Predicting Bladder Cancer Survival with High Accuracy: Insights from MAPK Pathway-related Genes

Predicting Bladder Cancer Survival with High Accuracy: Insights from MAPK Pathway-related Genes | 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 Article Predicting Bladder Cancer Survival with High Accuracy: Insights from MAPK Pathway-related Genes Gaungyang Cheng, Shiqi Li, Zhaokai Zhou, Yan Wang, Zhuo Ye, Chuanchuan Ren This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3872147/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 May, 2024 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract The mitogen-activated protein kinase (MAPK) pathway plays a critical role in tumor development and immunotherapy. Nevertheless, additional research is necessary to comprehend the relationship between the MAPK pathway and the prognosis of bladder cancer (BLCA), as well as its influence on the tumor immune microenvironment.To create prognostic models, we screened ten genes associated with the MAPK pathway using COX and least absolute shrinkage and selection operator (LASSO) regression analysis. These models were validated in the Genomic Data Commons (GEO) cohort and further examined for immune infiltration, somatic mutation, and drug sensitivity characteristics. Finally, the findings were validated using The Human Protein Atlas (HPA) database and through Quantitative Real-time PCR (qRT-PCR).Patients were classified into high-risk and low-risk groups based on the prognosis-related genes of the MAPK pathway. The high-risk group had poorer overall survival than the low-risk group and showed increased immune infiltration compared to the low-risk group. Additionally, the nomograms built using the risk scores and clinical factors exhibited high accuracy in predicting the survival of BLCA patients.The prognostic profiling of MAPK pathway-associated genes represents a potent clinical prediction tool, serving as the foundation for precise clinical treatment of bladder cancer. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Introduction BLCA is one of the most frequently occurring cancers and is the most dominant malignant tumor in the urinary system, ranking in the top ten 1 . Every year, there are about 550,000 fresh instances of bladder cancer documented worldwide, making up roughly 3.0% of new cancer detections and 2.1% of fatalities caused by cancer 2 . The majority of newly diagnosed cases (75%) are non-muscle-invasive (NMIBC), while 25% are muscle-invasive (MIBC) 3 . NMIBC has a relatively better prognosis but tends to recur frequently 4 . A growing agreement indicates that individuals diagnosed with MIBC encounter a worse outlook and a heightened likelihood of metastasis, resulting in a survival rate below 50% within five years 5 – 7 . Despite established treatment options, radical cystectomy, and pelvic lymph node dissection, approximately 50% of patients who undergo surgery for MIBC often face recurrence afterward, primarily caused by distant metastases 8 . Hence, early disease diagnosis and the identification of prognostic markers are crucial for managing bladder cancer effectively. In recent years, various signaling pathway-related models like the Notch pathway, EMT pathway, TGF-β pathway, and PI3K pathway have been established to predict the survival of bladder cancer patients 9 – 12 . However, a risk profile related to the MAPK pathway for predicting survival in BLCA patients has not yet been established. In mammalian cells, MAPK signaling is a fundamental mechanism, transmitting signals related to proliferation, apoptosis, and differentiation 13 – 15 . ERK, p38, JNK, and ERK5, which are a set of serine-threonine kinases conserved throughout evolution, serve as the classical MAPK pathways 16 . These pathways involve different MAPKs associated with specific MAPK kinases (MAPKK) and MAPK-kinase-kinase (MAPKKK), forming a conserved tertiary enzymatic cascade (MAPKKK→MAPKK→MAPK) 17 – 19 . Aberrant mutations in certain components of the MAPK pathway have been identified as significant contributors to various cancers 20 , 21 . Consequently, intervention in this pathway has been explored as a strategy for tumor therapy. Previous research indicates that 45% of potential therapeutic targets in bladder cancer are related to the MAPK pathway 22 , and this association correlates with the prognosis in BLCA 23 . As part of this study, a prediction model was developed based on genes associated with the MAPK signaling pathway. Importantly, we aimed to investigate the potential mechanisms by which the MAPK signaling pathway affects prognosis and immunotherapy response. Our research findings provide a foundation for future advancements in precision medicine for BLCA. Results Differential expression analysis of MAPK pathway-related genes To analyze the DEGs in TCGA-BLCA tumor tissues and normal tissues, RNA-seq data from log-transformed TCGA consisting of 406 tumor samples and 19 normal tissue samples were subjected to analysis using the "Deseq2" R package. A total of 4731 DEGs were identified, applying criteria of log2FC > 1 and F.adj < 0.05. By intersecting these DEGs with MAPK-related genes, 103 intersected genes were obtained using the VennDiagram package. The analysis resulted in the identification of these 103 genes, and Venn plots (Fig. 2 B), volcano plots (Fig. 2 C), and a heatmap (Fig. 2 A) illustrating the expression of these 103 differential genes were generated. Development and validation of prognostic gene signatures During the analysis of the 103 intersecting genes, COX regression analysis was performed, resulting in the identification of 26 genes significantly associated with survival (P < 0.05), as depicted in Fig. 2 D. The differential expression of these genes between the tumor and normal groups is illustrated in Fig. 2 E. Subsequent LASSO regression analysis on the 26 prognosis-related genes revealed 10 candidate genes at the minimum lambda value (Fig. 3 A). Risk scores were then calculated for each patient based on the mRNA expression levels of these 10 genes and the corresponding coefficients from the LASSO regression analysis. Using the median value of the risk score from the training cohort as the cutoff, patients across different cohorts were categorized into two groups. Principal Component Analysis (PCA) demonstrated the efficacy of the model genes in clustering patients within the TCGA-BLCA dataset (Fig. 3 B and C). The Kaplan-Meier analysis demonstrated a notable decrease in the likelihood of survival in the high-risk group when compared to the low-risk group across these cohorts (Fig. 3 D). ROC curves (receiver operating characteristic curves) yielded an Area Under the Curve (AUC) value of 0.751 for survival at 5 years (Fig. 3 E). The prognostic gene expression profiles are presented in a heatmap (Fig. 3 F). Association of risk profiles with the tumor microenvironment To further investigate the differences in immune infiltration between the two subgroups, the CIBERSORT algorithm was employed to calculate the proportion of immune cell infiltration for all samples, as shown in Supplementary Figure S1 A. Additionally, StromalScore and ImmuneScore were determined for all samples using the ESTIMATE algorithm, and correlation analyses showed higher StromalScore, ImmuneScore, and ESTIMATEScore in the high-risk group (Fig. 4 A). Further correlation analysis demonstrated a positive correlation between risk scores and both stromal scores and immune scores, as well as a positive correlation with the ESTIMATEScore (Fig. 4 B). Analysis of the differences in immune infiltration between the two risk groups revealed that patients in the high-risk score group exhibited a higher percentage of resting memory naive B cells, CD4 T cells, M0 macrophages, M1 macrophages, and M2 macrophages. In contrast, plasma cells, CD8 T cells, regulatory T cells, and activated dendritic cells were more prevalent in patients in the low-risk score group (Fig. 4 C). Finally, an analysis of the differences in the expression of 34 immune checkpoints between the high-risk and low-risk groups was conducted (Supplementary Table S2 , P.adj < 0.05). Among these, 21 immune checkpoints with P < 0.01 were selected and plotted in a box line plot. All of them were found to be highly expressed in the high-risk scoring group, except for TNFRSF14 (Fig. 4 D). Nomogram construction To construct nomograms for predicting patient survival, we conducted univariate and multivariate Cox regression analyses involving risk scores and clinical factors. Univariate Cox regression analyses revealed significant associations between RiskScore (p < 0.001, risk ratio [HR] = 3.157, 95% confidence interval [CI] = 2.286–4.359), Clinical stage (p < 0.001, HR = 1.561, 95% CI = 1.282–1.902), and Age (p < 0.001, HR = 1.027, 95% CI = 1.012–1.043) with Overall Survival (OS) in the TCGA-BLCA cohort (Fig. 5 A). In multivariate Cox regression analyses, RiskScore, Clinical stage, and Age were similarly statistically significant (Fig. 5 B). Nomograms, integrating multiple risk factors, were developed for predicting survival in the TCGA-BLCA cohort. The model incorporated three independent risk factors: age, stage, and RiskScore, and could predict survival probabilities by calculating the cumulative total score for each independent factor for each patient (Fig. 5 C). The nomogram's predictive performance was validated through calibration curves and ROC curves, indicating that the actual OS aligned well with the OS predicted by the nomogram at 1, 3, and 5 years. The area under the ROC curve value reached 0.799, demonstrating good predictive performance (Fig. 5 D and E, Supplementary Figure S1 B). MAPK-related gene prognostic models concerning tumor mutation load and immunotherapy response Afterward, we performed a study to compare the variances in immunotherapy reactions among the two subgroups. The findings from Tumor Immune Dysfunction and Exclusion (TIDE) indicated that samples categorized as the low-risk category showed a greater rate of response to immunosuppressive medications (Fig. 6 A). Furthermore, we examined the tumor somatic mutation landscapes in two groups. In both subgroups, the results indicated that the mutation rates of TP53, TTN, KMT2D, MUC16, and ARID1A genes were higher than 20%, as shown in Supplementary Figure S1 C. Analysis of TMB status between the two groups showed that TMB was significantly increased in the low-risk group (Fig. 6 B). Kaplan-Meier survival analysis showed that the high TMB group had a better prognosis. Notably, patients with low TMB and concomitant high risk had the worst prognosis (Fig. 6 C, p < 0.001). Ultimately, by utilizing the 'oncoPredict' R package, we conducted a comparison of the variances in medication responsiveness among the two cohorts. The findings indicated that the half inhibitory concentrations (IC 50 ) values of oxaliplatin, gemcitabine, and vincristine were considerably lower in the low-risk group. This implies that patients with lower risk scores could potentially gain greater advantages from utilizing these medications (Fig. 8 D). Among several recognized cancer-related pathway drugs, low-risk scoring cases had significantly lower IC50s for KRAS (G12 C) inhibitor-12, JAK1_8709, Wnt-C59, and LY2109761, and higher sensitivity to AZ960 (Fig. 6 E). SNV, CNV, and drug sensitivity analysis We utilized the GEPIA2 website to generate a heat map illustrating the survival analysis for the 10 model genes across 33 different tumors (Fig. 6 A). We further investigated the association of 10 model genes with immune cell infiltration in pan-cancer (Fig. 6 B). The analysis revealed high expression of Th2 cells, natural killer T cells, macrophage cells, iTreg cells, cytotoxic cells, NK cells, Tr1 cells, central memory cells, CD4 T cells, and Tfh cells in most tumors, indicating a potential association with tumor progression. In contrast, neutrophil and effector memory cells showed low expression. Additionally, SNV and CNV percentage heatmaps for the 10 model genes in 32 tumors were plotted using the GSCA website (Figs. 6 C and 6 D). The analysis of heatmaps revealed that PDGFRA and PTPRR demonstrated high SNV across various cancers, whereas PTPRR and RAC3 exhibited elevated CNV in the majority of cancer types. We also examined the association between the 10 model genes and drug sensitivity (Fig. 6 E). The analysis revealed that elevated expression of JUN and PTPRR genes was associated with increased drug sensitivity, while IGF1 and DUSP2 genes displayed a negative correlation. GSEA analysis of model genes in BLCA Significant enrichments in different pathways were revealed through the analysis of Gene Set Enrichment Analysis (GSEA) conducted on the two groups. In Fig. 8 A, the group at high risk showed notable enhancement in pathways associated with Cytokine-cytokine receptor interaction, ECM receptor interaction, focal adhesion, Neuroactive ligand-receptor interaction and receptors, and Regulation of actin cytoskeleton. The group with low risk exhibited notable enhancement in pathways linked to Linoleic acid metabolism, Oxidative phosphorylation, Pentose and glucuronate interconversions, and Ribosome (Fig. 8 B). Further analysis of the model gene GSEA-KEGG pathway revealed enrichment of each gene in numerous pathways. The top 5 elevated and reduced pathways for each gene were selected for presentation (Fig. 8 C-L). Immunohistochemical images of model genes from the HPA database. For the validation of the protein expression of the 10 prognostic genes, we retrieved the expression data of the model genes from the HPA database. Compared to normal tissues, tumor tissues displayed a significant increase in staining intensity for PTPRR and RAC3 (Fig. 8 A and B). Conversely, genes such as MAP3K8 , MAP3K20 , IGF1 , JUN , PDGFD , and PDGFRA displayed low staining intensity relative to normal tissues (Fig. 9 C-H). qRT-PCR-based validation of differential mRNA expression in BLCA clinical samples To further validate the expression of the model genes in clinical samples, we gathered 10 pairs of bladder cancer and paracancerous tissues. Subsequently, we extracted RNA from these samples, performed reverse transcription, and conducted qRT-PCR. The results demonstrated that the genes RAC3 and PTPRR were significantly overexpressed compared to normal tissues (Figs. 10 A and 10 B). In contrast, genes such as MAP3K20 , PDGFD , DUSP2 , IGF1 , MAP3K8 , and JUN exhibited significant downregulation relative to normal tissues. However, PDGFRA and NRTN did not show statistically significant differences (Fig. 10 C-G). Discussion Metastasis-prone MIBC continues to present a significant clinical challenge due to its high mortality rate, with the current first-line treatment typically involving platinum-based chemotherapy. However, the overall prognosis remains poor 24 . Despite the increasing use of inhibitors targeting immune checkpoints such as PD-1 and PD-L1 in the treatment of bladder cancer, the efficacy of immunotherapy is still suboptimal, with a remission rate of about 25 percent 25 . Advances in next-generation sequencing technologies over the past few years have underscored the importance of identifying new molecular biomarkers in bladder cancer for accurately predicting patient prognosis, a critical aspect of clinical decision-making 26 . Newly discovered evidence indicates that the MAPK pathway is a viable option for treating cancer, while the ERK pathway stands out as a significant and widely employed area of interest in clinical practice 27 . Additionally, the JNK and p38 pathways, while playing crucial regulatory roles, present challenges in predicting cancer cell responses to targeted therapies and chemotherapy due to their dependence on upstream and downstream environments 28 . In this investigation, we identified 26 prognostic genes associated with bladder cancer survival through COX analysis within the MAPK pathway. Subsequently, a novel prognostic signature for bladder cancer patients was developed utilizing LASSO-COX analysis, focusing on 10 MAPK pathway-related prognostic genes ( NRTN, RAC3, JUN, IGF1, DUSP2, MAP3K8, PDGFD, MAP3K20, PTPRR , and PDGFRA ). The validation of this signature was confirmed both internally and externally, indicating that patients with high-risk scores had notably worse overall survival. The prognostic model demonstrated strong performance with an AUC of 0.751. Both univariate and multivariate Cox regression analyses confirmed that the correlation model of MAPK served as a separate predictor for the overall survival in BLCA. To improve clinical applicability, nomograms were constructed, and calibration curves showed well-validated and stable predictive performance. Finally, we substantiated the expression of model genes in BLCA, further confirming the differential expression of MAPK pathway-related genes. Among the 10 model genes, RAC3 stands out as one of the three isoforms within the Rho GTPase subfamily 29 , 30 . Studies have demonstrated that RAC3 enhances tumor cell proliferation, migration, and invasion by activating the JAK/STAT signaling pathway 31 , 32 . Furthermore, high expression of RAC3 predicts a poor prognosis of BLCA 33 . Our study also confirmed high RAC3 expression, which provides a basis for using RAC3 as a therapeutic target for BLCA. Conversely, PTPRR exhibits a dual function. Suppression of PTPRR expression in rectal cancer triggers the Ras/ERK/c-Fos signaling pathway, thereby facilitating the development of rectal carcinogenesis 34 .In ovarian cancer, PTPRR functions as a suppressor of tumors by dephosphorylating and rendering β-conjugated proteins inactive 35 , suggesting a potentially protective role. NRTN is a ligand of the neurotrophic factor family, and recent studies have shown that NRTN is associated with rectal, pancreatic, and hepatocellular cancer progression 36 – 38 . IGF-1 is a growth hormone target gene that binds to and activates the receptor tyrosine kinase IGF1 receptor (IGF1R) 39 . Multiple studies have demonstrated its association with resistance to anticancer drugs and highlighted the potential benefits of targeting IGF1 in anticancer therapy 40 , 41 . Additionally, a large case-control study has indicated a significant association between IGF1 and a reduced risk of bladder cancer. 42 . MAP3K8 is an oncogene encoding a member of the serine/threonine protein kinase family, and studies have shown that high levels of MAP3K8 phosphorylation are associated with progression and poor prognosis in patients with bladder cancer 43 . Studies have indicated that PDGFD , a part of the platelet-derived growth factor family, is linked to gemcitabine resistance in BLCA patients and unfavorable prognosis in advanced uroepithelial carcinoma and pancreatic ductal adenocarcinoma (PDAC) 44 , 45 . PDGFRA , a receptor for tyrosine kinase, is frequently mutated in gastrointestinal mesenchymal stromal tumors (GIST) and serves as a target for anticancer medications like avastinib 46 , 47 . JUN has been implicated in APF-mediated growth inhibition of bladder tumor cells and is a potential target of APF in patients with invasive bladder cancer 48 , and overexpression of JUN protein is also closely associated with the invasive growth of BLCA 49 . DUSP2 belongs to the nuclear DUSP family, specifically type I, and it inhibits the activation of MAPK while having a crucial function in immune processes, inflammatory responses, and the advancement of cancer. Deletion of DUSP2 connotes a poor prognosis for patients with bladder cancer 50 . MAP3K20 , a member of the MAP3K subfamily 51 , 52 , has been associated with the regulation of HCC cell proliferation and apoptosis 53 . Finally, high MAP3K20 expression has been found to promote cancer progression in gastric, breast, bladder, and colorectal cancers 54 – 57 . In conclusion, we have developed and validated a novel prognostic model centered on MAPK pathway-associated genes. Despite its strengths, the study has several limitations. Initially, it was a retrospective analysis using TCGA and GEO databases, necessitating additional prospective real-world data to confirm the accuracy of the MAPK-related gene model. Furthermore, our validation was limited to basic preliminary qPCR analysis, requiring further experiments related to biological functions to elucidate the roles and mechanisms of these genes in bladder cancer development. Lastly, although the model was validated using the GEO database, further extensive integration tests with centralized cohorts are essential to comprehensively evaluate the model's performance. Conclusion Overall, we developed a comprehensive model capable of predicting the prognosis, immune microenvironment, and drug response in BLCA patients. This model relies on 10 MAPK pathway-related genes and prognosis-related clinical factors. Validation through both GEO data analysis and qRT-PCR experiments demonstrated the model's potential for reliable prognostic predictions in BLCA. Methods Data acquisition From the Genomic Data Commons (GDC) database, we acquired RNA-seq data and clinical information for 403 tumor tissue samples and 19 normal tissue samples. A log2 (TPM + 1) transformation was applied to the downloaded transcripts per million (TPM) data, and genes with total expression values less than 1 in all samples were excluded. The GEO database was used to retrieve expression profiles and clinical data for GSE32894. Probe IDs were converted to corresponding gene symbols, and batch effects were mitigated using the "sva" R package. For clinical data, patients with a survival time of fewer than 30 days and those with missing essential information were excluded. MAPK pathway-related genes were obtained from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database (Supplementary Table S1 ). The study's workflow is illustrated in Fig. 1 . Differential expression analysis The analysis of differential expression in TCGA-Counts data was conducted using the "DESeq2" R package. Differential genes meeting the criteria (Log2FC > 1, P.adj < 0.05) were identified and visualized in a volcano plot. To obtain the intersection between these differentially expressed genes (DEGs) and MAPK-related genes, the "VennDiagram" R package was employed, and a Venn plot was generated. Construction and validation of prognostic gene signatures The TCGA dataset was divided into training and testing sets randomly, with a ratio of 7:3. The intersected genes underwent univariate Cox regression analyses using the "Survival" R package. A LASSO regression analysis was performed using the R package "glmnet" to determine the final model genes and calculate correlation coefficients for each gene after screening for genes with prognostic significance. Risk scores were computed for the training group, validation group, entire TCGA cohort, and GSE32894 cohort by utilizing the formula: \(riskscore={\sum }_{i=1}^{n}ki*Xi\) , in which k denotes the relative expression level of the model genes, and X signifies the regression coefficients. Afterward, the patients were categorized into groups of high-risk and low-risk, using the median of the risk score from the training group as the threshold value. The distribution of risk scores and a heatmap for all cohorts were plotted to visually present the results. Tumor immune infiltration analysis Application of the CIBERSORT function of the "IOBR "R package to perform immune infiltration analysis, and the ESTIMATE function to calculate the immune score and stromal score 58 – 60 . Construction of nomograms A nomogram was created by utilizing the 'rms' R package, which included age, pathological stage, and risk score. The total score was calculated based on the contributions of these independent factors in the nomogram, aiming to predict the corresponding survival rate for patients with BLCA. The accuracy of the nomogram predictions was assessed using calibration curves. Tumor mutation analysis and immunotherapy analysis Tumor mutational burden (TMB) quantifies the number of non-synonymous mutations in somatic cells within a specific genomic region, indirectly reflecting a tumor's capacity and extent for neoantigen production. TMB serves as a predictive indicator for the effectiveness of immunotherapy across a broad spectrum of tumors 61 , 62 . Simple nucleotide variant datasets from bladder cancer patients were obtained from the GDC website, and TMBs for individual samples were calculated using the "maftools" R package. Drug sensitivity analysis data were sourced from the Genomics of Drug Sensitivity in Cancer (GDSC) website. The relationship between high and low-risk groups and IC 50 values of anticancer drugs was analyzed using the "oncoPredict" R package. GEPIA website and GSCA website We employed the Gene Expression Profiling Interactive Analysis (GEPIA) website for mapping the Hazard Ratios (HR) of model genes across various cancers 63 . Additionally, the Gene Set Cancer Analysis (GSCA) website was utilized for conducting analyses on Single Nucleotide Variations (SNV), Copy Number Variations (CNV), immune infiltration, and drug sensitivity related to the model genes 64 . Human Protein Atlas Database HPA database stores massive amounts of protein data from human tissues. In this study, we utilized the HPA database to retrieve histopathological data associated with the model genes. Clinical Sample Acquisition The study received approval from the Ethics Committee of the First Affiliated Hospital of Zhengzhou University, and all volunteers signed informed consent forms before participation. This study adhered strictly to the ethical principles for medical research involving human subjects, as outlined in the Declaration of Helsinki 65 . Clinical samples were sourced from the First Affiliated Hospital of Zhengzhou University, involving patients previously diagnosed with bladder cancer through pathological examination. Paracancerous tissues were collected from normal tissues within a 3 cm region near the tumor. Following sampling, tissue samples were promptly preserved in liquid nitrogen and transferred to a -80°C refrigerator to maintain their integrity for subsequent analyses. Quantitative Real-time PCR experiments Total RNA was extracted from the collected BLCA tumor tissues and adjacent normal tissues using the RNAeasy™ Animal RNA Extraction Kit (Beyotime). Subsequently, the reverse transcription process was performed using the PrimeScript™ RT reagent Kit (Takara), and qRT-PCR was conducted with the TB Green® Premix Ex Taq™ II Kit (Takara), following the manufacturer's instructions. The primer sequences are shown in Table 1 . Table 1 The list of the primers used for qRT-PCR. Gene symbol Forward or reverse primer Primer sequence (5'-3') GAPDH Forward GGAAGCTTGTCATCAATGGAAATC Reverse TGATGACCCTTTTGGCTCCC NRTN Forward ACCCTGGACGCCCGGATT Reverse CGCAGTAGCGGAACAGCACC MAP3K8 Forward TCGCTCAGCCTATCCCTCCTA Reverse GTTCCAGCTCCTTCCTACTCAG RAC3 Forward CTCCTACCCCCAAACTGACG Reverse TTCACAGAGCCCACCAATCTC PDGFD Forward GGTGAAAGGAAACGGCTACG Reverse CTCTAATAATGGTACTGGTTTCGGA JUN Forward TGGGTGCCAACTCATGCTAA Reverse TTCTTCGTTGCCCCTCAGC MAP3K20 Forward GTTAGATACTCTGAGGATGCGGC Reverse GTTGATACTTAATGGGCACCTGG IGF1 Forward GGTGGATGCTCTTCAGTTCGT Reverse GCAATACATCTCCAGCCTCCTTA PTPRR Forward GCAGGAATAGGTAGAACAGGGTG Reverse GCACCATTCCACCTCTATCCA DUSP2 Forward TGCTGTCCCGATCTGTGCT Reverse CAGGAACAGGTAGGGCAAGA PDGFRA Forward CTTTGGATTGAACCCTGCTGA Reverse GACATCTCGTGCCAACTCCA Statistical analysis Bioinformatics analysis was performed using R version 4.3.1. The comparison of continuous data utilized either the student’s t-test or the Wilcoxon test, depending on the nature of the data, with statistical significance established at a two-sided p-value < 0.05. Declarations Funding This work was supported by National Natural Science Foundation of China (Grant numbers [82000724]). Competing Interests The authors have no relevant financial or non-financial interests to disclose. Author contributions GYC was responsible for conducting data analysis, and visualization, and contributed to writing the paper. SQL and ZKZ were responsible for designing the research and conducting the literature review. YW and ZY collaborated on proofreading the paper. CCR provided project supervision and contributed to revising the paper. All authors have approved the final manuscript. Availability of data and materials Public data used in this work can be acquired from the TCGA Research Network portal (https://portal.gdc.cancer.gov/), Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo/), Kyoto Encyclopedia of Genes and Genomes (https://www.genome.jp/kegg/pathway.html/), Genomics of Drug Sensitivity in Cancer (https://www.cancerrxgene.org/), Gene Expression Profiling Interactive Analysis (http://gepia.cancer-pku.cn/), Gene Set Cancer Analysis (https://guolab.wchscu.cn/GSCA/), and The Human Protein Atlas database (http://www.proteinatlas.org/). Ethics approval All ethical aspects of this study were approved by the Ethics Committee of the First Affiliated Hospital of Zhengzhou University. Consent to participate Informed consent was obtained from all individual participants included in the study. Consent to publish Not applicable. References Bray, F. et al. 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Supplementary Files SupplementaryFiguresS1.pdf SupplementaryTable1.xlsx SupplementaryTable2.xlsx Cite Share Download PDF Status: Published Journal Publication published 07 May, 2024 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 20 Mar, 2024 Reviews received at journal 06 Mar, 2024 Reviews received at journal 04 Mar, 2024 Reviews received at journal 26 Feb, 2024 Reviewers agreed at journal 21 Feb, 2024 Reviewers agreed at journal 20 Feb, 2024 Reviewers invited by journal 09 Feb, 2024 Editor assigned by journal 06 Feb, 2024 Editor invited by journal 23 Jan, 2024 Submission checks completed at journal 23 Jan, 2024 First submitted to journal 17 Jan, 2024 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-3872147","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":268952605,"identity":"af4c85cb-5a6a-431e-962c-344fdb500f65","order_by":0,"name":"Gaungyang Cheng","email":"","orcid":"","institution":"The First Affiliated Hospital of Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Gaungyang","middleName":"","lastName":"Cheng","suffix":""},{"id":268952606,"identity":"f10ee945-0a98-4b46-895e-e3118340c0d8","order_by":1,"name":"Shiqi Li","email":"","orcid":"","institution":"The First Affiliated Hospital of Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Shiqi","middleName":"","lastName":"Li","suffix":""},{"id":268952607,"identity":"d96c6ea7-649f-4a35-91fc-ceff940f25d4","order_by":2,"name":"Zhaokai Zhou","email":"","orcid":"","institution":"The First Affiliated Hospital of Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Zhaokai","middleName":"","lastName":"Zhou","suffix":""},{"id":268952608,"identity":"6dd90332-167f-4596-a50f-99b5cd434f2f","order_by":3,"name":"Yan Wang","email":"","orcid":"","institution":"The First Affiliated Hospital of Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Wang","suffix":""},{"id":268952609,"identity":"9f0ce117-c35d-4cfb-ad06-d5db595240de","order_by":4,"name":"Zhuo Ye","email":"","orcid":"","institution":"The First Affiliated Hospital of Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Zhuo","middleName":"","lastName":"Ye","suffix":""},{"id":268952610,"identity":"5723bb17-a510-4d55-aea2-d206b6a3eba7","order_by":5,"name":"Chuanchuan Ren","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtElEQVRIiWNgGAWjYPACGwjFQ4KWNNK1HCZBC3/7AbYPH3ect5efkcD44G0bg7w5IS0SZxKYZ848c5uZcUYCs+HcNgbDnQ0EtBhIMDAz87bdZmOWSGCT5m1jSDA4QIyWv23neNgkEth/E6+Fse2ABA/QFmaitID8wtjblmwgwfOwWXLOOQnDDYS0AEOMmeFnm529fHvywQ9vymzkCdoC1PQBymBsANlKUP0oGAWjYBSMAiIAAJUSMp0ji4WVAAAAAElFTkSuQmCC","orcid":"","institution":"The First Affiliated Hospital of Zhengzhou University","correspondingAuthor":true,"prefix":"","firstName":"Chuanchuan","middleName":"","lastName":"Ren","suffix":""}],"badges":[],"createdAt":"2024-01-17 07:16:54","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3872147/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3872147/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-61302-0","type":"published","date":"2024-05-07T04:01:49+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":50182721,"identity":"1f9edbf8-8a6a-40f7-a829-aeedf73cc339","added_by":"auto","created_at":"2024-01-25 18:58:54","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":427145,"visible":true,"origin":"","legend":"\u003cp\u003eDiagram illustrating the workflows of this research.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3872147/v1/0aaf78825008683d55c5db3f.jpg"},{"id":50182012,"identity":"c680e820-523c-489a-9a28-5d5f3f0bd937","added_by":"auto","created_at":"2024-01-25 18:50:54","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1802008,"visible":true,"origin":"","legend":"\u003cp\u003eScreening of survival-related genes. (\u003cstrong\u003eA)\u003c/strong\u003e Heatmap displaying the differential expression of genes associated with the MAPK pathway. (\u003cstrong\u003eB)\u003c/strong\u003e Volcano plot illustrating the differential expression of MAPK pathway-related genes. (\u003cstrong\u003eC\u003c/strong\u003e) Venn diagram depicting the intersection of MAPK pathway-related genes with differentially expressed genes. \u003cstrong\u003e(D)\u003c/strong\u003e Forest plot representing the results of COX regression analysis. (\u003cstrong\u003eE)\u003c/strong\u003e Box plots showing the expression levels of survival-related MAPK pathway genes in tumor versus normal tissues.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3872147/v1/f34d0c819df067d058bd67d1.jpg"},{"id":50182724,"identity":"8dbdf745-e81e-4715-abe5-c9fcd22a7802","added_by":"auto","created_at":"2024-01-25 18:58:54","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1402492,"visible":true,"origin":"","legend":"\u003cp\u003eConstruction of MAPK prognostic gene signature and survival analysis. (\u003cstrong\u003eA)\u003c/strong\u003e LASSO regression correlation coefficient and LASSO regression screening model genes for 26 survival-related genes, with the best parameter (lambda min) as the first dashed line on the left. (\u003cstrong\u003eB)\u003c/strong\u003e PCA analysis of the clustering effect of all genes. \u003cstrong\u003eC\u003c/strong\u003e PCA analysis of the clustering effect of model genes. (\u003cstrong\u003eD)\u003c/strong\u003eKaplan-Meier survival analysis of the TCGA-BLCA training cohort, the validation cohort, the overall cohort, and the GEO32894 cohort for high-risk and low-risk groups. (\u003cstrong\u003eE)\u003c/strong\u003e ROC curves for the TCGA-BLCA training cohort, validation cohort, overall cohort, and GEO32894 cohort. (\u003cstrong\u003eF)\u003c/strong\u003e Risk score distribution plots for the TCGA-BLCA training cohort, validation cohort, overall cohort, and GEO32894 cohort.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3872147/v1/bf7a95a1c761d1a240633b7b.jpg"},{"id":50182023,"identity":"9b2f84b8-5c77-4602-ac63-b58629689951","added_by":"auto","created_at":"2024-01-25 18:50:54","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":791673,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between risk models and tumor immune microenvironment. (\u003cstrong\u003eA)\u003c/strong\u003e ESTIMATES analysis of differences in StromalScore, ImmuneScore, and ESTIMATEScore between high and low-risk groups. (\u003cstrong\u003eB)\u003c/strong\u003e Correlation of risk scores with StromalScore and ImmuneScore. Histograms on the horizontal axis show the distribution of samples with different risk scores, and histograms on the vertical axis show the distribution of samples with different StromalScore and ImmuneScore. (\u003cstrong\u003eC)\u003c/strong\u003e Box plots of immune cell infiltration in the high-risk group versus the low-risk group (adjusted p-value \u0026lt; 0.05). (\u003cstrong\u003eD)\u003c/strong\u003e Box plots of immune checkpoint expression levels in two groups (adjusted p-value).\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3872147/v1/6b7ae41d629031a7b63ac74b.jpg"},{"id":50182723,"identity":"60de8ec6-db14-447c-8634-58dc1fbf695e","added_by":"auto","created_at":"2024-01-25 18:58:54","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":882056,"visible":true,"origin":"","legend":"\u003cp\u003eConstruction and accuracy testing of nomograms. (\u003cstrong\u003eA)\u003c/strong\u003e One-way COX regression analysis of risk scores versus clinical factors. (\u003cstrong\u003eB)\u003c/strong\u003e Multifactor COX regression analysis of risk score versus clinical factors. (\u003cstrong\u003eC)\u003c/strong\u003e Nomogram constructed with Age, Stage, and RiskScore as risk factors. (\u003cstrong\u003eD)\u003c/strong\u003e Calibration curves for nomograms. (\u003cstrong\u003eE)\u003c/strong\u003e ROC curves for nomogram, RiskScore, and other clinical factors.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3872147/v1/c04c9bce40c98d01cbf075f8.jpg"},{"id":50182016,"identity":"0f20863d-e007-40e7-bc93-ba962eaaecf4","added_by":"auto","created_at":"2024-01-25 18:50:54","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":670946,"visible":true,"origin":"","legend":"\u003cp\u003eTIDE analysis and mutation assessment. (\u003cstrong\u003eA) \u003c/strong\u003eTIDE analysis of immune checkpoint inhibitor responses in two subgroups. (\u003cstrong\u003eB)\u003c/strong\u003e TMB differences between two groups. (\u003cstrong\u003eC)\u003c/strong\u003eKaplan-Meier curves between different TMB subgroups. (\u003cstrong\u003eD)\u003c/strong\u003e Sensitivity analysis of anticancer medications in two groups.\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3872147/v1/b141fdd664c91c39135fadfb.jpg"},{"id":50182022,"identity":"cd89f048-45c4-4cf8-b78b-ddd0a6b026b2","added_by":"auto","created_at":"2024-01-25 18:50:54","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":6907575,"visible":true,"origin":"","legend":"\u003cp\u003ePan-cancer analysis of model genes was performed using the GEPIA and GSCA websites. (\u003cstrong\u003eA)\u003c/strong\u003eHeatmap of overall survival analysis of model genes in pan-cancer. (\u003cstrong\u003eB)\u003c/strong\u003e GSVA analysis of the level of immune cell infiltration in different tumors, with a positive correlation in red and a negative correlation in blue. (\u003cstrong\u003eC)\u003c/strong\u003e The pie chart illustrates the distribution of SNV of model genes across various tumor types. (\u003cstrong\u003eD)\u003c/strong\u003eThe heatmap provides an overview of the CNV proportions in the model genes across different cancers. It uses a color scheme to indicate various types of CNVs: light red for heterozygous amplification (Hete. amp), dark red for homozygous amplification (Homo. amp), light green for heterozygous deletion (Hete. del), dark green for homozygous deletion (Homo. del), and grey to denote the absence of CNVs. (\u003cstrong\u003eE)\u003c/strong\u003e Model gene correlation analysis with anticancer drug sensitivity.\u003c/p\u003e","description":"","filename":"Figure7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3872147/v1/e92a00256711748abcac219c.jpg"},{"id":50182020,"identity":"4a4df03f-a106-4725-9cc8-eb87e25dc05f","added_by":"auto","created_at":"2024-01-25 18:50:54","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":845584,"visible":true,"origin":"","legend":"\u003cp\u003eGSEA analysis of modeled genes in the BLCA. (\u003cstrong\u003eA)\u003c/strong\u003e High-risk group. (\u003cstrong\u003eB)\u003c/strong\u003eLow-risk group. (\u003cstrong\u003eC)\u003c/strong\u003e IGF1. (\u003cstrong\u003eD)\u003c/strong\u003e JUN. (\u003cstrong\u003eE)\u003c/strong\u003e NRTN. (\u003cstrong\u003eF)\u003c/strong\u003eMAP3K20. (\u003cstrong\u003eG)\u003c/strong\u003e PDGFRA. (\u003cstrong\u003eH)\u003c/strong\u003e RAC3. (\u003cstrong\u003eI)\u003c/strong\u003e MAP3K8. (\u003cstrong\u003eJ)\u003c/strong\u003ePDGFD. (\u003cstrong\u003eK)\u003c/strong\u003e PTPRR. (\u003cstrong\u003eL)\u003c/strong\u003e DUSP2.\u003c/p\u003e","description":"","filename":"Figure8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3872147/v1/87618b3d00e838d43361797f.jpg"},{"id":50182017,"identity":"7a9ca487-dfc0-40d9-b2aa-ba781be8ee6f","added_by":"auto","created_at":"2024-01-25 18:50:54","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":3807496,"visible":true,"origin":"","legend":"\u003cp\u003eImmunohistochemical images of model genes from the HPA database.\u003c/p\u003e","description":"","filename":"Figure9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3872147/v1/b305b030211e0268dac51569.jpg"},{"id":50182024,"identity":"b83e8cfd-72d7-435e-9b70-60e32956ca19","added_by":"auto","created_at":"2024-01-25 18:50:54","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":429206,"visible":true,"origin":"","legend":"\u003cp\u003eqRT-PCR results of clinically collected tissues. (\u003cstrong\u003eA)\u003c/strong\u003e RAC3. (\u003cstrong\u003eB)\u003c/strong\u003e PTPRR. (\u003cstrong\u003eC)\u003c/strong\u003eMAP3K20. (\u003cstrong\u003eD)\u003c/strong\u003e PDGFRA. (\u003cstrong\u003eE)\u003c/strong\u003e PDGFD. (\u003cstrong\u003eF)\u003c/strong\u003e DUSP2. (\u003cstrong\u003eG)\u003c/strong\u003eNRTN. (\u003cstrong\u003eH)\u003c/strong\u003e IGF1. (\u003cstrong\u003eI) \u003c/strong\u003eMAP3K8. (\u003cstrong\u003eJ)\u003c/strong\u003e JUN.\u003c/p\u003e","description":"","filename":"Figure10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3872147/v1/e18fb1d0f3a8ea649a9f07c2.jpg"},{"id":56140443,"identity":"7da06bc5-73f9-45d0-870e-a4b909295355","added_by":"auto","created_at":"2024-05-09 04:25:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2391773,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3872147/v1/f24df8d5-23ed-4567-90ee-4121dc052b7d.pdf"},{"id":50182722,"identity":"9c9ef7ca-0c7e-49db-baa2-1f9dbc15be65","added_by":"auto","created_at":"2024-01-25 18:58:54","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":4469993,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFiguresS1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3872147/v1/b41b9927202917e87f42892c.pdf"},{"id":50182014,"identity":"95650ff7-4d88-4db0-b5bd-bc1843bc566f","added_by":"auto","created_at":"2024-01-25 18:50:54","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":18333,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3872147/v1/af8319c24a9ff1b0ee3eb385.xlsx"},{"id":50183090,"identity":"f6b3e2b6-89a5-4cff-afb8-9c459f3ff0dd","added_by":"auto","created_at":"2024-01-25 19:06:54","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":11466,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3872147/v1/c7013d99d71f3894cc2417ae.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predicting Bladder Cancer Survival with High Accuracy: Insights from MAPK Pathway-related Genes","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBLCA is one of the most frequently occurring cancers and is the most dominant malignant tumor in the urinary system, ranking in the top ten \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Every year, there are about 550,000 fresh instances of bladder cancer documented worldwide, making up roughly 3.0% of new cancer detections and 2.1% of fatalities caused by cancer \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. The majority of newly diagnosed cases (75%) are non-muscle-invasive (NMIBC), while 25% are muscle-invasive (MIBC) \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. NMIBC has a relatively better prognosis but tends to recur frequently\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. A growing agreement indicates that individuals diagnosed with MIBC encounter a worse outlook and a heightened likelihood of metastasis, resulting in a survival rate below 50% within five years \u003csup\u003e\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Despite established treatment options, radical cystectomy, and pelvic lymph node dissection, approximately 50% of patients who undergo surgery for MIBC often face recurrence afterward, primarily caused by distant metastases\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Hence, early disease diagnosis and the identification of prognostic markers are crucial for managing bladder cancer effectively. In recent years, various signaling pathway-related models like the Notch pathway, EMT pathway, TGF-β pathway, and PI3K pathway have been established to predict the survival of bladder cancer patients \u003csup\u003e\u003cspan additionalcitationids=\"CR10 CR11\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. However, a risk profile related to the MAPK pathway for predicting survival in BLCA patients has not yet been established.\u003c/p\u003e \u003cp\u003eIn mammalian cells, MAPK signaling is a fundamental mechanism, transmitting signals related to proliferation, apoptosis, and differentiation\u003csup\u003e\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. ERK, p38, JNK, and ERK5, which are a set of serine-threonine kinases conserved throughout evolution, serve as the classical MAPK pathways \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. These pathways involve different MAPKs associated with specific MAPK kinases (MAPKK) and MAPK-kinase-kinase (MAPKKK), forming a conserved tertiary enzymatic cascade (MAPKKK\u0026rarr;MAPKK\u0026rarr;MAPK) \u003csup\u003e\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Aberrant mutations in certain components of the MAPK pathway have been identified as significant contributors to various cancers \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Consequently, intervention in this pathway has been explored as a strategy for tumor therapy.\u003c/p\u003e \u003cp\u003ePrevious research indicates that 45% of potential therapeutic targets in bladder cancer are related to the MAPK pathway\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e, and this association correlates with the prognosis in BLCA \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. As part of this study, a prediction model was developed based on genes associated with the MAPK signaling pathway. Importantly, we aimed to investigate the potential mechanisms by which the MAPK signaling pathway affects prognosis and immunotherapy response. Our research findings provide a foundation for future advancements in precision medicine for BLCA.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eDifferential expression analysis of MAPK pathway-related genes\u003c/h2\u003e \u003cp\u003eTo analyze the DEGs in TCGA-BLCA tumor tissues and normal tissues, RNA-seq data from log-transformed TCGA consisting of 406 tumor samples and 19 normal tissue samples were subjected to analysis using the \"Deseq2\" R package. A total of 4731 DEGs were identified, applying criteria of log2FC\u0026thinsp;\u0026gt;\u0026thinsp;1 and F.adj\u0026thinsp;\u0026lt;\u0026thinsp;0.05. By intersecting these DEGs with MAPK-related genes, 103 intersected genes were obtained using the VennDiagram package. The analysis resulted in the identification of these 103 genes, and Venn plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eB), volcano plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eC), and a heatmap (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eA) illustrating the expression of these 103 differential genes were generated.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eDevelopment and validation of prognostic gene signatures\u003c/h2\u003e \u003cp\u003eDuring the analysis of the 103 intersecting genes, COX regression analysis was performed, resulting in the identification of 26 genes significantly associated with survival (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eD. The differential expression of these genes between the tumor and normal groups is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eE. Subsequent LASSO regression analysis on the 26 prognosis-related genes revealed 10 candidate genes at the minimum lambda value (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Risk scores were then calculated for each patient based on the mRNA expression levels of these 10 genes and the corresponding coefficients from the LASSO regression analysis. Using the median value of the risk score from the training cohort as the cutoff, patients across different cohorts were categorized into two groups. Principal Component Analysis (PCA) demonstrated the efficacy of the model genes in clustering patients within the TCGA-BLCA dataset (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eB and C). The Kaplan-Meier analysis demonstrated a notable decrease in the likelihood of survival in the high-risk group when compared to the low-risk group across these cohorts (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). ROC curves (receiver operating characteristic curves) yielded an Area Under the Curve (AUC) value of 0.751 for survival at 5 years (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). The prognostic gene expression profiles are presented in a heatmap (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eAssociation of risk profiles with the tumor microenvironment\u003c/h2\u003e \u003cp\u003eTo further investigate the differences in immune infiltration between the two subgroups, the CIBERSORT algorithm was employed to calculate the proportion of immune cell infiltration for all samples, as shown in Supplementary Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA. Additionally, StromalScore and ImmuneScore were determined for all samples using the ESTIMATE algorithm, and correlation analyses showed higher StromalScore, ImmuneScore, and ESTIMATEScore in the high-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Further correlation analysis demonstrated a positive correlation between risk scores and both stromal scores and immune scores, as well as a positive correlation with the ESTIMATEScore (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Analysis of the differences in immune infiltration between the two risk groups revealed that patients in the high-risk score group exhibited a higher percentage of resting memory naive B cells, CD4 T cells, M0 macrophages, M1 macrophages, and M2 macrophages. In contrast, plasma cells, CD8 T cells, regulatory T cells, and activated dendritic cells were more prevalent in patients in the low-risk score group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Finally, an analysis of the differences in the expression of 34 immune checkpoints between the high-risk and low-risk groups was conducted (Supplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e, P.adj\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Among these, 21 immune checkpoints with P\u0026thinsp;\u0026lt;\u0026thinsp;0.01 were selected and plotted in a box line plot. All of them were found to be highly expressed in the high-risk scoring group, except for TNFRSF14 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eNomogram construction\u003c/h2\u003e \u003cp\u003eTo construct nomograms for predicting patient survival, we conducted univariate and multivariate Cox regression analyses involving risk scores and clinical factors. Univariate Cox regression analyses revealed significant associations between RiskScore (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, risk ratio [HR]\u0026thinsp;=\u0026thinsp;3.157, 95% confidence interval [CI]\u0026thinsp;=\u0026thinsp;2.286\u0026ndash;4.359), Clinical stage (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, HR\u0026thinsp;=\u0026thinsp;1.561, 95% CI\u0026thinsp;=\u0026thinsp;1.282\u0026ndash;1.902), and Age (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, HR\u0026thinsp;=\u0026thinsp;1.027, 95% CI\u0026thinsp;=\u0026thinsp;1.012\u0026ndash;1.043) with Overall Survival (OS) in the TCGA-BLCA cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). In multivariate Cox regression analyses, RiskScore, Clinical stage, and Age were similarly statistically significant (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNomograms, integrating multiple risk factors, were developed for predicting survival in the TCGA-BLCA cohort. The model incorporated three independent risk factors: age, stage, and RiskScore, and could predict survival probabilities by calculating the cumulative total score for each independent factor for each patient (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). The nomogram's predictive performance was validated through calibration curves and ROC curves, indicating that the actual OS aligned well with the OS predicted by the nomogram at 1, 3, and 5 years. The area under the ROC curve value reached 0.799, demonstrating good predictive performance (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eD and E, Supplementary Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eB).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eMAPK-related gene prognostic models concerning tumor mutation load and immunotherapy response\u003c/h2\u003e \u003cp\u003eAfterward, we performed a study to compare the variances in immunotherapy reactions among the two subgroups. The findings from Tumor Immune Dysfunction and Exclusion (TIDE) indicated that samples categorized as the low-risk category showed a greater rate of response to immunosuppressive medications (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). Furthermore, we examined the tumor somatic mutation landscapes in two groups. In both subgroups, the results indicated that the mutation rates of TP53, TTN, KMT2D, MUC16, and ARID1A genes were higher than 20%, as shown in Supplementary Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eC. Analysis of TMB status between the two groups showed that TMB was significantly increased in the low-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). Kaplan-Meier survival analysis showed that the high TMB group had a better prognosis. Notably, patients with low TMB and concomitant high risk had the worst prognosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eC, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Ultimately, by utilizing the 'oncoPredict' R package, we conducted a comparison of the variances in medication responsiveness among the two cohorts. The findings indicated that the half inhibitory concentrations (IC\u003csub\u003e50\u003c/sub\u003e) values of oxaliplatin, gemcitabine, and vincristine were considerably lower in the low-risk group. This implies that patients with lower risk scores could potentially gain greater advantages from utilizing these medications (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e8\u003c/span\u003eD). Among several recognized cancer-related pathway drugs, low-risk scoring cases had significantly lower IC50s for KRAS (G12 C) inhibitor-12, JAK1_8709, Wnt-C59, and LY2109761, and higher sensitivity to AZ960 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSNV, CNV, and drug sensitivity analysis\u003c/h2\u003e \u003cp\u003eWe utilized the GEPIA2 website to generate a heat map illustrating the survival analysis for the 10 model genes across 33 different tumors (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). We further investigated the association of 10 model genes with immune cell infiltration in pan-cancer (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). The analysis revealed high expression of Th2 cells, natural killer T cells, macrophage cells, iTreg cells, cytotoxic cells, NK cells, Tr1 cells, central memory cells, CD4 T cells, and Tfh cells in most tumors, indicating a potential association with tumor progression. In contrast, neutrophil and effector memory cells showed low expression. Additionally, SNV and CNV percentage heatmaps for the 10 model genes in 32 tumors were plotted using the GSCA website (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eC and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). The analysis of heatmaps revealed that PDGFRA and PTPRR demonstrated high SNV across various cancers, whereas PTPRR and RAC3 exhibited elevated CNV in the majority of cancer types. We also examined the association between the 10 model genes and drug sensitivity (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eE). The analysis revealed that elevated expression of JUN and PTPRR genes was associated with increased drug sensitivity, while IGF1 and DUSP2 genes displayed a negative correlation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eGSEA analysis of model genes in BLCA\u003c/h2\u003e \u003cp\u003eSignificant enrichments in different pathways were revealed through the analysis of Gene Set Enrichment Analysis (GSEA) conducted on the two groups. In Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e8\u003c/span\u003eA, the group at high risk showed notable enhancement in pathways associated with Cytokine-cytokine receptor interaction, ECM receptor interaction, focal adhesion, Neuroactive ligand-receptor interaction and receptors, and Regulation of actin cytoskeleton. The group with low risk exhibited notable enhancement in pathways linked to Linoleic acid metabolism, Oxidative phosphorylation, Pentose and glucuronate interconversions, and Ribosome (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e8\u003c/span\u003eB). Further analysis of the model gene GSEA-KEGG pathway revealed enrichment of each gene in numerous pathways. The top 5 elevated and reduced pathways for each gene were selected for presentation (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e8\u003c/span\u003eC-L).\u003c/p\u003e \u003cp\u003e \u003cb\u003eImmunohistochemical images of model genes from the HPA database.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFor the validation of the protein expression of the 10 prognostic genes, we retrieved the expression data of the model genes from the HPA database. Compared to normal tissues, tumor tissues displayed a significant increase in staining intensity for \u003cem\u003ePTPRR\u003c/em\u003e and \u003cem\u003eRAC3\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e8\u003c/span\u003eA and B). Conversely, genes such as \u003cem\u003eMAP3K8\u003c/em\u003e, \u003cem\u003eMAP3K20\u003c/em\u003e, \u003cem\u003eIGF1\u003c/em\u003e, \u003cem\u003eJUN\u003c/em\u003e, \u003cem\u003ePDGFD\u003c/em\u003e, and \u003cem\u003ePDGFRA\u003c/em\u003e displayed low staining intensity relative to normal tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e9\u003c/span\u003eC-H).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eqRT-PCR-based validation of differential mRNA expression in BLCA clinical samples\u003c/h2\u003e \u003cp\u003eTo further validate the expression of the model genes in clinical samples, we gathered 10 pairs of bladder cancer and paracancerous tissues. Subsequently, we extracted RNA from these samples, performed reverse transcription, and conducted qRT-PCR. The results demonstrated that the genes \u003cem\u003eRAC3\u003c/em\u003e and \u003cem\u003ePTPRR\u003c/em\u003e were significantly overexpressed compared to normal tissues (Figs.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e10\u003c/span\u003eA and \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e10\u003c/span\u003eB). In contrast, genes such as \u003cem\u003eMAP3K20\u003c/em\u003e, \u003cem\u003ePDGFD\u003c/em\u003e, \u003cem\u003eDUSP2\u003c/em\u003e, \u003cem\u003eIGF1\u003c/em\u003e, \u003cem\u003eMAP3K8\u003c/em\u003e, and \u003cem\u003eJUN\u003c/em\u003e exhibited significant downregulation relative to normal tissues. However, \u003cem\u003ePDGFRA\u003c/em\u003e and \u003cem\u003eNRTN\u003c/em\u003e did not show statistically significant differences (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e10\u003c/span\u003eC-G).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eMetastasis-prone MIBC continues to present a significant clinical challenge due to its high mortality rate, with the current first-line treatment typically involving platinum-based chemotherapy. However, the overall prognosis remains poor \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Despite the increasing use of inhibitors targeting immune checkpoints such as PD-1 and PD-L1 in the treatment of bladder cancer, the efficacy of immunotherapy is still suboptimal, with a remission rate of about 25 percent \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Advances in next-generation sequencing technologies over the past few years have underscored the importance of identifying new molecular biomarkers in bladder cancer for accurately predicting patient prognosis, a critical aspect of clinical decision-making\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Newly discovered evidence indicates that the MAPK pathway is a viable option for treating cancer, while the ERK pathway stands out as a significant and widely employed area of interest in clinical practice\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Additionally, the JNK and p38 pathways, while playing crucial regulatory roles, present challenges in predicting cancer cell responses to targeted therapies and chemotherapy due to their dependence on upstream and downstream environments \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn this investigation, we identified 26 prognostic genes associated with bladder cancer survival through COX analysis within the MAPK pathway. Subsequently, a novel prognostic signature for bladder cancer patients was developed utilizing LASSO-COX analysis, focusing on 10 MAPK pathway-related prognostic genes (\u003cem\u003eNRTN, RAC3, JUN, IGF1, DUSP2, MAP3K8, PDGFD, MAP3K20, PTPRR\u003c/em\u003e, and \u003cem\u003ePDGFRA\u003c/em\u003e). The validation of this signature was confirmed both internally and externally, indicating that patients with high-risk scores had notably worse overall survival. The prognostic model demonstrated strong performance with an AUC of 0.751. Both univariate and multivariate Cox regression analyses confirmed that the correlation model of MAPK served as a separate predictor for the overall survival in BLCA. To improve clinical applicability, nomograms were constructed, and calibration curves showed well-validated and stable predictive performance. Finally, we substantiated the expression of model genes in BLCA, further confirming the differential expression of MAPK pathway-related genes.\u003c/p\u003e \u003cp\u003eAmong the 10 model genes, \u003cem\u003eRAC3\u003c/em\u003e stands out as one of the three isoforms within the Rho GTPase subfamily \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Studies have demonstrated that \u003cem\u003eRAC3\u003c/em\u003e enhances tumor cell proliferation, migration, and invasion by activating the JAK/STAT signaling pathway \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Furthermore, high expression of RAC3 predicts a poor prognosis of BLCA \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Our study also confirmed high \u003cem\u003eRAC3\u003c/em\u003e expression, which provides a basis for using \u003cem\u003eRAC3\u003c/em\u003e as a therapeutic target for BLCA. Conversely, \u003cem\u003ePTPRR\u003c/em\u003e exhibits a dual function. Suppression of \u003cem\u003ePTPRR\u003c/em\u003e expression in rectal cancer triggers the Ras/ERK/c-Fos signaling pathway, thereby facilitating the development of rectal carcinogenesis \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e.In ovarian cancer, PTPRR functions as a suppressor of tumors by dephosphorylating and rendering β-conjugated proteins inactive\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e, suggesting a potentially protective role.\u003cem\u003eNRTN\u003c/em\u003e is a ligand of the neurotrophic factor family, and recent studies have shown that NRTN is associated with rectal, pancreatic, and hepatocellular cancer progression \u003csup\u003e\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e.\u003cem\u003eIGF-1\u003c/em\u003e is a growth hormone target gene that binds to and activates the receptor tyrosine kinase IGF1 receptor (IGF1R)\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Multiple studies have demonstrated its association with resistance to anticancer drugs and highlighted the potential benefits of targeting IGF1 in anticancer therapy \u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Additionally, a large case-control study has indicated a significant association between IGF1 and a reduced risk of bladder cancer. \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eMAP3K8\u003c/em\u003e is an oncogene encoding a member of the serine/threonine protein kinase family, and studies have shown that high levels of MAP3K8 phosphorylation are associated with progression and poor prognosis in patients with bladder cancer \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Studies have indicated that \u003cem\u003ePDGFD\u003c/em\u003e, a part of the platelet-derived growth factor family, is linked to gemcitabine resistance in BLCA patients and unfavorable prognosis in advanced uroepithelial carcinoma and pancreatic ductal adenocarcinoma (PDAC) \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e.\u003cem\u003ePDGFRA\u003c/em\u003e, a receptor for tyrosine kinase, is frequently mutated in gastrointestinal mesenchymal stromal tumors (GIST) and serves as a target for anticancer medications like avastinib \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e,\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eJUN\u003c/em\u003e has been implicated in APF-mediated growth inhibition of bladder tumor cells and is a potential target of APF in patients with invasive bladder cancer \u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e, and overexpression of \u003cem\u003eJUN\u003c/em\u003e protein is also closely associated with the invasive growth of BLCA \u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e.\u003cem\u003eDUSP2\u003c/em\u003e belongs to the nuclear DUSP family, specifically type I, and it inhibits the activation of MAPK while having a crucial function in immune processes, inflammatory responses, and the advancement of cancer. Deletion of \u003cem\u003eDUSP2\u003c/em\u003e connotes a poor prognosis for patients with bladder cancer \u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e.\u003cem\u003eMAP3K20\u003c/em\u003e, a member of the MAP3K subfamily \u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e,\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e, has been associated with the regulation of HCC cell proliferation and apoptosis \u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. Finally, high \u003cem\u003eMAP3K20\u003c/em\u003e expression has been found to promote cancer progression in gastric, breast, bladder, and colorectal cancers \u003csup\u003e\u003cspan additionalcitationids=\"CR55 CR56\" citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn conclusion, we have developed and validated a novel prognostic model centered on MAPK pathway-associated genes. Despite its strengths, the study has several limitations. Initially, it was a retrospective analysis using TCGA and GEO databases, necessitating additional prospective real-world data to confirm the accuracy of the MAPK-related gene model. Furthermore, our validation was limited to basic preliminary qPCR analysis, requiring further experiments related to biological functions to elucidate the roles and mechanisms of these genes in bladder cancer development. Lastly, although the model was validated using the GEO database, further extensive integration tests with centralized cohorts are essential to comprehensively evaluate the model's performance.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOverall, we developed a comprehensive model capable of predicting the prognosis, immune microenvironment, and drug response in BLCA patients. This model relies on 10 MAPK pathway-related genes and prognosis-related clinical factors. Validation through both GEO data analysis and qRT-PCR experiments demonstrated the model's potential for reliable prognostic predictions in BLCA.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e "},{"header":"Methods","content":"\u003ch2\u003eData acquisition\u003c/h2\u003e\u003cp\u003eFrom the Genomic Data Commons (GDC) database, we acquired RNA-seq data and clinical information for 403 tumor tissue samples and 19 normal tissue samples. A log2 (TPM + 1) transformation was applied to the downloaded transcripts per million (TPM) data, and genes with total expression values less than 1 in all samples were excluded. The GEO database was used to retrieve expression profiles and clinical data for GSE32894. Probe IDs were converted to corresponding gene symbols, and batch effects were mitigated using the \"sva\" R package. For clinical data, patients with a survival time of fewer than 30 days and those with missing essential information were excluded. MAPK pathway-related genes were obtained from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The study's workflow is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003ch2\u003eDifferential expression analysis\u003c/h2\u003e\u003cp\u003eThe analysis of differential expression in TCGA-Counts data was conducted using the \"DESeq2\" R package. Differential genes meeting the criteria (Log2FC \u0026gt; 1, P.adj \u0026lt; 0.05) were identified and visualized in a volcano plot. To obtain the intersection between these differentially expressed genes (DEGs) and MAPK-related genes, the \"VennDiagram\" R package was employed, and a Venn plot was generated.\u003c/p\u003e\u003ch2\u003eConstruction and validation of prognostic gene signatures\u003c/h2\u003e\u003cp\u003eThe TCGA dataset was divided into training and testing sets randomly, with a ratio of 7:3. The intersected genes underwent univariate Cox regression analyses using the \"Survival\" R package. A LASSO regression analysis was performed using the R package \"glmnet\" to determine the final model genes and calculate correlation coefficients for each gene after screening for genes with prognostic significance. Risk scores were computed for the training group, validation group, entire TCGA cohort, and GSE32894 cohort by utilizing the formula: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(riskscore={\\sum }_{i=1}^{n}ki*Xi\\)\u003c/span\u003e\u003c/span\u003e, in which k denotes the relative expression level of the model genes, and X signifies the regression coefficients. Afterward, the patients were categorized into groups of high-risk and low-risk, using the median of the risk score from the training group as the threshold value. The distribution of risk scores and a heatmap for all cohorts were plotted to visually present the results.\u003c/p\u003e\u003ch2\u003eTumor immune infiltration analysis\u003c/h2\u003e\u003cp\u003eApplication of the CIBERSORT function of the \"IOBR \"R package to perform immune infiltration analysis, and the ESTIMATE function to calculate the immune score and stromal score \u003csup\u003e\u003cspan additionalcitationids=\"CR59\" citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e–\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003ch2\u003eConstruction of nomograms\u003c/h2\u003e\u003cp\u003eA nomogram was created by utilizing the 'rms' R package, which included age, pathological stage, and risk score. The total score was calculated based on the contributions of these independent factors in the nomogram, aiming to predict the corresponding survival rate for patients with BLCA. The accuracy of the nomogram predictions was assessed using calibration curves.\u003c/p\u003e\u003ch2\u003eTumor mutation analysis and immunotherapy analysis\u003c/h2\u003e\u003cp\u003eTumor mutational burden (TMB) quantifies the number of non-synonymous mutations in somatic cells within a specific genomic region, indirectly reflecting a tumor's capacity and extent for neoantigen production. TMB serves as a predictive indicator for the effectiveness of immunotherapy across a broad spectrum of tumors\u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e,\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e. Simple nucleotide variant datasets from bladder cancer patients were obtained from the GDC website, and TMBs for individual samples were calculated using the \"maftools\" R package. Drug sensitivity analysis data were sourced from the Genomics of Drug Sensitivity in Cancer (GDSC) website. The relationship between high and low-risk groups and IC\u003csub\u003e50\u003c/sub\u003e values of anticancer drugs was analyzed using the \"oncoPredict\" R package.\u003c/p\u003e\u003ch2\u003eGEPIA website and GSCA website\u003c/h2\u003e\u003cp\u003eWe employed the Gene Expression Profiling Interactive Analysis (GEPIA) website for mapping the Hazard Ratios (HR) of model genes across various cancers \u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e. Additionally, the Gene Set Cancer Analysis (GSCA) website was utilized for conducting analyses on Single Nucleotide Variations (SNV), Copy Number Variations (CNV), immune infiltration, and drug sensitivity related to the model genes \u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003ch2\u003eHuman Protein Atlas Database\u003c/h2\u003e\u003cp\u003eHPA database stores massive amounts of protein data from human tissues. In this study, we utilized the HPA database to retrieve histopathological data associated with the model genes.\u003c/p\u003e\u003ch2\u003eClinical Sample Acquisition\u003c/h2\u003e\u003cp\u003e The study received approval from the Ethics Committee of the First Affiliated Hospital of Zhengzhou University, and all volunteers signed informed consent forms before participation. This study adhered strictly to the ethical principles for medical research involving human subjects, as outlined in the Declaration of Helsinki\u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e. Clinical samples were sourced from the First Affiliated Hospital of Zhengzhou University, involving patients previously diagnosed with bladder cancer through pathological examination. Paracancerous tissues were collected from normal tissues within a 3 cm region near the tumor. Following sampling, tissue samples were promptly preserved in liquid nitrogen and transferred to a -80°C refrigerator to maintain their integrity for subsequent analyses.\u003c/p\u003e\u003ch2\u003eQuantitative Real-time PCR experiments\u003c/h2\u003e\u003cp\u003eTotal RNA was extracted from the collected BLCA tumor tissues and adjacent normal tissues using the RNAeasy™ Animal RNA Extraction Kit (Beyotime). Subsequently, the reverse transcription process was performed using the PrimeScript™ RT reagent Kit (Takara), and qRT-PCR was conducted with the TB Green® Premix Ex Taq™ II Kit (Takara), following the manufacturer's instructions. The primer sequences are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\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\u003eThe list of the primers used for qRT-PCR.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene symbol\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward or reverse primer\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrimer sequence (5'-3')\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGAPDH\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGGAAGCTTGTCATCAATGGAAATC\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReverse\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTGATGACCCTTTTGGCTCCC\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNRTN\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eACCCTGGACGCCCGGATT\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReverse\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCGCAGTAGCGGAACAGCACC\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMAP3K8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTCGCTCAGCCTATCCCTCCTA\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReverse\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGTTCCAGCTCCTTCCTACTCAG\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRAC3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCTCCTACCCCCAAACTGACG\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReverse\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTTCACAGAGCCCACCAATCTC\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePDGFD\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGGTGAAAGGAAACGGCTACG\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReverse\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCTCTAATAATGGTACTGGTTTCGGA\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eJUN\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTGGGTGCCAACTCATGCTAA\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReverse\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTTCTTCGTTGCCCCTCAGC\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMAP3K20\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGTTAGATACTCTGAGGATGCGGC\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReverse\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGTTGATACTTAATGGGCACCTGG\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eIGF1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGGTGGATGCTCTTCAGTTCGT\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReverse\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGCAATACATCTCCAGCCTCCTTA\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePTPRR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGCAGGAATAGGTAGAACAGGGTG\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReverse\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGCACCATTCCACCTCTATCCA\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDUSP2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTGCTGTCCCGATCTGTGCT\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReverse\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCAGGAACAGGTAGGGCAAGA\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePDGFRA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCTTTGGATTGAACCCTGCTGA\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReverse\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGACATCTCGTGCCAACTCCA\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eBioinformatics analysis was performed using R version 4.3.1. The comparison of continuous data utilized either the student’s t-test or the Wilcoxon test, depending on the nature of the data, with statistical significance established at a two-sided p-value \u0026lt; 0.05.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by National Natural Science Foundation of China (Grant numbers [82000724]).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGYC was responsible for conducting data analysis, and visualization, and contributed to writing the paper. SQL and ZKZ were responsible for designing the research and conducting the literature review. YW and ZY collaborated on proofreading the paper. CCR provided project supervision and contributed to revising the paper. All authors have approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePublic data used in this work can be acquired from the TCGA Research Network portal (https://portal.gdc.cancer.gov/), Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo/), Kyoto Encyclopedia of Genes and Genomes (https://www.genome.jp/kegg/pathway.html/), Genomics of Drug Sensitivity in Cancer (https://www.cancerrxgene.org/), Gene Expression Profiling Interactive Analysis (http://gepia.cancer-pku.cn/), Gene Set Cancer Analysis (https://guolab.wchscu.cn/GSCA/), and The Human Protein Atlas database (http://www.proteinatlas.org/). \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll ethical aspects of this study were approved by the Ethics Committee of the First Affiliated Hospital of Zhengzhou University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all individual participants included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBray, F.\u003cem\u003e et al.\u003c/em\u003e Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. \u003cem\u003eCA Cancer J Clin\u003c/em\u003e \u003cstrong\u003e68\u003c/strong\u003e, 394-424, doi:10.3322/caac.21492 (2018).\u003c/li\u003e\n\u003cli\u003eRichters, A., Aben, K. 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Nevertheless, additional research is necessary to comprehend the relationship between the MAPK pathway and the prognosis of bladder cancer (BLCA), as well as its influence on the tumor immune microenvironment.To create prognostic models, we screened ten genes associated with the MAPK pathway using COX and least absolute shrinkage and selection operator (LASSO) regression analysis. These models were validated in the Genomic Data Commons (GEO) cohort and further examined for immune infiltration, somatic mutation, and drug sensitivity characteristics. Finally, the findings were validated using The Human Protein Atlas (HPA) database and through Quantitative Real-time PCR (qRT-PCR).Patients were classified into high-risk and low-risk groups based on the prognosis-related genes of the MAPK pathway. The high-risk group had poorer overall survival than the low-risk group and showed increased immune infiltration compared to the low-risk group. Additionally, the nomograms built using the risk scores and clinical factors exhibited high accuracy in predicting the survival of BLCA patients.The prognostic profiling of MAPK pathway-associated genes represents a potent clinical prediction tool, serving as the foundation for precise clinical treatment of bladder cancer.\u003c/p\u003e","manuscriptTitle":"Predicting Bladder Cancer Survival with High Accuracy: Insights from MAPK Pathway-related Genes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-25 18:50:49","doi":"10.21203/rs.3.rs-3872147/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-03-20T14:25:09+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-03-07T02:33:17+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-03-04T11:24:11+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-02-27T00:16:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"51bcd08f-1ee2-4154-b08f-eaea04e9895e","date":"2024-02-21T12:26:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"e51e7b9d-0c4e-4066-89f6-67932463ba0a","date":"2024-02-20T18:15:40+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-02-09T19:16:18+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-02-06T15:36:44+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-01-23T16:58:01+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-01-23T16:56:37+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-01-17T07:15:07+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f57cf62e-08b5-408b-8f15-a592202622f0","owner":[],"postedDate":"January 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-05-09T04:01:50+00:00","versionOfRecord":{"articleIdentity":"rs-3872147","link":"https://doi.org/10.1038/s41598-024-61302-0","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2024-05-07 04:01:49","publishedOnDateReadable":"May 7th, 2024"},"versionCreatedAt":"2024-01-25 18:50:49","video":"","vorDoi":"10.1038/s41598-024-61302-0","vorDoiUrl":"https://doi.org/10.1038/s41598-024-61302-0","workflowStages":[]},"version":"v1","identity":"rs-3872147","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3872147","identity":"rs-3872147","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}