Systematic Pan-Cancer Analysis Showed that P2RY13 was Associated with Immune Microenvironment and Prognosis

Systematic Pan-Cancer Analysis Showed that P2RY13 was Associated with Immune Microenvironment and Prognosis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Systematic Pan-Cancer Analysis Showed that P2RY13 was Associated with Immune Microenvironment and Prognosis Zaishan Li, Zhenzhen Meng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4557714/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 P2RY13, a purinergic receptor belonging to the P2Y family of G-protein-coupled receptors, has garnered increasing attention for its potential involvement in cancer biology. This review explores the multifaceted role of P2RY13 in cancer development and progression, aiming to provide a comprehensive understanding of its implications for cancer diagnosis, prognosis, and therapy. Utilizing data mining techniques and bioinformatics analysis on publicly available datasets, we investigated the differential expression of P2RY13 across various cancer types and its correlation with clinical features, survival outcomes, tumor immune microenvironment, and molecular characteristics. Our analysis revealed significant dysregulation of P2RY13 expression in tumors, with distinct associations with malignant features and patient prognosis. Notably, P2RY13 expression was found to correlate with immune-related biomarkers and tumor stemness, highlighting its potential role in modulating tumor immunity and heterogeneity. Furthermore, our study identified correlations between P2RY13 expression and immune cell infiltration, immune checkpoint genes, and other immune regulatory genes, underscoring its importance in tumor immune modulation. Despite the insights gained, further mechanistic studies are warranted to elucidate the precise role of P2RY13 in cancer biology and to explore its therapeutic potential as a target for cancer treatment. Biological sciences/Cancer/Cancer genetics Biological sciences/Immunology/Immunogenetics P2RY13 tumor immune microenvironment pan-cancer bioinformatics analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction P2RY13, a purinergic receptor encoded by the P2RY13 gene, belongs to the P2Y family of G-protein-coupled receptors (GPCRs) that respond to extracellular nucleotides, particularly UDP-glucose( 1 ). It is predominantly expressed in immune cells, including microglia, macrophages, and dendritic cells, suggesting its involvement in immune responses and inflammatory processes( 2 , 3 ). Emerging evidence indicates that P2RY13 plays a crucial role in various physiological and pathological conditions, including cancer( 4 – 7 ). In recent years, studies have increasingly focused on understanding the role of P2RY13 in cancer development and progression( 4 , 8 – 10 ). Accumulating evidence suggests that dysregulation of P2RY13 expression may contribute to tumor initiation, growth, metastasis, and therapy resistance. However, the precise mechanisms underlying the involvement of P2RY13 in cancer biology remain poorly understood. This review aims to provide an overview of the current understanding of P2RY13 in cancer. We will explore its expression patterns across different cancer types, its potential roles in tumor progression, its associations with clinical outcomes and prognosis, and its implications for cancer therapy. Therefore, we conducted a data mining investigation on the public database by multiple bioinformatics methods in this paper. We analyzed the differential expressions of P2RY13 in pan-cancer and explored its correlation with prognosis, tumor stemness, tumor immunity, and tumor heterogeneity, aiming to preliminary explore the potential mechanism of P2RY13 in cancer development. Materials and Methods Gene expression data acquisition Unified pan-cancer datasets were downloaded from The Cancer Genome Atlas (TCGA) and Therapeutically Applicable Research to Generate Effective Treatments (TARGET) databases, and normal tissue datasets were downloaded from Genotype-Tissue Expression Project (GTEx) as control (PANCAN, N = 19131, G = 60499)( 11 ). P2RY13 (ENSG00000181631) gene expression data were extracted from each sample. All expression values were log2(x + 0.001) transformed. Differential expression analysis and clinical feature analysis were performed in R software (version 3.6.4). The abbreviation of each cancer type was listed in Table 1 . This part mainly refers to Liu's previously published papers( 11 ). Table 1 Abbreviation of each cancer type. Abbreviation Name TCGA-ACC Adrenocortical carcinoma TCGA-BLCA Bladder Urothelial Carcinoma TCGA-BRCA Breast invasive carcinoma TCGA-CESC Cervical squamous cell carcinoma and endocervical adenocarcinoma TCGA-CHOL Cholangiocarcinoma TCGA-COAD Colon adenocarcinoma TCGA-COADREAD Colon adenocarcinoma/Rectum adenocarcinoma Esophageal carcinoma TCGA-DLBC Lymphoid Neoplasm Diffuse Large B-cell Lymphoma TCGA-ESCA Esophageal carcinoma TCGA-FPPP FFPE Pilot Phase II TCGA-GBM Glioblastoma multiforme TCGA-GBMLGG Glioma TCGA-HNSC Head and Neck squamous cell carcinoma TCGA-KICH Kidney Chromophobe TCGA-KIPAN Pan-kidney cohort (KICH + KIRC + KIRP) TCGA-KIRC Kidney renal clear cell carcinoma TCGA-KIRP Kidney renal papillary cell carcinoma TCGA-LAML Acute Myeloid Leukemia TCGA-LGG Brain Lower Grade Glioma TCGA-LIHC Liver hepatocellular carcinoma TCGA-LUAD Lung adenocarcinoma TCGA-LUSC Lung squamous cell carcinoma TCGA-MESO Mesothelioma TCGA-OV Ovarian serous cystadenocarcinoma TCGA-PAAD Pancreatic adenocarcinoma TCGA-PCPG Pheochromocytoma and Paraganglioma TCGA-PRAD Prostate adenocarcinoma TCGA-READ Rectum adenocarcinoma TCGA-SARC Sarcoma TCGA-STAD Stomach adenocarcinoma TCGA-SKCM Skin Cutaneous Melanoma TCGA-SKCM-M Metastatic Skin Cutaneous Melanoma TCGA-SKCM-P Primary Skin Cutaneous Melanoma TCGA-STES Stomach and Esophageal carcinoma TCGA-TGCT Testicular Germ Cell Tumors TCGA-THCA Thyroid carcinoma TCGA-THYM Thymoma TCGA-UCEC Uterine Corpus Endometrial Carcinoma TCGA-UCS Uterine Carcinosarcoma TCGA-UVM Uveal Melanoma TARGET-OS Osteosarcoma TARGET-ALL Acute Lymphoblastic Leukemia TARGET-ALL-R Recurrent Acute Lymphoblastic Leukemia TARGET-NB Neuroblastoma TARGET-WT High-Risk Wilms Tumor Survival analysis A thorough prognostic dataset sourced from a study based on TCGA was acquired( 12 ). The CoxPH function in R was utilized to develop the Cox proportional hazards regression model. The optimal threshold for CD40LG expression was identified utilizing the MaxStat feature within the R programming environment, where the range for grouping was specified between the 25th and 75th percentiles. Each type of cancer was categorized into cohorts based on high and low levels of CD40LG expression. The survfit function in the R programming environment was employed to assess disparities in PFS and OS. Immune cell infiltration analysis Four independent tumor-infiltrating immune cells (TICs) analysis methods from the R package IOBR (version 0.99.9) ( 13 ) deconvo_EPIC ( 14 ), deconvo_MCPcounter ( 15 ) and deconvo_quanTIseq (16) were performed in this study. All 44 cancer types and 10180 samples were available in EPIC, MCPcounter, quanTIseq, and ESTIMATE score method. This part mainly refers to Liu's previously published papers( 11 ). Tumor heterogeneity, TMB, MSI, and stemness scoring Mutation data were gathered as described earlier. Mutant-allele tumor heterogeneity (MATH) and tumor mutational burden (TMB) were determined using the inferHeterogeneity and tmb functions from the R package maftools (version 2.8.05). Microsatellite instability (MSI) was assessed following the methodology outlined by R. Bonneville et al. ( 17 ). The stemness score was assessed with the algorithm developed by T.M. Malta et al. ( 18 ), which computes the mRNAsi score based on mRNA signatures and the mDNAsi score based on methylation signatures. Cancer types with fewer than three samples were excluded from this analysis. This part mainly refers to Liu's previously published papers( 11 ). Cell culture and cell transfection The cell lines employed in this study were obtained from the American Tissue Culture Collection (ATCC). Specifically, PANC1, A549, and BEAS-2B cell lines were cultured in RPMI 1640 medium (Gibco, USA), while MCF-7 and THLE-3 cell lines were grown in DMEM (Gibco, USA) and BEGM (Lonza, USA), respectively. The BEGM medium was fortified with 10% fetal bovine serum (FBS), phosphoethanolamine (70 ng/mL), and EGF (5 ng/mL), whereas the other media were supplemented with 10% FBS. Plasmids were sourced from Biomed Gene Technology Co., LTD (Beijing, China). Following this, either pCDNA3.1-P2RY13 or pCDNA3.1 with a nonspecific sequence was transfected into each cancer cell line using Lipofectamine 2000 (Invitrogen, USA). This part mainly refers to Liu's previously published papers( 11 ). Cell counting Kit-8 (CCK8) assay Cell suspensions were prepared and distributed into individual wells of 96-well plates, with a seeding density of 4000 cells per well. Following a respective incubation period of 24, 48, and 72 hours, 10µl of CCK8 solution (obtained from APExBIO, USA) was introduced into each well and allowed to incubate for 1 hour. Subsequently, the optical density (OD) at 450nm was quantified using a microplate reader. Western blot and qPCR Protein samples were extracted and quantified, and agarose gel electrophoresis was performed using 10% separating gels. Proteins were then transferred onto PVDF membranes (Millipore, Billerica, MA, United States) using a semi-dry transfer system. The membranes were then blocked with 5% skim milk at room temperature for 2 h. The primary antibodies (anti-P2RY13(Pronteintech, 20335-1-AP, 1:1000), anti-GAPDH(Pronteintech, 60004-1-lg, 1:1000) and membranes were incubated overnight at 4°C. Subsequently, the membranes and secondary antibody (1: 5000 dilution; Thermo Fisher Scientific, Inc.) were incubated for 1 h at room temperature. The bands were visualized using the Pierce ECL substrate (Thermo Fisher Scientific, Inc.).Real-time PCR was performed to evaluate the plasmid’s transfection effectiveness. Briefly, total RNA was extracted by TRIzol reagent (Invitrogen, USA), and cDNA were synthesized using PrimeScript RT Reagent Kit (TaKaRa, China). Each sample was tested in triplicate, and results were normalized by qPCR of cDNA with β-actin. The P2RY13 forward primer was designed as TGGCATCAGGTGGTCAGTCACA, and the β-actin forward primer was TCGTGCGTGACATTAAGGAGAAGC. Statistical analysis Statistical analyses were conducted using R software (version 3.6.4). For unpaired datasets, the Wilcoxon Rank Sum and Signed Rank Tests were applied. In cases involving multiple group comparisons, the Kruskal-Wallis test was employed. Pearson's correlation coefficient was utilized to assess correlations, while the Log-rank test was implemented for survival analyses. A P-value of ≤ 0.05 was considered indicative of statistical significance. This part mainly refers to Liu's previously published papers( 11 ). Results P2RY13 was significantly overexpressed in most tumors and occasionally negatively correlated with malignant clinical features. Sixteen of 34 cancer types presented P2RY13 significantly up-regulated in tumor samples, including GBM, GBMLGG, LGG, BRCA, CESC, ESCA, STES, KIRP, KIPAN, STAD, KIRC, SKCM, THCA, OV, PAAD, and TGCT.(Fig. 1 ). Additionally, we observed significant downregulation of P2RY13 in 10 tumor types, including UCEC, LUAD, LUSC, WT, READ, UCS, ALL, LAML, ACC, and KICH. (Fig. 1 ) Meanwhile, in LGG, GBMLGG, and SKCM, the expression of P2RY13 was significantly negatively correlated with age (Supplementary Fig. 1A). In ESCA, THYM, and LUAD, the expression of P2RY13 was significantly positive correlated with age (Supplementary Fig. 1A). Female patients in LUAD, BRCA, STES, HNSC, LUSC, PAAD, BLCA, and MESO presented significantly higher P2RY13 expression than females (Supplementary Fig. 1D). Decreased P2RY13 expression also indicated high-grade differentiation in LUAD, BRCA, STES, KIPAN, STAD, HNSC, THCA, SKCM, and KICH, higher T stage in BRCA, KIPAN, HNSC, ACC, and KICH.(Supplementary Fig. 1B, C). Up-regulated RAD51AP1 usually indicated a good prognosis Overall survival analysis showed that higher expression of P2RY13 correlated to better prognosis in SKCM, SKCM-M, KIRC, LUAD,etc. 9 cancer types, and worse prognosis in only 1 cancer types LAML (Fig. 2 A). Meanwhile, progression-free interval analysis showed that highly expressed RAD51AP1 was also related to good prognosis in 11 cancer types, including SARC, CESC, KIRC,etc. (Fig. 2 B). In seven types of tumors, high expression of P2RY13 is associated with better prognosis in both overall survival and progression-free survival. These correlations were reconfirmed using the Log-rank test (shown in Supplementary Fig. 2 and Supplementary Fig. 3 for OS and PFS, respectively). P2RY13 expression correlates with various immune therapy biomarkers across different tumor types. Pearson correlation analysis revealed significant associations between P2RY13 expression and Tumor Mutational Burden (TMB) (Fig. 3 A), Neoantigen burden(Fig. 3 B), Microsatellite Instability (MSI) score(Fig. 3 C), and Loss of Heterozygosity (LOH) score(Fig. 3 D). Notably, positive correlations with P2RY13 were observed in certain tumor types, including COAD, COADREAD, and LAML, while negative correlations were observed in others such as GBM, LUAD, LIHC, and CHOL for TMB. Similarly, positive and negative correlations were observed with Neoantigen burden, MSI score, and LOH score across different tumor types. Therefore, we compared the expression of P2RY13 with other conventional immunotherapy biomarkers for predicting immunotherapy efficacy. We found that the expression of P2RY13 exhibited remarkably high efficacy in predicting immunotherapy efficacy in certain datasets, with the area under the receiver operating characteristic (ROC) curve reaching up to 0.94.(Fig. 3 E) P2RY13 is generally negatively correlated with tumor stemness as well as tumor heterogeneity We conducted Pearson correlation analyses between P2RY13 expression and purity scores across 34 tumor types (Fig. 4 A). We observed significant negative correlations in various tumor types, including GBM, GBMLGG, LGG, CESC, LUAD, COAD, COADREAD, BRCA, ESCA, STES, SARC, KIRP, KIPAN, STAD, PRAD, UCEC, HNSC, KIRC, LUSC, LIHC, THCA, MESO, READ, OV, TGCT, PCPG, SKCM, UVM, UCS, BLCA, ACC, KICH, CHOL, and DLBC(Fig. 4 A). Additionally, we evaluated the Pearson correlation between MATH scores and P2RY13 expression, revealing significant associations in 11 tumor types(Fig. 4 B). Specifically, positive correlations were observed in GBMLGG, while negative correlations were found in LUAD, BRCA, STES, KIPAN, STAD, PRAD, KIRC, LUSC, TGCT, and BLCA, among others(Fig. 4 B). Thirty-seven cancer types were available to perform mDNAsi scoring and mRNAsi scoring analysis in this study. Results showed that the mDNAsi score of 21 cancer types, including BLCA, HNSC, STES, LUAD, and STAD, etc. were significant negative related to P2RY13 expression (Fig. 4 C). The mRNAsi score of GBM, LGG, SKCM, LUAD, LUSC, BRCA, etc. 33 cancer types were also significant positive correlated with RAD51AP1 (Fig. 4 D). On the contrary, in THYM and THCA, the mDNAsi score was significant positive correlated with RAD51AP1 (Fig. 4 C). Multiple algorithms verified that P2RY13 was closely related to tumor immune cell infiltration. Based on gene expression assessment, we evaluated the MHC, EC, SC, CP, AZ, and IPS infiltration scores for each patient in each tumor and conducted correlation analyses with P2RY13. We found that in the majority of tumors, P2RY13 exhibited positive correlations with MHC and EC infiltration scores, which respectively reflect the tumor's antigen presentation capacity and the degree of cytotoxic immune cell infiltration (Fig. 5 A). Using the QUANTISEQ method, we calculated the infiltration of 11 immune cell types and analyzed their correlation with P2RY13. The results revealed a positive correlation between P2RY13 and infiltration levels of M1/M2 macrophages as well as CD8 + T cells (Fig. 5 B). Furthermore, we recalculated the infiltration levels of immune cells within tumors using the EPIC and MCPcounter methods. In the EPIC method, we observed a strong positive correlation between P2RY13 and macrophage infiltration levels in most tumors, and in some tumors, a positive correlation with CD8 + T cells was also evident (Supplementary Fig. 4A). Similarly, in the MCPcounter method, we found a strong positive correlation between P2RY13 and monocyte lineage infiltration levels in most tumors, with a positive correlation with CD8 + T cells observed in some tumors as well (Supplementary Fig. 4B). P2RY13 correlated with most immune regulatory genes and immune checkpoint genes, including PD-L1 in multiple cancers P2RY13 expression was significantly correlated with chemokine and its receptors genes such as CXC and CC family, MHC class I and II (Fig. 6 ), immune suppression, and stimulation genes (Fig. 6 ) in most cancers. For instance, in SKCM, the expression of P2RY13 was positively related to CXCL-5, CXCL-6, CXCL-8, CXCL-9, CXCL-10, etc. chemokine genes, and positively related to HLA-DMA, HLA-DMB, etc. MHC genes. We also found that the expression of immune checkpoint inhibitory (Fig. 7 ) and stimulatory (Fig. 7 ) genes were significantly correlated with P2RY13 in various cancer types. Thirty-seven types of cancer with highly expressed RAD51AP1 presented high CD274 (PD-L1) expression, including LUAD, SKCM, KIRC, and PAAD. P2RY13 is downregulated in various cell lines, concomitantly inhibiting cancer cell proliferation To further authenticate the carcinostasis role of P2RY13 in cancers, we tested the protein expression level of P2RY13 in each cell line by western blot. The expression of P2RY13 in lung cancer cell line A549, breast cancer cell line MCF-7, ovarian cancer cell line OVCAR3, and pancreatic cancer cell line PANC1 were higher than normal lung epithelial cell line BEAS-2B (Fig. 8 A). We further transfected vector or P2RY13 OE plasmids in A549, MCF-7, OVCAR3, and PANC1. The transfection effectiveness in each cell line was tested by qPCR (Fig. 8 B). Using the CCK8 kit, we found that upregulation of P2RY13 significantly inhibited cell viability in all five cancer cell lines (Fig. 8 C-F). Discussion The findings presented in this study shed light on the multifaceted role of P2RY13 in cancer biology, offering insights into its potential as a biomarker and therapeutic target. Here, we discuss the implications of our results in the context of existing literature and highlight avenues for future research. Our analysis revealed significant dysregulation of P2RY13 expression across various cancer types, with distinct associations with clinical features. Notably, P2RY13 was found to be significantly upregulated in the majority of tumors studied, indicating its potential as a biomarker for cancer diagnosis. However, contradictory findings were observed in certain cancer types, where P2RY13 downregulation was associated with aggressive tumor characteristics, such as high-grade differentiation and advanced tumor stage. These findings underscore the complex and context-dependent role of P2RY13 in tumor biology, warranting further investigation into its mechanistic involvement in tumor progression. Our survival analysis revealed intriguing associations between P2RY13 expression levels and patient outcomes. Higher expression of P2RY13 was generally associated with favorable prognosis in several cancer types, suggesting a potential tumor-suppressive role for P2RY13. Conversely, in a minority of cases, elevated P2RY13 expression correlated with poorer prognosis, highlighting the context-specific nature of its prognostic significance. These findings underscore the need for comprehensive molecular profiling to accurately assess the prognostic value of P2RY13 in different cancer contexts. Our study identified significant correlations between P2RY13 expression and immune-related biomarkers across diverse tumor types. Notably, positive associations were observed between P2RY13 expression and immune activation markers, such as Tumor Mutational Burden (TMB) and Neoantigen burden, in certain tumor types. These findings suggest a potential role for P2RY13 in modulating antitumor immune responses, possibly through its influence on tumor antigenicity and immunogenicity. Furthermore, our analysis revealed correlations between P2RY13 expression and immune checkpoint genes, including PD-L1, highlighting its potential as a predictive biomarker for immunotherapy response. Our analysis demonstrated associations between P2RY13 expression and tumor stemness as well as tumor heterogeneity across multiple cancer types. Negative correlations were observed between P2RY13 expression and tumor heterogeneity indices, suggesting a potential role for P2RY13 in regulating tumor cell plasticity and clonal diversity. Conversely, positive correlations were observed between P2RY13 expression and stemness scores in certain cancer types, indicating a potential association with cancer stem cell properties. These findings suggest that P2RY13 may contribute to tumor aggressiveness and therapeutic resistance by promoting stemness and heterogeneity. Our analysis revealed significant associations between P2RY13 expression and tumor immune cell infiltration, particularly macrophages and CD8 + T cells. These findings suggest a potential role for P2RY13 in modulating the tumor immune microenvironment, possibly through its effects on immune cell recruitment and activation. Furthermore, our analysis identified correlations between P2RY13 expression and immune regulatory genes, further supporting its involvement in immune modulation within the tumor microenvironment. Our western blot results indicate a sustained downregulation of P2RY13 expression in lung cancer (A549), breast cancer (MCF-7), ovarian cancer (OVCAR3), and pancreatic cancer (PANC1) cell lines compared to normal lung epithelial cells (BEAS-2B). This downregulation suggests a potential tumor-suppressive role for P2RY13 in these cancer types. To further authenticate the tumor-suppressive function of P2RY13, we conducted overexpression experiments by transfecting P2RY13 overexpression plasmids into the aforementioned cancer cell lines. Our findings demonstrate successful transfection, as evidenced by increased P2RY13 expression levels in transfected cells compared to controls, as confirmed by qPCR analysis. Furthermore, functional assays using the CCK8 kit revealed a significant inhibition of cell viability following P2RY13 overexpression in all tested cancer cell lines. This sustained suppression of cell viability further supports the notion that P2RY13 plays a crucial role in regulating cancer cell proliferation and suggests its potential as a therapeutic target in cancer treatment. In summary, our study provides valuable insights into the involvement of P2RY13 in cancer progression, highlighting its potential as a novel therapeutic target for various cancer types. However, further research is necessary to elucidate the underlying molecular mechanisms governing the tumor-suppressive effects of P2RY13 and its therapeutic implications in cancer treatment. These findings underscore the importance of exploring P2RY13-targeted therapies as a promising approach to combat cancer progression and improve patient prognosis. Despite the insights gained from our analysis, several limitations should be acknowledged. Firstly, the retrospective nature of our study limits causal inference and requires validation in prospective cohorts. Additionally, the heterogeneity of cancer types and the complexity of tumor biology necessitate further mechanistic studies to elucidate the functional significance of P2RY13 dysregulation in cancer. Future research should focus on elucidating the molecular mechanisms underlying P2RY13-mediated effects on tumor immunity, stemness, and heterogeneity, with the aim of identifying novel therapeutic strategies targeting P2RY13 in cancer. Declarations Author Contribution Z.L. and Z.M. wrote the main manuscript text and Z.L.prepared all figures. All authors reviewed the manuscript. Data Availability The authors affirm that all data necessary for confirming the conclusions of the article are present within the article, figures, and tables. 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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-4557714","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":313718805,"identity":"9dc59a54-4c34-4acc-89c4-3deb75566fc8","order_by":0,"name":"Zaishan Li","email":"","orcid":"","institution":"Linyi People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zaishan","middleName":"","lastName":"Li","suffix":""},{"id":313718806,"identity":"a5a29126-68bb-4884-a85d-19438505ecda","order_by":1,"name":"Zhenzhen Meng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIie3QMWuDQBjG8VcC53LQ1dBit85PEFya4tAvEhGui4RkzCYUOildA/kS/QjKS93aWTDQTJ3TzcEh2jGE0zHD/eGW437cvUdkMl1hgsjKCXM3+OHiQBhBbrqV00p5VIkIo8g06cmRw6SSD86oh6HO8vwI9qxdKjbNan9PNn9+aMn+e1Fs8eJO7r7KWuJ3lkilKi2pYrDEoydoqWoCW4kjfS0JetJiEqYU++sGHAyS/1sIz+HWiX2S6P5hkPSzpFAeZBnddiR6G5ylzopD085d2K/FX9Py07vNpZaQI882hPb4RWIymUym807JHFN7MFXRXgAAAABJRU5ErkJggg==","orcid":"","institution":"Linyi People's Hospital","correspondingAuthor":true,"prefix":"","firstName":"Zhenzhen","middleName":"","lastName":"Meng","suffix":""}],"badges":[],"createdAt":"2024-06-10 11:26:01","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4557714/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4557714/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":59525860,"identity":"e42d51dc-7023-446d-a989-b204c938f929","added_by":"auto","created_at":"2024-07-02 20:57:09","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1947090,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferential expression analysis of P2RY13 in pan-cancer. \u003c/strong\u003eViolin plot shows the expression difference of p2ry13 between cancer and adjacent cancer in Pan cancer.\u003c/p\u003e","description":"","filename":"figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4557714/v1/afbbdcfac6fa5c4325bd73a8.jpg"},{"id":59524833,"identity":"08e251b4-5aaa-4a40-9016-7d74d44d563c","added_by":"auto","created_at":"2024-07-02 20:49:10","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":4430491,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSurvival analysis of RAD51AP1 in pan-cancer\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e: 43 cancer types were analyzed via coxph function in R software. High expression P2RY13 significantly correlated to the better OS in ACC, THCA, SKCM, CESC (from TCGA and TARGET database), LUAD, SARC, KIRC, SKCM-M, and SKCM. Conversely, in LAML, High expression of P2RY13 is significantly linked to poor prognosis. \u003cstrong\u003eB\u003c/strong\u003e: PFS analysis of 38 cancer types showed good prognosis of SARC, LIHC, CESC, CHOL, HNSC, KIRC, LUAD, BRCA, SKCM-M, SKCM, and ACC related to highly expressed P2RY13.\u003c/p\u003e","description":"","filename":"figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4557714/v1/c45c68c3b38c5f465c39046f.jpg"},{"id":59524836,"identity":"32eae04c-3c95-472f-bbb7-1de21e123a0d","added_by":"auto","created_at":"2024-07-02 20:49:10","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":6577502,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation between P2RY13 expression and biomarkers of cancer immunotherapy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA:\u003c/strong\u003e Correlation between P2RY13 and TMB.\u003cstrong\u003e B:\u003c/strong\u003e Correlation between P2RY13 and Neoantigen Burden.\u003cstrong\u003e C:\u003c/strong\u003eCorrelation between P2RY13 and MSI score. \u003cstrong\u003eD:\u003c/strong\u003eCorrelation between P2RY13 and LOH score.\u003cstrong\u003eE:\u003c/strong\u003e Comparison of AUC values of ROC analysis between P2RY13 and other immunotherapy biomarkers.\u003c/p\u003e","description":"","filename":"figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4557714/v1/cd794e6a9e5589ec49f340cd.jpg"},{"id":59524835,"identity":"4095ebb4-80d7-4c6e-a581-d288089666f2","added_by":"auto","created_at":"2024-07-02 20:49:10","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":3892948,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation between P2RY13 expression and cancer stemness\u003c/strong\u003e \u003cstrong\u003eas well as tumor heterogeneity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA:\u003c/strong\u003e Correlation between P2RY13 and tumor purity score. \u003cstrong\u003eB:\u003c/strong\u003e Correlation between P2RY13 and MATH score. \u003cstrong\u003eC:\u003c/strong\u003e Correlation between P2RY13 and mDNAsi score. \u003cstrong\u003eD:\u003c/strong\u003e Correlation between P2RY13 and mRNAs score.\u003c/p\u003e","description":"","filename":"figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4557714/v1/88c7867c1a2fa6ef7cdf43a4.jpg"},{"id":59525861,"identity":"a6cd32bd-8818-4bf1-889b-386a8c095789","added_by":"auto","created_at":"2024-07-02 20:57:10","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":7673055,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation analysis between P2RY13 and tumor immune cell infiltration in IPS and QUANTISEQ\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA:\u003c/strong\u003e Correlation between P2RY13 and tumor immune related functions calculated by IPS. \u003cstrong\u003eB:\u003c/strong\u003e Correlation between P2RY13 and tumor immune related functions calculated by QUANTISEQ.\u003c/p\u003e","description":"","filename":"figure501.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4557714/v1/bfbe5a691fdc3987906dd186.jpg"},{"id":59524839,"identity":"2b9a7355-5809-4fca-8a1c-a774954d08c9","added_by":"auto","created_at":"2024-07-02 20:49:10","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":12285749,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRelationship between P2RY13 and immune regulatory genes in Pan-cancer\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Heatmap shows the correlation between P2RY13 and immune regulatory genes.\u003c/p\u003e","description":"","filename":"figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4557714/v1/be341d7ed462c12554244176.jpg"},{"id":59524838,"identity":"2b85e1e1-88da-42d7-b166-5516f1f916e9","added_by":"auto","created_at":"2024-07-02 20:49:10","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":7115286,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRelationship between P2RY13 and immune checkpoint genes in Pan-cancer\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Heatmap shows the correlation between P2RY13 and immune checkpoint genes.\u003c/p\u003e","description":"","filename":"figure7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4557714/v1/b3539624cb7c9241064b80fb.jpg"},{"id":59524841,"identity":"f55f83ce-1fed-44c6-8b36-b2631116ff06","added_by":"auto","created_at":"2024-07-02 20:49:10","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1102473,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eValidation of the carcinostasis role of P2RY13 by vitro experiment.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe protein expression level of P2RY13 using western blot. P2RY13 was down regulated in various cancer cells, including A549, MCF-7, OVCAR3, and PANC1, compared to normal cells BEAS-2B (A). After successful transfected P2RY13 OE plasmids in each cancer cell line, which were verified via qPCR (B), we performed CCK8 assays. Results showed that RAD51AP1 significantly enhanced cancer cells viability (C-F).\u003c/p\u003e","description":"","filename":"figure8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4557714/v1/371f9a7d2a347c267bcfed81.jpg"},{"id":62330574,"identity":"ec69e542-ac5c-4720-adba-a29b88c441fd","added_by":"auto","created_at":"2024-08-13 03:49:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":45739331,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4557714/v1/25afbde3-371d-4c4a-8985-7225d1c6ddfd.pdf"},{"id":59524843,"identity":"70723d80-5518-455d-bed8-fc8e28f048a3","added_by":"auto","created_at":"2024-07-02 20:49:10","extension":"jpg","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":3121997,"visible":true,"origin":"","legend":"","description":"","filename":"supplementaryfigure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4557714/v1/6aeb864dbc2952af9469553c.jpg"},{"id":59525862,"identity":"c55071ec-85d8-4f0e-b401-2064ff5733f2","added_by":"auto","created_at":"2024-07-02 20:57:10","extension":"jpg","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":2999685,"visible":true,"origin":"","legend":"","description":"","filename":"supplementaryfigure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4557714/v1/725da41aee30fa4c7df9c8c6.jpg"},{"id":59524840,"identity":"b94225a4-7055-466a-869d-0e4b3213dfd4","added_by":"auto","created_at":"2024-07-02 20:49:10","extension":"jpg","order_by":13,"title":"","display":"","copyAsset":false,"role":"supplement","size":2961408,"visible":true,"origin":"","legend":"","description":"","filename":"supplementaryfigure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4557714/v1/081d656fb57bdc5bddb0d49b.jpg"},{"id":59524844,"identity":"be2efcbc-2d3b-43f7-b6e5-2002fccea4bc","added_by":"auto","created_at":"2024-07-02 20:49:11","extension":"jpg","order_by":14,"title":"","display":"","copyAsset":false,"role":"supplement","size":7607111,"visible":true,"origin":"","legend":"","description":"","filename":"supplementaryfigure401.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4557714/v1/6cd1275ab0b339cc3fb15fb4.jpg"}],"financialInterests":"No competing interests reported.","formattedTitle":"Systematic Pan-Cancer Analysis Showed that P2RY13 was Associated with Immune Microenvironment and Prognosis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eP2RY13, a purinergic receptor encoded by the P2RY13 gene, belongs to the P2Y family of G-protein-coupled receptors (GPCRs) that respond to extracellular nucleotides, particularly UDP-glucose(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). It is predominantly expressed in immune cells, including microglia, macrophages, and dendritic cells, suggesting its involvement in immune responses and inflammatory processes(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Emerging evidence indicates that P2RY13 plays a crucial role in various physiological and pathological conditions, including cancer(\u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn recent years, studies have increasingly focused on understanding the role of P2RY13 in cancer development and progression(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Accumulating evidence suggests that dysregulation of P2RY13 expression may contribute to tumor initiation, growth, metastasis, and therapy resistance. However, the precise mechanisms underlying the involvement of P2RY13 in cancer biology remain poorly understood.\u003c/p\u003e \u003cp\u003eThis review aims to provide an overview of the current understanding of P2RY13 in cancer. We will explore its expression patterns across different cancer types, its potential roles in tumor progression, its associations with clinical outcomes and prognosis, and its implications for cancer therapy.\u003c/p\u003e \u003cp\u003eTherefore, we conducted a data mining investigation on the public database by multiple bioinformatics methods in this paper. We analyzed the differential expressions of P2RY13 in pan-cancer and explored its correlation with prognosis, tumor stemness, tumor immunity, and tumor heterogeneity, aiming to preliminary explore the potential mechanism of P2RY13 in cancer development.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eGene expression data acquisition\u003c/h2\u003e \u003cp\u003eUnified pan-cancer datasets were downloaded from The Cancer Genome Atlas (TCGA) and Therapeutically Applicable Research to Generate Effective Treatments (TARGET) databases, and normal tissue datasets were downloaded from Genotype-Tissue Expression Project (GTEx) as control (PANCAN, N\u0026thinsp;=\u0026thinsp;19131, G\u0026thinsp;=\u0026thinsp;60499)(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). P2RY13 (ENSG00000181631) gene expression data were extracted from each sample. All expression values were log2(x\u0026thinsp;+\u0026thinsp;0.001) transformed. Differential expression analysis and clinical feature analysis were performed in R software (version 3.6.4). The abbreviation of each cancer type was listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. This part mainly refers to Liu's previously published papers(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAbbreviation of each cancer type.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbbreviation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eName\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTCGA-ACC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdrenocortical carcinoma\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTCGA-BLCA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBladder Urothelial Carcinoma\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTCGA-BRCA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBreast invasive carcinoma\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTCGA-CESC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCervical squamous cell carcinoma and endocervical adenocarcinoma\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTCGA-CHOL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCholangiocarcinoma\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTCGA-COAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eColon adenocarcinoma\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTCGA-COADREAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eColon adenocarcinoma/Rectum adenocarcinoma Esophageal carcinoma\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTCGA-DLBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLymphoid Neoplasm Diffuse Large B-cell Lymphoma\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTCGA-ESCA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEsophageal carcinoma\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTCGA-FPPP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFFPE Pilot Phase II\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTCGA-GBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlioblastoma multiforme\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTCGA-GBMLGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlioma\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTCGA-HNSC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHead and Neck squamous cell carcinoma\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTCGA-KICH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKidney Chromophobe\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTCGA-KIPAN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePan-kidney cohort (KICH\u0026thinsp;+\u0026thinsp;KIRC\u0026thinsp;+\u0026thinsp;KIRP)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTCGA-KIRC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKidney renal clear cell carcinoma\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTCGA-KIRP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKidney renal papillary cell carcinoma\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTCGA-LAML\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAcute Myeloid Leukemia\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTCGA-LGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBrain Lower Grade Glioma\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTCGA-LIHC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLiver hepatocellular carcinoma\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTCGA-LUAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLung adenocarcinoma\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTCGA-LUSC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLung squamous cell carcinoma\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTCGA-MESO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMesothelioma\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTCGA-OV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOvarian serous cystadenocarcinoma\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTCGA-PAAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePancreatic adenocarcinoma\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTCGA-PCPG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePheochromocytoma and Paraganglioma\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTCGA-PRAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProstate adenocarcinoma\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTCGA-READ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRectum adenocarcinoma\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTCGA-SARC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSarcoma\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTCGA-STAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStomach adenocarcinoma\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTCGA-SKCM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSkin Cutaneous Melanoma\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTCGA-SKCM-M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMetastatic Skin Cutaneous Melanoma\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTCGA-SKCM-P\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimary Skin Cutaneous Melanoma\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTCGA-STES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStomach and Esophageal carcinoma\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTCGA-TGCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTesticular Germ Cell Tumors\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTCGA-THCA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThyroid carcinoma\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTCGA-THYM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThymoma\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTCGA-UCEC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUterine Corpus Endometrial Carcinoma\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTCGA-UCS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUterine Carcinosarcoma\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTCGA-UVM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUveal Melanoma\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTARGET-OS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOsteosarcoma\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTARGET-ALL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAcute Lymphoblastic Leukemia\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTARGET-ALL-R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRecurrent Acute Lymphoblastic Leukemia\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTARGET-NB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNeuroblastoma\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTARGET-WT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh-Risk Wilms Tumor\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eSurvival analysis\u003c/h2\u003e \u003cp\u003eA thorough prognostic dataset sourced from a study based on TCGA was acquired(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). The CoxPH function in R was utilized to develop the Cox proportional hazards regression model. The optimal threshold for CD40LG expression was identified utilizing the MaxStat feature within the R programming environment, where the range for grouping was specified between the 25th and 75th percentiles. Each type of cancer was categorized into cohorts based on high and low levels of CD40LG expression. The survfit function in the R programming environment was employed to assess disparities in PFS and OS.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eImmune cell infiltration analysis\u003c/h2\u003e \u003cp\u003eFour independent tumor-infiltrating immune cells (TICs) analysis methods from the R package IOBR (version 0.99.9) (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e) deconvo_EPIC (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e), deconvo_MCPcounter (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e) and deconvo_quanTIseq (16) were performed in this study. All 44 cancer types and 10180 samples were available in EPIC, MCPcounter, quanTIseq, and ESTIMATE score method. This part mainly refers to Liu's previously published papers(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eTumor heterogeneity, TMB, MSI, and stemness scoring\u003c/h2\u003e \u003cp\u003eMutation data were gathered as described earlier. Mutant-allele tumor heterogeneity (MATH) and tumor mutational burden (TMB) were determined using the inferHeterogeneity and tmb functions from the R package maftools (version 2.8.05). Microsatellite instability (MSI) was assessed following the methodology outlined by R. Bonneville et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). The stemness score was assessed with the algorithm developed by T.M. Malta et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), which computes the mRNAsi score based on mRNA signatures and the mDNAsi score based on methylation signatures. Cancer types with fewer than three samples were excluded from this analysis. This part mainly refers to Liu's previously published papers(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eCell culture and cell transfection\u003c/h2\u003e \u003cp\u003eThe cell lines employed in this study were obtained from the American Tissue Culture Collection (ATCC). Specifically, PANC1, A549, and BEAS-2B cell lines were cultured in RPMI 1640 medium (Gibco, USA), while MCF-7 and THLE-3 cell lines were grown in DMEM (Gibco, USA) and BEGM (Lonza, USA), respectively. The BEGM medium was fortified with 10% fetal bovine serum (FBS), phosphoethanolamine (70 ng/mL), and EGF (5 ng/mL), whereas the other media were supplemented with 10% FBS. Plasmids were sourced from Biomed Gene Technology Co., LTD (Beijing, China). Following this, either pCDNA3.1-P2RY13 or pCDNA3.1 with a nonspecific sequence was transfected into each cancer cell line using Lipofectamine 2000 (Invitrogen, USA). This part mainly refers to Liu's previously published papers(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCell counting Kit-8 (CCK8) assay\u003c/h2\u003e \u003cp\u003eCell suspensions were prepared and distributed into individual wells of 96-well plates, with a seeding density of 4000 cells per well. Following a respective incubation period of 24, 48, and 72 hours, 10\u0026micro;l of CCK8 solution (obtained from APExBIO, USA) was introduced into each well and allowed to incubate for 1 hour. Subsequently, the optical density (OD) at 450nm was quantified using a microplate reader.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eWestern blot and qPCR\u003c/h2\u003e \u003cp\u003eProtein samples were extracted and quantified, and agarose gel electrophoresis was performed using 10% separating gels. Proteins were then transferred onto PVDF membranes (Millipore, Billerica, MA, United States) using a semi-dry transfer system. The membranes were then blocked with 5% skim milk at room temperature for 2 h. The primary antibodies (anti-P2RY13(Pronteintech, 20335-1-AP, 1:1000), anti-GAPDH(Pronteintech, 60004-1-lg, 1:1000) and membranes were incubated overnight at 4\u0026deg;C. Subsequently, the membranes and secondary antibody (1: 5000 dilution; Thermo Fisher Scientific, Inc.) were incubated for 1 h at room temperature. The bands were visualized using the Pierce ECL substrate (Thermo Fisher Scientific, Inc.).Real-time PCR was performed to evaluate the plasmid\u0026rsquo;s transfection effectiveness. Briefly, total RNA was extracted by TRIzol reagent (Invitrogen, USA), and cDNA were synthesized using PrimeScript RT Reagent Kit (TaKaRa, China). Each sample was tested in triplicate, and results were normalized by qPCR of cDNA with β-actin. The P2RY13 forward primer was designed as TGGCATCAGGTGGTCAGTCACA, and the β-actin forward primer was TCGTGCGTGACATTAAGGAGAAGC.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were conducted using R software (version 3.6.4). For unpaired datasets, the Wilcoxon Rank Sum and Signed Rank Tests were applied. In cases involving multiple group comparisons, the Kruskal-Wallis test was employed. Pearson's correlation coefficient was utilized to assess correlations, while the Log-rank test was implemented for survival analyses. A P-value of \u0026le;\u0026thinsp;0.05 was considered indicative of statistical significance. This part mainly refers to Liu's previously published papers(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":" \u003cdiv id=\"Sec11\" type=\"Results\" class=\"Section2\"\u003e \u003cp\u003e \u003cb\u003eP2RY13 was significantly overexpressed in most tumors and occasionally negatively correlated with malignant clinical features.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eSixteen of 34 cancer types presented P2RY13 significantly up-regulated in tumor samples, including GBM, GBMLGG, LGG, BRCA, CESC, ESCA, STES, KIRP, KIPAN, STAD, KIRC, SKCM, THCA, OV, PAAD, and TGCT.(Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Additionally, we observed significant downregulation of P2RY13 in 10 tumor types, including UCEC, LUAD, LUSC, WT, READ, UCS, ALL, LAML, ACC, and KICH. (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) Meanwhile, in LGG, GBMLGG, and SKCM, the expression of P2RY13 was significantly negatively correlated with age (Supplementary Fig.\u0026nbsp;1A). In ESCA, THYM, and LUAD, the expression of P2RY13 was significantly positive correlated with age (Supplementary Fig.\u0026nbsp;1A). Female patients in LUAD, BRCA, STES, HNSC, LUSC, PAAD, BLCA, and MESO presented significantly higher P2RY13 expression than females (Supplementary Fig.\u0026nbsp;1D). Decreased P2RY13 expression also indicated high-grade differentiation in LUAD, BRCA, STES, KIPAN, STAD, HNSC, THCA, SKCM, and KICH, higher T stage in BRCA, KIPAN, HNSC, ACC, and KICH.(Supplementary Fig.\u0026nbsp;1B, C).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eUp-regulated RAD51AP1 usually indicated a good prognosis\u003c/h2\u003e \u003cp\u003eOverall survival analysis showed that higher expression of P2RY13 correlated to better prognosis in SKCM, SKCM-M, KIRC, LUAD,etc. 9 cancer types, and worse prognosis in only 1 cancer types LAML (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Meanwhile, progression-free interval analysis showed that highly expressed RAD51AP1 was also related to good prognosis in 11 cancer types, including SARC, CESC, KIRC,etc. (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). In seven types of tumors, high expression of P2RY13 is associated with better prognosis in both overall survival and progression-free survival. These correlations were reconfirmed using the Log-rank test (shown in Supplementary Fig.\u0026nbsp;2 and Supplementary Fig.\u0026nbsp;3 for OS and PFS, respectively).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eP2RY13 expression correlates with various immune therapy biomarkers across different tumor types.\u003c/b\u003e \u003c/p\u003e \u003cp\u003ePearson correlation analysis revealed significant associations between P2RY13 expression and Tumor Mutational Burden (TMB) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA), Neoantigen burden(Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB), Microsatellite Instability (MSI) score(Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC), and Loss of Heterozygosity (LOH) score(Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). Notably, positive correlations with P2RY13 were observed in certain tumor types, including COAD, COADREAD, and LAML, while negative correlations were observed in others such as GBM, LUAD, LIHC, and CHOL for TMB. Similarly, positive and negative correlations were observed with Neoantigen burden, MSI score, and LOH score across different tumor types. Therefore, we compared the expression of P2RY13 with other conventional immunotherapy biomarkers for predicting immunotherapy efficacy. We found that the expression of P2RY13 exhibited remarkably high efficacy in predicting immunotherapy efficacy in certain datasets, with the area under the receiver operating characteristic (ROC) curve reaching up to 0.94.(Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eP2RY13 is generally negatively correlated with tumor stemness as well as tumor heterogeneity\u003c/h2\u003e \u003cp\u003eWe conducted Pearson correlation analyses between P2RY13 expression and purity scores across 34 tumor types (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). We observed significant negative correlations in various tumor types, including GBM, GBMLGG, LGG, CESC, LUAD, COAD, COADREAD, BRCA, ESCA, STES, SARC, KIRP, KIPAN, STAD, PRAD, UCEC, HNSC, KIRC, LUSC, LIHC, THCA, MESO, READ, OV, TGCT, PCPG, SKCM, UVM, UCS, BLCA, ACC, KICH, CHOL, and DLBC(Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Additionally, we evaluated the Pearson correlation between MATH scores and P2RY13 expression, revealing significant associations in 11 tumor types(Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Specifically, positive correlations were observed in GBMLGG, while negative correlations were found in LUAD, BRCA, STES, KIPAN, STAD, PRAD, KIRC, LUSC, TGCT, and BLCA, among others(Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThirty-seven cancer types were available to perform mDNAsi scoring and mRNAsi scoring analysis in this study. Results showed that the mDNAsi score of 21 cancer types, including BLCA, HNSC, STES, LUAD, and STAD, etc. were significant negative related to P2RY13 expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). The mRNAsi score of GBM, LGG, SKCM, LUAD, LUSC, BRCA, etc. 33 cancer types were also significant positive correlated with RAD51AP1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). On the contrary, in THYM and THCA, the mDNAsi score was significant positive correlated with RAD51AP1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003cb\u003eMultiple algorithms verified that P2RY13 was closely related to tumor immune cell infiltration.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eBased on gene expression assessment, we evaluated the MHC, EC, SC, CP, AZ, and IPS infiltration scores for each patient in each tumor and conducted correlation analyses with P2RY13. We found that in the majority of tumors, P2RY13 exhibited positive correlations with MHC and EC infiltration scores, which respectively reflect the tumor's antigen presentation capacity and the degree of cytotoxic immune cell infiltration (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Using the QUANTISEQ method, we calculated the infiltration of 11 immune cell types and analyzed their correlation with P2RY13. The results revealed a positive correlation between P2RY13 and infiltration levels of M1/M2 macrophages as well as CD8\u0026thinsp;+\u0026thinsp;T cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Furthermore, we recalculated the infiltration levels of immune cells within tumors using the EPIC and MCPcounter methods. In the EPIC method, we observed a strong positive correlation between P2RY13 and macrophage infiltration levels in most tumors, and in some tumors, a positive correlation with CD8\u0026thinsp;+\u0026thinsp;T cells was also evident (Supplementary Fig.\u0026nbsp;4A). Similarly, in the MCPcounter method, we found a strong positive correlation between P2RY13 and monocyte lineage infiltration levels in most tumors, with a positive correlation with CD8\u0026thinsp;+\u0026thinsp;T cells observed in some tumors as well (Supplementary Fig.\u0026nbsp;4B).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eP2RY13 correlated with most immune regulatory genes and immune checkpoint genes, including PD-L1 in multiple cancers\u003c/b\u003e \u003c/p\u003e \u003cp\u003eP2RY13 expression was significantly correlated with chemokine and its receptors genes such as CXC and CC family, MHC class I and II (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), immune suppression, and stimulation genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) in most cancers. For instance, in SKCM, the expression of P2RY13 was positively related to CXCL-5, CXCL-6, CXCL-8, CXCL-9, CXCL-10, etc. chemokine genes, and positively related to HLA-DMA, HLA-DMB, etc. MHC genes. We also found that the expression of immune checkpoint inhibitory (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e) and stimulatory (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e) genes were significantly correlated with P2RY13 in various cancer types. Thirty-seven types of cancer with highly expressed RAD51AP1 presented high CD274 (PD-L1) expression, including LUAD, SKCM, KIRC, and PAAD.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eP2RY13 is downregulated in various cell lines, concomitantly inhibiting cancer cell proliferation\u003c/h2\u003e \u003cp\u003eTo further authenticate the carcinostasis role of P2RY13 in cancers, we tested the protein expression level of P2RY13 in each cell line by western blot. The expression of P2RY13 in lung cancer cell line A549, breast cancer cell line MCF-7, ovarian cancer cell line OVCAR3, and pancreatic cancer cell line PANC1 were higher than normal lung epithelial cell line BEAS-2B (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA). We further transfected vector or P2RY13 OE plasmids in A549, MCF-7, OVCAR3, and PANC1. The transfection effectiveness in each cell line was tested by qPCR (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB). Using the CCK8 kit, we found that upregulation of P2RY13 significantly inhibited cell viability in all five cancer cell lines (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC-F).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe findings presented in this study shed light on the multifaceted role of P2RY13 in cancer biology, offering insights into its potential as a biomarker and therapeutic target. Here, we discuss the implications of our results in the context of existing literature and highlight avenues for future research.\u003c/p\u003e \u003cp\u003eOur analysis revealed significant dysregulation of P2RY13 expression across various cancer types, with distinct associations with clinical features. Notably, P2RY13 was found to be significantly upregulated in the majority of tumors studied, indicating its potential as a biomarker for cancer diagnosis. However, contradictory findings were observed in certain cancer types, where P2RY13 downregulation was associated with aggressive tumor characteristics, such as high-grade differentiation and advanced tumor stage. These findings underscore the complex and context-dependent role of P2RY13 in tumor biology, warranting further investigation into its mechanistic involvement in tumor progression.\u003c/p\u003e \u003cp\u003eOur survival analysis revealed intriguing associations between P2RY13 expression levels and patient outcomes. Higher expression of P2RY13 was generally associated with favorable prognosis in several cancer types, suggesting a potential tumor-suppressive role for P2RY13. Conversely, in a minority of cases, elevated P2RY13 expression correlated with poorer prognosis, highlighting the context-specific nature of its prognostic significance. These findings underscore the need for comprehensive molecular profiling to accurately assess the prognostic value of P2RY13 in different cancer contexts.\u003c/p\u003e \u003cp\u003eOur study identified significant correlations between P2RY13 expression and immune-related biomarkers across diverse tumor types. Notably, positive associations were observed between P2RY13 expression and immune activation markers, such as Tumor Mutational Burden (TMB) and Neoantigen burden, in certain tumor types. These findings suggest a potential role for P2RY13 in modulating antitumor immune responses, possibly through its influence on tumor antigenicity and immunogenicity. Furthermore, our analysis revealed correlations between P2RY13 expression and immune checkpoint genes, including PD-L1, highlighting its potential as a predictive biomarker for immunotherapy response.\u003c/p\u003e \u003cp\u003eOur analysis demonstrated associations between P2RY13 expression and tumor stemness as well as tumor heterogeneity across multiple cancer types. Negative correlations were observed between P2RY13 expression and tumor heterogeneity indices, suggesting a potential role for P2RY13 in regulating tumor cell plasticity and clonal diversity. Conversely, positive correlations were observed between P2RY13 expression and stemness scores in certain cancer types, indicating a potential association with cancer stem cell properties. These findings suggest that P2RY13 may contribute to tumor aggressiveness and therapeutic resistance by promoting stemness and heterogeneity.\u003c/p\u003e \u003cp\u003eOur analysis revealed significant associations between P2RY13 expression and tumor immune cell infiltration, particularly macrophages and CD8\u0026thinsp;+\u0026thinsp;T cells. These findings suggest a potential role for P2RY13 in modulating the tumor immune microenvironment, possibly through its effects on immune cell recruitment and activation. Furthermore, our analysis identified correlations between P2RY13 expression and immune regulatory genes, further supporting its involvement in immune modulation within the tumor microenvironment.\u003c/p\u003e \u003cp\u003eOur western blot results indicate a sustained downregulation of P2RY13 expression in lung cancer (A549), breast cancer (MCF-7), ovarian cancer (OVCAR3), and pancreatic cancer (PANC1) cell lines compared to normal lung epithelial cells (BEAS-2B). This downregulation suggests a potential tumor-suppressive role for P2RY13 in these cancer types. To further authenticate the tumor-suppressive function of P2RY13, we conducted overexpression experiments by transfecting P2RY13 overexpression plasmids into the aforementioned cancer cell lines. Our findings demonstrate successful transfection, as evidenced by increased P2RY13 expression levels in transfected cells compared to controls, as confirmed by qPCR analysis. Furthermore, functional assays using the CCK8 kit revealed a significant inhibition of cell viability following P2RY13 overexpression in all tested cancer cell lines. This sustained suppression of cell viability further supports the notion that P2RY13 plays a crucial role in regulating cancer cell proliferation and suggests its potential as a therapeutic target in cancer treatment.\u003c/p\u003e \u003cp\u003eIn summary, our study provides valuable insights into the involvement of P2RY13 in cancer progression, highlighting its potential as a novel therapeutic target for various cancer types. However, further research is necessary to elucidate the underlying molecular mechanisms governing the tumor-suppressive effects of P2RY13 and its therapeutic implications in cancer treatment. These findings underscore the importance of exploring P2RY13-targeted therapies as a promising approach to combat cancer progression and improve patient prognosis.\u003c/p\u003e \u003cp\u003eDespite the insights gained from our analysis, several limitations should be acknowledged. Firstly, the retrospective nature of our study limits causal inference and requires validation in prospective cohorts. Additionally, the heterogeneity of cancer types and the complexity of tumor biology necessitate further mechanistic studies to elucidate the functional significance of P2RY13 dysregulation in cancer. Future research should focus on elucidating the molecular mechanisms underlying P2RY13-mediated effects on tumor immunity, stemness, and heterogeneity, with the aim of identifying novel therapeutic strategies targeting P2RY13 in cancer.\u003c/p\u003e "},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eZ.L. and Z.M. wrote the main manuscript text and Z.L.prepared all figures. All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe authors affirm that all data necessary for confirming the conclusions of the article are present within the article, figures, and tables.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLe Duc D, Schulz A, Lede V, Schulze A, Thor D, Br\u0026uuml;ser A, Sch\u0026ouml;neberg T. P2Y Receptors in Immune Response and Inflammation. Adv Immunol. 2017;136:85\u0026ndash;121.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoche D, Gordon MN. Diversity of transcriptomic microglial phenotypes in aging and Alzheimer's disease. Alzheimers Dement. 2022;18(2):360\u0026ndash;376.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSousa C, Golebiewska A, Poovathingal SK, Kaoma T, Pires-Afonso Y, Martina S, et al. Single-cell transcriptomics reveals distinct inflammation-induced microglia signatures. 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Front Genet. 2022;13:971033.\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":"P2RY13, tumor immune microenvironment, pan-cancer, bioinformatics analysis","lastPublishedDoi":"10.21203/rs.3.rs-4557714/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4557714/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eP2RY13, a purinergic receptor belonging to the P2Y family of G-protein-coupled receptors, has garnered increasing attention for its potential involvement in cancer biology. This review explores the multifaceted role of P2RY13 in cancer development and progression, aiming to provide a comprehensive understanding of its implications for cancer diagnosis, prognosis, and therapy. Utilizing data mining techniques and bioinformatics analysis on publicly available datasets, we investigated the differential expression of P2RY13 across various cancer types and its correlation with clinical features, survival outcomes, tumor immune microenvironment, and molecular characteristics. Our analysis revealed significant dysregulation of P2RY13 expression in tumors, with distinct associations with malignant features and patient prognosis. Notably, P2RY13 expression was found to correlate with immune-related biomarkers and tumor stemness, highlighting its potential role in modulating tumor immunity and heterogeneity. Furthermore, our study identified correlations between P2RY13 expression and immune cell infiltration, immune checkpoint genes, and other immune regulatory genes, underscoring its importance in tumor immune modulation. Despite the insights gained, further mechanistic studies are warranted to elucidate the precise role of P2RY13 in cancer biology and to explore its therapeutic potential as a target for cancer treatment.\u003c/p\u003e","manuscriptTitle":"Systematic Pan-Cancer Analysis Showed that P2RY13 was Associated with Immune Microenvironment and Prognosis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-02 20:49:04","doi":"10.21203/rs.3.rs-4557714/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":"a81fa4a2-6296-4d48-a963-ce5f26aded93","owner":[],"postedDate":"July 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":33173838,"name":"Biological sciences/Cancer/Cancer genetics"},{"id":33173839,"name":"Biological sciences/Immunology/Immunogenetics"}],"tags":[],"updatedAt":"2024-08-13T03:41:18+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-02 20:49:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4557714","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4557714","identity":"rs-4557714","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}