PTPRC as a pan-cancer biomarker: Prognostic significance and immune microenvironment interactions

PTPRC as a pan-cancer biomarker: Prognostic significance and immune microenvironment interactions | 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 PTPRC as a pan-cancer biomarker: Prognostic significance and immune microenvironment interactions Lizhu Tang, Ting Hu, Dingshi Liu, Changqiao Huang, Wenli Yin, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6233301/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 This study investigated the role of Protein Tyrosine Phosphatase Receptor type C (PTPRC) in various cancers using bioinformatics analyses and experimental validation. We analyzed public databases (TIMER2.0, GEPIA2, cBioPortal) and single-cell sequencing data to evaluate PTPRC expression differences, patient prognosis, and immune microenvironment associations in tumors. RT-qPCR was employed to validated PTPRC expression in nasopharyngeal carcinoma (CNE2) and hepatocellular carcinoma (HePG2) cell lines. Our findings revealed that PTPRC was upregulated in 11 tumor types (P < 0.05) and associated with worse survival in 6 cancers (P < 0.05). It was correlated with immune cell infiltration, immune checkpoint genes (ICGs), cancer-associated fibroblasts (CAFs), tumor mutation burden (TMB), and microsatellite instability (MSI) (P < 0.05). Single-cell analysis indicated PTPRC is closely related to angiogenesis, differentiation, proliferation, and quiescence in certain tumors. Functional enrichment analysis emphasized PTPRC's involvement in T-cell receptor (TCR) and PD-L1/PD-1 signaling pathways, highlighting its role in T-cell activation, immune tolerance, and tumor progression. Experimental validation confirmed that PTPRC was upregulated in CNE2 cells compared to normal nasopharyngeal epithelial cells and downregulated in HePG2 cells compared to normal hepatocytes, consistent with bioinformatics results. In conclusion, abnormal PTPRC expression in pan-cancer may drive tumor development through multiple mechanisms, indicating its potential as a therapeutic target, especially in immunotherapy. PTPRC Pan-cancer immune microenvironment Gene expression analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 1. Introduction Tumors, characterized by high incidence and mortality rates, present a significant threat to human health. Although immunotherapy, particularly immune checkpoint blockade, has made progress, it remains effective for only a small subset of patients, with the majority exhibiting drug resistance[ 1 – 3 ]. Consequently, there is an urgent demand for early diagnosis and effective treatment strategies to improve patient outcomes. To address the limitations of current therapies, it is crucial to identify new therapeutic targets and strategies. The development of pan-cancer databases has significantly advanced this field, providing a powerful tool for discovering novel targets and guiding tumor diagnosis and treatment. By integrating data from multiple tumor types, pan-cancer analysis can reveal common mechanisms of tumorigenesis and progression, as well as potential therapeutic targets, thereby paving the way for more effective treatment methods. Protein tyrosine phosphatase receptor type C (PTPRC, also known as CD45), a key member of the protein tyrosine phosphatase (PTP) family, plays a crucial role in signal transduction pathways. PTPs collaborate with protein tyrosine kinases (PTKs) to regulate numerous cellular processes, such as cell proliferation, differentiation, and mitosis. Aberrant expression or dysfunction of PTPs has been associated with autoimmune diseases, metabolic syndrome, and tumor development[ 4 ]. PTPRC has been shown to be involved in multiple fundamental biological processes, and its abnormalities can lead to immunodeficiency, autoimmune diseases, or malignant tumors[ 5 ]. In recent years, the role of PTPRC in tumors has attracted increasing attention. Studies have shown that PTPRC can function as both a tumor promoter and a tumor suppressor in certain cancers. For example, Helicobacter pylori-induced PTPRC overexpression is positively correlated with gastric cancer occurrence[ 6 ], while CD45 deficiency is linked to the progression and poor prognosis of multiple myeloma[ 7 ]. Moreover, PTPRC may serve as a potential diagnostic or therapeutic target for various cancers, including inflammatory breast cancer[ 8 ], nasopharyngeal carcinoma[ 9 ], and renal cell carcinoma[ 10 ], although its underlying mechanisms are not fully understood. However, most current studies focus on specific tumor types, and there is a lack of comprehensive pan-cancer analysis of PTPRC. Given its crucial role in cellular processes and complex functions in tumors, a systematic study of PTPRC expression differences in pan-cancer, its correlation with tumor prognosis, and its role in the tumor immune microenvironment is essential for revealing its potential mechanisms in tumorigenesis and tumor development. Therefore, this study aims to comprehensively elucidate the potential mechanisms of PTPRC in tumorigenesis and tumor development from a pan-cancer perspective. We have thoroughly analyzed the associations between PTPRC expression and tumor prognosis, DNA methylation, microsatellite instability (MSI), tumor mutation burden (TMB), immune checkpoint genes (ICGs), and immune infiltration levels. Through single-cell functional analysis and functional enrichment analysis, we have further elaborated on the biological functions of PTPRC in tumors and uncovered its mechanism of action in the tumor immune microenvironment. By integrating multi-dimensional data, this study not only reveals the expression patterns and prognostic values of PTPRC in different tumors but also explores its role in the tumor immune microenvironment. These findings provide potential targets for the development of innovative immunotherapy strategies and offer theoretical bases and practical guidance for the clinical diagnosis and treatment of tumors. 2. Materials and Methods 2.1 Materials 2.1.1 Databases Gene expression differences were assessed using TIMER2.0 ( http://timer.cistrome.org/ ) and UALCAN ( http://ualcan.Path.uab.edu/ ) databases. Protein expression levels were evaluated via the UALCAN database, while immunohistochemistry (IHC) staining data were obtained from the HPA database ( https://www.proteinatlas.org/ ). Patient survival curves were generated using the GEPIA2 ( http://gepia2.cancer-pku.cn/#analysis ) database. PTPRC methylation levels and mutation frequencies were analyzed using UALCAN and cBioPortal ( https://www.cbioportal.org ), respectively. Correlations between PTPRC expression and immune cell infiltration, TMB, and MSI were analyzed using UCSCXenaShiny database ( https://shiny.hiPlot-academic.com/ucsc-xena-shiny/ ). CancerSEA ( https://ngdc.cncb.ac.cn/databasecommons/database/id/6092 ) was used to analyze PTPRC’s correlation with tumor functional states. The STRING database ( https://www.string-db.org/ ) constructed a PTPRC protein-protein interaction (PPI) network, and DAVID ( https://david.ncifcrf.gov/home.jsp ) performed functional enrichment analysis. 2.1.2 Cell source NP69 (Immortalized human nasopharyngeal epithelial cells), CNE-2 (nasopharyngeal carcinoma cells), LO2 (normal human hepatocytes), and HepG2 (hepatocellular carcinoma cells). These cell lines were purchased from Procell Life Science & Technology Co., Ltd. (Wuhan, China). 2.1.3 Reagents Cells were cultured under standard conditions as per the supplier’s guidelines: NP69 in DMEM/F12 medium with 10% FBS, and other cell lines in RPMI 1640 medium with 10% FBS at 37°C and 5% CO₂. The main reagents used in this study included: Total RNA extraction kit (Solarbio Science & Technology Co., Ltd., Shanghai, China); First Strand cDNA Synthesis Kit (Beyotime Biotechnology Co., Ltd., Shanghai, China); Hiff™ QPCR SYBR® Green Mix (TransGen Biotech Co., Ltd., Shanghai, China). 2.2 Methods 2.2.1 Gene Expression Analysis PTPRC expression differences between tumor and normal tissues were analyzed using the “Gene_DE” module within the “Cancer Exploration” section of the TIMER2.0 database. For TCGA tumors lacking normal tissue data, expression differences were analyzed using R packages in the TCGA and GTEx databases. Specifically, the “limma” package was used to perform differential expression analysis, adjusting for batch effects and other covariates. Protein expression levels were assessed via the UALCAN database’s CPTAC dataset, which provides quantitative proteomics data. IHC staining data were obtained from the HPA database’s “TISSUE” and “PATHOLOGY” modules to further validate protein expression patterns. 2.2.2 Survival Prognosis Analysis To evaluate the relationship between PTPRC expression and patient survival outcomes, we used the GEPIA2 database’s “Survival Analysis” module to generate Kaplan-Meier survival curves for overall survival (OS) and disease-free survival (DFS). Patients were stratified into high and low PTPRC expression groups based on the median expression level. The log-rank test was applied to assess significant differences in survival times between groups, and hazard ratios (HR) with 95% confidence intervals (CI) were calculated. 2.2.3 Methylation and Genetic Variation Analysis PTPRC methylation levels were analyzed using the “methylation” module within the UALCAN database’s “TCGA” section, comparing methylation status between tumor and normal tissues. Mutation frequencies and types were assessed via the cBioPortal database’s “TCGA PanCancer Atlas Studies”. The “Cancer Types Summary” module was used to analyze the mutation frequencies of PTPRC across different tumors, and the “Mutations” module was employed to identify the specific mutation types and their locations. This comprehensive approach allowed us to explore both epigenetic and genetic factors influencing PTPRC expression and function in cancer. 2.2.4 Immune Infiltration Analysis Correlations between PTPRC expression and immune cell infiltration, TMB, and MSI were analyzed using the UCSCXenaShiny database. The “Quick PanCan Analysis” module was selected, and PTPRC was input as the gene of interest. Relevant cancer datasets were chosen, and the analysis was executed to obtain correlation coefficients and p-values. For the analysis of cancer-associated fibroblasts (CAFs) and ICGs, the TIMER2.0 and SangerBox databases were utilized. In TIMER2.0, PTPRC was input, and modules were selected to estimate CAF abundance based on gene expression signatures. SangerBox was used to search for known ICGs, and their expression patterns in relation to PTPRC were analyzed by selecting relevant genes and cancer datasets, identifying significant correlations or differential expression patterns. 2.2.5 Single-Cell Functional Analysis PTPRC’s correlation with tumor functional states was analyzed using the CancerSEA database’s “Correlation Plot” and “Functional relevance” modules. To perform this analysis, the CancerSEA database was accessed, and PTPRC was input as the gene of interest. The “Correlation Plot” module was used to visualize the relationship between PTPRC expression and various tumor functional states, while the "Functional relevance" module provided insights into the biological significance of these correlations. Single-cell expression profiles were visualized as t-SNE plots, which allowed for the identification of distinct cell populations and their functional states within the tumor microenvironment. 2.2.6 Functional Enrichment Analysis The PTPRC protein-protein interaction (PPI) network was constructed using the STRING database (minimum confidence score: 0.700). Hub genes were identified using Cytoscape’s CytoHubba plugin (MCC algorithm) and prioritized by maximal clique centrality. Functional enrichment analysis (Gene Ontology and KEGG pathways) of the top 10 hub genes was performed using DAVID with a significance threshold (Benjamini-Hochberg adjusted P < 0.05). Results were visualized as bubble charts and network diagrams through the Microbial Bioinformatics Cloud Platform. 2.2.7 Experimental Verification Total RNA was extracted from LO2, HepG2, NP69, and CNE-2 cells using a Total RNA extraction kit. Reverse transcription was performed using the First Strand cDNA Synthesis Kit with 1 µg RNA in a 20 µL reaction (42°C for 30 min, 85°C for 5 s). RT-qPCR amplification protocol included an initial denaturation(95°C, 5 min), 40 cycles (95°C, 10 s; 60°C, 30 s), and melt curve analysis (65–95°C) to confirm primer specificity. GAPDH served as the endogenous control, and relative PTPRC expression was calculated via the 2 − ΔΔCt method. Experiments were independently repeated three times. Primer sequences are listed in Table 1 . Table 1 Primers for RT-qPCR Gene Forward primer (5'-3') Reverse primer (5'-3') PTPRC TGAAGCAAAGGAACAGGCTGAAGG GCTGGACTTGCAGGACCATTGAC GAPDH GCACCGTCAAGGCTGAGAAC TGGTGAAGACGCCAGTGTA 2.3 Statistical Analysis Data were analyzed using database-provided methods. The Wilcoxon rank-sum test was applied to assess PTPRC expression differences between tumor and normal tissues. Survival outcomes were compared via the Log-rank test with Kaplan-Meier curves, and HR with 95% CIs were derived from Cox proportional hazards models. One-way ANOVA evaluated PTPRC protein and methylation differences, assuming homogeneity of variances (Levene’s test, P > 0.1). Spearman correlation analyzed associations between PTPRC expression and immune cell infiltration, TMB, MSI, and CAF/ICG relationships. Welch's t-test compared PTPRC mRNA levels in cancer versus normal cells. All tests were two-tailed, and statistical significance was defined as P < 0.05. 3. Results 3.1 Pan-cancer Analysis of PTPRC Expression Analysis of the TIMER2.0 database revealed significant upregulation of PTPRC in five tumor types compared to normal tissues, including esophageal carcinoma (ESCA), glioblastoma multiform (GBM), head and neck squamous cell carcinoma (HNSC), kidney renal clear cell carcinoma (KIRC), and stomach adenocarcinoma (STAD) ( P < 0.05, Fig. 1 A). Conversely, PTPRC was downregulated in bladder urogenital carcinoma (BLCA), colon adenocarcinoma (COAD), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), pancreatic adenocarcinoma (PAAD), rectum adenocarcinoma (READ), thyroid carcinoma (THCA), and uterine corpus endometrial carcinoma (UCEC) ( P < 0.05, Fig. 1 A). For tumors without matched normal tissue data in TIMER2.0 database, integration of TCGA and GTEx datasets via R packages demonstrated PTPRC overexpression in lymphoid neoplasm diffuse large B-cell lymphoma (DLBC), acute myeloid leukemia (LAML), brain lower grade glioma (LGG), ovarian serous cystadenocarcinoma (OV), skin cutaneous melanoma (SKCM), and testicular germ cell tumors (TGCT). Conversely, reduced expression was observed in adrenocortical carcinoma (ACC), thymoma (THYM), and uterine carcinosarcoma (UCS) ( P < 0.05, Fig. 1 B). Protein-level validation using the UALCAN database confirmed elevated PTPRC expression in clear cell RCC (CCRCC), GBM, HNSC, PAAD, and UCEC, whereas decreased levels were detected in LIHC, LUAD, and OV ( P < 0.05, Fig. 2 A). Immunohistochemical data from the HPA database further supported these findings, showing concordant low PTPRC expression in LUAD tumor tissues compared to normal lung tissues ( P < 0.05, Fig. 2 B). 3.2 Pan-cancer Analysis of the Association between PTPRC Expression and Patient Prognosis Analysis of the GEPIA2 database revealed a significant association between PTPRC expression and patient survival outcomes. OS analysis demonstrated that high PTPRC expression correlated with improved prognosis in HNSC ( P = 0.014), LUAD ( P = 0.0013), and SKCM ( P = 1.9 e-05). Conversely, elevated PTPRC expression predicted poor OS in LGG ( P = 0.017) and uveal melanoma (UVM, P = 0.008) (Fig. 3 A). For DFS, high PTPRC expression was associated with favorable outcomes in patients with cholangiocarcinoma (CHOL, P = 0.019), LIHC ( P = 0.034), and SKCM ( P = 0.017). In contrast, increased PTPRC expression indicated worse DFS in LGG ( P = 0.041, Fig. 3 B). 3.3 Pan-cancer Analysis of PTPRC Methylation Analysis of DNA methylation profiles via the UALCAN database demonstrated widespread hypomethylation of PTPRC in tumor tissues compared to adjacent normal tissues. Significant hypomethylation ( P < 0.05) was observed in BLCA, breast invasive carcinoma (BRCA), COAD, ESCA, HNSC, KIRC, kidney renal papillary cell carcinoma (KIRP), LIHC, LUAD, LUSC, PAAD, prostate adenocarcinoma (PRAD), READ, sarcoma (SARC), TGCT, and UCEC. In contrast, cholangiocarcinoma (CHOL) exhibited tumor-specific hypermethylation of PTPRC ( P < 0.05, Fig. 4 ). 3.4 Pan-cancer Analysis of PTPRC Genetic Variations Genomic alteration analysis via the cBioPortal database identified distinct patterns of PTPRC variations across cancers. Mutation frequency was highest in SKCM (13.51%), predominantly characterized by somatic missense mutations. Other tumors with detectable PTPRC mutations (> 0.71%) included DLBC, LGG, LAML, KIRC, and KIRP. In contrast, gene amplification was the sole alteration type in THYM, mesothelioma (MESO), THCA, and ACC (Fig. 5 A). Further characterization revealed that missense mutations accounted for the majority of PTPRC genomic variations. The most recurrent mutation site mapped to codon 292 (T292M/P) within the cytoplasmic tyrosine phosphatase domain (Fig. 5 B). 3.5 Pan-cancer Analysis of the Association between PTPRC Expression and the Infiltration Levels of Different Immune Cell Subtypes Analysis of the UCSCXenaShiny database revealed distinct correlations between PTPRC expression and immune cell infiltration (Fig. 6 ). Positively correlations were observed with immunosuppressive subsets, including regulatory T cells (Tregs), M2 macrophages, and resting dendritic cells. Adaptive immune effectors such as CD8 + T cells, γδ T cells, and activated memory CD4 + T cells also showed significant positive associations. In contrast, it was negatively correlated with activated dendritic cells, M0 macrophages, activated natural killer (NK) cells, and memory B cells. Resting NK cells and naive CD4 + T cells were inversely associated with PTPRC expression. 3.6 Pan-cancer Analysis of the Association between PTPRC Expression and the Infiltration Level of CAFs TIMER2.0 database analysis revealed a positive correlation between PTPRC expression and CAF infiltration levels across various tumors (Fig. 7 A-B). Notably, in BLCA, PTPRC expression showed a significant positive correlation with CAF infiltration (Rho = 0.295, P = 7.53e-09) using the MCPCOUNTER method. Similarly, in breast invasive carcinoma of Luminal A subtype (BRCA-LumA), a positive correlation was observed (Rho = 0.259, P = 2.34e-09) via the EPIC method. Other cancer types exhibited comparable trends, including COAD (Rho = 0.516, P = 4.30e-20), HNSC (Rho = 0.318, P = 4.64e-13), LGG (Rho = 0.343, P = 1.22e-14), LUAD (Rho = 0.277, P = 3.83e-10), LUSC (Rho = 0.328, P = 1.84e-13), and PAAD (Rho = 0.506, P = 1.70e-12), all analyzed using the EPIC or XCELL methods. These findings collectively indicate that PTPRC expression is positively associated with CAF infiltration across multiple cancer types, suggesting a potential role of PTPRC in tumor-stroma interactions and tumor progression. 3.7 Pan-cancer Analysis of the Association between PTPRC Expression and ICGs Analysis of the SangerBox database revealed pan-cancer correlations between the PTPRC expression and ICGs across 20 tumor types (Fig. 8 ). PTPRC expression showed significant positive associations with ICGs (including CTLA4, TIGIT, CD274, and BTLA), in kidney chromophobe (KICH), LIHC, pheochromocytoma and paraganglioma (PCPG), KIRP, UCS, BRCA, LUAD, STAD, ESCA, neuroblastoma (NB), COAD, READ, OV, PAAD, PRAD, acute lymphoblastic leukemia (ALL), glioma (GBM), and LGG. In contrast, THYM, exhibited inverse correlations between PTPRC expression and multiple ICGs, suggesting a unique immune evasion phenotype in this tumor type. 3.8 Pan-cancer Analysis of the Correlations between PTPRC Expression and TMB and MSI Analysis using the UCSCXenaShiny database revealed tumor-specific associations between PTPRC expression and genomic instability markers. TMB was positively correlated with PTPRC expression in COAD and UCEC, while negative correlations were observed in LIHC, TGCT, and THCA ( P < 0.05, Fig. 9 A). MSI showed inverse associations with PTPRC expression in eight tumor types, including BLCA, GBM, HNSC, KIRC, LUAD, LUSC, PAAD, and PCPG ( P < 0.05, Fig. 9 B). 3.9 Single-cell Functional Analysis of PTPRC The CancerSEA database was used to analyze the functional associations of PTPRC at the single-cell level. In ALL, PTPRC expression positively correlated with cellular quiescence but inversely associated with EMT and cell cycle progression ( P < 0.05). Prostate cancer (PC) exhibited PTPRC co-expression with pro-tumorigenic processes, including inflammation, proliferation, differentiation, stemness, and EMT ( P < 0.05). melanoma (MEL) showed a PTPRC-proliferation linkage, while UVM demonstrated broad suppression of oncogenic pathways, with PTPRC inversely correlated with DNA repair, invasion, metastasis, DNA damage, apoptosis, inflammation, and quiescence. t-SNE plots visualized PTPRC expression heterogeneity within single-cell clusters of these tumors (Fig. 10 ). 3.10 Enrichment Analysis of PTPRC-related Genes The STRING database was used to construct a PPI network, identifying 50 genes that interact with PTPRC (Fig. 11 A). The CytoHubba plugin was then employed to screen for hub genes, with the top 10 genes with the highest MCC scores being CD4, CD8A, ITGAM, SELL, PECAM1, CD19, CD44, ITGAX, and FCGR3A (Fig. 11 B). Functional enrichment analysis using the DAVID database revealed that these hub genes were primarily involved in biological processes such as transmembrane receptor protein tyrosine kinase signaling pathway, cell surface receptor signaling pathway, T-cell activation, adaptive immune response, peptidyl-tyrosine phosphorylation, positive regulation of interleukin-2 production, T-cell differentiation, and positive regulation of peptidyl-tyrosine phosphorylation. They were also associated with cellular components including the outer side of the plasma membrane, T-cell receptor (TCR) complex, part of the plasma membrane, part of the membrane, and membrane raft. Additionally, these genes were involved in molecular functions such as protein kinase binding and transmembrane signaling receptor activity. In terms of signaling pathways, they were enriched in seven KEGG pathways: TCR signaling pathway, primary immunodeficiency, PD-L1 expression in cancer and PD-1 checkpoint pathway (PD-L1/PD-1 signaling pathway), Th1 and Th2 cell differentiation, Th17 cell differentiation, human immunodeficiency virus type 1 infection, and human T-cell leukemia virus type 1 infection. The results were visualized as bubble charts on the Microbial Bioinformatics Cloud Platform (Fig. 11 C-D). 3.11 Expression Verification of PTPRC mRNA in Hepatocellular Carcinoma and Nasopharyngeal Carcinoma To validate the expression differences of PTPRC in tumor cells, we employed RT-qPCR to detect PTPRC expression in HepG2 and CNE-2. The results revealed that PTPRC expression was significantly upregulated in CNE-2 compared to normal nasopharyngeal epithelial cells ( P < 0.05) and downregulated in HepG2 compared to normal hepatocytes ( P < 0.05). These findings are consistent with the bioinformatics analysis, further confirming the differential expression of PTPRC in LIHC and nasopharyngeal carcinoma (Fig. 12 A-B). 4. Discussion PTPRC (CD45), a key member of the PTP family, plays a crucial role in regulating the JAK-STAT signaling pathway, which is crucial for immune regulation and tumorigenesis[ 11 – 13 ]. Our comprehensive analysis, combining bioinformatics analysis and experimental validation, uncovered significant variations in PTPRC expression across different tumor types and its impact on patient prognostic. Specifically, PTPRC was notably upregulated in 11 tumor types, including KIRC and LGG, while downregulated in others like LUAD and LIHC. This heterogeneity suggests diverse roles of PTPRC in different tumor contexts. For instance, higher PTPRC expression in LUAD correlated with better overall survival, whereas its elevated expression in LGG was associated with a poor prognosis. These findings highlight the importance of evaluating PTPRC's clinical value within specific tumor types and microenvironments. Methylation, an important epigenetic modification, significantly impacts tumor development and progression[ 14 – 16 ]. Our analysis revealed that PTPRC methylation levels were markedly reduced in most tumors types, consistent with its overexpression trend, suggesting that DNA hypomethylation may drive its transcriptional activation. Previous studies have shown that CD45 methylation status can influence TCR signaling pathway activity, affecting T-cell differentiation and function[ 17 ]. Additionally, missense mutations in PTPRC are frequently observed in certain tumors [ 18 ]. These epigenetic and genetic alterations may jointly determine the functional diversity of PTPRC in tumors, highlighting the need for further functional experiments to elucidate its roles in different cancer contexts. Tumor immune infiltration is closely associated with tumor prognosis, with immune cells playing a supportive role in tumor progression[ 19 ]. Our study found that PTPRC expression was significantly correlated with the infiltration levels of various immune cells, including M2 macrophages, Tregs, and CAFs. These immune cells are key players in tumor development. For example, macrophages can enhance tumor cell invasion, metastasis, and angiogenesis while suppressing anti-tumor immune surveillance[ 20 ]. Moreover, the positive correlation between PTPRC and immune checkpoint genes (e.g., CTLA4 and TIGIT) highlights its potential role in immune checkpoint regulation. These findings suggest that PTPRC may significantly influence the tumor immune landscape by modulating immune cell infiltration and immune checkpoint gene expression. Single-cell functional analysis further underscored the context-dependent roles in tumor biology. In ALL, elevated PTPRC expression marked cellular quiescence, potentially reflecting a dormant state resistant to conventional therapies. Conversely, in PC, PTPRC co-expression with inflammation, proliferation, differentiation, and stemness suggests its role in sustaining tumor plasticity and therapy resistance. Strikingly, UVM exhibited broad suppression of oncogenic pathways (e.g., DNA repair, invasion, metastasis, DNA damage, apoptosis, and inflammation) with PTPRC downregulation, implying a tumor-suppressive function in this context. Functional enrichment of PTPRC-interacting hub genes (CD4, CD8A, PD-L1) highlighted its centrality in immune checkpoint regulation. The TCR signaling pathway — essential for T-cell activation and immune tolerance — and the PD-L1/PD-1 axis emerged as dominant mechanisms. The TCR signaling pathway is essential for T cell development, activation, and immune tolerance, and its dysregulation can lead to immune escape or autoimmune reactions[ 21 ]. The PD-L1/PD-1 signaling pathway, highly expressed in various tumors, inhibits anti-tumor T-cell activity, promoting tumor immune escape[ 22 ]. These findings provide a molecular basis for PTPRC as a potential immunotherapy target and suggest directions for future research. While our study provides valuable insights into the role of PTPRC in various cancers, it has some limitations. The experimental validation was limited to hepatocellular carcinoma and nasopharyngeal carcinoma, which restricts the universality of our conclusions. Future studies should verify PTPRC’s role in more tumor types to comprehensively assess its potential as a pan-ncer prognostic and immune biomarker. Furthermore, additional functional experiments, including in vitro and in vivo studies, are needed to elucidate the precise mechanisms of PTPRC in tumorigenesis and development. For example, CRISPR-Cas9 gene editing technology could be used to precisely knockout or mutate the PTPRC gene to study its impact on tumor cell behavior. PPI network analysis could also identify key PTPRC-interacting proteins, further clarifying its role in cell signaling. 5. Conclusion In conclusion, our pan-cancer analysis disclosed the differential expression of PTPRC between tumor and normal tissues and highlighted its potential as a prognostic factor for multiple tumor types. PTPRC expression was significantly correlated with protein levels, methylation, genetic variations, the tumor immune microenvironment, immune-related genes, TMB, and MSI. These results suggest that PTPRC could serve as a potential prognostic and immune-related biomarker for tumors, laying the foundation for further research on its precise mechanisms in different tumors and the development of treatment strategies. Declarations Author contributions Lizhu Tang conceptualized and wrote the manuscript. Ting Hu reviewed and revised the manuscript. Dingshi Liu, Changqiao Huang and Wenli Yin performed the result analysis and graphic. Sijing Wei,Ruimin Xu and Chengliang Yang made the data collection and analysis. Yulian Tang and Yueyong Li conceived, designed, and reviewed this manuscript. All authors have both read and approved the submitted version of the manuscript. All authors contributed equally to this work. Funding This research was supported by Guangxi Natural Science Foundation of China (No. 2024JJH140141). Availability of supporting data The data supporting the results of this study comes from the following available resources in the public domain: TIMER2.0 database (http://timer.cistrome.org/); UALCAN database (http://ualcan.path.uab.edu/); GEPIA2 database (http://gepia2.cancer-pku.cn/#analysis); HPA database (https://www.proteinatlas.org/); cBioPortal database (https://www.cbioportal.org); UCSCXenaShiny database (https://shiny.hiplot-academic.com/ucsc-xena-shiny/); SangerBox database (http://sangerbox.com/home.html); CancerSEA database (https://ngdc.cncb.ac.cn/databasecommons/database/id/6092); STRING database (https://www.string-db.org/); DAVID database (https://david.ncifcrf.gov/home.jsp); and the Microsense Cloud Platform (http://www.bioinformatics.com.cn). Ethical Statement: This study has no ethical implications. Consent for publication: All relevant authors agree to publish. Competing interests: The authors declare no competing interests. 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Regulation of CD45 phosphatase by oncogenic ALK in anaplastic large cell lymphoma, Frontiers in Oncology 2022;12:1085672. Li X, Yue Z, Wang D et al. PTPRC functions as a prognosis biomarker in the tumor microenvironment of cutaneous melanoma, Scientific Reports 2023;13:20617. Zhang Y, Zhang Z. The history and advances in cancer immunotherapy: understanding the characteristics of tumor-infiltrating immune cells and their therapeutic implications, Cellular & Molecular Immunology 2020;17:807-821. Xiao Y, Yu D. Tumor microenvironment as a therapeutic target in cancer, PHARMACOLOGY & THERAPEUTICS 2021;221:107753. Shah K, Al-Haidari A, Sun J et al. T cell receptor (TCR) signaling in health and disease, Signal Transduction and Targeted Therapy 2021;6:412. Xie W, Medeiros LJ, Li S et al. PD-1/PD-L1 Pathway and Its Blockade in Patients with Classic Hodgkin Lymphoma and Non-Hodgkin Large-Cell Lymphomas, Current Hematologic Malignancy Reports 2020;15:372-381. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6233301","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":439053251,"identity":"d66ef1fe-92ee-4605-9933-4ec3c42afcb6","order_by":0,"name":"Lizhu Tang","email":"","orcid":"","institution":"Jinan University","correspondingAuthor":false,"prefix":"","firstName":"Lizhu","middleName":"","lastName":"Tang","suffix":""},{"id":439053252,"identity":"f7d9a5b5-1ec6-420f-90a9-99c9fa8fad2c","order_by":1,"name":"Ting Hu","email":"","orcid":"","institution":"Youjiang Medical University for Nationalities","correspondingAuthor":false,"prefix":"","firstName":"Ting","middleName":"","lastName":"Hu","suffix":""},{"id":439053253,"identity":"0f9f73d9-ba84-415a-8db7-8d4a55c38e05","order_by":2,"name":"Dingshi Liu","email":"","orcid":"","institution":"Youjiang Medical University for Nationalities","correspondingAuthor":false,"prefix":"","firstName":"Dingshi","middleName":"","lastName":"Liu","suffix":""},{"id":439053254,"identity":"20fcd31f-3c26-4ba1-98c7-6eb15dfa2435","order_by":3,"name":"Changqiao Huang","email":"","orcid":"","institution":"Youjiang Medical University for Nationalities","correspondingAuthor":false,"prefix":"","firstName":"Changqiao","middleName":"","lastName":"Huang","suffix":""},{"id":439053256,"identity":"028f56c2-a963-4847-b37e-188589b0df10","order_by":4,"name":"Wenli Yin","email":"","orcid":"","institution":"Youjiang Medical University for Nationalities","correspondingAuthor":false,"prefix":"","firstName":"Wenli","middleName":"","lastName":"Yin","suffix":""},{"id":439053257,"identity":"9c169d13-5aa3-4b32-b747-91667b86ce35","order_by":5,"name":"Sijing Wei","email":"","orcid":"","institution":"Youjiang Medical University for Nationalities","correspondingAuthor":false,"prefix":"","firstName":"Sijing","middleName":"","lastName":"Wei","suffix":""},{"id":439053259,"identity":"dbf81d0c-a18c-42f0-b641-581f4cd9e121","order_by":6,"name":"Chengliang Yang","email":"","orcid":"","institution":"Key Laboratory of Biomedical Material Research of Guangxi (Cultivation)","correspondingAuthor":false,"prefix":"","firstName":"Chengliang","middleName":"","lastName":"Yang","suffix":""},{"id":439053260,"identity":"7c1a2c9c-ee80-4335-9fdb-d4186f1c5353","order_by":7,"name":"Ruimin Xu","email":"","orcid":"","institution":"Changsha Central Hospital Affiliated to University of South China","correspondingAuthor":false,"prefix":"","firstName":"Ruimin","middleName":"","lastName":"Xu","suffix":""},{"id":439053261,"identity":"ef7f1468-38fe-4ded-9105-1bf45e5413e3","order_by":8,"name":"Yulian Tang","email":"","orcid":"","institution":"Youjiang Medical University for Nationalities","correspondingAuthor":false,"prefix":"","firstName":"Yulian","middleName":"","lastName":"Tang","suffix":""},{"id":439053262,"identity":"0a6d714c-0d27-4ef6-b7d8-9ebc16b7d13b","order_by":9,"name":"Yueyong Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzElEQVRIiWNgGAWjYBACeWb+BwcS/9nUN7Y3EKnFsL2H8cEDtjTG5p4DxFpz5gyz4QO2Q4ztMxKI1ME4I/eYRALPAWbemY833mCosYkmqIVdIi9NIkHiDpvk7LRiC4ZjabkNhG1JMJNIMHjGYzg7x0yCseEwYS0MN0BaEg5L2N88Q6yWM2eMDRIOHDZgnMFDpBbD9rbEB4kNaQmMPUC/JBDjF3lm5gMHfzbYJDC2H95440ONDREOQwIGEgmkKIdoIVXHKBgFo2AUjAwAACGBRKvI8XcjAAAAAElFTkSuQmCC","orcid":"","institution":"Jinan University","correspondingAuthor":true,"prefix":"","firstName":"Yueyong","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2025-03-15 14:08:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6233301/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6233301/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":80710567,"identity":"d934bcac-92e8-4007-a49f-dcc51628e4e7","added_by":"auto","created_at":"2025-04-16 09:00:35","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1022651,"visible":true,"origin":"","legend":"\u003cp\u003ePan-cancer Analysis of PTPRC Expression\u003c/p\u003e\n\u003cp\u003eNote: (A) This panel shows the expression levels of PTPRC across various tumor types compared to normal tissues, as analyzed by the TIMER2.0 database. (B) This panel displays PTPRC expression in tumors without matched normal tissue data in the TIMER2.0 database, utilizing integrated TCGA and GTEx datasets via R packages. *P \u0026lt; 0.05; **P \u0026lt; 0.01; ***P \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Fig1.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6233301/v1/b619539a5f5db34a3e95531e.jpg"},{"id":80712509,"identity":"14cbaa7c-7cb1-459e-9766-8ec8127aace9","added_by":"auto","created_at":"2025-04-16 09:16:36","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":885209,"visible":true,"origin":"","legend":"\u003cp\u003eExpression of PTPRC in normal and tumor tissues\u003c/p\u003e\n\u003cp\u003eNote: (A) This panel presents protein expression levels of PTPRC in various tumor types compared to normal tissues, as analyzed by the UALCAN database. (B) This panel provides representative immunohistochemical images of PTPRC expression from the HPA database, comparing normal and tumor tissues across different organs. *P \u0026lt; 0.05; **P \u0026lt; 0.01; ***P \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Fig2.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6233301/v1/0e37056354476080f74498f1.jpg"},{"id":80710571,"identity":"d10569ef-54fb-4697-af50-142d94112114","added_by":"auto","created_at":"2025-04-16 09:00:36","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":633307,"visible":true,"origin":"","legend":"\u003cp\u003eSurvival Prognosis Status of PTPRC Expression in Pan-cancer\u003c/p\u003e\n\u003cp\u003eNote: (A) This panel shows the association between PTPRC expression and overall survival (OS) across various cancer types. (B) This panel illustrates the relationship between PTPRC expression and disease-free survival (DFS) in different cancers.\u003c/p\u003e","description":"","filename":"Fig3.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6233301/v1/3596c684b9713203c478c5f5.jpg"},{"id":80711850,"identity":"d055e09d-d120-445d-972a-3e45a5ed1a20","added_by":"auto","created_at":"2025-04-16 09:08:36","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":544894,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between PTPRC Expression and Methylation in Pan-cancer\u003c/p\u003e\n\u003cp\u003eNote: This panel displays the methylation status of PTPRC across various tumor types compared to normal tissues, highlighting significant differences in methylation levels.\u003c/p\u003e\n\u003cp\u003e*P \u0026lt; 0.05; **P \u0026lt; 0.01; ***P \u0026lt; 0.001\u003c/p\u003e","description":"","filename":"Fig4.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6233301/v1/f4f714810270c905fdfd9a14.jpg"},{"id":80710577,"identity":"91b70e12-0b22-45f2-9163-05b06ef4e352","added_by":"auto","created_at":"2025-04-16 09:00:36","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":608112,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between PTPRC Expression and Genetic Variation in Pan-cancer\u003c/p\u003e\n\u003cp\u003eNote: (A) This panel shows the genetic variation analysis of PTPRC across different cancer types, including mutation frequencies and types. (B) This panel provides a detailed view of specific genetic alterations in PTPRC across various tumors, highlighting the distribution and types of mutations such as missense, truncating, and splice mutations.\u003c/p\u003e","description":"","filename":"Fig5.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6233301/v1/fac8c56ddc9e2bcf85317e99.jpg"},{"id":80710578,"identity":"e68294b8-e046-480e-b798-abf9c15f6ce1","added_by":"auto","created_at":"2025-04-16 09:00:36","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":634668,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation of PTPRC Expression with the Level of Infiltration of Different Immune Cell Subtypes\u003c/p\u003e\n\u003cp\u003eNote: This panel shows the correlation between PTPRC expression and the infiltration levels of various immune cell subtypes across different cancer types, as analyzed by the UCSCXenaShiny database. The heat map visually represents these correlations, with red indicating positive associations and blue indicating negative associations.\u003c/p\u003e","description":"","filename":"Fig6.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6233301/v1/fa3e4ae820ac3f1e2ea513db.jpg"},{"id":80711854,"identity":"6e5e3c18-fc39-416a-8082-1f427bad6f9a","added_by":"auto","created_at":"2025-04-16 09:08:36","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":892630,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between PTPRC Expression and CAFs Infiltration Level in Pan-cancer\u003c/p\u003e\n\u003cp\u003eNote: (A) This panel presents a heatmap of the correlation between PTPRC expression and cancer-associated fibroblast (CAF) infiltration levels across various tumor types, analyzed using different methods (EPIC, MCPCOUNTER, XCELL, and TIDE). (B) This panel provides scatter plots illustrating the correlation between PTPRC expression and CAF infiltration levels in specific tumors, showing the distribution and strength of these correlations.\u003c/p\u003e","description":"","filename":"Fig7.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6233301/v1/3fede7029f47aa538b399bbf.jpg"},{"id":80710579,"identity":"5a620e59-0312-4b6e-8353-bbc37aff9893","added_by":"auto","created_at":"2025-04-16 09:00:36","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1279506,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between PTPRC expression and ICG in pan-cancer\u003c/p\u003e\n\u003cp\u003eNote: This panel shows the correlation between PTPRC expression and immune checkpoint genes (ICGs) across various cancer types, as analyzed by the SangerBox database.\u003c/p\u003e","description":"","filename":"Fig8.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6233301/v1/92bc0bb5806e408d932fd1bf.jpg"},{"id":80710573,"identity":"bc1c63f8-3be4-4795-9228-f08ab422088e","added_by":"auto","created_at":"2025-04-16 09:00:36","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":222947,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelations between PTPRC Expression and Genomic Instability Markers in Pan-cancer\u003c/p\u003e\n\u003cp\u003eNote: (A) This panel shows the positive correlation between PTPRC expression and tumor mutation burden (TMB) in specific cancer types. (B) This panel illustrates the inverse correlation between PTPRC expression and microsatellite instability (MSI) across eight tumor types.\u003c/p\u003e","description":"","filename":"Fig9.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6233301/v1/bacddf66b8b40ae6e10c8d4d.jpg"},{"id":80711852,"identity":"89b607ad-9bd6-46f8-8843-1a30b7194c01","added_by":"auto","created_at":"2025-04-16 09:08:36","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":585599,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional Analysis of PTPRC at the Single-Cell Level\u003c/p\u003e\n\u003cp\u003eNote: (A) This panel shows the functional status of PTPRC in different tumors, with red indicating positive correlations and blue indicating negative correlations. (B) This panel presents the correlation of functional status with PTPRC in acute lymphoblastic leukemia (ALL), prostate cancer (PC), melanoma (MEL), and uveal melanoma (UVM), along with expression profiles of PTPRC in individual cells.\u003c/p\u003e","description":"","filename":"Fig10.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6233301/v1/117f529b45f407a4d560e84a.jpg"},{"id":80712510,"identity":"6d2a9807-acd7-4310-89d5-cd04a277a1d3","added_by":"auto","created_at":"2025-04-16 09:16:36","extension":"jpg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":1176714,"visible":true,"origin":"","legend":"\u003cp\u003ePTPRC Interaction Protein Network and Hub Gene Function Enrichment Analysis\u003c/p\u003e\n\u003cp\u003eNote: (A) This panel shows the PTPRC protein interaction network constructed by the STRING database, identifying 50 genes that interact with PTPRC. (B) This panel presents the hub gene network mapped by Cytoscape 3.9.1 software, highlighting the top 10 hub genes with the highest MCC scores. (C) This panel provides the Gene Ontology functional classification of these hub genes. (D) This panel shows the KEGG pathway analysis of these hub genes.\u003c/p\u003e","description":"","filename":"Fig11.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6233301/v1/df73a7868ac1735dd738a972.jpg"},{"id":80711859,"identity":"098bebe4-251f-4066-85b9-6cb0644c4b9a","added_by":"auto","created_at":"2025-04-16 09:08:36","extension":"jpg","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":227118,"visible":true,"origin":"","legend":"\u003cp\u003eExpression Verification of PTPRC mRNA in Hepatocellular Carcinoma and Nasopharyngeal Carcinoma\u003c/p\u003e\n\u003cp\u003eNote: (A) This panel shows the relative expression levels of PTPRC mRNA in HepG2 compared to LO2. (B) This panel shows the relative expression levels of PTPRC mRNA in CNE-2 compared to NP69.\u003c/p\u003e","description":"","filename":"Fig12.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6233301/v1/17ebd064d5e78666f95afc1b.jpg"},{"id":87037550,"identity":"4a205264-4eac-4267-bc2c-f0cd236774cb","added_by":"auto","created_at":"2025-07-18 13:24:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9668298,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6233301/v1/13d1c588-3ab5-4ddb-8b12-55b16812145f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"PTPRC as a pan-cancer biomarker: Prognostic significance and immune microenvironment interactions","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eTumors, characterized by high incidence and mortality rates, present a significant threat to human health. Although immunotherapy, particularly immune checkpoint blockade, has made progress, it remains effective for only a small subset of patients, with the majority exhibiting drug resistance[\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Consequently, there is an urgent demand for early diagnosis and effective treatment strategies to improve patient outcomes. To address the limitations of current therapies, it is crucial to identify new therapeutic targets and strategies. The development of pan-cancer databases has significantly advanced this field, providing a powerful tool for discovering novel targets and guiding tumor diagnosis and treatment. By integrating data from multiple tumor types, pan-cancer analysis can reveal common mechanisms of tumorigenesis and progression, as well as potential therapeutic targets, thereby paving the way for more effective treatment methods. Protein tyrosine phosphatase receptor type C (PTPRC, also known as CD45), a key member of the protein tyrosine phosphatase (PTP) family, plays a crucial role in signal transduction pathways. PTPs collaborate with protein tyrosine kinases (PTKs) to regulate numerous cellular processes, such as cell proliferation, differentiation, and mitosis. Aberrant expression or dysfunction of PTPs has been associated with autoimmune diseases, metabolic syndrome, and tumor development[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. PTPRC has been shown to be involved in multiple fundamental biological processes, and its abnormalities can lead to immunodeficiency, autoimmune diseases, or malignant tumors[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn recent years, the role of PTPRC in tumors has attracted increasing attention. Studies have shown that PTPRC can function as both a tumor promoter and a tumor suppressor in certain cancers. For example, Helicobacter pylori-induced PTPRC overexpression is positively correlated with gastric cancer occurrence[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], while CD45 deficiency is linked to the progression and poor prognosis of multiple myeloma[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Moreover, PTPRC may serve as a potential diagnostic or therapeutic target for various cancers, including inflammatory breast cancer[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], nasopharyngeal carcinoma[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], and renal cell carcinoma[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], although its underlying mechanisms are not fully understood. However, most current studies focus on specific tumor types, and there is a lack of comprehensive pan-cancer analysis of PTPRC. Given its crucial role in cellular processes and complex functions in tumors, a systematic study of PTPRC expression differences in pan-cancer, its correlation with tumor prognosis, and its role in the tumor immune microenvironment is essential for revealing its potential mechanisms in tumorigenesis and tumor development.\u003c/p\u003e \u003cp\u003eTherefore, this study aims to comprehensively elucidate the potential mechanisms of PTPRC in tumorigenesis and tumor development from a pan-cancer perspective. We have thoroughly analyzed the associations between PTPRC expression and tumor prognosis, DNA methylation, microsatellite instability (MSI), tumor mutation burden (TMB), immune checkpoint genes (ICGs), and immune infiltration levels. Through single-cell functional analysis and functional enrichment analysis, we have further elaborated on the biological functions of PTPRC in tumors and uncovered its mechanism of action in the tumor immune microenvironment. By integrating multi-dimensional data, this study not only reveals the expression patterns and prognostic values of PTPRC in different tumors but also explores its role in the tumor immune microenvironment. These findings provide potential targets for the development of innovative immunotherapy strategies and offer theoretical bases and practical guidance for the clinical diagnosis and treatment of tumors.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Materials\u003c/h2\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003e2.1.1 Databases\u003c/h2\u003e \u003cp\u003eGene expression differences were assessed using TIMER2.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://timer.cistrome.org/\u003c/span\u003e\u003cspan address=\"http://timer.cistrome.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and UALCAN (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://ualcan.Path.uab.edu/\u003c/span\u003e\u003cspan address=\"http://ualcan.Path.uab.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) databases. Protein expression levels were evaluated via the UALCAN database, while immunohistochemistry (IHC) staining data were obtained from the HPA database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.proteinatlas.org/\u003c/span\u003e\u003cspan address=\"https://www.proteinatlas.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Patient survival curves were generated using the GEPIA2 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://gepia2.cancer-pku.cn/#analysis\u003c/span\u003e\u003cspan address=\"http://gepia2.cancer-pku.cn/#analysis\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) database. PTPRC methylation levels and mutation frequencies were analyzed using UALCAN and cBioPortal (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cbioportal.org\u003c/span\u003e\u003cspan address=\"https://www.cbioportal.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), respectively. Correlations between PTPRC expression and immune cell infiltration, TMB, and MSI were analyzed using UCSCXenaShiny database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://shiny.hiPlot-academic.com/ucsc-xena-shiny/\u003c/span\u003e\u003cspan address=\"https://shiny.hiPlot-academic.com/ucsc-xena-shiny/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). CancerSEA (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ngdc.cncb.ac.cn/databasecommons/database/id/6092\u003c/span\u003e\u003cspan address=\"https://ngdc.cncb.ac.cn/databasecommons/database/id/6092\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to analyze PTPRC\u0026rsquo;s correlation with tumor functional states. The STRING database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.string-db.org/\u003c/span\u003e\u003cspan address=\"https://www.string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) constructed a PTPRC protein-protein interaction (PPI) network, and DAVID (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://david.ncifcrf.gov/home.jsp\u003c/span\u003e\u003cspan address=\"https://david.ncifcrf.gov/home.jsp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) performed functional enrichment analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.1.2 Cell source\u003c/h2\u003e \u003cp\u003eNP69 (Immortalized human nasopharyngeal epithelial cells), CNE-2 (nasopharyngeal carcinoma cells), LO2 (normal human hepatocytes), and HepG2 (hepatocellular carcinoma cells). These cell lines were purchased from Procell Life Science \u0026amp; Technology Co., Ltd. (Wuhan, China).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.1.3 Reagents\u003c/h2\u003e \u003cp\u003eCells were cultured under standard conditions as per the supplier\u0026rsquo;s guidelines: NP69 in DMEM/F12 medium with 10% FBS, and other cell lines in RPMI 1640 medium with 10% FBS at 37\u0026deg;C and 5% CO₂.\u003c/p\u003e \u003cp\u003eThe main reagents used in this study included: Total RNA extraction kit (Solarbio Science \u0026amp; Technology Co., Ltd., Shanghai, China); First Strand cDNA Synthesis Kit (Beyotime Biotechnology Co., Ltd., Shanghai, China); Hiff\u0026trade; QPCR SYBR\u0026reg; Green Mix (TransGen Biotech Co., Ltd., Shanghai, China).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Methods\u003c/h2\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 Gene Expression Analysis\u003c/h2\u003e \u003cp\u003ePTPRC expression differences between tumor and normal tissues were analyzed using the \u0026ldquo;Gene_DE\u0026rdquo; module within the \u0026ldquo;Cancer Exploration\u0026rdquo; section of the TIMER2.0 database. For TCGA tumors lacking normal tissue data, expression differences were analyzed using R packages in the TCGA and GTEx databases. Specifically, the \u0026ldquo;limma\u0026rdquo; package was used to perform differential expression analysis, adjusting for batch effects and other covariates. Protein expression levels were assessed via the UALCAN database\u0026rsquo;s CPTAC dataset, which provides quantitative proteomics data. IHC staining data were obtained from the HPA database\u0026rsquo;s \u0026ldquo;TISSUE\u0026rdquo; and \u0026ldquo;PATHOLOGY\u0026rdquo; modules to further validate protein expression patterns.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 Survival Prognosis Analysis\u003c/h2\u003e \u003cp\u003eTo evaluate the relationship between PTPRC expression and patient survival outcomes, we used the GEPIA2 database\u0026rsquo;s \u0026ldquo;Survival Analysis\u0026rdquo; module to generate Kaplan-Meier survival curves for overall survival (OS) and disease-free survival (DFS). Patients were stratified into high and low PTPRC expression groups based on the median expression level. The log-rank test was applied to assess significant differences in survival times between groups, and hazard ratios (HR) with 95% confidence intervals (CI) were calculated.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3 Methylation and Genetic Variation Analysis\u003c/h2\u003e \u003cp\u003ePTPRC methylation levels were analyzed using the \u0026ldquo;methylation\u0026rdquo; module within the UALCAN database\u0026rsquo;s \u0026ldquo;TCGA\u0026rdquo; section, comparing methylation status between tumor and normal tissues. Mutation frequencies and types were assessed via the cBioPortal database\u0026rsquo;s \u0026ldquo;TCGA PanCancer Atlas Studies\u0026rdquo;. The \u0026ldquo;Cancer Types Summary\u0026rdquo; module was used to analyze the mutation frequencies of PTPRC across different tumors, and the \u0026ldquo;Mutations\u0026rdquo; module was employed to identify the specific mutation types and their locations. This comprehensive approach allowed us to explore both epigenetic and genetic factors influencing PTPRC expression and function in cancer.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.2.4 Immune Infiltration Analysis\u003c/h2\u003e \u003cp\u003eCorrelations between PTPRC expression and immune cell infiltration, TMB, and MSI were analyzed using the UCSCXenaShiny database. The \u0026ldquo;Quick PanCan Analysis\u0026rdquo; module was selected, and PTPRC was input as the gene of interest. Relevant cancer datasets were chosen, and the analysis was executed to obtain correlation coefficients and p-values. For the analysis of cancer-associated fibroblasts (CAFs) and ICGs, the TIMER2.0 and SangerBox databases were utilized. In TIMER2.0, PTPRC was input, and modules were selected to estimate CAF abundance based on gene expression signatures. SangerBox was used to search for known ICGs, and their expression patterns in relation to PTPRC were analyzed by selecting relevant genes and cancer datasets, identifying significant correlations or differential expression patterns.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.2.5 Single-Cell Functional Analysis\u003c/h2\u003e \u003cp\u003ePTPRC\u0026rsquo;s correlation with tumor functional states was analyzed using the CancerSEA database\u0026rsquo;s \u0026ldquo;Correlation Plot\u0026rdquo; and \u0026ldquo;Functional relevance\u0026rdquo; modules. To perform this analysis, the CancerSEA database was accessed, and PTPRC was input as the gene of interest. The \u0026ldquo;Correlation Plot\u0026rdquo; module was used to visualize the relationship between PTPRC expression and various tumor functional states, while the \"Functional relevance\" module provided insights into the biological significance of these correlations. Single-cell expression profiles were visualized as t-SNE plots, which allowed for the identification of distinct cell populations and their functional states within the tumor microenvironment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e2.2.6 Functional Enrichment Analysis\u003c/h2\u003e \u003cp\u003eThe PTPRC protein-protein interaction (PPI) network was constructed using the STRING database (minimum confidence score: 0.700). Hub genes were identified using Cytoscape\u0026rsquo;s CytoHubba plugin (MCC algorithm) and prioritized by maximal clique centrality. Functional enrichment analysis (Gene Ontology and KEGG pathways) of the top 10 hub genes was performed using DAVID with a significance threshold (Benjamini-Hochberg adjusted \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Results were visualized as bubble charts and network diagrams through the Microbial Bioinformatics Cloud Platform.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e2.2.7 Experimental Verification\u003c/h2\u003e \u003cp\u003eTotal RNA was extracted from LO2, HepG2, NP69, and CNE-2 cells using a Total RNA extraction kit. Reverse transcription was performed using the First Strand cDNA Synthesis Kit with 1 \u0026micro;g RNA in a 20 \u0026micro;L reaction (42\u0026deg;C for 30 min, 85\u0026deg;C for 5 s). RT-qPCR amplification protocol included an initial denaturation(95\u0026deg;C, 5 min), 40 cycles (95\u0026deg;C, 10 s; 60\u0026deg;C, 30 s), and melt curve analysis (65\u0026ndash;95\u0026deg;C) to confirm primer specificity. GAPDH served as the endogenous control, and relative PTPRC expression was calculated via the 2\u0026thinsp;\u0026minus;\u0026thinsp;ΔΔCt method. Experiments were independently repeated three times. Primer sequences are listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\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\u003ePrimers for RT-qPCR\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward primer (5'-3')\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReverse primer (5'-3')\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePTPRC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTGAAGCAAAGGAACAGGCTGAAGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGCTGGACTTGCAGGACCATTGAC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGAPDH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGCACCGTCAAGGCTGAGAAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTGGTGAAGACGCCAGTGTA\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 \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Statistical Analysis\u003c/h2\u003e \u003cp\u003eData were analyzed using database-provided methods. The Wilcoxon rank-sum test was applied to assess PTPRC expression differences between tumor and normal tissues. Survival outcomes were compared via the Log-rank test with Kaplan-Meier curves, and HR with 95% CIs were derived from Cox proportional hazards models. One-way ANOVA evaluated PTPRC protein and methylation differences, assuming homogeneity of variances (Levene\u0026rsquo;s test, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.1). Spearman correlation analyzed associations between PTPRC expression and immune cell infiltration, TMB, MSI, and CAF/ICG relationships. Welch's t-test compared PTPRC mRNA levels in cancer versus normal cells. All tests were two-tailed, and statistical significance was defined as \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Pan-cancer Analysis of PTPRC Expression\u003c/h2\u003e \u003cp\u003eAnalysis of the TIMER2.0 database revealed significant upregulation of PTPRC in five tumor types compared to normal tissues, including esophageal carcinoma (ESCA), glioblastoma multiform (GBM), head and neck squamous cell carcinoma (HNSC), kidney renal clear cell carcinoma (KIRC), and stomach adenocarcinoma (STAD) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Conversely, PTPRC was downregulated in bladder urogenital carcinoma (BLCA), colon adenocarcinoma (COAD), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), pancreatic adenocarcinoma (PAAD), rectum adenocarcinoma (READ), thyroid carcinoma (THCA), and uterine corpus endometrial carcinoma (UCEC) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor tumors without matched normal tissue data in TIMER2.0 database, integration of TCGA and GTEx datasets via R packages demonstrated PTPRC overexpression in lymphoid neoplasm diffuse large B-cell lymphoma (DLBC), acute myeloid leukemia (LAML), brain lower grade glioma (LGG), ovarian serous cystadenocarcinoma (OV), skin cutaneous melanoma (SKCM), and testicular germ cell tumors (TGCT). Conversely, reduced expression was observed in adrenocortical carcinoma (ACC), thymoma (THYM), and uterine carcinosarcoma (UCS) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eProtein-level validation using the UALCAN database confirmed elevated PTPRC expression in clear cell RCC (CCRCC), GBM, HNSC, PAAD, and UCEC, whereas decreased levels were detected in LIHC, LUAD, and OV (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Immunohistochemical data from the HPA database further supported these findings, showing concordant low PTPRC expression in LUAD tumor tissues compared to normal lung tissues (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Pan-cancer Analysis of the Association between PTPRC Expression and Patient Prognosis\u003c/h2\u003e \u003cp\u003eAnalysis of the GEPIA2 database revealed a significant association between PTPRC expression and patient survival outcomes. OS analysis demonstrated that high PTPRC expression correlated with improved prognosis in HNSC (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.014), LUAD (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0013), and SKCM (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.9 e-05). Conversely, elevated PTPRC expression predicted poor OS in LGG (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.017) and uveal melanoma (UVM, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). For DFS, high PTPRC expression was associated with favorable outcomes in patients with cholangiocarcinoma (CHOL, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.019), LIHC (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.034), and SKCM (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.017). In contrast, increased PTPRC expression indicated worse DFS in LGG (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.041, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Pan-cancer Analysis of PTPRC Methylation\u003c/h2\u003e \u003cp\u003eAnalysis of DNA methylation profiles via the UALCAN database demonstrated widespread hypomethylation of PTPRC in tumor tissues compared to adjacent normal tissues. Significant hypomethylation (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) was observed in BLCA, breast invasive carcinoma (BRCA), COAD, ESCA, HNSC, KIRC, kidney renal papillary cell carcinoma (KIRP), LIHC, LUAD, LUSC, PAAD, prostate adenocarcinoma (PRAD), READ, sarcoma (SARC), TGCT, and UCEC. In contrast, cholangiocarcinoma (CHOL) exhibited tumor-specific hypermethylation of PTPRC (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Pan-cancer Analysis of PTPRC Genetic Variations\u003c/h2\u003e \u003cp\u003eGenomic alteration analysis via the cBioPortal database identified distinct patterns of PTPRC variations across cancers. Mutation frequency was highest in SKCM (13.51%), predominantly characterized by somatic missense mutations. Other tumors with detectable PTPRC mutations (\u0026gt;\u0026thinsp;0.71%) included DLBC, LGG, LAML, KIRC, and KIRP. In contrast, gene amplification was the sole alteration type in THYM, mesothelioma (MESO), THCA, and ACC (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Further characterization revealed that missense mutations accounted for the majority of PTPRC genomic variations. The most recurrent mutation site mapped to codon 292 (T292M/P) within the cytoplasmic tyrosine phosphatase domain (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e3.5 Pan-cancer Analysis of the Association between PTPRC Expression and the Infiltration Levels of Different Immune Cell Subtypes\u003c/p\u003e \u003cp\u003eAnalysis of the UCSCXenaShiny database revealed distinct correlations between PTPRC expression and immune cell infiltration (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Positively correlations were observed with immunosuppressive subsets, including regulatory T cells (Tregs), M2 macrophages, and resting dendritic cells. Adaptive immune effectors such as CD8\u0026thinsp;+\u0026thinsp;T cells, γδ T cells, and activated memory CD4\u0026thinsp;+\u0026thinsp;T cells also showed significant positive associations. In contrast, it was negatively correlated with activated dendritic cells, M0 macrophages, activated natural killer (NK) cells, and memory B cells. Resting NK cells and naive CD4\u0026thinsp;+\u0026thinsp;T cells were inversely associated with PTPRC expression.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Pan-cancer Analysis of the Association between PTPRC Expression and the Infiltration Level of CAFs\u003c/h2\u003e \u003cp\u003eTIMER2.0 database analysis revealed a positive correlation between PTPRC expression and CAF infiltration levels across various tumors (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA-B). Notably, in BLCA, PTPRC expression showed a significant positive correlation with CAF infiltration (Rho\u0026thinsp;=\u0026thinsp;0.295, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7.53e-09) using the MCPCOUNTER method. Similarly, in breast invasive carcinoma of Luminal A subtype (BRCA-LumA), a positive correlation was observed (Rho\u0026thinsp;=\u0026thinsp;0.259, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.34e-09) via the EPIC method. Other cancer types exhibited comparable trends, including COAD (Rho\u0026thinsp;=\u0026thinsp;0.516, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.30e-20), HNSC (Rho\u0026thinsp;=\u0026thinsp;0.318, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.64e-13), LGG (Rho\u0026thinsp;=\u0026thinsp;0.343, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.22e-14), LUAD (Rho\u0026thinsp;=\u0026thinsp;0.277, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.83e-10), LUSC (Rho\u0026thinsp;=\u0026thinsp;0.328, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.84e-13), and PAAD (Rho\u0026thinsp;=\u0026thinsp;0.506, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.70e-12), all analyzed using the EPIC or XCELL methods. These findings collectively indicate that PTPRC expression is positively associated with CAF infiltration across multiple cancer types, suggesting a potential role of PTPRC in tumor-stroma interactions and tumor progression.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Pan-cancer Analysis of the Association between PTPRC Expression and ICGs\u003c/h2\u003e \u003cp\u003eAnalysis of the SangerBox database revealed pan-cancer correlations between the PTPRC expression and ICGs across 20 tumor types (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). PTPRC expression showed significant positive associations with ICGs (including CTLA4, TIGIT, CD274, and BTLA), in kidney chromophobe (KICH), LIHC, pheochromocytoma and paraganglioma (PCPG), KIRP, UCS, BRCA, LUAD, STAD, ESCA, neuroblastoma (NB), COAD, READ, OV, PAAD, PRAD, acute lymphoblastic leukemia (ALL), glioma (GBM), and LGG. In contrast, THYM, exhibited inverse correlations between PTPRC expression and multiple ICGs, suggesting a unique immune evasion phenotype in this tumor type.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e3.8 Pan-cancer Analysis of the Correlations between PTPRC Expression and TMB and MSI\u003c/h2\u003e \u003cp\u003eAnalysis using the UCSCXenaShiny database revealed tumor-specific associations between PTPRC expression and genomic instability markers. TMB was positively correlated with PTPRC expression in COAD and UCEC, while negative correlations were observed in LIHC, TGCT, and THCA (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA). MSI showed inverse associations with PTPRC expression in eight tumor types, including BLCA, GBM, HNSC, KIRC, LUAD, LUSC, PAAD, and PCPG (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e3.9 Single-cell Functional Analysis of PTPRC\u003c/h2\u003e \u003cp\u003eThe CancerSEA database was used to analyze the functional associations of PTPRC at the single-cell level. In ALL, PTPRC expression positively correlated with cellular quiescence but inversely associated with EMT and cell cycle progression (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Prostate cancer (PC) exhibited PTPRC co-expression with pro-tumorigenic processes, including inflammation, proliferation, differentiation, stemness, and EMT (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). melanoma (MEL) showed a PTPRC-proliferation linkage, while UVM demonstrated broad suppression of oncogenic pathways, with PTPRC inversely correlated with DNA repair, invasion, metastasis, DNA damage, apoptosis, inflammation, and quiescence. t-SNE plots visualized PTPRC expression heterogeneity within single-cell clusters of these tumors (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e3.10 Enrichment Analysis of PTPRC-related Genes\u003c/h2\u003e \u003cp\u003eThe STRING database was used to construct a PPI network, identifying 50 genes that interact with PTPRC (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eA). The CytoHubba plugin was then employed to screen for hub genes, with the top 10 genes with the highest MCC scores being CD4, CD8A, ITGAM, SELL, PECAM1, CD19, CD44, ITGAX, and FCGR3A (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eB). Functional enrichment analysis using the DAVID database revealed that these hub genes were primarily involved in biological processes such as transmembrane receptor protein tyrosine kinase signaling pathway, cell surface receptor signaling pathway, T-cell activation, adaptive immune response, peptidyl-tyrosine phosphorylation, positive regulation of interleukin-2 production, T-cell differentiation, and positive regulation of peptidyl-tyrosine phosphorylation. They were also associated with cellular components including the outer side of the plasma membrane, T-cell receptor (TCR) complex, part of the plasma membrane, part of the membrane, and membrane raft. Additionally, these genes were involved in molecular functions such as protein kinase binding and transmembrane signaling receptor activity. In terms of signaling pathways, they were enriched in seven KEGG pathways: TCR signaling pathway, primary immunodeficiency, PD-L1 expression in cancer and PD-1 checkpoint pathway (PD-L1/PD-1 signaling pathway), Th1 and Th2 cell differentiation, Th17 cell differentiation, human immunodeficiency virus type 1 infection, and human T-cell leukemia virus type 1 infection. The results were visualized as bubble charts on the Microbial Bioinformatics Cloud Platform (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eC-D).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e3.11 Expression Verification of PTPRC mRNA in Hepatocellular Carcinoma and Nasopharyngeal Carcinoma\u003c/h2\u003e \u003cp\u003eTo validate the expression differences of PTPRC in tumor cells, we employed RT-qPCR to detect PTPRC expression in HepG2 and CNE-2. The results revealed that PTPRC expression was significantly upregulated in CNE-2 compared to normal nasopharyngeal epithelial cells (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and downregulated in HepG2 compared to normal hepatocytes (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). These findings are consistent with the bioinformatics analysis, further confirming the differential expression of PTPRC in LIHC and nasopharyngeal carcinoma (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003eA-B).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003ePTPRC (CD45), a key member of the PTP family, plays a crucial role in regulating the JAK-STAT signaling pathway, which is crucial for immune regulation and tumorigenesis[\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Our comprehensive analysis, combining bioinformatics analysis and experimental validation, uncovered significant variations in PTPRC expression across different tumor types and its impact on patient prognostic. Specifically, PTPRC was notably upregulated in 11 tumor types, including KIRC and LGG, while downregulated in others like LUAD and LIHC. This heterogeneity suggests diverse roles of PTPRC in different tumor contexts. For instance, higher PTPRC expression in LUAD correlated with better overall survival, whereas its elevated expression in LGG was associated with a poor prognosis. These findings highlight the importance of evaluating PTPRC's clinical value within specific tumor types and microenvironments.\u003c/p\u003e \u003cp\u003eMethylation, an important epigenetic modification, significantly impacts tumor development and progression[\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Our analysis revealed that PTPRC methylation levels were markedly reduced in most tumors types, consistent with its overexpression trend, suggesting that DNA hypomethylation may drive its transcriptional activation. Previous studies have shown that CD45 methylation status can influence TCR signaling pathway activity, affecting T-cell differentiation and function[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Additionally, missense mutations in PTPRC are frequently observed in certain tumors [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. These epigenetic and genetic alterations may jointly determine the functional diversity of PTPRC in tumors, highlighting the need for further functional experiments to elucidate its roles in different cancer contexts.\u003c/p\u003e \u003cp\u003eTumor immune infiltration is closely associated with tumor prognosis, with immune cells playing a supportive role in tumor progression[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Our study found that PTPRC expression was significantly correlated with the infiltration levels of various immune cells, including M2 macrophages, Tregs, and CAFs. These immune cells are key players in tumor development. For example, macrophages can enhance tumor cell invasion, metastasis, and angiogenesis while suppressing anti-tumor immune surveillance[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Moreover, the positive correlation between PTPRC and immune checkpoint genes (e.g., CTLA4 and TIGIT) highlights its potential role in immune checkpoint regulation. These findings suggest that PTPRC may significantly influence the tumor immune landscape by modulating immune cell infiltration and immune checkpoint gene expression.\u003c/p\u003e \u003cp\u003eSingle-cell functional analysis further underscored the context-dependent roles in tumor biology. In ALL, elevated PTPRC expression marked cellular quiescence, potentially reflecting a dormant state resistant to conventional therapies. Conversely, in PC, PTPRC co-expression with inflammation, proliferation, differentiation, and stemness suggests its role in sustaining tumor plasticity and therapy resistance. Strikingly, UVM exhibited broad suppression of oncogenic pathways (e.g., DNA repair, invasion, metastasis, DNA damage, apoptosis, and inflammation) with PTPRC downregulation, implying a tumor-suppressive function in this context. Functional enrichment of PTPRC-interacting hub genes (CD4, CD8A, PD-L1) highlighted its centrality in immune checkpoint regulation. The TCR signaling pathway \u0026mdash; essential for T-cell activation and immune tolerance \u0026mdash; and the PD-L1/PD-1 axis emerged as dominant mechanisms. The TCR signaling pathway is essential for T cell development, activation, and immune tolerance, and its dysregulation can lead to immune escape or autoimmune reactions[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The PD-L1/PD-1 signaling pathway, highly expressed in various tumors, inhibits anti-tumor T-cell activity, promoting tumor immune escape[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. These findings provide a molecular basis for PTPRC as a potential immunotherapy target and suggest directions for future research.\u003c/p\u003e \u003cp\u003eWhile our study provides valuable insights into the role of PTPRC in various cancers, it has some limitations. The experimental validation was limited to hepatocellular carcinoma and nasopharyngeal carcinoma, which restricts the universality of our conclusions. Future studies should verify PTPRC\u0026rsquo;s role in more tumor types to comprehensively assess its potential as a pan-ncer prognostic and immune biomarker. Furthermore, additional functional experiments, including in vitro and in vivo studies, are needed to elucidate the precise mechanisms of PTPRC in tumorigenesis and development. For example, CRISPR-Cas9 gene editing technology could be used to precisely knockout or mutate the PTPRC gene to study its impact on tumor cell behavior. PPI network analysis could also identify key PTPRC-interacting proteins, further clarifying its role in cell signaling.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn conclusion, our pan-cancer analysis disclosed the differential expression of PTPRC between tumor and normal tissues and highlighted its potential as a prognostic factor for multiple tumor types. PTPRC expression was significantly correlated with protein levels, methylation, genetic variations, the tumor immune microenvironment, immune-related genes, TMB, and MSI. These results suggest that PTPRC could serve as a potential prognostic and immune-related biomarker for tumors, laying the foundation for further research on its precise mechanisms in different tumors and the development of treatment strategies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLizhu Tang conceptualized and wrote the manuscript.\u003c/p\u003e\n\u003cp\u003eTing Hu reviewed and revised the manuscript.\u003c/p\u003e\n\u003cp\u003eDingshi Liu, Changqiao Huang and Wenli Yin performed the result analysis and graphic.\u003c/p\u003e\n\u003cp\u003eSijing Wei,Ruimin Xu and Chengliang Yang made the data collection and analysis.\u003c/p\u003e\n\u003cp\u003eYulian Tang and Yueyong Li conceived, designed, and reviewed this manuscript.\u003c/p\u003e\n\u003cp\u003eAll authors have both read and approved the submitted version of the manuscript.\u003c/p\u003e\n\u003cp\u003eAll authors contributed equally to this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by Guangxi Natural Science Foundation of China (No. 2024JJH140141).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of supporting data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data supporting the results of this study comes from the following available resources in the public domain:\u003c/p\u003e\n\u003cp\u003eTIMER2.0 database (http://timer.cistrome.org/); UALCAN database (http://ualcan.path.uab.edu/); GEPIA2 database (http://gepia2.cancer-pku.cn/#analysis); HPA database (https://www.proteinatlas.org/); cBioPortal database (https://www.cbioportal.org); UCSCXenaShiny database (https://shiny.hiplot-academic.com/ucsc-xena-shiny/); SangerBox database (http://sangerbox.com/home.html); CancerSEA database (https://ngdc.cncb.ac.cn/databasecommons/database/id/6092); STRING database (https://www.string-db.org/); DAVID database (https://david.ncifcrf.gov/home.jsp); and the Microsense Cloud Platform (http://www.bioinformatics.com.cn).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Statement:\u003c/strong\u003e This study has no ethical implications.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e All relevant authors agree to publish.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eTasoudis P, Manaki V, Iwai Y et al. 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The history and advances in cancer immunotherapy: understanding the characteristics of tumor-infiltrating immune cells and their therapeutic implications, Cellular \u0026amp; Molecular Immunology 2020;17:807-821.\u003c/li\u003e\n\u003cli\u003eXiao Y, Yu D. Tumor microenvironment as a therapeutic target in cancer, PHARMACOLOGY \u0026amp; THERAPEUTICS 2021;221:107753.\u003c/li\u003e\n\u003cli\u003eShah K, Al-Haidari A, Sun J et al. T cell receptor (TCR) signaling in health and disease, Signal Transduction and Targeted Therapy 2021;6:412.\u003c/li\u003e\n\u003cli\u003eXie W, Medeiros LJ, Li S et al. PD-1/PD-L1 Pathway and Its Blockade in Patients with Classic Hodgkin Lymphoma and Non-Hodgkin Large-Cell Lymphomas, Current Hematologic Malignancy Reports 2020;15:372-381.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"PTPRC, Pan-cancer, immune microenvironment, Gene expression analysis","lastPublishedDoi":"10.21203/rs.3.rs-6233301/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6233301/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigated the role of Protein Tyrosine Phosphatase Receptor type C (PTPRC) in various cancers using bioinformatics analyses and experimental validation. We analyzed public databases (TIMER2.0, GEPIA2, cBioPortal) and single-cell sequencing data to evaluate PTPRC expression differences, patient prognosis, and immune microenvironment associations in tumors. RT-qPCR was employed to validated PTPRC expression in nasopharyngeal carcinoma (CNE2) and hepatocellular carcinoma (HePG2) cell lines. Our findings revealed that PTPRC was upregulated in 11 tumor types (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and associated with worse survival in 6 cancers (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). It was correlated with immune cell infiltration, immune checkpoint genes (ICGs), cancer-associated fibroblasts (CAFs), tumor mutation burden (TMB), and microsatellite instability (MSI) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Single-cell analysis indicated PTPRC is closely related to angiogenesis, differentiation, proliferation, and quiescence in certain tumors. Functional enrichment analysis emphasized PTPRC's involvement in T-cell receptor (TCR) and PD-L1/PD-1 signaling pathways, highlighting its role in T-cell activation, immune tolerance, and tumor progression. Experimental validation confirmed that PTPRC was upregulated in CNE2 cells compared to normal nasopharyngeal epithelial cells and downregulated in HePG2 cells compared to normal hepatocytes, consistent with bioinformatics results. In conclusion, abnormal PTPRC expression in pan-cancer may drive tumor development through multiple mechanisms, indicating its potential as a therapeutic target, especially in immunotherapy.\u003c/p\u003e","manuscriptTitle":"PTPRC as a pan-cancer biomarker: Prognostic significance and immune microenvironment interactions","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-16 09:00:31","doi":"10.21203/rs.3.rs-6233301/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":"7688b82c-48ce-4667-90c2-638a54359fd6","owner":[],"postedDate":"April 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-18T13:23:52+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-16 09:00:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6233301","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6233301","identity":"rs-6233301","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}