SLC9A3R1-2 and si-SLC9A3R1-3) or a non-targeting negative control (si-NC) using a commercially available transfection reagent according to the manufacturer’s instructions. Transfection efficiency was assessed by quantitative real-time PCR (qRT-PCR), and the siRNA exhibiting the highest knockdown efficiency was selected for subsequent experiments. 2.9. CCK-8 assay Cell proliferation was evaluated using the Cell Counting Kit-8 (CCK-8) assay. Briefly, transfected T24 and UM-UC-3 cells were seeded into 96-well plates at an appropriate density and cultured for the indicated time periods. At each time point, CCK-8 reagent was added to each well and incubated according to the manufacturer’s instructions. The absorbance at 450 nm was measured using a microplate reader to determine cell viability. 2.10. Transwell migration assay and wound-healing assay Cell migratory ability was assessed using Transwell migration and wound-healing assays. For the Transwell migration assay, transfected cells suspended in serum-free medium were added to the upper chambers, while medium supplemented with fetal bovine serum was placed in the lower chambers as a chemoattractant. After incubation, cells that had migrated to the lower surface of the membrane were fixed, stained and counted under a microscope.For the wound-healing assay, transfected cells were seeded into 6-well plates and cultured until reaching near confluence. A linear scratch was created using a sterile pipette tip, and detached cells were removed by washing with phosphate-buffered saline. Images were captured at the indicated time points, and the wound area was measured to evaluate migratory capacity. 2.11. EdU incorporation assay Cell proliferative activity was further assessed using the 5-ethynyl-2'-deoxyuridine (EdU) incorporation assay. Transfected cells were seeded into appropriate culture plates and incubated with EdU reagent according to the manufacturer’s protocol. After fixation, permeabilization and fluorescent staining, images were acquired under a fluorescence microscope. The proportion of EdU-positive cells was quantified from randomly selected fields to evaluate DNA-synthesis activity. 2.12. Statistical analysis All statistical analyses were conducted using R software (version 4.2.1) with the aid of established bioinformatics and statistical packages. Group comparisons were performed using two-sided Student’s t-tests for normally distributed continuous variables and χ² tests for categorical variables, as appropriate. When parametric assumptions were not met, non-parametric tests (Mann–Whitney U test) were applied. A p-value < 0.05 was considered statistically significant. All tests were two-sided, and data visualization was carried out using standard graphical tools in R. 3. Results 3.1. Expression and prognostic analysis of SLC9A3R1 in pan-cancer In the TCGA pan-cancer dataset, SLC9A3R1 mRNA abundance exceeded that of matched normal tissues in 23 malignancies ( Fig. 1 A ) , whereas a significant reduction was detected in seven cancer entities ( Fig. 1 A ) . Meanwhile, according to OS analysis, poor prognosis in 3 tumor types was associated with high SLC9A3R1 expression, while better prognosis in 7 tumor types was associated with high SLC9A3R1 expression ( Fig. 1 B ) . Disease-specific survival (DSS) modelling demonstrated that elevated SLC9A3R1 transcript levels were associated with inferior outcomes in two cancer entities, whereas attenuated SLC9A3R1 expression correlated with reduced survival in five additional malignancies ( Fig. 1 C ) . In the TCGA-BLCA cohort, heightened SLC9A3R1 abundance predicted prolonged disease-free interval (DFI) ( Fig. 1 D ) . Progression-free interval (PFI) analysis further revealed that elevated SLC9A3R1 signaling conferred an adverse prognosis in three tumor lineages, yet was linked to favorable clinical trajectories in five others ( Fig. 1 E ) . 3.2. SLC9A3R1 expression in human cancers in relation to genomic heterogeneity and tumor stem cells To interrogate the interplay between SLC9A3R1 transcriptional activity and fundamental tumor biological traits across multiple cancer entities, we quantified its relationships with two genomic-heterogeneity indices and two stemness metrics. First, Spearman correlation revealed a significant association between SLC9A3R1 mRNA abundance and tumor mutational burden (TMB) in ten malignancies; five exhibited positive coefficients ( Fig. 2 A ) , Similarly, microsatellite-instability (MSI) scores were significantly linked to SLC9A3R1 expression in eleven cancer types, six of which displayed positive correlations ( Fig. 2 B ) . DNA-methylation-based stemness (DNAss) exhibited a positive correlation with SLC9A3R1 in nine tumor lineages and an inverse correlation in seven others ( Fig. 2 C ) . RNA-expression-based stemness (RNAss) was positively associated with SLC9A3R1 in seventeen cancer entities and negatively associated in three entities ( Fig. 2 D ) . 3.3. Landscape of SLC9A3R1 genetic alterations across human cancers Pan-cancer mutational profiles of SLC9A3R1 were interrogated through the TCGA data portal ( Fig. 3 A ) . Subsequently, pairwise correlations between SLC9A3R1 transcript abundance and representative genes defining five distinct immune pathways were computed, revealing an extensive regulatory impact of SLC9A3R1 on RNA-modifying processes. Indeed, SLC9A3R1 exhibited robust co-expression with m1A-, m5C- and m6A-associated enzymes across diverse malignancies ( Fig. 3 B ) . Specifically, in certain tumor types, such as DLBC, KIPAN and GBM, SLC9A3R1 expression was strongly positively correlated with m1A modification-related genes, while in BLCA, READ and KIRC, SLC9A3R1 expression showed strong positive correlation with m6A modification-related writer, reader and eraser genes, which suggests that SLC9A3R1 plays a role in the m6A regulatory network in these tumor types. In addition, in some other tumor types, such as THCA and COAD, SLC9A3R1 also showed significant negative correlation with m5C modification genes, suggesting that SLC9A3R1 affects the immune characteristics of these tumors by inhibiting m5C modification. This result reflects the heterogeneity of RNA modification regulatory mechanisms in different tumor types. 3.4. Immunological landscape of SLC9A3R1 in urothelial carcinoma Employing two complementary in-silico deconvolution frameworks— single-sample Gene Set Enrichment Analysis (ssGSEA) and CIBERSORT, we systematically quantified the relationship between SLC9A3R1 transcript abundance and immune-cell infiltration across multiple tumor entities. Based on ssGSEA, elevated SLC9A3R1 levels were significantly coupled with increased signatures of activated CD8 + T lymphocytes, natural killer (NK) cells and M1-polarised macrophages in several cancers, most notably KIRC, DLBC and BRCA (Spearman r > 0.4, FDR < 0.05) (Figure. 4A) . Conversely, a pronounced inverse association was observed between SLC9A3R1 expression and regulatory T-cell (Treg) signatures within these same malignancies. Focusing specifically on the TCGA-BLCA cohort, CIBERSORT-derived fractions revealed that heightened SLC9A3R1 transcription associated positively with both CD8 + T-cell infiltrationand cytotoxic T-lymphocyte activity, as well as with NK-cell and M1 macrophage infiltration, underscoring a potential role for SLC9A3R1 in recruiting or activating anti-tumor effector populations. In contrast, SLC9A3R1 abundance exhibited significant negative correlations with both Treg infiltration and M2 macrophage polarization, implying a suppressive influence on immunoregulatory cell subsets within the bladder-cancer microenvironment. Consistent with these findings, CIBERSORT-based analysis ( Fig. 4 B ) showed that higher SLC9A3R1 expression was significantly associated with an increase in the estimated proportions of CD8 + T cells and M1 macrophages in KIRC and DLBC ( p < 0.05). Meanwhile, in tumors with elevated SLC9A3R1 expression (e.g., STAD), the proportions of M2 macrophages and regulatory T cells were significantly lower, suggesting that high SLC9A3R1 expression is associated with an immune-activated tumor microenvironment, which promotes anti-tumor immune response by inhibiting the infiltration or polarization of immune-suppressive cells. In BLCA specimens, SLC9A3R1 transcript abundance exhibited a robust positive association with the estimated abundance of CD8⁺ cytotoxic T cells and activated natural killer cells, indicating a facilitative role in the accumulation of effector lymphocytes within the tumor bed. Conversely, CIBERSORT deconvolution revealed a pronounced inverse relationship between SLC9A3R1 levels and regulatory T-cell (Treg) infiltration. Moreover, heightened SLC9A3R1 expression was significantly negatively coupled with M2-polarised macrophages and monocyte fractions, implying that SLC9A3R1 attenuates the differentiation or recruitment of myeloid-derived suppressor populations, thereby reinforcing anti-tumor immunity. 3.5. Expression pattern and experimental validation of SLC9A3R1 in BLCA Capitalizing on prior advances in urothelial carcinoma biology, we interrogated the transcriptional landscape of SLC9A3R1 within the TCGA-BLCA cohort (n = 408). Relative to adjacent urothelium, tumor specimens displayed markedly elevated SLC9A3R1 mRNA levels ( Fig. 5 A and B) . To determine whether SLC9A3R1 abundance is modulated by demographic or disease-related variables, patients were stratified by age (≤ 70 years vs > 70 years), sex, histological subtype (papillary vs non-papillary), self-reported race (Asian, Black/African American, White) and pathological TNM stage (T1-T4, N0-N3, M0-M1) ( Fig. 5 C-I ) . Transcript abundance differed significantly according to histological subtype, racial background and nodal status (all p < 0.05). Specifically, non-papillary tumors exhibited higher SLC9A3R1 expression than papillary lesions ( p < 0.001), and progressive increments were noted from T1 to T4 and from N0 to N2 ( p < 0.05). Conversely, age, sex and M stage exerted no discernible influence on SLC9A3R1 abundance. Clinically, heightened SLC9A3R1 expression was associated with prolonged overall, disease-specific and progression-free survival ( Fig. 5 J-L ) .The clinical characteristics of patients with bladder cancer in the validation cohort are summarized in Table 1. To corroborate these findings at the protein level, we subjected paired bladder-cancer and adjacent normal specimens to immunohistochemistry. Consistent with the transcriptomic data, tumor tissues displayed markedly elevated SLC9A3R1 protein abundance as quantified by composite IHC scores ( Fig. 5 M and N) . Collectively, these observations indicate that SLC9A3R1 is transcriptionally and translationally up-regulated in bladder cancer and may serve as a favorable prognostic biomarker. 3.6. Functional landscape of SLC9A3R1 in bladder cancer Leveraging TCGA-BLCA transcriptomes, we dichotomized tumors (n = 204 per arm) into SLC9A3R1-high and SLC9A3R1-low cohorts and identified 167 differentially expressed genes (DEGs; |log₂FC| ≥ 1, FDR < 0.05), comprising 113 up-regulated and 54 down-regulated transcripts ( Fig. 6 A ) . We further identified the most significantly upregulated and downregulated genes, focusing on the top 20 in each category ( Fig. 6 B ) . Up-regulated genes were dominated by keratinization-associated entities ( KRT6C , KLK5/6/7/8/10/13 , SPRR2A/2D/2E ), whereas down-regulated transcripts were enriched for extracellular-matrix modulators ( CRTACI , VWC2L ) and metabolic enzymes ( CYP2C9 , ADH7 ). To elucidate the biological processes driven by the high expression of SLC9A3R1, we performed GO functional classification and KEGG pathway enrichment analysis (FDR < 0.05) on the differentially expressed genes. The results of GO functional annotation showed that enrichment analysis revealed that the expression profile of SLC9A3R1 was associated with biological processes such as epidermal development, skin development, epidermal cell differentiation, keratinocyte differentiation and biological processes such as keratinization ( Fig. 6 C ) . The significantly enriched cell component pathway was mainly concentrated in the keratinized envelope (FDR = 6×10 - ⁶) and intermediate filament cytoskeleton (FDR = 1×10 - ³) ( Fig. 6 D ) ; at the molecular function level ( Fig. 6 E ) , differential genes were significantly enriched in serine-type endopeptidase activity (FDR = 2×10 - ⁶) and serine hydrolase activity (FDR = 1×10 - ⁶). SLC9A3R1 overexpression drives malignant progression of bladder cancer by intensifying the keratinization program, remodeling the intermediate filament network, and disrupting exogenous metabolic pathways. KEGG pathway analysis showed ( Fig. 6 F ) that neuroactive ligand-receptor interaction (FDR = 0.04), metabolism of xenobiotics by cytochrome P450 (FDR = 0.02), and retinol metabolism (FDR = 0.02) were the most important metabolic pathways in bladder cancer. 3.7. Silencing SLC9A3R1 restrains proliferative and migratory phenotypes in bladder-cancer cells To experimentally validate the oncogenic role of SLC9A3R1 in bladder cancer, we performed loss-of-function assays in T24 and UM-UC-3 cells. Three independent siRNAs were designed against SLC9A3R1, and qRT-PCR analysis confirmed efficient knockdown in both cell lines, with si-SLC9A3R1-2 exhibiting the most pronounced silencing efficiency; this construct was therefore selected for subsequent functional experiments (Fig. 7 A). CCK-8 assays demonstrated that SLC9A3R1 depletion significantly attenuated cell proliferation over time compared with the si-NC group in both T24 and UM-UC-3 cells ( Fig. 7 B ) . Consistently, EdU incorporation analysis revealed a marked reduction in the proportion of EdU-positive cells following SLC9A3R1 knockdown, indicating impaired proliferative capacity (Fig. 7 E). We next assessed whether SLC9A3R1 modulates the migratory behavior of bladder-cancer cells. Transwell assays showed that the number of migrated cells was significantly decreased after SLC9A3R1 silencing in both T24 and UM-UC-3 cells ( Fig. 7 C). In parallel, wound-healing experiments demonstrated that depletion of SLC9A3R1 substantially delayed scratch closure, as reflected by a larger residual wound area at the indicated time points relative to control cells ( Fig. 7 D ) . Collectively, these findings indicate that SLC9A3R1 promotes the proliferative and migratory phenotypes of bladder-cancer cells in vitro. 4. Discussion While considerable advancements have been achieved, effective cancer management is still limited by multifactorial barriers, including the dynamic nature of tumor ecosystems and suboptimal efficacy of existing interventions. Clonal diversity within tumors has been widely implicated in the development of resistance following initial response to targeted and immunotherapeutic regimens[ 21 , 22 ]. Resistance to targeted drugs is frequently acquired through mechanisms such as genetic mutation or pathway remodeling[ 23 , 24 ]. Moreover, despite the paradigm shift brought about by immune checkpoint inhibitors in cancer treatment, durable responses are restricted to a subset of malignancies, and on-target off-tumor immune activation often leads to treatment-related inflammatory complications[ 25 , 26 ]. While significant challenges persist, the therapeutic landscape in oncology is evolving rapidly, fueled by breakthroughs in biotechnology and synergistic efforts across research domains.[ 27 , 28 ]. Emerging research has highlighted the pathological relevance of SLC9A3R1 across multiple disease states. Dysregulation of SLC9A3R1 expression or activity has been implicated in hypertension, acute renal failure, and breast carcinogenesis. Notably, the involvement of SLC9A3R1 in tumorigenesis is gaining strong experimental support, positioning it as a candidate modulator in cancer biology [ 29 , 30 ]. Initially characterized as a scaffolding protein for epithelial sodium-phosphate cotransporters, SLC9A3R1 has emerged in recent years as a key regulator of cell polarity, invasive potential, and tumor microenvironment remodeling across multiple solid malignancies. It is ubiquitously expressed in epithelial tissues and has been increasingly implicated in carcinogenesis. The functional role of SLC9A3R1 in tumors appears context-dependent, particularly influenced by its subcellular localization. Membrane-localized SLC9A3R1 exerts tumor-suppressive effects, whereas mislocalization or loss of plasma membrane expression is linked to oncogenic phenotypes.[ 31 ]. Conversely, cytoplasmic mislocalization or loss of membrane expression impairs the tumor-suppressive function of SLC9A3R1 and is linked to more aggressive tumor phenotypes[ 31 ]. The functional impact of SLC9A3R1 on tumor behavior has been proposed through multiple mechanisms in diverse malignancies, including melanoma[ 32 ], breast[ 33 – 38 ], reproductive[ 8 , 39 – 41 ], gastrointestinal[ 42 – 46 ], hepatocellular[ 47 – 49 ], lung[ 50 ], and central nervous system cancers[ 51 – 54 ]. The tumor-modulating effects of SLC9A3R1 are thought to involve multiple molecular pathways. These include impaired interaction with the epidermal growth factor receptor, leading to attenuated EGFR signaling[ 55 ], increased cytoplasmic accumulation of VEGF and VEGFR1[ 56 ]; dysregulation of matrix metalloproteinase expression[ 48 ]; activation of Wnt signaling[ 57 ]; and disruption of the actin cytoskeleton[ 38 ]. Recent evidence demonstrates that SLC9A3R1 overexpression in hepatocellular carcinoma cells promotes cellular proliferation and suppresses reactive oxygen species accumulation.[ 58 ]. Suppression of SLC9A3R1 expression inhibits tumor cell proliferation through downregulation of cyclin D1 and cyclin-dependent kinase 4, coupled with upregulation of p27, p53, and phosphatase and tensin homolog. This cell cycle arrest is mediated by increased reactive oxygen species production triggered by SLC9A3R1 loss[ 58 ] . With the development of immunotherapy, genomic features such as TMB and MSI have become important predictors of immune checkpoint inhibitor efficacy[ 59 , 60 ]. High TMB usually implies more neoantigen production and enhances tumor immunogenicity, thereby improving ICI response rate[ 61 ], while MSI, which stems from DNA mismatch repair defects, similarly enhances immune recognition[ 62 ]. Notably, our study suggests that SLC9A3R1 expression may be associated with these key genomic features. Low SLC9A3R1 levels may be associated with higher TMB or MSI status, thus indirectly affecting tumor sensitivity to immunotherapy. In addition, the potential association of SLC9A3R1 with DNAss and RNAss may also be involved in the regulation of immune response by maintaining genomic stability. Future studies should explore in depth the role of SLC9A3R1 in the regulation of TMB, MSI and overall genome stability and assess its potential as a predictive biomarker for immunotherapy. In the specific context of bladder cancer, the disease has a high recurrence rate, strong aggressiveness and complex molecular mechanisms[ 63 , 64 ]. It has been shown that low expression of SLC9A3R1 is associated with high-grade tumors, lymph node metastasis and poor prognosis, suggesting that it plays an oncogenic role by regulating cell adhesion and migration. Meanwhile, increasing evidence supports that SLC9A3R1 also has an anti-cancer function in bladder cancer, and its expression level is closely related to patient prognosis and tumor microenvironment characteristics[ 65 ]. We further propose that SLC9A3R1 may affect the anti-tumor immune response in bladder cancer by regulating the immune microenvironment. Specifically, we hypothesized that SLC9A3R1 could alleviate the immunosuppressive state by inhibiting macrophage polarization toward the M2 type and reducing immunosuppressive cytokine secretion. In addition, SLC9A3R1 may also modulate the expression of immune checkpoint molecules (e.g., PD-L1), which in turn affects the sensitivity of tumor cells to ICIs. However, in our validation cohort, despite the observed upregulation of SLC9A3R1 expression in tumor tissues, its correlation with PD-L1 expression or CD8 + T-cell infiltration did not reach statistical significance, which may be limited by the small sample size. Therefore, larger clinical cohorts and functional experiments are still needed to validate the interaction between SLC9A3R1 and immune cell infiltration and to clarify its specific role in immunotherapy response. While these findings offer new insights, they require validation in larger experimental cohorts and clinical datasets. In bladder cancer, reduced SLC9A3R1 expression correlates with adverse prognosis and features of immune evasion. Elucidating the role of SLC9A3R1 in immune modulation, genomic instability, and response to immunotherapy may uncover key regulatory mechanisms and inform the development of novel precision therapeutic strategies. 5. Conclusion Our pan-cancer analysis, validated in a bladder cancer cohort, underscores SLC9A3R1 as a clinically significant modulator of tumor progression and patient survival. Beyond its intrinsic regulation of cancer cell behavior, SLC9A3R1 contributes to the dynamic reshaping of the tumor immune microenvironment. Its expression correlates with patterns of immune cell infiltration, genomic instability, and core oncogenic signaling pathways, implicating SLC9A3R1 in the establishment of an immunosuppressive niche. These results support its potential as a prognostic biomarker and a novel target for immunotherapeutic strategies in bladder cancer, meriting dedicated translational exploration. Declarations Author Contributions WYM: Data interpretation, manuscript drafting, WXX: Literature research. SC,CX: Conceptual advice, technical support. BT: Study design. All authors reviewed and approved the final manuscript. Funding None. Data availability The datasets analyzed in this study are publicly available. Pan-cancer transcriptomic and clinical matrices were retrieved from TCGA via the UCSC Xena repository (https://xena.ucsc.edu/; original TCGA portal at https://portal.gdc.cancer.gov). Matched normal-tissue expression for SLC9A3R1 was obtained from GTEx (https://gtexportal.org). Public GEO cohorts were downloaded from the NCBI GEO database (https://www.ncbi.nlm.nih.gov/geo) (GSE154261, GSE70691, GSE69795, GSE52219, GSE48276, GSE48075, GSE39281, GSE37815, GSE31684, GSE19423, GSE13507).. Somatic mutation (SNV) calls were obtained from the Genomic Data Commons (GDC) (https://portal.gdc.cancer.gov), and SLC9A3R1 cancer-specific mutation frequencies were queried from cBioPortal (https://www.cbioportal.org). Processed data generated and used in this study are available via the BEST platform (https://rookieutopia.com). Raw data:https://www.jianguoyun.com/p/DboakWAQxPD6DRirgJYGIAA Conflict of interest The authors declare no competing interests. Ethical statment: Tissue microarray chips and clinical data were obtained with patients’ informed consent in accordance with the Helsinki Declaration and approved by the institutional ethics committee of Shanghai Zhuoli Biotechnology Co., Ltd. (No. LLS M-15-01). As all data analyzed were derived from public databases and no human subjects were directly recruited by our institution, additional ethical approval was not required. Informed consent: Consent to Participate and Consent to Publish were obtained from all participants. Clinical trial number: not applicable References Kamat AM, Hahn NM, Efstathiou JA, Lerner SP, Malmström PU, Choi W, Guo CC, Lotan Y, Kassouf W. Bladder cancer. Lancet (London England). 2016;388(10061):2796–810. Lenis AT, Lec PM, Chamie K, Mshs MD. Bladder Cancer: A Review. JAMA. 2020;324(19):1980–91. Feng DC, Li DX, Wu RC, Wang J, Xiao YH, Yoo KH, Ye X, Wei WR, Kong DP, Tuo ZT. Global burden and cross-country inequalities in urinary tumors from 1990 to 2021 and predicted incidence changes to 2046. Military Med Res. 2025;12(1):12. Theivendran S, Lazarev S, Yu C. Mesoporous silica/organosilica nanoparticles for cancer immunotherapy. Explor (Beijing China). 2023;3(3):20220086. Li AQ, Fang JH. Anti-angiogenic therapy enhances cancer immunotherapy: Mechanism and clinical application. Interdisciplinary Med 2024, 2(1). Chen T, Cheng L, Ma J, Yuan J, Pi C, Xiong L, Chen J, Liu H, Tang J, Zhong Y, et al. Molecular mechanisms of rapid-acting antidepressants: New perspectives for developing antidepressants. Pharmacol Res. 2023;194:106837. Letai A, Bhola P, Welm AL. Functional precision oncology: Testing tumors with drugs to identify vulnerabilities and novel combinations. Cancer Cell. 2022;40(1):26–35. Kreimann EL, Ratajska M, Kuzniacka A, Demacopulo B, Stukan M, Limon J. A novel splicing mutation in the SLC9A3R1 gene in tumors from ovarian cancer patients. Oncol Lett. 2015;10(6):3722–6. Liu H, Ma Y, He HW, Wang JP, Jiang JD, Shao RG. SLC9A3R1 stimulates autophagy via BECN1 stabilization in breast cancer cells. Autophagy. 2015;11(12):2323–34. Poplawski P, Alseekh S, Jankowska U, Skupien-Rabian B, Iwanicka-Nowicka R, Kossowska H, Fogtman A, Rybicka B, Bogusławska J, Adamiok-Ostrowska A, et al. Coordinated reprogramming of renal cancer transcriptome, metabolome and secretome associates with immune tumor infiltration. Cancer Cell Int. 2023;23(1):2. Xiong L, Luo Y, Yuan T, Lin W, Lin B, Wu C, Duan Y, Ou Y. Prognostic 7-SLC-Gene Signature Identified via Weighted Gene Co-Expression Network Analysis for Patients with Hepatocellular Carcinoma. Journal of oncology 2023, 2023:4364654. Feng D, Shi X, Zhu W, Zhang F, Li D, Han P, Wei Q, Yang L. A pan-cancer analysis of the oncogenic role of leucine zipper protein 2 in human cancer. Experimental Hematol Oncol. 2022;11(1):55. Shen W, Song Z, Zhong X, Huang M, Shen D, Gao P, Qian X, Wang M, He X, Wang T et al. Sangerbox: A comprehensive, interaction-friendly clinical bioinformatics analysis platform. iMeta 2022, 1(3):e36. Liu J, Lichtenberg T, Hoadley KA, Poisson LM, Lazar AJ, Cherniack AD, Kovatich AJ, Benz CC, Levine DA, Lee AV, et al. An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics. Cell. 2018;173(2):400–e416411. Andersen PK, Gill RD. Cox's Regression Model for Counting Processes: A Large Sample Study. Annals Stat 1982, 10(4). Barretina J, Caponigro G, Stransky N, Venkatesan K, Margolin AA, Kim S, Wilson CJ, Lehár J, Kryukov GV, Sonkin D, et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature. 2012;483(7391):603–7. Yu Z, Lu W, Su C, Lv Y, Ye Y, Guo B, Liu D, Yan H, Mi H, Li T, et al. Single-Cell RNA-seq Identification of the Cellular Molecular Characteristics of Sporadic Bilateral Clear Cell Renal Cell Carcinoma. Front Oncol. 2021;11:659251. Sun D, Wang J, Han Y, Dong X, Ge J, Zheng R, Shi X, Wang B, Li Z, Ren P, et al. TISCH: a comprehensive web resource enabling interactive single-cell transcriptome visualization of tumor microenvironment. Nucleic Acids Res. 2021;49(D1):D1420–30. Beroukhim R, Mermel CH, Porter D, Wei G, Raychaudhuri S, Donovan J, Barretina J, Boehm JS, Dobson J, Urashima M, et al. The landscape of somatic copy-number alteration across human cancers. Nature. 2010;463(7283):899–905. Newman AM, Liu CL, Green MR, Gentles AJ, Feng W, Xu Y, Hoang CD, Diehn M, Alizadeh AA. Robust enumeration of cell subsets from tissue expression profiles. Nat Methods. 2015;12(5):453–7. Du S, Yan J, Xue Y, Zhong Y, Dong Y. Adoptive cell therapy for cancer treatment. Explor (Beijing China). 2023;3(4):20210058. Wang XL, Xu HW, Liu NN. Oral Microbiota: A New Insight into Cancer Progression, Diagnosis and Treatment. Phenomics (Cham Switzerland). 2023;3(5):535–47. Liu H, Chen X, Jia Y, Chen H, Wang X, Liu G, Luo Y. Facing inevitable PARPis resistance: Mechanisms and therapeutic strategies for breast cancer treatment. Interdisciplinary Med 2023, 1(2). Chen Z, Vallega KA, Chen H, Zhou J, Ramalingam SS, Sun SY. The natural product berberine synergizes with osimertinib preferentially against MET-amplified osimertinib-resistant lung cancer via direct MET inhibition. Pharmacol Res. 2022;175:105998. Wong RS, Ong RJ, Lim JS. Immune checkpoint inhibitors in breast cancer: development, mechanisms of resistance and potential management strategies. Cancer drug Resist (Alhambra Calif). 2023;6(4):768–87. Cerella C, Dicato M, Diederich M. Enhancing personalized immune checkpoint therapy by immune archetyping and pharmacological targeting. Pharmacol Res. 2023;196:106914. Wang X, Mao K, Zhang X, Zhang Y, Yang YG, Sun T. Red blood cell derived nanocarrier drug delivery system: A promising strategy for tumor therapy. Interdisciplinary Med 2024, 2(3). Wang Z, Liu Z, Qu J, Sun Y, Zhou W. Role of natural products in tumor therapy from basic research and clinical perspectives. Acta Materia Med 2024, 3(2). Yao W, Feng D, Bian W, Yang L, Li Y, Yang Z, Xiong Y, Zheng J, Zhai R, He J. EBP50 inhibits EGF-induced breast cancer cell proliferation by blocking EGFR phosphorylation. Amino Acids. 2012;43(5):2027–35. Li D, Yu Q, Wu R, Tuo Z, Wang J, Ye L, Shao F, Chaipanichkul P, Yoo KH, Wei W, et al. Interactions between oxidative stress and senescence in cancer: Mechanisms, therapeutic implications, and future perspectives. Redox Biol. 2024;73:103208. Vaquero J, Nguyen Ho-Bouldoires TH, Clapéron A, Fouassier L. Role of the PDZ-scaffold protein NHERF1/EBP50 in cancer biology: from signaling regulation to clinical relevance. Oncogene. 2017;36(22):3067–79. Fang XY, Song R, Chen W, Yang YY, Gu YH, Shu YQ, Wu XD, Wu XF, Sun Y, Shen Y, et al. PRL-3 Promotes the Malignant Progression of Melanoma via Triggering Dephosphorylation and Cytoplasmic Localization of NHERF1. J Invest Dermatol. 2015;135(9):2273–82. Bellizzi A, Mangia A, Malfettone A, Cardone RA, Simone G, Reshkin SJ, Paradiso A. Na+/H+ exchanger regulatory factor 1 expression levels in blood and tissue predict breast tumour clinical behaviour. Histopathology. 2011;58(7):1086–95. Cardone RA, Bellizzi A, Busco G, Weinman EJ, Dell'Aquila ME, Casavola V, Azzariti A, Mangia A, Paradiso A, Reshkin SJ. The NHERF1 PDZ2 domain regulates PKA-RhoA-p38-mediated NHE1 activation and invasion in breast tumor cells. Mol Biol Cell. 2007;18(5):1768–80. Karn T, Ruckhäberle E, Hanker L, Müller V, Schmidt M, Solbach C, Gätje R, Gehrmann M, Holtrich U, Kaufmann M, et al. Gene expression profiling of luminal B breast cancers reveals NHERF1 as a new marker of endocrine resistance. Breast Cancer Res Treat. 2011;130(2):409–20. Malfettone A, Saponaro C, Paradiso A, Simone G, Mangia A. Peritumoral vascular invasion and NHERF1 expression define an immunophenotype of grade 2 invasive breast cancer associated with poor prognosis. BMC Cancer. 2012;12:106. Mangia A, Chiriatti A, Bellizzi A, Malfettone A, Stea B, Zito FA, Reshkin SJ, Simone G, Paradiso A. Biological role of NHERF1 protein expression in breast cancer. Histopathology. 2009;55(5):600–8. Song J, Bai J, Yang W, Gabrielson EW, Chan DW, Zhang Z. Expression and clinicopathological significance of oestrogen-responsive ezrin-radixin-moesin-binding phosphoprotein 50 in breast cancer. Histopathology. 2007;51(1):40–53. Accardi R, Rubino R, Scalise M, Gheit T, Shahzad N, Thomas M, Banks L, Indiveri C, Sylla BS, Cardone RA, et al. E6 and E7 from human papillomavirus type 16 cooperate to target the PDZ protein Na/H exchange regulatory factor 1. J Virol. 2011;85(16):8208–16. Bartholow TL, Becich MJ, Chandran UR, Parwani AV. Immunohistochemical analysis of ezrin-radixin-moesin-binding phosphoprotein 50 in prostatic adenocarcinoma. BMC Urol. 2011;11:12. Tabrizi AD, Kalloger SE, Köbel M, Cipollone J, Roskelley CD, Mehl E, Gilks CB. Primary ovarian mucinous carcinoma of intestinal type: significance of pattern of invasion and immunohistochemical expression profile in a series of 31 cases. Int J Gynecol pathology: official J Int Soc Gynecol Pathologists. 2010;29(2):99–107. Ji MY, Fan DK, Lv XG, Peng XL, Lei XF, Dong WG. The detection of EBP50 expression using quantum dot immunohistochemistry in pancreatic cancer tissue and down-regulated EBP50 effect on PC-2 cells. J Mol Histol. 2012;43(5):517–26. Malfettone A, Silvestris N, Paradiso A, Mattioli E, Simone G, Mangia A. Overexpression of nuclear NHERF1 in advanced colorectal cancer: association with hypoxic microenvironment and tumor invasive phenotype. Exp Mol Pathol. 2012;92(3):296–303. Mangia A, Caldarola L, Dell'Endice S, Scarpi E, Saragoni L, Monti M, Santini D, Brunetti O, Simone G, Silvestris N. The potential predictive role of nuclear NHERF1 expression in advanced gastric cancer patients treated with epirubicin/oxaliplatin/capecitabine first line chemotherapy. Cancer Biol Ther. 2015;16(8):1140–7. Schirosi L, Mazzotta A, Opinto G, Pinto R, Graziano G, Tommasi S, Fucci L, Simone G, Mangia A. β-catenin interaction with NHERF1 and RASSF1A methylation in metastatic colorectal cancer patients. Oncotarget. 2016;7(42):67841–50. Wang L, Du YR, Ji MY, Wang W, Zhan N, Zhou QS, Dong WG. Reduced EBP50 expression or mis-localization of the EBP50 protein is associated with the malignant progression of esophageal squamous cell carcinoma. Eur Rev Med Pharmacol Sci. 2014;18(24):3854–63. Fouassier L, Duan CY, Feranchak AP, Yun CH, Sutherland E, Simon F, Fitz JG, Doctor RB. Ezrin-radixin-moesin-binding phosphoprotein 50 is expressed at the apical membrane of rat liver epithelia. Hepatology (Baltimore MD). 2001;33(1):166–76. Nguyen Ho-Bouldoires TH, Clapéron A, Mergey M, Wendum D, Desbois-Mouthon C, Tahraoui S, Fartoux L, Chettouh H, Merabtene F, Scatton O, et al. Mitogen-activated protein kinase-activated protein kinase 2 mediates resistance to hydrogen peroxide-induced oxidative stress in human hepatobiliary cancer cells. Free Radic Biol Med. 2015;89:34–46. Shibata T, Chuma M, Kokubu A, Sakamoto M, Hirohashi S. EBP50, a beta-catenin-associating protein, enhances Wnt signaling and is over-expressed in hepatocellular carcinoma. Hepatology (Baltimore MD). 2003;38(1):178–86. Mangia A, Partipilo G, Schirosi L, Saponaro C, Galetta D, Catino A, Scattone A, Simone G. Fine Needle Aspiration Cytology: A Tool to Study NHERF1 Expression as a Potential Marker of Aggressiveness in Lung Cancer. Mol Biotechnol. 2015;57(6):549–57. Georgescu MM, Yell P, Mobley BC, Shang P, Georgescu T, Wang SH, Canoll P, Hatanpaa KJ, White CL 3rd, Raisanen JM. NHERF1/EBP50 is an organizer of polarity structures and a diagnostic marker in ependymoma. Acta Neuropathol Commun. 2015;3:11. Kislin KL, McDonough WS, Eschbacher JM, Armstrong BA, Berens ME. NHERF-1: modulator of glioblastoma cell migration and invasion. Neoplasia (New York NY). 2009;11(4):377–87. Molina JR, Agarwal NK, Morales FC, Hayashi Y, Aldape KD, Cote G, Georgescu MM. PTEN, NHERF1 and PHLPP form a tumor suppressor network that is disabled in glioblastoma. Oncogene. 2012;31(10):1264–74. Molina JR, Morales FC, Hayashi Y, Aldape KD, Georgescu MM. Loss of PTEN binding adapter protein NHERF1 from plasma membrane in glioblastoma contributes to PTEN inactivation. Cancer Res. 2010;70(17):6697–703. Du G, Hao C, Gu Y, Wang Z, Jiang WG, He J, Cheng S. A Novel NHERF1 Mutation in Human Breast Cancer Inactivates Inhibition by NHERF1 Protein in EGFR Signaling. Anticancer Res. 2016;36(3):1165–73. Saponaro C, Vagheggini A, Scarpi E, Centonze M, Catacchio I, Popescu O, Pastena MI, Giotta F, Silvestris N, Mangia A. NHERF1 and tumor microenvironment: a new scene in invasive breast carcinoma. J experimental Clin cancer research: CR. 2018;37(1):96. Saponaro C, Sergio S, Coluccia A, De Luca M, La Regina G, Mologni L, Famiglini V, Naccarato V, Bonetti D, Gautier C, et al. : β-catenin knockdown promotes NHERF1-mediated survival of colorectal cancer cells: implications for a double-targeted therapy. Oncogene. 2018;37(24):3301–16. Sun J, Li P, Yang J. Repressing of NHERF1 inhibits liver cancer progression by promoting the production of ROS. Biochem Biophys Res Commun. 2019;509(1):8–15. Wu R, Li D, Zhang S, Wang J, Chen K, Tuo Z, Miyamoto A, Yoo KH, Wei W, Zhang C, et al. A pan-cancer analysis of the oncogenic and immunological roles of transglutaminase 1 (TGM1) in human cancer. J Cancer Res Clin Oncol. 2024;150(3):123. Yan H, Song L, Li Y, Xu Q, Guo W, Lin S, Jiang W, Wang Z, Deng L, Huang Z, et al. Clinical evidence for efficacy of pembrolizumab in MSI-H and TMB-H advanced solid tumor: results from three cancer centers in China. Cancer Immunol immunotherapy: CII. 2024;73(4):74. Palmeri M, Mehnert J, Silk AW, Jabbour SK, Ganesan S, Popli P, Riedlinger G, Stephenson R, de Meritens AB, Leiser A, et al. Real-world application of tumor mutational burden-high (TMB-high) and microsatellite instability (MSI) confirms their utility as immunotherapy biomarkers. ESMO open. 2022;7(1):100336. Zhang X, Wu T, Cai X, Dong J, Xia C, Zhou Y, Ding R, Yang R, Tan J, Zhang L, et al. Neoadjuvant Immunotherapy for MSI-H/dMMR Locally Advanced Colorectal Cancer: New Strategies and Unveiled Opportunities. Front Immunol. 2022;13:795972. Tuo Z, Zhang Y, Li D, Wang Y, Wu R, Wang J, Yu Q, Ye L, Shao F, Wusiman D, et al. Relationship between clonal evolution and drug resistance in bladder cancer: A genomic research review. Pharmacol Res. 2024;206:107302. Compérat E, Amin MB, Cathomas R, Choudhury A, De Santis M, Kamat A, Stenzl A, Thoeny HC, Witjes JA. Current best practice for bladder cancer: a narrative review of diagnostics and treatments. Lancet (London England). 2022;400(10364):1712–21. Liu X, Ren B, Fang Y, Ren J, Wang X, Gu M, Zhou F, Xiao R, Luo X, You L, et al. Comprehensive analysis of bulk and single-cell transcriptomic data reveals a novel signature associated with endoplasmic reticulum stress, lipid metabolism, and liver metastasis in pancreatic cancer. J translational Med. 2024;22(1):393. Table 1 Table 1 not available with this version Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9211687","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":619431450,"identity":"72e0301e-1fb9-4579-a7e1-7b527c1bd4fe","order_by":0,"name":"Yimin Wang","email":"","orcid":"","institution":"Anqing City Municipal Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yimin","middleName":"","lastName":"Wang","suffix":""},{"id":619431451,"identity":"adac805e-6ccb-43a0-9a97-d4989d1bf87e","order_by":1,"name":"Xiaoxiang Wu","email":"","orcid":"","institution":"Anqing City Municipal Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiaoxiang","middleName":"","lastName":"Wu","suffix":""},{"id":619431452,"identity":"9fe596db-c5a1-4dce-bbde-1c7199a35186","order_by":2,"name":"Chong Shu","email":"","orcid":"","institution":"Anqing City Municipal Hospital","correspondingAuthor":false,"prefix":"","firstName":"Chong","middleName":"","lastName":"Shu","suffix":""},{"id":619431455,"identity":"daec0bff-cdd9-4b0d-8e18-d901f705041f","order_by":3,"name":"Xin Chen","email":"","orcid":"","institution":"Second Hospital of Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Chen","suffix":""},{"id":619431456,"identity":"da40ef09-3151-4ed2-92de-0b89e3bbbef9","order_by":4,"name":"Tong Bao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABC0lEQVRIiWNgGAWjYBACAzhLAsrlY29sfPgBvxbGBiQtBgxsPIebjSWI1wLks0mktwnw4NFizn74+IOPO2rl+W43H/5cUPBHnk3yYRtQv52cbgN2LZY9aYmNM88cN5x551ia9AwDA8M26cS2BwUMycZmB3A47ECOYTNv2zHGDTdyzJh5DAwYgVraDSQYDiRuw6Xl/BuwFnugFuPPQC32bZIH2yR48Gm5AbalJhGoxUAaqCWxTYIRvxbLGc8SZ85sO5A880YayC/GyW08icBANsDtF3P+5AMfPrbV2fbdSAaG2B8523724w8ffqiwk8OlBQoOMzAAFTAjORivchCoQ9cyCkbBKBgFowABAPMAYgUrsnCXAAAAAElFTkSuQmCC","orcid":"","institution":"Anqing City Municipal Hospital","correspondingAuthor":true,"prefix":"","firstName":"Tong","middleName":"","lastName":"Bao","suffix":""}],"badges":[],"createdAt":"2026-03-24 12:09:52","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9211687/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9211687/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106835974,"identity":"e805162c-41d4-4f51-8733-143b5e9fbbba","added_by":"auto","created_at":"2026-04-14 02:03:38","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3750589,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSLC9A3R1 expression and prognosis analysis in human cancers.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A)Correlation analysis between SLC9A3R1 expression and clinical features in pan-cancer.\u003c/p\u003e\n\u003cp\u003e(B)Transcriptional expression analysis of SLC9A3R1 in pan-cancer.\u003c/p\u003e\n\u003cp\u003e(C)Pan-cancer analysis of SLC9A3R1 for overall survival (OS).。\u003c/p\u003e\n\u003cp\u003e(D)Pan-cancer analysis of SLC9A3R1 for disease-specific survival (DSS).\u003c/p\u003e\n\u003cp\u003e(E)Pan-cancer analysis of SLC9A3R1 for disease-free interval (DFI).\u003c/p\u003e\n\u003cp\u003e(F)Pan-cancer analysis of SLC9A3R1 for progression-free interval (PFI). *P \u0026lt; 0.05; **P \u0026lt; 0.01; ***P \u0026lt; 0.001; ****P \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-9211687/v1/1acc8a4f7a932fbc9e6b3528.png"},{"id":106960730,"identity":"27383a6e-e76d-4da1-92c1-4289f2c8b0c5","added_by":"auto","created_at":"2026-04-15 09:22:51","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":4188820,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe correlation analysis of tumor heterogeneity and stemness in pan-cancer.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A)The correlation between TMB and SLC9A3R1 level.\u003c/p\u003e\n\u003cp\u003e(B)The correlation between MSI and SLC9A3R1 level.\u003c/p\u003e\n\u003cp\u003e(C)The correlation between DNAss and SLC9A3R1 level.\u003c/p\u003e\n\u003cp\u003e(D)The correlation between RNAss and SLC9A3R1 level.\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-9211687/v1/31f65d34a63cda77b1466918.png"},{"id":106960729,"identity":"455134ab-9553-4979-a171-d7cc5515540f","added_by":"auto","created_at":"2026-04-15 09:22:51","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":7784026,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe analysis of mutation landscape in pan-cancer.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A)Mutation landscapes of SLC9A3R1 for pan-cancer.\u003c/p\u003e\n\u003cp\u003e(B)The correlation of SLC9A3R1 expression with immune regulatory genes.\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-9211687/v1/a8fd804d02be2ff54cdbedd3.png"},{"id":106835976,"identity":"19b8c35c-8afa-400f-a4fa-40018e95fd78","added_by":"auto","created_at":"2026-04-14 02:03:38","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":12252870,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation of SLC9A3R1 Expression with Immune Cell Infiltration Across Cancer Types\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A)The correlation between SLC9A3R1 expression and various immune cell types across different cancer types using single-sample Gene Set Enrichment Analysis.\u003c/p\u003e\n\u003cp\u003e(B)The correlation between SLC9A3R1 expression and various immune cell types across different cancer types using CIBERSORT.\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-9211687/v1/bf19b05e4a315ceec9b13b32.png"},{"id":106960006,"identity":"4cfa93cc-27f1-48df-9009-dab316078a7d","added_by":"auto","created_at":"2026-04-15 09:17:45","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":6061826,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe correlation between SLC9A3R1 expression and various immune cell types across different cancer types using single-sample Gene Set Enrichment Analysis.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) An unpaired expression of SLC9A3R1 in BLCA was analyzed.\u003c/p\u003e\n\u003cp\u003e(B) A paired expression of SLC9A3R1 in BLCA was analyzed.\u003c/p\u003e\n\u003cp\u003e(C) SLC9A3R1 expression in BLCA by age.\u003c/p\u003e\n\u003cp\u003e(D) SLC9A3R1 expression in BLCA by gender.\u003c/p\u003e\n\u003cp\u003e(E) SLC9A3R1 expression in BLCA by histological morphology.\u003c/p\u003e\n\u003cp\u003e(F) SLC9A3R1 expression in BLCA by race.\u003c/p\u003e\n\u003cp\u003e(G) SLC9A3R1 expression in BLCA by pathologic M stage.\u003c/p\u003e\n\u003cp\u003e(H) SLC9A3R1 expression in BLCA by pathologic T stage.\u003c/p\u003e\n\u003cp\u003e(I) SLC9A3R1 expression in BLCA by pathologic N stage.\u003c/p\u003e\n\u003cp\u003e(J) Overall Survival Analysis Based on SLC9A3R1 Expression\u003c/p\u003e\n\u003cp\u003e(K) Disease-Specific Survival Analysis Based on SLC9A3R1 Expression\u003c/p\u003e\n\u003cp\u003e(L) Progression-Free Interval Analysis Based on SLC9A3R1 Expression\u003c/p\u003e\n\u003cp\u003e(M) Representative pictures of IHC staining of SLC9A3R1 in tumor and matched adjacent normal tissues.\u003c/p\u003e\n\u003cp\u003e(N) IHC scores were performed on the bladder cancer and adjacent tissues.\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-9211687/v1/2d3233f5f96b87f74a373ff4.png"},{"id":106961088,"identity":"ca697eea-d996-4c3e-ba95-ecbbf76e4dcf","added_by":"auto","created_at":"2026-04-15 09:24:13","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":3329157,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional enrichment analysis of SLC9A3R1 in bladder cancer.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A)Volcano plot of gene expression differences between high and low SLC9A3R1 expression groups.\u003c/p\u003e\n\u003cp\u003e(B)Heatmap showing the top 20 up-regulated genes and top 20 down-regulated genes.\u003c/p\u003e\n\u003cp\u003e(C)GO analysis of biological processes terms for SLC9A3R1 expression in BLCA.\u003c/p\u003e\n\u003cp\u003e(D)GO analysis of cellular component terms for SLC9A3R1 expression in BLCA.\u003c/p\u003e\n\u003cp\u003e(E) GO analysis of molecular function terms for SLC9A3R1 expression in BLCA.\u003c/p\u003e\n\u003cp\u003e(F) KEGG analysis results of SLC9A3R1 expression in bladder cancer.\u003c/p\u003e","description":"","filename":"Fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-9211687/v1/af791e09439a082b9ae9fd16.png"},{"id":106835980,"identity":"006a520a-4b7c-4ca3-8d16-535dbb2ebff0","added_by":"auto","created_at":"2026-04-14 02:03:38","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":22731792,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional validation of SLC9A3R1 in bladder-cancer cells.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Knockdown efficiency of three siRNAs targeting SLC9A3R1 was evaluated by qRT-PCR, and si-SLC9A3R1-2 was selected for subsequent experiments.\u003c/p\u003e\n\u003cp\u003e(B) CCK-8 assays were performed to assess the proliferative capacity of T24 and UM-UC-3 cells after SLC9A3R1 silencing.\u003c/p\u003e\n\u003cp\u003e(C) Transwell migration assays showing the migratory ability of T24 and UM-UC-3 cells after transfection with si-NC or si-SLC9A3R1-2; representative images and quantitative analysis are presented.\u003c/p\u003e\n\u003cp\u003e(D) Wound-healing assays evaluating migration in T24 and UM-UC-3 cells following SLC9A3R1 knockdown. Representative images were captured at 0, 12 and 24 h, and residual wound area was quantified.\u003c/p\u003e\n\u003cp\u003e(E) EdU incorporation assays showing the proliferative activity of T24 and UM-UC-3 cells after SLC9A3R1 silencing; representative fluorescence images and quantification of EdU-positive cells are shown.\u003c/p\u003e","description":"","filename":"Fig7.png","url":"https://assets-eu.researchsquare.com/files/rs-9211687/v1/4e45beed500007534bc387fb.png"},{"id":107524335,"identity":"11c6b461-64ba-4acc-b484-175cd7031e52","added_by":"auto","created_at":"2026-04-22 09:28:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":58810065,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9211687/v1/fb7d8b20-2345-44ae-8e4c-a4066d2514fa.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Pan-cancer analysis and verification revealed the clinical significance of SLC9A3R1 in bladder cancer cohort","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eBladder cancer (BLCA) is a malignant tumor of the urinary system with high morbidity and mortality, and is reported to be the tenth most common cancer[\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]with more than 430,000 new cases each year. Advances in conventional surgery, chemotherapy and radiotherapy, as well as the advent of targeted therapies and immunotherapies, have brought new hope to cancer patients[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. However, despite these advances, significant challenges remain. Despite these advances, formidable obstacles persist: precision agents remain constrained by inter-tumor hetero[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]geneity and inter-individual variability[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], and the paucity of effective regimens for invasive or metastatic urothelial carcinoma represents a pressing clinical unmet need. Consequently, the identification of innovative molecular targets capable of restraining local invasion and systemic dissemination of bladder cancer is urgently warranted.\u003c/p\u003e \u003cp\u003eSLC9A3R1 (solute carrier family 9 member A3 regulator 1), located at 17q25.1 and comprising six exons, encodes a scaffolding protein with dual PDZ domains and a C-terminal ERM-binding motif[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. This multifunctional adaptor modulates ion-channel localization, receptor trafficking and signal-transduction cascades, and emerging evidence implicates its dysregulation in malignant progression[\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, we combined pan-cancer analysis with experimental validation of BLCA to comprehensively explore the oncogenic and immunologic roles of SLC9A3R1. Collectively, our findings establish SLC9A3R1 as a pivotal determinant of tumor aggressiveness and immune evasion in urothelial carcinoma, thereby providing a rational framework for future targeted therapeutic development.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Data collection, processing, and analysis of variance and prognosis\u003c/h2\u003e \u003cp\u003eAs similar to previous studies[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], we downloaded an integrated pan-cancer transcriptome and clinical cohort from the UCSC Xena repository. To contextualise tumor-specific expression, we appended matched normal-tissue data for SLC9A3R1 retrieved from the GTEx portal (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gtexportal.org\u003c/span\u003e\u003cspan address=\"https://gtexportal.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). In addition, we obtained prognostic information for 34 tumor types, including progression-free interval (PFI), disease-specific survival (DSS), overall survival (OS), and disease-free interval (DFI) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. We conducted prognostic analyses utilizing the Cox proportional hazards regression model, which was executed with the \"survival\" package. Statistical significance was evaluated using the log-rank test[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Genomic heterogeneity and tumor stemness metrics\u003c/h2\u003e \u003cp\u003eSingle-nucleotide-variant (SNV) profiles for each cancer type were retrieved from the Genomic Data Commons (GDC) repository, and tumor mutational burden (TMB) was subsequently estimated with the R package \"maftools\" [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The association between microsatellite-instability (MSI) scores and SLC9A3R1 transcript levels was evaluated following the analytical framework reported earlier. Moreover, stemness indices (DNAss and RNAss) together with matched gene-expression matrices were compiled from prior work to quantify the relationship between DNA-based and RNA-based stemness signatures across tumor lineages[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Immune landscapes and RNA-modification signatures\u003c/h2\u003e \u003cp\u003eTranscript-level quantifications for 150 immunomodulators were compiled across TCGA tumor types, and their pairwise relationships with SLC9A3R1 were systematically interrogated with Spearman correlation. In parallel, the same analytical workflow was applied to 60 immune-checkpoint genes[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Furthermore, expression matrices for 44 RNA-modifying enzymes were extracted, and their co-expression patterns with SLC9A3R1 were evaluated across malignancies[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Somatic mutation landscape of SLC9A3R1\u003c/h2\u003e \u003cp\u003eSomatic mutation calls across the entire TCGA cohort were consolidated, and the R package \"maftools\" was employed to annotate affected protein domains. Cancer-specific mutation frequencies of SLC9A3R1 were subsequently extracted from the cBioPortal resource.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Functional enrichment profiling\u003c/h2\u003e \u003cp\u003eTo dissect the biological roles and signaling cascades linked to SLC9A3R1-associated genes, we conducted Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) using the \"clusterProfiler\" R package[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Briefly, SLC9A3R1-correlated genes (|r| \u0026ge; 0.3 and FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were first identified across all tumor types via weighted gene co-expression network analysis (WGCNA). These gene sets were then subjected to over-representation tests against the org.Hs.eg.db (v3.17) background for GO and KEGG terms, with Benjamini\u0026ndash;Hochberg correction applied. Visualization of enriched terms was performed with the enrichplot and ggplot2 packages, focusing on the top ten pathways ranked by normalized enrichment score (NES).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Immunological value analysis\u003c/h2\u003e \u003cp\u003eUsing the CIBERSORT deconvolution algorithm, we estimated the relative abundance of 22 tumor-infiltrating immune cell subsets in the TCGA-BLCA dataset and subsequently correlated these estimates with SLC9A3R1 transcript levels in urothelial carcinoma patients [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. To ensure robustness, proportions were inferred from log2-transformed TPM matrices with 1,000 permutations and a CIBERSORT p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 set as the quality threshold. Additionally, single-sample gene-set enrichment analysis (ssGSEA) was executed with the GSVA R package (v1.44.0) to generate immune-signature scores across individual tumors. Resulting enrichment scores were z-standardized per cancer type, and Spearman correlations with SLC9A3R1 expression were calculated; statistical significance was declared at FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Immune cell infiltration patterns were visualized with the ggpubr and ComplexHeatmap packages.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7. Immunohistochemistry (IHC) Analysis\u003c/h2\u003e \u003cp\u003eA commercially available bladder cancer tissue microarray (Cat. ZL-BlaU961; Shanghai Zhuoli Biotechnology Co., Ltd., China) was used to evaluate SLC9A3R1 expression. The TMA contained 36 primary bladder tumor tissues and 10 matched adjacent non-tumor bladder tissues. IHC staining was performed using a standard immunoperoxidase protocol. Following deparaffinization, rehydration and heat-induced epitope retrieval, endogenous peroxidase activity was quenched with 3% H₂O₂. Sections were then incubated overnight at 4\u0026deg;C with rabbit anti-SLC9A3R1 polyclonal antibody (Affinity, USA). Immune complexes were visualized with an HRP-polymer detection system (ZSGB-BIO, China) and 3,3'-diaminobenzidine chromogen according to the manufacturer's protocol. Staining intensity (0\u0026ndash;3). Cases were dichotomized into low (score\u0026thinsp;\u0026le;\u0026thinsp;3) and high (score\u0026thinsp;\u0026gt;\u0026thinsp;3) SLC9A3R1 expression groups for subsequent statistical analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8. Cell culture and transient transfection\u003c/h2\u003e \u003cp\u003eHuman bladder-cancer cell T24 and UM-UC-3 were cultured under standard conditions in a humidified incubator containing 5% CO\u003csub\u003e2\u003c/sub\u003e at 37\u0026deg;C. To investigate the biological role of SLC9A3R1 in bladder cancer, cells were transiently transfected with three independent small interfering RNAs targeting SLC9A3R1 (si-SLC9A3R1-1, si-SLC9A3R1-2 and si-SLC9A3R1-3) or a non-targeting negative control (si-NC) using a commercially available transfection reagent according to the manufacturer\u0026rsquo;s instructions. Transfection efficiency was assessed by quantitative real-time PCR (qRT-PCR), and the siRNA exhibiting the highest knockdown efficiency was selected for subsequent experiments.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9. CCK-8 assay\u003c/h2\u003e \u003cp\u003eCell proliferation was evaluated using the Cell Counting Kit-8 (CCK-8) assay. Briefly, transfected T24 and UM-UC-3 cells were seeded into 96-well plates at an appropriate density and cultured for the indicated time periods. At each time point, CCK-8 reagent was added to each well and incubated according to the manufacturer\u0026rsquo;s instructions. The absorbance at 450 nm was measured using a microplate reader to determine cell viability.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.10. Transwell migration assay and wound-healing assay\u003c/h2\u003e \u003cp\u003eCell migratory ability was assessed using Transwell migration and wound-healing assays. For the Transwell migration assay, transfected cells suspended in serum-free medium were added to the upper chambers, while medium supplemented with fetal bovine serum was placed in the lower chambers as a chemoattractant. After incubation, cells that had migrated to the lower surface of the membrane were fixed, stained and counted under a microscope.For the wound-healing assay, transfected cells were seeded into 6-well plates and cultured until reaching near confluence. A linear scratch was created using a sterile pipette tip, and detached cells were removed by washing with phosphate-buffered saline. Images were captured at the indicated time points, and the wound area was measured to evaluate migratory capacity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.11. EdU incorporation assay\u003c/h2\u003e \u003cp\u003eCell proliferative activity was further assessed using the 5-ethynyl-2'-deoxyuridine (EdU) incorporation assay. Transfected cells were seeded into appropriate culture plates and incubated with EdU reagent according to the manufacturer\u0026rsquo;s protocol. After fixation, permeabilization and fluorescent staining, images were acquired under a fluorescence microscope. The proportion of EdU-positive cells was quantified from randomly selected fields to evaluate DNA-synthesis activity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.12. Statistical analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were conducted using R software (version 4.2.1) with the aid of established bioinformatics and statistical packages. Group comparisons were performed using two-sided Student\u0026rsquo;s t-tests for normally distributed continuous variables and χ\u0026sup2; tests for categorical variables, as appropriate. When parametric assumptions were not met, non-parametric tests (Mann\u0026ndash;Whitney U test) were applied. A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. All tests were two-sided, and data visualization was carried out using standard graphical tools in R.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Expression and prognostic analysis of SLC9A3R1 in pan-cancer\u003c/h2\u003e \u003cp\u003eIn the TCGA pan-cancer dataset, SLC9A3R1 mRNA abundance exceeded that of matched normal tissues in 23 malignancies \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e, whereas a significant reduction was detected in seven cancer entities \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. Meanwhile, according to OS analysis, poor prognosis in 3 tumor types was associated with high SLC9A3R1 expression, while better prognosis in 7 tumor types was associated with high SLC9A3R1 expression \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e. Disease-specific survival (DSS) modelling demonstrated that elevated SLC9A3R1 transcript levels were associated with inferior outcomes in two cancer entities, whereas attenuated SLC9A3R1 expression correlated with reduced survival in five additional malignancies \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC\u003cb\u003e)\u003c/b\u003e. In the TCGA-BLCA cohort, heightened SLC9A3R1 abundance predicted prolonged disease-free interval (DFI) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD\u003cb\u003e)\u003c/b\u003e. Progression-free interval (PFI) analysis further revealed that elevated SLC9A3R1 signaling conferred an adverse prognosis in three tumor lineages, yet was linked to favorable clinical trajectories in five others \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.2. SLC9A3R1 expression in human cancers in relation to genomic heterogeneity and tumor stem cells\u003c/h2\u003e \u003cp\u003eTo interrogate the interplay between SLC9A3R1 transcriptional activity and fundamental tumor biological traits across multiple cancer entities, we quantified its relationships with two genomic-heterogeneity indices and two stemness metrics. First, Spearman correlation revealed a significant association between SLC9A3R1 mRNA abundance and tumor mutational burden (TMB) in ten malignancies; five exhibited positive coefficients \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e, Similarly, microsatellite-instability (MSI) scores were significantly linked to SLC9A3R1 expression in eleven cancer types, six of which displayed positive correlations \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e. DNA-methylation-based stemness (DNAss) exhibited a positive correlation with SLC9A3R1 in nine tumor lineages and an inverse correlation in seven others \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC\u003cb\u003e)\u003c/b\u003e. RNA-expression-based stemness (RNAss) was positively associated with SLC9A3R1 in seventeen cancer entities and negatively associated in three entities \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Landscape of SLC9A3R1 genetic alterations across human cancers\u003c/h2\u003e \u003cp\u003ePan-cancer mutational profiles of SLC9A3R1 were interrogated through the TCGA data portal \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. Subsequently, pairwise correlations between SLC9A3R1 transcript abundance and representative genes defining five distinct immune pathways were computed, revealing an extensive regulatory impact of SLC9A3R1 on RNA-modifying processes. Indeed, SLC9A3R1 exhibited robust co-expression with m1A-, m5C- and m6A-associated enzymes across diverse malignancies \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e. Specifically, in certain tumor types, such as DLBC, KIPAN and GBM, SLC9A3R1 expression was strongly positively correlated with m1A modification-related genes, while in BLCA, READ and KIRC, SLC9A3R1 expression showed strong positive correlation with m6A modification-related writer, reader and eraser genes, which suggests that SLC9A3R1 plays a role in the m6A regulatory network in these tumor types. In addition, in some other tumor types, such as THCA and COAD, SLC9A3R1 also showed significant negative correlation with m5C modification genes, suggesting that SLC9A3R1 affects the immune characteristics of these tumors by inhibiting m5C modification. This result reflects the heterogeneity of RNA modification regulatory mechanisms in different tumor types.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Immunological landscape of SLC9A3R1 in urothelial carcinoma\u003c/h2\u003e \u003cp\u003eEmploying two complementary in-silico deconvolution frameworks\u0026mdash; single-sample Gene Set Enrichment Analysis (ssGSEA) and CIBERSORT, we systematically quantified the relationship between SLC9A3R1 transcript abundance and immune-cell infiltration across multiple tumor entities. Based on ssGSEA, elevated SLC9A3R1 levels were significantly coupled with increased signatures of activated CD8\u003csup\u003e+\u003c/sup\u003e T lymphocytes, natural killer (NK) cells and M1-polarised macrophages in several cancers, most notably KIRC, DLBC and BRCA (Spearman r\u0026thinsp;\u0026gt;\u0026thinsp;0.4, FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05) \u003cb\u003e(Figure. 4A)\u003c/b\u003e. Conversely, a pronounced inverse association was observed between SLC9A3R1 expression and regulatory T-cell (Treg) signatures within these same malignancies. Focusing specifically on the TCGA-BLCA cohort, CIBERSORT-derived fractions revealed that heightened SLC9A3R1 transcription associated positively with both CD8\u003csup\u003e+\u003c/sup\u003e T-cell infiltrationand cytotoxic T-lymphocyte activity, as well as with NK-cell and M1 macrophage infiltration, underscoring a potential role for SLC9A3R1 in recruiting or activating anti-tumor effector populations. In contrast, SLC9A3R1 abundance exhibited significant negative correlations with both Treg infiltration and M2 macrophage polarization, implying a suppressive influence on immunoregulatory cell subsets within the bladder-cancer microenvironment.\u003c/p\u003e \u003cp\u003eConsistent with these findings, CIBERSORT-based analysis \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e showed that higher SLC9A3R1 expression was significantly associated with an increase in the estimated proportions of CD8\u003csup\u003e+\u003c/sup\u003e T cells and M1 macrophages in KIRC and DLBC (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Meanwhile, in tumors with elevated SLC9A3R1 expression (e.g., STAD), the proportions of M2 macrophages and regulatory T cells were significantly lower, suggesting that high SLC9A3R1 expression is associated with an immune-activated tumor microenvironment, which promotes anti-tumor immune response by inhibiting the infiltration or polarization of immune-suppressive cells. In BLCA specimens, SLC9A3R1 transcript abundance exhibited a robust positive association with the estimated abundance of CD8⁺ cytotoxic T cells and activated natural killer cells, indicating a facilitative role in the accumulation of effector lymphocytes within the tumor bed. Conversely, CIBERSORT deconvolution revealed a pronounced inverse relationship between SLC9A3R1 levels and regulatory T-cell (Treg) infiltration. Moreover, heightened SLC9A3R1 expression was significantly negatively coupled with M2-polarised macrophages and monocyte fractions, implying that SLC9A3R1 attenuates the differentiation or recruitment of myeloid-derived suppressor populations, thereby reinforcing anti-tumor immunity.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Expression pattern and experimental validation of SLC9A3R1 in BLCA\u003c/h2\u003e \u003cp\u003eCapitalizing on prior advances in urothelial carcinoma biology, we interrogated the transcriptional landscape of SLC9A3R1 within the TCGA-BLCA cohort (n\u0026thinsp;=\u0026thinsp;408). Relative to adjacent urothelium, tumor specimens displayed markedly elevated SLC9A3R1 mRNA levels \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA \u003cb\u003eand B)\u003c/b\u003e. To determine whether SLC9A3R1 abundance is modulated by demographic or disease-related variables, patients were stratified by age (\u0026le;\u0026thinsp;70 years vs\u0026thinsp;\u0026gt;\u0026thinsp;70 years), sex, histological subtype (papillary vs non-papillary), self-reported race (Asian, Black/African American, White) and pathological TNM stage (T1-T4, N0-N3, M0-M1) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC-I\u003cb\u003e)\u003c/b\u003e. Transcript abundance differed significantly according to histological subtype, racial background and nodal status (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Specifically, non-papillary tumors exhibited higher SLC9A3R1 expression than papillary lesions (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and progressive increments were noted from T1 to T4 and from N0 to N2 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Conversely, age, sex and M stage exerted no discernible influence on SLC9A3R1 abundance. Clinically, heightened SLC9A3R1 expression was associated with prolonged overall, disease-specific and progression-free survival \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eJ-L\u003cb\u003e)\u003c/b\u003e.The clinical characteristics of patients with bladder cancer in the validation cohort are summarized in \u003cb\u003eTable\u0026nbsp;1.\u003c/b\u003eTo corroborate these findings at the protein level, we subjected paired bladder-cancer and adjacent normal specimens to immunohistochemistry. Consistent with the transcriptomic data, tumor tissues displayed markedly elevated SLC9A3R1 protein abundance as quantified by composite IHC scores \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eM \u003cb\u003eand N)\u003c/b\u003e. Collectively, these observations indicate that SLC9A3R1 is transcriptionally and translationally up-regulated in bladder cancer and may serve as a favorable prognostic biomarker.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.6. Functional landscape of SLC9A3R1 in bladder cancer\u003c/h2\u003e \u003cp\u003eLeveraging TCGA-BLCA transcriptomes, we dichotomized tumors (n\u0026thinsp;=\u0026thinsp;204 per arm) into SLC9A3R1-high and SLC9A3R1-low cohorts and identified 167 differentially expressed genes (DEGs; |log₂FC| \u0026ge; 1, FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05), comprising 113 up-regulated and 54 down-regulated transcripts \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. We further identified the most significantly upregulated and downregulated genes, focusing on the top 20 in each category \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e. Up-regulated genes were dominated by keratinization-associated entities (\u003cem\u003eKRT6C\u003c/em\u003e, \u003cem\u003eKLK5/6/7/8/10/13\u003c/em\u003e, \u003cem\u003eSPRR2A/2D/2E\u003c/em\u003e), whereas down-regulated transcripts were enriched for extracellular-matrix modulators (\u003cem\u003eCRTACI\u003c/em\u003e, \u003cem\u003eVWC2L\u003c/em\u003e) and metabolic enzymes (\u003cem\u003eCYP2C9\u003c/em\u003e, \u003cem\u003eADH7\u003c/em\u003e). To elucidate the biological processes driven by the high expression of SLC9A3R1, we performed GO functional classification and KEGG pathway enrichment analysis (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05) on the differentially expressed genes. The results of GO functional annotation showed that enrichment analysis revealed that the expression profile of SLC9A3R1 was associated with biological processes such as epidermal development, skin development, epidermal cell differentiation, keratinocyte differentiation and biological processes such as keratinization \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC\u003cb\u003e)\u003c/b\u003e. The significantly enriched cell component pathway was mainly concentrated in the keratinized envelope (FDR\u0026thinsp;=\u0026thinsp;6\u0026times;10\u003csup\u003e-\u003c/sup\u003e⁶) and intermediate filament cytoskeleton (FDR\u0026thinsp;=\u0026thinsp;1\u0026times;10\u003csup\u003e-\u003c/sup\u003e\u0026sup3;) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD\u003cb\u003e)\u003c/b\u003e; at the molecular function level \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE\u003cb\u003e)\u003c/b\u003e, differential genes were significantly enriched in serine-type endopeptidase activity (FDR\u0026thinsp;=\u0026thinsp;2\u0026times;10\u003csup\u003e-\u003c/sup\u003e⁶) and serine hydrolase activity (FDR\u0026thinsp;=\u0026thinsp;1\u0026times;10\u003csup\u003e-\u003c/sup\u003e⁶). SLC9A3R1 overexpression drives malignant progression of bladder cancer by intensifying the keratinization program, remodeling the intermediate filament network, and disrupting exogenous metabolic pathways. KEGG pathway analysis showed \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF\u003cb\u003e)\u003c/b\u003e that neuroactive ligand-receptor interaction (FDR\u0026thinsp;=\u0026thinsp;0.04), metabolism of xenobiotics by cytochrome P450 (FDR\u0026thinsp;=\u0026thinsp;0.02), and retinol metabolism (FDR\u0026thinsp;=\u0026thinsp;0.02) were the most important metabolic pathways in bladder cancer.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.7. Silencing SLC9A3R1 restrains proliferative and migratory phenotypes in bladder-cancer cells\u003c/h2\u003e \u003cp\u003eTo experimentally validate the oncogenic role of SLC9A3R1 in bladder cancer, we performed loss-of-function assays in T24 and UM-UC-3 cells. Three independent siRNAs were designed against SLC9A3R1, and qRT-PCR analysis confirmed efficient knockdown in both cell lines, with si-SLC9A3R1-2 exhibiting the most pronounced silencing efficiency; this construct was therefore selected for subsequent functional experiments (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). CCK-8 assays demonstrated that SLC9A3R1 depletion significantly attenuated cell proliferation over time compared with the si-NC group in both T24 and UM-UC-3 cells \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e. Consistently, EdU incorporation analysis revealed a marked reduction in the proportion of EdU-positive cells following SLC9A3R1 knockdown, indicating impaired proliferative capacity (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE). We next assessed whether SLC9A3R1 modulates the migratory behavior of bladder-cancer cells. Transwell assays showed that the number of migrated cells was significantly decreased after SLC9A3R1 silencing in both T24 and UM-UC-3 cells \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC). In parallel, wound-healing experiments demonstrated that depletion of SLC9A3R1 substantially delayed scratch closure, as reflected by a larger residual wound area at the indicated time points relative to control cells \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD\u003cb\u003e)\u003c/b\u003e. Collectively, these findings indicate that SLC9A3R1 promotes the proliferative and migratory phenotypes of bladder-cancer cells in vitro.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eWhile considerable advancements have been achieved, effective cancer management is still limited by multifactorial barriers, including the dynamic nature of tumor ecosystems and suboptimal efficacy of existing interventions. Clonal diversity within tumors has been widely implicated in the development of resistance following initial response to targeted and immunotherapeutic regimens[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Resistance to targeted drugs is frequently acquired through mechanisms such as genetic mutation or pathway remodeling[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Moreover, despite the paradigm shift brought about by immune checkpoint inhibitors in cancer treatment, durable responses are restricted to a subset of malignancies, and on-target off-tumor immune activation often leads to treatment-related inflammatory complications[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. While significant challenges persist, the therapeutic landscape in oncology is evolving rapidly, fueled by breakthroughs in biotechnology and synergistic efforts across research domains.[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEmerging research has highlighted the pathological relevance of SLC9A3R1 across multiple disease states. Dysregulation of SLC9A3R1 expression or activity has been implicated in hypertension, acute renal failure, and breast carcinogenesis. Notably, the involvement of SLC9A3R1 in tumorigenesis is gaining strong experimental support, positioning it as a candidate modulator in cancer biology [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Initially characterized as a scaffolding protein for epithelial sodium-phosphate cotransporters, SLC9A3R1 has emerged in recent years as a key regulator of cell polarity, invasive potential, and tumor microenvironment remodeling across multiple solid malignancies. It is ubiquitously expressed in epithelial tissues and has been increasingly implicated in carcinogenesis. The functional role of SLC9A3R1 in tumors appears context-dependent, particularly influenced by its subcellular localization. Membrane-localized SLC9A3R1 exerts tumor-suppressive effects, whereas mislocalization or loss of plasma membrane expression is linked to oncogenic phenotypes.[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Conversely, cytoplasmic mislocalization or loss of membrane expression impairs the tumor-suppressive function of SLC9A3R1 and is linked to more aggressive tumor phenotypes[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The functional impact of SLC9A3R1 on tumor behavior has been proposed through multiple mechanisms in diverse malignancies, including melanoma[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], breast[\u003cspan additionalcitationids=\"CR34 CR35 CR36 CR37\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], reproductive[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan additionalcitationids=\"CR40\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], gastrointestinal[\u003cspan additionalcitationids=\"CR43 CR44 CR45\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], hepatocellular[\u003cspan additionalcitationids=\"CR48\" citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], lung[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e], and central nervous system cancers[\u003cspan additionalcitationids=\"CR52 CR53\" citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. The tumor-modulating effects of SLC9A3R1 are thought to involve multiple molecular pathways. These include impaired interaction with the epidermal growth factor receptor, leading to attenuated EGFR signaling[\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e], increased cytoplasmic accumulation of VEGF and VEGFR1[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]; dysregulation of matrix metalloproteinase expression[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]; activation of Wnt signaling[\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]; and disruption of the actin cytoskeleton[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Recent evidence demonstrates that SLC9A3R1 overexpression in hepatocellular carcinoma cells promotes cellular proliferation and suppresses reactive oxygen species accumulation.[\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Suppression of SLC9A3R1 expression inhibits tumor cell proliferation through downregulation of cyclin D1 and cyclin-dependent kinase 4, coupled with upregulation of p27, p53, and phosphatase and tensin homolog. This cell cycle arrest is mediated by increased reactive oxygen species production triggered by SLC9A3R1 loss[\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e] .\u003c/p\u003e \u003cp\u003eWith the development of immunotherapy, genomic features such as TMB and MSI have become important predictors of immune checkpoint inhibitor efficacy[\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. High TMB usually implies more neoantigen production and enhances tumor immunogenicity, thereby improving ICI response rate[\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e], while MSI, which stems from DNA mismatch repair defects, similarly enhances immune recognition[\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. Notably, our study suggests that SLC9A3R1 expression may be associated with these key genomic features. Low SLC9A3R1 levels may be associated with higher TMB or MSI status, thus indirectly affecting tumor sensitivity to immunotherapy. In addition, the potential association of SLC9A3R1 with DNAss and RNAss may also be involved in the regulation of immune response by maintaining genomic stability. Future studies should explore in depth the role of SLC9A3R1 in the regulation of TMB, MSI and overall genome stability and assess its potential as a predictive biomarker for immunotherapy.\u003c/p\u003e \u003cp\u003eIn the specific context of bladder cancer, the disease has a high recurrence rate, strong aggressiveness and complex molecular mechanisms[\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. It has been shown that low expression of SLC9A3R1 is associated with high-grade tumors, lymph node metastasis and poor prognosis, suggesting that it plays an oncogenic role by regulating cell adhesion and migration. Meanwhile, increasing evidence supports that SLC9A3R1 also has an anti-cancer function in bladder cancer, and its expression level is closely related to patient prognosis and tumor microenvironment characteristics[\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. We further propose that SLC9A3R1 may affect the anti-tumor immune response in bladder cancer by regulating the immune microenvironment. Specifically, we hypothesized that SLC9A3R1 could alleviate the immunosuppressive state by inhibiting macrophage polarization toward the M2 type and reducing immunosuppressive cytokine secretion. In addition, SLC9A3R1 may also modulate the expression of immune checkpoint molecules (e.g., PD-L1), which in turn affects the sensitivity of tumor cells to ICIs. However, in our validation cohort, despite the observed upregulation of SLC9A3R1 expression in tumor tissues, its correlation with PD-L1 expression or CD8\u003csup\u003e+\u003c/sup\u003e T-cell infiltration did not reach statistical significance, which may be limited by the small sample size. Therefore, larger clinical cohorts and functional experiments are still needed to validate the interaction between SLC9A3R1 and immune cell infiltration and to clarify its specific role in immunotherapy response.\u003c/p\u003e \u003cp\u003eWhile these findings offer new insights, they require validation in larger experimental cohorts and clinical datasets. In bladder cancer, reduced SLC9A3R1 expression correlates with adverse prognosis and features of immune evasion. Elucidating the role of SLC9A3R1 in immune modulation, genomic instability, and response to immunotherapy may uncover key regulatory mechanisms and inform the development of novel precision therapeutic strategies.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eOur pan-cancer analysis, validated in a bladder cancer cohort, underscores SLC9A3R1 as a clinically significant modulator of tumor progression and patient survival. Beyond its intrinsic regulation of cancer cell behavior, SLC9A3R1 contributes to the dynamic reshaping of the tumor immune microenvironment. Its expression correlates with patterns of immune cell infiltration, genomic instability, and core oncogenic signaling pathways, implicating SLC9A3R1 in the establishment of an immunosuppressive niche. These results support its potential as a prognostic biomarker and a novel target for immunotherapeutic strategies in bladder cancer, meriting dedicated translational exploration.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWYM: Data interpretation, manuscript drafting, WXX: Literature research. SC,CX: Conceptual advice, technical support. BT: Study design. All authors reviewed and approved the final manuscript.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analyzed in this study are publicly available. Pan-cancer transcriptomic and clinical matrices were retrieved from TCGA via the UCSC Xena repository (https://xena.ucsc.edu/; original TCGA portal at https://portal.gdc.cancer.gov). Matched normal-tissue expression for SLC9A3R1 was obtained from GTEx (https://gtexportal.org). Public GEO cohorts were downloaded from the NCBI GEO database (https://www.ncbi.nlm.nih.gov/geo) (GSE154261, GSE70691, GSE69795, GSE52219, GSE48276, GSE48075, GSE39281, GSE37815, GSE31684, GSE19423, GSE13507).. Somatic mutation (SNV) calls were obtained from the Genomic Data Commons (GDC) (https://portal.gdc.cancer.gov), and SLC9A3R1 cancer-specific mutation frequencies were queried from cBioPortal (https://www.cbioportal.org). Processed data generated and used in this study are available via the BEST platform (https://rookieutopia.com). \u003c/p\u003e\n\u003cp\u003eRaw data:https://www.jianguoyun.com/p/DboakWAQxPD6DRirgJYGIAA\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e \u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical statment:\u003c/strong\u003e Tissue microarray chips and clinical data were obtained with patients\u0026rsquo; informed consent in accordance with the Helsinki Declaration and approved by the institutional ethics committee of Shanghai Zhuoli Biotechnology Co., Ltd. (No. LLS M-15-01). As all data analyzed were derived from public databases and no human subjects were directly recruited by our institution, additional ethical approval was not required. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent: \u003c/strong\u003eConsent to Participate and Consent to Publish were obtained from all participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u003c/strong\u003e not applicable\u003cbr clear=\"all\"\u003e \u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKamat AM, Hahn NM, Efstathiou JA, Lerner SP, Malmstr\u0026ouml;m PU, Choi W, Guo CC, Lotan Y, Kassouf W. Bladder cancer. Lancet (London England). 2016;388(10061):2796\u0026ndash;810.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLenis AT, Lec PM, Chamie K, Mshs MD. Bladder Cancer: A Review. JAMA. 2020;324(19):1980\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFeng DC, Li DX, Wu RC, Wang J, Xiao YH, Yoo KH, Ye X, Wei WR, Kong DP, Tuo ZT. Global burden and cross-country inequalities in urinary tumors from 1990 to 2021 and predicted incidence changes to 2046. Military Med Res. 2025;12(1):12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTheivendran S, Lazarev S, Yu C. Mesoporous silica/organosilica nanoparticles for cancer immunotherapy. Explor (Beijing China). 2023;3(3):20220086.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi AQ, Fang JH. Anti-angiogenic therapy enhances cancer immunotherapy: Mechanism and clinical application. Interdisciplinary Med 2024, 2(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen T, Cheng L, Ma J, Yuan J, Pi C, Xiong L, Chen J, Liu H, Tang J, Zhong Y, et al. Molecular mechanisms of rapid-acting antidepressants: New perspectives for developing antidepressants. Pharmacol Res. 2023;194:106837.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLetai A, Bhola P, Welm AL. Functional precision oncology: Testing tumors with drugs to identify vulnerabilities and novel combinations. Cancer Cell. 2022;40(1):26\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKreimann EL, Ratajska M, Kuzniacka A, Demacopulo B, Stukan M, Limon J. A novel splicing mutation in the SLC9A3R1 gene in tumors from ovarian cancer patients. Oncol Lett. 2015;10(6):3722\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu H, Ma Y, He HW, Wang JP, Jiang JD, Shao RG. SLC9A3R1 stimulates autophagy via BECN1 stabilization in breast cancer cells. Autophagy. 2015;11(12):2323\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePoplawski P, Alseekh S, Jankowska U, Skupien-Rabian B, Iwanicka-Nowicka R, Kossowska H, Fogtman A, Rybicka B, Bogusławska J, Adamiok-Ostrowska A, et al. Coordinated reprogramming of renal cancer transcriptome, metabolome and secretome associates with immune tumor infiltration. Cancer Cell Int. 2023;23(1):2.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXiong L, Luo Y, Yuan T, Lin W, Lin B, Wu C, Duan Y, Ou Y. Prognostic 7-SLC-Gene Signature Identified via Weighted Gene Co-Expression Network Analysis for Patients with Hepatocellular Carcinoma. \u003cem\u003eJournal of oncology\u003c/em\u003e 2023, 2023:4364654.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFeng D, Shi X, Zhu W, Zhang F, Li D, Han P, Wei Q, Yang L. A pan-cancer analysis of the oncogenic role of leucine zipper protein 2 in human cancer. Experimental Hematol Oncol. 2022;11(1):55.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShen W, Song Z, Zhong X, Huang M, Shen D, Gao P, Qian X, Wang M, He X, Wang T et al. Sangerbox: A comprehensive, interaction-friendly clinical bioinformatics analysis platform. \u003cem\u003eiMeta\u003c/em\u003e 2022, 1(3):e36.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu J, Lichtenberg T, Hoadley KA, Poisson LM, Lazar AJ, Cherniack AD, Kovatich AJ, Benz CC, Levine DA, Lee AV, et al. An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics. Cell. 2018;173(2):400\u0026ndash;e416411.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAndersen PK, Gill RD. Cox's Regression Model for Counting Processes: A Large Sample Study. Annals Stat 1982, 10(4).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarretina J, Caponigro G, Stransky N, Venkatesan K, Margolin AA, Kim S, Wilson CJ, Leh\u0026aacute;r J, Kryukov GV, Sonkin D, et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature. 2012;483(7391):603\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu Z, Lu W, Su C, Lv Y, Ye Y, Guo B, Liu D, Yan H, Mi H, Li T, et al. Single-Cell RNA-seq Identification of the Cellular Molecular Characteristics of Sporadic Bilateral Clear Cell Renal Cell Carcinoma. Front Oncol. 2021;11:659251.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun D, Wang J, Han Y, Dong X, Ge J, Zheng R, Shi X, Wang B, Li Z, Ren P, et al. TISCH: a comprehensive web resource enabling interactive single-cell transcriptome visualization of tumor microenvironment. Nucleic Acids Res. 2021;49(D1):D1420\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBeroukhim R, Mermel CH, Porter D, Wei G, Raychaudhuri S, Donovan J, Barretina J, Boehm JS, Dobson J, Urashima M, et al. The landscape of somatic copy-number alteration across human cancers. Nature. 2010;463(7283):899\u0026ndash;905.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNewman AM, Liu CL, Green MR, Gentles AJ, Feng W, Xu Y, Hoang CD, Diehn M, Alizadeh AA. Robust enumeration of cell subsets from tissue expression profiles. Nat Methods. 2015;12(5):453\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDu S, Yan J, Xue Y, Zhong Y, Dong Y. Adoptive cell therapy for cancer treatment. Explor (Beijing China). 2023;3(4):20210058.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang XL, Xu HW, Liu NN. Oral Microbiota: A New Insight into Cancer Progression, Diagnosis and Treatment. Phenomics (Cham Switzerland). 2023;3(5):535\u0026ndash;47.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu H, Chen X, Jia Y, Chen H, Wang X, Liu G, Luo Y. Facing inevitable PARPis resistance: Mechanisms and therapeutic strategies for breast cancer treatment. Interdisciplinary Med 2023, 1(2).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen Z, Vallega KA, Chen H, Zhou J, Ramalingam SS, Sun SY. The natural product berberine synergizes with osimertinib preferentially against MET-amplified osimertinib-resistant lung cancer via direct MET inhibition. Pharmacol Res. 2022;175:105998.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWong RS, Ong RJ, Lim JS. Immune checkpoint inhibitors in breast cancer: development, mechanisms of resistance and potential management strategies. Cancer drug Resist (Alhambra Calif). 2023;6(4):768\u0026ndash;87.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCerella C, Dicato M, Diederich M. Enhancing personalized immune checkpoint therapy by immune archetyping and pharmacological targeting. Pharmacol Res. 2023;196:106914.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang X, Mao K, Zhang X, Zhang Y, Yang YG, Sun T. Red blood cell derived nanocarrier drug delivery system: A promising strategy for tumor therapy. Interdisciplinary Med 2024, 2(3).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Z, Liu Z, Qu J, Sun Y, Zhou W. Role of natural products in tumor therapy from basic research and clinical perspectives. Acta Materia Med 2024, 3(2).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYao W, Feng D, Bian W, Yang L, Li Y, Yang Z, Xiong Y, Zheng J, Zhai R, He J. EBP50 inhibits EGF-induced breast cancer cell proliferation by blocking EGFR phosphorylation. Amino Acids. 2012;43(5):2027\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi D, Yu Q, Wu R, Tuo Z, Wang J, Ye L, Shao F, Chaipanichkul P, Yoo KH, Wei W, et al. Interactions between oxidative stress and senescence in cancer: Mechanisms, therapeutic implications, and future perspectives. Redox Biol. 2024;73:103208.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVaquero J, Nguyen Ho-Bouldoires TH, Clap\u0026eacute;ron A, Fouassier L. Role of the PDZ-scaffold protein NHERF1/EBP50 in cancer biology: from signaling regulation to clinical relevance. Oncogene. 2017;36(22):3067\u0026ndash;79.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFang XY, Song R, Chen W, Yang YY, Gu YH, Shu YQ, Wu XD, Wu XF, Sun Y, Shen Y, et al. PRL-3 Promotes the Malignant Progression of Melanoma via Triggering Dephosphorylation and Cytoplasmic Localization of NHERF1. J Invest Dermatol. 2015;135(9):2273\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBellizzi A, Mangia A, Malfettone A, Cardone RA, Simone G, Reshkin SJ, Paradiso A. Na+/H+ exchanger regulatory factor 1 expression levels in blood and tissue predict breast tumour clinical behaviour. Histopathology. 2011;58(7):1086\u0026ndash;95.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCardone RA, Bellizzi A, Busco G, Weinman EJ, Dell'Aquila ME, Casavola V, Azzariti A, Mangia A, Paradiso A, Reshkin SJ. The NHERF1 PDZ2 domain regulates PKA-RhoA-p38-mediated NHE1 activation and invasion in breast tumor cells. Mol Biol Cell. 2007;18(5):1768\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKarn T, Ruckh\u0026auml;berle E, Hanker L, M\u0026uuml;ller V, Schmidt M, Solbach C, G\u0026auml;tje R, Gehrmann M, Holtrich U, Kaufmann M, et al. Gene expression profiling of luminal B breast cancers reveals NHERF1 as a new marker of endocrine resistance. Breast Cancer Res Treat. 2011;130(2):409\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMalfettone A, Saponaro C, Paradiso A, Simone G, Mangia A. Peritumoral vascular invasion and NHERF1 expression define an immunophenotype of grade 2 invasive breast cancer associated with poor prognosis. BMC Cancer. 2012;12:106.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMangia A, Chiriatti A, Bellizzi A, Malfettone A, Stea B, Zito FA, Reshkin SJ, Simone G, Paradiso A. Biological role of NHERF1 protein expression in breast cancer. Histopathology. 2009;55(5):600\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSong J, Bai J, Yang W, Gabrielson EW, Chan DW, Zhang Z. Expression and clinicopathological significance of oestrogen-responsive ezrin-radixin-moesin-binding phosphoprotein 50 in breast cancer. Histopathology. 2007;51(1):40\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAccardi R, Rubino R, Scalise M, Gheit T, Shahzad N, Thomas M, Banks L, Indiveri C, Sylla BS, Cardone RA, et al. E6 and E7 from human papillomavirus type 16 cooperate to target the PDZ protein Na/H exchange regulatory factor 1. J Virol. 2011;85(16):8208\u0026ndash;16.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBartholow TL, Becich MJ, Chandran UR, Parwani AV. Immunohistochemical analysis of ezrin-radixin-moesin-binding phosphoprotein 50 in prostatic adenocarcinoma. BMC Urol. 2011;11:12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTabrizi AD, Kalloger SE, K\u0026ouml;bel M, Cipollone J, Roskelley CD, Mehl E, Gilks CB. Primary ovarian mucinous carcinoma of intestinal type: significance of pattern of invasion and immunohistochemical expression profile in a series of 31 cases. Int J Gynecol pathology: official J Int Soc Gynecol Pathologists. 2010;29(2):99\u0026ndash;107.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJi MY, Fan DK, Lv XG, Peng XL, Lei XF, Dong WG. The detection of EBP50 expression using quantum dot immunohistochemistry in pancreatic cancer tissue and down-regulated EBP50 effect on PC-2 cells. J Mol Histol. 2012;43(5):517\u0026ndash;26.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMalfettone A, Silvestris N, Paradiso A, Mattioli E, Simone G, Mangia A. Overexpression of nuclear NHERF1 in advanced colorectal cancer: association with hypoxic microenvironment and tumor invasive phenotype. Exp Mol Pathol. 2012;92(3):296\u0026ndash;303.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMangia A, Caldarola L, Dell'Endice S, Scarpi E, Saragoni L, Monti M, Santini D, Brunetti O, Simone G, Silvestris N. The potential predictive role of nuclear NHERF1 expression in advanced gastric cancer patients treated with epirubicin/oxaliplatin/capecitabine first line chemotherapy. Cancer Biol Ther. 2015;16(8):1140\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchirosi L, Mazzotta A, Opinto G, Pinto R, Graziano G, Tommasi S, Fucci L, Simone G, Mangia A. β-catenin interaction with NHERF1 and RASSF1A methylation in metastatic colorectal cancer patients. Oncotarget. 2016;7(42):67841\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang L, Du YR, Ji MY, Wang W, Zhan N, Zhou QS, Dong WG. Reduced EBP50 expression or mis-localization of the EBP50 protein is associated with the malignant progression of esophageal squamous cell carcinoma. Eur Rev Med Pharmacol Sci. 2014;18(24):3854\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFouassier L, Duan CY, Feranchak AP, Yun CH, Sutherland E, Simon F, Fitz JG, Doctor RB. Ezrin-radixin-moesin-binding phosphoprotein 50 is expressed at the apical membrane of rat liver epithelia. Hepatology (Baltimore MD). 2001;33(1):166\u0026ndash;76.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNguyen Ho-Bouldoires TH, Clap\u0026eacute;ron A, Mergey M, Wendum D, Desbois-Mouthon C, Tahraoui S, Fartoux L, Chettouh H, Merabtene F, Scatton O, et al. Mitogen-activated protein kinase-activated protein kinase 2 mediates resistance to hydrogen peroxide-induced oxidative stress in human hepatobiliary cancer cells. Free Radic Biol Med. 2015;89:34\u0026ndash;46.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShibata T, Chuma M, Kokubu A, Sakamoto M, Hirohashi S. EBP50, a beta-catenin-associating protein, enhances Wnt signaling and is over-expressed in hepatocellular carcinoma. Hepatology (Baltimore MD). 2003;38(1):178\u0026ndash;86.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMangia A, Partipilo G, Schirosi L, Saponaro C, Galetta D, Catino A, Scattone A, Simone G. Fine Needle Aspiration Cytology: A Tool to Study NHERF1 Expression as a Potential Marker of Aggressiveness in Lung Cancer. Mol Biotechnol. 2015;57(6):549\u0026ndash;57.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGeorgescu MM, Yell P, Mobley BC, Shang P, Georgescu T, Wang SH, Canoll P, Hatanpaa KJ, White CL 3rd, Raisanen JM. NHERF1/EBP50 is an organizer of polarity structures and a diagnostic marker in ependymoma. Acta Neuropathol Commun. 2015;3:11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKislin KL, McDonough WS, Eschbacher JM, Armstrong BA, Berens ME. NHERF-1: modulator of glioblastoma cell migration and invasion. Neoplasia (New York NY). 2009;11(4):377\u0026ndash;87.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMolina JR, Agarwal NK, Morales FC, Hayashi Y, Aldape KD, Cote G, Georgescu MM. PTEN, NHERF1 and PHLPP form a tumor suppressor network that is disabled in glioblastoma. Oncogene. 2012;31(10):1264\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMolina JR, Morales FC, Hayashi Y, Aldape KD, Georgescu MM. Loss of PTEN binding adapter protein NHERF1 from plasma membrane in glioblastoma contributes to PTEN inactivation. Cancer Res. 2010;70(17):6697\u0026ndash;703.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDu G, Hao C, Gu Y, Wang Z, Jiang WG, He J, Cheng S. A Novel NHERF1 Mutation in Human Breast Cancer Inactivates Inhibition by NHERF1 Protein in EGFR Signaling. Anticancer Res. 2016;36(3):1165\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaponaro C, Vagheggini A, Scarpi E, Centonze M, Catacchio I, Popescu O, Pastena MI, Giotta F, Silvestris N, Mangia A. NHERF1 and tumor microenvironment: a new scene in invasive breast carcinoma. J experimental Clin cancer research: CR. 2018;37(1):96.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaponaro C, Sergio S, Coluccia A, De Luca M, La Regina G, Mologni L, Famiglini V, Naccarato V, Bonetti D, Gautier C, et al. : β-catenin knockdown promotes NHERF1-mediated survival of colorectal cancer cells: implications for a double-targeted therapy. Oncogene. 2018;37(24):3301\u0026ndash;16.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun J, Li P, Yang J. Repressing of NHERF1 inhibits liver cancer progression by promoting the production of ROS. Biochem Biophys Res Commun. 2019;509(1):8\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu R, Li D, Zhang S, Wang J, Chen K, Tuo Z, Miyamoto A, Yoo KH, Wei W, Zhang C, et al. A pan-cancer analysis of the oncogenic and immunological roles of transglutaminase 1 (TGM1) in human cancer. J Cancer Res Clin Oncol. 2024;150(3):123.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYan H, Song L, Li Y, Xu Q, Guo W, Lin S, Jiang W, Wang Z, Deng L, Huang Z, et al. Clinical evidence for efficacy of pembrolizumab in MSI-H and TMB-H advanced solid tumor: results from three cancer centers in China. Cancer Immunol immunotherapy: CII. 2024;73(4):74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePalmeri M, Mehnert J, Silk AW, Jabbour SK, Ganesan S, Popli P, Riedlinger G, Stephenson R, de Meritens AB, Leiser A, et al. Real-world application of tumor mutational burden-high (TMB-high) and microsatellite instability (MSI) confirms their utility as immunotherapy biomarkers. ESMO open. 2022;7(1):100336.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang X, Wu T, Cai X, Dong J, Xia C, Zhou Y, Ding R, Yang R, Tan J, Zhang L, et al. Neoadjuvant Immunotherapy for MSI-H/dMMR Locally Advanced Colorectal Cancer: New Strategies and Unveiled Opportunities. Front Immunol. 2022;13:795972.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTuo Z, Zhang Y, Li D, Wang Y, Wu R, Wang J, Yu Q, Ye L, Shao F, Wusiman D, et al. Relationship between clonal evolution and drug resistance in bladder cancer: A genomic research review. Pharmacol Res. 2024;206:107302.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eComp\u0026eacute;rat E, Amin MB, Cathomas R, Choudhury A, De Santis M, Kamat A, Stenzl A, Thoeny HC, Witjes JA. Current best practice for bladder cancer: a narrative review of diagnostics and treatments. Lancet (London England). 2022;400(10364):1712\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu X, Ren B, Fang Y, Ren J, Wang X, Gu M, Zhou F, Xiao R, Luo X, You L, et al. Comprehensive analysis of bulk and single-cell transcriptomic data reveals a novel signature associated with endoplasmic reticulum stress, lipid metabolism, and liver metastasis in pancreatic cancer. J translational Med. 2024;22(1):393.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Table 1","content":"\u003cp\u003eTable 1 not available with this version\u003c/p\u003e\n"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"SLC9A3R1, Pan-cancer analysis, Prognostic biomarker, Immune cell, Bladder cancer","lastPublishedDoi":"10.21203/rs.3.rs-9211687/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9211687/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eEmerging evidence indicates that SLC9A3R1 participates in oncogenesis, yet its prognostic relevance and immune-regulatory circuitry remain largely undefined.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA harmonized pan-cancer transcriptomic compendium was retrieved from public repositories, and the clinical cohort was employed for bladder-cancer validation of expression patterns and biological function. Cox regression models were constructed to quantify the prognostic impact of SLC9A3R1, while immunohistochemistry on paired tumor and adjacent urothelium was performed to corroborate protein abundance and clinicopathological associations. Oncogenic and immunological roles were subsequently interrogated using R v4.2.1 and associated bioinformatics packages.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003ePan-cancer profiling demonstrated widespread SLC9A3R1 dysregulation that correlated with patient outcome across malignancies. Moreover, its expression aligned with genomic-heterogeneity indices and stemness scores in multiple tumor entities. Immunohistochemistry confirmed elevated SLC9A3R1 protein in bladder tumors, and, within the same cohort, transcript abundance paralleled tumor-associated fibroblast distribution.SLC9A3R1 up-regulation sustains stem-like traits, migration, chemoresistance and immune escape, driving bladder-cancer aggressiveness.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eSLC9A3R1 constitutes a multi-dimensional prognostic biomarker that integrates tumor progression, immune contexture and patient survival, thereby offering a rational target for precision oncology.\u003c/p\u003e","manuscriptTitle":"Pan-cancer analysis and verification revealed the clinical significance of SLC9A3R1 in bladder cancer cohort","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-14 02:03:33","doi":"10.21203/rs.3.rs-9211687/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"98595043-3955-421d-a5f3-5cb3d5355fda","owner":[],"postedDate":"April 14th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-22T09:24:24+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-14 02:03:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9211687","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9211687","identity":"rs-9211687","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}