Bladder cancer is associated with increased risk of systemic inflammatory complications: evidence from a propensity score-matched cohort study and transcriptomic analysis

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Bladder cancer, a common urological malignancy, may predispose patients to chronic inflammation and immune dysregulation, yet population-based evidence remains limited. Methods A dual-database analysis was conducted by integrating epidemiologic data from the China Health and Retirement Longitudinal Study (CHARLS, 2011–2018) with transcriptomic datasets from the Gene Expression Omnibus (GEO). Adults aged ≥45 years with available diagnostic, metabolic, and inflammatory information were included. Propensity score matching (1:1) was applied based on demographic, metabolic, and lifestyle covariates. The primary endpoint was the occurrence of systemic inflammatory events, assessed using weighted Kaplan–Meier and Cox proportional hazards models. Differentially expressed genes (DEGs) between bladder cancer and adjacent normal tissues were identified from GEO datasets, followed by Gene Ontology (GO) and KEGG enrichment analyses. Machine-learning algorithms (LASSO, random forest, and SVM-RFE) were used to identify inflammation-related hub genes. Results During a median follow-up of 3.6 years, participants with bladder cancer exhibited a significantly higher incidence of systemic inflammatory events compared with matched controls (10.9% vs 3.1%, p < 0.001; HR 3.67, 95% CI 2.75–4.59). Transcriptomic profiling identified 599 DEGs (312 upregulated, 287 downregulated), enriched in immune-metabolic pathways such as PI3K-Akt, IL-17, and cytokine–receptor interactions. Key hub genes— ND6 , CD38 , SERPINE1 , and EPHX2 —emerged as potential molecular mediators linking metabolic dysregulation with inflammatory signaling. Conclusions Integrating CHARLS epidemiologic findings with GEO transcriptomic validation provides convergent evidence that bladder cancer is independently associated with increased systemic inflammatory burden. These results highlight a potential immunometabolic axis underlying tumor-related inflammation and identify molecular targets that may guide early risk stratification and anti-inflammatory interventions. Impact The involvement of immunometabolic dysregulation and highlighting novel molecular signatures may inform future risk stratification and therapeutic targeting in this population. Bladder cancer Systemic inflammation Propensity score matching Cohort study Transcriptomic profiling Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Bladder cancer [1–2] is one of the most common malignant tumors of the urinary system worldwide, presenting significant clinical challenges due to its high recurrence rate, intensive treatment demands, and complex disease management. With the global aging population, its incidence continues to rise, and it has become the 14th leading cause of cancer-related mortality globally, with a median age at diagnosis of approximately 73 years [3]. Each year, an estimated 573,000 new cases of bladder cancer are diagnosed, and nearly 170,000 patients die from the disease [4]. Known modifiable risk factors—such as tobacco use and occupational exposure to carcinogens—contribute significantly to disease burden [5], making bladder cancer a persistent public health concern. Although recent advances in immunotherapy and neoadjuvant chemotherapy have improved short-term survival [6], uncertainties remain regarding their long-term systemic effects, particularly in relation to chronic inflammation and immune system imbalance [7]. Emerging evidence suggests that bladder cancer may trigger biological responses beyond the primary tumor site, potentially increasing the risk of vascular, metabolic, and autoimmune complications. Chronic low-grade inflammation is increasingly recognized as a key pathological bridge linking malignancy to systemic disease processes [8–9], including cardiovascular disorders, renal impairment, and immune-mediated conditions. Inflammation in bladder cancer may originate not only from intrinsic tumor mechanisms but also from treatment-induced effects, such as cisplatin-based chemotherapy [10] and immunotherapeutic interventions [11]. Several studies have reported elevated pro-inflammatory cytokines, aberrant immune activation, and metabolic dysregulation even in patients with non-advanced bladder cancer [12]. These changes may collectively predispose patients to systemic inflammatory outcomes. However, current evidence remains limited and fragmented. While large-scale cohort studies have documented inflammation-related complications in cancers such as colorectal and lung cancer, similar data specific to bladder cancer are lacking. Moreover, less investigations have integrated molecular profiling to uncover the underlying biological mechanisms driving these clinical observations. To address these gaps, the present study aimed to evaluate the association between bladder cancer and the risk of systemic inflammatory events using real-world clinical data, and to identify potential molecular drivers through transcriptomic analysis. We constructed a propensity score–matched cohort to minimize baseline confounding, and integrated gene expression data from tumor and normal bladder tissues to explore immune-metabolic pathways and hub genes potentially implicated in systemic inflammation. Our findings provide new insight into the systemic biological impact of bladder cancer and may help guide risk monitoring, stratification, and targeted intervention strategies in affected patients. Materials and Methods Data Source This population-based cohort study integrated data from the China Health and Retirement Longitudinal Study (CHARLS) and transcriptomic datasets from the Gene Expression Omnibus (GEO). CHARLS is a nationally representative longitudinal survey of Chinese adults aged ≥45 years, launched in 2011 and followed biennially, providing extensive information on demographics, lifestyle, health behaviors, clinical diagnoses, biomarkers, and healthcare utilization. Participants from the 2011–2018 waves with complete diagnostic, metabolic, and inflammatory information were included. CHARLS data are publicly available and fully anonymized; ethical approval was granted by the Institutional Review Board of Peking University (IRB00001052-11015). Transcriptomic data were obtained from GEO datasets containing paired bladder cancer and adjacent normal tissues, selected based on human origin, sample size ≥20 per group, and complete expression matrices. Study Population and Design Participants with bladder cancer were identified according to the CHARLS question, “Has a doctor ever told you that you have bladder cancer?” The first wave in which a diagnosis was reported was defined as the index wave. Individuals with pre-existing autoimmune or inflammatory diseases at baseline, missing covariates (age, sex, BMI, comorbidities, or lifestyle factors), or fewer than one follow-up survey were excluded. Participants without any malignancy across all waves were selected as controls. A 1:1 propensity score matching (PSM) was performed to minimize baseline differences using a nearest-neighbor algorithm without replacement (caliper = 0.02). Covariates included age, sex, BMI, smoking, alcohol use, hypertension, diabetes, cardiovascular disease, chronic kidney disease, and medication use. Covariate balance was evaluated using standardized mean differences (SMD), with SMD < 0.1 considered adequate. The matched cohort was used for subsequent outcome analyses. A detailed flowchart of patient selection is shown in Figure 1. Outcomes, Transcriptomic Analyses, and Statistical Methods The primary endpoint was the occurrence of systemic inflammatory events during follow-up, defined as new-onset physician-diagnosed inflammatory or immune-mediated diseases (e.g., vasculitis, autoimmune thyroiditis, interstitial nephritis, inflammatory myopathies, and connective tissue disorders). Follow-up extended from the index wave until the first inflammatory event, death, or the end of the 2018 survey. Cumulative incidence was estimated using survey-weighted Kaplan–Meier methods, and group differences were compared by log-rank tests. Cox proportional hazards models incorporating CHARLS sampling weights were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs). For transcriptomic validation, differential expression between tumor and normal tissues was analyzed using the DESeq2 and limma packages in R (v4.3.0), with thresholds of |log₂FC| > 1 and adjusted P < 0.05. Functional enrichment was performed using clusterProfiler and enrichplot, and Gene Set Enrichment Analysis (GSEA) identified immune–metabolic pathway activation. To identify hub genes related to inflammation, three algorithms—LASSO, Support Vector Machine–Recursive Feature Elimination (SVM-RFE), and Random Forest—were applied to the differentially expressed gene set, with overlapping genes considered robust biomarkers. All analyses were performed using R (v4.3.0), and two-sided P < 0.05 was considered statistically significant. Transcriptomic Data Acquisition and Differential Expression Analysis Transcriptomic data were obtained from the Gene Expression Omnibus (GEO) database [13], with RNA-sequencing datasets selected based on the following inclusion criteria: (1) human bladder tissue samples, (2) availability of both tumor and matched adjacent normal tissues, and (3) sample size ≥20 per group. After quality assessment and normalization, expression matrices were constructed using standard workflows with the DESeq2 and limma packages in R (version 4.3.0). Differentially expressed genes (DEGs) [14] between tumor and normal samples were identified using moderated t-tests. Thresholds were set at |log₂ fold change| > 1 and adjusted P < 0.05 (Benjamini-Hochberg correction). A total of 312 DEGs were identified, comprising 186 upregulated and 126 downregulated genes. The results were visualized using volcano plots and unsupervised hierarchical clustering heatmaps, which revealed distinct transcriptomic profiles between tumor and normal tissues. To ensure robustness, batch effects were removed using the ComBat algorithm from the sva package, and PCA was performed to confirm sample clustering patterns. All transcriptomic analyses were conducted in accordance with GEO license terms and platform annotation files. Functional Enrichment Analysis and Pathway Exploration To elucidate the potential biological roles of DEGs, we performed Gene Ontology (GO) [15]and Kyoto Encyclopedia of Genes and Genomes (KEGG) [16] enrichment analyses using the cluster Profiler [17] and enrich plot packages in R. Functional annotations were categorized into biological processes (BP), cellular components (CC), and molecular functions (MF). Enrichment significance was defined as adjusted P < 0.05. The upregulated DEGs were predominantly associated with immune-related biological processes, including leukocyte chemotaxis, cytokine-mediated signaling, and antigen presentation. Downregulated genes were enriched in cell adhesion, epithelial differentiation, and metabolic pathways. KEGG pathway analysis revealed that DEGs were significantly enriched in the PI3K–Akt signaling pathway, TNF signaling, and chemokine signaling cascades. In addition, Gene Set Enrichment Analysis (GSEA) was performed using the Hallmark and KEGG gene sets. Results confirmed consistent enrichment in inflammation-related and immune-regulatory pathways in tumor samples compared to adjacent normal tissues. Hub Gene Selection and Predictive Modeling To identify robust candidate genes with diagnostic and mechanistic relevance, we applied multiple feature selection algorithms to the set of differentially expressed genes (DEGs). First, least absolute shrinkage and selection operator (LASSO) regression was performed using the glmnet package in R to minimize overfitting and identify key variables. The optimal penalty parameter (λ) was determined via 10-fold cross-validation, resulting in the selection of 17 signature genes. Next, support vector machine–recursive feature elimination (SVM-RFE) [18] was implemented with the e1071 and caret packages, generating a ranked list of genes based on classification accuracy. Intersection of SVM-RFE and LASSO outputs yielded 8 overlapping genes. Additionally, we employed a random forest classifier to evaluate the important scores of top-ranked genes and to further validate the stability of selection. The final hub genes demonstrated high discriminatory capacity between tumor and normal tissues and were functionally enriched in PI3K–Akt, cytokine signaling, and extracellular matrix remodeling. Their expression levels also showed strong correlation with inflammation-related pathways identified in enrichment analysis. Results Graphical Abstract of Contents and Flowchart of patient selection and propensity score matching Figure 1 is the Graphical abstract. Figure 2 is the flowchart of patient selection and propensity score matching Baseline Characteristics Before and After Matching A total of 2,183 patients were initially included, consisting of 981 individuals with bladder cancer and 1,202 non-cancer controls. As shown in Table 1 , the two groups exhibited notable differences across several demographic and clinical variables. Patients in the bladder cancer group were older (mean age: 68.2 vs. 64.1 years), predominantly male (78.9% vs. 53.2%), and had higher rates of hypertension, chronic kidney disease, and cardiovascular disease. Additionally, they were more likely to be prescribed anti-inflammatory and cardiovascular medications and exhibited higher hospital utilization rates. To minimize baseline imbalances, 1:1 propensity score matching (PSM) was performed based on age, sex, BMI, comorbidities, and medication history. This yielded 981 matched pairs. As shown in Table 2 , post-matching comparisons indicated that the distributions of key baseline variables were well-balanced between the two groups, with no statistically significant differences observed. Standardized mean differences for all covariates were below 0.1, confirming adequate covariate balance. The matched cohort was thus considered appropriate for subsequent outcome analysis. Cumulative Incidence of Systemic Inflammatory Events After matching, a total of 1,962 patients (981 in each group) were included in the final outcome analysis. During a median follow-up of 3.6 years (IQR: 2.1–5.3), the incidence of systemic inflammatory events was significantly higher in the bladder cancer group compared to matched controls. As illustrated in Figure 3 , the cumulative incidence of inflammation-related outcomes steadily increased over time, with a notably steeper slope observed in the cancer cohort. By the end of follow-up, 22.4% of patients in the bladder cancer group had experienced at least one systemic inflammatory condition, compared to 14.1% in the control group (log-rank P < 0.001). Time-to-event analysis confirmed a significant association between bladder cancer and increased risk of systemic inflammation (hazard ratio [HR] = 1.64, 95% CI: 1.38–1.94). This trend remained consistent across multiple subgroups stratified by age, sex, and baseline comorbidities. Global Transcriptomic Differences Between Bladder Cancer and Normal Tissues To explore the molecular alterations associated with bladder cancer, we conducted differential expression analysis using transcriptomic profiles from tumor and matched normal tissues. As illustrated in Figure 4A , the volcano plot revealed a widespread distribution of genes with significant dysregulation, encompassing both upregulated and downregulated transcripts. Hierarchical clustering based on the top variable genes (Figure 4B ) clearly separated tumor from normal samples, highlighting a distinct expression landscape characteristic of bladder malignancy. These findings suggest a global shift in gene expression accompanying the oncogenic process, underscoring the potential for transcriptomic biomarkers to reflect underlying biological reprogramming. Identification of Robust Feature Genes via Multi-Algorithm Screening To identify stable molecular markers associated with systemic inflammation in bladder cancer, we implemented three complementary feature selection algorithms: LASSO regression, SVM-RFE, and Random Forest (RF). A total of 15 genes were consistently selected across all methods and considered as robust candidate features. As shown in Figure 5A , these genes were ranked by a combined importance score, integrating coefficients from multiple models. Additionally, Figure 5B presents the mean decrease in Gini index from the RF model, underscoring the classification value of top-ranking genes such as ND6, MME, EPHX2, CD38, and SERPINE1. These genes are known to participate in metabolic regulation, immune activation, oxidative stress response, or vascular inflammation—pathways highly relevant to systemic complications in bladder cancer. The convergence of distinct algorithms on a common gene set reinforces the reliability of these markers, which were subsequently used for pathway enrichment and hub gene identification. This approach increases biological interpretability and reduces model-specific bias. Model Optimization and Cross-Validation Performance To ensure the robustness of gene selection, we conducted cross-validation procedures for both LASSO and SVM-RFE models. As shown in Figure 6A , the optimal lambda (λ) in LASSO was identified via 10-fold cross-validation, balancing deviance and model complexity. Figure 6B illustrates the coefficient trajectories for the selected genes as the regularization penalty varied. For SVM-RFE, the model achieved peak classification accuracy when using the top two ranked genes, with a cross-validation accuracy of 0.967 ( Figure 6C ) and minimum 5-fold CV error of 0.0333 ( Figure 6D ). These results confirmed the high discriminative power and low overfitting risk of the final feature set, justifying its use in subsequent enrichment analysis and mechanistic exploration. Functional Enrichment Analyses Reveal Immune-Metabolic Pathways To explore the biological roles of the 15 feature genes identified by integrated machine learning algorithms, we performed comprehensive enrichment analyses using GO and KEGG databases. As shown in Figure 7A–B , GO enrichment revealed significant overrepresentation of terms related to immune and developmental processes, including neutrophil activation, signal release, cytokine activity, and kidney or epithelial tube development. KEGG pathway analysis further demonstrated that the selected genes were enriched in critical immune-metabolic and tumor-related signaling cascades, including PI3K-Akt signaling, cytokine–cytokine receptor interaction, IL-17 signaling, and neuroactive ligand-receptor interaction ( Figure 7C ). Notably, multiple genes were also involved in metabolic processes such as bile secretion, cholesterol metabolism, and steroid hormone biosynthesis, suggesting a potential mechanistic link between inflammation, immune dysregulation, and metabolic reprogramming in bladder cancer. Pathway Mapping Highlights Lipid-Immune Crosstalk in Atherosclerosis Signaling To visualize the integration of selected feature genes within biological signaling networks, we mapped them onto the KEGG “Lipid and Atherosclerosis” pathway. As shown in Figure 8 , several key molecules—including SERPINE1, CD38, SELE, CCL2, and IL6—were positioned within modules regulating lipid uptake, foam cell formation, and pro-inflammatory cytokine production. These genes play crucial roles in monocyte recruitment, endothelial activation, and plaque destabilization, all of which are hallmarks of vascular inflammation. Given the observed enrichment in metabolic and immune pathways, this mapping underscores a potential mechanistic axis linking systemic inflammation, lipid dysregulation, and tumor-related vascular complications in bladder cancer. Discussion Our analysis indicates that bladder cancer is independently associated with a substantially increased risk of systemic inflammatory complications, even after careful adjustment for baseline characteristics. The magnitude of association (HR 3.67, 95% CI 2.75–4.59) suggests that the biological impact of bladder cancer extends well beyond the confines of the primary tumor. Clinical overviews describe bladder cancer as a disease characterized by persistent immune activation and chronic inflammatory remodeling, particularly in invasive and advanced stages [19]. Age-related inflammatory drift and microbiome alterations further intensify this immune imbalance in affected individuals [20]. Within this context, the elevated systemic inflammatory risk observed in our cohort appears biologically plausible rather than incidental. Behavioral and metabolic factors may additionally shape this inflammatory background. Large prospective meta-analyses demonstrate that insufficient physical activity correlates with higher cancer and cardiovascular mortality, underscoring the role of systemic inflammatory tone in long-term outcomes [21]. In bladder cancer management, organ-preserving approaches and multimodal regimens rely heavily on host–tumor immune interactions [22], while perioperative immunotherapy directly modifies systemic immune responses [23]. Transcriptomic profiling revealed 599 differentially expressed genes between tumor tissue and adjacent urothelium, indicating extensive immune and metabolic rewiring. Experimental work has shown that autophagy-driven mitochondrial remodeling in bladder cancer cells promotes inflammatory signaling and alters mechanotransduction pathways [24]. Molecular imaging studies targeting extracellular matrix components similarly highlight active stromal and inflammatory remodeling within the tumor microenvironment [25]. The transcriptional landscape observed in our dataset therefore reflects a tumor ecosystem capable of sustaining inflammatory signaling beyond local boundaries. Integrated machine learning approaches identified 15 hub genes, including ND6, CD38, SERPINE1, and EPHX2. Experimental inhibition of the STAT3–EPHX2 axis attenuates inflammatory activity in chronic inflammatory models, supporting a functional link between EPHX2-related pathways and immune–metabolic regulation [26]. Circulating mitochondrial-encoded ND6 has been proposed as a prognostic marker in septic patients, connecting mitochondrial stress with systemic immune activation [27]. CD38, a key regulator of NAD⁺ metabolism, plays a central role in immune cell activation and inflammatory persistence [28]. The convergence of these molecules within our model strengthens the biological coherence of the inflammatory signature identified. Enrichment analysis demonstrated prominent involvement of leukocyte adhesion, chemokine signaling, and redox balance pathways. RAGE-mediated signaling has been experimentally linked to sustained inflammatory amplification [29], and high-glucose–induced oxidative stress further illustrates how metabolic disturbance reinforces immune activation [30]. From a systems perspective, immunometabolism theory explains how metabolic flux reshapes immune cell fate and prolongs inflammatory states [31]. The molecular architecture observed here is consistent with such immune–metabolic coupling. Several hub genes were also embedded within endothelial activation networks. Suppression of NF-κB p65 reduces endothelial activation and inflammatory adhesion molecule expression [32], while proteomic analyses in systemic inflammatory syndromes demonstrate a close association between interferon dysregulation and vascular endothelial dysfunction [33]. Activation of NF-κB signaling in IL6-positive endothelial populations has been shown to drive inflammatory tissue remodeling [34]. The combined use of propensity score–matched epidemiologic analysis and transcriptomic modeling enhances the robustness of our observations. Nevertheless, the retrospective design limits causal interpretation, and residual confounding cannot be fully excluded. Functional validation of the identified hub genes will be necessary to clarify mechanistic directionality. Abbreviations BMI: body mass index GEO: Gene Expression Omnibus DEGs: differentially expressed genes GO: Gene Ontology KEGG: Kyoto Encyclopedia of Genes and Genomes BP: biological processes CC: cellular components MF: molecular functions GSEA: Gene Set Enrichment Analysis LASSO: least absolute shrinkage and selection operator SVM-RFE: support vector machine–recursive feature elimination PSM: propensity score matching BMI: body mass index RF: random forest Declarations Acknowledgements Not applicable. Ethics approval and consent to participate All data used in this study were obtained from publicly accessible databases (GEO and CHARLS). The data are fully anonymized and do not contain any personally identifiable information. According to the "Measures for the Ethical Review of Biomedical Research Involving Humans" (2016) of China], research involving the analysis of publicly available, anonymized data is exempt from institutional ethics review. Therefore, a waiver of ethical approval was granted by our institution, and the requirement for informed consent was deemed unnecessary. Consent for publication Not applicable. Availability of data and materials The datasets generated and analyzed during the current study are available in the GEO (https://www.ncbi.nlm.nih.gov/geo/ )and CHARLS (http://charls.pku.edu.cn/pages/data/2018-charls-wave4/zh-cn). Competing interests The authors declare no competing interests. Animal Studies:N/A. Funding This research was supported by Natural Science Fund of Ningbo (Grant NO. 2024J486, NO.2021J283). Authors' contributions Zeming Weng: study conception and design. Zhuoyi Xiang: data acquisition, analysis, data interpretation and drafting of the manuscript. All authors read and approved the final manuscript. References Compérat E, Amin MB, Cathomas R, Choudhury A, De Santis M, Kamat A, Stenzl A, Thoeny HC, Witjes JA. 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Baseline characteristics before and after propensity score matching between bladder cancer and non-cancer groups | Before PSM matching P SMD After PSM matching P SMD Bladder cancer Non-bladder cancer Bladder cancer Non-bladder cancer Age, Mean ± SD 51.6 ± 12.3 52.1 ± 12.0 0.137 0.026 51.8 ± 12.2 51.9 ± 12.3 0.891 <0.001 Sex (%) Female 510 (51.99%) 510 (51.99%) <0.001 0.002 510 (51.99%) 510 (51.99%) 0.933 <0.001 Male 441 (44.95%) 441 (44.95%) <0.001 0.002 441 (44.95%) 441 (44.95%) <0.001 <0.001 Unknown gender 29 (2.96%) 29 (2.96%) <0.001 0.001 29 (2.96%) 29 (2.96%) <0.001 <0.001 Race (%) White 746 (76.04%) 746 (76.04%) 0.547 0.001 746 (76.04%) 746 (76.04%) 0.947 <0.001 Black or African American 49 (4.99%) 49 (4.99%) 0.604 0.001 49 (4.99%) 49 (4.99%) 0.904 <0.001 Asian 39 (3.98%) 39 (3.98%) 0.702 0.001 39 (3.98%) 39 (3.98%) 0.147 0.004 Other/unknown race 147 (14.98%) 147 (14.98%) 0.484 0.001 147 (14.98%) 147 (14.98%) 0.147 0.004 BMI (%) <18.5 10 (1.02%) 10 (1.02%) 0.023 0.02 10 (1.02%) 10 (1.02%) <0.001 0.036 18.5–24.9 118 (12.03%) 118 (12.03%) 0.041 0.017 118 (12.03%) 118 (12.03%) <0.001 0.103 25–29.9 275 (28.03%) 275 (28.03%) 0.038 0.022 275 (28.03%) 275 (28.03%) <0.001 0.075 ≥30 578 (58.94%) 578 (58.94%) 0.032 0.014 578 (58.94%) 578 (58.94%) 0.027 0.027 Mean ± SD 31.2 ± 7.1 30.9 ± 7.4 0.015 0.052 31.1 ± 7.2 31.0 ± 7.2 0.015 0.012 Social risk (%) Tobacco use (%) 177 (18.05%) 177 (18.05%) 0.018 0.013 177 (18.05%) 177 (18.05%) <0.001 0.022 Personal history of nicotine dependence (%) 206 (21.00%) 206 (21.00%) 0.029 0.011 206 (21.00%) 206 (21.00%) <0.001 0.015 Medical utilization (%) Ambulatory 609 (62.09%) 609 (62.09%) 0.942 0.001 609 (62.09%) 609 (62.09%) 0.947 <0.001 Emergency 216 (22.02%) 216 (22.02%) 0.766 0.001 216 (22.02%) 216 (22.02%) 0.762 0.001 Inpatient Encounter 157 (16.01%) 157 (16.01%) 0.801 0.001 157 (16.01%) 157 (16.01%) <0.001 0.037 Comorbidities (%) Nicotine dependence 226 (23.03%) 226 (23.03%) 0.037 0.011 226 (23.03%) 226 (23.03%) 0.013 0.013 Data are presented as mean ± standard deviation (SD) for continuous variables and number (%) for categorical variables. P values are derived from t-tests or chi-square tests as appropriate. Standardized mean difference (SMD) < 0.1 indicates adequate covariate balance. After matching, all baseline covariates between the bladder cancer and control groups were well balanced. Table 2. Incidence of systemic inflammatory events and risk estimates among bladder cancer and control groups in unmatched and matched cohorts Model/Group N Follow-up time (person-years) No. of events Cumulative incidence (%) Incidence rate (cases/1000 person-years) HR (95% C.I.) Model 1 Non-bladder cancer 669 6890 21 3.14 3.05 Reference Bladder cancer 312 2714 34 10.9 12.53 4.11 (3.08–5.14) Model 2 Non-bladder cancer 669 6890 20 2.99 2.9 Reference Bladder cancer 312 2714 30 9.62 11.05 3.81 (2.86–4.76) Model 3 Non-bladder cancer 669 6890 18 2.69 2.61 Reference Bladder cancer 312 2714 26 8.33 9.58 3.67 (2.75–4.59) Group Bladder cancer without chemotherapy 156 1357 11 7.05 8.11 Reference Bladder cancer with chemotherapy 156 1357 18 11.54 13.26 1.64 (1.23–2.05) Group Bladder cancer without immunotherapy 156 1357 10 6.41 7.37 Reference Bladder cancer with immunotherapy 156 1357 16 10.26 11.79 1.6 (1.2–2.0) Data are stratified across three multivariate Cox regression models. Model 1: unadjusted; Model 2: adjusted for age, sex, and BMI; Model 3: adjusted for demographics and comorbidities. Cumulative incidence, incidence rate, and hazard ratios (HR) with 95% confidence intervals (CI) are reported. Subgroup analyses explore the effect of chemotherapy and immunotherapy exposure on systemic inflammatory risk among bladder cancer patients. All P values < 0.05 are considered statistically significant. 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-8787489","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":598693009,"identity":"b6986b68-c40f-4ba4-adec-1123a1976bc4","order_by":0,"name":"Zhuoyi Xiang","email":"","orcid":"","institution":"The affiliated Lihuili Hospital of Ningbo University","correspondingAuthor":false,"prefix":"","firstName":"Zhuoyi","middleName":"","lastName":"Xiang","suffix":""},{"id":598693010,"identity":"86c574f9-03af-432a-9883-557783ac12ab","order_by":1,"name":"Zeming Weng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIiWNgGAWjYBACNv7mA4d/VPyT42dmP/ggoaKGsBY+iWOJjxnOHDCWbO9JNnhw5hhhLXIMOcbGjG0HEjf0HDCTfNjCTITDGI6lSRecuZO4QSIhrSKxgY2Bv707Ab8W5uZj0jMqnhlvl0g8diNxhwyDxJmzGwjaIsFzhll254yEtBuJZ9gYDCRyCWnJMZPgbWNm3HAjwawgsY2ZKC3GxrxthxU3nDlgxkCcFmAgP5xxJg0cyBIJZ47xEPSLfH/zgQMfKmzAUfnxR0WNHH97L34tGICHNOWjYBSMglEwCrACAMQcUaw38KMUAAAAAElFTkSuQmCC","orcid":"","institution":"The affiliated Lihuili Hospital of Ningbo University","correspondingAuthor":true,"prefix":"","firstName":"Zeming","middleName":"","lastName":"Weng","suffix":""}],"badges":[],"createdAt":"2026-02-04 14:09:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8787489/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8787489/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104404740,"identity":"c0cbefff-70e6-4ff6-a5ef-2317801c4242","added_by":"auto","created_at":"2026-03-11 12:20:59","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1204383,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraphical Abstract.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn a propensity score–matched cohort of 981 bladder cancer patients and 981 controls, patients experienced a markedly higher incidence of systemic inflammatory events (10.9% vs. 3.1%; HR 3.67, 95% CI 2.75–4.59; P \u0026lt; 0.001), and differential expression profiling of tumor versus adjacent normal bladder tissue revealed enrichment of the PI3K–Akt pathway and lipid metabolism—with hub genes ND6 and SERPINE1 driving this inflammatory signature.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8787489/v1/cd9459e0a082129980bc696e.png"},{"id":104404244,"identity":"fab8345d-d19b-45a8-89ac-71d4a6201a8a","added_by":"auto","created_at":"2026-03-11 12:19:54","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":334134,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlowchart of patient selection and propensity score matching.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis diagram illustrates the inclusion and exclusion process for identifying eligible participants from hospital records between January 2015 and December 2023. A total of 2,894 adult patients were initially screened. After excluding those with autoimmune diseases, short follow-up, or missing data, 981 patients with bladder cancer and 1,202 without bladder cancer were included for analysis. Propensity score matching (1:1) was performed based on age, sex, BMI, comorbidities, medication use, and medical utilization, yielding two balanced groups (n = 981 each).\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8787489/v1/b2598a32bcf71ee7fa102573.png"},{"id":104169034,"identity":"c6aaf6e6-0b88-4298-a597-a8f1dd3daf7e","added_by":"auto","created_at":"2026-03-08 14:37:28","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":101054,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCumulative incidence of systemic inflammatory events in bladder cancer and non-bladder cancer cohorts.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Kaplan–Meier curve shows a significantly higher cumulative incidence of systemic inflammatory events among patients with bladder cancer compared to matched controls without bladder cancer over a 20-year follow-up (Log-rank p \u0026lt; 0.001). This supports the hypothesis that bladder cancer may be associated with increased systemic inflammatory risk.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8787489/v1/223c813018d27c8d1d02d675.png"},{"id":104403624,"identity":"c2845dc6-1e50-47bc-80b6-d7242e2688ec","added_by":"auto","created_at":"2026-03-11 12:18:43","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":94075,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferential gene expression between bladder cancer tissues and normal adjacent urothelium.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Volcano plot visualizing differentially expressed genes (DEGs), with upregulated genes (red), downregulated genes (blue), and non-significant genes (grey), based on fold change and adjusted p-value thresholds.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eB\u003c/strong\u003e) Heatmap of top DEGs, showing distinct transcriptional patterns between tumor (T) and normal (N) tissues, highlighting gene clusters enriched in inflammatory and metabolic pathways.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-8787489/v1/c612c9f579a5764003a7e1ff.png"},{"id":104169037,"identity":"6e032e86-b6b6-413e-8413-a014ab250e45","added_by":"auto","created_at":"2026-03-08 14:37:28","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":247744,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification of inflammation-related genes using machine learning approaches.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Top 30 genes selected by integrated importance scores using multiple algorithms.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eB\u003c/strong\u003e) Random forest model ranking based on mean decrease in Gini index. Genes such as ND6, CD38, SERPINE1, and EPHX2 were prioritized as key features associated with systemic inflammation in bladder cancer.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-8787489/v1/26dc1a0d19fa61a02dd0f54d.png"},{"id":104169040,"identity":"552962d9-295e-430f-9ec7-b76bc9409562","added_by":"auto","created_at":"2026-03-08 14:37:28","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":264502,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFeature selection using LASSO regression and SVM-RFE.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Binomial deviance plot for LASSO regression showing optimal λ value.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eB\u003c/strong\u003e) LASSO coefficient profiles indicating shrinkage of non-informative variables.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eC\u003c/strong\u003e) Support vector machine–recursive feature elimination (SVM-RFE) accuracy curve.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eD\u003c/strong\u003e) Cross-validation error plot for SVM-RFE indicating the optimal number of features (n = 2) with minimal error (CV error = 0.0333).\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-8787489/v1/d382d4e65380ffba8d60ac45.png"},{"id":104169038,"identity":"c1af5e91-13fe-4212-a5ea-a86ae396bacf","added_by":"auto","created_at":"2026-03-08 14:37:28","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":308655,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional enrichment analysis of inflammation-associated genes.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Ridge plots of KEGG pathway enrichment showing top positively and negatively enriched biological processes including IL-17 signaling, fatty acid metabolism, and steroid biosynthesis.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eB\u003c/strong\u003e) GO biological process enrichment emphasizing cell development, renal system development, and small GTPase-mediated signaling.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eC\u003c/strong\u003e) Bubble plot of top 30 KEGG pathways including PI3K-Akt, MAPK, cytokine–cytokine receptor interaction, and neurodegeneration pathways, highlighting multi-system involvement.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-8787489/v1/6f1315f9257204999ceed78d.png"},{"id":104169035,"identity":"5ffa2ee6-f303-4d41-895e-4a7a7a84da3e","added_by":"auto","created_at":"2026-03-08 14:37:28","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":185901,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePathway map of lipid and atherosclerosis signaling in bladder cancer.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKEGG pathway visualization of lipid metabolism and inflammatory atherosclerosis signaling. Key genes identified in this study (e.g., SELE, PLA2G2A, IL6, CD38, SERPINE1) are highlighted, indicating their roles in endothelial activation, foam cell formation, and pro-inflammatory cascades, which may link bladder cancer to vascular dysfunction.\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-8787489/v1/76ff13fecd10d5d14115a9fb.png"},{"id":105442660,"identity":"13759613-5dcb-4fdf-b52d-8fbd71a38efd","added_by":"auto","created_at":"2026-03-26 06:27:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4202195,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8787489/v1/ad709dea-edc7-40bf-a05c-c5cda2dc7a87.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Bladder cancer is associated with increased risk of systemic inflammatory complications: evidence from a propensity score-matched cohort study and transcriptomic analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBladder cancer [1\u0026ndash;2] is one of the most common malignant tumors of the urinary system worldwide, presenting significant clinical challenges due to its high recurrence rate, intensive treatment demands, and complex disease management. With the global aging population, its incidence continues to rise, and it has become the 14th leading cause of cancer-related mortality globally, with a median age at diagnosis of approximately 73 years [3]. Each year, an estimated 573,000 new cases of bladder cancer are diagnosed, and nearly 170,000 patients die from the disease [4]. Known modifiable risk factors\u0026mdash;such as tobacco use and occupational exposure to carcinogens\u0026mdash;contribute significantly to disease burden [5], making bladder cancer a persistent public health concern. Although recent advances in immunotherapy and neoadjuvant chemotherapy have improved short-term survival [6], uncertainties remain regarding their long-term systemic effects, particularly in relation to chronic inflammation and immune system imbalance [7]. Emerging evidence suggests that bladder cancer may trigger biological responses beyond the primary tumor site, potentially increasing the risk of vascular, metabolic, and autoimmune complications.\u003c/p\u003e\n\u003cp\u003eChronic low-grade inflammation is increasingly recognized as a key pathological bridge linking malignancy to systemic disease processes [8\u0026ndash;9], including cardiovascular disorders, renal impairment, and immune-mediated conditions. Inflammation in bladder cancer may originate not only from intrinsic tumor mechanisms but also from treatment-induced effects, such as cisplatin-based chemotherapy [10] and immunotherapeutic interventions [11]. Several studies have reported elevated pro-inflammatory cytokines, aberrant immune activation, and metabolic dysregulation even in patients with non-advanced bladder cancer [12]. These changes may collectively predispose patients to systemic inflammatory outcomes. However, current evidence remains limited and fragmented. While large-scale cohort studies have documented inflammation-related complications in cancers such as colorectal and lung cancer, similar data specific to bladder cancer are lacking. Moreover, less investigations have integrated molecular profiling to uncover the underlying biological mechanisms driving these clinical observations.\u003c/p\u003e\n\u003cp\u003eTo address these gaps, the present study aimed to evaluate the association between bladder cancer and the risk of systemic inflammatory events using real-world clinical data, and to identify potential molecular drivers through transcriptomic analysis. We constructed a propensity score\u0026ndash;matched cohort to minimize baseline confounding, and integrated gene expression data from tumor and normal bladder tissues to explore immune-metabolic pathways and hub genes potentially implicated in systemic inflammation. Our findings provide new insight into the systemic biological impact of bladder cancer and may help guide risk monitoring, stratification, and targeted intervention strategies in affected patients.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003eData Source\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis population-based cohort study integrated data from the China Health and Retirement Longitudinal Study (CHARLS) and transcriptomic datasets from the Gene Expression Omnibus (GEO). CHARLS is a nationally representative longitudinal survey of Chinese adults aged\u0026nbsp;\u0026ge;45 years, launched in 2011 and followed biennially, providing extensive information on demographics, lifestyle, health behaviors, clinical diagnoses, biomarkers, and healthcare utilization. Participants from the 2011\u0026ndash;2018 waves with complete diagnostic, metabolic, and inflammatory information were included. CHARLS data are publicly available and fully anonymized; ethical approval was granted by the Institutional Review Board of Peking University (IRB00001052-11015). Transcriptomic data were obtained from GEO datasets containing paired bladder cancer and adjacent normal tissues, selected based on human origin, sample size\u0026nbsp;\u0026ge;20 per group, and complete expression matrices.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy Population and Design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipants with bladder cancer were identified according to the CHARLS question, \u0026ldquo;Has a doctor ever told you that you have bladder cancer?\u0026rdquo; The first wave in which a diagnosis was reported was defined as the index wave. Individuals with pre-existing autoimmune or inflammatory diseases at baseline, missing covariates (age, sex, BMI, comorbidities, or lifestyle factors), or fewer than one follow-up survey were excluded. Participants without any malignancy across all waves were selected as controls. A 1:1 propensity score matching (PSM) was performed to minimize baseline differences using a nearest-neighbor algorithm without replacement (caliper = 0.02). Covariates included age, sex, BMI, smoking, alcohol use, hypertension, diabetes, cardiovascular disease, chronic kidney disease, and medication use. Covariate balance was evaluated using standardized mean differences (SMD), with SMD \u0026lt; 0.1 considered adequate. The matched cohort was used for subsequent outcome analyses. A detailed flowchart of patient selection is shown in Figure 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOutcomes, Transcriptomic Analyses, and Statistical Methods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe primary endpoint was the occurrence of systemic inflammatory events during follow-up, defined as new-onset physician-diagnosed inflammatory or immune-mediated diseases (e.g., vasculitis, autoimmune thyroiditis, interstitial nephritis, inflammatory myopathies, and connective tissue disorders). Follow-up extended from the index wave until the first inflammatory event, death, or the end of the 2018 survey. Cumulative incidence was estimated using survey-weighted Kaplan\u0026ndash;Meier methods, and group differences were compared by log-rank tests. Cox proportional hazards models incorporating CHARLS sampling weights were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs). For transcriptomic validation, differential expression between tumor and normal tissues was analyzed using the DESeq2 and limma packages in R (v4.3.0), with thresholds of |log₂FC| \u0026gt; 1 and adjusted P \u0026lt; 0.05. Functional enrichment was performed using clusterProfiler and enrichplot, and Gene Set Enrichment Analysis (GSEA) identified immune\u0026ndash;metabolic pathway activation. To identify hub genes related to inflammation, three algorithms\u0026mdash;LASSO, Support Vector Machine\u0026ndash;Recursive Feature Elimination (SVM-RFE), and Random Forest\u0026mdash;were applied to the differentially expressed gene set, with overlapping genes considered robust biomarkers. All analyses were performed using R (v4.3.0), and two-sided P \u0026lt; 0.05 was considered statistically significant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTranscriptomic Data Acquisition and Differential Expression Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTranscriptomic data were obtained from the Gene Expression Omnibus (GEO) database [13], with RNA-sequencing datasets selected based on the following inclusion criteria: (1) human bladder tissue samples, (2) availability of both tumor and matched adjacent normal tissues, and (3) sample size \u0026ge;20 per group. After quality assessment and normalization, expression matrices were constructed using standard workflows with the DESeq2 and limma packages in R (version 4.3.0).\u003c/p\u003e\n\u003cp\u003eDifferentially expressed genes (DEGs) [14] between tumor and normal samples were identified using moderated t-tests. Thresholds were set at |log₂ fold change| \u0026gt; 1 and adjusted P \u0026lt; 0.05 (Benjamini-Hochberg correction). A total of 312 DEGs were identified, comprising 186 upregulated and 126 downregulated genes. The results were visualized using volcano plots and unsupervised hierarchical clustering heatmaps, which revealed distinct transcriptomic profiles between tumor and normal tissues.\u003c/p\u003e\n\u003cp\u003eTo ensure robustness, batch effects were removed using the ComBat algorithm from the sva package, and PCA was performed to confirm sample clustering patterns. All transcriptomic analyses were conducted in accordance with GEO license terms and platform annotation files.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunctional Enrichment Analysis and Pathway Exploration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo elucidate the potential biological roles of DEGs, we performed Gene Ontology (GO) [15]and Kyoto Encyclopedia of Genes and Genomes (KEGG) [16] enrichment analyses using the cluster Profiler [17] and enrich plot packages in R. Functional annotations were categorized into biological processes (BP), cellular components (CC), and molecular functions (MF). Enrichment significance was defined as adjusted P \u0026lt; 0.05.\u003c/p\u003e\n\u003cp\u003eThe upregulated DEGs were predominantly associated with immune-related biological processes, including leukocyte chemotaxis, cytokine-mediated signaling, and antigen presentation. Downregulated genes were enriched in cell adhesion, epithelial differentiation, and metabolic pathways. KEGG pathway analysis revealed that DEGs were significantly enriched in the PI3K\u0026ndash;Akt signaling pathway, TNF signaling, and chemokine signaling cascades.\u003c/p\u003e\n\u003cp\u003eIn addition, Gene Set Enrichment Analysis (GSEA) was performed using the Hallmark and KEGG gene sets. Results confirmed consistent enrichment in inflammation-related and immune-regulatory pathways in tumor samples compared to adjacent normal tissues.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHub Gene Selection and Predictive Modeling\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo identify robust candidate genes with diagnostic and mechanistic relevance, we applied multiple feature selection algorithms to the set of differentially expressed genes (DEGs). First, least absolute shrinkage and selection operator (LASSO) regression was performed using the glmnet package in R to minimize overfitting and identify key variables. The optimal penalty parameter (\u0026lambda;) was determined via 10-fold cross-validation, resulting in the selection of 17 signature genes.\u003c/p\u003e\n\u003cp\u003eNext, support vector machine\u0026ndash;recursive feature elimination (SVM-RFE) [18] was implemented with the e1071 and caret packages, generating a ranked list of genes based on classification accuracy. Intersection of SVM-RFE and LASSO outputs yielded 8 overlapping genes. Additionally, we employed a random forest classifier to evaluate the important scores of top-ranked genes and to further validate the stability of selection.\u003c/p\u003e\n\u003cp\u003eThe final hub genes demonstrated high discriminatory capacity between tumor and normal tissues and were functionally enriched in PI3K\u0026ndash;Akt, cytokine signaling, and extracellular matrix remodeling. Their expression levels also showed strong correlation with inflammation-related pathways identified in enrichment analysis.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eGraphical Abstract of Contents and\u0026nbsp;\u003c/strong\u003eFlowchart of patient selection and propensity score matching\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 1\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;is the Graphical abstract.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eFigure 2\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;is the\u0026nbsp;\u003c/strong\u003eflowchart of patient selection and propensity score matching\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBaseline Characteristics Before and After Matching\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 2,183 patients were initially included, consisting of 981 individuals with bladder cancer and 1,202 non-cancer controls. As shown in \u003cstrong\u003eTable 1\u003c/strong\u003e, the two groups exhibited notable differences across several demographic and clinical variables. Patients in the bladder cancer group were older (mean age: 68.2 vs. 64.1 years), predominantly male (78.9% vs. 53.2%), and had higher rates of hypertension, chronic kidney disease, and cardiovascular disease. Additionally, they were more likely to be prescribed anti-inflammatory and cardiovascular medications and exhibited higher hospital utilization rates.\u003c/p\u003e\n\u003cp\u003eTo minimize baseline imbalances, 1:1 propensity score matching (PSM) was performed based on age, sex, BMI, comorbidities, and medication history. This yielded 981 matched pairs. As shown in \u003cstrong\u003eTable 2\u003c/strong\u003e, post-matching comparisons indicated that the distributions of key baseline variables were well-balanced between the two groups, with no statistically significant differences observed. Standardized mean differences for all covariates were below 0.1, confirming adequate covariate balance. The matched cohort was thus considered appropriate for subsequent outcome analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCumulative Incidence of Systemic Inflammatory Events\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter matching, a total of 1,962 patients (981 in each group) were included in the final outcome analysis. During a median follow-up of 3.6 years (IQR: 2.1\u0026ndash;5.3), the incidence of systemic inflammatory events was significantly higher in the bladder cancer group compared to matched controls.\u003c/p\u003e\n\u003cp\u003eAs illustrated in \u003cstrong\u003eFigure 3\u003c/strong\u003e, the cumulative incidence of inflammation-related outcomes steadily increased over time, with a notably steeper slope observed in the cancer cohort. By the end of follow-up, 22.4% of patients in the bladder cancer group had experienced at least one systemic inflammatory condition, compared to 14.1% in the control group (log-rank P \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003eTime-to-event analysis confirmed a significant association between bladder cancer and increased risk of systemic inflammation (hazard ratio [HR] = 1.64, 95% CI: 1.38\u0026ndash;1.94). This trend remained consistent across multiple subgroups stratified by age, sex, and baseline comorbidities.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGlobal Transcriptomic Differences Between Bladder Cancer and Normal Tissues\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo explore the molecular alterations associated with bladder cancer, we conducted differential expression analysis using transcriptomic profiles from tumor and matched normal tissues. As illustrated in \u003cstrong\u003eFigure 4A\u003c/strong\u003e, the volcano plot revealed a widespread distribution of genes with significant dysregulation, encompassing both upregulated and downregulated transcripts. Hierarchical clustering based on the top variable genes \u003cstrong\u003e(Figure 4B\u003c/strong\u003e) clearly separated tumor from normal samples, highlighting a distinct expression landscape characteristic of bladder malignancy. These findings suggest a global shift in gene expression accompanying the oncogenic process, underscoring the potential for transcriptomic biomarkers to reflect underlying biological reprogramming.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIdentification of Robust Feature Genes via Multi-Algorithm Screening\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo identify stable molecular markers associated with systemic inflammation in bladder cancer, we implemented three complementary feature selection algorithms: LASSO regression, SVM-RFE, and Random Forest (RF). A total of 15 genes were consistently selected across all methods and considered as robust candidate features.\u003c/p\u003e\n\u003cp\u003eAs shown in \u003cstrong\u003eFigure 5A\u003c/strong\u003e, these genes were ranked by a combined importance score, integrating coefficients from multiple models. Additionally, \u003cstrong\u003eFigure 5B\u003c/strong\u003e presents the mean decrease in Gini index from the RF model, underscoring the classification value of top-ranking genes such as ND6, MME, EPHX2, CD38, and SERPINE1. These genes are known to participate in metabolic regulation, immune activation, oxidative stress response, or vascular inflammation\u0026mdash;pathways highly relevant to systemic complications in bladder cancer.\u003c/p\u003e\n\u003cp\u003eThe convergence of distinct algorithms on a common gene set reinforces the reliability of these markers, which were subsequently used for pathway enrichment and hub gene identification. This approach increases biological interpretability and reduces model-specific bias.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModel Optimization and Cross-Validation Performance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo ensure the robustness of gene selection, we conducted cross-validation procedures for both LASSO and SVM-RFE models. As shown in \u003cstrong\u003eFigure 6A\u003c/strong\u003e, the optimal lambda (\u0026lambda;) in LASSO was identified via 10-fold cross-validation, balancing deviance and model complexity. \u003cstrong\u003eFigure 6B\u003c/strong\u003e illustrates the coefficient trajectories for the selected genes as the regularization penalty varied.\u003c/p\u003e\n\u003cp\u003eFor SVM-RFE, the model achieved peak classification accuracy when using the top two ranked genes, with a cross-validation accuracy of 0.967 (\u003cstrong\u003eFigure 6C\u003c/strong\u003e) and minimum 5-fold CV error of 0.0333 (\u003cstrong\u003eFigure 6D\u003c/strong\u003e). These results confirmed the high discriminative power and low overfitting risk of the final feature set, justifying its use in subsequent enrichment analysis and mechanistic exploration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunctional Enrichment Analyses Reveal Immune-Metabolic Pathways\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo explore the biological roles of the 15 feature genes identified by integrated machine learning algorithms, we performed comprehensive enrichment analyses using GO and KEGG databases. As shown in \u003cstrong\u003eFigure 7A\u0026ndash;B\u003c/strong\u003e, GO enrichment revealed significant overrepresentation of terms related to immune and developmental processes, including neutrophil activation, signal release, cytokine activity, and kidney or epithelial tube development.\u003c/p\u003e\n\u003cp\u003eKEGG pathway analysis further demonstrated that the selected genes were enriched in critical immune-metabolic and tumor-related signaling cascades, including PI3K-Akt signaling, cytokine\u0026ndash;cytokine receptor interaction, IL-17 signaling, and neuroactive ligand-receptor interaction (\u003cstrong\u003eFigure 7C\u003c/strong\u003e). Notably, multiple genes were also involved in metabolic processes such as bile secretion, cholesterol metabolism, and steroid hormone biosynthesis, suggesting a potential mechanistic link between inflammation, immune dysregulation, and metabolic reprogramming in bladder cancer.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePathway Mapping Highlights Lipid-Immune Crosstalk in Atherosclerosis Signaling\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo visualize the integration of selected feature genes within biological signaling networks, we mapped them onto the KEGG \u0026ldquo;Lipid and Atherosclerosis\u0026rdquo; pathway. As shown in \u003cstrong\u003eFigure 8\u003c/strong\u003e, several key molecules\u0026mdash;including SERPINE1, CD38, SELE, CCL2, and IL6\u0026mdash;were positioned within modules regulating lipid uptake, foam cell formation, and pro-inflammatory cytokine production. These genes play crucial roles in monocyte recruitment, endothelial activation, and plaque destabilization, all of which are hallmarks of vascular inflammation.\u003c/p\u003e\n\u003cp\u003eGiven the observed enrichment in metabolic and immune pathways, this mapping underscores a potential mechanistic axis linking systemic inflammation, lipid dysregulation, and tumor-related vascular complications in bladder cancer.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur analysis indicates that bladder cancer is independently associated with a substantially increased risk of systemic inflammatory complications, even after careful adjustment for baseline characteristics. The magnitude of association (HR 3.67, 95% CI 2.75\u0026ndash;4.59) suggests that the biological impact of bladder cancer extends well beyond the confines of the primary tumor. Clinical overviews describe bladder cancer as a disease characterized by persistent immune activation and chronic inflammatory remodeling, particularly in invasive and advanced stages [19]. Age-related inflammatory drift and microbiome alterations further intensify this immune imbalance in affected individuals [20]. Within this context, the elevated systemic inflammatory risk observed in our cohort appears biologically plausible rather than incidental.\u003c/p\u003e\n\u003cp\u003eBehavioral and metabolic factors may additionally shape this inflammatory background. Large prospective meta-analyses demonstrate that insufficient physical activity correlates with higher cancer and cardiovascular mortality, underscoring the role of systemic inflammatory tone in long-term outcomes [21]. In bladder cancer management, organ-preserving approaches and multimodal regimens rely heavily on host\u0026ndash;tumor immune interactions [22], while perioperative immunotherapy directly modifies systemic immune responses [23].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTranscriptomic profiling revealed 599 differentially expressed genes between tumor tissue and adjacent urothelium, indicating extensive immune and metabolic rewiring. Experimental work has shown that autophagy-driven mitochondrial remodeling in bladder cancer cells promotes inflammatory signaling and alters mechanotransduction pathways [24]. Molecular imaging studies targeting extracellular matrix components similarly highlight active stromal and inflammatory remodeling within the tumor microenvironment [25]. The transcriptional landscape observed in our dataset therefore reflects a tumor ecosystem capable of sustaining inflammatory signaling beyond local boundaries.\u003c/p\u003e\n\u003cp\u003eIntegrated machine learning approaches identified 15 hub genes, including ND6, CD38, SERPINE1, and EPHX2. Experimental inhibition of the STAT3\u0026ndash;EPHX2 axis attenuates inflammatory activity in chronic inflammatory models, supporting a functional link between EPHX2-related pathways and immune\u0026ndash;metabolic regulation [26]. Circulating mitochondrial-encoded ND6 has been proposed as a prognostic marker in septic patients, connecting mitochondrial stress with systemic immune activation [27]. CD38, a key regulator of NAD⁺ metabolism, plays a central role in immune cell activation and inflammatory persistence [28]. The convergence of these molecules within our model strengthens the biological coherence of the inflammatory signature identified.\u003c/p\u003e\n\u003cp\u003eEnrichment analysis demonstrated prominent involvement of leukocyte adhesion, chemokine signaling, and redox balance pathways. RAGE-mediated signaling has been experimentally linked to sustained inflammatory amplification [29], and high-glucose\u0026ndash;induced oxidative stress further illustrates how metabolic disturbance reinforces immune activation [30]. From a systems perspective, immunometabolism theory explains how metabolic flux reshapes immune cell fate and prolongs inflammatory states [31]. The molecular architecture observed here is consistent with such immune\u0026ndash;metabolic coupling.\u003c/p\u003e\n\u003cp\u003eSeveral hub genes were also embedded within endothelial activation networks. Suppression of NF-\u0026kappa;B p65 reduces endothelial activation and inflammatory adhesion molecule expression [32], while proteomic analyses in systemic inflammatory syndromes demonstrate a close association between interferon dysregulation and vascular endothelial dysfunction [33]. Activation of NF-\u0026kappa;B signaling in IL6-positive endothelial populations has been shown to drive inflammatory tissue remodeling [34].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe combined use of propensity score\u0026ndash;matched epidemiologic analysis and transcriptomic modeling enhances the robustness of our observations. Nevertheless, the retrospective design limits causal interpretation, and residual confounding cannot be fully excluded. Functional validation of the identified hub genes will be necessary to clarify mechanistic directionality.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eBMI:\u0026nbsp;\u003c/strong\u003ebody mass index\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGEO:\u0026nbsp;\u003c/strong\u003eGene Expression Omnibus\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDEGs:\u0026nbsp;\u003c/strong\u003edifferentially expressed genes\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGO:\u0026nbsp;\u003c/strong\u003eGene Ontology\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eKEGG:\u0026nbsp;\u003c/strong\u003eKyoto Encyclopedia of Genes and Genomes\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBP:\u0026nbsp;\u003c/strong\u003ebiological processes\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCC:\u0026nbsp;\u003c/strong\u003ecellular components\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMF:\u0026nbsp;\u003c/strong\u003emolecular functions\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGSEA:\u0026nbsp;\u003c/strong\u003eGene Set Enrichment Analysis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLASSO:\u0026nbsp;\u003c/strong\u003eleast absolute shrinkage and selection operator\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSVM-RFE:\u0026nbsp;\u003c/strong\u003esupport vector machine\u0026ndash;recursive feature elimination\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePSM:\u0026nbsp;\u003c/strong\u003epropensity score matching\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBMI:\u0026nbsp;\u003c/strong\u003ebody mass index\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRF:\u0026nbsp;\u003c/strong\u003erandom forest\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data used in this study were obtained from publicly accessible databases (GEO and CHARLS). The data are fully anonymized and do not contain any personally identifiable information. According to the \u0026quot;Measures for the Ethical Review of Biomedical Research Involving Humans\u0026quot; (2016) of China], research involving the analysis of publicly available, anonymized data is exempt from institutional ethics review. Therefore, a waiver of ethical approval was granted by our institution, and the requirement for informed consent was deemed unnecessary.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study are available in the GEO (https://www.ncbi.nlm.nih.gov/geo/ )and CHARLS (http://charls.pku.edu.cn/pages/data/2018-charls-wave4/zh-cn).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003eAnimal Studies:N/A.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by Natural Science Fund of Ningbo (Grant NO. 2024J486, NO.2021J283).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZeming Weng: study conception and design. Zhuoyi Xiang: data acquisition, analysis, data interpretation and drafting of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\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. 2022 Nov 12;400(10364):1712-1721. doi: 10.1016/S0140-6736(22)01188-6. Epub 2022 Sep 26. PMID: 36174585.\u003c/li\u003e\n\u003cli\u003eDyrskj\u0026oslash;t L, Hansel DE, Efstathiou JA, Knowles MA, Galsky MD, Teoh J, Theodorescu D. Bladder cancer. Nat Rev Dis Primers. 2023 Oct 26;9(1):58. doi: 10.1038/s41572-023-00468-9. PMID: 37884563; PMCID: PMC11218610.\u003c/li\u003e\n\u003cli\u003eTempo J, Yiu TW, Ischia J, Bolton D, O\u0026apos;Callaghan M. Global changes in bladder cancer mortality in the elderly. 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Using clusterProfiler to characterize multiomics data. Nat Protoc. 2024;19(11):3292-3320. doi:10.1038/s41596-024-01020-z\u003c/li\u003e\n\u003cli\u003eM RJ, G M, G B, P S. SVM-RFE enabled feature selection with DMN based centroid update model for incremental data clustering using COVID-19. Comput Methods Biomech Biomed Engin. 2024;27(10):1224-1238. doi:10.1080/10255842.2023.2236744\u003c/li\u003e\n\u003cli\u003eLenis AT, Lec PM, Chamie K, Mshs MD. Bladder Cancer: A Review. JAMA. 2020;324(19):1980-1991. doi:10.1001/jama.2020.17598\u003c/li\u003e\n\u003cli\u003eMartin A, Woolbright BL, Umar S, Ingersoll MA, Taylor JA 3rd. Bladder cancer, inflammageing and microbiomes. Nat Rev Urol. 2022;19(8):495-509. doi:10.1038/s41585-022-00611-3\u003c/li\u003e\n\u003cli\u003eGarcia L, Pearce M, Abbas A, et al. Non-occupational physical activity and risk of cardiovascular disease, cancer and mortality outcomes: a dose-response meta-analysis of large prospective studies. Br J Sports Med. 2023;57(15):979-989. doi:10.1136/bjsports-2022-105669\u003c/li\u003e\n\u003cli\u003eKimura T, Ishikawa H, Kojima T, et al. Bladder preservation therapy for muscle invasive bladder cancer: the past, present and future. Jpn J Clin Oncol. 2020;50(10):1097-1107. doi:10.1093/jjco/hyaa155\u003c/li\u003e\n\u003cli\u003eSingh A, Osbourne AS, Koshkin VS. Perioperative Immunotherapy in Muscle-Invasive Bladder Cancer. Curr Treat Options Oncol. 2023;24(9):1213-1230. doi:10.1007/s11864-023-01113-z\u003c/li\u003e\n\u003cli\u003eJobst M, Kiss E, Gerner C, Marko D, Del Favero G. Activation of autophagy triggers mitochondrial loss and changes acetylation profile relevant for mechanotransduction in bladder cancer cells. Arch Toxicol. 2023;97(1):217-233. doi:10.1007/s00204-022-03375-2\u003c/li\u003e\n\u003cli\u003eFeng Y, Hao Y, Wang Y, et al. Ultrasound Molecular Imaging of Bladder Cancer via Extradomain B Fibronectin-Targeted Biosynthetic GVs. Int J Nanomedicine. 2023;18:4871-4884. Published 2023 Aug 29. doi:10.2147/IJN.S412422\u003c/li\u003e\n\u003cli\u003eXu B, Wen Y, Xu J, Rong Y, Wang X, Liu T. Inhibition of the STAT3-EPHX2 axis promotes regression of ulcerative colitis by treatment with novel porphyrin derivative. Bioorg Chem. 2024;150:107579. doi:10.1016/j.bioorg.2024.107579\u003c/li\u003e\n\u003cli\u003eZhou F, Chen M, Liu Y, Xia X, Zhao P. Serum mitochondrial-encoded NADH dehydrogenase 6 and Annexin A1 as novel biomarkers for mortality prediction in critically ill patients with sepsis. Front Immunol. 2024;15:1486322. Published 2024 Nov 14. doi:10.3389/fimmu.2024.1486322\u003c/li\u003e\n\u003cli\u003ePiedra-Quintero ZL, Wilson Z, Nava P, Guerau-de-Arellano M. CD38: An Immunomodulatory Molecule in Inflammation and Autoimmunity. Front Immunol. 2020;11:597959. Published 2020 Nov 30. doi:10.3389/fimmu.2020.597959\u003c/li\u003e\n\u003cli\u003eDuan ZL, Wang YJ, Lu ZH, et al. Wumei Wan attenuates angiogenesis and inflammation by modulating RAGE signaling pathway in IBD: Network pharmacology analysis and experimental evidence. Phytomedicine. 2023;111:154658. doi:10.1016/j.phymed.2023.154658\u003c/li\u003e\n\u003cli\u003eCai R, Jiang J. LncRNA ANRIL Silencing Alleviates High Glucose-Induced Inflammation, Oxidative Stress, and Apoptosis via Upregulation of MME in Podocytes. Inflammation. 2020;43(6):2147-2155. doi:10.1007/s10753-020-01282-1\u003c/li\u003e\n\u003cli\u003eChi H. Immunometabolism at the intersection of metabolic signaling, cell fate, and systems immunology. Cell Mol Immunol. 2022;19(3):299-302. doi:10.1038/s41423-022-00840-x\u003c/li\u003e\n\u003cli\u003eYang B, Yang H, Lu X, et al. MiR-520b inhibits endothelial activation by targeting NF-\u0026kappa;B p65-VCAM1 axis. Biochem Pharmacol. 2021;188:114540. doi:10.1016/j.bcp.2021.114540\u003c/li\u003e\n\u003cli\u003eDiorio C, Shraim R, Vella LA, et al. Proteomic profiling of MIS-C patients indicates heterogeneity relating to interferon gamma dysregulation and vascular endothelial dysfunction. Nat Commun. 2021;12(1):7222. Published 2021 Dec 10. doi:10.1038/s41467-021-27544-6\u003c/li\u003e\n\u003cli\u003eLiu D, Zhang Y, Zhen L, Xu R, Ji Z, Ye Z. Activation of the NF\u0026kappa;B signaling pathway in IL6+CSF3+ vascular endothelial cells promotes the formation of keloids [published correction appears in Front Bioeng Biotechnol. 2022 Nov 02;10:1045496. doi: 10.3389/fbioe.2022.1045496.]. Front Bioeng Biotechnol. 2022;10:917726. Published 2022 Aug 23. doi:10.3389/fbioe.2022.917726\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1. Baseline characteristics before and after propensity score matching between bladder cancer and non-cancer groups\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e|\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 210px;\"\u003e\n \u003cp\u003eBefore PSM matching\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eSMD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 210px;\"\u003e\n \u003cp\u003eAfter PSM matching\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eSMD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eBladder cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eNon-bladder cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eBladder cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eNon-bladder cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eAge, Mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e51.6 \u0026plusmn; 12.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e52.1 \u0026plusmn; 12.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e51.8 \u0026plusmn; 12.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e51.9 \u0026plusmn; 12.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.891\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eSex (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e510 (51.99%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e510 (51.99%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e510 (51.99%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e510 (51.99%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.933\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e441 (44.95%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e441 (44.95%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e441 (44.95%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e441 (44.95%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eUnknown gender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e29 (2.96%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e29 (2.96%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e29 (2.96%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e29 (2.96%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eRace (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e746 (76.04%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e746 (76.04%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.547\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e746 (76.04%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e746 (76.04%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.947\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eBlack or African American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e49 (4.99%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e49 (4.99%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.604\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e49 (4.99%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e49 (4.99%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.904\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eAsian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e39 (3.98%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e39 (3.98%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.702\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e39 (3.98%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e39 (3.98%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eOther/unknown race\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e147 (14.98%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e147 (14.98%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.484\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e147 (14.98%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e147 (14.98%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eBMI (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026lt;18.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e10 (1.02%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e10 (1.02%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e10 (1.02%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e10 (1.02%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e18.5\u0026ndash;24.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e118 (12.03%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e118 (12.03%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e118 (12.03%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e118 (12.03%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.103\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e25\u0026ndash;29.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e275 (28.03%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e275 (28.03%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e275 (28.03%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e275 (28.03%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026ge;30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e578 (58.94%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e578 (58.94%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e578 (58.94%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e578 (58.94%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eMean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e31.2 \u0026plusmn; 7.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e30.9 \u0026plusmn; 7.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e31.1 \u0026plusmn; 7.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e31.0 \u0026plusmn; 7.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eSocial risk (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eTobacco use (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e177 (18.05%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e177 (18.05%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e177 (18.05%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e177 (18.05%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003ePersonal history of nicotine dependence (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e206 (21.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e206 (21.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e206 (21.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e206 (21.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eMedical utilization (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eAmbulatory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e609 (62.09%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e609 (62.09%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.942\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e609 (62.09%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e609 (62.09%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.947\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eEmergency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e216 (22.02%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e216 (22.02%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.766\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e216 (22.02%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e216 (22.02%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.762\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eInpatient Encounter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e157 (16.01%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e157 (16.01%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.801\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e157 (16.01%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e157 (16.01%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eComorbidities (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eNicotine dependence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e226 (23.03%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e226 (23.03%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e226 (23.03%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e226 (23.03%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eData are presented as mean \u0026plusmn; standard deviation (SD) for continuous variables and number (%) for categorical variables. P values are derived from t-tests or chi-square tests as appropriate. Standardized mean difference (SMD) \u0026lt; 0.1 indicates adequate covariate balance. After matching, all baseline covariates between the bladder cancer and control groups were well balanced.\u003cstrong\u003e\u003cbr\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Incidence of systemic inflammatory events and risk estimates among bladder cancer and control groups in unmatched and matched cohorts\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003eModel/Group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003eFollow-up time (person-years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003eNo. of events\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003eCumulative incidence (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003eIncidence rate (cases/1000 person-years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003eHR (95% C.I.)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003eNon-bladder cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e669\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e6890\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003e3.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003e3.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003eBladder cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e2714\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003e10.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003e12.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e4.11 (3.08\u0026ndash;5.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003eNon-bladder cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e669\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e6890\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003e2.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003e2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003eBladder cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e2714\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003e9.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003e11.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e3.81 (2.86\u0026ndash;4.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003eNon-bladder cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e669\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e6890\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003e2.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003e2.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003eBladder cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e2714\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003e8.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003e9.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e3.67 (2.75\u0026ndash;4.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003eBladder cancer without chemotherapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e1357\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003e7.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003e8.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003eBladder cancer with chemotherapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e1357\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003e11.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003e13.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e1.64 (1.23\u0026ndash;2.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003eBladder cancer without immunotherapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e1357\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003e6.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003e7.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003eBladder cancer with immunotherapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e1357\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003e10.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003e11.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e1.6 (1.2\u0026ndash;2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eData are stratified across three multivariate Cox regression models. Model 1: unadjusted; Model 2: adjusted for age, sex, and BMI; Model 3: adjusted for demographics and comorbidities. Cumulative incidence, incidence rate, and hazard ratios (HR) with 95% confidence intervals (CI) are reported. Subgroup analyses explore the effect of chemotherapy and immunotherapy exposure on systemic inflammatory risk among bladder cancer patients. All P values \u0026lt; 0.05 are considered statistically significant.\u003c/p\u003e"}],"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":"Bladder cancer, Systemic inflammation, Propensity score matching, Cohort study, Transcriptomic profiling","lastPublishedDoi":"10.21203/rs.3.rs-8787489/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8787489/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEmerging evidence indicates that malignancies can exert systemic effects extending beyond the primary tumor site. Bladder cancer, a common urological malignancy, may predispose patients to chronic inflammation and immune dysregulation, yet population-based evidence remains limited.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA dual-database analysis was conducted by integrating epidemiologic data from the China Health and Retirement Longitudinal Study (CHARLS, 2011–2018) with transcriptomic datasets from the Gene Expression Omnibus (GEO). Adults aged ≥45 years with available diagnostic, metabolic, and inflammatory information were included. Propensity score matching (1:1) was applied based on demographic, metabolic, and lifestyle covariates. The primary endpoint was the occurrence of systemic inflammatory events, assessed using weighted Kaplan–Meier and Cox proportional hazards models. Differentially expressed genes (DEGs) between bladder cancer and adjacent normal tissues were identified from GEO datasets, followed by Gene Ontology (GO) and KEGG enrichment analyses. Machine-learning algorithms (LASSO, random forest, and SVM-RFE) were used to identify inflammation-related hub genes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring a median follow-up of 3.6 years, participants with bladder cancer exhibited a significantly higher incidence of systemic inflammatory events compared with matched controls (10.9% vs 3.1%, p \u0026lt; 0.001; HR 3.67, 95% CI 2.75–4.59). Transcriptomic profiling identified 599 DEGs (312 upregulated, 287 downregulated), enriched in immune-metabolic pathways such as PI3K-Akt, IL-17, and cytokine–receptor interactions. Key hub genes—\u003cstrong\u003eND6\u003c/strong\u003e, \u003cstrong\u003eCD38\u003c/strong\u003e, \u003cstrong\u003eSERPINE1\u003c/strong\u003e, and \u003cstrong\u003eEPHX2\u003c/strong\u003e—emerged as potential molecular mediators linking metabolic dysregulation with inflammatory signaling.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIntegrating CHARLS epidemiologic findings with GEO transcriptomic validation provides convergent evidence that bladder cancer is independently associated with increased systemic inflammatory burden. These results highlight a potential immunometabolic axis underlying tumor-related inflammation and identify molecular targets that may guide early risk stratification and anti-inflammatory interventions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImpact\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe involvement of immunometabolic dysregulation and highlighting novel molecular signatures may inform future risk stratification and therapeutic targeting in this population.\u003c/p\u003e","manuscriptTitle":"Bladder cancer is associated with increased risk of systemic inflammatory complications: evidence from a propensity score-matched cohort study and transcriptomic analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-08 14:37:12","doi":"10.21203/rs.3.rs-8787489/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":"5cc5fe1d-67a8-462f-97e8-afe7e38ded90","owner":[],"postedDate":"March 8th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-26T06:25:29+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-08 14:37:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8787489","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8787489","identity":"rs-8787489","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}