Integrated spatial transcriptome and metabolism study reveals metabolic heterogeneity in human bladder cancer

Integrated spatial transcriptome and metabolism study reveals metabolic heterogeneity in human bladder cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Integrated spatial transcriptome and metabolism study reveals metabolic heterogeneity in human bladder cancer Yu Lu, Fangdie Ye, Xuedan Han, Zihan Wang, Wenzhou Zhang, Hai Qin, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5894269/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 Bladder cancer (BC) is a malignancy that originates from the cells lining the bladder and is one of the most common cancers of the urinary system, capable of occurring in any part of the bladder. However, the molecular mechanisms underlying the malignant transformation of BC have not been systematically studied. This study integrated cutting-edge techniques of spatial transcriptomics (ST) and spatial metabolomics (SM) to capture the transcriptomic and metabolomic landscapes of both BC and adjacent normal tissues. ST results revealed a significant upregulation of genes associated with choline metabolism and glucose metabolism, while genes related to sphingolipid metabolism and tryptophan metabolism were significantly downregulated. Additionally, significant metabolic reprogramming was observed in BC tissues, including the upregulation of choline metabolism and glucose metabolism, as well as the downregulation of sphingolipid metabolism and tryptophan metabolism. These alterations may play a crucial role in promoting tumorigenesis and immune evasion of BC. The interpretation of ST and SM data in this study offers new insights into the molecular mechanisms underlying BC progression and provides valuable clues for the prevention and treatment of BC. Bladder cancer Spatial transcriptome Spatial metabolomics Choline metabolism TCA cycle Sphingolipid metabolism Tryptophan metabolism Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Bladder cancer (BC) is a type of cancer that originates in the cells lining the bladder, a hollow organ that stores urine[ 1 ]. Advanced age, male sex and smoking contribute to the development and progression of BC[ 2 ]. BC is typically categorized into two main types based on its behavior: non-muscle invasive bladder cancer (NMIBC) and muscle-invasive bladder cancer (MIBC)[ 3 ]. NMIBC is confined to the innermost layer of the bladder lining and does not invade the muscle layer[ 4 ]. MIBC invades the bladder wall and may spread to nearby lymph nodes or distant organs[ 5 ]. For NMIBC, treatments mainly include transurethral resection of bladder tumors (TURBT) combined with chemotherapy or immunotherapy to reduce the risk of recurrence[ 6 ]. For MIBC, a radical cystectomy is performed, often followed by reconstructive surgery to create a new way for the body to store and eliminate urine[ 7 ]. Occasionally, radiation therapy or chemotherapy is used as an alternative or adjunct to surgery[ 8 ]. Immunotherapies, such as PD-1 inhibitors or checkpoint inhibitors, are also commonly used to treat advanced BC, especially in cases where the cancer has spread to other parts of the body[ 9 – 11 ]. However, the identification of specific druggable targets for BC remains incomplete, and effective therapies to address resistance to immunotherapy have yet to be developed. Compared to normal cells, cancer cells often exhibit alterations in gene expression and metabolic reprogramming[ 12 ]. These changes drive the hallmarks of cancer, including uncontrolled proliferation, evasion of growth suppressors, resistance to cell death, the ability to invade surrounding tissues, and changes in the tumor microenvironment (TME) [ 13 ]. By comparing gene expression and metabolic profiles between cancerous tissues and adjacent normal tissues, specific dysregulated genes and pathways in cancer cells can be identified, pinpointing the molecular drivers of cancer[ 14 ]. Understanding the differences in gene expression and metabolite profiles between cancerous and adjacent normal tissues is crucial, as it provides insights into the molecular mechanisms of cancer initiation, progression, metastasis, and the TME[ 15 , 16 ]. These differences can help identify potential biomarkers for early diagnosis, reveal therapeutic targets, and enhance our understanding of how tumors interact with their microenvironment[ 17 ]. Spatial transcriptomics (ST) is an emerging field that combines high-throughput RNA sequencing with tissue samples that retain spatial resolution, enabling the mapping of gene expression distribution within tissues at high resolution[ 18 ]. Unlike traditional transcriptomics, which provides global gene expression data, ST preserves the tissue’s spatial structure, allowing for the analysis of how gene expression varies across different regions of tissues, organs, or organisms[ 19 , 20 ]. This technology can help elucidate complex tissue heterogeneity, study cell interactions, and offer insights into tissue development, disease mechanisms, and the TME [ 21 ]. Several studies have mapped the ST landscape of BC, yet these investigations primarily focus on the integration with single-cell techniques or the identification of specific cancer cell subpopulations[ 22 , 23 ]. However, no research has analyzed the gene expression differences between BC and adjacent normal tissues from a metabolic perspective. Spatial metabolomics (SM) refers to the study of metabolites and their distribution within tissues, organs, or organisms while retaining the spatial context[ 24 ]. This technique combines high-resolution mass spectrometry or other analytical tools with imaging technologies to map metabolite levels in biological samples[ 25 ]. By providing spatial information on the metabolome, SM enables researchers to understand how metabolic processes are localized, how they interact with tissue structures, and how they contribute to diseases such as cancer, neurodegenerative diseases, and metabolic disorders[ 26 ]. The integration of ST and SM provides a powerful approach to studying biological systems comprehensively[ 27 , 28 ]. ST maps gene expression within tissues, while SM captures the distribution of metabolites, the ultimate products of cellular processes[ 29 , 30 ]. By combining these two modalities, researchers can gain a more holistic understanding of the molecular landscape of tissues, revealing how gene expression and metabolism are spatially coordinated within the same tissue environment[ 31 ]. However, there has not yet been an analysis of differential genes and metabolites in BC utilizing ST and SM. This study, based on ST and SM, integrates the analysis of upregulated and downregulated pathway-related genes and metabolites in BC tissues, aiming to develop potential diagnostic biomarkers, accurately pinpoint the molecular drivers of BC, and provide a reference for the development of targeted therapies and precision medicine. 2. Materials and Methods 2.1. Human subjects BLCA and adjacent tissue samples were collected with the patients’ written informed consent and approved by the Human Research Ethics Committee of the Huashan Hospital, Fudan University (KY2011-009), and used for ST and SM analysis. 2.2. Spatial transcriptome (ST) 2.2.1. Sample preparation Following the embedding of fresh tissue, cryosections were prepared, and ten cryosections were placed into enzyme-free tubes for RNA extraction. The RNA Integrity Number (RIN) was required to exceed 7. As the optimal permeabilization time can vary based on species and tissue type, it is essential to optimize the tissue before performing ST sequencing. The tissue optimization chip eight capture areas. Cryosections were affixed to the designated areas on the chip, followed by hematoxylin-eosin (H&E) staining and imaging. Tissue permeabilization was subsequently conducted, allowing mRNAs released from cells to be captured by probes on the chip, resulting in the formation of cDNA labeled with fluorescent tags. The optimal permeabilization time was determined through the analysis of fluorescence imaging results. 2.2.2. ST Library Construction and Sequencing The spatial gene expression chip required for library construction features either two or four capture areas, each comprising 4,992 spots. Each spot was uniquely identified by a spatial barcode and contains millions of nucleotide primers. After tissue permeabilization, the mRNAs released from cells bound to the barcoded primers, facilitating the synthesis of cDNA and the construction of sequencing libraries. Depending on the tissue type, each barcode might correspond to 1 to 10 cells. Upon optimizing the permeabilization conditions, the process of constructing the ST library and sequencing can commence. Frozen tissue sections were mounted onto the capture areas of the gene expression chip, followed by fixation, H&E staining, and imaging. Tissue permeabilization was conducted according to the established conditions. The mRNAs released from cells was captured by the primers on the spots. Reverse transcription reagents (RT Master Mix) were then added to the permeabilized tissue sections, leading to the synthesis of full-length cDNA tagged with spatial barcodes during the incubation period. The cDNA served as a template for PCR amplification. Once amplification was completed, the products underwent quality control (QC) to evaluate the size and yield of the amplified fragments. Upon passing QC, the sequencing library was constructed. Initially, the cDNA was fragmented into approximately 200–300 bp pieces using chemical methods. The fragmented cDNA underwent end repair and A-tailing, followed by selection of cDNA fragments. A P7 adapter was ligated to the cDNA fragments, and dual-end sample indexes were incorporated via PCR amplification. Finally, the desired cDNA fragments were selected to create a sequencing-ready cDNA library. 2.2.3. Data processing 1) The raw sequencing data was preserved in GEO repository as GSE285715 and processed using the official 10 x Genomics software, Space Ranger ( https://support.10xgenomics.com/spatial-gene-expression/software/overview/welcome ). This included the steps of filtering, alignment, and quantification, which collectively produced a gene expression matrix for the detected spots. 2) For conducting differential analysis and visualizing data, we utilized Seurat ( https://satijalab.org/seurat/ ), allowing us to examine and visualize the dataset's heterogeneity by identifying clusters of cells or spots based on their gene expression patterns. To maintain data quality and align sequences to the reference genome, we performed quality control on the raw sequencing data using Space Ranger. This software employs the STAR algorithm for aligning Reads2 to the reference genome, prioritizing uniquely mapped sequences for further analysis. Additionally, Space Ranger employs a GFP annotation file to classify reads as exonic, intronic, or intergenic. Specifically, reads aligning to an exon for at least 50% of their bases are categorized as exonic, while those aligning to a gene but not to an exon were labeled as intronic; remaining reads were classified as intergenic. 3) Space Ranger facilitates single-sample clustering and visualization to investigated sample heterogeneity through gene expression levels across spots. This clustering process involved UMI normalization, PCA for dimensionality reduction, and clustering using Graph-based and k-means (k = 2…10) methods. The results of PCA were then visualized with t-SNE and UMAP to gain deeper insights into the data structure. 4) The integration of multiple samples was conducted using the “aggr” function provided by Space Ranger, which consolidates gene expression data from various samples to enable thorough multi-sample analysis across the spots. 2.3. Spatial metabolomics (SM) The fundamental principle of Matrix-Assisted Laser Desorption/Ionization (MALDI) mass spectrometry imaging is based on the co-crystallization of matrix molecules with the analytes of interest. These matrix molecules are capable of absorbing UV laser radiation at wavelengths of 337 nm or 355 nm, thereby facilitating the ionization of the analytes. During the process, each sample point was irradiated by the laser beam, which leads to the ionization of the target compounds. The sample was subsequently moved across the XY plane using a two-dimensional stage, enabling the separation and detection of ions from the tissue sample by the mass spectrometer. This process generated a mass spectrum that is correlated with the spatial distribution of the sample. Specialized software (SCiLS Lab) was then employed to analyze the mass spectrometric data, ultimately producing spatial distribution maps of metabolites within the range of 50-1300 Da present in the tissue sample. 2.3.1. Sample preparation The tissue was sectioned into 10 µm slices utilizing a Leica CM1950 cryostat. Initially, the tissue sample, which had been stored in a -80°C freezer, was equilibrated at -20°C within the cryostat chamber for one hour. The sample was then mounted onto the specimen holder, with careful adjustments made to its angle and position. The specimen holder was subsequently secured onto the orientation head of the cryostat. Following the operational manual of the cryostat, tissue sectioning was carried out. The freshly cut sections were transferred to pre-chilled ITO slides using a cooled brush. The back of the slide was gently pressed against the hand to allow body heat to melt the tissue section until it became transparent. Once transparency was achieved, the back of the slide was rubbed with a finger to evaporate moisture from the tissue surface, resulting in the section transitioning from transparent to white. After sectioning, the ITO slides containing the tissue sections were dried in a vacuum desiccator for 30 minutes. A 15 mg/mL solution of 2,5-dihydroxybenzoic acid (DHB) was prepared using a solvent mixture of 90% acetonitrile and 10% water. This DHB matrix solution was uniformly applied to the tissue sections mounted on the ITO slides using a TM-Sprayer matrix sprayer. The instrument parameters were set as follows: temperature at 60°C, flow rate at 0.12 mL/min, pressure at 6 psi, and a total of 30 spray cycles, with a drying time of 5 seconds allowed between each cycle. 2.3.2. Mass Spectrometry Imaging Detection The ITO slide, pre-coated with matrix, was positioned on the mass spectrometry target plate. Utilizing the DataImaging software (Bruker), the tissue region of interest was selected, and the imaging resolution was configured to 50 µm, indicating that the smallest unit in the two-dimensional grid measured 50 µm × 50 µm. The imaging area was then divided into a two-dimensional array of points according to its dimensions, with the imaging range set from 50 to 1300 Da. The tissue samples were analyzed under constant laser energy, where a laser beam, directed through a grating, irradiated the designated tissue region on the target plate. The interaction between the tissue samples and the matrix, induced by laser excitation, resulted in ionization and desorption of the molecules. These ions were subsequently identified by the mass spectrometer, yielding data on the mass-to-charge ratio (m/z) and peak intensity for each pixel. The raw data were imported into SCiLS Lab software for processing, which included smoothing and Root Mean Square (RMS) normalization. This analysis provided relative intensity information for various m/z values at each spatial point, which was then transformed into a pixelated heat map for imaging analysis. 2.3.3. Substance Identification Using the primary mass spectral (MS Class Ⅰ) peaks identified through marker peaks, we selected high-intensity target peaks to conduct in situ tandem mass spectrometry (MS/MS) fragmentation on the tissue samples. This approach facilitated the collection of secondary fragment ion spectra (MS/MS fragmentation data) from the tissue. The acquired MS/MS spectra were subsequently compared against our in-house database, augmented with public databases, to identify the substances present in the tissue samples. For target peaks exhibiting lower intensities, where secondary spectra could not be obtained, identification was performed based on the MS1 information. By searching both the in-house and public databases for compounds with molecular weights that matched the MS1 data within a 10 ppm error range, we identified substances with molecular weights closest to those detected by the instrument. 2.3.4. Data processing 1) Spatial Segmentation Analysis Spatial segmentation analysis provides a comprehensive overview of mass spectrometry imaging datasets, facilitating the rapid detection of significant features. This method employs statistical approaches to assess the similarity of mass spectra within a designated region, effectively grouping pixels that exhibit similar spectral data. Each group of pixels is then assigned a specific color to visually represent the segmentation. 2) Spatially-Aware Nearest Shrunken Centroids Clustering To obtain partitioning information, spatially-aware nearest shrunken centroids clustering was applied to the intensity data of all metabolites identified in the sample. Pixels within the same region typically displayed a consistent metabolic profile, and this analysis aided in the selection of regions of interest (ROIs) for further investigation. Parameter settings: Neighborhood smoothing radius (r = 2), Shrinkage parameter (s = 0), Maximum number of partitionable regions (k = 10). 3) Metabolite Co-localization Analysis Metabolite co-localization analysis was employed to identify metabolites that exhibit similar spatial distribution patterns relative to a target metabolite. This analysis could reveal potential interactions between molecules. Co-localization was typically quantified using Pearson’s Correlation Coefficient and Manders’ Colocalization Coefficient. 4) Relative Quantification of Metabolites The relative abundance of different substances across various regions of the tissues was represented by the intensity of the target peak, which was normalized using the Root Mean Square (RMS) method. A higher intensity value indicated a greater abundance of the target metabolite in that specific region. 2.4. Kaplan-Meier plotter database analysis We assessed the survival data and gene expression levels in BC patients through Kaplan-Meier plotter database ( http://kmplot.com ). Our search for BC cohorts (n = 405) was conducted through the NCBI Gene Expression Omnibus (GEO; https://www.ncbi.nlm.nih.gov/geo/ ) and the Genomic Data Commons Data Portal ( https://portal.gdc.cancer.gov/ )[ 32 ]. Due to the limited transcriptomic data available for BC, we used 10 unpaired normal tissue samples and 144 BC tissue samples for a comparative analysis of gene expression. 2.5. Hallmarks of Cancer analysis CancerHallmarks online platform ( www.cancerhallmarks.com ) was used to analyze the correlation between genes and their association with any identified cancer hallmarks. This database established a consensus list of cancer hallmark genes by merging 6,763 genes from available mapping resources[ 33 ]. 3. Results 3.1. ST and Metabolomics Reveal Spatial Heterogeneity in BC and Adjacent Tissues To explore the spatial landscape of BC, we collected cancerous tissues (T418, T419, T421) and adjacent normal tissues (N423, N424, N455) from post-surgery BC patients. ST sequencing was performed based on the 10 × Genomics platform. The schematic workflow of sample collection and spatial multi-omics detection was shown in Fig. 1 A. Uniform Manifold Approximation and Projection (UMAP) and t-distributed Stochastic Neighbor Embedding (t-SNE) were applied to each sample for dimensionality reduction (Figs. 1 B – C ). The resulting clusters were spatially visualized (Fig. 1 D), and cell-type annotation was performed based on the spatial clustering results (Fig. 1 E). Integration of the ST data from the six samples using t-SNE (Fig. 1 F) and UMAP (Fig. 1 G) multi-sample analyses demonstrated significant gene expression differences between cancer and adjacent tissues, as visualized through violin plots (Fig. 1 H). The samples were further divided into 20 subsets (Figs. 1 I – J, Supplementary Fig. S1 – 6 , and a heatmap displaying the top 10 differentially expressed genes in each subgroup was generated (Fig. 1 K). These results demonstrated that the regional complexity of the BC tissues greatly exceeded that of the adjacent tissues, indicating that BC tissues displayed significantly greater heterogeneity than adjacent tissues. 3.2. SM Reveals Spatial Heterogeneity in BC and Adjacent Tissues Subsequently, the KEGG enrichment analysis based on differential genes was conducted on three groups of cancer tissues and their corresponding adjacent tissues (T418-N424, T419-N455, T421-N423) (Figs. 2 A – B ). The results revealed that genes related to choline metabolism and carbon metabolism, especially the tricarboxylic acid (TCA) cycle, were significantly upregulated in cancer tissues; while genes associated with tryptophan metabolism, steroid biosynthesis, and AMPK signaling pathways were significantly downregulated. Orthogonal partial least squares discriminant analysis (OPLS-DA) was then used to further analyze the gene distribution differences between cancerous and adjacent tissues (Figs. 2 C – D ), again revealing marked gene expression differences. Since traditional metabolic studies could not revealed spatial distribution and heterogeneity, we further performed SM analysis (Fig. 2 E), identifying a total of 1362 metabolites, of which 507 were secondarily identified (Figs. 2 F – G, Supplementary Table. S1 ). Spatially-aware nearest shrunken centroids clustering was used on the intensity data of all identified metabolites to obtain partition information. The overall metabolic trend within the same regions was consistent. These results promoted us to combine the KEGG results of differentially expressed genes to further analyze the expression of key genes and their corresponding metabolites in the choline metabolism, TCA cycle, tryptophan metabolism, and steroid biosynthesis pathways in BC tissues and adjacent tissues. 3.3. Upregulation of Choline Metabolism in BC Tissues Aberrant choline metabolism is a recognized metabolic hallmark associated with tumorigenesis and progression[ 34 – 36 ]. Dysregulation of enzymes controlling both anabolic and catabolic pathways leads to elevated levels of choline-containing precursors and phospholipid breakdown products[ 37 ]. In cancer cells, the increased levels of phosphocholine (PCho) are not only linked to cellular proliferation, but are also closely associated with the TME, particularly the hypoxia TME (Fig. 3 A) [ 38 ]. Studies have demonstrated that hypoxia enhances both PCho and total choline levels in xenograft models of human prostate cancer[ 39 – 41 ]. In our investigation, we analyzed the expression levels of genes related to choline kinase α (CHKα) and CTP: phosphocholine cytidylyltransferase (CCT). Compared to normal tissues, the expression of CHKα, CCT2, CCT3, CCT6A, and CCT8 genes showed a general trend of upregulation in cancer tissues (Figs. 3 B – G ). Notably, the expression of these genes, except for CHKα, was negatively correlated with the overall survival (OS) of BC patients, and the expression of these genes was upregulated in BC tissues compared with normal tissues ( Supplementary Fig. S7 ). Additionally, we examined the spatial expression levels of capryloylcholine, PCho, and CDP-Cho, intermediates of the choline metabolic pathway, and found them to be consistently elevated (Figs. 3 H – J ). Furthermore, in the hypoxic core regions of the tumors, both choline metabolism-related genes and intermediate products exhibited higher expression levels compared to peripheral regions (Figs. 3 B – F , Figs. 3 H – J ). We also found the downregulation of the AMPK signaling in cancer tissues (Fig. 2 B). Since AMPK signaling pathway can inhibit energy consumption processes such as fatty acid synthesis, cholesterol synthesis, and protein synthesis[ 42 – 44 ], we also investigated the expression levels of AMPK signaling-related genes, and found that they were all downregulated ( Supplementary Fig. S8 ). These findings suggest that monitoring choline levels could potentially provide a non-invasive means to assess tumor therapeutic response. Targeting inhibitors of CHK and CCT could represent a promising therapeutic strategy for the treatment of BC. 3.4. Upregulation of Carbon Metabolism, Particularly the TCA Cycle Pathway, in BC Tissues. The TCA cycle is a fundamental metabolic pathway consisting of eight enzymatic reactions that provide reducing agents to drive ATP production in mitochondria[ 45 ]. Intermediates from the TCA cycle also facilitate anabolic pathways for the synthesis of lipids, nucleic acids, and proteins (Fig. 4 A). Cancer cells can disrupt the integrity of the TCA cycle, primarily evidenced by dysregulation in the expression levels of isocitrate dehydrogenase (IDH), succinate dehydrogenase (SDH), and fumarate hydratase (FH) [ 46 ]. The altered expression of these enzymes can further impact the levels of TCA cycle metabolites, consequently promoting cancer progression[ 47 ]. We observed an increase in the expression levels of the genes IDH1, IDH2, SDHA, SDHB, FH, and malate dehydrogenase 2 (MDH2) in cancerous tissues (Figs. 4 B – G , Fig. 4 I). Conversely, the expression of the aconitase (ACO) gene, ACO1, was found to be reduced (Fig. 4 H). ACO functions not only as an enzyme in the TCA cycle but also plays a critical role in cellular iron homeostasis, thus being referred to as iron regulatory protein-1 (IRP1). Therefore, further studies are warranted to determine whether the dysregulation of aconitase 1 (ACO1) and TCA cycle metabolites in tumors reflects a cross-talk in ACO/IRP1 activity. Notably, the expression of ACO1, IDH1, IDH2, SDHA, FH, and MDH2 was negatively correlated with the OS of BC patients too, and the expression of these genes, except for ACO1, SDHA, MDH2, was upregulated in BC tissues compared with normal tissues. ( Supplementary Fig. S9 ). In cancerous tissues, we observed an elevation in succinate expression, particularly in the intermediate hypoxic regions of tumors, compared to the surrounding areas (Fig. 4 J). Furthermore, we also noted an increase in the levels of phosphoenolpyruvate in cancer tissues (Fig. 4 K). Cancer cells secrete succinate, which promotes tumorigenesis through both autocrine and paracrine mechanisms[ 48 ]. Succinate binds to the succinate receptor 1 (SUCNR1) on cancer cells, facilitating tumor metastasis via the AKT/mTOR/HIF-1α signaling axis[ 49 ]. Under hypoxic conditions, succinate accumulates within tumors, inhibiting prolyl hydroxylases (PHDs) and leading to a pseudohypoxic state[ 50 ]. Studies have shown that succinate can activate the PI3K-AKT signaling pathway, further promoting cancer progression[ 51 ]. These findings indicate a general uptrend in the levels of TCA cycle-related genes and metabolites within BC tissues. 3.5. Downregulation of Sphingolipid Metabolism in BC Tissues The role of sphingolipid metabolism in cancer initiation and progression is complex and may vary across different cancer types[ 52 ]. Bioactive sphingolipids, particularly ceramides and sphingomyelins, are extensively involved in the regulation of cell migration, differentiation, proliferation, apoptosis, and stress responses, playing a significant role in immune-dependent and vascular-associated chronic inflammatory diseases[ 53 , 54 ]. When attempting to influence cancer cell growth, merely altering the level of a single sphingolipid is insufficient; rather, multiple sphingolipids must work in concert or in opposition to ultimately dictate cancer cell proliferation or apoptosis (Fig. 5 A). In BC tissues, we observed a downregulation of genes associated with the Sphingosine kinase 1 (SK1) enzyme, including ceramide kinase (CERK) and sphingosine kinase 1 (SPHK1), as well as the glutamate-cysteine ligase (GCS) enzyme-related gene mannosyl-oligosaccharide glucosidase (MOGS) and the sphingosine-1-phosphate lyase 1 (SPL1) enzyme-related gene SGPL1 (Figs. 5 B – F ). This decrease in expression levels was accompanied by a reduction in the levels of various metabolites, including 3-Dehydrosphinganine, N-Palmitoyl sphingomyelin, C20 Sphingomyelin, and Galactosyl ceramide (d18:1/20:0, d18:1/22:0, d18:1/24:0) (Figs. 5 G – J ). In consistent, the expression of CERK and SPHK1 was negatively correlated with the OS of BC patientsand the expression of CERK and SPHK1 was downregulated in BC tissues compared with normal tissues. ( Supplementary Fig. S10 ) The diminished levels of sphingolipid metabolites may lead to increased cancer cell proliferation and migration, potentially facilitating tumor progression by affecting immune cells in the TME. However, the specific mechanisms by which these metabolites influence tumor behavior require further investigation. 3.6. Downregulation of Tryptophan Metabolism in BC Tissues Tryptophan is an essential amino acid for humans, obtained solely through dietary sources. It plays a crucial role in protein biosynthesis and serves as a precursor for various important bioactive compounds[ 55 ]. The tryptophan metabolism primarily consists of the kynurenine pathway, the serotonin (5-HT) pathway, and the indole pathway[ 56 ]. Key rate-limiting enzymes involved in tryptophan metabolism include indoleamine-2,3-dioxygenase (TDO), tryptophan-2,3-dioxygenase (IDO), kynurenine-3-monooxygenase (KMO), and tryptophan hydroxylase (TPH) (Fig. 6 A). Interestingly, despite the upregulation of the tryptophan metabolic pathway in most cancers and the clinical trials involving IDO1 inhibitors, our findings indicate that the expression levels of this pathway in cancer tissues are downregulated[ 57 ]. Specifically, the expression levels of rate-limiting enzymes associated with the tryptophan metabolic pathway, such as IDO1, KMO, and TPH1, were significantly lower in cancerous tissues compared to normal tissues (Figs. 6 B – E ). This downregulation was accompanied by decreased levels of the metabolic products L-Tryptophanamide and D-Kynurenine (Figs. 6 F – G ). Notably, the expression of these genes was positively correlated with the OS of BC patients, and the expression of KMO was downregulated in BC tissues compared with normal tissues. ( Supplementary Fig. S11 ). The mechanisms by which the tryptophan metabolic pathway enzymes and their metabolites function in BC cells warrant further investigation. 4. Discussion BC, as a common malignancy of the urinary system, severely impacts patients' survival and health[ 58 ]. Clinically, BC treatment remains to be limited to surgical interventions and radiotherapy, with a lack of precision therapeutic strategies[ 59 ]. By combining ST and SM, we have revealed significant heterogeneity between BC tissues and normal tissues. In the malignant progression of BC, key genes associated with choline metabolism and the TCA cycle were significantly upregulated, while key genes related to sphingolipid metabolism and tryptophan metabolism were significantly downregulated. Further analysis of SM data revealed a significant upregulation of metabolites related to choline metabolism and the TCA cycle, alongside a notable downregulation of metabolites associated with sphingolipid and tryptophan metabolism. The downregulation of the AMPK pathway in cancer tissues promoted choline generation and carbon metabolism, thereby facilitating the malignant progression of BC. Among our findings, CHKα and the CCT family are crucial enzymes involved in the metabolism of cellular membrane phospholipids, participating in the synthesis of PC, a key component of the cell membrane. Studies have shown that the CHKα and the CCT family are closely associated with the proliferation, migration, and survival of cancer cells[ 60 – 63 ]. Given their essential roles in cancer cell metabolism and membrane synthesis, these enzymes are considered promising targets for cancer therapy. Currently, several inhibitors targeting CHKα, such as MN58b, EB3D, and TCD-717, are undergoing preclinical research[ 64 – 66 ]. These compounds hold promise for the treatment of solid tumors as well as for overcoming resistance to chemotherapy and radiotherapy. Besides, the downregulation of IDO in BC suggests that IDO inhibitors may not be suitable for BC. Graphic Abstract. Schematic illustration of BC metabolic reprogramming. Furthermore, we analyzed the survival data of these genes in clinical BC samples through Kaplan Meier plotter databases, as well as the differences in gene expression levels between BC tissues and normal tissues ( Supplementary Fig. S7, S9, S10, S11 ). These findings largely corroborate our conclusions, providing additional support for our hypotheses. Some discrepancies in the gene analysis results may arise due to our limited sample size or the relatively small number of normal bladder tissue samples available in the online databases. Moreover, the adjacent normal tissue samples and cancer samples analyzed through online databases are not paired, which may account for the discrepancies between the results of certain genes and our findings. In addition, we analyzed the correlation between genes and their association with any identified cancer hallmarks ( Supplementary Fig. S12, Supplementary Table. S2 ). The genes we identified are primarily involved in the process of reprogramming energy metabolism, resisting cell death and sustaining proliferative signaling. Energy metabolism reprogramming is a key characteristic of cancer cells, referring to the process by which cancer cells alter their metabolic pathways to adapt to the TME and promote tumor growth during their proliferation[ 67 ]. This metabolic reprogramming enables cancer cells to adapt to hostile microenvironments, driving tumor growth, metastasis, and resistance to therapy[ 68 ]. Cancer biomarkers involved in energy metabolism reprogramming are typically closely associated with these altered metabolic pathways[ 69 ]. These biomarkers primarily support the rapid proliferation, survival, and therapeutic resistance of cancer cells by regulating their metabolic processes. The alterations in these pathways are not only linked to tumor initiation and progression, but also offer potential targets for research into therapeutic resistance. Interventions targeting these metabolic markers may provide new directions for targeted therapies in cancer treatment. By comparing the spatial distribution of differentially expressed genes and their metabolites, we found that most upregulated genes and their associated metabolites are predominantly located in the hypoxic regions of the tumor. The hypoxic microenvironment is a common and significant characteristic of most solid tumors[ 70 ]. Hypoxia has profound effects on the biological behavior and malignant phenotype of cancer cells, mediating the efficacy of chemotherapy, radiotherapy, and immunotherapy through complex mechanisms, and is closely associated with poor prognosis in various cancer patients[ 71 ]. Our study reveals that the upregulation of choline metabolism and glycolysis levels in cancer cells under hypoxic conditions reflects alterations in signaling pathways that enable cancer cells to adapt to low oxygen environments. This may also contribute to mechanisms of hypoxia-induced immune tolerance, chemotherapy resistance, and enhanced radiotherapy resistance in cancer. In conclusion, our findings indicate that there is significant metabolic reprogramming during the onset and progression of BC. This study provides new insights into the malignant progression and immune evasion mechanisms of BC, as well as new clues for the development of targeted clinical therapies. Given the limited number of clinical samples, some degree of bias may be present. Furthermore, our findings only highlight the pathways, associated genes, and metabolites that are upregulated and downregulated in BC, while the underlying mechanisms require further investigation. Declarations Study approval BLCA and adjacent tissue samples were collected with the patients’ written informed consent and approved by the Human Research Ethics Committee of the Huashan Hospital, Fudan University (KY2011-009), and used for ST and SM analysis. Data availability statement All data were presented within the article, as well as supplementary online data. Author contributions Lufeng Zheng, Qianqian Guo, and Hai Qin designed the research. Yu Lu, Fangdie Ye, Xuedan Han, Zihan Wang and Wenzhou Zhang analyzed the data. Yu Lu and Fangdie Ye performed the research. Yu Lu and Fangdie Ye wrote the paper. Lufeng Zheng, Qianqian, and Hai Qin reviewed this paper. All authors read and approved the final manuscript. Acknowledgments This work was supported by the National Natural Science Foundation of China (Grant No. 82473955, 82173842), Guizhou Provincial Basic Research Program(Natural Science) (Qian Ke He Ji Chu-[32] Youth 020), and the Priority Academic Program Development (PAPD) of Jiangsu Higher Education Institutions. We would like to thank the Wuhan Metware Biotechnology Co., Ltd. (Wuhan, China) for spatial multi-omics analysis. Conflict-of-interest statement The authors have declared that no conflict of interest exists. References Compérat E, et al. Current best practice for bladder cancer: a narrative review of diagnostics and treatments. Lancet (London England). 2022;400(10364):1712–21. Lenis AT, et al. Bladder Cancer: Rev JAMA. 2020;324(19):1980–91. Dobruch J, Oszczudłowski M. 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Comprehensive prognostic and immunological analysis of CCT2 in pan-cancer. Front Oncol. 2022;12:986990. Liu W, et al. Current understanding on the role of CCT3 in cancer research. Front Oncol. 2022;12:961733. Lacal JC et al. Choline Kinase α Inhibitors MN58b and RSM932A Enhances the Antitumor Response to Cisplatin in Lung Tumor Cells. Pharmaceutics, 2022. 14(6). Mariotto E, et al. EB-3D a novel choline kinase inhibitor induces deregulation of the AMPK-mTOR pathway and apoptosis in leukemia T-cells. Biochem Pharmacol. 2018;155:213–23. de la Cueva A, et al. Combined 5-FU and ChoKα inhibitors as a new alternative therapy of colorectal cancer: evidence in human tumor-derived cell lines and mouse xenografts. PLoS ONE. 2013;8(6):e64961. Xia L, et al. The cancer metabolic reprogramming and immune response. Mol Cancer. 2021;20(1):28. Martínez-Reyes I, Chandel NS. Cancer metabolism: looking forward. Nat Rev Cancer. 2021;21(10):669–80. Pavlova NN, Thompson CB. Emerg Hallm Cancer Metabolism Cell Metabolism. 2016;23(1):27–47. Meng W, et al. Exosome-orchestrated hypoxic tumor microenvironment. Mol Cancer. 2019;18(1):57. Chen Z, et al. Hypoxic microenvironment in cancer: molecular mechanisms and therapeutic interventions. Signal Transduct Target Therapy. 2023;8(1):70. Additional Declarations No competing interests reported. Supplementary Files Graphicalabstract.tif Graphic Abstract. Schematic illustration of BC metabolic reprogramming. SupplementaryInformation.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-5894269","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":406597523,"identity":"cb0bd719-0bc3-42ef-a119-229a69571340","order_by":0,"name":"Yu Lu","email":"","orcid":"","institution":"China Pharmaceutical University","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Lu","suffix":""},{"id":406597524,"identity":"bad1f0d7-159b-43ea-b13b-a4eb5d04f1ee","order_by":1,"name":"Fangdie Ye","email":"","orcid":"","institution":"Fudan 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gene expression.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eF\u003c/strong\u003e) A t-SNE plot highlights the genetic differences between cancer tissues and normal tissues.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eG\u003c/strong\u003e) A UMAP plot demonstrates the genetic disparities between cancer tissues and normal tissues.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eH\u003c/strong\u003e) A violin plot displays the number of genes detected in the samples.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eI\u003c/strong\u003e) The clustering results from six tissue samples are mapped to their spatial localization.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eJ\u003c/strong\u003e) A bar chart shows the proportion of each tissue sample across different clusters.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eK\u003c/strong\u003e) A heatmap represents the top 10 gene expressions within each cluster for the six tissue samples.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5894269/v1/9099768516351118bde9e836.png"},{"id":74940393,"identity":"9b368e02-fda2-49f5-b177-960a06521e58","added_by":"auto","created_at":"2025-01-28 14:02:01","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2197532,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe differential analysis of BC and adjacent tissues through the integration of SM.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eA\u003c/strong\u003e) KEGG pathways in upregulated DEGs in tumor tissues.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eB\u003c/strong\u003e) KEGG pathways in downregulated DEGs in tumor tissues.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eC\u003c/strong\u003e - \u003cstrong\u003eD\u003c/strong\u003e) OPLS-DA in multiple statistical comparison to differentiate sample groups.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eE\u003c/strong\u003e) Spatially-aware nearest shrunken centroids clustering of 6 samples.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eF\u003c/strong\u003e) Ring diagram of metabolites (Class I) in BC and adjacent tissues.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eG\u003c/strong\u003e) Expression levels of 1362 metabolites in BC and adjacent tissues.\u003c/p\u003e","description":"","filename":"OnlineFigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5894269/v1/91b9aece0e4892bd58d91b59.png"},{"id":74941277,"identity":"2a84cd0d-46df-47a3-9f1f-0f4bc77d0613","added_by":"auto","created_at":"2025-01-28 14:09:56","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":5680758,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eUpregulation of Choline Metabolism in BC Tissues\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Mechanistic overview of choline metabolism in cancer.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eB\u003c/strong\u003e - \u003cstrong\u003eF\u003c/strong\u003e) Distribution of CHKα, CCT2, CCT3, CCT6A, and CCT8 in cancerous and adjacent non-cancerous tissues.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eG\u003c/strong\u003e) Violin plots illustrating the expression levels of genes in cancerous versus adjacent non-cancerous tissues.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eH\u003c/strong\u003e - \u003cstrong\u003eJ\u003c/strong\u003e) Distribution of Capryloyl choline, PCho, and CDP-Cho in cancerous and adjacent non-cancerous tissues.\u003c/p\u003e","description":"","filename":"OnlineFigure3.png","url":"https://assets-eu.researchsquare.com/files/rs-5894269/v1/c924880712bd0dd38b6562d1.png"},{"id":74940343,"identity":"5379b95c-e130-4516-86f1-1aa98b0661b4","added_by":"auto","created_at":"2025-01-28 14:01:58","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":19141257,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIn BC tissues, carbon metabolism, particularly the TCA cycle pathway, is upregulated.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eA\u003c/strong\u003e) A mechanistic diagram of the TCA cycle illustrates the key enzymatic steps involved.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eB\u003c/strong\u003e - \u003cstrong\u003eH\u003c/strong\u003e) The distribution of MDH2, IDH1, IDH2, SDHA, SDHB, FH, and ACO1in both cancerous and adjacent non-cancerous tissues is presented.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eI\u003c/strong\u003e) A violin plot displays the expression levels of these genes in cancerous tissues compared to adjacent non-cancerous tissues.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eJ\u003c/strong\u003e - \u003cstrong\u003eK\u003c/strong\u003e) The distributions of dimethyl succinate and phosphoenolpyruvate in cancerous and adjacent non-cancerous tissues are also analyzed, highlighting differences in metabolic profiles.\u003c/p\u003e","description":"","filename":"OnlineFigure4.png","url":"https://assets-eu.researchsquare.com/files/rs-5894269/v1/c802113513db4614d0dcc01e.png"},{"id":74940354,"identity":"9f83c037-5983-4995-9ba7-6bac574b22f1","added_by":"auto","created_at":"2025-01-28 14:01:59","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":12778440,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDownregulation of Sphingolipid Metabolism in BC Tissues.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Mechanistic diagram of sphingolipid metabolism.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eB\u003c/strong\u003e - \u003cstrong\u003eE\u003c/strong\u003e) Distribution of CERK, SPHK1, MOGS, and SGPL1 in cancerous and adjacent non-cancerous tissues.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eF\u003c/strong\u003e) Violin plots showing the expression levels of genes in cancerous and adjacent non-cancerous tissues.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eG\u003c/strong\u003e - \u003cstrong\u003eJ\u003c/strong\u003e) Distribution of 3-Dehydrosphinganine, N-Palmitoyl sphingomyelin, C20 Sphingomyelin, and Galactosyl ceramide in cancerous and adjacent non-cancerous tissues.\u003c/p\u003e","description":"","filename":"OnlineFigure5.png","url":"https://assets-eu.researchsquare.com/files/rs-5894269/v1/4752d288119b9b161cc175d5.png"},{"id":74940338,"identity":"9688e167-fef1-49e6-a71b-c6e24eb1a937","added_by":"auto","created_at":"2025-01-28 14:01:58","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":9909863,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDownregulation of Tryptophan Metabolism in BC Tissues.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Mechanistic diagram of the tryptophan metabolic pathway.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eB\u003c/strong\u003e - \u003cstrong\u003eD\u003c/strong\u003e) Distribution of IDO1, KMO, and TPH1 in cancerous tissues compared to adjacent non-cancerous tissues.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eE\u003c/strong\u003e) Violin plots illustrating the expression levels of genes in cancerous and adjacent non-cancerous tissues.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eF\u003c/strong\u003e - \u003cstrong\u003eG\u003c/strong\u003e) Distribution of L-Tryptophanamide and D-Kynurenine in cancerous tissues versus adjacent non-cancerous tissues.\u003c/p\u003e","description":"","filename":"OnlineFigure6.png","url":"https://assets-eu.researchsquare.com/files/rs-5894269/v1/942eb8874cc884305bbba7b0.png"},{"id":75187150,"identity":"82488711-71e9-4cb8-9f50-ae6dfaf87a21","added_by":"auto","created_at":"2025-01-31 17:46:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":11606613,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5894269/v1/0871ac1e-b9d4-434e-9cf2-f0fa1005b11b.pdf"},{"id":74940331,"identity":"efc15d1e-afaf-4d1d-8fc1-7eba4c231a71","added_by":"auto","created_at":"2025-01-28 14:01:55","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":26818552,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraphic Abstract. Schematic illustration of BC metabolic reprogramming.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Graphicalabstract.tif","url":"https://assets-eu.researchsquare.com/files/rs-5894269/v1/76f607b9f5008e683232533b.tif"},{"id":74940333,"identity":"65a625ba-a37c-4494-a5fd-c4944e27b326","added_by":"auto","created_at":"2025-01-28 14:01:55","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":14672531,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-5894269/v1/18b967c953bd4e5b8098aca9.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrated spatial transcriptome and metabolism study reveals metabolic heterogeneity in human bladder cancer","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eBladder cancer (BC) is a type of cancer that originates in the cells lining the bladder, a hollow organ that stores urine[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Advanced age, male sex and smoking contribute to the development and progression of BC[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. BC is typically categorized into two main types based on its behavior: non-muscle invasive bladder cancer (NMIBC) and muscle-invasive bladder cancer (MIBC)[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. NMIBC is confined to the innermost layer of the bladder lining and does not invade the muscle layer[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. MIBC invades the bladder wall and may spread to nearby lymph nodes or distant organs[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. For NMIBC, treatments mainly include transurethral resection of bladder tumors (TURBT) combined with chemotherapy or immunotherapy to reduce the risk of recurrence[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. For MIBC, a radical cystectomy is performed, often followed by reconstructive surgery to create a new way for the body to store and eliminate urine[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Occasionally, radiation therapy or chemotherapy is used as an alternative or adjunct to surgery[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Immunotherapies, such as PD-1 inhibitors or checkpoint inhibitors, are also commonly used to treat advanced BC, especially in cases where the cancer has spread to other parts of the body[\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. However, the identification of specific druggable targets for BC remains incomplete, and effective therapies to address resistance to immunotherapy have yet to be developed.\u003c/p\u003e \u003cp\u003eCompared to normal cells, cancer cells often exhibit alterations in gene expression and metabolic reprogramming[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. These changes drive the hallmarks of cancer, including uncontrolled proliferation, evasion of growth suppressors, resistance to cell death, the ability to invade surrounding tissues, and changes in the tumor microenvironment (TME) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. By comparing gene expression and metabolic profiles between cancerous tissues and adjacent normal tissues, specific dysregulated genes and pathways in cancer cells can be identified, pinpointing the molecular drivers of cancer[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Understanding the differences in gene expression and metabolite profiles between cancerous and adjacent normal tissues is crucial, as it provides insights into the molecular mechanisms of cancer initiation, progression, metastasis, and the TME[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. These differences can help identify potential biomarkers for early diagnosis, reveal therapeutic targets, and enhance our understanding of how tumors interact with their microenvironment[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSpatial transcriptomics (ST) is an emerging field that combines high-throughput RNA sequencing with tissue samples that retain spatial resolution, enabling the mapping of gene expression distribution within tissues at high resolution[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Unlike traditional transcriptomics, which provides global gene expression data, ST preserves the tissue\u0026rsquo;s spatial structure, allowing for the analysis of how gene expression varies across different regions of tissues, organs, or organisms[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. This technology can help elucidate complex tissue heterogeneity, study cell interactions, and offer insights into tissue development, disease mechanisms, and the TME [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Several studies have mapped the ST landscape of BC, yet these investigations primarily focus on the integration with single-cell techniques or the identification of specific cancer cell subpopulations[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. However, no research has analyzed the gene expression differences between BC and adjacent normal tissues from a metabolic perspective.\u003c/p\u003e \u003cp\u003eSpatial metabolomics (SM) refers to the study of metabolites and their distribution within tissues, organs, or organisms while retaining the spatial context[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. This technique combines high-resolution mass spectrometry or other analytical tools with imaging technologies to map metabolite levels in biological samples[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. By providing spatial information on the metabolome, SM enables researchers to understand how metabolic processes are localized, how they interact with tissue structures, and how they contribute to diseases such as cancer, neurodegenerative diseases, and metabolic disorders[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The integration of ST and SM provides a powerful approach to studying biological systems comprehensively[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. ST maps gene expression within tissues, while SM captures the distribution of metabolites, the ultimate products of cellular processes[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. By combining these two modalities, researchers can gain a more holistic understanding of the molecular landscape of tissues, revealing how gene expression and metabolism are spatially coordinated within the same tissue environment[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. However, there has not yet been an analysis of differential genes and metabolites in BC utilizing ST and SM.\u003c/p\u003e \u003cp\u003eThis study, based on ST and SM, integrates the analysis of upregulated and downregulated pathway-related genes and metabolites in BC tissues, aiming to develop potential diagnostic biomarkers, accurately pinpoint the molecular drivers of BC, and provide a reference for the development of targeted therapies and precision medicine.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1. Human subjects\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eBLCA and adjacent tissue samples were collected with the patients\u0026rsquo; written informed consent and approved by the Human Research Ethics Committee of the Huashan Hospital, Fudan University (KY2011-009), and used for ST and SM analysis.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2. Spatial transcriptome (ST)\u003c/h2\u003e\n \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\n \u003ch2\u003e2.2.1. Sample preparation\u003c/h2\u003e\n \u003cp\u003eFollowing the embedding of fresh tissue, cryosections were prepared, and ten cryosections were placed into enzyme-free tubes for RNA extraction. The RNA Integrity Number (RIN) was required to exceed 7. As the optimal permeabilization time can vary based on species and tissue type, it is essential to optimize the tissue before performing ST sequencing. The tissue optimization chip eight capture areas. Cryosections were affixed to the designated areas on the chip, followed by hematoxylin-eosin (H\u0026amp;E) staining and imaging. Tissue permeabilization was subsequently conducted, allowing mRNAs released from cells to be captured by probes on the chip, resulting in the formation of cDNA labeled with fluorescent tags. The optimal permeabilization time was determined through the analysis of fluorescence imaging results.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\n \u003ch2\u003e2.2.2. ST Library Construction and Sequencing\u003c/h2\u003e\n \u003cp\u003eThe spatial gene expression chip required for library construction features either two or four capture areas, each comprising 4,992 spots. Each spot was uniquely identified by a spatial barcode and contains millions of nucleotide primers. After tissue permeabilization, the mRNAs released from cells bound to the barcoded primers, facilitating the synthesis of cDNA and the construction of sequencing libraries. Depending on the tissue type, each barcode might correspond to 1 to 10 cells.\u003c/p\u003e\n \u003cp\u003eUpon optimizing the permeabilization conditions, the process of constructing the ST library and sequencing can commence. Frozen tissue sections were mounted onto the capture areas of the gene expression chip, followed by fixation, H\u0026amp;E staining, and imaging. Tissue permeabilization was conducted according to the established conditions. The mRNAs released from cells was captured by the primers on the spots. Reverse transcription reagents (RT Master Mix) were then added to the permeabilized tissue sections, leading to the synthesis of full-length cDNA tagged with spatial barcodes during the incubation period.\u003c/p\u003e\n \u003cp\u003eThe cDNA served as a template for PCR amplification. Once amplification was completed, the products underwent quality control (QC) to evaluate the size and yield of the amplified fragments. Upon passing QC, the sequencing library was constructed. Initially, the cDNA was fragmented into approximately 200\u0026ndash;300 bp pieces using chemical methods. The fragmented cDNA underwent end repair and A-tailing, followed by selection of cDNA fragments. A P7 adapter was ligated to the cDNA fragments, and dual-end sample indexes were incorporated via PCR amplification. Finally, the desired cDNA fragments were selected to create a sequencing-ready cDNA library.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\n \u003ch2\u003e2.2.3. Data processing\u003c/h2\u003e\n \u003cp\u003e1) The raw sequencing data was preserved in GEO repository as GSE285715 and processed using the official 10 x Genomics software, Space Ranger (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://support.10xgenomics.com/spatial-gene-expression/software/overview/welcome\u003c/span\u003e\u003c/span\u003e). This included the steps of filtering, alignment, and quantification, which collectively produced a gene expression matrix for the detected spots.\u003c/p\u003e\n \u003cp\u003e2) For conducting differential analysis and visualizing data, we utilized Seurat (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://satijalab.org/seurat/\u003c/span\u003e\u003c/span\u003e), allowing us to examine and visualize the dataset\u0026apos;s heterogeneity by identifying clusters of cells or spots based on their gene expression patterns. To maintain data quality and align sequences to the reference genome, we performed quality control on the raw sequencing data using Space Ranger. This software employs the STAR algorithm for aligning Reads2 to the reference genome, prioritizing uniquely mapped sequences for further analysis. Additionally, Space Ranger employs a GFP annotation file to classify reads as exonic, intronic, or intergenic. Specifically, reads aligning to an exon for at least 50% of their bases are categorized as exonic, while those aligning to a gene but not to an exon were labeled as intronic; remaining reads were classified as intergenic.\u003c/p\u003e\n \u003cp\u003e3) Space Ranger facilitates single-sample clustering and visualization to investigated sample heterogeneity through gene expression levels across spots. This clustering process involved UMI normalization, PCA for dimensionality reduction, and clustering using Graph-based and k-means (k\u0026thinsp;=\u0026thinsp;2\u0026hellip;10) methods. The results of PCA were then visualized with t-SNE and UMAP to gain deeper insights into the data structure.\u003c/p\u003e\n \u003cp\u003e4) The integration of multiple samples was conducted using the \u0026ldquo;aggr\u0026rdquo; function provided by Space Ranger, which consolidates gene expression data from various samples to enable thorough multi-sample analysis across the spots.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3. Spatial metabolomics (SM)\u003c/h2\u003e\n \u003cp\u003eThe fundamental principle of Matrix-Assisted Laser Desorption/Ionization (MALDI) mass spectrometry imaging is based on the co-crystallization of matrix molecules with the analytes of interest. These matrix molecules are capable of absorbing UV laser radiation at wavelengths of 337 nm or 355 nm, thereby facilitating the ionization of the analytes. During the process, each sample point was irradiated by the laser beam, which leads to the ionization of the target compounds. The sample was subsequently moved across the XY plane using a two-dimensional stage, enabling the separation and detection of ions from the tissue sample by the mass spectrometer. This process generated a mass spectrum that is correlated with the spatial distribution of the sample. Specialized software (SCiLS Lab) was then employed to analyze the mass spectrometric data, ultimately producing spatial distribution maps of metabolites within the range of 50-1300 Da present in the tissue sample.\u003c/p\u003e\n \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\n \u003ch2\u003e2.3.1. Sample preparation\u003c/h2\u003e\n \u003cp\u003eThe tissue was sectioned into 10 \u0026micro;m slices utilizing a Leica CM1950 cryostat. Initially, the tissue sample, which had been stored in a -80\u0026deg;C freezer, was equilibrated at -20\u0026deg;C within the cryostat chamber for one hour. The sample was then mounted onto the specimen holder, with careful adjustments made to its angle and position. The specimen holder was subsequently secured onto the orientation head of the cryostat. Following the operational manual of the cryostat, tissue sectioning was carried out. The freshly cut sections were transferred to pre-chilled ITO slides using a cooled brush. The back of the slide was gently pressed against the hand to allow body heat to melt the tissue section until it became transparent. Once transparency was achieved, the back of the slide was rubbed with a finger to evaporate moisture from the tissue surface, resulting in the section transitioning from transparent to white. After sectioning, the ITO slides containing the tissue sections were dried in a vacuum desiccator for 30 minutes. A 15 mg/mL solution of 2,5-dihydroxybenzoic acid (DHB) was prepared using a solvent mixture of 90% acetonitrile and 10% water. This DHB matrix solution was uniformly applied to the tissue sections mounted on the ITO slides using a TM-Sprayer matrix sprayer. The instrument parameters were set as follows: temperature at 60\u0026deg;C, flow rate at 0.12 mL/min, pressure at 6 psi, and a total of 30 spray cycles, with a drying time of 5 seconds allowed between each cycle.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\n \u003ch2\u003e2.3.2. Mass Spectrometry Imaging Detection\u003c/h2\u003e\n \u003cp\u003eThe ITO slide, pre-coated with matrix, was positioned on the mass spectrometry target plate. Utilizing the DataImaging software (Bruker), the tissue region of interest was selected, and the imaging resolution was configured to 50 \u0026micro;m, indicating that the smallest unit in the two-dimensional grid measured 50 \u0026micro;m \u0026times; 50 \u0026micro;m. The imaging area was then divided into a two-dimensional array of points according to its dimensions, with the imaging range set from 50 to 1300 Da. The tissue samples were analyzed under constant laser energy, where a laser beam, directed through a grating, irradiated the designated tissue region on the target plate. The interaction between the tissue samples and the matrix, induced by laser excitation, resulted in ionization and desorption of the molecules. These ions were subsequently identified by the mass spectrometer, yielding data on the mass-to-charge ratio (m/z) and peak intensity for each pixel.\u003c/p\u003e\n \u003cp\u003eThe raw data were imported into SCiLS Lab software for processing, which included smoothing and Root Mean Square (RMS) normalization. This analysis provided relative intensity information for various m/z values at each spatial point, which was then transformed into a pixelated heat map for imaging analysis.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\n \u003ch2\u003e2.3.3. Substance Identification\u003c/h2\u003e\n \u003cp\u003eUsing the primary mass spectral (MS Class Ⅰ) peaks identified through marker peaks, we selected high-intensity target peaks to conduct in situ tandem mass spectrometry (MS/MS) fragmentation on the tissue samples. This approach facilitated the collection of secondary fragment ion spectra (MS/MS fragmentation data) from the tissue. The acquired MS/MS spectra were subsequently compared against our in-house database, augmented with public databases, to identify the substances present in the tissue samples.\u003c/p\u003e\n \u003cp\u003eFor target peaks exhibiting lower intensities, where secondary spectra could not be obtained, identification was performed based on the MS1 information. By searching both the in-house and public databases for compounds with molecular weights that matched the MS1 data within a 10 ppm error range, we identified substances with molecular weights closest to those detected by the instrument.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\n \u003ch2\u003e2.3.4. Data processing\u003c/h2\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003ch3\u003e1) Spatial Segmentation Analysis\u003c/h3\u003e\n\u003cp\u003eSpatial segmentation analysis provides a comprehensive overview of mass spectrometry imaging datasets, facilitating the rapid detection of significant features. This method employs statistical approaches to assess the similarity of mass spectra within a designated region, effectively grouping pixels that exhibit similar spectral data. Each group of pixels is then assigned a specific color to visually represent the segmentation.\u003c/p\u003e\n\u003ch3\u003e2) Spatially-Aware Nearest Shrunken Centroids Clustering\u003c/h3\u003e\n\u003cp\u003eTo obtain partitioning information, spatially-aware nearest shrunken centroids clustering was applied to the intensity data of all metabolites identified in the sample. Pixels within the same region typically displayed a consistent metabolic profile, and this analysis aided in the selection of regions of interest (ROIs) for further investigation.\u003c/p\u003e\n\u003cp\u003eParameter settings: Neighborhood smoothing radius (r\u0026thinsp;=\u0026thinsp;2), Shrinkage parameter (s\u0026thinsp;=\u0026thinsp;0), Maximum number of partitionable regions (k\u0026thinsp;=\u0026thinsp;10).\u003c/p\u003e\n\u003ch3\u003e3) Metabolite Co-localization Analysis\u003c/h3\u003e\n\u003cp\u003eMetabolite co-localization analysis was employed to identify metabolites that exhibit similar spatial distribution patterns relative to a target metabolite. This analysis could reveal potential interactions between molecules. Co-localization was typically quantified using Pearson\u0026rsquo;s Correlation Coefficient and Manders\u0026rsquo; Colocalization Coefficient.\u003c/p\u003e\n\u003ch3\u003e4) Relative Quantification of Metabolites\u003c/h3\u003e\n\u003cp\u003eThe relative abundance of different substances across various regions of the tissues was represented by the intensity of the target peak, which was normalized using the Root Mean Square (RMS) method. A higher intensity value indicated a greater abundance of the target metabolite in that specific region.\u003c/p\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003e2.4. Kaplan-Meier plotter database analysis\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eWe assessed the survival data and gene expression levels in BC patients through Kaplan-Meier plotter database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://kmplot.com\u003c/span\u003e\u003c/span\u003e). Our search for BC cohorts (n\u0026thinsp;=\u0026thinsp;405) was conducted through the NCBI Gene Expression Omnibus (GEO; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003c/span\u003e) and the Genomic Data Commons Data Portal (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov/\u003c/span\u003e\u003c/span\u003e)[\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e]. Due to the limited transcriptomic data available for BC, we used 10 unpaired normal tissue samples and 144 BC tissue samples for a comparative analysis of gene expression.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003e2.5. Hallmarks of Cancer analysis\u003c/h2\u003e\n \u003cp\u003eCancerHallmarks online platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.cancerhallmarks.com\u003c/span\u003e\u003c/span\u003e) was used to analyze the correlation between genes and their association with any identified cancer hallmarks. This database established a consensus list of cancer hallmark genes by merging 6,763 genes from available mapping resources[\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1. ST and Metabolomics Reveal Spatial Heterogeneity in BC and Adjacent Tissues\u003c/h2\u003e\n \u003cp\u003eTo explore the spatial landscape of BC, we collected cancerous tissues (T418, T419, T421) and adjacent normal tissues (N423, N424, N455) from post-surgery BC patients. ST sequencing was performed based on the 10 \u0026times; Genomics platform. The schematic workflow of sample collection and spatial multi-omics detection was shown in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA. Uniform Manifold Approximation and Projection (UMAP) and t-distributed Stochastic Neighbor Embedding (t-SNE) were applied to each sample for dimensionality reduction (Figs. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eB \u003cstrong\u003e\u0026ndash; C\u003c/strong\u003e). The resulting clusters were spatially visualized (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eD), and cell-type annotation was performed based on the spatial clustering results (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eE). Integration of the ST data from the six samples using t-SNE (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eF) and UMAP (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eG) multi-sample analyses demonstrated significant gene expression differences between cancer and adjacent tissues, as visualized through violin plots (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eH). The samples were further divided into 20 subsets (Figs. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eI \u003cstrong\u003e\u0026ndash; J, Supplementary Fig. \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e\u0026ndash; 6\u003c/strong\u003e, and a heatmap displaying the top 10 differentially expressed genes in each subgroup was generated (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eK). These results demonstrated that the regional complexity of the BC tissues greatly exceeded that of the adjacent tissues, indicating that BC tissues displayed significantly greater heterogeneity than adjacent tissues.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2. SM Reveals Spatial Heterogeneity in BC and Adjacent Tissues\u003c/h2\u003e\n \u003cp\u003eSubsequently, the KEGG enrichment analysis based on differential genes was conducted on three groups of cancer tissues and their corresponding adjacent tissues (T418-N424, T419-N455, T421-N423) (Figs. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA \u003cstrong\u003e\u0026ndash; B\u003c/strong\u003e). The results revealed that genes related to choline metabolism and carbon metabolism, especially the tricarboxylic acid (TCA) cycle, were significantly upregulated in cancer tissues; while genes associated with tryptophan metabolism, steroid biosynthesis, and AMPK signaling pathways were significantly downregulated. Orthogonal partial least squares discriminant analysis (OPLS-DA) was then used to further analyze the gene distribution differences between cancerous and adjacent tissues (Figs. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eC \u003cstrong\u003e\u0026ndash; D\u003c/strong\u003e), again revealing marked gene expression differences. Since traditional metabolic studies could not revealed spatial distribution and heterogeneity, we further performed SM analysis (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eE), identifying a total of 1362 metabolites, of which 507 were secondarily identified (Figs. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eF \u003cstrong\u003e\u0026ndash; G, Supplementary Table. S1\u003c/strong\u003e). Spatially-aware nearest shrunken centroids clustering was used on the intensity data of all identified metabolites to obtain partition information. The overall metabolic trend within the same regions was consistent. These results promoted us to combine the KEGG results of differentially expressed genes to further analyze the expression of key genes and their corresponding metabolites in the choline metabolism, TCA cycle, tryptophan metabolism, and steroid biosynthesis pathways in BC tissues and adjacent tissues.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3. Upregulation of Choline Metabolism in BC Tissues\u003c/h2\u003e\n \u003cp\u003eAberrant choline metabolism is a recognized metabolic hallmark associated with tumorigenesis and progression[\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e]. Dysregulation of enzymes controlling both anabolic and catabolic pathways leads to elevated levels of choline-containing precursors and phospholipid breakdown products[\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e]. In cancer cells, the increased levels of phosphocholine (PCho) are not only linked to cellular proliferation, but are also closely associated with the TME, particularly the hypoxia TME (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA) [\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e]. Studies have demonstrated that hypoxia enhances both PCho and total choline levels in xenograft models of human prostate cancer[\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eIn our investigation, we analyzed the expression levels of genes related to choline kinase \u0026alpha; (CHK\u0026alpha;) and CTP: phosphocholine cytidylyltransferase (CCT). Compared to normal tissues, the expression of CHK\u0026alpha;, CCT2, CCT3, CCT6A, and CCT8 genes showed a general trend of upregulation in cancer tissues (Figs. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB \u003cstrong\u003e\u0026ndash; G\u003c/strong\u003e). Notably, the expression of these genes, except for CHK\u0026alpha;, was negatively correlated with the overall survival (OS) of BC patients, and the expression of these genes was upregulated in BC tissues compared with normal tissues (\u003cstrong\u003eSupplementary Fig. S7\u003c/strong\u003e). Additionally, we examined the spatial expression levels of capryloylcholine, PCho, and CDP-Cho, intermediates of the choline metabolic pathway, and found them to be consistently elevated (Figs. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eH \u003cstrong\u003e\u0026ndash; J\u003c/strong\u003e). Furthermore, in the hypoxic core regions of the tumors, both choline metabolism-related genes and intermediate products exhibited higher expression levels compared to peripheral regions (Figs. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB \u003cstrong\u003e\u0026ndash; F\u003c/strong\u003e, Figs. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eH \u003cstrong\u003e\u0026ndash; J\u003c/strong\u003e). We also found the downregulation of the AMPK signaling in cancer tissues (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB). Since AMPK signaling pathway can inhibit energy consumption processes such as fatty acid synthesis, cholesterol synthesis, and protein synthesis[\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e], we also investigated the expression levels of AMPK signaling-related genes, and found that they were all downregulated (\u003cstrong\u003eSupplementary Fig. S8\u003c/strong\u003e). These findings suggest that monitoring choline levels could potentially provide a non-invasive means to assess tumor therapeutic response. Targeting inhibitors of CHK and CCT could represent a promising therapeutic strategy for the treatment of BC.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4. Upregulation of Carbon Metabolism, Particularly the TCA Cycle Pathway, in BC Tissues.\u003c/h2\u003e\n \u003cp\u003eThe TCA cycle is a fundamental metabolic pathway consisting of eight enzymatic reactions that provide reducing agents to drive ATP production in mitochondria[\u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e]. Intermediates from the TCA cycle also facilitate anabolic pathways for the synthesis of lipids, nucleic acids, and proteins (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA). Cancer cells can disrupt the integrity of the TCA cycle, primarily evidenced by dysregulation in the expression levels of isocitrate dehydrogenase (IDH), succinate dehydrogenase (SDH), and fumarate hydratase (FH) [\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e]. The altered expression of these enzymes can further impact the levels of TCA cycle metabolites, consequently promoting cancer progression[\u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eWe observed an increase in the expression levels of the genes IDH1, IDH2, SDHA, SDHB, FH, and malate dehydrogenase 2 (MDH2) in cancerous tissues (Figs. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB \u003cstrong\u003e\u0026ndash; G\u003c/strong\u003e, Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eI). Conversely, the expression of the aconitase (ACO) gene, ACO1, was found to be reduced (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eH). ACO functions not only as an enzyme in the TCA cycle but also plays a critical role in cellular iron homeostasis, thus being referred to as iron regulatory protein-1 (IRP1). Therefore, further studies are warranted to determine whether the dysregulation of aconitase 1 (ACO1) and TCA cycle metabolites in tumors reflects a cross-talk in ACO/IRP1 activity. Notably, the expression of ACO1, IDH1, IDH2, SDHA, FH, and MDH2 was negatively correlated with the OS of BC patients too, and the expression of these genes, except for ACO1, SDHA, MDH2, was upregulated in BC tissues compared with normal tissues. (\u003cstrong\u003eSupplementary Fig. S9\u003c/strong\u003e).\u003c/p\u003e\n \u003cp\u003eIn cancerous tissues, we observed an elevation in succinate expression, particularly in the intermediate hypoxic regions of tumors, compared to the surrounding areas (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eJ). Furthermore, we also noted an increase in the levels of phosphoenolpyruvate in cancer tissues (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eK). Cancer cells secrete succinate, which promotes tumorigenesis through both autocrine and paracrine mechanisms[\u003cspan class=\"CitationRef\"\u003e48\u003c/span\u003e]. Succinate binds to the succinate receptor 1 (SUCNR1) on cancer cells, facilitating tumor metastasis via the AKT/mTOR/HIF-1\u0026alpha; signaling axis[\u003cspan class=\"CitationRef\"\u003e49\u003c/span\u003e]. Under hypoxic conditions, succinate accumulates within tumors, inhibiting prolyl hydroxylases (PHDs) and leading to a pseudohypoxic state[\u003cspan class=\"CitationRef\"\u003e50\u003c/span\u003e]. Studies have shown that succinate can activate the PI3K-AKT signaling pathway, further promoting cancer progression[\u003cspan class=\"CitationRef\"\u003e51\u003c/span\u003e]. These findings indicate a general uptrend in the levels of TCA cycle-related genes and metabolites within BC tissues.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5. Downregulation of Sphingolipid Metabolism in BC Tissues\u003c/h2\u003e\n \u003cp\u003eThe role of sphingolipid metabolism in cancer initiation and progression is complex and may vary across different cancer types[\u003cspan class=\"CitationRef\"\u003e52\u003c/span\u003e]. Bioactive sphingolipids, particularly ceramides and sphingomyelins, are extensively involved in the regulation of cell migration, differentiation, proliferation, apoptosis, and stress responses, playing a significant role in immune-dependent and vascular-associated chronic inflammatory diseases[\u003cspan class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e54\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eWhen attempting to influence cancer cell growth, merely altering the level of a single sphingolipid is insufficient; rather, multiple sphingolipids must work in concert or in opposition to ultimately dictate cancer cell proliferation or apoptosis (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA). In BC tissues, we observed a downregulation of genes associated with the Sphingosine kinase 1 (SK1) enzyme, including ceramide kinase (CERK) and sphingosine kinase 1 (SPHK1), as well as the glutamate-cysteine ligase (GCS) enzyme-related gene mannosyl-oligosaccharide glucosidase (MOGS) and the sphingosine-1-phosphate lyase 1 (SPL1) enzyme-related gene SGPL1 (Figs. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eB \u003cstrong\u003e\u0026ndash; F\u003c/strong\u003e). This decrease in expression levels was accompanied by a reduction in the levels of various metabolites, including 3-Dehydrosphinganine, N-Palmitoyl sphingomyelin, C20 Sphingomyelin, and Galactosyl ceramide (d18:1/20:0, d18:1/22:0, d18:1/24:0) (Figs. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eG \u003cstrong\u003e\u0026ndash; J\u003c/strong\u003e). In consistent, the expression of CERK and SPHK1 was negatively correlated with the OS of BC patientsand the expression of CERK and SPHK1 was downregulated in BC tissues compared with normal tissues. (\u003cstrong\u003eSupplementary Fig. S10\u003c/strong\u003e)\u003c/p\u003e\n \u003cp\u003eThe diminished levels of sphingolipid metabolites may lead to increased cancer cell proliferation and migration, potentially facilitating tumor progression by affecting immune cells in the TME. However, the specific mechanisms by which these metabolites influence tumor behavior require further investigation.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec25\" class=\"Section2\"\u003e\n \u003ch2\u003e3.6. Downregulation of Tryptophan Metabolism in BC Tissues\u003c/h2\u003e\n \u003cp\u003eTryptophan is an essential amino acid for humans, obtained solely through dietary sources. It plays a crucial role in protein biosynthesis and serves as a precursor for various important bioactive compounds[\u003cspan class=\"CitationRef\"\u003e55\u003c/span\u003e]. The tryptophan metabolism primarily consists of the kynurenine pathway, the serotonin (5-HT) pathway, and the indole pathway[\u003cspan class=\"CitationRef\"\u003e56\u003c/span\u003e]. Key rate-limiting enzymes involved in tryptophan metabolism include indoleamine-2,3-dioxygenase (TDO), tryptophan-2,3-dioxygenase (IDO), kynurenine-3-monooxygenase (KMO), and tryptophan hydroxylase (TPH) (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eA).\u003c/p\u003e\n \u003cp\u003eInterestingly, despite the upregulation of the tryptophan metabolic pathway in most cancers and the clinical trials involving IDO1 inhibitors, our findings indicate that the expression levels of this pathway in cancer tissues are downregulated[\u003cspan class=\"CitationRef\"\u003e57\u003c/span\u003e]. Specifically, the expression levels of rate-limiting enzymes associated with the tryptophan metabolic pathway, such as IDO1, KMO, and TPH1, were significantly lower in cancerous tissues compared to normal tissues (Figs. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eB \u003cstrong\u003e\u0026ndash; E\u003c/strong\u003e). This downregulation was accompanied by decreased levels of the metabolic products L-Tryptophanamide and D-Kynurenine (Figs. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eF \u003cstrong\u003e\u0026ndash; G\u003c/strong\u003e). Notably, the expression of these genes was positively correlated with the OS of BC patients, and the expression of KMO was downregulated in BC tissues compared with normal tissues. (\u003cstrong\u003eSupplementary Fig. S11\u003c/strong\u003e). The mechanisms by which the tryptophan metabolic pathway enzymes and their metabolites function in BC cells warrant further investigation.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eBC, as a common malignancy of the urinary system, severely impacts patients' survival and health[\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Clinically, BC treatment remains to be limited to surgical interventions and radiotherapy, with a lack of precision therapeutic strategies[\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. By combining ST and SM, we have revealed significant heterogeneity between BC tissues and normal tissues. In the malignant progression of BC, key genes associated with choline metabolism and the TCA cycle were significantly upregulated, while key genes related to sphingolipid metabolism and tryptophan metabolism were significantly downregulated. Further analysis of SM data revealed a significant upregulation of metabolites related to choline metabolism and the TCA cycle, alongside a notable downregulation of metabolites associated with sphingolipid and tryptophan metabolism. The downregulation of the AMPK pathway in cancer tissues promoted choline generation and carbon metabolism, thereby facilitating the malignant progression of BC.\u003c/p\u003e \u003cp\u003eAmong our findings, CHKα and the CCT family are crucial enzymes involved in the metabolism of cellular membrane phospholipids, participating in the synthesis of PC, a key component of the cell membrane. Studies have shown that the CHKα and the CCT family are closely associated with the proliferation, migration, and survival of cancer cells[\u003cspan additionalcitationids=\"CR61 CR62\" citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. Given their essential roles in cancer cell metabolism and membrane synthesis, these enzymes are considered promising targets for cancer therapy. Currently, several inhibitors targeting CHKα, such as MN58b, EB3D, and TCD-717, are undergoing preclinical research[\u003cspan additionalcitationids=\"CR65\" citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. These compounds hold promise for the treatment of solid tumors as well as for overcoming resistance to chemotherapy and radiotherapy. Besides, the downregulation of IDO in BC suggests that IDO inhibitors may not be suitable for BC.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eGraphic Abstract. Schematic illustration of BC metabolic reprogramming.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFurthermore, we analyzed the survival data of these genes in clinical BC samples through Kaplan Meier plotter databases, as well as the differences in gene expression levels between BC tissues and normal tissues (\u003cb\u003eSupplementary Fig. S7, S9, S10, S11\u003c/b\u003e). These findings largely corroborate our conclusions, providing additional support for our hypotheses. Some discrepancies in the gene analysis results may arise due to our limited sample size or the relatively small number of normal bladder tissue samples available in the online databases. Moreover, the adjacent normal tissue samples and cancer samples analyzed through online databases are not paired, which may account for the discrepancies between the results of certain genes and our findings.\u003c/p\u003e \u003cp\u003eIn addition, we analyzed the correlation between genes and their association with any identified cancer hallmarks (\u003cb\u003eSupplementary Fig. S12, Supplementary Table. S2\u003c/b\u003e). The genes we identified are primarily involved in the process of reprogramming energy metabolism, resisting cell death and sustaining proliferative signaling. Energy metabolism reprogramming is a key characteristic of cancer cells, referring to the process by which cancer cells alter their metabolic pathways to adapt to the TME and promote tumor growth during their proliferation[\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. This metabolic reprogramming enables cancer cells to adapt to hostile microenvironments, driving tumor growth, metastasis, and resistance to therapy[\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. Cancer biomarkers involved in energy metabolism reprogramming are typically closely associated with these altered metabolic pathways[\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. These biomarkers primarily support the rapid proliferation, survival, and therapeutic resistance of cancer cells by regulating their metabolic processes. The alterations in these pathways are not only linked to tumor initiation and progression, but also offer potential targets for research into therapeutic resistance. Interventions targeting these metabolic markers may provide new directions for targeted therapies in cancer treatment.\u003c/p\u003e \u003cp\u003eBy comparing the spatial distribution of differentially expressed genes and their metabolites, we found that most upregulated genes and their associated metabolites are predominantly located in the hypoxic regions of the tumor. The hypoxic microenvironment is a common and significant characteristic of most solid tumors[\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. Hypoxia has profound effects on the biological behavior and malignant phenotype of cancer cells, mediating the efficacy of chemotherapy, radiotherapy, and immunotherapy through complex mechanisms, and is closely associated with poor prognosis in various cancer patients[\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. Our study reveals that the upregulation of choline metabolism and glycolysis levels in cancer cells under hypoxic conditions reflects alterations in signaling pathways that enable cancer cells to adapt to low oxygen environments. This may also contribute to mechanisms of hypoxia-induced immune tolerance, chemotherapy resistance, and enhanced radiotherapy resistance in cancer.\u003c/p\u003e \u003cp\u003eIn conclusion, our findings indicate that there is significant metabolic reprogramming during the onset and progression of BC. This study provides new insights into the malignant progression and immune evasion mechanisms of BC, as well as new clues for the development of targeted clinical therapies. Given the limited number of clinical samples, some degree of bias may be present. Furthermore, our findings only highlight the pathways, associated genes, and metabolites that are upregulated and downregulated in BC, while the underlying mechanisms require further investigation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eStudy approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBLCA and adjacent tissue samples were collected with the patients\u0026rsquo; written informed consent and approved by the Human Research Ethics Committee of the Huashan Hospital, Fudan University (KY2011-009), and used for ST and SM analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data were presented within the article, as well as supplementary online data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLufeng Zheng, Qianqian Guo, and Hai Qin designed the research. Yu Lu, Fangdie Ye, Xuedan Han, Zihan Wang and Wenzhou Zhang analyzed the data. Yu Lu and Fangdie Ye performed the research. Yu Lu and Fangdie Ye wrote the paper. Lufeng Zheng, Qianqian, and Hai Qin reviewed this paper. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China (Grant No. 82473955, 82173842), Guizhou Provincial Basic Research Program(Natural Science) \u0026nbsp;(Qian Ke He Ji Chu-[32] Youth 020), and the Priority Academic Program Development (PAPD) of Jiangsu Higher Education Institutions. We would like to thank the Wuhan Metware Biotechnology Co., Ltd. (Wuhan, China) for spatial multi-omics analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict-of-interest statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have declared that no conflict of interest exists.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eComp\u0026eacute;rat E, et al. Current best practice for bladder cancer: a narrative review of diagnostics and treatments. Lancet (London England). 2022;400(10364):1712\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLenis AT, et al. Bladder Cancer: Rev JAMA. 2020;324(19):1980\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDobruch J, Oszczudłowski M. \u003cem\u003eBladder Cancer: Current Challenges and Future Directions.\u003c/em\u003e Medicina (Kaunas, Lithuania), 2021. 57(8).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWoldu SL, Bagrodia A, Lotan Y. 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Signal Transduct Target Therapy. 2023;8(1):70.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Bladder cancer, Spatial transcriptome, Spatial metabolomics, Choline metabolism, TCA cycle, Sphingolipid metabolism, Tryptophan metabolism","lastPublishedDoi":"10.21203/rs.3.rs-5894269/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5894269/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBladder cancer (BC) is a malignancy that originates from the cells lining the bladder and is one of the most common cancers of the urinary system, capable of occurring in any part of the bladder. However, the molecular mechanisms underlying the malignant transformation of BC have not been systematically studied. This study integrated cutting-edge techniques of spatial transcriptomics (ST) and spatial metabolomics (SM) to capture the transcriptomic and metabolomic landscapes of both BC and adjacent normal tissues. ST results revealed a significant upregulation of genes associated with choline metabolism and glucose metabolism, while genes related to sphingolipid metabolism and tryptophan metabolism were significantly downregulated. Additionally, significant metabolic reprogramming was observed in BC tissues, including the upregulation of choline metabolism and glucose metabolism, as well as the downregulation of sphingolipid metabolism and tryptophan metabolism. These alterations may play a crucial role in promoting tumorigenesis and immune evasion of BC. The interpretation of ST and SM data in this study offers new insights into the molecular mechanisms underlying BC progression and provides valuable clues for the prevention and treatment of BC.\u003c/p\u003e","manuscriptTitle":"Integrated spatial transcriptome and metabolism study reveals metabolic heterogeneity in human bladder cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-28 14:01:40","doi":"10.21203/rs.3.rs-5894269/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":"90466934-ba1e-482f-9c51-cd6b16fc5227","owner":[],"postedDate":"January 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-31T17:38:30+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-28 14:01:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5894269","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5894269","identity":"rs-5894269","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}