Exploring the transcriptomic landscape of BLCA: SHTN1 as a key player in photodynamic therapy response

Exploring the transcriptomic landscape of BLCA: SHTN1 as a key player in photodynamic therapy response | 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 Exploring the transcriptomic landscape of BLCA: SHTN1 as a key player in photodynamic therapy response Zhengang Shen, Jiayi Lu, Haojin Cheng, Xiaodi Tang, Yunlong Li, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4021160/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background A high recurrence rate and a tendency to progress to more advanced, invasive stages characterize bladder urothelial carcinoma (BLCA), the ninth most common malignant tumor worldwide. Despite its potential, photodynamic therapy (PDT), a minimally invasive treatment, remains underutilized in BLCA management. This study focuses on identifying key genes that influence BLCA progression and prognosis, specifically in the context of PDT therapy. Methods According to the Cancer Genome Atlas (TCGA), we analyzed the mRNA expression profiles as well as clinical data for BLCA patients. Our approach included differential analysis, gene set intersection using GSEA databases, univariate regression analysis, and ROC curve plotting. Additionally, we validated our findings using BLCA patients' genes from the GEO dataset. To explore the role of SHTN1, we employed various methods such as GO, KEGG, GSEA, and GeneMANIA. We also examined the immunological environments associated with SHTN1 using tools like ESTIMATE, CIBERSORT, ssGSEA, and ICB to compare SHTN1 subgroups. Results A positive correlation was found between SHTN1 expression and clinical stage and distant metastasis of BLCA, while a negative correlation was found between SHTN1 expression and patient survival. There were a number of genes associated with tumor formation and development in the high SHTN1-expressing group. Immune characteristics assessment using ESTIMATE, CIBERSORT, and ssGSEA showed that the high SHTN1-expressing group showed improved immune characteristics. Conclusion According to our research, SHTN1 can both be a prognostic factor for BLCA and a therapeutic target. TCGA GEO GSEA BLCA PDT SHTN1 Bladder Cancer Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Bladder urothelial carcinoma (BLCA) is one of the most common malignancies in females and the fourth most common in males. The disease is estimated to cause 500,000 new cases and 200,000 deaths worldwide, ranking it 13th among cancer-related deaths [ 1 ]. A high recurrence rate is associated with BLCA, which can progress to higher-grade muscle-invasive bladder cancer (MIBC) [ 2 ]. Despite thorough Transurethral Resection of Bladder Tumors (TuRBT) and intravesical Bacillus Calmette-Guérin (BCG) therapy, over 50% of NMIBC tumors recur within a year, and approximately 30% progress to MIBC, potentially requiring definitive surgery, immunotherapy, or chemoradiotherapy [ 3 , 4 ]. In recent years, photodynamic therapy (PDT) has gained attention as a novel cancer treatment. To induce a photochemical reaction, PDT uses a photosensitizer, light, and oxygen, triggering localized inflammatory responses that lead to tumor ablation while activating humoral and cell-mediated anti-tumor immunity [ 5 ]. Clinical studies have shown that PDT can cure early-stage tumors and, on the other hand, extend the survival of late-stage cancer patients, significantly improving the quality of life. PDT has minimal toxicity to normal tissues, with almost negligible systemic effects, and can significantly reduce morbidity. This treatment has good cosmetic and organ-preserving effects, making it a valuable option in combination therapies [ 6 ]. With technological improvements, PDT is expected to become mainstream in cancer treatment. Currently, PDT combined with chemotherapy is being used to treat lung cancer, colorectal cancer, cervical cancer, and breast cancer [ 7 ]. However, its application in the treatment of BLCA remains a challenge. Currently, various photosensitizers, such as hypericin and curcumin, are considered for use in PDT for treating bladder cancer. Curcumin, as a photosensitizer, not only induces apoptosis in three types of urological cancers but also limits their proliferative potential. Additionally, curcumin can inhibit the invasion of urological tumors through the epithelial-mesenchymal transition (EMT) [ 8 ]. In our study, we analyzed 822 bladder cancer cells sourced from the GSEA database to investigate the gene set upregulation in response to PDT stress. Multiple genes expressed at elevated levels in the study, including those involved in heterogeneous metabolic processes, cell death induced by stress, autophagy, proliferation, inflammation, and carcinogenesis. Notably, we found that the induction of these genes by apoptosis plays a crucial role in limiting the proliferative potential across three types of urological cancers. Furthermore, our study highlights the potential of curcumin as an inhibitor of urological tumor invasion, particularly through its impact on the EMT, as referenced in the source [ 8 ]. The TCGA BLCA RNAseq dataset was used along with the GSEA database's upregulated gene set in this study targeting bladder cancer cells under PDT. Our data mining efforts revealed a notable upregulation of SHTN1 in both datasets. SHTN1, a protein-coding gene located in the cytoplasm, is primarily involved in neurogenesis and interacts with L1CAM, a key player in muscle actin cytoskeleton organization, as indicated in source [ 9 ]. Notably, current research lacks evidence of a direct correlation between SHTN1 and BLCA. Additionally, we found a positive correlation between SHTN1 expression and BLCA clinical pathology, including clinical stage and distant metastases, and a negative correlation with survival. The results of GSEA suggest SHTN1 may promote BLCA progression through various pathways, such as drug metabolism and epithelial- EMT. Additionally, we conducted an immunological analysis by comparing the high and low SHTN1 expression groups using tools like ESTIMATE, CIBERSORT, ssGSEA, and other immune checkpoint methods. This comparison provided deeper bioinformatics insights into BLCA's diagnosis, development, and prognosis. Materials and methods Data collection We conducted transcriptional analysis using data acquired from the TCGA-BLCA project. This data, along with clinical information, was downloaded via R (version 4.0.2) utilizing the TCGAbiolinks package. The dataset comprises 422 cases, including RNA-seq data from 403 tumor tissues and 19 adjacent normal tissues. TCGA provided comprehensive clinical data, including age, gender, T stage, N stage, and M stage, as well as prognosis. ( https://portal.gdc.cancer.gov/ ). Additionally, we obtained data on genes upregulated in response to PDT in 822 bladder cancer cells from the GSEA database. For independent validation, we used the GSE13507 dataset, which encompasses 256 samples, split between 188 normal tissues and 68 tumor tissues. Gene identification We utilized a Venn diagram to intersect the TCGA-BLCA DEG UP and GSEA PDT UP datasets, leading to the identification of 113 key genes. A univariate Cox regression analysis was conducted using the survival package in R. Based on the univariate Cox regression analysis, the top 10 genes are shown in the following figure. Through the integration of ROC curve analysis, SHTN1 was determined as the primary focus of our study. This selection was further validated using the GEO database. Differential analysis of scores and clinical stages Download clinical and pathological feature data for BLCA samples from TCGA. Using RStudio, analyze and compare data based on clinical stages using either Wilcoxon rank sums or Kruskal-Wallis rank-sum tests. Survival analysis Perform Statistical analysis of survival using RStudio with the package's survival and survminer. Among the 403 tumor samples, 436 cases have detailed survival time records spanning from 0 to 13.8 years, Kaplan-Meier method was used for survival analysis; the log-rank test was used as a statistical significance test; P < 0.05 was considered significant. Functional estimation and enrichment analysis To elucidate common patterns in gene sequences, GO and KEGG analyses have been performed, and GSEA V4.3.2 software was used to analyze gene set enrichment. The GO database was utilized to determine the relevance of target genes, which were further categorized into three domains: Bioinformatics analyses at the pathway level were done primarily with the KEGG database, determining Biological Processes (BPs), Cellular Components (CCs), and Molecular Functions (MFs). The GSEA enabled us to identify differentially regulated pathways and signaling pathways. Additionally, we conducted a functional analysis of hub genes and their interacting genes using GeneMANIA ( http://genemania.org/ ). This analysis predicted various associations, including protein-protein and genetic interactions, pathways, co-expression, co-localization, and protein domain similarity. Tumor immune cell (TIC) analysis To evaluate the tumor purity of each BLCA sample, TCGA gene expression data was used to calculate stromal and immune scores using the ESTIMATE algorithm ( https://bioinformatics.mdanderson.org/estimate/ ). A P- value less than 0.05 was considered statistically significant when analyzing the relationship between candidate diagnostic biomarkers and infiltrating immune cells in BLCA. The visualization of these relationships was facilitated using the R package 'ggplot2', and further enhanced through the Sanger Box platform ( https://sourceforge.net/projects/ggplot2.mir-ror/ ). Data analysis and visualization All data were analyzed using RStudio 4.3.1, SPSS Statistics 20.0, and Sanger box 3.0 ( http://vip.sangerbox.com/home.html ). Correlations were calculated using the Spearman method. Results Analysis process of this study This study's methodology is depicted in Fig. 1 In the TCGA database, 422 transcriptome RNA-seq samples were downloaded. A differential analysis between TCGA BLCA tumor tissues and normal tissues was conducted, identifying upregulated genes. An intersection with GSEA's PDT data revealed 113 key genes. These genes were analyzed for their involvement in biological processes using GO and KEGG enrichment analyses, cellular components, physiological activities, and signaling pathways. Genes were then analyzed using univariate Cox regression, focusing on the top 10 with the most significant p-values. Correlation analysis and ROC curves were conducted for these top genes. HR values, AUC values, and p-values were thoroughly compared and evaluated, leading to the identification of SHTN1 as the most significant gene. SHTN1 was subsequently selected for deeper analysis, which included correlation with survival and clinical pathological features, Regression of COX expression, GSEA, and correlation with TICs. Intersection genes and functions The Wilcoxon rank-sum test was used to determine which genes were differentially expressed between tumor tissues and normal tissues in the TCGA-BLCA dataset. We set significance thresholds of q 0.5 after log2 transformation. 7875 DEGs were found, including 3164 down-regulated genes and 4711 upregulated genes (Fig. 2A). The expression pattern of these DEGs is shown. Additionally, we intersected the 4711 upregulated genes in the TCGA BLCA dataset with 822 genes upregulated in BLCA under PDT from the GSEA database. The Venn diagram displayed a total of 113 intersecting genes between the two datasets (Fig. 2B-2C). We conducted GO and KEGG enrichment analyses on these 113 differential genes. Notably, KEGG pathway analysis indicated that pathways related to iron death were significantly enriched in this gene set (Fig. 2D). This suggests that the overall function of these DEGs may be linked to tissue metabolism and iron-dependent activities that regulate cell death. While the connection between iron-death mechanisms and bladder cancer has not been extensively explored, iron plays a crucial role in many biochemical reactions in the body, including processes that lead to disordered cell death. The relationship with tumors might involve regulatory mechanisms of cell survival and death [ 10 ]. As a result, it appears that these 113 key genes play a crucial role in the progression of bladder cancer [ 11 ]. Screening and Validation of SHTN1 A COX regression analysis was conducted on the 113 genes identified earlier, and the top 10 genes with the smallest to largest P -values were selected for further analysis (Fig. 2E). These genes include SHTN1, UAP1L1, GCLM, ZNF83, ADCY7, Trib3, SLC3A2, SLC7A11, ADAM17, and IP6K2. We performed correlation analysis (Fig. 2F) and ROC analysis (Fig. 3) for these ten genes. The combined results indicated that SHTN1 stood out with the smallest P -value and the highest HR and AUC values, establishing it as the most significant key gene. To validate this finding, we incorporated data from the GSE13507 database. 256 transcriptome RNA-seq data from GSE13507 were downloaded, which included both tumor and normal tissues, and we conducted a differential analysis afterward. A Wilcoxon rank-sum test was used to determine differentially expressed genes, with significance thresholds set at q 0.5 after log2 transformation. A total of 2112 DEGs were identified, with 1424 down-regulated genes and 688 upregulated genes (Fig. 4A and 4B). Notably, SHTN1 was identified as an upregulated gene in both TCGA BLCA and GSE13507 datasets. Compared to normal tissues, tumor tissues expressed the protein significantly more (Fig. 4C and 4D). A similar trend was observed when normal and tumor tissues were analyzed together (Fig. 4E). Hence, SHTN1 was chosen for further investigation. Clinicopathologic staging and survival analysis of patients with BLCA A retrospective analysis of relevant clinical data from BLCA cases in the TCGA database was conducted to explore the association between SHTN1 expression and the clinicopathology of BLCA. In this study, BLCA samples were classified into groups based on the median expression level of SHTN1, and subsequent analyses were conducted to assess differences. The results of the TNM and clinicopathologic staging indicated a positive correlation between SHTN1 levels and both survival and TNM stage in BLCA patients (Fig. 4F-4G). Kaplan-Meier curves were employed to evaluate the survival differences between the high-risk group (N = 201) and the low-risk group (N = 202). The results of the survival analysis indicate a statistically significant difference in overall survival rates between the low-risk and high-risk groups based on SHTN1 expression. These findings suggest that elevated levels of SHTN1 are correlated with an unfavorable prognosis (Fig. 4H). Functional analysis of SHTN1 gene To validate the impact of SHTN1 on BLCA at a functional level, we conducted comprehensive functional analyses utilizing GO, KEGG, and studies on hub genes and their interaction networks. These analyses aimed to explore the function of DEGs associated with SHTN1. SHTN1 DEGs were analyzed for BPs, CCs, and MFs. These analyses revealed significant enrichment in pathways related to epidermal differentiation and keratinization (Fig. 5A). KEGG analysis revealed that SHTN1 DEGs were primarily enriched in signaling pathways related to chemical and drug metabolism, including chemical carcinogenicity (DNA adducts), drug metabolism involving cytochrome P450, and the interaction of neuromast (Fig. 5B). Subsequently, a functional analysis of SHTN1 and its interacting genes was performed using GeneMANIA. This analysis showed that the functions of SHTN1 and its interacting genes were predominantly associated with actin filament bundles, trans-epithelial cell transport, immune response regulation, cell surface receptor signaling pathways, phagocytosis, and Fc receptor signaling pathways. Given the known association of bladder cancer with chemical exposures, such as smoking, a major risk factor closely linked to BLCA [ 12 ], and the linkage of abnormal lipid metabolism with the progression of BLCA [ 13 ], these findings suggest that chemical and drug metabolism-related signaling pathways may directly correlate with tumor malignancy and poor prognosis (Fig. 5C). Genetic correlation analysis In GSEA, the high-expression SHTN1 DEGs were identified as mainly involved in inflammation, Tumor Necrosis Factors (TNF), and EMTs (Fig. 5D). In the context of chronic inflammation, inflammation is recognized as a significant factor in the development of various cancers, including bladder cancer. During inflammation, white blood cells release inflammatory mediators, such as TNF, which are suggested by some studies to be implicated in the development and progression of bladder cancer. TNF's role is largely through the activation of the nuclear factor-kappa B (NF-κB) pathway, a critical pathway in both inflammation and cancer [ 14 ]. Moreover, the differentiation of the epidermis and the transformation of the epithelial stroma are key factors in bladder cancer metastasis. Recent research has identified TEAD4 as a prognostic biomarker that induces EMT through the PI3K/Akt pathway in bladder cancer [ 15 ]. Consequently, we conducted a gene correlation analysis between SHTN1 and TEAD4. The results showed a significant difference ( P < 0.05) and a positive correlation between SHTN1 and TEAD4 (Fig. 5E). This finding underscores the role of SHTN1 in promoting BLCA formation and development, potentially via mechanisms involving EMT. Correlation between SHTN1 and TIC ratio Comparing immunogenic characteristics between high and low SHTN1 expression groups was carried out using ESTIMATE and CIBERSORT methods(Fig. 6A-6E). In the ESTIMATE analysis, the high SHTN1 expression group demonstrated elevated stromal, immune, and ESTIMATE scores, suggesting a correlation between high SHTN1 expression and an active tumor immune microenvironment(Fig. 6A-6D). The CIBERSORT analysis revealed a significant increase in the proportion of CD 4+ T cells(Fig. 6E and 6F) and M0, M1 macrophages in the high SHTN1 expression group. Moreover, ssGSEA analysis showed a noticeable increase in T helper cells (CD 4+ )(Fig. 6G), dendritic cells (DCs), natural killer (NK) cells, natural killer T (NKT) cells, and macrophages in the high SHTN1 expression group, highlighting their potential significance. It has been documented that dendritic cells are pivotal antigen-presenting cells, activating T cells to foster anti-tumor immunity. NK cells, which have cytotoxic capabilities in anti-tumor immunity, also recruit conventional type 1 dendritic cells into tumor microenvironments upon stimulation. Their extensive infiltration is related to favorable outcomes in B cells. Immune checkpoint inhibitors like PD-1, PD-L1, and CTLA-4, approved by the FDA, have been shown to be effective in cancer treatment [ 16 , 17 ]. The high SHTN1 expression group exhibited more expression of immunosuppressive checkpoints(Fig. 6H), indicating a potential for a better response to immunotherapy. According to CIBERSORT analysis, the high SHTN1 expression group also contains more M1 subtype macrophages, indicating a predisposition towards anti-tumor Th1 responses. Our study of the immune environment indicated that the high SHTN1 expression group had more extensive infiltration of immune cells compared to the low expression group. Therefore, patients with high SHTN1 expression might be more likely to benefit from immunotherapy [ 18 ]. Discussion The role of SHTN1 (salt-inducible kinase 3) in cancer has been relatively unexplored. This study demonstrates that SHTN1 expression is significantly elevated in bladder cancer and intensifies with advancing clinical stages. This suggests that SHTN1 contributes to the progression of bladder cancer by affecting cytochrome P450 drug metabolism and the EMT pathway. Bladder cancer is a complex and multifactorial disease influenced by a mix of genetic, environmental, behavioral, lifestyle factors, including chemical exposures that elevate the risk of development. Specific chemicals and drugs, such as p-phenylenediamine, cyclophosphamide, praziquantel, and their metabolites [ 19 ]. A complex biological process is EMT, in which epithelial cells undergo a molecular or cytological transformation to become mesenchymal cells [ 20 ]. This transformation is crucial in the context of bladder cancer. As bladder cancer cells undergo EMT, they gain a heightened ability to migrate and infiltrate. This increased capacity enables them to penetrate the basement membrane and invade adjacent tissues. Such progression is a key factor in the spread and metastasis of bladder cancer, marking a critical phase in the disease's advancement [ 21 ]. Chemical exposure can impact various mechanisms such as cell adhesion, intercellular signaling pathways, autophagy, and gene expression regulation, triggering the initiation of EMT [ 22 ]. EMT not only remodels the surrounding stroma and alters the cytoskeleton but also is intricately linked to drug resistance in bladder cancer. Patients undergoing treatment may develop resistance to therapeutic drugs, a phenomenon closely associated with EMT, as evidenced by several studies [ 23 ]. Furthermore, the Cytochrome P450 enzyme family, vital in drug metabolism and clearance, particularly in the liver and other tissues, might interact with tumor development and EMT, especially during liver-based drug metabolism [ 24 ]. This interaction can lead to tumor cells developing resistance to conventional therapies, complicating treatment efforts [ 20 ]. Genes regulating EMT mechanisms present potential therapeutic targets for malignant urinary system tumors. In this context, In the fight against bladder cancer, photodynamic therapy has emerged as a highly targeted treatment option. SHTN1 is notably upregulated in response to PDT, which may indicate its regulatory role and response mechanism to cellular stress and photo infection. However, it's important to note that tissue hypoxia is a prevalent characteristic in nearly all solid tumors. This hypoxic environment prominently influences the EMT, with lncRNAs, microRNAs, EIF5A2, Notch-4, and hypoxia itself being major regulators. Moreover, the epigenetic regulation of EMT is largely centered around hypoxia and TGF-β, emphasizing the complexity of cancer biology and the response to treatments like PDT [ 25 ]. It appears that lncRNAs, microRNAs, EIF5A2, Notch-4, and hypoxia are the primary regulators of EMTs. Importantly, the epigenetic regulation of EMT is profoundly influenced by hypoxia and TGF-β, highlighting their central roles in this critical cellular transformation [ 26 ]. In GSEA, SHTN1 demonstrated a significant up-regulation in response to hypoxia. This finding is particularly relevant in the context of PDT, which necessitates substantial oxygen consumption during the treatment process. Addressing the resultant hypoxia within tumors is crucial for enhancing the effectiveness of PDT [ 27 ]. Several studies have focused on overcoming hypoxia in tumor photodynamic therapy. These include employing micro/nano motors to enhance oxygen utilization within the tumor environment, using biosynthetic living organisms to supplement oxygen, and repairing tumor blood circulation to augment oxygen supply. Additionally, other innovative methods are being explored to address this challenge effectively [ 19 , 28 ]. These innovative pathways have the potential to alleviate hypoxia-related issues in tumor treatment. Additionally, compounds like curcumin and its nanomedicine formulations, metformin, and Nitric Oxide have been effectively used to reduce oxygen consumption, thereby enhancing the efficiency of PDT. Given these advancements, it's recommended that PDT be integrated with other treatment modalities, such as radiotherapy and chemotherapy, to augment the overall therapeutic effectiveness against BLCA. The microenvironment of BLCA is highly dynamic, comprising various cell types such as cancer cells, stem cells, and a range of immune cells, including neutrophils, macrophages, adipocytes, neurons, and neuroendocrine cells. These diverse cells interact and collectively influence the local tumor milieu. Crucially, the balance and functional activation of these immune cells are pivotal in determining tumor prognosis, and their activation is significantly influenced by the TME. Immune checkpoint inhibitors have emerged as a transformative therapy, enhancing treatment options and oncological outcomes for patients with urinary system cancers. In light of these insights, patients with high SHTN1 expression might exhibit a more favorable response to immunotherapy [ 29 , 30 ]. While SHTN1 is identified as a key risk factor for bladder cancer, its role extends beyond this, significantly influencing the response to photodynamic therapy. This dual functionality of SHTN1 not only underscores its importance in disease progression but also highlights its potential as a therapeutic target in bladder cancer treatment. Further supporting its role, our analysis reveals that SHTN1 is associated with an immune environment conducive to treatment, reinforcing its relevance in BLCA therapeutic strategies. Declarations Author contributions Zhengang Shen: Writing-original draft, investigation, methodology, conceptualization. Jiayi Lu: Drawing of Figures & editing. HaoJin Cheng: drawing figures. Xiaodi Tang: Writing-review. Li Chen: project administration. Guangqiang Hu, Yong Yu, Jun-feng Liu, Xingyue Han: Data Collection. Corresponding Authors：Hong Liao, Shukui Zhou：Manuscript review and design. Funding This work was supported by no findings. Data and code availability The datasets involved in our study were extracted from TCGA (https://portal.gdc.cancer.gov/), GEO (https://www.ncbi.nlm.nih.gov/geo), and GSE (https://www.ncbi.nlm.nih.gov/geo/). If necessary, the code will be made available upon request via email at: [email protected] . Ethical approval This article does not contain any studies with human participants performed by any of the authors. Competing interests The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Declaration During the preparation of this work, the authors used ChatGPT in order to improve the language and readability of the manuscript. After using this tool, the authors reviewed and edited the content as needed. References Lenis AT, Lec PM, Chamie K, Mshs MD. Bladder Cancer: A Review. Jama. 2020;324(19):1980-91. doi:10.1001/jama.2020.17598. 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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-4021160","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":284374458,"identity":"719f405e-7850-4f6c-ba7e-441101225e02","order_by":0,"name":"Zhengang Shen","email":"","orcid":"","institution":"Sichuan Cancer Hospital \u0026 Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China","correspondingAuthor":false,"prefix":"","firstName":"Zhengang","middleName":"","lastName":"Shen","suffix":""},{"id":284374459,"identity":"912a1fa0-cb73-4f15-b5c6-257fb07d2da2","order_by":1,"name":"Jiayi Lu","email":"","orcid":"","institution":"Chengdu University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jiayi","middleName":"","lastName":"Lu","suffix":""},{"id":284374460,"identity":"c4ec0588-5f44-4a91-a737-eff2629341ac","order_by":2,"name":"Haojin Cheng","email":"","orcid":"","institution":"Chengdu University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Haojin","middleName":"","lastName":"Cheng","suffix":""},{"id":284374461,"identity":"9dd3b636-100a-45ed-81e9-e560915bffac","order_by":3,"name":"Xiaodi Tang","email":"","orcid":"","institution":"Chengdu Medical College","correspondingAuthor":false,"prefix":"","firstName":"Xiaodi","middleName":"","lastName":"Tang","suffix":""},{"id":284374462,"identity":"7c024a25-0a22-4b69-900b-b9e0e4d8c792","order_by":4,"name":"Yunlong Li","email":"","orcid":"","institution":"Sichuan Cancer Hospital \u0026 Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China","correspondingAuthor":false,"prefix":"","firstName":"Yunlong","middleName":"","lastName":"Li","suffix":""},{"id":284374464,"identity":"6c1889fc-b3a9-4085-a110-63f5e5873360","order_by":5,"name":"Li Chen","email":"","orcid":"","institution":"Sichuan Cancer Hospital \u0026 Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Chen","suffix":""},{"id":284374465,"identity":"ab778774-aa20-4f07-a85b-d13eb36958aa","order_by":6,"name":"Junfeng Liu","email":"","orcid":"","institution":"Sichuan Cancer Hospital \u0026 Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China","correspondingAuthor":false,"prefix":"","firstName":"Junfeng","middleName":"","lastName":"Liu","suffix":""},{"id":284374466,"identity":"da6ccdb3-6e42-4c5f-82cd-05a3a7057505","order_by":7,"name":"Guangqiang Hu","email":"","orcid":"","institution":"Sichuan Cancer Hospital \u0026 Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China","correspondingAuthor":false,"prefix":"","firstName":"Guangqiang","middleName":"","lastName":"Hu","suffix":""},{"id":284374467,"identity":"2e9deb69-1a76-4bee-8692-89f1968bd961","order_by":8,"name":"Yong Yu","email":"","orcid":"","institution":"Sichuan Cancer Hospital \u0026 Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China","correspondingAuthor":false,"prefix":"","firstName":"Yong","middleName":"","lastName":"Yu","suffix":""},{"id":284374469,"identity":"6a16d38d-5107-45b3-802d-f5048e20b33e","order_by":9,"name":"Xingyue Han","email":"","orcid":"","institution":"Sichuan Cancer Hospital \u0026 Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China","correspondingAuthor":false,"prefix":"","firstName":"Xingyue","middleName":"","lastName":"Han","suffix":""},{"id":284374471,"identity":"7871766f-3b87-4219-adba-787326f12ffd","order_by":10,"name":"Hong Liao","email":"","orcid":"","institution":"Sichuan Cancer Hospital \u0026 Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China","correspondingAuthor":false,"prefix":"","firstName":"Hong","middleName":"","lastName":"Liao","suffix":""},{"id":284374472,"identity":"7d87b56c-1ea8-4893-9da0-9c33bd8e6c63","order_by":11,"name":"Shukui Zhou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyUlEQVRIiWNgGAWjYBACxgYGZhAtx8AOZBKv5QADgzEDM7FagACsJbGBmWj17YcfG3+ouJPe38zcJs27g0GeX+wAAYf1pBknHDjzLHfGYUagljMMhjNnJxDQ0pDDfOBg2+HcBrCWNoYEg9uEtPS/AWr5dzhdnngtM3KYEw42HE4wIEHLM2ODM8cOG248zNhsObdNgrBfDPuTH0tU1ByWlzve/vDG2zYbeX5pQloaEGwWCQYGCfzKQUAeic38gbD6UTAKRsEoGIkAAMIgRI6M2RsWAAAAAElFTkSuQmCC","orcid":"","institution":"Sichuan Cancer Hospital \u0026 Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China","correspondingAuthor":true,"prefix":"","firstName":"Shukui","middleName":"","lastName":"Zhou","suffix":""}],"badges":[],"createdAt":"2024-03-06 13:38:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4021160/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4021160/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":53754908,"identity":"e43cf1b7-a69a-4e0e-bfe1-5fc379624a34","added_by":"auto","created_at":"2024-03-29 18:58:59","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":316142,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis workflow of this study.\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-4021160/v1/9a60ad3d6732bd6ae0f22a55.png"},{"id":53754909,"identity":"a2f44d05-12ea-45b5-acb5-e48290b6f752","added_by":"auto","created_at":"2024-03-29 18:58:59","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3637893,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of the function of intersection genes. (A) Heatmap of differential analysis between TCGA-BLCA tumor and normal tissues. (B) Venn diagram of upregulated genes in TCGA-BLCA and PDT. (C-D) Results of GO and KEGG enrichment analyses for 113 intersection genes. (E) Multifactorial Cox regression analysis of 113 intersection genes, focusing on the top ten genes. (F) Correlation analysis of the top ten genes.\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-4021160/v1/66a43a9665ad7990975d1321.png"},{"id":53754912,"identity":"c9c58429-8594-4563-be9f-86fc37653d56","added_by":"auto","created_at":"2024-03-29 18:58:59","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":5140213,"visible":true,"origin":"","legend":"\u003cp\u003eScreening and identification of SHTN1. (A-J) ROC curves for top genes. (K-M) Ranking of top genes by AUC, HR values, and \u003cem\u003eP\u003c/em\u003e values.\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-4021160/v1/bad9aacaf7c669fa5c99dfcc.png"},{"id":53754910,"identity":"68a607c5-94ba-46d2-83f5-4828c5cb91fa","added_by":"auto","created_at":"2024-03-29 18:58:59","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":714198,"visible":true,"origin":"","legend":"\u003cp\u003eExpression of SHTN1 and its correlation with clinical staging and survival curves. (A/C) Volcano plots from GSE13507 showing DEGs between tumor and normal groups, and the expression of SHTN1 in tumor and normal tissues. (B/D) TCGA volcano plot showing DEGs between tumor and normal groups, and the expression of SHTN1 in tumor and normal tissues. (E) Paired plot of SHTN1 in tumor and normal tissues in the TCGA database. (F/G) Expression of SHTN1 in clinicopathologic stage and T stage classification. (H) Survival analysis of BLCA patients with high and low median expression of SHTN1.\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-4021160/v1/d2b54b0a0e99c9f62d1a4edc.png"},{"id":53754913,"identity":"7dc916d6-bb6d-4e33-9701-0a2402ddf2b7","added_by":"auto","created_at":"2024-03-29 18:58:59","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":5841048,"visible":true,"origin":"","legend":"\u003cp\u003eSHTN1 enrichment analysis. (A) Gene ontology (GO) diagram for high and low SHTN1 expression. (B) KEGG Pathway chart for high and low SHTN1 expression. (C) Functional analysis of Hub genes and their interaction networks. (D) GSEA chart for high and low expression of SHTN1. (E) Correlation scatter plot between SHTN1 and TEAD4.\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-4021160/v1/923fec54212795803f04b464.png"},{"id":53754911,"identity":"854e406c-2e46-4e1b-9bd0-a4fe7b60a9b1","added_by":"auto","created_at":"2024-03-29 18:58:59","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":6944800,"visible":true,"origin":"","legend":"\u003cp\u003eThe immune environment of SHTN1. (A-D) Correlation of SHTN1 Expression with Immune, Stroma, and ESTIMATE Scores, and Tumor Purity. (E) Histogram depicting the distribution of 21 TIC types in BLCA tumor samples, with columns labeled as sample IDs. (F) PCA of 21 immune cell types, calculated using the CIBERSORT algorithm for abundance estimation. (G) PCA of 28 immune cell types, abundance calculated using the Sages algorithm. (H) Group comparison chart showing SHTN1 expression levels in relation to immune checkpoints.\u003c/p\u003e","description":"","filename":"Fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-4021160/v1/66b4c67b3a6812570bc33126.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploring the transcriptomic landscape of BLCA: SHTN1 as a key player in photodynamic therapy response","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBladder urothelial carcinoma (BLCA) is one of the most common malignancies in females and the fourth most common in males. The disease is estimated to cause 500,000 new cases and 200,000 deaths worldwide, ranking it 13th among cancer-related deaths [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. A high recurrence rate is associated with BLCA, which can progress to higher-grade muscle-invasive bladder cancer (MIBC) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Despite thorough Transurethral Resection of Bladder Tumors (TuRBT) and intravesical Bacillus Calmette-Gu\u0026eacute;rin (BCG) therapy, over 50% of NMIBC tumors recur within a year, and approximately 30% progress to MIBC, potentially requiring definitive surgery, immunotherapy, or chemoradiotherapy [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn recent years, photodynamic therapy (PDT) has gained attention as a novel cancer treatment. To induce a photochemical reaction, PDT uses a photosensitizer, light, and oxygen, triggering localized inflammatory responses that lead to tumor ablation while activating humoral and cell-mediated anti-tumor immunity [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Clinical studies have shown that PDT can cure early-stage tumors and, on the other hand, extend the survival of late-stage cancer patients, significantly improving the quality of life. PDT has minimal toxicity to normal tissues, with almost negligible systemic effects, and can significantly reduce morbidity. This treatment has good cosmetic and organ-preserving effects, making it a valuable option in combination therapies [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. With technological improvements, PDT is expected to become mainstream in cancer treatment. Currently, PDT combined with chemotherapy is being used to treat lung cancer, colorectal cancer, cervical cancer, and breast cancer [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, its application in the treatment of BLCA remains a challenge. Currently, various photosensitizers, such as hypericin and curcumin, are considered for use in PDT for treating bladder cancer. Curcumin, as a photosensitizer, not only induces apoptosis in three types of urological cancers but also limits their proliferative potential. Additionally, curcumin can inhibit the invasion of urological tumors through the epithelial-mesenchymal transition (EMT) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn our study, we analyzed 822 bladder cancer cells sourced from the GSEA database to investigate the gene set upregulation in response to PDT stress. Multiple genes expressed at elevated levels in the study, including those involved in heterogeneous metabolic processes, cell death induced by stress, autophagy, proliferation, inflammation, and carcinogenesis. Notably, we found that the induction of these genes by apoptosis plays a crucial role in limiting the proliferative potential across three types of urological cancers. Furthermore, our study highlights the potential of curcumin as an inhibitor of urological tumor invasion, particularly through its impact on the EMT, as referenced in the source [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe TCGA BLCA RNAseq dataset was used along with the GSEA database's upregulated gene set in this study targeting bladder cancer cells under PDT. Our data mining efforts revealed a notable upregulation of SHTN1 in both datasets. SHTN1, a protein-coding gene located in the cytoplasm, is primarily involved in neurogenesis and interacts with L1CAM, a key player in muscle actin cytoskeleton organization, as indicated in source [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Notably, current research lacks evidence of a direct correlation between SHTN1 and BLCA. Additionally, we found a positive correlation between SHTN1 expression and BLCA clinical pathology, including clinical stage and distant metastases, and a negative correlation with survival. The results of GSEA suggest SHTN1 may promote BLCA progression through various pathways, such as drug metabolism and epithelial- EMT. Additionally, we conducted an immunological analysis by comparing the high and low SHTN1 expression groups using tools like ESTIMATE, CIBERSORT, ssGSEA, and other immune checkpoint methods. This comparison provided deeper bioinformatics insights into BLCA's diagnosis, development, and prognosis.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData collection\u003c/h2\u003e \u003cp\u003eWe conducted transcriptional analysis using data acquired from the TCGA-BLCA project. This data, along with clinical information, was downloaded via R (version 4.0.2) utilizing the TCGAbiolinks package. The dataset comprises 422 cases, including RNA-seq data from 403 tumor tissues and 19 adjacent normal tissues. TCGA provided comprehensive clinical data, including age, gender, T stage, N stage, and M stage, as well as prognosis. (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov/\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Additionally, we obtained data on genes upregulated in response to PDT in 822 bladder cancer cells from the GSEA database. For independent validation, we used the GSE13507 dataset, which encompasses 256 samples, split between 188 normal tissues and 68 tumor tissues.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eGene identification\u003c/h2\u003e \u003cp\u003eWe utilized a Venn diagram to intersect the TCGA-BLCA DEG UP and GSEA PDT UP datasets, leading to the identification of 113 key genes. A univariate Cox regression analysis was conducted using the survival package in R. Based on the univariate Cox regression analysis, the top 10 genes are shown in the following figure. Through the integration of ROC curve analysis, SHTN1 was determined as the primary focus of our study. This selection was further validated using the GEO database.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eDifferential analysis of scores and clinical stages\u003c/h2\u003e \u003cp\u003eDownload clinical and pathological feature data for BLCA samples from TCGA. Using RStudio, analyze and compare data based on clinical stages using either Wilcoxon rank sums or Kruskal-Wallis rank-sum tests.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eSurvival analysis\u003c/h2\u003e \u003cp\u003ePerform Statistical analysis of survival using RStudio with the package's survival and survminer. Among the 403 tumor samples, 436 cases have detailed survival time records spanning from 0 to 13.8 years, Kaplan-Meier method was used for survival analysis; the log-rank test was used as a statistical significance test; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u003cb\u003e\u0026lt;\u003c/b\u003e\u0026thinsp;0.05 was considered significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eFunctional estimation and enrichment analysis\u003c/h2\u003e \u003cp\u003eTo elucidate common patterns in gene sequences, GO and KEGG analyses have been performed, and GSEA V4.3.2 software was used to analyze gene set enrichment. The GO database was utilized to determine the relevance of target genes, which were further categorized into three domains: Bioinformatics analyses at the pathway level were done primarily with the KEGG database, determining Biological Processes (BPs), Cellular Components (CCs), and Molecular Functions (MFs). The GSEA enabled us to identify differentially regulated pathways and signaling pathways. Additionally, we conducted a functional analysis of hub genes and their interacting genes using GeneMANIA (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://genemania.org/\u003c/span\u003e\u003cspan address=\"http://genemania.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). This analysis predicted various associations, including protein-protein and genetic interactions, pathways, co-expression, co-localization, and protein domain similarity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eTumor immune cell (TIC) analysis\u003c/h2\u003e \u003cp\u003eTo evaluate the tumor purity of each BLCA sample, TCGA gene expression data was used to calculate stromal and immune scores using the ESTIMATE algorithm (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bioinformatics.mdanderson.org/estimate/\u003c/span\u003e\u003cspan address=\"https://bioinformatics.mdanderson.org/estimate/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). A \u003cem\u003eP-\u003c/em\u003evalue less than 0.05 was considered statistically significant when analyzing the relationship between candidate diagnostic biomarkers and infiltrating immune cells in BLCA. The visualization of these relationships was facilitated using the R package 'ggplot2', and further enhanced through the Sanger Box platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://sourceforge.net/projects/ggplot2.mir-ror/\u003c/span\u003e\u003cspan address=\"https://sourceforge.net/projects/ggplot2.mir-ror/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eData analysis and visualization\u003c/h2\u003e \u003cp\u003eAll data were analyzed using RStudio 4.3.1, SPSS Statistics 20.0, and Sanger box 3.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://vip.sangerbox.com/home.html\u003c/span\u003e\u003cspan address=\"http://vip.sangerbox.com/home.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Correlations were calculated using the Spearman method.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis process of this study\u003c/h2\u003e \u003cp\u003eThis study's methodology is depicted in Fig.\u0026nbsp;1 In the TCGA database, 422 transcriptome RNA-seq samples were downloaded. A differential analysis between TCGA BLCA tumor tissues and normal tissues was conducted, identifying upregulated genes. An intersection with GSEA's PDT data revealed 113 key genes. These genes were analyzed for their involvement in biological processes using GO and KEGG enrichment analyses, cellular components, physiological activities, and signaling pathways. Genes were then analyzed using univariate Cox regression, focusing on the top 10 with the most significant p-values. Correlation analysis and ROC curves were conducted for these top genes. HR values, AUC values, and p-values were thoroughly compared and evaluated, leading to the identification of SHTN1 as the most significant gene. SHTN1 was subsequently selected for deeper analysis, which included correlation with survival and clinical pathological features, Regression of COX expression, GSEA, and correlation with TICs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eIntersection genes and functions\u003c/h2\u003e \u003cp\u003eThe Wilcoxon rank-sum test was used to determine which genes were differentially expressed between tumor tissues and normal tissues in the TCGA-BLCA dataset. We set significance thresholds of q\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |log2(fold-change)| \u0026gt; 0.5 after log2 transformation. 7875 DEGs were found, including 3164 down-regulated genes and 4711 upregulated genes (Fig.\u0026nbsp;2A). The expression pattern of these DEGs is shown.\u003c/p\u003e \u003cp\u003eAdditionally, we intersected the 4711 upregulated genes in the TCGA BLCA dataset with 822 genes upregulated in BLCA under PDT from the GSEA database. The Venn diagram displayed a total of 113 intersecting genes between the two datasets (Fig.\u0026nbsp;2B-2C). We conducted GO and KEGG enrichment analyses on these 113 differential genes. Notably, KEGG pathway analysis indicated that pathways related to iron death were significantly enriched in this gene set (Fig.\u0026nbsp;2D). This suggests that the overall function of these DEGs may be linked to tissue metabolism and iron-dependent activities that regulate cell death. While the connection between iron-death mechanisms and bladder cancer has not been extensively explored, iron plays a crucial role in many biochemical reactions in the body, including processes that lead to disordered cell death. The relationship with tumors might involve regulatory mechanisms of cell survival and death [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. As a result, it appears that these 113 key genes play a crucial role in the progression of bladder cancer [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eScreening and Validation of SHTN1\u003c/h2\u003e \u003cp\u003eA COX regression analysis was conducted on the 113 genes identified earlier, and the top 10 genes with the smallest to largest \u003cem\u003eP\u003c/em\u003e-values were selected for further analysis (Fig.\u0026nbsp;2E). These genes include SHTN1, UAP1L1, GCLM, ZNF83, ADCY7, Trib3, SLC3A2, SLC7A11, ADAM17, and IP6K2. We performed correlation analysis (Fig.\u0026nbsp;2F) and ROC analysis (Fig.\u0026nbsp;3) for these ten genes. The combined results indicated that SHTN1 stood out with the smallest \u003cem\u003eP\u003c/em\u003e-value and the highest HR and AUC values, establishing it as the most significant key gene.\u003c/p\u003e \u003cp\u003eTo validate this finding, we incorporated data from the GSE13507 database. 256 transcriptome RNA-seq data from GSE13507 were downloaded, which included both tumor and normal tissues, and we conducted a differential analysis afterward. A Wilcoxon rank-sum test was used to determine differentially expressed genes, with significance thresholds set at q\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |log2(fold-change)| \u0026gt; 0.5 after log2 transformation. A total of 2112 DEGs were identified, with 1424 down-regulated genes and 688 upregulated genes (Fig.\u0026nbsp;4A and 4B). Notably, SHTN1 was identified as an upregulated gene in both TCGA BLCA and GSE13507 datasets. Compared to normal tissues, tumor tissues expressed the protein significantly more (Fig.\u0026nbsp;4C and 4D). A similar trend was observed when normal and tumor tissues were analyzed together (Fig.\u0026nbsp;4E). Hence, SHTN1 was chosen for further investigation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eClinicopathologic staging and survival analysis of patients with BLCA\u003c/h2\u003e \u003cp\u003eA retrospective analysis of relevant clinical data from BLCA cases in the TCGA database was conducted to explore the association between SHTN1 expression and the clinicopathology of BLCA. In this study, BLCA samples were classified into groups based on the median expression level of SHTN1, and subsequent analyses were conducted to assess differences. The results of the TNM and clinicopathologic staging indicated a positive correlation between SHTN1 levels and both survival and TNM stage in BLCA patients (Fig.\u0026nbsp;4F-4G). Kaplan-Meier curves were employed to evaluate the survival differences between the high-risk group (N\u0026thinsp;=\u0026thinsp;201) and the low-risk group (N\u0026thinsp;=\u0026thinsp;202). The results of the survival analysis indicate a statistically significant difference in overall survival rates between the low-risk and high-risk groups based on SHTN1 expression. These findings suggest that elevated levels of SHTN1 are correlated with an unfavorable prognosis (Fig.\u0026nbsp;4H).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eFunctional analysis of SHTN1 gene\u003c/h2\u003e \u003cp\u003eTo validate the impact of SHTN1 on BLCA at a functional level, we conducted comprehensive functional analyses utilizing GO, KEGG, and studies on hub genes and their interaction networks. These analyses aimed to explore the function of DEGs associated with SHTN1. SHTN1 DEGs were analyzed for BPs, CCs, and MFs. These analyses revealed significant enrichment in pathways related to epidermal differentiation and keratinization (Fig.\u0026nbsp;5A). KEGG analysis revealed that SHTN1 DEGs were primarily enriched in signaling pathways related to chemical and drug metabolism, including chemical carcinogenicity (DNA adducts), drug metabolism involving cytochrome P450, and the interaction of neuromast (Fig.\u0026nbsp;5B). Subsequently, a functional analysis of SHTN1 and its interacting genes was performed using GeneMANIA. This analysis showed that the functions of SHTN1 and its interacting genes were predominantly associated with actin filament bundles, trans-epithelial cell transport, immune response regulation, cell surface receptor signaling pathways, phagocytosis, and Fc receptor signaling pathways. Given the known association of bladder cancer with chemical exposures, such as smoking, a major risk factor closely linked to BLCA [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], and the linkage of abnormal lipid metabolism with the progression of BLCA [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], these findings suggest that chemical and drug metabolism-related signaling pathways may directly correlate with tumor malignancy and poor prognosis (Fig.\u0026nbsp;5C).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eGenetic correlation analysis\u003c/h2\u003e \u003cp\u003eIn GSEA, the high-expression SHTN1 DEGs were identified as mainly involved in inflammation, Tumor Necrosis Factors (TNF), and EMTs (Fig.\u0026nbsp;5D). In the context of chronic inflammation, inflammation is recognized as a significant factor in the development of various cancers, including bladder cancer. During inflammation, white blood cells release inflammatory mediators, such as TNF, which are suggested by some studies to be implicated in the development and progression of bladder cancer. TNF's role is largely through the activation of the nuclear factor-kappa B (NF-κB) pathway, a critical pathway in both inflammation and cancer [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Moreover, the differentiation of the epidermis and the transformation of the epithelial stroma are key factors in bladder cancer metastasis. Recent research has identified TEAD4 as a prognostic biomarker that induces EMT through the PI3K/Akt pathway in bladder cancer [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Consequently, we conducted a gene correlation analysis between SHTN1 and TEAD4. The results showed a significant difference (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and a positive correlation between SHTN1 and TEAD4 (Fig.\u0026nbsp;5E). This finding underscores the role of SHTN1 in promoting BLCA formation and development, potentially via mechanisms involving EMT.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation between SHTN1 and TIC ratio\u003c/h2\u003e \u003cp\u003eComparing immunogenic characteristics between high and low SHTN1 expression groups was carried out using ESTIMATE and CIBERSORT methods(Fig.\u0026nbsp;6A-6E). In the ESTIMATE analysis, the high SHTN1 expression group demonstrated elevated stromal, immune, and ESTIMATE scores, suggesting a correlation between high SHTN1 expression and an active tumor immune microenvironment(Fig.\u0026nbsp;6A-6D). The CIBERSORT analysis revealed a significant increase in the proportion of CD\u003csub\u003e4+\u003c/sub\u003e T cells(Fig.\u0026nbsp;6E and 6F) and M0, M1 macrophages in the high SHTN1 expression group. Moreover, ssGSEA analysis showed a noticeable increase in T helper cells (CD\u003csub\u003e4+\u003c/sub\u003e)(Fig.\u0026nbsp;6G), dendritic cells (DCs), natural killer (NK) cells, natural killer T (NKT) cells, and macrophages in the high SHTN1 expression group, highlighting their potential significance.\u003c/p\u003e \u003cp\u003eIt has been documented that dendritic cells are pivotal antigen-presenting cells, activating T cells to foster anti-tumor immunity. NK cells, which have cytotoxic capabilities in anti-tumor immunity, also recruit conventional type 1 dendritic cells into tumor microenvironments upon stimulation. Their extensive infiltration is related to favorable outcomes in B cells. Immune checkpoint inhibitors like PD-1, PD-L1, and CTLA-4, approved by the FDA, have been shown to be effective in cancer treatment [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The high SHTN1 expression group exhibited more expression of immunosuppressive checkpoints(Fig.\u0026nbsp;6H), indicating a potential for a better response to immunotherapy. According to CIBERSORT analysis, the high SHTN1 expression group also contains more M1 subtype macrophages, indicating a predisposition towards anti-tumor Th1 responses. Our study of the immune environment indicated that the high SHTN1 expression group had more extensive infiltration of immune cells compared to the low expression group. Therefore, patients with high SHTN1 expression might be more likely to benefit from immunotherapy [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe role of SHTN1 (salt-inducible kinase 3) in cancer has been relatively unexplored. This study demonstrates that SHTN1 expression is significantly elevated in bladder cancer and intensifies with advancing clinical stages. This suggests that SHTN1 contributes to the progression of bladder cancer by affecting cytochrome P450 drug metabolism and the EMT pathway.\u003c/p\u003e\n\u003cp\u003eBladder cancer is a complex and multifactorial disease influenced by a mix of genetic, environmental, behavioral, lifestyle factors, including chemical exposures that elevate the risk of development. Specific chemicals and drugs, such as p-phenylenediamine, cyclophosphamide, praziquantel, and their metabolites [\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e]. A complex biological process is EMT, in which epithelial cells undergo a molecular or cytological transformation to become mesenchymal cells [\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e]. This transformation is crucial in the context of bladder cancer. As bladder cancer cells undergo EMT, they gain a heightened ability to migrate and infiltrate. This increased capacity enables them to penetrate the basement membrane and invade adjacent tissues. Such progression is a key factor in the spread and metastasis of bladder cancer, marking a critical phase in the disease\u0026apos;s advancement [\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eChemical exposure can impact various mechanisms such as cell adhesion, intercellular signaling pathways, autophagy, and gene expression regulation, triggering the initiation of EMT [\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e]. EMT not only remodels the surrounding stroma and alters the cytoskeleton but also is intricately linked to drug resistance in bladder cancer. Patients undergoing treatment may develop resistance to therapeutic drugs, a phenomenon closely associated with EMT, as evidenced by several studies [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e]. Furthermore, the Cytochrome P450 enzyme family, vital in drug metabolism and clearance, particularly in the liver and other tissues, might interact with tumor development and EMT, especially during liver-based drug metabolism [\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e]. This interaction can lead to tumor cells developing resistance to conventional therapies, complicating treatment efforts [\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e]. Genes regulating EMT mechanisms present potential therapeutic targets for malignant urinary system tumors. In this context, In the fight against bladder cancer, photodynamic therapy has emerged as a highly targeted treatment option.\u003c/p\u003e\n\u003cp\u003eSHTN1 is notably upregulated in response to PDT, which may indicate its regulatory role and response mechanism to cellular stress and photo infection. However, it\u0026apos;s important to note that tissue hypoxia is a prevalent characteristic in nearly all solid tumors. This hypoxic environment prominently influences the EMT, with lncRNAs, microRNAs, EIF5A2, Notch-4, and hypoxia itself being major regulators. Moreover, the epigenetic regulation of EMT is largely centered around hypoxia and TGF-\u0026beta;, emphasizing the complexity of cancer biology and the response to treatments like PDT [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e]. It appears that lncRNAs, microRNAs, EIF5A2, Notch-4, and hypoxia are the primary regulators of EMTs. Importantly, the epigenetic regulation of EMT is profoundly influenced by hypoxia and TGF-\u0026beta;, highlighting their central roles in this critical cellular transformation [\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e]. In GSEA, SHTN1 demonstrated a significant up-regulation in response to hypoxia. This finding is particularly relevant in the context of PDT, which necessitates substantial oxygen consumption during the treatment process. Addressing the resultant hypoxia within tumors is crucial for enhancing the effectiveness of PDT [\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e]. Several studies have focused on overcoming hypoxia in tumor photodynamic therapy. These include employing micro/nano motors to enhance oxygen utilization within the tumor environment, using biosynthetic living organisms to supplement oxygen, and repairing tumor blood circulation to augment oxygen supply. Additionally, other innovative methods are being explored to address this challenge effectively [\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e]. These innovative pathways have the potential to alleviate hypoxia-related issues in tumor treatment. Additionally, compounds like curcumin and its nanomedicine formulations, metformin, and Nitric Oxide have been effectively used to reduce oxygen consumption, thereby enhancing the efficiency of PDT. Given these advancements, it\u0026apos;s recommended that PDT be integrated with other treatment modalities, such as radiotherapy and chemotherapy, to augment the overall therapeutic effectiveness against BLCA.\u003c/p\u003e\n\u003cp\u003eThe microenvironment of BLCA is highly dynamic, comprising various cell types such as cancer cells, stem cells, and a range of immune cells, including neutrophils, macrophages, adipocytes, neurons, and neuroendocrine cells. These diverse cells interact and collectively influence the local tumor milieu. Crucially, the balance and functional activation of these immune cells are pivotal in determining tumor prognosis, and their activation is significantly influenced by the TME. Immune checkpoint inhibitors have emerged as a transformative therapy, enhancing treatment options and oncological outcomes for patients with urinary system cancers. In light of these insights, patients with high SHTN1 expression might exhibit a more favorable response to immunotherapy [\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eWhile SHTN1 is identified as a key risk factor for bladder cancer, its role extends beyond this, significantly influencing the response to photodynamic therapy. This dual functionality of SHTN1 not only underscores its importance in disease progression but also highlights its potential as a therapeutic target in bladder cancer treatment. Further supporting its role, our analysis reveals that SHTN1 is associated with an immune environment conducive to treatment, reinforcing its relevance in BLCA therapeutic strategies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAuthor contributions\u003c/p\u003e\n\u003cp\u003eZhengang Shen: Writing-original draft, investigation, methodology, conceptualization. Jiayi Lu: Drawing of Figures \u0026amp; editing. HaoJin Cheng: drawing figures. Xiaodi Tang: Writing-review. Li Chen: project administration. Guangqiang Hu, Yong Yu, Jun-feng Liu, Xingyue Han: Data Collection.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCorresponding Authors：Hong Liao, Shukui Zhou：Manuscript review and design.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis work was\u0026nbsp;supported by no findings.\u003c/p\u003e\n\u003cp\u003eData and code availability\u003c/p\u003e\n\u003cp\u003eThe datasets involved in our study were extracted from TCGA (https://portal.gdc.cancer.gov/), GEO (https://www.ncbi.nlm.nih.gov/geo), and GSE (https://www.ncbi.nlm.nih.gov/geo/).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;If necessary, the code will be made available upon request via email at: [email protected].\u003c/p\u003e\n\u003cp\u003eEthical approval This article does not contain any studies with human participants performed by any of the authors.\u003c/p\u003e\n\u003cp\u003eCompeting interests The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003eDeclaration During the preparation of this work, the authors used ChatGPT in order to improve the language and readability of the manuscript. After using this tool, the authors reviewed and edited the content as needed.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLenis AT, Lec PM, Chamie K, Mshs MD. Bladder Cancer: A Review. Jama. 2020;324(19):1980-91. doi:10.1001/jama.2020.17598.\u003c/li\u003e\n\u003cli\u003eProut GR, Jr., Barton BA, Griffin PP, Friedell GH. Treated history of noninvasive grade 1 transitional cell carcinoma. The National Bladder Cancer Group. J Urol. 1992;148(5):1413-9. doi:10.1016/s0022-5347(17)36924-0.\u003c/li\u003e\n\u003cli\u003eLobo N, Mount C, Omar K, Nair R, Thurairaja R, Khan MS. Landmarks in the treatment of muscle-invasive bladder cancer. Nat Rev Urol. 2017;14(9):565-74. doi:10.1038/nrurol.2017.82.\u003c/li\u003e\n\u003cli\u003eSiegel RL, Miller KD, Wagle NS, Jemal A. Cancer statistics, 2023. CA Cancer J Clin. 2023;73(1):17-48. doi:10.3322/caac.21763.\u003c/li\u003e\n\u003cli\u003eAgostinis P, Berg K, Cengel KA, Foster TH, Girotti AW, Gollnick SO, et al. Photodynamic therapy of cancer: an update. CA Cancer J Clin. 2011;61(4):250-81. doi:10.3322/caac.20114.\u003c/li\u003e\n\u003cli\u003eSobhani N, Samadani AA. Implications of photodynamic cancer therapy: an overview of PDT mechanisms basically and practically. J Egypt Natl Canc Inst. 2021;33(1):34. doi:10.1186/s43046-021-00093-1.\u003c/li\u003e\n\u003cli\u003eDąbrowski JM, Arnaut LG. Photodynamic therapy (PDT) of cancer: from local to systemic treatment. Photochem Photobiol Sci. 2015;14(10):1765-80. doi:10.1039/c5pp00132c.\u003c/li\u003e\n\u003cli\u003eHafez Ghoran S, Calcaterra A, Abbasi M, Taktaz F, Nieselt K, Babaei E. Curcumin-Based Nanoformulations: A Promising Adjuvant towards Cancer Treatment. Molecules. 2022;27(16). doi:10.3390/molecules27165236.\u003c/li\u003e\n\u003cli\u003eErgin V, Zheng S. Putative Coiled-Coil Domain-Dependent Autoinhibition and Alternative Splicing Determine SHTN1\u0026apos;s Actin-Binding Activity. J Mol Biol. 2020;432(14):4154-66. doi:10.1016/j.jmb.2020.04.025.\u003c/li\u003e\n\u003cli\u003eJiang X, Stockwell BR, Conrad M. Ferroptosis: mechanisms, biology and role in disease. Nat Rev Mol Cell Biol. 2021;22(4):266-82. doi:10.1038/s41580-020-00324-8.\u003c/li\u003e\n\u003cli\u003eLiang Y, Ye F, Xu C, Zou L, Hu Y, Hu J, et al. A novel survival model based on a Ferroptosis-related gene signature for predicting overall survival in bladder cancer. BMC Cancer. 2021;21(1):943. doi:10.1186/s12885-021-08687-7.\u003c/li\u003e\n\u003cli\u003eGlaser AP, Fantini D, Shilatifard A, Schaeffer EM, Meeks JJ. The evolving genomic landscape of urothelial carcinoma. Nat Rev Urol. 2017;14(4):215-29. doi:10.1038/nrurol.2017.11.\u003c/li\u003e\n\u003cli\u003eZhu K, Xiaoqiang L, Deng W, Wang G, Fu B. Development and validation of a novel lipid metabolism-related gene prognostic signature and candidate drugs for patients with bladder cancer. Lipids Health Dis. 2021;20(1):146. doi:10.1186/s12944-021-01554-1.\u003c/li\u003e\n\u003cli\u003eLi H, Liu S, Li C, Xiao Z, Hu J, Zhao C. TNF Family-Based Signature Predicts Prognosis, Tumor Microenvironment, and Molecular Subtypes in Bladder Carcinoma. Front Cell Dev Biol. 2021;9:800967. doi:10.3389/fcell.2021.800967.\u003c/li\u003e\n\u003cli\u003eChi M, Liu J, Mei C, Shi Y, Liu N, Jiang X, et al. TEAD4 functions as a prognostic biomarker and triggers EMT via PI3K/AKT pathway in bladder cancer. J Exp Clin Cancer Res. 2022;41(1):175. doi:10.1186/s13046-022-02377-3.\u003c/li\u003e\n\u003cli\u003eSharma P, Siddiqui BA, Anandhan S, Yadav SS, Subudhi SK, Gao J, et al. The Next Decade of Immune Checkpoint Therapy. Cancer Discov. 2021;11(4):838-57. doi:10.1158/2159-8290.Cd-20-1680.\u003c/li\u003e\n\u003cli\u003eShiravand Y, Khodadadi F, Kashani SMA, Hosseini-Fard SR, Hosseini S, Sadeghirad H, et al. Immune Checkpoint Inhibitors in Cancer Therapy. Curr Oncol. 2022;29(5):3044-60. doi:10.3390/curroncol29050247.\u003c/li\u003e\n\u003cli\u003eXiao Y, Yu D. Tumor microenvironment as a therapeutic target in cancer. Pharmacol Ther. 2021;221:107753. doi:10.1016/j.pharmthera.2020.107753.\u003c/li\u003e\n\u003cli\u003eAmara CS, Vantaku V, Lotan Y, Putluri N. Recent advances in the metabolomic study of bladder cancer. Expert Rev Proteomics. 2019;16(4):315-24. doi:10.1080/14789450.2019.1583105.\u003c/li\u003e\n\u003cli\u003ePastushenko I, Blanpain C. EMT Transition States during Tumor Progression and Metastasis. Trends Cell Biol. 2019;29(3):212-26. doi:10.1016/j.tcb.2018.12.001.\u003c/li\u003e\n\u003cli\u003eGundamaraju R, Lu W, Paul MK, Jha NK, Gupta PK, Ojha S, et al. Autophagy and EMT in cancer and metastasis: Who controls whom? Biochim Biophys Acta Mol Basis Dis. 2022;1868(9):166431. doi:10.1016/j.bbadis.2022.166431.\u003c/li\u003e\n\u003cli\u003eRamesh V, Brabletz T, Ceppi P. Targeting EMT in Cancer with Repurposed Metabolic Inhibitors. Trends Cancer. 2020;6(11):942-50. doi:10.1016/j.trecan.2020.06.005.\u003c/li\u003e\n\u003cli\u003ePan G, Liu Y, Shang L, Zhou F, Yang S. EMT-associated microRNAs and their roles in cancer stemness and drug resistance. Cancer Commun (Lond). 2021;41(3):199-217. doi:10.1002/cac2.12138.\u003c/li\u003e\n\u003cli\u003eGuengerich FP. Cytochrome p450 and chemical toxicology. Chem Res Toxicol. 2008;21(1):70-83. doi:10.1021/tx700079z.\u003c/li\u003e\n\u003cli\u003eJing X, Yang F, Shao C, Wei K, Xie M, Shen H, et al. Role of hypoxia in cancer therapy by regulating the tumor microenvironment. Mol Cancer. 2019;18(1):157. doi:10.1186/s12943-019-1089-9.\u003c/li\u003e\n\u003cli\u003eLin YT, Wu KJ. Epigenetic regulation of epithelial-mesenchymal transition: focusing on hypoxia and TGF-\u0026beta; signaling. J Biomed Sci. 2020;27(1):39. doi:10.1186/s12929-020-00632-3.\u003c/li\u003e\n\u003cli\u003eLarue L, Myrzakhmetov B, Ben-Mihoub A, Moussaron A, Thomas N, Arnoux P, et al. Fighting Hypoxia to Improve PDT. Pharmaceuticals (Basel). 2019;12(4). doi:10.3390/ph12040163.\u003c/li\u003e\n\u003cli\u003eWan Y, Fu LH, Li C, Lin J, Huang P. Conquering the Hypoxia Limitation for Photodynamic Therapy. Adv Mater. 2021;33(48):e2103978. doi:10.1002/adma.202103978.\u003c/li\u003e\n\u003cli\u003eMancini M, Righetto M, Noessner E. Checkpoint Inhibition in Bladder Cancer: Clinical Expectations, Current Evidence, and Proposal of Future Strategies Based on a Tumor-Specific Immunobiological Approach. Cancers (Basel). 2021;13(23). doi:10.3390/cancers13236016.\u003c/li\u003e\n\u003cli\u003eViveiros N, Flores BC, Lobo J, Martins-Lima C, Cantante M, Lopes P, et al. Detailed bladder cancer immunoprofiling reveals new clues for immunotherapeutic strategies. Clin Transl Immunology. 2022;11(9):e1402. doi:10.1002/cti2.1402.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":false,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"TCGA, GEO, GSEA, BLCA, PDT, SHTN1, Bladder Cancer","lastPublishedDoi":"10.21203/rs.3.rs-4021160/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4021160/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eA high recurrence rate and a tendency to progress to more advanced, invasive stages characterize bladder urothelial carcinoma (BLCA), the ninth most common malignant tumor worldwide. Despite its potential, photodynamic therapy (PDT), a minimally invasive treatment, remains underutilized in BLCA management. This study focuses on identifying key genes that influence BLCA progression and prognosis, specifically in the context of PDT therapy.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eAccording to the Cancer Genome Atlas (TCGA), we analyzed the mRNA expression profiles as well as clinical data for BLCA patients. Our approach included differential analysis, gene set intersection using GSEA databases, univariate regression analysis, and ROC curve plotting. Additionally, we validated our findings using BLCA patients' genes from the GEO dataset. To explore the role of SHTN1, we employed various methods such as GO, KEGG, GSEA, and GeneMANIA. We also examined the immunological environments associated with SHTN1 using tools like ESTIMATE, CIBERSORT, ssGSEA, and ICB to compare SHTN1 subgroups.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA positive correlation was found between SHTN1 expression and clinical stage and distant metastasis of BLCA, while a negative correlation was found between SHTN1 expression and patient survival. There were a number of genes associated with tumor formation and development in the high SHTN1-expressing group. Immune characteristics assessment using ESTIMATE, CIBERSORT, and ssGSEA showed that the high SHTN1-expressing group showed improved immune characteristics.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eAccording to our research, SHTN1 can both be a prognostic factor for BLCA and a therapeutic target.\u003c/p\u003e","manuscriptTitle":"Exploring the transcriptomic landscape of BLCA: SHTN1 as a key player in photodynamic therapy response","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-29 18:58:54","doi":"10.21203/rs.3.rs-4021160/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b8e946e6-1e99-4d60-b83b-fc888e1906d9","owner":[],"postedDate":"March 29th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-06-13T14:22:05+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-29 18:58:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4021160","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4021160","identity":"rs-4021160","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}