Identification of Prognostic Signature Genes and Immune Microenvironment Features Associated with Lymph Node Metastasis in Bladder Cancer

Identification of Prognostic Signature Genes and Immune Microenvironment Features Associated with Lymph Node Metastasis in Bladder Cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Identification of Prognostic Signature Genes and Immune Microenvironment Features Associated with Lymph Node Metastasis in Bladder Cancer Yifan Sun, Meng Ding, Jiyuan Sun, Qing Zhang, Wenming Cao, Wei Chen, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5816202/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Lymph node (LN) metastasis is related to poor prognosis in bladder cancer (BLCA). To explore novel signature genes associated with LN metastasis in BLCA, we identified 17 signature genes with non-zero coefficients to construct the prognostic model, which demonstrated a prognostic accuracy with an area under the curve of 0.706 at 1 year, 0.701 at 3 years, and 0.688 at 5 years. EPN2 , CYP4F12 and especially FKBP10 , three of the above signature genes, exhibited significant upregulation in BLCA with LN metastasis, thereby contributing to the unfavorable survival of BLCA patients. Meanwhile, we validated that FKBP10 exerts a biological function in bladder cancer metastasis through cytological experiments. Moreover, by utilizing the CIBERSORT algorithm and immunofluorescence assay, we identified and validated a significant upregulation of M0 macrophages, alongside a downregulation of activated NK cells and CD8 + T cells, which were associated with the presence of LN metastasis in BLCA. Conclusively, These results will provide new insights for future improvements in diagnosis, treatment, and prognosis evaluation for BLCA patients with LN metastasis. Bladder cancer Lymph node metastasis Prognostic model Tumor infiltrating immunocytes Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Bladder cancer (BLCA) is one of the most common malignancy in the urinary system, with approximately 573,000 newly diagnosed patients per year worldwide.[ 1 ] Approximately 25% BLCA are pathologically identified as muscle-invasive BLCA. Muscle-invasive BLCA is considered as one of the most aggressive and lethal malignancies because of its propensity to metastasize to lymph nodes and distant organs. Previous studies have indicated that BLCA lymph node (LN) involvement possesses significant prognostic implications. The identification of LN metastasis is considered as a crucial determinant in predicting the survival outcome for patients with BLCA,[ 2 , 3 ] which reduces the 5-year survival rate to only 18.6%.[ 4 ] Cisplatin-based chemotherapy remains the primary choice for treating individuals with LN involvement, and there has been an increasing utilization of targeted therapies, particularly immunotherapy, as a complementary approach to manage BLCA patients with positive LNs in recent years.[ 5 ] It has been established that therapies targeting the VEGF-C and VEGF-D/VEGFR-3 axis could potentially inhibit LN metastasis in BLCA. Moreover, combining ramucirumab (a drug inhibiting the VEGF signal transduction) with docetaxel resulted in better progression-free survival for metastatic patients with platinum-refractory advanced urothelial carcinoma.[ 6 ] However, due to a lack of sufficient well-performed prospective studies and available evidence, not enough recommendations have been provided by various guidelines for BLCA patients with positive LNs. Considering the significant impact of lymph node metastasis on BLCA patient prognosis, preoperative evaluation of lymph node metastasis holds great significance in patient management. Although, traditional radiology cannot detect the micro metastasis of lymph nodes, nomogram based on radiomics exhibited great precision in predicting BLCA LN metastasis.[ 7 , 8 ] Nevertheless, the external validation is necessary prior to their clinical implementation. In addition, the urine-based liquid biopsy in BLCA was proven to distinguish the LN status and evaluate the risk of LN metastasis, but the number of participants in both studies was relatively limited and did not meet the requirements for making conclusive clinical decisions.[ 9 , 10 ] Consequently, it is vital to discover new potential diagnostic and prognostic biomarkers for BLCA with LN metastasis. Some LN metastasis-related genes which are of great significance for diagnosis and prognosis have been observed in previous studies with BLCA patients, including a five-mRNA classifier ( ADRA1D , COL10A1 , DKK2 , HIST2K3D , and MMP11 ) [ 11 ] and KNN51 [ 12 ], with AUCs ranging from 0.72 to 0.82. Another series of aging-related signature genes ( EFEMP1 , UCHL1 , TP63 , ELN ) has demonstrated the potential to predict BLCA LN metastasis with an AUC of 0.67.[ 13 ] More researches need to be conducted to identify predictive biomarkers and investigate the underlying mechanism of BLCA with LN metastasis. Here, we aimed to construct a prognostic model in BLCA and explore novel signature genes related to lymph node metastasis. Furthermore, we intended to predict and validate the efficacy of chemotherapy and immunotherapy based on the prognostic model, which could guide high-risk BLCA patients to early multimodal therapy and provide new perspectives for exploring therapeutic options for BLCA patients with LN metastasis. Materials and Methods Data Retrieve and Processing The mRNA sequence and the clinicopathological data, including clinical stage, clinical grade, patient survival, patient gender, and LN metastasis of the BLCA patients were obtained from the Cancer Genome Atlas (TCGA) dataset (Project: TCGA-BLCA), and the gene expression matrix were retrieved from the Gene Expression Omnibus (GEO) dataset (GSE106534). All data were derived from bladder carcinoma tissues and categorized into two groups based on the presence or absence of LN metastasis. Identification of differentially expressed genes and gene set enrichment analysis We used R 4.1.0 version “edgeR” package to perform mRNA expression profile normalization and compare the mRNA expression of LN metastasis (–) group and LN metastasis (+) group to screen differentially expressed genes (DEGs). In order to elucidate the enrichment of candidate genes, analyses including Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis and gene set enrichment analysis (GSEA) were carried out. Fisher’s exact test was employed to find out which functions and pathways were most closely related to candidate genes. Survival analysis was conducted based on the two groups of data using R package “survival”. Development and validation of a risk model A total of 372 samples were randomly assigned to the training set or test set in a ratio of 1:1. Least absolute shrinkage and selection operator (LASSO) regression algorithm was employed for analysis of the above candidate genes with respect to patient prognosis and the prognostic risk prediction model was constructed. We obtained the expression profile data of the candidate genes from the training set and incorporated them into model to calculate the risk score. Based on this, receiver operating characteristic curve (ROC) analysis was applied for prognostic classification of the risk score. The efficiency of our model in predicting prognosis was evaluated using samples from the training set, while samples from the test set were used for validation. Evaluation of Immune infiltration and Tumor mutation burden landscape CIBERSORT algorithm was applied to evaluate the immune infiltration landscape. Furthermore, we also used “maftools” package for evaluating the tumor mutation burden (TMB) score of each sample and compared the TMB landscape in above four groups by using “wilcox.test”. Prediction of anti-tumor drug response According to the TCGA and genomics of drug sensitivity in cancer (GDSC) dataset, “oncoPredict” package was utilized for predicting chemotherapeutic and targeted drug response. In addition, to further predict the responses of BLCA patients towards immunotherapy, analyses of T cell-inflammation and tumor immune dysfunction and exclusion (TIDE) were also conducted. The prediction power for the signature genes was also identified. Sample collection 16 pairs of fresh bladder cancer tissues and 30 pairs of paraffin-embedded bladder cancer tissues were collected from BLCA patients who underwent radical cystectomy and pelvic lymph nodes dissection in the Nanjing Drum Tower Hospital from June 2018 to Dec 2023. BLCA patients were pathologically diagnosed and divided into LN metastasis (–) group and LN metastasis (+) group by at least two experienced pathologists. RNA isolation and quantitative real-time PCR (RT-qPCR) RNA was isolated using TRIzol reagent (Invitrogen), followed by reverse transcription using a HiScriptIII RT SuperMix for qPCR (+ gDNA wiper) (Vazyme, Nanjing, China). We performed RT-qPCR using ChamQ SYBR qPCR Master Mix (Vazyme, Nanjing, China) on a QuantStudio™ 6 Flex (Applied Biosystems, Foster City, CA, USA). All primers were designed and synthesized by Shanghai Generay Biotech Co., Ltd. β-actin was employed as the internal reference. The primers are listed in Table 1 . The relative expression levels of genes were determined using the 2 −ΔΔCt method. Table 1 Primer sequences Gene Forward primer (5’-3’) Reverse primer (5’-3’) ASB13 CCTCATCGAGATGCTTATCGAGT AGTCAGAGGTGTCTTTTCGTAGT CARD11 GGACGCCTTGTGGGAGAATG TCAATGACCTTACACTGACGC CYP4F12 TGTCGGCCACCTATTCCCA GGTGATAGACCGGATGGTGTC EPN2 ACCTCACTGTAGCCTGGTCA TGGTAAGTGCGCACCCTATG FKBP10 CATGGGCATGTGTGTCAACG GAATGAGCCCCGCCAGG ST3GAL5 AGGAATGTCGTCCCAAGTTTG GGAGTAAGTCCACGCTATACCT Immunohistochemistry (IHC) and immunofluorescence (IF) Paraffin-embedded BLCA tissues were collected, then sliced at 5-µm intervals and placed onto glass slides. Slides were incubated at 75°C for 2 h, deparaffinized, and rehydrated. We blocked the slides and incubated them with primary antibodies for NCAM (1:125 dilution, A11770, Abclonal, China), CD8A (1:125 dilution, A0663, Abclonal, China), ASB13 (1:500 dilution, 25616-1-AP, Proteintech, USA), CARD11 (1:200 dilution, A9652, Abclonal, China), CYP4F12 (1:200 dilution, DF2614, Affinity, USA), EPN2 (1:200 dilution, A16106, Abclonal, China), FKBP10 (1:500 dilution, 12172-1-AP, Proteintech, USA) or ST3GAL5 (1:500 dilution, 14614-1-AP, Proteintech, USA) overnight at 4°C, followed by incubation with HRP-conjugated secondary antibodies for 1 h at room temperature. Finally, Each slide was reacted with 3.3′-diaminobenzidine (DAB) solution for 30 seconds and then washed with water before counterstaining with hematoxylin. To identify the subpopulations of macrophage, double immunofluorescence staining to detect CD68 and inducible NO synthase (iNOS) (or CD163) were conducted. CD68 was identified as a pan-macrophage marker, while CD163 was a marker for M2-polarized macrophages (CD68 + /CD163 + ) and iNOS was a marker for M1-polarized macrophages (CD68 + /iNOS + ). CD68 + /CD163 − /iNOS − cells were identified as M0-polarized macrophages. Formalin-fixed tissue slides were deparaffinized, rehydrated before epitope retrieval. After blocking with 10% BSA for 1 h, incubation with the primary antibody for CD68 (1:100 dilution, A15037, Abclonal, China), iNOS (1:100 dilution, A14031, Abclonal, China) or CD163 (1:100 dilution, A22619, Abclonal, China) was performed overnight at 4°C. Alexa Fluor plus 488 and 594 goat anti-rabbit IgG (H + L) (1:1000 dilution, Invitrogen, America) were used as secondary antibodies. Incubation was performed for 60 minutes at room temperature. Nuclei were labeled using Hoechst 33342. Captured images were acquired with the EVOS FL Auto 2 (Invitrogen, America). The positive cells were counted manually in the random fields (40× magnifications). RNA interference Small interfering RNAs (siRNAs) specific to human FKBP10 were purchased from Generay (Shanghai, China). The details of these siRNAs can be found in Supplementary Table S4. The T24 cells were transiently transfected with siRNAs targeting human FKBP10 and with the negative control siRNA by means of jetPRIME (Polyplus, France). Western blotting Protein extracts were prepared on ice using a lysis buffer containing protease inhibitors. The samples were denatured, and proteins were separated via SDS-PAGE before being transferred onto polyvinylidene difluoride (PVDF) membranes. After blocking, the membranes were sequentially incubated with primary and secondary antibodies. The FKBP10 antibody (12172-1-AP) was obtained from Proteintech (Rosemont, IL, USA). Protein signals were visualized using an enhanced chemiluminescence (ECL) detection system (Vazyme, Nanjing, China), and images were captured with the ChemiScope 3300 Mini Imaging System (CLiNX, Shanghai, China). Cell migration and invasion assays In the wound healing experiment, the cells were cultivated until reaching near-full confluence, after which a scratch was created on the cell layer's surface. Subsequently, the cells were kept in serum-free medium for a predetermined duration. Upon observing and photographing the cells, the migration distance was measured and calculated. Regarding the Transwell migration assay, 6 × 10 4 cells per well were placed in the upper chambers (8 µm) and maintained in serum-free medium, while conditioned medium was introduced into the lower chambers. In the Transwell invasion assay, Matrigel was spread across the bottom of each upper chamber and permitted to solidify at 37°C. Then, the cells were seeded in the upper chambers for the assessment of invasion. Following incubation at 37°C, the cells were fixed, stained, and photographed. Statistical analysis The software utilized for statistical analysis and data processing was R 4.1.0. The survival differences were evaluated using Kaplan-Meier (K-M) analysis. Gene expression correlation was determined through Pearson correlation analysis. Prognostic value of the risk score was assessed using multivariate Cox analysis. The predictive value of the risk score was estimated by ROC curve analysis. Statistical significance was defined as two-sided p ≤ 0.05. Results Identification of Differentially Expressed Genes To explore the differences in gene expression and functional heterogeneity between BLCA patients with and without LN metastasis, we screened candidate genes in the TCGA and GEO dataset. A total of 372 samples from TCGA-BLCA dataset and 15 samples from GSE106534 dataset were identified and selected in our study (Table 2 , 3 ). There were 2918 differentially expressed genes in TCGA-BLCA dataset, and 1715 DEGs in GSE106534 dataset (Fig. 1 A, LN metastasis (+) vs. LN metastasis (–), | log 2 FC | >1, p < 0.05). The top 20 up-regulated and down-regulated DEGs of the two datasets were presented respectively in Fig. 1 B, C. Additionally, we carried out differential analysis of clinical characteristics including age, gender, tumor stage, tumor grade and tumor metastasis. The patient age and the ratios of high grade and advanced stage were significantly higher in BLCA patients with lymph node metastasis in TCGA-BLCA dataset ( Supplementary Fig. 1A, B, 1D–F ). There were no significant differences in patient gender or overall survival in TCGA-BLCA dataset, nor in any clinical features in GSE106534 dataset ( Supplementary Fig. 1C, G, 2A–D ). Table 2 The clinical data of TCGA-BLCA dataset Variables Primary tumor (N = 239) Metastatic LN (N = 131) Age 67.45 ± 8.81 69.64 ± 8.76 Sex (%) Male 174 (72.8) 99 (75.57) Female 65 (27.2) 32 (24.43) T_Stage (%) T0 1 (0.42) 0 T1 1 (0.42) 0 T2 90 (37.66) 16 (12.21) T3 111 (46.44) 78 (59.54) T4 22 (9.21) 34 (25.95) NA 14 (5.86) 3 (2.29) N Stage (%) N0 239 (100) 0 N1 0 47 (35.88) N2 0 76 (58.01) N3 0 8 (6.11) M Stage (%) M0 133 (55.65) 44 (33.59) M1 0 8 (6.11) Mx 1 (0.42) 0 NA 105 (43.93) 79 (60.3) Pathologic Stage (%) I 1 (0.42) 0 II 103 (43.1) 0 Ⅲ 131 (54.81) 0 Ⅳ 3 (1.25) 130 (99.24) NA 1 (0.42) 1 (0.76) Histologic Grade (%) Low 19 (7.95) 2 (1.53) High 219 (91.63) 129 (98.47) NA 1 (0.42) 0 Survival Months 17.71 (12-35.52) 16.12 (8.12–28.02) Table 3 The clinical data of GSE106534 dataset Variables Primary tumor (N = 7) Metastatic LN (N = 8) Age 66.43 ± 8.73 67.88 ± 8.43 Sex (%) Male 5 (71.4) 6 (75) Female 2 (28.6) 2 (25) T_Stage (%) T1 0 0 T2a 0 1 (12.5) T2b 0 1 (12.5) T3a 3 (42.86) 4 (50) T3b 4 (57.14) 2 (25) N Stage (%) N0 7 (100) 0 N1 0 7 (87.5) N2 0 1 (12.5) N3 0 0 M Stage (%) M0 7 (100) 7 (87.5) M1 0 1 (12.5) Pathological Grade (%) I 0 0 II 0 0 Ⅲ 7 (100) 0 Ⅳ 0 8 (100) Functional Enrichment Analysis of DEGs To further explore the underlying mechanisms concerning LN metastasis in BLCA, we performed GO and KEGG enrichment analysis in the above datasets. KEGG enrichment chord diagram showed the top 50 log 2 FC genes corresponding to the top significantly enriched 10 KEGG terms (Fig. 1 D). Calcium signaling pathway, focal adhesion, and PI3K-Akt signaling pathway were the most potentially associated with lymph node metastasis in TCGA-BLCA dataset. The top fifteen terms from the GO results were displayed in dotplot (Fig. 1 E–G). In the biological process (BP) group, DEGs showed significant enrichment in animal organ development, multicellular organism development and tissue development. Besides, cell migration, cell motility and chemotaxis might also play crucial roles in lymph node metastasis. In the molecular function (MF) group, DEGs were mostly enriched in metal ion transmembrane transporter activity, cation transmembrane transporter activity, inorganic cation transmembrane transporter activity and calcium ion binding. The alterations in ion transmembrane transporter activity appeared to be responsible for the enhanced migration of cancer cells, thus seemingly contributing to lymph node metastasis. In addition, calcium-binding protein may also facilitate cell adhesion and contribute to the process of cancer metastasis.[ 14 ] Similar analyses were performed using GSE106534 dataset. Related results were shown in Supplementary Fig. 3A–E . There were a total of 3 KEGG terms and 83 GO terms enriched in both TCGA-BLCA and GSE106534 datasets ( Supplementary Fig. 3F–G ). Construction and validation of prognostic model To construct a prognostic model, BLCA patients from the TCGA dataset were randomly assigned into the training set or test set in a 1:1 ratio. No significant differences were found in any of clinical variables examined between the two groups (Table 4 ). We screened the common up- and down- regulated DEGs (43 up-regulated and 78 down-regulated in total) and selected 40 genes as candidates which exhibited remarkable correlations with prognosis ( Supplementary Table 1 ). We subsequently incorporated all candidate genes into the LASSO logistic regression model (Fig. 2 A, B). The LASSO logistic regression model generated a forest plot illustrating 17 signature genes with non-zero coefficients (Fig. 2 C). When the error was minimized, the model indicated the value of the harmonic parameter λ was 0.0356. The risk score was calculated using the following formula: Risk score = [(− 0.0233 × ANKRD20A5P) + (0.0217 × ARHGAP29) + (− 0.0089 × ASB13) + (− 0.1094 × BCL2L14) + (− 0.2173 × C19orf71) + (0.0469 × CADM3) + (− 0.0434 × CARD11) + (0.0122 × CCDC102B) + (− 0.0854 × CD3D) + (− 0.0098 × CYP4F12) + (0.1117 × EPN2) + (0.0359 × FKBP10) + (− 0.0556 × IKZF3) + (-0.0251 × RPL34) + (− 0.0251 × ST3GAL5) + (0.1208 × SVIL) + (0.0458 × ZC3HAV1L)]. Patients were categorized into two risk groups according to the median risk score. Supplementary Fig. 4A–H presents the differential analysis of clinical characteristics such as age, gender, tumor stage, tumor grade and tumor metastasis between high- and low-risk groups. We found that the patient age, the ratios of high grade and advanced stage were significantly higher among patients at high risk, while the tumor purity was contrary. Risk score was considered as the most significant risk factor for survival (hazard ratio, 1.52; p < 0.001) ( Supplementary Fig. 4I–J) . In the total population, K-M survival curves demonstrated a significant decline in the survival rates among patients at high risk (hazard ratio, 2.803; p < 0.001) (Fig. 2 D). ROC curves revealed that the prognostic model had a prognostic accuracy with the AUC values of 0.706 in one year, 0.701 in three years, and 0.688 in five years, respectively (Fig. 2 E). ROC curves and K-M survival curves for the training and test sets are displayed in Figs. 2 F–I. The training set achieved AUC values of 0.743, 0.718 and 0.686 in one, three, five years respectively, while the test set obtained AUC values of 0.707, 0.655 and 0.677 at the same time point. K-M survival curves of these two sets both indicated significant disparities in survival rate when comparing high- with low-risk groups (Training set, hazard ratio, 3.592, p < 0.001; Test set, hazard ratio, 2,32, p < 0.001). Table 4 Comparison of TCGA clinical data between training set and test set. Variables Total Training set (N = 203) Test set (N = 203) P value Age 68.05 ± 10.6 67.88 ± 11.01 68.22 ± 10.19 0.743 Status (%) 0.230 Alive 227 (55.91) 120 (59.11) 107 (52.71) Dead 179 (44.09) 83 (40.89) 96 (47.29) Sex (%) 0.499 Male 299 (73.65) 146 (71.92) 153 (75.37) Female 107 (26.35) 57 (28.08) 50 (24.63) T_Stage (%) 0.464 T0 1 (0.25) 1 (0.49) 0 (0) T1 3 (0.74) 1 (0.49) 2 (0.99) T2 118 (29.06) 61 (30.05) 57 (28.08) T3 193 (47.54) 101 (49.75) 92 (45.32) T4 58 (14.29) 27 (13.3) 31 (15.27) Unknow 33 (8.13) 12 (5.91) 21 (10.34) N_Stage (%) 0.517 N0 236 (58.13) 126 (62.07) 110 (54.19) N1 46 (11.33) 21 (10.34) 25 (12.32) N2 75 (18.47) 36 (17.73) 39 (19.21) N3 7 (1.72) 3 (1.48) 4 (1.97) Unknow 42 (10.34) 17 (8.37) 25 (12.32) M_Stage (%) 0.135 M0 195 (48.03) 92 (45.32) 103 (50.74) M1 11 (2.71) 3 (1.48) 8 (3.94) Unknow 200 (49.26) 108 (53.2) 92 (45.32) Pathologic_Stage (%) 0.933 Stage I 2 (0.49) 1 (0.49) 1 (0.49) Stage II 129 (31.77) 65 (32.02) 64 (31.53) Stage III 140 (34.48) 73 (35.96) 67 (33) Stage IV 133 (32.76) 63 (31.03) 70 (34.48) Unknow 2 (0.49) 1 (0.49) 1 (0.49) Moreover, this model was applied to an external independent dataset, GSE13507 dataset, which provides overall survival data. ROC analysis manifested that the AUC values of this model (0.719 in one year, 0.635 in three years, and 0.633 in five years) were close to that of the TCGA-BLCA, and K-M survival curves also showed significant difference in survival rate (hazard ratio, 2.032; p < 0.01), suggesting this model is robust in other datasets (Fig. 2 J, K). Functional enrichment analysis and mutational characteristics of risk score GO and KEGG enrichment analyses were utilized to assess the function of genes ranked by risk score. The top 3 terms of KEGG, BP, cellular component (CC), MF based on high- and low-risk patients respectively are shown in Fig. 3 A. We identified that high-risk patients were enriched in KEGG terms including “Glycosaminoglycan biosynthesis-chondroitin sulfate; Dermatan sulfate”, “DNA replication” and “Biosynthesis of unsaturated fatty acids”, while the low-risk patients were enriched in “Ascorbate and aldarate metabolism”, “Pentose and glucuronate interconversions” and “Chemical carcinogenesis”. To further explore the genomic differences between high- and low-risk groups, as well as between the LN metastasis (+) and LN metastasis (–) groups. we conducted an analysis of the mutational landscape and compared the mutation rate and TMB in these groups. Specially, TP53 , TTN , KDM6A, MUC16 and KMT2D were the most frequently mutated genes (Fig. 3 B, C, Supplementary Fig. 5A, B) . Missense mutations accounted for the most of mutation types in TTN , TP53 , and MUC16 mutations. Compared to the low-risk group, the mutation frequency of FGFR3 , NEO1 , FBN2 , TTI1 , ZNF750 , LRRC37B and AP3D1 was significantly lower, while the mutation frequency of TP53 , ZC3H14 and PRDM5 was notably higher (Fig. 3 D, E). In the LN metastasis (+) group, a significantly lower mutation frequency was observed for NUP107 , MSLNL , SLC37A3 , TBC1D1 and CCT3 , whereas a significantly higher mutation frequency was noted for MED1 , ARHGAP35 , SORCS1 , CKAP5 , and EXOC4 (Supplementary Fig. 5C, D) . Moreover, the TMB distribution diagram of risk score was shown in Fig. 3 F and Supplementary Fig. 5E . The TMB level of patients in the low-risk and LN metastasis (–) group was slightly elevated compared to that of patients in the high-risk and LN metastasis (+) group (Fig. 3 G, Supplementary Fig. 5F ), which suggested a better therapeutic effect of immunotherapy in low-risk and LN metastasis (–) group. The outcomes indicated that BLCA patients in both high-risk group and LN metastasis (+) group were prone to TP53 , TTN and KMT2D mutations. In addition, mutations in TP53 , ZC3H14 , and PRDM5 , known as tumor suppressor genes, contributed to the progression of high-risk BLCA. Tumor-infiltrating immunocytes in tumor microenvironment (TME) Tumor lymph node metastasis is driven by both the inherent characteristics of tumor cells and the interaction between tumor cells and tumor immune microenvironment.[ 15 ] To further examine the relationship between tumor-infiltrating immunocytes and LN metastasis status, we performed CIBERSORT algorithm to assess the subpopulations of immunocytes and the difference of immune signature in the above two datasets. The fractions of M0 macrophages were significantly higher in LN metastasis (+) group and high-risk group, while fractions of activated NK cells and CD8 + T cells were remarkably higher in LN metastasis (–) group and low-risk group (Fig. 4 A, B). To validate the results, IHC and IF were performed to investigate the proportion of M0 macrophages, activated NK cells and CD8 + T cells in 10 pairs of paraffin-embedded bladder cancer tissues. As shown in Fig. 4 C–H, the infiltration of M0 macrophages was significantly upregulated, while activated NK cells and CD8 + T cells were less accumulated in BLCA patients with LN metastasis. Additionally, we observed a positive relationship between M1 macrophages and CD8 + T cells, activated NK cells and CD8 + T cells, as well as a negative relationship between CD8 + T cells and M0 macrophages, CD8 + T cells and M2 macrophages respectively ( Supplementary Fig. 6A, B ). These alterations of specific immunocytes within the TME could be manipulated by tumor cells via releasing more or less chemokines, especially CXCL and CCL families.[ 16 , 17 ] The occurrence of CD8 + T cells, activated NK cells, and M1 macrophages in the TME is typically linked to tumor regression as well as a positive prognosis, while the infiltration of M2 macrophages facilitate tumor progression and lymphatic metastasis.[ 18 , 19 ] Significance of risk score in predicting therapeutic efficacy of BLCA Considering the potential of risk score to predict the prognosis of BLCA patients and its strong association with mutational characteristics and infiltration of immunocytes, we further wondered whether it could predict the effectiveness of clinical treatments. Correlation analysis between risk score and IC50 of drugs in GDSC demonstrated that high-risk BLCA patients were more sensitive to the treatment of sorafenib, gemcitabine and oxaliplatin, which were frequently used in BLCA (Fig. 5 A, B). T cell inflammation score was positively correlated with clinical efficacy of tumor immunotherapy, but it did not differ significantly based on the risk score (Fig. 5 C). Moreover, by evaluating scores in TIDE and TME, we observed a notable decrease in the TIDE score and a significant increase in the TME score in the low-risk group, both indicating a potential enhancement in the effectiveness of immunotherapy for low-risk BLCA patients (Fig. 5 D, E). To further identify the predictive capability of the risk score in immunotherapy efficacy, we utilized Imvigor210 to evaluate the capacity of the above 17 signature genes to predict the efficacy of anti-PD-L1 immunotherapy. However, the expression of the 17 signature genes did not affect the efficacy of immunotherapy (Fig. 5 F). K-M analysis indicated that a higher risk score was linked to decreased survival rates (Fig. 5 G). In conclusion, our data suggest that traditional chemotherapy agents such as gemcitabine and oxaliplatin may offer greater advantages for high-risk BLCA patients, whereas low-risk BLCA patients may benefit more from immunotherapy. The clinical significance of the signature genes To further assess the clinical significance of signature genes, we collected 16 paired fresh bladder cancer tissues ( Supplementary Table 2 ) (LN metastasis (+) vs. LN metastasis (–)) and conducted RT-qPCR analysis to explore the mRNA expression levels of six signature genes including ASB13 , CARD11 , CYP4F12 , EPN2 , FKBP10 and ST3GAL5 , which were obtained through the intersection of prognostic genes from GSE13507 dataset and aforementioned 17 signature genes. The results indicated a notable upregulation in the relative mRNA expression levels of EPN2 and FKBP10 in bladder cancer tissues with LN metastasis (Fig. 6 B). In addition, we assembled 30 pairs of paraffin-embedded bladder cancer tissues (LN metastasis (+) vs. LN metastasis (–)) to evaluate the expression level and clinical significance of the above 6 signature genes ( Supplementary Table 3 ). Staining of tissue sections revealed significantly increased levels of CYP4F12 and FKBP10, while decreased levels of ASB13 and ST3GAL5 in LN metastasis (+) group (Fig. 6 A). Prognostic analysis showed elevated levels of FKBP10, EPN2 and CYP4F12 were significantly associated with poorer overall survival and reduced progression-free survival among BLCA patients (Fig. 6 E–G, Supplementary Fig. 7C – E) , but ASB13, CARD11 and ST3GAL5 were not significantly related to the prognosis of BLCA patients (Fig. 6 C, D, H, Supplementary Fig. 7A, B, F ). Furthermore, to further investigate the biological function of FKBP10, we established a T24-siFKBP10 cell line with transient FKBP10 knockdown (Fig. 6 K, L). the wound healing assay results demonstrated that silencing FKBP10 reduced cell migration and the outcomes of Transwell assays also indicated that transient knockdown of FKBP10 remarkably inhibited cell migration and invasion (Fig. 6 I, J ) . These findings suggested that EPN2 , CYP4F12 and especially FKBP10 could serve as novel biomarkers, playing an important role in the preoperative diagnosis and prognosis guidance of LN metastasis in BLCA. Discussion Bladder cancer holds the fourth most common cancer in males and the eleventh in females.[ 20 ] LN metastasis has a notable impact on the spread of bladder cancer, and its involvement is an independent factor for predicting disease progression.[ 21 ] Although medical imaging approaches including multiparametric magnetic resonance imaging (mpMRI), contrast-enhanced computed tomography (ceCT) and positron emission computed tomography (PET) have achieved great advancements in pre-surgical diagnosis, occult LN metastases are frequently discovered after radical cystectomy for BLCA.[ 22 ] Accordingly, we intended to established and validate a personalized prognostic model based on a signature of lymph node metastasis to predict outcomes and guide therapy decisions for patients with BLCA. The impact of the TME on LN metastasis in tumors has been widely recognized.[ 23 ] In our study, we have noticed a significant correlation between BLCA LN metastasis and infiltrating levels of specific immunocytes such as M0 macrophages, activated NK cells, and CD8 + T cells. Previous researches have demonstrated that macrophages play a pivotal role not only in tumor metastasis but also in regulating the immune microenvironment within tumors. Naive M0 macrophages could differentiate into M2 macrophages, which are linked to the development of LN metastasis at an early stage.[ 24 , 25 ] In addition to differentiating into M2 macrophages, M0 macrophages are capable to secrete MMP-9 during early stages of pancreatic cancer growth, promoting mesenchymal transition and facilitating tumor progression and spread.[ 26 ] NK cells, as essential parts of the innate immune system, are vital in combating LN metastasis. NK cells can display strong anti-metastatic effects that are independent of MHC-mediated antigen presentation through multiple pathways, including the release of pre-formed granules containing PRF1 and GZMB, the secretion of IFN-γ, and the exposure of death receptor ligands such as FASLG and TRAIL.[ 27 ] Furthermore, suppression of CD8 + T cells leads to increased cancer spread and reduced survival rates, particularly in BLCA patients.[ 28 ] Elevated IL-8 have been demonstrated to up-regulated PD-1 expression in CD8 + T cells, leading to immunosuppression within tumors and tumor-draining lymph nodes, which enhances LN metastasis of gastric cancer.[ 29 ] Apart from the levels of tumor-infiltrating immunocytes, some genes may also contribute to LN metastasis of BLCA via exerting biological functions and mediating related signaling pathways. Liu et al. have reviewed the coding genes associated with LN metastasis including CCR7 , PTBP-1 , and UPK-1B with increased expression and GATA-6 , NONO , and TCF-21 with decreased expression.[ 30 ] In our study, bioinformatic analysis and clinical sample validation both demonstrated augmentation of FKBP10 in BLCA tissues with positive lymph node metastasis and suggested it may be a potential marker for poor prognosis. FKBP10 is also known as FKBP65 (FK506-binding protein 10, 65kDa), a member of immunophilins that possess repeats of the peptidylprolyl isomerase domain and acts as a protein chaperone for collagen I in the endoplasmic reticulum.[ 31 ] Notably, FKBP10 has been suggested to contribute to the progression of tumors and act as an unfavorable prognostic factor across vaious malignancies including kidney, lung gastric, and prostate cancers.[ 32 – 35 ] Repression of FKBP10 hinders the growth and movement of renal cancer cells via reducing heat shock protein 90 levels and inducing cell cycle arrest.[ 32 ] Moreover, FKBP10 may enhance the adhesion of gastric cancer cells through integrin/AKT pathway, thus promoting lymph node metastasis,[ 34 ] targeting FKBP10 with YK-4-279, an inhibitor of ETV1, could prevent the progression of prostate cancer.[ 35 ] However, there remains a need to elucidate the biological role and regulatory mechanism of FKBP10 in BLCA. EPN2 and CYP4F12 have been identified as two other upregulated genes in LN metastasis (+) group, as confirmed by RT-qPCR and IHC assays separately. There have been few studies on the roles of EPN2 and CYP4F12 in cancer. Song et al. have emphasized that EPN2 might drive breast cancer development by promoting NF-κB essential modulator linear ubiquitination via linear ubiquitin chain assembly complex.[ 36 ] Another study has noted the upregulation of EPN2 in prostate cancer promoted the stabilization of cell surface receptor complexes, thus offering a mechanism to amplifying signals that stimulate tumor proliferation.[ 37 ] CYP4F12 exhibited low expression levels in tumor tissues, influencing various phenotypic changes in head and neck squamous cell carcinoma (HNSC). The overexpression of CYP4F12 impacted immune cell infiltration, inhibited cell migration, and promoted cell-matrix adhesion by suppressing the epithelial-mesenchymal transition (EMT) pathway in HNSC cells.[ 38 ] In addition, ASB13 and ST3GAL5 were the genes found to be downregulated in LN metastasis (+) group. ASB13 has been identified as a suppressor for breast cancer metastasis since it promotes SNAI2 degradation while relieving its transcriptional repression on YAP.[ 39 ] Furthermore, Zhang et al. have discovered that the expression of ST3GAL5 is relatively low in lung cancer tissues compared to nearby nonmalignant tissues, and this lower expression is linked to a more favorable prognosis.[ 40 ] Exosomes secreted by cancer cells with high levels of ST3GAL5 have the potential to facilitate peritoneal dissemination through the creation of a pre-metastatic niche via the recruitment of cancer-associated macrophages and promotion of immunosuppression.[ 41 ] Our findings of the analysis are subject to limitations. This study is based on the TCGA and GEO databases, and validation with small clinical samples may lead to the bias of results. More samples and experiments are required to confirm the applicability of this prognostic model in future, which could provide a dependable predictor and therapeutic target for BLCA patients with LN metastasis. Conclusion In general, this study presents signature genes related with LN metastasis in BLCA. We develop and validate a prognostic model via identifying novel gene markers. The presence of tumor immunocytes including M0 macrophages, activated NK cells, and CD8 + T cells is correlated with lymph node metastasis. Based on the bioinformatic results and clinical sample analysis, elevated levels of EPN2 , CYP4F12 and especially FKBP10 in BLCA can predict LN metastasis as well as poor prognosis. Abbreviations LN Lymph node BLCA Bladder cancer TCGA The Cancer Genome Atlas GEO Gene Expression Omnibus DEG Differentially expressed gene GO Gene Ontology KEGG Kyoto Encyclopedia of Genes and Genomes GSEA Gene set enrichment analysis LASSO Least absolute shrinkage and selection operator ROC Receiver operating characteristic curve TMB Tumor mutation burden GDSC Genomics of drug sensitivity in cancer TIDE Tumor immune dysfunction and exclusion RT-qPCR Quantitative real-time PCR IHC Immunohistochemistry IF Immunofluorescence iNOS Inducible NO synthase K-M Kaplan-Meier BP Biological process MF Molecular function CC Cellular component TME Tumor-infiltrating immunocytes in tumor microenvironment mpMRI Multiparametric magnetic resonance imaging ceCT Contrast-enhanced computed tomography PET Positron emission computed tomography HNSC Head and neck squamous cell carcinoma EMT Epithelial-mesenchymal transition Declarations Ethics approval and consent to participate Our research was granted approval by the Ethics Committee at Nanjing Drum Tower Hospital, and each participant provided written consent. Consent for publication Not applicable. Availability of data and materials Sequence data that support the findings of this study are derived from the TCGA-BLCA and GEO databases (GSE106534). The clinical data of the patients are provided within the supplementary information. Competing Interests No conflict of interest exists in the submission of this manuscript. Funding This research was supported by the National Natural Science Foundation of China (82173160 to W.D.) and Nanjing Health Technology Development Fund (YKK23107). Authors' contributions HG and WD conceived and designed the research studies. YS analyzed data, performed experiments and wrote the manuscript. JS and XW assisted experiments. MD and QZ provided the clinical information. Wei Chen and Wenming Cao guided experiments. All authors read and approved the manuscript. Acknowledgements We would like to thank all patients who agreed to participate in this study and all colleagues who helped with this research. 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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-5816202","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":401816992,"identity":"b7e6d476-57b0-4c55-940f-827cffaa335c","order_by":0,"name":"Yifan Sun","email":"","orcid":"","institution":"Nanjing Drum Tower Hospital, Nanjing University","correspondingAuthor":false,"prefix":"","firstName":"Yifan","middleName":"","lastName":"Sun","suffix":""},{"id":401816993,"identity":"559bac9c-69d7-4a3a-bbb6-251996b54331","order_by":1,"name":"Meng Ding","email":"","orcid":"","institution":"Nanjing Drum Tower Hospital, Nanjing 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03:08:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5816202/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5816202/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":73898995,"identity":"9e2def67-3180-4d0e-a155-f7ec0729e6a7","added_by":"auto","created_at":"2025-01-15 17:05:45","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":424422,"visible":true,"origin":"","legend":"\u003cp\u003eDEGs between LN metastasis (+) group and LN metastasis (–) group in two datasets. \u003cstrong\u003e(A) \u003c/strong\u003eVolcano plot for DEGs (Left: TCGA-BLCA, Right: GSE106534). \u003cstrong\u003e(B\u003c/strong\u003e, \u003cstrong\u003eC) \u003c/strong\u003eHierarchical clustering heatmap based on the DEGs in two datasets.\u003cstrong\u003e (D) \u003c/strong\u003eKEGG enrichment chord diagram for the “pathway-gene” network in TCGA-BLCA dataset. \u003cstrong\u003e(E\u003c/strong\u003e–\u003cstrong\u003eG) \u003c/strong\u003eDotplots of GO enrichment analysis in TCGA-BLCA dataset.\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5816202/v1/ef1ce7ec83127f981c877d1a.png"},{"id":73898978,"identity":"433cb906-1e33-4967-befd-bad5405c92e0","added_by":"auto","created_at":"2025-01-15 17:05:43","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":427652,"visible":true,"origin":"","legend":"\u003cp\u003eConstruction and validation of prognostic signature. \u003cstrong\u003e(A\u003c/strong\u003e, \u003cstrong\u003eB) \u003c/strong\u003eLASSO regression analysis for screening candidate genes.\u003cstrong\u003e (C) \u003c/strong\u003eForestplot for\u003cstrong\u003e \u003c/strong\u003eunivariate COX regression results of signature genes. \u003cstrong\u003e(D\u003c/strong\u003e, \u003cstrong\u003eE) \u003c/strong\u003eOverall survival analysis and time-dependent ROC curves of prognostic signature for predicting 1-year, 3-year, and 5-year survival in the TCGA database. \u003cstrong\u003e(F\u003c/strong\u003e, \u003cstrong\u003eG) \u003c/strong\u003eSurvival analysis result and time-dependent ROC curves for the training set.\u003cstrong\u003e (H\u003c/strong\u003e, \u003cstrong\u003eI) \u003c/strong\u003eSurvival analysis result and time-dependent ROC curves for the test set. \u003cstrong\u003e(J\u003c/strong\u003e, \u003cstrong\u003eK) \u003c/strong\u003eSurvival analysis result and time-dependent ROC curves for external validation with the GEO database.\u003c/p\u003e","description":"","filename":"Onlinefloatimage21.png","url":"https://assets-eu.researchsquare.com/files/rs-5816202/v1/4aed8d6502f947a2abc4939f.png"},{"id":73898983,"identity":"74b3ee67-67da-4ba3-ab55-bda839d2e62b","added_by":"auto","created_at":"2025-01-15 17:05:43","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":294634,"visible":true,"origin":"","legend":"\u003cp\u003eOverview of\u003cstrong\u003e \u003c/strong\u003emutational signatures between high- and low-risk groups in TCGA-BLCA dataset. \u003cstrong\u003e(A) \u003c/strong\u003eGO enrichment analysis (BP, CC, MF) and\u003cstrong\u003e \u003c/strong\u003eKEGG enrichment analysis for risk score. \u003cstrong\u003e(B)\u003c/strong\u003e Oncoplot for mutational signatures in high-risk group. \u003cstrong\u003e(C)\u003c/strong\u003e Oncoplot for mutational signatures in low-risk group.\u003cstrong\u003e (D\u003c/strong\u003e, \u003cstrong\u003eE) \u003c/strong\u003eOncoplot and forestplot for the mutation frequency of top 10 differentially significant genes. \u003cstrong\u003e(F\u003c/strong\u003e, \u003cstrong\u003eG)\u003c/strong\u003e Scatter gram and boxplot for tumor mutation burden.\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5816202/v1/9d20fe15fb20f721d0826c50.png"},{"id":73898980,"identity":"0aed93ab-0086-4218-ac2b-e6ac4b5b295d","added_by":"auto","created_at":"2025-01-15 17:05:43","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":434818,"visible":true,"origin":"","legend":"\u003cp\u003eOverview of\u003cstrong\u003e \u003c/strong\u003eimmune cell infiltration patterns. \u003cstrong\u003e(A\u003c/strong\u003e, \u003cstrong\u003eB)\u003c/strong\u003e Boxplots for immune cell infiltration patterns between LN metastasis (+) group and LN metastasis (–) group, high- and low-risk groups. \u003cstrong\u003e(C\u003c/strong\u003e, \u003cstrong\u003eD)\u003c/strong\u003e Representative immunofluorescence images showing the distributions of M0 macrophages (CD68\u003csup\u003e+\u003c/sup\u003e/CD163\u003csup\u003e-\u003c/sup\u003e/iNOS\u003csup\u003e-\u003c/sup\u003e), M1 macrophages (CD68\u003csup\u003e+\u003c/sup\u003e/iNOS\u003csup\u003e+\u003c/sup\u003e) and M2 macrophages (CD68\u003csup\u003e+\u003c/sup\u003e/CD163\u003csup\u003e+\u003c/sup\u003e) in BLCA tissue from patients with or without LN metastasis. \u003cstrong\u003e(E\u003c/strong\u003e–\u003cstrong\u003eH)\u003c/strong\u003e Representative immunohistochemistry images showing the distributions of activated NK cells and CD8\u003csup\u003e+\u003c/sup\u003e T cells in BLCA tissue from patients with or without LN metastasis. Quantitative evaluation of NCAM and CD8A expression represented as IOD/area. The graphs show mean ± SD. *p ≤ 0.05, **p ≤ 0.01.\u003c/p\u003e","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-5816202/v1/832dc178ab87f40ceeba6c0a.png"},{"id":73898981,"identity":"cf930fba-3ba7-4a6d-b723-e95e10016ec9","added_by":"auto","created_at":"2025-01-15 17:05:43","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":208878,"visible":true,"origin":"","legend":"\u003cp\u003eThe prognostic model to predict therapeutic effects. \u003cstrong\u003e(A)\u003c/strong\u003e Barplot for the drug sensitivity among high- and low-risk groups. \u003cstrong\u003e(B) \u003c/strong\u003eBoxplot for the sensitivity of selected drugs (|t| \u0026gt;5, P \u0026lt; 0.05). \u003cstrong\u003e(C)\u003c/strong\u003e Boxplot for the t cell inflammation score. \u003cstrong\u003e(D) \u003c/strong\u003eBoxplot for the TIDE score. \u003cstrong\u003e(E) \u003c/strong\u003eBoxplot for the TME score.\u003cstrong\u003e (F)\u003c/strong\u003e The expression of signature genes between CR/PR and SD/PDgroups. \u003cstrong\u003e(G)\u003c/strong\u003e Survival analysis results among high- and low-risk groups.\u003c/p\u003e","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-5816202/v1/f28ab92c31a4b8304ba3571c.png"},{"id":73900270,"identity":"e9ed89e1-2950-4a33-a6af-ed4b2a209e01","added_by":"auto","created_at":"2025-01-15 17:13:43","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":829166,"visible":true,"origin":"","legend":"\u003cp\u003eClinical significance of the signature genes. \u003cstrong\u003e(A)\u003c/strong\u003e Representative IHC images of signature gene expression in cancer tissues from BLCA patients with and without lymph node metastasis. Quantitative evaluation of signature gene expression levels represented as IOD/area. \u003cstrong\u003e(B)\u003c/strong\u003e Relative mRNA expression levels of the signature genes in BLCA patients with and without lymph node metastasis. \u003cstrong\u003e(C\u003c/strong\u003e–\u003cstrong\u003eH) \u003c/strong\u003eOverall survival of patients with low and high expression levels of the signature genes. The graphs show mean ± SEM. *p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001, ****p ≤ 0.0001. \u003cstrong\u003e(I, J) \u003c/strong\u003eCells were transfected with FKBP10-specific siRNAs or the control siRNA. Interference with FKBP10 expression significantly reduced the migration of T24 cells in the wound healing assay and the migration and invasion of T24 cells in the Transwell assays. \u003cstrong\u003e(K) \u003c/strong\u003eThe protein levels of FKBP10 in T24-siNC and T24-siFKBP10 cells. \u003cstrong\u003e(L) \u003c/strong\u003eThe mRNA levels of FKBP10 in T24-siNC and T24-siFKBP10 cells.\u003c/p\u003e","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-5816202/v1/0567716f31cb8ca5abafff5c.png"},{"id":79641693,"identity":"47bf23d8-d145-45af-bb97-4b7976d4533f","added_by":"auto","created_at":"2025-04-01 06:10:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4765738,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5816202/v1/4b147479-64a1-4f75-bbf9-d85409d34a6b.pdf"},{"id":73898984,"identity":"8318c11c-baf2-4b0c-942c-0ba3b7f11b66","added_by":"auto","created_at":"2025-01-15 17:05:43","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":9153536,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-5816202/v1/2f4a41f6145b15ee3714a7dc.docx"},{"id":73898979,"identity":"3eaf1ecd-1cac-4d40-ab64-0b25b158338a","added_by":"auto","created_at":"2025-01-15 17:05:43","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":81615,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable.docx","url":"https://assets-eu.researchsquare.com/files/rs-5816202/v1/c5de4604501cdd5099be0a39.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification of Prognostic Signature Genes and Immune Microenvironment Features Associated with Lymph Node Metastasis in Bladder Cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBladder cancer (BLCA) is one of the most common malignancy in the urinary system, with approximately 573,000 newly diagnosed patients per year worldwide.[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] Approximately 25% BLCA are pathologically identified as muscle-invasive BLCA. Muscle-invasive BLCA is considered as one of the most aggressive and lethal malignancies because of its propensity to metastasize to lymph nodes and distant organs. Previous studies have indicated that BLCA lymph node (LN) involvement possesses significant prognostic implications. The identification of LN metastasis is considered as a crucial determinant in predicting the survival outcome for patients with BLCA,[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] which reduces the 5-year survival rate to only 18.6%.[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] Cisplatin-based chemotherapy remains the primary choice for treating individuals with LN involvement, and there has been an increasing utilization of targeted therapies, particularly immunotherapy, as a complementary approach to manage BLCA patients with positive LNs in recent years.[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] It has been established that therapies targeting the VEGF-C and VEGF-D/VEGFR-3 axis could potentially inhibit LN metastasis in BLCA. Moreover, combining ramucirumab (a drug inhibiting the VEGF signal transduction) with docetaxel resulted in better progression-free survival for metastatic patients with platinum-refractory advanced urothelial carcinoma.[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] However, due to a lack of sufficient well-performed prospective studies and available evidence, not enough recommendations have been provided by various guidelines for BLCA patients with positive LNs.\u003c/p\u003e \u003cp\u003eConsidering the significant impact of lymph node metastasis on BLCA patient prognosis, preoperative evaluation of lymph node metastasis holds great significance in patient management. Although, traditional radiology cannot detect the micro metastasis of lymph nodes, nomogram based on radiomics exhibited great precision in predicting BLCA LN metastasis.[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] Nevertheless, the external validation is necessary prior to their clinical implementation. In addition, the urine-based liquid biopsy in BLCA was proven to distinguish the LN status and evaluate the risk of LN metastasis, but the number of participants in both studies was relatively limited and did not meet the requirements for making conclusive clinical decisions.[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] Consequently, it is vital to discover new potential diagnostic and prognostic biomarkers for BLCA with LN metastasis. Some LN metastasis-related genes which are of great significance for diagnosis and prognosis have been observed in previous studies with BLCA patients, including a five-mRNA classifier (\u003cem\u003eADRA1D\u003c/em\u003e, \u003cem\u003eCOL10A1\u003c/em\u003e, \u003cem\u003eDKK2\u003c/em\u003e, \u003cem\u003eHIST2K3D\u003c/em\u003e, and \u003cem\u003eMMP11\u003c/em\u003e) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] and \u003cem\u003eKNN51\u003c/em\u003e [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], with AUCs ranging from 0.72 to 0.82. Another series of aging-related signature genes (\u003cem\u003eEFEMP1\u003c/em\u003e, \u003cem\u003eUCHL1\u003c/em\u003e, \u003cem\u003eTP63\u003c/em\u003e, \u003cem\u003eELN\u003c/em\u003e) has demonstrated the potential to predict BLCA LN metastasis with an AUC of 0.67.[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] More researches need to be conducted to identify predictive biomarkers and investigate the underlying mechanism of BLCA with LN metastasis.\u003c/p\u003e \u003cp\u003eHere, we aimed to construct a prognostic model in BLCA and explore novel signature genes related to lymph node metastasis. Furthermore, we intended to predict and validate the efficacy of chemotherapy and immunotherapy based on the prognostic model, which could guide high-risk BLCA patients to early multimodal therapy and provide new perspectives for exploring therapeutic options for BLCA patients with LN metastasis.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Retrieve and Processing\u003c/h2\u003e \u003cp\u003eThe mRNA sequence and the clinicopathological data, including clinical stage, clinical grade, patient survival, patient gender, and LN metastasis of the BLCA patients were obtained from the Cancer Genome Atlas (TCGA) dataset (Project: TCGA-BLCA), and the gene expression matrix were retrieved from the Gene Expression Omnibus (GEO) dataset (GSE106534). All data were derived from bladder carcinoma tissues and categorized into two groups based on the presence or absence of LN metastasis.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eIdentification of differentially expressed genes and gene set enrichment analysis\u003c/h3\u003e\n\u003cp\u003eWe used R 4.1.0 version \u0026ldquo;edgeR\u0026rdquo; package to perform mRNA expression profile normalization and compare the mRNA expression of LN metastasis (\u0026ndash;) group and LN metastasis (+) group to screen differentially expressed genes (DEGs). In order to elucidate the enrichment of candidate genes, analyses including Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis and gene set enrichment analysis (GSEA) were carried out. Fisher\u0026rsquo;s exact test was employed to find out which functions and pathways were most closely related to candidate genes. Survival analysis was conducted based on the two groups of data using R package \u0026ldquo;survival\u0026rdquo;.\u003c/p\u003e\n\u003ch3\u003eDevelopment and validation of a risk model\u003c/h3\u003e\n\u003cp\u003eA total of 372 samples were randomly assigned to the training set or test set in a ratio of 1:1. Least absolute shrinkage and selection operator (LASSO) regression algorithm was employed for analysis of the above candidate genes with respect to patient prognosis and the prognostic risk prediction model was constructed. We obtained the expression profile data of the candidate genes from the training set and incorporated them into model to calculate the risk score. Based on this, receiver operating characteristic curve (ROC) analysis was applied for prognostic classification of the risk score. The efficiency of our model in predicting prognosis was evaluated using samples from the training set, while samples from the test set were used for validation.\u003c/p\u003e\n\u003ch3\u003eEvaluation of Immune infiltration and Tumor mutation burden landscape\u003c/h3\u003e\n\u003cp\u003eCIBERSORT algorithm was applied to evaluate the immune infiltration landscape. Furthermore, we also used \u0026ldquo;maftools\u0026rdquo; package for evaluating the tumor mutation burden (TMB) score of each sample and compared the TMB landscape in above four groups by using \u0026ldquo;wilcox.test\u0026rdquo;.\u003c/p\u003e\n\u003ch3\u003ePrediction of anti-tumor drug response\u003c/h3\u003e\n\u003cp\u003eAccording to the TCGA and genomics of drug sensitivity in cancer (GDSC) dataset, \u0026ldquo;oncoPredict\u0026rdquo; package was utilized for predicting chemotherapeutic and targeted drug response. In addition, to further predict the responses of BLCA patients towards immunotherapy, analyses of T cell-inflammation and tumor immune dysfunction and exclusion (TIDE) were also conducted. The prediction power for the signature genes was also identified.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSample collection\u003c/h2\u003e \u003cp\u003e16 pairs of fresh bladder cancer tissues and 30 pairs of paraffin-embedded bladder cancer tissues were collected from BLCA patients who underwent radical cystectomy and pelvic lymph nodes dissection in the Nanjing Drum Tower Hospital from June 2018 to Dec 2023. BLCA patients were pathologically diagnosed and divided into LN metastasis (\u0026ndash;) group and LN metastasis (+) group by at least two experienced pathologists.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eRNA isolation and quantitative real-time PCR (RT-qPCR)\u003c/h3\u003e\n\u003cp\u003eRNA was isolated using TRIzol reagent (Invitrogen), followed by reverse transcription using a HiScriptIII RT SuperMix for qPCR (+\u0026thinsp;gDNA wiper) (Vazyme, Nanjing, China). We performed RT-qPCR using ChamQ SYBR qPCR Master Mix (Vazyme, Nanjing, China) on a QuantStudio\u0026trade; 6 Flex (Applied Biosystems, Foster City, CA, USA). All primers were designed and synthesized by Shanghai Generay Biotech Co., Ltd. β-actin was employed as the internal reference. The primers are listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The relative expression levels of genes were determined using the 2\u003csup\u003e\u0026minus;ΔΔCt\u003c/sup\u003e method.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrimer sequences\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward primer (5\u0026rsquo;-3\u0026rsquo;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReverse primer (5\u0026rsquo;-3\u0026rsquo;)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eASB13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCCTCATCGAGATGCTTATCGAGT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAGTCAGAGGTGTCTTTTCGTAGT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCARD11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGGACGCCTTGTGGGAGAATG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTCAATGACCTTACACTGACGC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCYP4F12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTGTCGGCCACCTATTCCCA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGGTGATAGACCGGATGGTGTC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEPN2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eACCTCACTGTAGCCTGGTCA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTGGTAAGTGCGCACCCTATG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFKBP10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCATGGGCATGTGTGTCAACG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGAATGAGCCCCGCCAGG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eST3GAL5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAGGAATGTCGTCCCAAGTTTG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGGAGTAAGTCCACGCTATACCT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eImmunohistochemistry (IHC) and immunofluorescence (IF)\u003c/h3\u003e\n\u003cp\u003eParaffin-embedded BLCA tissues were collected, then sliced at 5-\u0026micro;m intervals and placed onto glass slides. Slides were incubated at 75\u0026deg;C for 2 h, deparaffinized, and rehydrated. We blocked the slides and incubated them with primary antibodies for NCAM (1:125 dilution, A11770, Abclonal, China), CD8A (1:125 dilution, A0663, Abclonal, China), ASB13 (1:500 dilution, 25616-1-AP, Proteintech, USA), CARD11 (1:200 dilution, A9652, Abclonal, China), CYP4F12 (1:200 dilution, DF2614, Affinity, USA), EPN2 (1:200 dilution, A16106, Abclonal, China), FKBP10 (1:500 dilution, 12172-1-AP, Proteintech, USA) or ST3GAL5 (1:500 dilution, 14614-1-AP, Proteintech, USA) overnight at 4\u0026deg;C, followed by incubation with HRP-conjugated secondary antibodies for 1 h at room temperature. Finally, Each slide was reacted with 3.3\u0026prime;-diaminobenzidine (DAB) solution for 30 seconds and then washed with water before counterstaining with hematoxylin.\u003c/p\u003e \u003cp\u003eTo identify the subpopulations of macrophage, double immunofluorescence staining to detect CD68 and inducible NO synthase (iNOS) (or CD163) were conducted. CD68 was identified as a pan-macrophage marker, while CD163 was a marker for M2-polarized macrophages (CD68\u003csup\u003e+\u003c/sup\u003e/CD163\u003csup\u003e+\u003c/sup\u003e) and iNOS was a marker for M1-polarized macrophages (CD68\u003csup\u003e+\u003c/sup\u003e/iNOS\u003csup\u003e+\u003c/sup\u003e). CD68\u003csup\u003e+\u003c/sup\u003e/CD163\u003csup\u003e\u0026minus;\u003c/sup\u003e/iNOS\u003csup\u003e\u0026minus;\u003c/sup\u003e cells were identified as M0-polarized macrophages. Formalin-fixed tissue slides were deparaffinized, rehydrated before epitope retrieval. After blocking with 10% BSA for 1 h, incubation with the primary antibody for CD68 (1:100 dilution, A15037, Abclonal, China), iNOS (1:100 dilution, A14031, Abclonal, China) or CD163 (1:100 dilution, A22619, Abclonal, China) was performed overnight at 4\u0026deg;C. Alexa Fluor plus 488 and 594 goat anti-rabbit IgG (H\u0026thinsp;+\u0026thinsp;L) (1:1000 dilution, Invitrogen, America) were used as secondary antibodies. Incubation was performed for 60 minutes at room temperature. Nuclei were labeled using Hoechst 33342. Captured images were acquired with the EVOS FL Auto 2 (Invitrogen, America). The positive cells were counted manually in the random fields (40\u0026times; magnifications).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eRNA interference\u003c/h2\u003e \u003cp\u003eSmall interfering RNAs (siRNAs) specific to human \u003cem\u003eFKBP10\u003c/em\u003e were purchased from Generay (Shanghai, China). The details of these siRNAs can be found in Supplementary Table S4. The T24 cells were transiently transfected with siRNAs targeting human \u003cem\u003eFKBP10\u003c/em\u003e and with the negative control siRNA by means of jetPRIME (Polyplus, France).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eWestern blotting\u003c/h2\u003e \u003cp\u003eProtein extracts were prepared on ice using a lysis buffer containing protease inhibitors. The samples were denatured, and proteins were separated via SDS-PAGE before being transferred onto polyvinylidene difluoride (PVDF) membranes. After blocking, the membranes were sequentially incubated with primary and secondary antibodies. The FKBP10 antibody (12172-1-AP) was obtained from Proteintech (Rosemont, IL, USA). Protein signals were visualized using an enhanced chemiluminescence (ECL) detection system (Vazyme, Nanjing, China), and images were captured with the ChemiScope 3300 Mini Imaging System (CLiNX, Shanghai, China).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eCell migration and invasion assays\u003c/h2\u003e \u003cp\u003eIn the wound healing experiment, the cells were cultivated until reaching near-full confluence, after which a scratch was created on the cell layer's surface. Subsequently, the cells were kept in serum-free medium for a predetermined duration. Upon observing and photographing the cells, the migration distance was measured and calculated. Regarding the Transwell migration assay, 6 \u0026times; 10\u003csup\u003e4\u003c/sup\u003e cells per well were placed in the upper chambers (8 \u0026micro;m) and maintained in serum-free medium, while conditioned medium was introduced into the lower chambers. In the Transwell invasion assay, Matrigel was spread across the bottom of each upper chamber and permitted to solidify at 37\u0026deg;C. Then, the cells were seeded in the upper chambers for the assessment of invasion. Following incubation at 37\u0026deg;C, the cells were fixed, stained, and photographed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe software utilized for statistical analysis and data processing was R 4.1.0. The survival differences were evaluated using Kaplan-Meier (K-M) analysis. Gene expression correlation was determined through Pearson correlation analysis. Prognostic value of the risk score was assessed using multivariate Cox analysis. The predictive value of the risk score was estimated by ROC curve analysis. Statistical significance was defined as two-sided \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of Differentially Expressed Genes\u003c/h2\u003e \u003cp\u003eTo explore the differences in gene expression and functional heterogeneity between BLCA patients with and without LN metastasis, we screened candidate genes in the TCGA and GEO dataset. A total of 372 samples from TCGA-BLCA dataset and 15 samples from GSE106534 dataset were identified and selected in our study (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). There were 2918 differentially expressed genes in TCGA-BLCA dataset, and 1715 DEGs in GSE106534 dataset (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, LN metastasis (+) vs. LN metastasis (\u0026ndash;), \u003cb\u003e|\u003c/b\u003elog\u003csub\u003e2\u003c/sub\u003eFC\u003cb\u003e|\u003c/b\u003e \u0026gt;1, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The top 20 up-regulated and down-regulated DEGs of the two datasets were presented respectively in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB, C. Additionally, we carried out differential analysis of clinical characteristics including age, gender, tumor stage, tumor grade and tumor metastasis. The patient age and the ratios of high grade and advanced stage were significantly higher in BLCA patients with lymph node metastasis in TCGA-BLCA dataset (\u003cb\u003eSupplementary Fig.\u0026nbsp;1A, B, 1D\u0026ndash;F\u003c/b\u003e). There were no significant differences in patient gender or overall survival in TCGA-BLCA dataset, nor in any clinical features in GSE106534 dataset (\u003cb\u003eSupplementary Fig.\u0026nbsp;1C, G, 2A\u0026ndash;D\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe clinical data of TCGA-BLCA dataset\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimary tumor (N\u0026thinsp;=\u0026thinsp;239)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMetastatic LN (N\u0026thinsp;=\u0026thinsp;131)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67.45\u0026thinsp;\u0026plusmn;\u0026thinsp;8.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69.64\u0026thinsp;\u0026plusmn;\u0026thinsp;8.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e174 (72.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e99 (75.57)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65 (27.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32 (24.43)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT_Stage (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90 (37.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (12.21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e111 (46.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78 (59.54)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (9.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34 (25.95)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (5.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (2.29)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN Stage (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e239 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47 (35.88)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76 (58.01)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (6.11)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM Stage (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e133 (55.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44 (33.59)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (6.11)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMx\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e105 (43.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79 (60.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePathologic Stage (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e103 (43.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eⅢ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e131 (54.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eⅣ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (1.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e130 (99.24)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0.76)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eHistologic Grade (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19 (7.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (1.53)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e219 (91.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e129 (98.47)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurvival Months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.71 (12-35.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.12 (8.12\u0026ndash;28.02)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe clinical data of GSE106534 dataset\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimary tumor (N\u0026thinsp;=\u0026thinsp;7)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMetastatic LN (N\u0026thinsp;=\u0026thinsp;8)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66.43\u0026thinsp;\u0026plusmn;\u0026thinsp;8.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67.88\u0026thinsp;\u0026plusmn;\u0026thinsp;8.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (71.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (75)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (28.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (25)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT_Stage (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (12.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (12.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (42.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (50)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (57.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (25)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN Stage (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (87.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (12.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM Stage (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (87.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (12.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePathological Grade (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eⅢ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eⅣ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (100)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eFunctional Enrichment Analysis of DEGs\u003c/h2\u003e \u003cp\u003eTo further explore the underlying mechanisms concerning LN metastasis in BLCA, we performed GO and KEGG enrichment analysis in the above datasets. KEGG enrichment chord diagram showed the top 50 log\u003csub\u003e2\u003c/sub\u003eFC genes corresponding to the top significantly enriched 10 KEGG terms (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). Calcium signaling pathway, focal adhesion, and PI3K-Akt signaling pathway were the most potentially associated with lymph node metastasis in TCGA-BLCA dataset. The top fifteen terms from the GO results were displayed in dotplot (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE\u0026ndash;G). In the biological process (BP) group, DEGs showed significant enrichment in animal organ development, multicellular organism development and tissue development. Besides, cell migration, cell motility and chemotaxis might also play crucial roles in lymph node metastasis. In the molecular function (MF) group, DEGs were mostly enriched in metal ion transmembrane transporter activity, cation transmembrane transporter activity, inorganic cation transmembrane transporter activity and calcium ion binding. The alterations in ion transmembrane transporter activity appeared to be responsible for the enhanced migration of cancer cells, thus seemingly contributing to lymph node metastasis. In addition, calcium-binding protein may also facilitate cell adhesion and contribute to the process of cancer metastasis.[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] Similar analyses were performed using GSE106534 dataset. Related results were shown in \u003cb\u003eSupplementary Fig.\u0026nbsp;3A\u0026ndash;E\u003c/b\u003e. There were a total of 3 KEGG terms and 83 GO terms enriched in both TCGA-BLCA and GSE106534 datasets (\u003cb\u003eSupplementary Fig.\u0026nbsp;3F\u0026ndash;G\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eConstruction and validation of prognostic model\u003c/h2\u003e \u003cp\u003eTo construct a prognostic model, BLCA patients from the TCGA dataset were randomly assigned into the training set or test set in a 1:1 ratio. No significant differences were found in any of clinical variables examined between the two groups (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). We screened the common up- and down- regulated DEGs (43 up-regulated and 78 down-regulated in total) and selected 40 genes as candidates which exhibited remarkable correlations with prognosis (\u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e). We subsequently incorporated all candidate genes into the LASSO logistic regression model (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, B). The LASSO logistic regression model generated a forest plot illustrating 17 signature genes with non-zero coefficients (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). When the error was minimized, the model indicated the value of the harmonic parameter λ was 0.0356. The risk score was calculated using the following formula: Risk score = [(\u0026minus;\u0026thinsp;0.0233 \u0026times; ANKRD20A5P) + (0.0217 \u0026times; ARHGAP29) + (\u0026minus;\u0026thinsp;0.0089 \u0026times; ASB13) + (\u0026minus;\u0026thinsp;0.1094 \u0026times; BCL2L14) + (\u0026minus;\u0026thinsp;0.2173 \u0026times; C19orf71) + (0.0469 \u0026times; CADM3) + (\u0026minus;\u0026thinsp;0.0434 \u0026times; CARD11) + (0.0122 \u0026times; CCDC102B) + (\u0026minus;\u0026thinsp;0.0854 \u0026times; CD3D) + (\u0026minus;\u0026thinsp;0.0098 \u0026times; CYP4F12) + (0.1117 \u0026times; EPN2) + (0.0359 \u0026times; FKBP10) + (\u0026minus;\u0026thinsp;0.0556 \u0026times; IKZF3) + (-0.0251 \u0026times; RPL34) + (\u0026minus;\u0026thinsp;0.0251 \u0026times; ST3GAL5) + (0.1208 \u0026times; SVIL) + (0.0458 \u0026times; ZC3HAV1L)]. Patients were categorized into two risk groups according to the median risk score. \u003cb\u003eSupplementary Fig.\u0026nbsp;4A\u0026ndash;H\u003c/b\u003e presents the differential analysis of clinical characteristics such as age, gender, tumor stage, tumor grade and tumor metastasis between high- and low-risk groups. We found that the patient age, the ratios of high grade and advanced stage were significantly higher among patients at high risk, while the tumor purity was contrary. Risk score was considered as the most significant risk factor for survival (hazard ratio, 1.52; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (\u003cb\u003eSupplementary Fig.\u0026nbsp;4I\u0026ndash;J)\u003c/b\u003e. In the total population, K-M survival curves demonstrated a significant decline in the survival rates among patients at high risk (hazard ratio, 2.803; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). ROC curves revealed that the prognostic model had a prognostic accuracy with the AUC values of 0.706 in one year, 0.701 in three years, and 0.688 in five years, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). ROC curves and K-M survival curves for the training and test sets are displayed in Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF\u0026ndash;I. The training set achieved AUC values of 0.743, 0.718 and 0.686 in one, three, five years respectively, while the test set obtained AUC values of 0.707, 0.655 and 0.677 at the same time point. K-M survival curves of these two sets both indicated significant disparities in survival rate when comparing high- with low-risk groups (Training set, hazard ratio, 3.592, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Test set, hazard ratio, 2,32, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of TCGA clinical data between training set and test set.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTraining set (N\u0026thinsp;=\u0026thinsp;203)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTest set (N\u0026thinsp;=\u0026thinsp;203)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e68.05\u0026thinsp;\u0026plusmn;\u0026thinsp;10.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67.88\u0026thinsp;\u0026plusmn;\u0026thinsp;11.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e68.22\u0026thinsp;\u0026plusmn;\u0026thinsp;10.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.743\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStatus (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.230\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e227 (55.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e120 (59.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e107 (52.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDead\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e179 (44.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e83 (40.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e96 (47.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.499\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e299 (73.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e146 (71.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e153 (75.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e107 (26.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57 (28.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50 (24.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT_Stage (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.464\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1 (0.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (0.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e3 (0.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (0.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e118 (29.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61 (30.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e57 (28.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e193 (47.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e101 (49.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e92 (45.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e58 (14.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27 (13.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31 (15.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e33 (8.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (5.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21 (10.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN_Stage (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.517\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e236 (58.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e126 (62.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e110 (54.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e46 (11.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21 (10.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25 (12.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e75 (18.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36 (17.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39 (19.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e7 (1.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (1.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 (1.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e42 (10.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17 (8.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25 (12.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM_Stage (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.135\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e195 (48.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e92 (45.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e103 (50.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e11 (2.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (1.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8 (3.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e200 (49.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e108 (53.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e92 (45.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePathologic_Stage (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.933\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e2 (0.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (0.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (0.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e129 (31.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65 (32.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e64 (31.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e140 (34.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73 (35.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e67 (33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e133 (32.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63 (31.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e70 (34.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e2 (0.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (0.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (0.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMoreover, this model was applied to an external independent dataset, GSE13507 dataset, which provides overall survival data. ROC analysis manifested that the AUC values of this model (0.719 in one year, 0.635 in three years, and 0.633 in five years) were close to that of the TCGA-BLCA, and K-M survival curves also showed significant difference in survival rate (hazard ratio, 2.032; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), suggesting this model is robust in other datasets (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eJ, K).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eFunctional enrichment analysis and mutational characteristics of risk score\u003c/h2\u003e \u003cp\u003eGO and KEGG enrichment analyses were utilized to assess the function of genes ranked by risk score. The top 3 terms of KEGG, BP, cellular component (CC), MF based on high- and low-risk patients respectively are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA. We identified that high-risk patients were enriched in KEGG terms including \u0026ldquo;Glycosaminoglycan biosynthesis-chondroitin sulfate; Dermatan sulfate\u0026rdquo;, \u0026ldquo;DNA replication\u0026rdquo; and \u0026ldquo;Biosynthesis of unsaturated fatty acids\u0026rdquo;, while the low-risk patients were enriched in \u0026ldquo;Ascorbate and aldarate metabolism\u0026rdquo;, \u0026ldquo;Pentose and glucuronate interconversions\u0026rdquo; and \u0026ldquo;Chemical carcinogenesis\u0026rdquo;.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo further explore the genomic differences between high- and low-risk groups, as well as between the LN metastasis (+) and LN metastasis (\u0026ndash;) groups. we conducted an analysis of the mutational landscape and compared the mutation rate and TMB in these groups. Specially, \u003cem\u003eTP53\u003c/em\u003e, \u003cem\u003eTTN\u003c/em\u003e, \u003cem\u003eKDM6A, MUC16\u003c/em\u003e and \u003cem\u003eKMT2D\u003c/em\u003e were the most frequently mutated genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, C, \u003cb\u003eSupplementary Fig.\u0026nbsp;5A, B)\u003c/b\u003e. Missense mutations accounted for the most of mutation types in \u003cem\u003eTTN\u003c/em\u003e, \u003cem\u003eTP53\u003c/em\u003e, and \u003cem\u003eMUC16\u003c/em\u003e mutations. Compared to the low-risk group, the mutation frequency of \u003cem\u003eFGFR3\u003c/em\u003e, \u003cem\u003eNEO1\u003c/em\u003e, \u003cem\u003eFBN2\u003c/em\u003e, \u003cem\u003eTTI1\u003c/em\u003e, \u003cem\u003eZNF750\u003c/em\u003e, \u003cem\u003eLRRC37B\u003c/em\u003e and \u003cem\u003eAP3D1\u003c/em\u003e was significantly lower, while the mutation frequency of \u003cem\u003eTP53\u003c/em\u003e, \u003cem\u003eZC3H14\u003c/em\u003e and \u003cem\u003ePRDM5\u003c/em\u003e was notably higher (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD, E). In the LN metastasis (+) group, a significantly lower mutation frequency was observed for \u003cem\u003eNUP107\u003c/em\u003e, \u003cem\u003eMSLNL\u003c/em\u003e, \u003cem\u003eSLC37A3\u003c/em\u003e, \u003cem\u003eTBC1D1\u003c/em\u003e and \u003cem\u003eCCT3\u003c/em\u003e, whereas a significantly higher mutation frequency was noted for \u003cem\u003eMED1\u003c/em\u003e, \u003cem\u003eARHGAP35\u003c/em\u003e, \u003cem\u003eSORCS1\u003c/em\u003e, \u003cem\u003eCKAP5\u003c/em\u003e, and \u003cem\u003eEXOC4\u003c/em\u003e \u003cb\u003e(Supplementary Fig.\u0026nbsp;5C, D)\u003c/b\u003e. Moreover, the TMB distribution diagram of risk score was shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF and \u003cb\u003eSupplementary Fig.\u0026nbsp;5E\u003c/b\u003e. The TMB level of patients in the low-risk and LN metastasis (\u0026ndash;) group was slightly elevated compared to that of patients in the high-risk and LN metastasis (+) group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG, \u003cb\u003eSupplementary Fig.\u0026nbsp;5F\u003c/b\u003e), which suggested a better therapeutic effect of immunotherapy in low-risk and LN metastasis (\u0026ndash;) group. The outcomes indicated that BLCA patients in both high-risk group and LN metastasis (+) group were prone to \u003cem\u003eTP53\u003c/em\u003e, \u003cem\u003eTTN\u003c/em\u003e and \u003cem\u003eKMT2D\u003c/em\u003e mutations. In addition, mutations in \u003cem\u003eTP53\u003c/em\u003e, \u003cem\u003eZC3H14\u003c/em\u003e, and \u003cem\u003ePRDM5\u003c/em\u003e, known as tumor suppressor genes, contributed to the progression of high-risk BLCA.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eTumor-infiltrating immunocytes in tumor microenvironment (TME)\u003c/h2\u003e \u003cp\u003eTumor lymph node metastasis is driven by both the inherent characteristics of tumor cells and the interaction between tumor cells and tumor immune microenvironment.[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] To further examine the relationship between tumor-infiltrating immunocytes and LN metastasis status, we performed CIBERSORT algorithm to assess the subpopulations of immunocytes and the difference of immune signature in the above two datasets. The fractions of M0 macrophages were significantly higher in LN metastasis (+) group and high-risk group, while fractions of activated NK cells and CD8\u003csup\u003e+\u003c/sup\u003e T cells were remarkably higher in LN metastasis (\u0026ndash;) group and low-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, B). To validate the results, IHC and IF were performed to investigate the proportion of M0 macrophages, activated NK cells and CD8\u003csup\u003e+\u003c/sup\u003e T cells in 10 pairs of paraffin-embedded bladder cancer tissues. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC\u0026ndash;H, the infiltration of M0 macrophages was significantly upregulated, while activated NK cells and CD8\u003csup\u003e+\u003c/sup\u003e T cells were less accumulated in BLCA patients with LN metastasis. Additionally, we observed a positive relationship between M1 macrophages and CD8\u003csup\u003e+\u003c/sup\u003e T cells, activated NK cells and CD8\u003csup\u003e+\u003c/sup\u003e T cells, as well as a negative relationship between CD8\u003csup\u003e+\u003c/sup\u003e T cells and M0 macrophages, CD8\u003csup\u003e+\u003c/sup\u003e T cells and M2 macrophages respectively (\u003cb\u003eSupplementary Fig.\u0026nbsp;6A, B\u003c/b\u003e). These alterations of specific immunocytes within the TME could be manipulated by tumor cells via releasing more or less chemokines, especially CXCL and CCL families.[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] The occurrence of CD8\u003csup\u003e+\u003c/sup\u003e T cells, activated NK cells, and M1 macrophages in the TME is typically linked to tumor regression as well as a positive prognosis, while the infiltration of M2 macrophages facilitate tumor progression and lymphatic metastasis.[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eSignificance of risk score in predicting therapeutic efficacy of BLCA\u003c/h2\u003e \u003cp\u003eConsidering the potential of risk score to predict the prognosis of BLCA patients and its strong association with mutational characteristics and infiltration of immunocytes, we further wondered whether it could predict the effectiveness of clinical treatments. Correlation analysis between risk score and IC50 of drugs in GDSC demonstrated that high-risk BLCA patients were more sensitive to the treatment of sorafenib, gemcitabine and oxaliplatin, which were frequently used in BLCA (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, B). T cell inflammation score was positively correlated with clinical efficacy of tumor immunotherapy, but it did not differ significantly based on the risk score (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). Moreover, by evaluating scores in TIDE and TME, we observed a notable decrease in the TIDE score and a significant increase in the TME score in the low-risk group, both indicating a potential enhancement in the effectiveness of immunotherapy for low-risk BLCA patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD, E).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo further identify the predictive capability of the risk score in immunotherapy efficacy, we utilized Imvigor210 to evaluate the capacity of the above 17 signature genes to predict the efficacy of anti-PD-L1 immunotherapy. However, the expression of the 17 signature genes did not affect the efficacy of immunotherapy (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF). K-M analysis indicated that a higher risk score was linked to decreased survival rates (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eG). In conclusion, our data suggest that traditional chemotherapy agents such as gemcitabine and oxaliplatin may offer greater advantages for high-risk BLCA patients, whereas low-risk BLCA patients may benefit more from immunotherapy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eThe clinical significance of the signature genes\u003c/h2\u003e \u003cp\u003eTo further assess the clinical significance of signature genes, we collected 16 paired fresh bladder cancer tissues (\u003cb\u003eSupplementary Table\u0026nbsp;2\u003c/b\u003e) (LN metastasis (+) vs. LN metastasis (\u0026ndash;)) and conducted RT-qPCR analysis to explore the mRNA expression levels of six signature genes including \u003cem\u003eASB13\u003c/em\u003e, \u003cem\u003eCARD11\u003c/em\u003e, \u003cem\u003eCYP4F12\u003c/em\u003e, \u003cem\u003eEPN2\u003c/em\u003e, \u003cem\u003eFKBP10\u003c/em\u003e and \u003cem\u003eST3GAL5\u003c/em\u003e, which were obtained through the intersection of prognostic genes from GSE13507 dataset and aforementioned 17 signature genes. The results indicated a notable upregulation in the relative mRNA expression levels of \u003cem\u003eEPN2\u003c/em\u003e and \u003cem\u003eFKBP10\u003c/em\u003e in bladder cancer tissues with LN metastasis (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). In addition, we assembled 30 pairs of paraffin-embedded bladder cancer tissues (LN metastasis (+) vs. LN metastasis (\u0026ndash;)) to evaluate the expression level and clinical significance of the above 6 signature genes (\u003cb\u003eSupplementary Table\u0026nbsp;3\u003c/b\u003e). Staining of tissue sections revealed significantly increased levels of CYP4F12 and FKBP10, while decreased levels of ASB13 and ST3GAL5 in LN metastasis (+) group (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). Prognostic analysis showed elevated levels of FKBP10, EPN2 and CYP4F12 were significantly associated with poorer overall survival and reduced progression-free survival among BLCA patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE\u0026ndash;G, \u003cb\u003eSupplementary Fig.\u0026nbsp;7C\u003c/b\u003e\u0026ndash;\u003cb\u003eE)\u003c/b\u003e, but ASB13, CARD11 and ST3GAL5 were not significantly related to the prognosis of BLCA patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC, D, H, \u003cb\u003eSupplementary Fig.\u0026nbsp;7A, B, F\u003c/b\u003e). Furthermore, to further investigate the biological function of FKBP10, we established a T24-siFKBP10 cell line with transient FKBP10 knockdown (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eK, L). the wound healing assay results demonstrated that silencing FKBP10 reduced cell migration and the outcomes of Transwell assays also indicated that transient knockdown of FKBP10 remarkably inhibited cell migration and invasion (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eI, J\u003cb\u003e)\u003c/b\u003e. These findings suggested that \u003cem\u003eEPN2\u003c/em\u003e, \u003cem\u003eCYP4F12\u003c/em\u003e and especially \u003cem\u003eFKBP10\u003c/em\u003e could serve as novel biomarkers, playing an important role in the preoperative diagnosis and prognosis guidance of LN metastasis in BLCA.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eBladder cancer holds the fourth most common cancer in males and the eleventh in females.[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] LN metastasis has a notable impact on the spread of bladder cancer, and its involvement is an independent factor for predicting disease progression.[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] Although medical imaging approaches including multiparametric magnetic resonance imaging (mpMRI), contrast-enhanced computed tomography (ceCT) and positron emission computed tomography (PET) have achieved great advancements in pre-surgical diagnosis, occult LN metastases are frequently discovered after radical cystectomy for BLCA.[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] Accordingly, we intended to established and validate a personalized prognostic model based on a signature of lymph node metastasis to predict outcomes and guide therapy decisions for patients with BLCA.\u003c/p\u003e \u003cp\u003eThe impact of the TME on LN metastasis in tumors has been widely recognized.[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] In our study, we have noticed a significant correlation between BLCA LN metastasis and infiltrating levels of specific immunocytes such as M0 macrophages, activated NK cells, and CD8\u003csup\u003e+\u003c/sup\u003e T cells. Previous researches have demonstrated that macrophages play a pivotal role not only in tumor metastasis but also in regulating the immune microenvironment within tumors. Naive M0 macrophages could differentiate into M2 macrophages, which are linked to the development of LN metastasis at an early stage.[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] In addition to differentiating into M2 macrophages, M0 macrophages are capable to secrete MMP-9 during early stages of pancreatic cancer growth, promoting mesenchymal transition and facilitating tumor progression and spread.[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] NK cells, as essential parts of the innate immune system, are vital in combating LN metastasis. NK cells can display strong anti-metastatic effects that are independent of MHC-mediated antigen presentation through multiple pathways, including the release of pre-formed granules containing PRF1 and GZMB, the secretion of IFN-γ, and the exposure of death receptor ligands such as FASLG and TRAIL.[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] Furthermore, suppression of CD8\u003csup\u003e+\u003c/sup\u003e T cells leads to increased cancer spread and reduced survival rates, particularly in BLCA patients.[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] Elevated IL-8 have been demonstrated to up-regulated PD-1 expression in CD8\u003csup\u003e+\u003c/sup\u003e T cells, leading to immunosuppression within tumors and tumor-draining lymph nodes, which enhances LN metastasis of gastric cancer.[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eApart from the levels of tumor-infiltrating immunocytes, some genes may also contribute to LN metastasis of BLCA via exerting biological functions and mediating related signaling pathways. Liu et al. have reviewed the coding genes associated with LN metastasis including \u003cem\u003eCCR7\u003c/em\u003e, \u003cem\u003ePTBP-1\u003c/em\u003e, and \u003cem\u003eUPK-1B\u003c/em\u003e with increased expression and \u003cem\u003eGATA-6\u003c/em\u003e, \u003cem\u003eNONO\u003c/em\u003e, and \u003cem\u003eTCF-21\u003c/em\u003e with decreased expression.[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] In our study, bioinformatic analysis and clinical sample validation both demonstrated augmentation of \u003cem\u003eFKBP10\u003c/em\u003e in BLCA tissues with positive lymph node metastasis and suggested it may be a potential marker for poor prognosis. FKBP10 is also known as FKBP65 (FK506-binding protein 10, 65kDa), a member of immunophilins that possess repeats of the peptidylprolyl isomerase domain and acts as a protein chaperone for collagen I in the endoplasmic reticulum.[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] Notably, FKBP10 has been suggested to contribute to the progression of tumors and act as an unfavorable prognostic factor across vaious malignancies including kidney, lung gastric, and prostate cancers.[\u003cspan additionalcitationids=\"CR33 CR34\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] Repression of FKBP10 hinders the growth and movement of renal cancer cells via reducing heat shock protein 90 levels and inducing cell cycle arrest.[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] Moreover, FKBP10 may enhance the adhesion of gastric cancer cells through integrin/AKT pathway, thus promoting lymph node metastasis,[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] targeting FKBP10 with YK-4-279, an inhibitor of ETV1, could prevent the progression of prostate cancer.[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] However, there remains a need to elucidate the biological role and regulatory mechanism of FKBP10 in BLCA.\u003c/p\u003e \u003cp\u003e \u003cem\u003eEPN2\u003c/em\u003e and \u003cem\u003eCYP4F12\u003c/em\u003e have been identified as two other upregulated genes in LN metastasis (+) group, as confirmed by RT-qPCR and IHC assays separately. There have been few studies on the roles of EPN2 and CYP4F12 in cancer. Song et al. have emphasized that EPN2 might drive breast cancer development by promoting NF-κB essential modulator linear ubiquitination via linear ubiquitin chain assembly complex.[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] Another study has noted the upregulation of EPN2 in prostate cancer promoted the stabilization of cell surface receptor complexes, thus offering a mechanism to amplifying signals that stimulate tumor proliferation.[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] CYP4F12 exhibited low expression levels in tumor tissues, influencing various phenotypic changes in head and neck squamous cell carcinoma (HNSC). The overexpression of CYP4F12 impacted immune cell infiltration, inhibited cell migration, and promoted cell-matrix adhesion by suppressing the epithelial-mesenchymal transition (EMT) pathway in HNSC cells.[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] In addition, \u003cem\u003eASB13\u003c/em\u003e and \u003cem\u003eST3GAL5\u003c/em\u003e were the genes found to be downregulated in LN metastasis (+) group. ASB13 has been identified as a suppressor for breast cancer metastasis since it promotes SNAI2 degradation while relieving its transcriptional repression on YAP.[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] Furthermore, Zhang et al. have discovered that the expression of ST3GAL5 is relatively low in lung cancer tissues compared to nearby nonmalignant tissues, and this lower expression is linked to a more favorable prognosis.[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] Exosomes secreted by cancer cells with high levels of ST3GAL5 have the potential to facilitate peritoneal dissemination through the creation of a pre-metastatic niche via the recruitment of cancer-associated macrophages and promotion of immunosuppression.[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eOur findings of the analysis are subject to limitations. This study is based on the TCGA and GEO databases, and validation with small clinical samples may lead to the bias of results. More samples and experiments are required to confirm the applicability of this prognostic model in future, which could provide a dependable predictor and therapeutic target for BLCA patients with LN metastasis.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn general, this study presents signature genes related with LN metastasis in BLCA. We develop and validate a prognostic model via identifying novel gene markers. The presence of tumor immunocytes including M0 macrophages, activated NK cells, and CD8\u003csup\u003e+\u003c/sup\u003e T cells is correlated with lymph node metastasis. Based on the bioinformatic results and clinical sample analysis, elevated levels of \u003cem\u003eEPN2\u003c/em\u003e, \u003cem\u003eCYP4F12\u003c/em\u003e and especially \u003cem\u003eFKBP10\u003c/em\u003e in BLCA can predict LN metastasis as well as poor prognosis.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eLN\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLymph node\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eBLCA\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBladder cancer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eTCGA\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eThe Cancer Genome Atlas\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eGEO\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGene Expression Omnibus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eDEG\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDifferentially expressed gene\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eGO\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGene Ontology\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eKEGG\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKyoto Encyclopedia of Genes and Genomes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eGSEA\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGene set enrichment analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eLASSO\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLeast absolute shrinkage and selection operator\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eROC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eReceiver operating characteristic curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eTMB\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTumor mutation burden\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eGDSC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGenomics of drug sensitivity in cancer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eTIDE\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTumor immune dysfunction and exclusion\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eRT-qPCR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eQuantitative real-time PCR\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eIHC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eImmunohistochemistry\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eIF\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eImmunofluorescence\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eiNOS\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInducible NO synthase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eK-M\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKaplan-Meier\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eBP\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBiological process\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eMF\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMolecular function\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCellular component\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eTME\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTumor-infiltrating immunocytes in tumor microenvironment\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003empMRI\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMultiparametric magnetic resonance imaging\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eceCT\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eContrast-enhanced computed tomography\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ePET\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePositron emission computed tomography\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eHNSC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHead and neck squamous cell carcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eEMT\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEpithelial-mesenchymal transition\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp skip=\"true\"\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur research was granted approval by the Ethics Committee at Nanjing Drum Tower Hospital, and each participant provided written consent.\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\u003eSequence data that support the findings of this study are derived from the TCGA-BLCA and GEO databases (GSE106534). \u0026nbsp;The clinical data of the patients are provided within the supplementary information.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo conflict of interest exists in the submission of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the National Natural Science Foundation of China (82173160 to W.D.) and Nanjing Health Technology Development Fund (YKK23107).\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHG and WD conceived and designed the research studies. YS analyzed data, performed experiments and wrote the manuscript. JS and XW assisted experiments. MD and QZ provided the clinical information. Wei Chen and Wenming Cao guided experiments. All authors read and approved the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank all patients who agreed to participate in this study and all colleagues who helped with this research.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSingh D, Vignat J, Lorenzoni V, Eslahi M, Ginsburg O, Lauby-Secretan B, Arbyn M, Basu P, Bray F, Vaccarella S. Global estimates of incidence and mortality of cervical cancer in 2020: a baseline analysis of the WHO Global Cervical Cancer Elimination Initiative. 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Mol Oncol. 2024;18(1):21\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e\u003c/ol\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, Lymph node metastasis, Prognostic model, Tumor infiltrating immunocytes","lastPublishedDoi":"10.21203/rs.3.rs-5816202/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5816202/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLymph node (LN) metastasis is related to poor prognosis in bladder cancer (BLCA). To explore novel signature genes associated with LN metastasis in BLCA, we identified 17 signature genes with non-zero coefficients to construct the prognostic model, which demonstrated a prognostic accuracy with an area under the curve of 0.706 at 1 year, 0.701 at 3 years, and 0.688 at 5 years. \u003cem\u003eEPN2\u003c/em\u003e, \u003cem\u003eCYP4F12\u003c/em\u003e and especially \u003cem\u003eFKBP10\u003c/em\u003e, three of the above signature genes, exhibited significant upregulation in BLCA with LN metastasis, thereby contributing to the unfavorable survival of BLCA patients. Meanwhile, we validated that FKBP10 exerts a biological function in bladder cancer metastasis through cytological experiments. Moreover, by utilizing the CIBERSORT algorithm and immunofluorescence assay, we identified and validated a significant upregulation of M0 macrophages, alongside a downregulation of activated NK cells and CD8\u003csup\u003e+\u003c/sup\u003e T cells, which were associated with the presence of LN metastasis in BLCA. Conclusively, These results will provide new insights for future improvements in diagnosis, treatment, and prognosis evaluation for BLCA patients with LN metastasis.\u003c/p\u003e","manuscriptTitle":"Identification of Prognostic Signature Genes and Immune Microenvironment Features Associated with Lymph Node Metastasis in Bladder Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-15 17:05:38","doi":"10.21203/rs.3.rs-5816202/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":"142da8f9-066c-4caa-966c-8df2da752b92","owner":[],"postedDate":"January 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-04-01T05:54:24+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-15 17:05:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5816202","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5816202","identity":"rs-5816202","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}