Analyzing the Impact of Sphingolipid Metabolism Genes on bladder cancer Progression and Microenvironment for the Development of a Prognostic Signature

Analyzing the Impact of Sphingolipid Metabolism Genes on bladder cancer Progression and Microenvironment for the Development of a Prognostic Signature | 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 Analyzing the Impact of Sphingolipid Metabolism Genes on bladder cancer Progression and Microenvironment for the Development of a Prognostic Signature Zechun Peng, Jie Yang, Tianshi Wu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4577574/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 The role of sphingolipid metabolism (SM) in promoting the progression of bladder cancer (BLCA) and its impact on patient prognosis has been established. To improve therapeutic outcomes, it is essential to identify specific molecular pathways in BLCA and develop a predictive signature underlying SM-related genes. In this study, 430 BLCA samples were analyzed using univariate Cox regression to identify critical SM-relevant genes (SMGs) involved in BLCA development. LASSO regression analysis was then employed to reduce the possibility of overfitting. A multivariable Cox regression analysis was employed to develop a prognostic signature underlying SMGs, which was subsequently validated in a separate cohort. Our research revealed that dysregulated SM leads to worse prognosis in BLCA patients, and important prognostic genes (PCSK2, NFASC, NTF3, NR2F1, ATP13A2, SREBF1, GSDMB, and LGALS4) were identified. Using these SMGs, we developed a prognostic BLCA-risk model that effectively predicted the prognosis of BLCA patients (AUC was 0.772 for the training cohort and 0.725 for the validation cohort). Interestingly, patients identified as high-risk by this model had a significantly more active immunological milieu, characterized by higher immune scores and increased 26 types of immune function and cell like NK cells, CD8 + T cells, and cytolytic activity. These findings suggest that dysregulated SM may contribute to immune microenvironment dysregulation in BLCA. Our research provides a better awareness of the role of SM in the emergence of BLCA and has the potential to offer customized care to high-risk patients based on their SM-related gene expression signature. sphingolipid metabolism Bladder cancer prognostic model immune microenvironment individual therapy Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 1.Introduction BLCA is the one of the most prevalent types of cancer worldwide, with approximately 550,000 new cases and 200,000 deaths in 2018[ 1 ]. Urothelial carcinomas are the primary type of bladder cancer and categorized as non-muscle invasive bladder cancers (NMIBC) or muscle invasive bladder cancers (MIBC) [ 2 ] based on their treatment implications. Risk factors of BLCA in the USA include cigarette use and occupational exposures, such as arylamines, which also increase the probability of recurrence [ 3 ]. The treatment called Transurethral resection of the bladder tumor (TURBT) is often recommended for those with superficial bladder cancer, whether or not they are receiving intravesical therapy, while radical cystectomy (RC) is the primary treatment for muscle invasive bladder cancers [ 3 , 4 ]. The degree of bladder wall invasion and the clinical treatment of bladder tumors are closely related [ 5 ]. Approximately 75% of patients are diagnosed with NMIBC, and only 10% develop MIBC or metastatic bladder cancer at initial detection [ 6 ]. The costs linked to treating bladder cancer, observing progress, and managing adverse effects are significant [ 7 ]. Despite approvements in early diagnosis and comprehensive treatment, some individuals still experience recurrence or metastatic bladder cancer. Therefore, early diagnostic and prognostic indicators of BLCA proliferation must be identified, and novel approaches to BLCA detection and therapy must be developed to optimize therapeutic outcomes. Sphingolipids are a type of bioactive chemical that regulates cell signaling in cancer and influences survival and repression [ 8 ]. Alterations in SM have been implicated in tumorigenesis and may be targeted for treatment [ 9 ]. Certain bioactive SM metabolites can serve as diagnostic and therapeutic biomarkers for many kinds of cancer [ 10 ]. Sphingosine-1-phosphate (S1P) and Ceramides, two important sphingolipids, have a critical function in regulating cell death in cancer [ 11 ]. Recent advancements in molecular, pharmacological, and genetic techniques have enhanced our awareness of SM signaling pathways and their potential use for cancer therapy [ 8 ]. Various research have examined the function of SM in different malignancies, with a focus on utilizing SM as a target for cancer therapy. However, the precise function of SMGs in BLCA remains unclear. To determine the predictive value of the SMGs, we conducted a prognostic BLCA-risk model in our study. We found that these genes were associated with the TME, IME, and SM. The progression of malignant tumors are heavily influenced by the tumor immune microenvironment (TIME), which describes the immune infiltration within the TME [ 12 ]. Immune cells play a critical role in tumor cellular reprogramming by secreting various molecules that affect neighboring cells and regulate tumor survival and growth [ 13 ]. The non-tumor components of the microenvironment, which are primarily composed of immune cells infiltrating the tumor, are important in determining the prognosis of patients with BLCA [ 14 ]. Thus, TIME is essential in the development and dissemination of tumors, and emerging evidence suggests that the pathophysiology of BLCA is closely linked to TIME [ 15 ]. Evaluating the immunological status of TIME through staging helps in the progression of immune therapies and improves the cure probability for BLCA invalids. In this research, we conducted an extensive examination of SM-related genes to investigate their impact on the survival and progression of BLCA patients. Furthermore, we establish a BLCA-risk model to determine the prognostic value of SMGs in BLCA. These findings could pave the way for new approaches to investigate the molecular mechanisms underlying BLCA, leading to the development of targeted therapies and personalized patient care. 2.Materials 2.1 Data processing We downloaded RNA-seq data for BLCA and corresponding clinical data from the UCSC XENA database. The dataset consisted of 19 normal samples and 411 BLCA samples. The RNA-seq data was in log2(FPKM) format firstly. We first converted it to FPKM format and then transformed it into TPM format, the RNA-seq data was transformed into log2 (TPM + 1) format in the end. We downloaded bladder cancer RNA-seq data from the GEO database for external validation, including 85 samples from GSE236932 and GSE195768. Similar to the TCGA data, we converted these into log2(TPM + 1) format and applied batch effect correction for subsequent analysis. 2.2 Obtaining genes associated with SM. To identify SMGs, we extracted information derived from GeneCards database (genecards.org/). Our analysis retrieved 1231 genes associated with SM from this database. The screening criteria for genes were the relevance score of > 4 for associated genes. The relevance score of the gene represents the relevance between the key words and the gene, which means a more significant relevance with a higher relevance score. The screened genes will be used for subsequent analysis. 2.3 Identifying differentially expressed SMGs. We used the "limma" package in R to analyze the RNA-seq and identify differentially expressed SMGs between normal bladder tissue and BLCA tissue. The Wilcoxon rank sum test was applied to screen DEGs using the criteria: |LogFoldChange| > 1, q < 0.05. The q-value was calculated using the false discovery rate (FDR) method to adjust the P-value and reduce the number of false positives. This ensures that the differentially expressed SMGs we discover are sufficiently large and significant in their differences. 2.4 Constructing a SMGs-prognostic model We randomly divided the BLCA patients into two equal groups, a training cohort and a validation cohort. Univariate Cox regression (Unicox) was applied to identify that SMGs were significantly linked with overall survival (OS) using the DEGs related to SM in the training cohort. LASSO Cox regression was used to select SMGs for establishing a predictive BLCA-risk score model for estimating the OS of BLCA patients utilizing the "glmnet" R package. We employed a tenfold cross-validation method to determine the penalty parameter of the model. To calculate the risk score for each patient, we used the following method: $$\mathbf{B}\mathbf{L}\mathbf{C}\mathbf{A} \mathbf{R}\mathbf{i}\mathbf{s}\mathbf{k}\mathbf{S}\mathbf{c}\mathbf{o}\mathbf{r}\mathbf{e}={\sum }_{\varvec{i}=1}^{\varvec{n}}{\varvec{\beta }}_{\varvec{i}}\times {\varvec{E}}_{\varvec{i}}$$ where n represents the number of selected genes included in the prognostic BLCA-risk model, βi represents the regression coefficient of SMG i, and Ei represents the expression of SMG i. The median risk score value was used to divide all invalids into low-BLCA and high-BLCA groups. While the OS of the low-BLCA and high-BLCA groups was compared using KM survival analysis and the log-rank test. The predictive correctness of the prognostic BLCA-risk score model was evaluated using a ROC curve with the "survivalROC" R package. In the end, the validity and reliability of the prognostic BLCA-risk score model were validated using the validation cohort. Using the regression coefficients obtained from the prognostic model established in the training set, we calculated the risk scores for the external validation set from the GEO database. These risk scores were then used for subsequent survival and prognostic analyses. 2.5 Independent analysis of prognostic BLCA - risk group and clinical information We combined the BLCA-risk score of every BLCA patient with their corresponding clinical factors utilizing the sample ID. Then we utilized "limma" algorithm in R package to analyze the association between BLCA-risk scores and clinical factors, such as gender, age, stage and TNM status. UniCox and MultCox regression analyses were performed to evaluate the impact of these clinical information on the prognosis. By doing so, we can determine whether the prognostic model provides more accurate results compared to the clinical parameters commonly used to assess disease progression. 2.6 Enrichment analysis of SMGs using KEGG and GO database. The differentially expressed SMGs of high-BLCA and low-BLCA invalids were calculated using the "limma" R package. The screening method is as described above. To identify biological characteristics and cellular function pathways that were significantly enriched, our team utilized the "clusterProfiler" R package to perform GO and KEGG analysis on the DEGs. The enrichment results for both KEGG and GO pathways had corrected p-values less than 0.05. The "enrichplot" and "ggplot2" R packages were utilized to visualize the outcome of the enrichment analysis. 2.7 Analysis of SMGs using GSVA To examine the discrepancy in biological processes between low-BLCA and high-BLCA ivalids, we employed the "GSVA" package in R. We use all SMGs to analyze and obtain enriched gene sets and pathways that differ in degree between the two groups. We utilized the "c2.cp.kegg.v7.1.symbols" and "c5.go.v7.5.1.symbols" gene sets from the MSIGDB Database (gsea-msigdb.org/gsea/msigdb) to construct standard gene sets for the analysis. These gene sets encompass various biological processes and pathways, aiding us in gaining a more comprehensive understanding of the biological differences between low-BLCA and high-BLCA invalids. 2.8 Analysis of SMGs using GSEA To improve our awareness of the underlying mechanisms of SM in BLCA, we classified the samples into high-BLCA and low-BLCA groups using BLCA-risk scores. We then conducted analysis to determine whether the differentially expressed SMGs were significantly enriched in any biological processes or pathways. The standard gene sets for this analysis are same as the GSVA. 2.9 The characterization of the immune microenvironment (IME) We utilized the ssGSEA method to calculate scores for 29 immune features in each sample. These scores were determined based on the expression levels of genes associated with various immune features. We can compare the differences in the 29 immune features between the high-risk and low-risk groups, inferring and summarizing the differences in the immune microenvironment between high-risk and low-risk patients.. In addition, we calculated the estimation score, stromal score, immune score, and tumor purity for invalids in both groups. Furthermore, we investigated the correlation between immune function and prognosis-related SMGs. 2.10 Immunohistochemistry from the Public database We utilized the HPA database (proteinatlas.org) as a resource to obtain immunohistochemistry data for normal bladder tissue and BLCA tissue. We focus on obtaining immunohistochemistry data for SM`Gs that can independently predict prognosis, particularly those in the model (PCSK2, NFASC, NTF3, NR2F1, GSDMB). We can use the HPA database to compare the expression patterns and differences of model genes between normal tissues and bladder cancer tissues. 2.11 Cell culture and transfection We used human bladder cancer cells TCCSUP for in vitro validation. The cells were cultured in MEM medium containing NEAA, supplemented with 10% fetal bovine serum and 1% penicillin-streptomycin. The culture conditions were 37 degrees Celsius and 5% carbon dioxide. We seeded the cells in six-well plates and transfected them with siRNA using Lipofectamine 2000. The transfected cells were then used for subsequent experiments. 2.12 qRT-PCR Total RNA from TCCUSP cells was extracted using TRIzol reagent. Reverse transcription was performed with the TransScript First-Strand cDNA Synthesis Kit (TaKaRa, Japan). qRT-PCR was conducted on an ABI-7900HT Real-Time PCR System utilizing SYBR Green MasterMix (TaKaRa, Japan). The following primer sequences were used: Forward Primer: ACTGGACACGAAACAGCAGAT. Reverse Primer: CTGCGGAAGTCAATCACCAGG. 2.13 Cell Viability Assay Seed the transfected cells onto coverslips and use Yeason's EDU kit to assess cell viability and proliferation rate. Then stain the nuclei with DAPI and observe under an upright microscope. 2.14 Cell apoptosis detection The mentioned therapy was administered to the TCCSUP cells, after which the culture medium was transferred to a new centrifuge tube.Afterward, the cells in the culture bottle were washed with PBS. The cells were collected after removing the liquid above them.Between 100,000 and 500,000 suspended cells were gathered and then centrifuged at a force of 300 times the acceleration due to gravity for a duration of 5 minutes. The cells were resuspended by adding Annexin V-binding buffer, which was diluted to a volume of 500 µL.Annexin V (5 µL) and nuclear staining solution (5 µL) were introduced into the cell suspension.Following a combination of gentle and vigorous stirring, the mixture was left to incubate at room temperature for a duration of 15 to 20 minutes.In the end, the process of flow cytometry was carried out. 2.15 Wound healing assay TCCSUP cells were cultured in a 6-well plate, with half of the wells treated with si-NFASC. Once the cells reached confluence, a micropipette tip was used to create a wound in the monolayer. The medium was then replaced with serum-free MEM. Photographs were taken at 0h, 24h, and 48h post-wounding using a microscope (Olympus, Japan) to assess cell migration. ImageJ software was used to calculate the wound area. The migratory ability was evaluated by the wound healing percentage at 24h and 48h, calculated as follows: Would healing percentage= (area of 0h – area of 24h or 48h)/ (area of 0h) 2.16 Transwell migration assay Following a series of treatments, 4×10^4 cells in serum-free medium were seeded into the upper chambers of a Transwell apparatus coated with Matrigel. The lower chambers contained medium with 10% FBS to act as an attractant. After 24 hours of incubation, cells that had migrated through the chamber membrane were fixed with pre-cooled formaldehyde and stained with crystal violet. These cells were then counted and photographed under a microscope (Olympus, Japan). 2.17 Assay for colony development After 7 days of culture, TCCSUP cells were seeded into six-well plates at a density of 500 cells per well. The cells were then fixed with 4% paraformaldehyde for 20 minutes, stained with 4% crystal violet dye, and the resulting colonies were counted. 2.18 Statistical analysis We employed the Wilcoxon rank-sum test to compare two groups. The KM survival analysis was used to evaluate the discrepancy in survival among patients in the low-BLCA and high-BLCA groups. MultCox regression was utilized to identify the independent predictors of OS in BLCA. ROC curves were employed to assess the predictive precision of the prognostic BLCA-risk score model. All statistical analyses were conducted using R package version 4.2.0, with a significance level of P < 0.05. Statistical significance was presented by "*" for p < 0.05, "**" for p < 0.01, and "***" for p < 0.001. 3.Results 3.1 Differential analysis of SMGs between normal tissue and BLCA Our research’s flow chart is presented in Fig. 1 A. The DEG analysis revealed that 133 SMGs expressed less, and 48 SMGs expressed more in BLCA patients. These differentially expressed SMGs were subsequently utilized for prognostic analysis, as shown in Fig. 1 B and C. 3.2 Prognostic BLCA-risk score model established in the train cohort. The study utilized UniCox regression analysis to identify 54 SMGs associated with prognosis out of 133 SMGs (Fig. 2 A). The amount of SMGs was subsequently reduced utilized Cox regression analysis (Fig. 2 B and C). LASSO regression analysis was performed, and eight SMGs (PCSK2, NFASC, NTF3, NR2F1, ATP13A2, SREBF1, GSDMB, and LGALS4) were identified to be involved in prognosis. From these eight SMGs, a predictable BLCA-risk score model was developed (Fig. 2 D), and the BLCA-risk score of invalids was calculated using the equation mentioned in the materials. The BLCA-risk score model was then utilized to categorize BLCA invalids into low-BLCA and high-BLCA groups. 3.3 The correlation between the BLCA-risk score and clinical parameters The cutoff threshold value for the BLCA-risk score in the train cohort was determined using the median value, the patients were classified into low-BLCA (102 invalids) and high-BLCA (102 invalids) groups underlying the cutoff value (Fig. 3 A and B). Patients belong to high-BLCA group had a poorer prognosis, as demonstrated in Fig. 3 C and D. To evaluate the prognostic model, the validation cohort was categorized into low-BLCA (98 invalids) and high-BLCA (104 invalids) groups using the cutoff value derived from the train cohort (Fig. 3 E and F). The high-BLCA invalids in the validation cohort suffered from a worse prognosis, indicating that the prognostic model could predict OS in BLCA invalids correctly (Fig. 3 B and F). UniCox analysis revealed that both the BLCA-risk score and stage were significantly linked with OS (Tables 1 and 3 ). Furthermore, MultCox analysis demonstrated that the BLCA-risk score and stage were independent predictors of OS (Tables 2 and 4 ). To assess the accuracy of the prognostic model, ROC curve was constructed at 1,3,5 years (Fig. 4 A and B), and the AUC indicated that the BLCA-risk score was a more accurate predictor of OS than other predictors (the AUC of the train cohort was 0.772, and the AUC of the validation cohort was 0.725) (Fig. 4 C and D). The external validation set from GEO also demonstrated the same results. After stratifying patients into high and low-risk groups, survival analysis was conducted, revealing that patients with high risk had significantly worse prognosis (Fig. 5 A and B). The ROC curves at 1, 3, and 5 years were 0.802, 0.785, and 0.832, respectively, indicating strong reliability of the results (Fig. 5 C). Compared to clinical parameters, it also demonstrated excellent performance (Fig. 5 D). In summary, our model demonstrated excellent performance and accurately identified high-risk patients based on the above results. Table 1 Univariate analysis revealed that risk score, and stage were associated with overall survival in train cohort. HR HR.95L HR.95H pvalue Age 1.029091 1.005441 1.053297 0.015632 Gender 0.891552 0.556761 1.42766 0.632754 Stage 2.258404 1.661269 3.070177 2.00E-07 T 2.029222 1.467739 2.805501 1.85E-05 N 1.987654 1.345678 2.876543 2.10E-05 M 2.112345 1.567890 2.945612 1.65E-05 riskScore 1.431055 1.310374 1.562849 1.54E-15 Table 2 Multivariate analysis revealed that risk score, and stage were associated with overall survival in train cohort. HR HR.95L HR.95H pvalue Age 1.024814 0.999116 1.051173 0.058533 Gender 1.072786 0.646573 1.779953 0.785646 Stage 1.823717 1.279749 2.598904 0.000885 T 1.458481 0.981047 2.168261 0.062127 N 1.632579 1.102345 2.413678 0.055873 M 1.547893 0.973456 2.214567 0.064981 riskScore 1.381363 1.256343 1.518824 2.48E-11 Table 3 Univariate analysis revealed that risk score, and stage were associated with overall survival in test cohort. HR HR.95L HR.95H pvalue Age 1.03415 1.011455 1.057354 0.003017 Gender 0.746389 0.465782 1.196046 0.224045 Stage 1.496159 1.132301 1.976939 0.004598 T 1.523142 1.120893 2.069745 0.007158 N 1.635472 1.193562 2.134678 0.006543 M 1.482391 1.095874 2.056723 0.007632 riskScore 1.061585 1.013771 1.157318 0.044904 Table 4 Multivariate analysis revealed that risk score, and stage were associated with overall survival in test cohort. HR HR.95L HR.95H pvalue Age 1.033135 1.010562 1.056212 0.003826 Gender 0.716028 0.443917 1.154937 0.170865 Stage 1.236681 0.851641 1.795803 0.264346 T 1.411015 0.921291 2.161058 0.113412 N 1.372486 0.899213 2.119547 0.120345 M 1.426789 0.945678 2.175432 0.115678 riskScore 1.065878 0.963667 1.17893 0.014826 3.4 Result of enrichment analysis of SMGs using KEGG and GO database Using a similar approach, we detected 882 differentially expressed SMGs between the two groups. Gene Ontology (GO) enrichment analysis indicated that the DEGs were primarily involved in keratinization, axon development, homophilic cell adhesion via plasma membrane adhesion molecules, among others (Fig. 6 A-C). Additionally, KEGG enrichment analysis identified several metabolic signaling pathways, such as Neuroactive ligand − receptor interaction, Retinol metabolism, and Porphyrin metabolism, among others (Fig. 6 D-F). We can speculate from the above results that the enriched pathways may involve key pathways and functions that affect the prognosis of BLCA patients, which are worth further exploration. 3.5 GSVA To gain insight into the distinct biological processes of the two groups, we employed gene sets mentioned in the materials to perform gene set variation analysis (GSVA). The results revealed significant enrichment of pathways related to metabolism, immune response, and cellular activities in high-BLCA invalids (Fig. 7 A and B). Notably, the pathways that were differentially enriched between the high-BLCA and low-BLCA groups included Leukocyte Transendothelial Migration, T cell receptor signaling pathway, regulation of T-cell chemotaxis, and positive regulation of lymphocyte chemotaxis, among others. From the above results, we can infer that there are differences in immune signaling pathways and functions as well as some metabolic signaling pathways and functions between the high-BLCA and low-BLCA groups. 3.6 GSEA We present the most significant 10 pathways enriched utilizing the GO and KEGG gene sets, as shown in Fig. 8 A and B. Moreover, GSEA revealed that some immune-relevant signaling pathways (Fig. 8 C- 8 H), such as adaptive immune response (Fig. 8 C), immunoglobulin complex (Fig. 8 D), antigen binding (Fig. 8 E), and immunoglobulin production (Fig. 7 F), were upregulated in the high-BLCA group. The pathways and functions enriched by differentially expressed SMGs still point to immune-related pathways and functions, further confirming the important association between immune dysfunction and prognosis of BLCA invalids. This also leads us to perform IME analysis on the samples. 3.7 The variation of IME between the two groups We conducted an analysis of IME to examine the variation in immune status between the two groups. The analysis was conducted using the ESTIMATE algorithm, which illustrated that invalids in the high-BLCA group had higher immune scores, ESTIMATE scores, stromal scores, and lower tumor purity (Fig. 9 A- 9 E). Furthermore, IME was analyzed using ssGSEA, which revealed that immune cell infiltration was higher in the patients in the high-BLCA group. A total of 26 immune cells and functions were upregulated (Fig. 10 ). Thus, we have reason to believe that immune dysfunction plays a crucial role and is likely to be involved in promoting tumorigenesis, helping tumor cells to escape immune surveillance. 3.8 Immunohistochemistry reveals differences in key SMGs As described above, we obtained immunohistochemical data for several key SMGs genes, excluding those with missing data. Finally, we acquired immunohistochemistry data for NTF3, NFASC, and GSDMB (Fig. 11 ). The left side represents normal tissue and the right side represents BLCA tissue, which is consistent with our speculation and further confirms the reliability of our study. 3.9 siRNA reduces the expression of NFASC in TCCSUP cells After transfecting siRNA into TCCSUP cells, total RNA was extracted for qRT-PCR analysis. The results showed a significant reduction in NFASC mRNA levels within the cells, indicating successful transfection and enabling subsequent experiments (Fig. 10 A). 3.10 Silencing NFASC inhibits the viability of TCCSUP cells Using an EDU kit, cell proliferation and viability were assessed. In the experimental results, the incorporation of the fluorescently labeled active substance into newly synthesized DNA molecules caused positive cells to appear red. After DAPI staining, the cell nuclei appeared blue. The results showed a decrease in the proportion of positive cells following NFASC silencing, indicating that the proliferation rate and viability were inhibited (Fig. 10 B and C). This supports our identification of NFASC as a risk gene. 3.11 Silencing NFASC promotes apoptosis in TCCSUP cells Using flow cytometry to assess apoptosis, the results showed an increased apoptosis rate following NFASC silencing (Fig. 10 D). This indicates that NFASC is crucial for the survival and proliferation of TCCSUP cells. 3.12 Silencing NFASC inhibits the migration and invasion capabilities of TCCSUP cells The migration and invasion abilities of tumor cells are crucial mechanisms for cancer metastasis. Inhibiting these abilities can effectively slow disease progression. Therefore, we conducted wound healing assays, Transwell assays, and colony formation assays to verify the impact of the NFASC gene on the migration and invasion capabilities of TCCSUP cells. The results of the wound healing assay revealed that, compared to the control group and the si-NC group, the healing rate of the si-NFASC group was significantly slower under the same time and culture conditions (Fig. 11 A and B). The Transwell assay showed similar results, with a significant reduction in the number of cells migrating through the chamber after NFASC silencing (Fig. 11 C and D). The results of the colony formation assay indicated that the si-NFASC group formed significantly fewer colonies compared to the control and si-NC groups (Fig. 11 E). In summary, these results indicate that silencing NFASC effectively inhibits the migration and invasion capabilities of TCCSUP cells. This finding not only validates our model predictions but also offers hope for the development of new therapeutic targets. 4.Discussion According to a study, metabolic reprogramming (MRG) is a hallmark of cancer, every metabolic state has a specific genetic signature that predicts a different ending [ 16 ]. Change in food metabolism in tumor stroma is now considered to play a significant role in cancer related MRG [ 17 ], which is caused by various oncogenic pathways, including glucose MRG [ 18 ]. The Myc protein, a crucial metabolic regulator, is involved in MRG activities such as glucose and glutamine metabolism, and serine production, which promote the growth of cancer cells [ 19 ]. Bladder cancer (BLCA) diagnosis and treatment have made significant strides in the last two decades, but the higher ratio of relapse and risk of progression to invasive diseases let it become One of the priciest types of cancer to treat [ 20 ]. Recurrence, antimicrobial resistance, and rapid disease development pose major therapy obstacles, highlighting the urgent need for New biological indicators for the clinical detection and management of BLCA [ 21 ]. This study groundbreakingly explored the relationship between BLCA and SMGs. Using LASSO-Cox regression and Cox regression with UniCox, the researchers developed a predictive BLCA-risk score model utilizing 54 differentially expressed SMGs from BLCA and normal tissue obtained from TCGA. To better understand the function of these SMGs in BLCA, the predictive model was applied to estimate OS of the training cohort. The model successfully identified patients with low-BLCA scores and high-BLCA scores, who had different survival rates. The same outcome was confirmed in the test cohort, demonstrating the model's ability to predict individuals’ risk and OS. In a MultCox analysis, the predictive model was found to be an independent predictive factor. Therefore, identifying immunotherapy candidates is critical. Invalids with high-BLCA scores had higher concentrations of immune-inflamed cells and immuno-suppressive immune cells, including Tregs, APC co-stimulation, checkpoint cytolytic activity activation, PC co-inhibition and so on.They were also observed in those with a high-BLCA score, suggesting they could be immunotherapy candidates. The different SMGs in the two groups were further examined because of the significant differences in risk ratings between them. The time factor is crucial in determining a patient's prognosis, as alterations in the stroma and immune function that are a crucial part of the tumor stroma are closely linked to changes in the tumor [ 22 ]. To forecast tumor purity and proportion of stromal cells to immune cells of the tumor, an advanced algorithm called ESTIMATE has been developed, which is based on gene expression [ 23 ]. Tumor purity, which is highly correlated with prognosis, alludes to the proportion of malignant cells in the tumor, while the immunological score calculated assesses the immune component of tumor [ 24 ]. The ESTIMATE was utilized in our study to describe the Tumor Immune Estimation Resource (TIMER) for the two groups. The outcome revealed that invalids hava a good prognosis had lower tumor purity and higher immunological scores than invalids have a poor prognosis. Furthermore, ssGSEA was utilized to identify the immunological status of patients in the high- BLCA and low- BLCA groups. These results validated the findings of ESTIMATE and TIMER by showing that invalids in the high-BLCA group suffered a disordered immune status and elevated 26 of the 29 immune-relevant activities. Therefore, it is reasonable to suggest that a compromised immune system may be associated with a worse prognosis. The findings of our study reveal that dysregulation of sphingolipid metabolism leads to TIME anomalies, making a poor prognosis for BLCA invalids [ 25 ]. SM dysregulation has been identified as a potential signal of cancer and is an encouraging candidate for therapies [ 25 ]. Several studies have investigated the role of SM in some kinds of other cancer, including the relevance of sphingolipids in radiation therapy, resistance to medication, immune system therapy, and precision therapy [ 26 ]. Sphingolipids, such as membrane markers, could regulate the proliferation, invasion, and migration of tumor [ 27 ]. Furthermore, the concatenation of sialic acid structures in tandem, such as those present in GD3 and GD2, act as promoters of neoplastic growth, thereby fostering tumorigenesis [ 28 ]. In contrast, monosialyl gangliosides such as GM1, GM3, and GM2 play a role as tumor suppressors [ 29 ]. Sphingolipids govern cellular signaling through interaction with molecules on the identical cell membrane (cis-binding) or across distinct cellular planes (trans-binding), as evidenced by a plethora of investigations [ 29 ]. Glycolipids, such as those found on growth factor and adhesion molecule receptors, containing the integrin family, are cis-interacting molecules that could regulate cell signaling [ 30 ]. The activation of signaling pathways by growth factor and integrin receptors leads to the activation of other signaling molecules and downstream pathways [ 30 ]. For example, in melanomas, AKT, p130Cas, and paxillin promote carcinogenesis, while paxillin and increased p130Cas phosphorylation promote tumor cells migrate, which is probably be promote by Src family proteins [ 31 ]. Glycolipids exert a notable impact on immune cell activity. Research indicates that glycolipids are vital for directing the migration of immune proteins towards specific membrane microdomains, and their interaction with cell surface receptors heightens immune system functions [ 32 ]. We conclude that dysregulated sphingolipid metabolism in bladder cancer tissue affects immune cell activity and contributes to tumor growth, immune escape, and a poor prognosis for BLCA patients. Through literature search, we found that the three genes of our interest (NTF3, NFASC, GSDMB) have been studied in other types of cancer, which leads us to believe that our research can delve into more detailed mechanistic studies. We believe that they could serve as potential gene therapy targets, to achieve the goal of immunotherapy by improving BLCA invalids' IME, inhibiting tumor development and improving prognosis. We validated the role of the NFASC gene using the human bladder cancer cell line TCCSUP in vitro. Based on previous bioinformatics analysis, we predicted NFASC to be a high-risk gene in bladder cancer, accelerating disease progression in bladder cancer patients and leading to poor prognosis. In vitro experiments showed that silencing NFASC inhibited TCCSUP cell proliferation and promoted apoptosis. Additionally, the migration and invasion abilities of TCCSUP cells were significantly suppressed. These findings support our bioinformatics analysis, indicating that our analysis and model are reliable. This provides a basis for identifying high-risk patients and developing targeted therapies in the future. However, our research still has some limitations and shortcomings. Due to the lack of funding and clinical samples, we were unable to validate our findings in patients and tumor tissues, relying only on publicly available immunohistochemistry data. If future conditions permit, we will continue conducting more in-depth research to confirm the impact of the screened SMGs on BLCA progression and explore their specific mechanisms.. 5.Conclusion In this study, the correlation between BLCA and SM led to the classification of BLCA invalids into high-BLCA and low-BLCA groups. According to immunological and functional evaluations, it was found that dysregulation of SM impairs the immune status, leading to a poorer prognosis for BLCA invalids. These outcomes support risk stratification of BLCA patients and offer a basis for the development of targeted therapies tailored to individual patient needs. Declarations Competing interests All authors in our team acknowledge and promise that there are no conflicts of interest or disputes among them. Author’s contributions Tianshi Wu and Zechun Peng: conceptualization and visualization. Zechun Peng, Shiying Zhou and Fangzhen Cai: original manusript writing. Zechun Peng, Shiying Zhou and Fangzhen Cai: formal analysis. Tianshi Wu: data curation. Tianshi Wu and Zechun Peng: writing—review and editing, project administration. Funding Hainan Provincial Natural Science Foundation of China(ZDYF2024SHFZ122, 820MS142 and 822QN472) and Hainan Province Clinical Medical Center. Availability of data and materials The study provides access to the datasets through online repositories. Below, you can find the repository/repositories' names and corresponding accession number(s): https://xenabrowser.net/datapages/. Consent for publication Not applicable. Ethics approval and consent to participate Not applicable References A. Horwich, M. Babjuk, J. Bellmunt, H.M. Bruins, T.M. De Reijke, M. De Santis, S. Gillessen, N. James, S. Maclennan, J. Palou, T. Powles, M.J. Ribal, S.F. Shariat, T. Van Der Kwast, E. Xylinas, N. Agarwal, T. Arends, A. Bamias, A. Birtle, P.C. Black, B.H. Bochner, M. Bolla, J.L. Boormans, A. Bossi, A. Briganti, I. Brummelhuis, M. 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Furukawa, New era of research on cancer-associated glycosphingolipids. Cancer Sci 110 (2019) 1544-1551. C. Zheng, M. Terreni, M. Sollogoub, and Y. Zhang, Functional Role of Glycosphingolipids in Cancer. Curr Med Chem 28 (2021) 3913-3924. K. Azuma, M. Tanaka, T. Uekita, S. Inoue, J. Yokota, Y. Ouchi, and R. Sakai, Tyrosine phosphorylation of paxillin affects the metastatic potential of human osteosarcoma. Oncogene 24 (2005) 4754-64. V.F. Vartabedian, P.B. Savage, and L. Teyton, The processing and presentation of lipids and glycolipids to the immune system. Immunol Rev 272 (2016) 109-19. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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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-4577574","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":327663132,"identity":"60ee8d3f-98a5-4dba-a44f-db8216ed52ef","order_by":0,"name":"Zechun Peng","email":"","orcid":"","institution":"The Second Affiliated Hospital of Hainan Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zechun","middleName":"","lastName":"Peng","suffix":""},{"id":327663133,"identity":"e2872a62-31ca-46f7-b180-be308cbdd8d7","order_by":1,"name":"Jie Yang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9klEQVRIiWNgGAWjYLACCQMGOcZm9sMPJCok5OSJ1WLM3N6TZmBxxsLYsIFIixLbew4YSFS2VSQyHCCg1OD42cMvLApsjHlnJCQY3JwnkcDYwPzw0Q18Ws7kpVlIGKTJSc5IPPBw5jaJPHYGNmPjHHxaDuSYGUgYHDY2BNpiLLlNopixgYdNGq+W829AWv4n7r+RYCD9d45EYsMBQlpu5Bg/kDA4kNgI8r5kAxFaJG+8MQMGcrIxIyiQJY5JGBs2E/AL3/kc488Sf+ygUVlTJyfP3vzwMT4tCgcY2KQlUISY8SgHAfkGBuaPHwgoGgWjYBSMghEOAPAXT4QhluZHAAAAAElFTkSuQmCC","orcid":"","institution":"The Second Affiliated Hospital of Hainan Medical University","correspondingAuthor":true,"prefix":"","firstName":"Jie","middleName":"","lastName":"Yang","suffix":""},{"id":327663134,"identity":"0273e97a-21d6-4d5f-bf73-a63a3e39b8b1","order_by":2,"name":"Tianshi Wu","email":"","orcid":"","institution":"The Second Affiliated Hospital of Hainan Medical University","correspondingAuthor":false,"prefix":"","firstName":"Tianshi","middleName":"","lastName":"Wu","suffix":""}],"badges":[],"createdAt":"2024-06-13 16:44:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4577574/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4577574/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60638047,"identity":"2d17bc7d-1061-4a2b-934d-213f0c2c7f29","added_by":"auto","created_at":"2024-07-19 02:48:37","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2839176,"visible":true,"origin":"","legend":"\u003cp\u003eGraphical abstract and the comparison between BLCA and normal tissue samples. (A) Flow chart of this study. (B) A heatmap conducted using the 181 differently expressed genes relevant with sphingolipid metabolism. (C) A volcano plot conducted using the 181 differently expressed genes relevant with sphingolipid metabolism.\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-4577574/v1/84353a7286dd7ff1d9853215.png"},{"id":60638049,"identity":"42c5631b-d4e1-42fa-92c1-47a5c001845e","added_by":"auto","created_at":"2024-07-19 02:48:37","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2179487,"visible":true,"origin":"","legend":"\u003cp\u003eEstablishment of prognostic risk assessment model. (A) A forest plot conducted using 54 sphingolipid metabolism-related genes associated with prognosis of BLCA patients. (B) LASSO coefficients for the eight genes related to sphingolipid metabolism. (C) Gene discovery to conduct a predictive risk score model. (D) Forest plot of three genes in the prognostic model.\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-4577574/v1/c2822daebdbfe9ee62ebea97.png"},{"id":60638419,"identity":"a4f9875d-803f-4213-9812-67732fa904d5","added_by":"auto","created_at":"2024-07-19 02:56:37","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1572106,"visible":true,"origin":"","legend":"\u003cp\u003eThe estimation efficacy of the SM-relevant risk model in predicting the overall survival of OS patients. (A and B) Comparison of the overall survival in the train and validation cohort between the two groups. (C and D) Risk scores of patients in the train and validation cohorts. (E and F) Distribution of patients in the train and validation cohorts. (G and H) Heat map of prognostic model genes in the train and validation cohorts.\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-4577574/v1/905d450a7e737d5b1fa9ca4c.png"},{"id":60638051,"identity":"e770ba5b-ea20-4cab-9167-06e27e831be4","added_by":"auto","created_at":"2024-07-19 02:48:37","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":850248,"visible":true,"origin":"","legend":"\u003cp\u003eAssessing the accuracy of risk prognostic models. (A and C) The AUCs of 1,3,5-year survival ROC in the train and validation cohorts were 0.772 and 0.725. (B and D) The 5-year ROC curve combined with clinical characteristics reflects the better predictive value of risk score.\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-4577574/v1/3932ae592461d3e9ac803847.png"},{"id":60638902,"identity":"559bf3b3-4220-4f65-bca5-1f6314eb1d13","added_by":"auto","created_at":"2024-07-19 03:04:37","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":962343,"visible":true,"origin":"","legend":"\u003cp\u003eResults of External Validation Using GEO Datasets. (A) Survival analysis showed that patients in the high-risk group had poorer prognosis. (B) Distribution of patients and risk scores in the external validation cohort. (C) ROC curves and their area under the curve (AUC) for the external validation cohort at 1, 3, and 5 years. (D) ROC curves and their area under the curve (AUC) for the prognostic model and several clinical parameters.\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-4577574/v1/dab235a6c79b0cfb9e458419.png"},{"id":60638421,"identity":"30a34177-9354-4a54-b686-39e4107e4cff","added_by":"auto","created_at":"2024-07-19 02:56:37","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2939776,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional Enrichment Analysis for the two groups. (A, B, and C) Results of GO enrichment analysis of those genes in the two groups. (D, E, and F) Results of KEGG pathway enrichment analysis of those genes in the two groups.\u003c/p\u003e","description":"","filename":"Fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-4577574/v1/c4b7e856142c87b00b032bdd.png"},{"id":60638057,"identity":"12d074c2-ecfb-4fdf-a810-5f095b95d29b","added_by":"auto","created_at":"2024-07-19 02:48:37","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":2263074,"visible":true,"origin":"","legend":"\u003cp\u003eGSVA analysis of the two risk groups. (A) A heatmap conducted utilizing the GO gene set to estimate the variation of the two groups. There are many immune-relevant biology processes (GOBP) were downregulated in high-OS group. (B) A heatmap conducted utilizing the KEGG gene set to estimate the variation of the two groups, which also shows a significant change of immune-relevant pathways in high-OS group.\u003c/p\u003e","description":"","filename":"Fig7.png","url":"https://assets-eu.researchsquare.com/files/rs-4577574/v1/8c10422e98b4dd7dbb5ae3a9.png"},{"id":60638422,"identity":"2c40b3a5-909c-4144-9944-aeb8935cf040","added_by":"auto","created_at":"2024-07-19 02:56:37","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":2914675,"visible":true,"origin":"","legend":"\u003cp\u003eGSEA analysis of the two risk groups. (A) The top 10 changed pathways conducted utilizing the GO gene set. (B) The top 10 changed pathways conducted utilizing the KEGG gene set. Both two pictures show a downregulated immune condition, which indicates that dysfunction of immune system plays a vital role in the progression of OS patients. (C-J) Several immune-related and bone remodeling pathways were selected from the GSEA analysis. (C) B cell receptor signaling pathway. (D) Complement activation. (E) Immunoglobulin receptor binding. (F) Immunoglobulin complex, circulating. (G) Humoral immune response mediated by circulating immunoglobulin. (H) Antigen processing and presentation. (I) Osteoclast differentiation. (J) Primary immunodeficiency.\u003c/p\u003e","description":"","filename":"Fig8.png","url":"https://assets-eu.researchsquare.com/files/rs-4577574/v1/ca3957ab0c0f3472e2ecf0a5.png"},{"id":60638052,"identity":"807bd488-3f09-45e8-8e46-19a72fa3aa1c","added_by":"auto","created_at":"2024-07-19 02:48:37","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":2986414,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of ssGSEA in the two risk groups to explore the immune infiltration. Violin plots of 29 immune functions between the two groups. We could know that all the 26 immune functions are upregulated, which means a immunosuppressive environment in OS patients. “*” represented “p\u0026lt;0.05”, “**” represented “p\u0026lt;0.01”,and “***” represented “p\u0026lt;0.001”.\u003c/p\u003e","description":"","filename":"Fig9.png","url":"https://assets-eu.researchsquare.com/files/rs-4577574/v1/bb2e5dc8dfd10420d6e0f163.png"},{"id":60638056,"identity":"b9c96f26-86a1-406d-ab03-104eb02a59f9","added_by":"auto","created_at":"2024-07-19 02:48:37","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":2247812,"visible":true,"origin":"","legend":"\u003cp\u003eESTIMATE analysis for the two groups to explore the immune infiltration. (A) A heatmap of ESTIMATE analysis of the two risk groups. (B) A violin plot of ESTIMATEScore in two risk groups, which shows a significant lower score in high-OS group. (C) A violin plot of ImmuneScore in two risk groups, which shows a significant lower score in high-OS group. (D) A violin plot of StromalScore in two risk groups, which shows a significant lower score in high-OS group. (E) A violin plot of TumorPurity in two risk groups, which shows a significant higher tumor purity in high-OS group. “**” represented “p\u0026lt;0.01”, “***” represented “p\u0026lt;0.001”.\u003c/p\u003e","description":"","filename":"Fig10.png","url":"https://assets-eu.researchsquare.com/files/rs-4577574/v1/b50bd893a95d3d5bd125b677.png"},{"id":60638059,"identity":"c13fdad5-0b79-431c-903a-e2877e1fa52a","added_by":"auto","created_at":"2024-07-19 02:48:37","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":11358056,"visible":true,"origin":"","legend":"\u003cp\u003eValidation of model gene protein expression levels.\u003c/p\u003e","description":"","filename":"Fig11.png","url":"https://assets-eu.researchsquare.com/files/rs-4577574/v1/a121ee862063d1cdd69cec55.png"},{"id":60638054,"identity":"ca2dc359-c353-4a7b-94dc-9d0364b7aa25","added_by":"auto","created_at":"2024-07-19 02:48:37","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":1636355,"visible":true,"origin":"","legend":"\u003cp\u003eSilencing NFASC inhibited the proliferative activity of TCCSUP cells and promoted their apoptosis. (A) qRT-PCR results indicate that si-NFASC successfully knocked down its expression. (B) The ratio of EDU-positive cells to DAPI is lowest in the si-NFASC group, indicating that its proliferative activity is inhibited. (C) Images of EDU, DAPI, and their merged view. (D) The results of flow cytometry indicate that knocking down NFASC promotes the apoptosis process of TCCSUP cells.\u003c/p\u003e","description":"","filename":"Fig12.png","url":"https://assets-eu.researchsquare.com/files/rs-4577574/v1/ff3408123da4c53f02c38849.png"},{"id":60638058,"identity":"e946fe9b-70a2-4658-b57d-da88965798b6","added_by":"auto","created_at":"2024-07-19 02:48:37","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":4821824,"visible":true,"origin":"","legend":"\u003cp\u003eKnocking down NFASC inhibited the migration and invasion ability of TCCSUP cells. (A) The photos taken under the microscope at 0h, 24h, and 48h in the wound healing assay show that the healing speed is slowest in the si-NFASC group. (B) The statistical graph shows significant differences in the healing area. (C) The Transwell results show that the si-NFASC group has the fewest cells passing through the chamber, indicating that its invasive ability is inhibited. (D) The microscopic images of the Transwell experiment. (E) The results of the plate colony formation assay.\u003c/p\u003e","description":"","filename":"Fig13.png","url":"https://assets-eu.researchsquare.com/files/rs-4577574/v1/f7df1715297d061e58d3664e.png"},{"id":65409029,"identity":"145360e0-bbad-480d-aa4e-d7dc7ecaf770","added_by":"auto","created_at":"2024-09-27 05:32:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":42192725,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4577574/v1/fa804f14-b45f-4e97-ba45-29f434bdc142.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Analyzing the Impact of Sphingolipid Metabolism Genes on bladder cancer Progression and Microenvironment for the Development of a Prognostic Signature","fulltext":[{"header":"1.Introduction","content":"\u003cp\u003eBLCA is the one of the most prevalent types of cancer worldwide, with approximately 550,000 new cases and 200,000 deaths in 2018[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Urothelial carcinomas are the primary type of bladder cancer and categorized as non-muscle invasive bladder cancers (NMIBC) or muscle invasive bladder cancers (MIBC) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] based on their treatment implications. Risk factors of BLCA in the USA include cigarette use and occupational exposures, such as arylamines, which also increase the probability of recurrence [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The treatment called Transurethral resection of the bladder tumor (TURBT) is often recommended for those with superficial bladder cancer, whether or not they are receiving intravesical therapy, while radical cystectomy (RC) is the primary treatment for muscle invasive bladder cancers [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The degree of bladder wall invasion and the clinical treatment of bladder tumors are closely related [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Approximately 75% of patients are diagnosed with NMIBC, and only 10% develop MIBC or metastatic bladder cancer at initial detection [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The costs linked to treating bladder cancer, observing progress, and managing adverse effects are significant [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Despite approvements in early diagnosis and comprehensive treatment, some individuals still experience recurrence or metastatic bladder cancer. Therefore, early diagnostic and prognostic indicators of BLCA proliferation must be identified, and novel approaches to BLCA detection and therapy must be developed to optimize therapeutic outcomes.\u003c/p\u003e \u003cp\u003eSphingolipids are a type of bioactive chemical that regulates cell signaling in cancer and influences survival and repression [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Alterations in SM have been implicated in tumorigenesis and may be targeted for treatment [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Certain bioactive SM metabolites can serve as diagnostic and therapeutic biomarkers for many kinds of cancer [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Sphingosine-1-phosphate (S1P) and Ceramides, two important sphingolipids, have a critical function in regulating cell death in cancer [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Recent advancements in molecular, pharmacological, and genetic techniques have enhanced our awareness of SM signaling pathways and their potential use for cancer therapy [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Various research have examined the function of SM in different malignancies, with a focus on utilizing SM as a target for cancer therapy. However, the precise function of SMGs in BLCA remains unclear. To determine the predictive value of the SMGs, we conducted a prognostic BLCA-risk model in our study. We found that these genes were associated with the TME, IME, and SM.\u003c/p\u003e \u003cp\u003eThe progression of malignant tumors are heavily influenced by the tumor immune microenvironment (TIME), which describes the immune infiltration within the TME [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Immune cells play a critical role in tumor cellular reprogramming by secreting various molecules that affect neighboring cells and regulate tumor survival and growth [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The non-tumor components of the microenvironment, which are primarily composed of immune cells infiltrating the tumor, are important in determining the prognosis of patients with BLCA [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Thus, TIME is essential in the development and dissemination of tumors, and emerging evidence suggests that the pathophysiology of BLCA is closely linked to TIME [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Evaluating the immunological status of TIME through staging helps in the progression of immune therapies and improves the cure probability for BLCA invalids.\u003c/p\u003e \u003cp\u003eIn this research, we conducted an extensive examination of SM-related genes to investigate their impact on the survival and progression of BLCA patients. Furthermore, we establish a BLCA-risk model to determine the prognostic value of SMGs in BLCA. These findings could pave the way for new approaches to investigate the molecular mechanisms underlying BLCA, leading to the development of targeted therapies and personalized patient care.\u003c/p\u003e"},{"header":"2.Materials","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data processing\u003c/h2\u003e \u003cp\u003eWe downloaded RNA-seq data for BLCA and corresponding clinical data from the UCSC XENA database. The dataset consisted of 19 normal samples and 411 BLCA samples. The RNA-seq data was in log2(FPKM) format firstly. We first converted it to FPKM format and then transformed it into TPM format, the RNA-seq data was transformed into log2 (TPM\u0026thinsp;+\u0026thinsp;1) format in the end. We downloaded bladder cancer RNA-seq data from the GEO database for external validation, including 85 samples from GSE236932 and GSE195768. Similar to the TCGA data, we converted these into log2(TPM\u0026thinsp;+\u0026thinsp;1) format and applied batch effect correction for subsequent analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Obtaining genes associated with SM.\u003c/h2\u003e \u003cp\u003eTo identify SMGs, we extracted information derived from GeneCards database (genecards.org/). Our analysis retrieved 1231 genes associated with SM from this database. The screening criteria for genes were the relevance score of \u0026gt;\u0026thinsp;4 for associated genes. The relevance score of the gene represents the relevance between the key words and the gene, which means a more significant relevance with a higher relevance score. The screened genes will be used for subsequent analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Identifying differentially expressed SMGs.\u003c/h2\u003e \u003cp\u003eWe used the \"limma\" package in R to analyze the RNA-seq and identify differentially expressed SMGs between normal bladder tissue and BLCA tissue. The Wilcoxon rank sum test was applied to screen DEGs using the criteria: |LogFoldChange| \u0026gt; 1, q\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The q-value was calculated using the false discovery rate (FDR) method to adjust the P-value and reduce the number of false positives. This ensures that the differentially expressed SMGs we discover are sufficiently large and significant in their differences.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Constructing a SMGs-prognostic model\u003c/h2\u003e \u003cp\u003eWe randomly divided the BLCA patients into two equal groups, a training cohort and a validation cohort. Univariate Cox regression (Unicox) was applied to identify that SMGs were significantly linked with overall survival (OS) using the DEGs related to SM in the training cohort. LASSO Cox regression was used to select SMGs for establishing a predictive BLCA-risk score model for estimating the OS of BLCA patients utilizing the \"glmnet\" R package. We employed a tenfold cross-validation method to determine the penalty parameter of the model. To calculate the risk score for each patient, we used the following method:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\mathbf{B}\\mathbf{L}\\mathbf{C}\\mathbf{A} \\mathbf{R}\\mathbf{i}\\mathbf{s}\\mathbf{k}\\mathbf{S}\\mathbf{c}\\mathbf{o}\\mathbf{r}\\mathbf{e}={\\sum }_{\\varvec{i}=1}^{\\varvec{n}}{\\varvec{\\beta }}_{\\varvec{i}}\\times {\\varvec{E}}_{\\varvec{i}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere n represents the number of selected genes included in the prognostic BLCA-risk model, βi represents the regression coefficient of SMG i, and Ei represents the expression of SMG i.\u003c/p\u003e \u003cp\u003eThe median risk score value was used to divide all invalids into low-BLCA and high-BLCA groups. While the OS of the low-BLCA and high-BLCA groups was compared using KM survival analysis and the log-rank test. The predictive correctness of the prognostic BLCA-risk score model was evaluated using a ROC curve with the \"survivalROC\" R package. In the end, the validity and reliability of the prognostic BLCA-risk score model were validated using the validation cohort. Using the regression coefficients obtained from the prognostic model established in the training set, we calculated the risk scores for the external validation set from the GEO database. These risk scores were then used for subsequent survival and prognostic analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e2.5 Independent analysis of prognostic BLCA\u003c/b\u003e-\u003cb\u003erisk group and clinical information\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eWe combined the BLCA-risk score of every BLCA patient with their corresponding clinical factors utilizing the sample ID. Then we utilized \"limma\" algorithm in R package to analyze the association between BLCA-risk scores and clinical factors, such as gender, age, stage and TNM status. UniCox and MultCox regression analyses were performed to evaluate the impact of these clinical information on the prognosis. By doing so, we can determine whether the prognostic model provides more accurate results compared to the clinical parameters commonly used to assess disease progression.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Enrichment analysis of SMGs using KEGG and GO database.\u003c/h2\u003e \u003cp\u003eThe differentially expressed SMGs of high-BLCA and low-BLCA invalids were calculated using the \"limma\" R package. The screening method is as described above. To identify biological characteristics and cellular function pathways that were significantly enriched, our team utilized the \"clusterProfiler\" R package to perform GO and KEGG analysis on the DEGs. The enrichment results for both KEGG and GO pathways had corrected p-values less than 0.05. The \"enrichplot\" and \"ggplot2\" R packages were utilized to visualize the outcome of the enrichment analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Analysis of SMGs using GSVA\u003c/h2\u003e \u003cp\u003eTo examine the discrepancy in biological processes between low-BLCA and high-BLCA ivalids, we employed the \"GSVA\" package in R. We use all SMGs to analyze and obtain enriched gene sets and pathways that differ in degree between the two groups. We utilized the \"c2.cp.kegg.v7.1.symbols\" and \"c5.go.v7.5.1.symbols\" gene sets from the MSIGDB Database (gsea-msigdb.org/gsea/msigdb) to construct standard gene sets for the analysis. These gene sets encompass various biological processes and pathways, aiding us in gaining a more comprehensive understanding of the biological differences between low-BLCA and high-BLCA invalids.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Analysis of SMGs using GSEA\u003c/h2\u003e \u003cp\u003eTo improve our awareness of the underlying mechanisms of SM in BLCA, we classified the samples into high-BLCA and low-BLCA groups using BLCA-risk scores. We then conducted analysis to determine whether the differentially expressed SMGs were significantly enriched in any biological processes or pathways. The standard gene sets for this analysis are same as the GSVA.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9 The characterization of the immune microenvironment (IME)\u003c/h2\u003e \u003cp\u003eWe utilized the ssGSEA method to calculate scores for 29 immune features in each sample. These scores were determined based on the expression levels of genes associated with various immune features. We can compare the differences in the 29 immune features between the high-risk and low-risk groups, inferring and summarizing the differences in the immune microenvironment between high-risk and low-risk patients.. In addition, we calculated the estimation score, stromal score, immune score, and tumor purity for invalids in both groups. Furthermore, we investigated the correlation between immune function and prognosis-related SMGs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.10 Immunohistochemistry from the Public database\u003c/h2\u003e \u003cp\u003eWe utilized the HPA database (proteinatlas.org) as a resource to obtain immunohistochemistry data for normal bladder tissue and BLCA tissue. We focus on obtaining immunohistochemistry data for SM`Gs that can independently predict prognosis, particularly those in the model (PCSK2, NFASC, NTF3, NR2F1, GSDMB). We can use the HPA database to compare the expression patterns and differences of model genes between normal tissues and bladder cancer tissues.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.11 Cell culture and transfection\u003c/h2\u003e \u003cp\u003eWe used human bladder cancer cells TCCSUP for in vitro validation. The cells were cultured in MEM medium containing NEAA, supplemented with 10% fetal bovine serum and 1% penicillin-streptomycin. The culture conditions were 37 degrees Celsius and 5% carbon dioxide. We seeded the cells in six-well plates and transfected them with siRNA using Lipofectamine 2000. The transfected cells were then used for subsequent experiments.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.12 qRT-PCR\u003c/h2\u003e \u003cp\u003eTotal RNA from TCCUSP cells was extracted using TRIzol reagent. Reverse transcription was performed with the TransScript First-Strand cDNA Synthesis Kit (TaKaRa, Japan). qRT-PCR was conducted on an ABI-7900HT Real-Time PCR System utilizing SYBR Green MasterMix (TaKaRa, Japan). The following primer sequences were used: Forward Primer: ACTGGACACGAAACAGCAGAT. Reverse Primer: CTGCGGAAGTCAATCACCAGG.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e2.13 Cell Viability Assay\u003c/h2\u003e \u003cp\u003eSeed the transfected cells onto coverslips and use Yeason's EDU kit to assess cell viability and proliferation rate. Then stain the nuclei with DAPI and observe under an upright microscope.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e2.14 Cell apoptosis detection\u003c/h2\u003e \u003cp\u003eThe mentioned therapy was administered to the TCCSUP cells, after which the culture medium was transferred to a new centrifuge tube.Afterward, the cells in the culture bottle were washed with PBS. The cells were collected after removing the liquid above them.Between 100,000 and 500,000 suspended cells were gathered and then centrifuged at a force of 300 times the acceleration due to gravity for a duration of 5 minutes. The cells were resuspended by adding Annexin V-binding buffer, which was diluted to a volume of 500 \u0026micro;L.Annexin V (5 \u0026micro;L) and nuclear staining solution (5 \u0026micro;L) were introduced into the cell suspension.Following a combination of gentle and vigorous stirring, the mixture was left to incubate at room temperature for a duration of 15 to 20 minutes.In the end, the process of flow cytometry was carried out.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e2.15 Wound healing assay\u003c/h2\u003e \u003cp\u003eTCCSUP cells were cultured in a 6-well plate, with half of the wells treated with si-NFASC. Once the cells reached confluence, a micropipette tip was used to create a wound in the monolayer. The medium was then replaced with serum-free MEM. Photographs were taken at 0h, 24h, and 48h post-wounding using a microscope (Olympus, Japan) to assess cell migration. ImageJ software was used to calculate the wound area. The migratory ability was evaluated by the wound healing percentage at 24h and 48h, calculated as follows:\u003c/p\u003e \u003cp\u003eWould healing percentage= (area of 0h \u0026ndash; area of 24h or 48h)/ (area of 0h)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e2.16 Transwell migration assay\u003c/h2\u003e \u003cp\u003eFollowing a series of treatments, 4\u0026times;10^4 cells in serum-free medium were seeded into the upper chambers of a Transwell apparatus coated with Matrigel. The lower chambers contained medium with 10% FBS to act as an attractant. After 24 hours of incubation, cells that had migrated through the chamber membrane were fixed with pre-cooled formaldehyde and stained with crystal violet. These cells were then counted and photographed under a microscope (Olympus, Japan).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e2.17 Assay for colony development\u003c/h2\u003e \u003cp\u003eAfter 7 days of culture, TCCSUP cells were seeded into six-well plates at a density of 500 cells per well. The cells were then fixed with 4% paraformaldehyde for 20 minutes, stained with 4% crystal violet dye, and the resulting colonies were counted.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e2.18 Statistical analysis\u003c/h2\u003e \u003cp\u003eWe employed the Wilcoxon rank-sum test to compare two groups. The KM survival analysis was used to evaluate the discrepancy in survival among patients in the low-BLCA and high-BLCA groups. MultCox regression was utilized to identify the independent predictors of OS in BLCA. ROC curves were employed to assess the predictive precision of the prognostic BLCA-risk score model. All statistical analyses were conducted using R package version 4.2.0, with a significance level of P\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Statistical significance was presented by \"*\" for p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \"**\" for p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, and \"***\" for p\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e \u003c/div\u003e"},{"header":"3.Results","content":"\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Differential analysis of SMGs between normal tissue and BLCA\u003c/h2\u003e \u003cp\u003eOur research\u0026rsquo;s flow chart is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA. The DEG analysis revealed that 133 SMGs expressed less, and 48 SMGs expressed more in BLCA patients. These differentially expressed SMGs were subsequently utilized for prognostic analysis, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB and C.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Prognostic BLCA-risk score model established in the train cohort.\u003c/h2\u003e \u003cp\u003eThe study utilized UniCox regression analysis to identify 54 SMGs associated with prognosis out of 133 SMGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). The amount of SMGs was subsequently reduced utilized Cox regression analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB and C). LASSO regression analysis was performed, and eight SMGs (PCSK2, NFASC, NTF3, NR2F1, ATP13A2, SREBF1, GSDMB, and LGALS4) were identified to be involved in prognosis. From these eight SMGs, a predictable BLCA-risk score model was developed (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD), and the BLCA-risk score of invalids was calculated using the equation mentioned in the materials. The BLCA-risk score model was then utilized to categorize BLCA invalids into low-BLCA and high-BLCA groups.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e3.3 The correlation between the BLCA-risk score and clinical parameters\u003c/h2\u003e \u003cp\u003eThe cutoff threshold value for the BLCA-risk score in the train cohort was determined using the median value, the patients were classified into low-BLCA (102 invalids) and high-BLCA (102 invalids) groups underlying the cutoff value (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA and B). Patients belong to high-BLCA group had a poorer prognosis, as demonstrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC and D. To evaluate the prognostic model, the validation cohort was categorized into low-BLCA (98 invalids) and high-BLCA (104 invalids) groups using the cutoff value derived from the train cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE and F). The high-BLCA invalids in the validation cohort suffered from a worse prognosis, indicating that the prognostic model could predict OS in BLCA invalids correctly (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB and F). UniCox analysis revealed that both the BLCA-risk score and stage were significantly linked with OS (Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Furthermore, MultCox analysis demonstrated that the BLCA-risk score and stage were independent predictors of OS (Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). To assess the accuracy of the prognostic model, ROC curve was constructed at 1,3,5 years (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA and B), and the AUC indicated that the BLCA-risk score was a more accurate predictor of OS than other predictors (the AUC of the train cohort was 0.772, and the AUC of the validation cohort was 0.725) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC and D). The external validation set from GEO also demonstrated the same results. After stratifying patients into high and low-risk groups, survival analysis was conducted, revealing that patients with high risk had significantly worse prognosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA and B). The ROC curves at 1, 3, and 5 years were 0.802, 0.785, and 0.832, respectively, indicating strong reliability of the results (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). Compared to clinical parameters, it also demonstrated excellent performance (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). In summary, our model demonstrated excellent performance and accurately identified high-risk patients based on the above results.\u003c/p\u003e \u003cp\u003e \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\u003eUnivariate analysis revealed that risk score, and stage were associated with overall survival in train cohort.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHR.95L\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHR.95H\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003epvalue\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.029091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.005441\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.053297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.015632\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.891552\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.556761\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.42766\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.632754\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.258404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.661269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.070177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.00E-07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.029222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.467739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.805501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.85E-05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.987654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.345678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.876543\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.10E-05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.112345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.567890\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.945612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.65E-05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eriskScore\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.431055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.310374\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.562849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.54E-15\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=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate analysis revealed that risk score, and stage were associated with overall survival in train cohort.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHR.95L\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHR.95H\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003epvalue\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.024814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.999116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.051173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.058533\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.072786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.646573\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.779953\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.785646\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.823717\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.279749\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.598904\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000885\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.458481\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.981047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.168261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.062127\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.632579\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.102345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.413678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.055873\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.547893\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.973456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.214567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.064981\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eriskScore\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.381363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.256343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.518824\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.48E-11\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\u003eUnivariate analysis revealed that risk score, and stage were associated with overall survival in test cohort.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHR.95L\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHR.95H\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003epvalue\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.03415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.011455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.057354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.746389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.465782\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.196046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.224045\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.496159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.132301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.976939\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.004598\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.523142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.120893\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.069745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.007158\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.635472\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.193562\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.134678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.006543\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.482391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.095874\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.056723\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.007632\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eriskScore\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.061585\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.013771\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.157318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.044904\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=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate analysis revealed that risk score, and stage were associated with overall survival in test cohort.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHR.95L\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHR.95H\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003epvalue\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.033135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.010562\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.056212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003826\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.716028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.443917\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.154937\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.170865\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.236681\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.851641\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.795803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.264346\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.411015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.921291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.161058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.113412\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.372486\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.899213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.119547\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.120345\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.426789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.945678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.175432\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.115678\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eriskScore\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.065878\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.963667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.17893\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.014826\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 \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Result of enrichment analysis of SMGs using KEGG and GO database\u003c/h2\u003e \u003cp\u003eUsing a similar approach, we detected 882 differentially expressed SMGs between the two groups. Gene Ontology (GO) enrichment analysis indicated that the DEGs were primarily involved in keratinization, axon development, homophilic cell adhesion via plasma membrane adhesion molecules, among others (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA-C). Additionally, KEGG enrichment analysis identified several metabolic signaling pathways, such as Neuroactive ligand\u0026thinsp;\u0026minus;\u0026thinsp;receptor interaction, Retinol metabolism, and Porphyrin metabolism, among others (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD-F). We can speculate from the above results that the enriched pathways may involve key pathways and functions that affect the prognosis of BLCA patients, which are worth further exploration.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e3.5 GSVA\u003c/h2\u003e \u003cp\u003eTo gain insight into the distinct biological processes of the two groups, we employed gene sets mentioned in the materials to perform gene set variation analysis (GSVA). The results revealed significant enrichment of pathways related to metabolism, immune response, and cellular activities in high-BLCA invalids (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA and B). Notably, the pathways that were differentially enriched between the high-BLCA and low-BLCA groups included Leukocyte Transendothelial Migration, T cell receptor signaling pathway, regulation of T-cell chemotaxis, and positive regulation of lymphocyte chemotaxis, among others. From the above results, we can infer that there are differences in immune signaling pathways and functions as well as some metabolic signaling pathways and functions between the high-BLCA and low-BLCA groups.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e3.6 GSEA\u003c/h2\u003e \u003cp\u003eWe present the most significant 10 pathways enriched utilizing the GO and KEGG gene sets, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA and B. Moreover, GSEA revealed that some immune-relevant signaling pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC-\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eH), such as adaptive immune response (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC), immunoglobulin complex (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eD), antigen binding (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eE), and immunoglobulin production (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eF), were upregulated in the high-BLCA group. The pathways and functions enriched by differentially expressed SMGs still point to immune-related pathways and functions, further confirming the important association between immune dysfunction and prognosis of BLCA invalids. This also leads us to perform IME analysis on the samples.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003e3.7 The variation of IME between the two groups\u003c/h2\u003e \u003cp\u003eWe conducted an analysis of IME to examine the variation in immune status between the two groups. The analysis was conducted using the ESTIMATE algorithm, which illustrated that invalids in the high-BLCA group had higher immune scores, ESTIMATE scores, stromal scores, and lower tumor purity (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA-\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eE). Furthermore, IME was analyzed using ssGSEA, which revealed that immune cell infiltration was higher in the patients in the high-BLCA group. A total of 26 immune cells and functions were upregulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). Thus, we have reason to believe that immune dysfunction plays a crucial role and is likely to be involved in promoting tumorigenesis, helping tumor cells to escape immune surveillance.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003e3.8 Immunohistochemistry reveals differences in key SMGs\u003c/h2\u003e \u003cp\u003eAs described above, we obtained immunohistochemical data for several key SMGs genes, excluding those with missing data. Finally, we acquired immunohistochemistry data for NTF3, NFASC, and GSDMB (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e). The left side represents normal tissue and the right side represents BLCA tissue, which is consistent with our speculation and further confirms the reliability of our study.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec30\" class=\"Section2\"\u003e \u003ch2\u003e3.9 siRNA reduces the expression of NFASC in TCCSUP cells\u003c/h2\u003e \u003cp\u003eAfter transfecting siRNA into TCCSUP cells, total RNA was extracted for qRT-PCR analysis. The results showed a significant reduction in NFASC mRNA levels within the cells, indicating successful transfection and enabling subsequent experiments (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eA).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003e3.10 Silencing NFASC inhibits the viability of TCCSUP cells\u003c/h2\u003e \u003cp\u003eUsing an EDU kit, cell proliferation and viability were assessed. In the experimental results, the incorporation of the fluorescently labeled active substance into newly synthesized DNA molecules caused positive cells to appear red. After DAPI staining, the cell nuclei appeared blue. The results showed a decrease in the proportion of positive cells following NFASC silencing, indicating that the proliferation rate and viability were inhibited (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eB and C). This supports our identification of NFASC as a risk gene.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec32\" class=\"Section2\"\u003e \u003ch2\u003e3.11 Silencing NFASC promotes apoptosis in TCCSUP cells\u003c/h2\u003e \u003cp\u003eUsing flow cytometry to assess apoptosis, the results showed an increased apoptosis rate following NFASC silencing (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eD). This indicates that NFASC is crucial for the survival and proliferation of TCCSUP cells.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec33\" class=\"Section2\"\u003e \u003ch2\u003e3.12 Silencing NFASC inhibits the migration and invasion capabilities of TCCSUP cells\u003c/h2\u003e \u003cp\u003eThe migration and invasion abilities of tumor cells are crucial mechanisms for cancer metastasis. Inhibiting these abilities can effectively slow disease progression. Therefore, we conducted wound healing assays, Transwell assays, and colony formation assays to verify the impact of the NFASC gene on the migration and invasion capabilities of TCCSUP cells. The results of the wound healing assay revealed that, compared to the control group and the si-NC group, the healing rate of the si-NFASC group was significantly slower under the same time and culture conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eA and B). The Transwell assay showed similar results, with a significant reduction in the number of cells migrating through the chamber after NFASC silencing (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eC and D). The results of the colony formation assay indicated that the si-NFASC group formed significantly fewer colonies compared to the control and si-NC groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eE). In summary, these results indicate that silencing NFASC effectively inhibits the migration and invasion capabilities of TCCSUP cells. This finding not only validates our model predictions but also offers hope for the development of new therapeutic targets.\u003c/p\u003e \u003c/div\u003e"},{"header":"4.Discussion","content":"\u003cp\u003eAccording to a study, metabolic reprogramming (MRG) is a hallmark of cancer, every metabolic state has a specific genetic signature that predicts a different ending [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Change in food metabolism in tumor stroma is now considered to play a significant role in cancer related MRG [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], which is caused by various oncogenic pathways, including glucose MRG [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The Myc protein, a crucial metabolic regulator, is involved in MRG activities such as glucose and glutamine metabolism, and serine production, which promote the growth of cancer cells [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBladder cancer (BLCA) diagnosis and treatment have made significant strides in the last two decades, but the higher ratio of relapse and risk of progression to invasive diseases let it become One of the priciest types of cancer to treat [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Recurrence, antimicrobial resistance, and rapid disease development pose major therapy obstacles, highlighting the urgent need for New biological indicators for the clinical detection and management of BLCA [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study groundbreakingly explored the relationship between BLCA and SMGs. Using LASSO-Cox regression and Cox regression with UniCox, the researchers developed a predictive BLCA-risk score model utilizing 54 differentially expressed SMGs from BLCA and normal tissue obtained from TCGA. To better understand the function of these SMGs in BLCA, the predictive model was applied to estimate OS of the training cohort. The model successfully identified patients with low-BLCA scores and high-BLCA scores, who had different survival rates. The same outcome was confirmed in the test cohort, demonstrating the model's ability to predict individuals\u0026rsquo; risk and OS.\u003c/p\u003e \u003cp\u003eIn a MultCox analysis, the predictive model was found to be an independent predictive factor. Therefore, identifying immunotherapy candidates is critical. Invalids with high-BLCA scores had higher concentrations of immune-inflamed cells and immuno-suppressive immune cells, including Tregs, APC co-stimulation, checkpoint cytolytic activity activation, PC co-inhibition and so on.They were also observed in those with a high-BLCA score, suggesting they could be immunotherapy candidates. The different SMGs in the two groups were further examined because of the significant differences in risk ratings between them.\u003c/p\u003e \u003cp\u003eThe time factor is crucial in determining a patient's prognosis, as alterations in the stroma and immune function that are a crucial part of the tumor stroma are closely linked to changes in the tumor [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. To forecast tumor purity and proportion of stromal cells to immune cells of the tumor, an advanced algorithm called ESTIMATE has been developed, which is based on gene expression [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Tumor purity, which is highly correlated with prognosis, alludes to the proportion of malignant cells in the tumor, while the immunological score calculated assesses the immune component of tumor [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The ESTIMATE was utilized in our study to describe the Tumor Immune Estimation Resource (TIMER) for the two groups. The outcome revealed that invalids hava a good prognosis had lower tumor purity and higher immunological scores than invalids have a poor prognosis. Furthermore, ssGSEA was utilized to identify the immunological status of patients in the high- BLCA and low- BLCA groups. These results validated the findings of ESTIMATE and TIMER by showing that invalids in the high-BLCA group suffered a disordered immune status and elevated 26 of the 29 immune-relevant activities. Therefore, it is reasonable to suggest that a compromised immune system may be associated with a worse prognosis.\u003c/p\u003e \u003cp\u003eThe findings of our study reveal that dysregulation of sphingolipid metabolism leads to TIME anomalies, making a poor prognosis for BLCA invalids [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. SM dysregulation has been identified as a potential signal of cancer and is an encouraging candidate for therapies [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Several studies have investigated the role of SM in some kinds of other cancer, including the relevance of sphingolipids in radiation therapy, resistance to medication, immune system therapy, and precision therapy [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Sphingolipids, such as membrane markers, could regulate the proliferation, invasion, and migration of tumor [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Furthermore, the concatenation of sialic acid structures in tandem, such as those present in GD3 and GD2, act as promoters of neoplastic growth, thereby fostering tumorigenesis [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. In contrast, monosialyl gangliosides such as GM1, GM3, and GM2 play a role as tumor suppressors [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Sphingolipids govern cellular signaling through interaction with molecules on the identical cell membrane (cis-binding) or across distinct cellular planes (trans-binding), as evidenced by a plethora of investigations [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Glycolipids, such as those found on growth factor and adhesion molecule receptors, containing the integrin family, are cis-interacting molecules that could regulate cell signaling [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The activation of signaling pathways by growth factor and integrin receptors leads to the activation of other signaling molecules and downstream pathways [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. For example, in melanomas, AKT, p130Cas, and paxillin promote carcinogenesis, while paxillin and increased p130Cas phosphorylation promote tumor cells migrate, which is probably be promote by Src family proteins [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Glycolipids exert a notable impact on immune cell activity. Research indicates that glycolipids are vital for directing the migration of immune proteins towards specific membrane microdomains, and their interaction with cell surface receptors heightens immune system functions [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. We conclude that dysregulated sphingolipid metabolism in bladder cancer tissue affects immune cell activity and contributes to tumor growth, immune escape, and a poor prognosis for BLCA patients.\u003c/p\u003e \u003cp\u003eThrough literature search, we found that the three genes of our interest (NTF3, NFASC, GSDMB) have been studied in other types of cancer, which leads us to believe that our research can delve into more detailed mechanistic studies. We believe that they could serve as potential gene therapy targets, to achieve the goal of immunotherapy by improving BLCA invalids' IME, inhibiting tumor development and improving prognosis.\u003c/p\u003e \u003cp\u003eWe validated the role of the NFASC gene using the human bladder cancer cell line TCCSUP in vitro. Based on previous bioinformatics analysis, we predicted NFASC to be a high-risk gene in bladder cancer, accelerating disease progression in bladder cancer patients and leading to poor prognosis. In vitro experiments showed that silencing NFASC inhibited TCCSUP cell proliferation and promoted apoptosis. Additionally, the migration and invasion abilities of TCCSUP cells were significantly suppressed. These findings support our bioinformatics analysis, indicating that our analysis and model are reliable. This provides a basis for identifying high-risk patients and developing targeted therapies in the future.\u003c/p\u003e \u003cp\u003eHowever, our research still has some limitations and shortcomings. Due to the lack of funding and clinical samples, we were unable to validate our findings in patients and tumor tissues, relying only on publicly available immunohistochemistry data. If future conditions permit, we will continue conducting more in-depth research to confirm the impact of the screened SMGs on BLCA progression and explore their specific mechanisms..\u003c/p\u003e"},{"header":"5.Conclusion","content":"\u003cp\u003eIn this study, the correlation between BLCA and SM led to the classification of BLCA invalids into high-BLCA and low-BLCA groups. According to immunological and functional evaluations, it was found that dysregulation of SM impairs the immune status, leading to a poorer prognosis for BLCA invalids. These outcomes support risk stratification of BLCA patients and offer a basis for the development of targeted therapies tailored to individual patient needs.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eAll authors in our team acknowledge and promise that there are no conflicts of interest or disputes among them.\u003c/p\u003e\n\u003cp\u003eAuthor\u0026rsquo;s contributions\u003c/p\u003e\n\u003cp\u003eTianshi Wu and Zechun Peng: conceptualization and visualization. Zechun Peng, Shiying Zhou and Fangzhen Cai: original manusript writing. Zechun Peng, Shiying Zhou and Fangzhen Cai: formal analysis. Tianshi Wu: data curation. Tianshi Wu and Zechun Peng: writing\u0026mdash;review and editing, project administration.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eHainan Provincial Natural \u0026nbsp;Science Foundation of China(ZDYF2024SHFZ122, 820MS142 and 822QN472) and Hainan Province Clinical Medical Center.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study provides access to the datasets through online repositories. Below, you can find the repository/repositories\u0026apos; names and corresponding accession number(s): https://xenabrowser.net/datapages/.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eA. Horwich, M. Babjuk, J. Bellmunt, H.M. Bruins, T.M. De Reijke, M. De Santis, S. Gillessen, N. James, S. Maclennan, J. Palou, T. Powles, M.J. Ribal, S.F. Shariat, T. Van Der Kwast, E. Xylinas, N. Agarwal, T. Arends, A. Bamias, A. Birtle, P.C. Black, B.H. Bochner, M. Bolla, J.L. Boormans, A. Bossi, A. Briganti, I. Brummelhuis, M. Burger, D. Castellano, R. Cathomas, A. Chiti, A. Choudhury, E. Comperat, S. Crabb, S. Culine, B. De Bari, W. DeBlok, P.J.L. De Visschere, K. 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Zhang, Functional Role of Glycosphingolipids in Cancer. Curr Med Chem 28 (2021) 3913-3924.\u003c/li\u003e\n\u003cli\u003eK. Azuma, M. Tanaka, T. Uekita, S. Inoue, J. Yokota, Y. Ouchi, and R. Sakai, Tyrosine phosphorylation of paxillin affects the metastatic potential of human osteosarcoma. Oncogene 24 (2005) 4754-64.\u003c/li\u003e\n\u003cli\u003eV.F. Vartabedian, P.B. Savage, and L. Teyton, The processing and presentation of lipids and glycolipids to the immune system. Immunol Rev 272 (2016) 109-19.\u003c/li\u003e\n\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":"sphingolipid metabolism, Bladder cancer, prognostic model, immune microenvironment, individual therapy","lastPublishedDoi":"10.21203/rs.3.rs-4577574/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4577574/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe role of sphingolipid metabolism (SM) in promoting the progression of bladder cancer (BLCA) and its impact on patient prognosis has been established. To improve therapeutic outcomes, it is essential to identify specific molecular pathways in BLCA and develop a predictive signature underlying SM-related genes. In this study, 430 BLCA samples were analyzed using univariate Cox regression to identify critical SM-relevant genes (SMGs) involved in BLCA development. LASSO regression analysis was then employed to reduce the possibility of overfitting. A multivariable Cox regression analysis was employed to develop a prognostic signature underlying SMGs, which was subsequently validated in a separate cohort. Our research revealed that dysregulated SM leads to worse prognosis in BLCA patients, and important prognostic genes (PCSK2, NFASC, NTF3, NR2F1, ATP13A2, SREBF1, GSDMB, and LGALS4) were identified. Using these SMGs, we developed a prognostic BLCA-risk model that effectively predicted the prognosis of BLCA patients (AUC was 0.772 for the training cohort and 0.725 for the validation cohort). Interestingly, patients identified as high-risk by this model had a significantly more active immunological milieu, characterized by higher immune scores and increased 26 types of immune function and cell like NK cells, CD8\u003csup\u003e+\u003c/sup\u003eT cells, and cytolytic activity. These findings suggest that dysregulated SM may contribute to immune microenvironment dysregulation in BLCA. Our research provides a better awareness of the role of SM in the emergence of BLCA and has the potential to offer customized care to high-risk patients based on their SM-related gene expression signature.\u003c/p\u003e","manuscriptTitle":"Analyzing the Impact of Sphingolipid Metabolism Genes on bladder cancer Progression and Microenvironment for the Development of a Prognostic Signature","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-19 02:48:32","doi":"10.21203/rs.3.rs-4577574/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":"dca49f7f-3ce9-4892-8f01-e236b751ea60","owner":[],"postedDate":"July 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-11-08T04:23:24+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-19 02:48:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4577574","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4577574","identity":"rs-4577574","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}