Abstract Background PTPN2, a member of the non-receptor protein tyrosine phosphatases family, holds a crucial role in tumorigenesis and cancer immunotherapy. However, most studies on the role of PTPN2 in cancer are limited to specific cancer types. Therefore, this study aimed to investigate the prognostic significance of PTPN2 in human cancers and its function in the tumor microenvironment. Methods To shed light on this matter, we investigated the expression level, prognostic value, genomic alterations, molecular function, immune function, and immunotherapeutic predictive ability of PTPN2 in human cancers using the TCGA, GTEx, CGGA, GEO, cBioPortal, STRING, TISCH, TIMER2.0, ESTIMATE, and TIDE databases. Furthermore, the CCK-8 assay was utilized to detect the effect of PTPN2 on cell proliferation. Cell immunofluorescence analysis was performed to probe the cellular localization of PTPN2. Western blot was applied to examine the molecular targets downstream of PTPN2. Finally, a Nomogram model was constructed using the TCGA-LGG cohort and evaluated with calibration curves and time-dependent ROCs. Results PTPN2 was highly expressed in most cancers and was linked to poor prognosis in ACC, GBM, LGG, KICH, and PAAD, while the opposite was true in OV, SKCM, and THYM. PTPN2 knockdown promoted the proliferation of melanoma cells, while significantly inhibiting proliferation in colon cancer and glioblastoma cells. In addition, TC-PTP, encoded by the PTPN2 gene, was primarily localized in the nucleus and cytoplasm and could negatively regulate the JAK/STAT and MEK/ERK pathways. Strikingly, PTPN2 knockdown significantly enhanced the abundance of PD-L1. PTPN2 was abundantly expressed in Mono/Macro cells and positively correlated with multiple immune infiltrating cells, especially CD8^+ T cells. Notably, DLBC, LAML, OV, and TGCT patients in the PTPN2-high group responded better to immunotherapy, while the opposite was true in ESCA, KIRC, KIRP, LIHC, and THCA. Finally, the construction of a Nomogram model on LGG exhibited a high prediction accuracy. Conclusion Immune checkpoint PTPN2 is a powerful biomarker for predicting prognosis and the efficacy of immunotherapy in cancers. Mechanistically, PTPN2 negatively regulates the JAK/STAT and MEK/ERK pathways and the abundance of PD-L1. Keywords: PTPN2, Pan-cancer, Prognosis, Immunotherapy, Biomarker 1. Introduction T-cell protein tyrosine phosphatase (TC-PTP), encoded by the protein tyrosine phosphatase non-receptor type 2 (PTPN2) gene, is a non-transmembrane protein consisting of a conserved catalytic structural domain and a non-catalytic c-terminal structural domain [[27]1]. PTPN2, a member of the family of intracellular non-receptor PTPs (PTPNs), belongs to the largest family of class I cysteine PTPs and is essential for the regulation of various biological processes by dephosphorylating a variety of substrate proteins, including but not limited to epidermal growth factor receptor (EGFR), insulin receptor (IR), Janus kinase 1 (JAK1), JAK3, signal transducer and activator of transcription 1 (STAT1), STAT3, and Src family kinases [[28]2]. Since the regulatory network of PTPN2 is extremely sophisticated and serves a unique function in various cancers, it can negatively regulate many signaling pathways and thus exert its pro- or anti-cancer function. For instance, PTPN2 acts as a tumor suppressor in breast and skin cancers. Specifically, loss of PTPN2 in breast cancer results in the activation of oncogenic signaling pathways, such as protein kinase B (AKT), Src family kinases (SFK), and STAT3 signaling pathways, and is also associated with resistance to tamoxifen [[29]3,[30]4]. In skin cancer, PTPN2 suppresses cancer cell proliferation and induces apoptosis by negatively regulating STAT1, STAT3, STAT5, phosphoinositide 3-kinase (PI3K)/AKT, and fetal liver kinase-1 (Flk-1)/c-Jun N-terminal kinase (JNK) signaling pathways [[31][5], [32][6], [33][7]]. In contrast, patients with pancreatic cancer, glioma, and glioblastoma with high expression of PTPN2 have a poor prognosis [[34]8,[35]9]. What's more, inflammatory responses or oxidative stress can induce up-regulation of PTPN2 expression, thereby promoting progression in patients with thyroid cancer, laryngeal cancer, and glioma [[36]10–[37]12]. To elucidate the role of PTPN2 in human cancers, a comprehensive pan-cancer analysis of PTPN2 is needed for further exploration. There is accumulating evidence indicating that PTPN2 occupies a crucial position in immunomodulation and immunotherapy. PTPN2 is a crucial anti-inflammatory regulator that restricts the activation of T cells by dephosphorylating and inactivating Src family kinases, thereby reducing the inflammatory response [[38]13,[39]14]. Furthermore, PTPN2 holds a similar role in dendritic cells (DC) [[40]15]. Of note, PTPN2 abolition in tumor cells enhances interferon-γ (IFN-γ)-mediated antigen presentation [[41]16], while the depletion of PTPN2 in CD8^+ T cells increases the proliferative capacity and cytotoxicity of Tim-3^+ cells [[42]17], both of which ultimately achieve antitumor effects. In brief, PTPN2 performs a “brake-like” function in the immune system, which is considered an emerging target for cancer immunotherapy. However, most studies on the role of PTPN2 in cancer are limited to specific cancer types and there are no pan-cancer analyses of PTPN2. In this study, we explored the function of PTPN2 in pan-cancer using multiple databases, including the Cancer Genome Atlas (TCGA), the Genotype-Tissue Expression (GTEx), Chinese Glioma Genome Atlas (CGGA), the Gene Expression Omnibus (GEO), cBioPortal, STRING, the Tumor Immune Single-cell Hub (TISCH), TIMER2.0, ESTIMATE, and the Tumor Immune Dysfunction and Exclusion (TIDE) databases. First, we evaluated the expression level of PTPN2 and its relationship with prognosis and further explored the cellular localization and molecular function of PTPN2. Next, we elucidated in detail the role of PTPN2 in the tumor microenvironment (TME) and utilized the TIDE algorithm to predict the response to immunotherapy. Finally, a Nomogram model was constructed to predict the overall survival in patients with brain lower-grade glioma (LGG) based on the expression of PTPN2. 2. Materials and methods 2.1. PTPN2 expression levels and relationship with prognosis in cancers The RNA-seq data of PTPN2 in each tumor and normal tissues were obtained from the TCGA database ([43]https://portal.gdc.cancer.gov/) and GTEx database by UCSC XENA ([44]https://xena.ucsc.edu/). Three types of prognosis data including overall survival (OS), disease-specific survival (DSS), and disease-free interval (DFI) were obtained from the TCGA database. Subsequently, CGGA ([45]http://www.cgga.org.cn/index.jsp) (n = 420), [46]GSE63885 (n = 70), and [47]GSE54467 (n = 79) were employed to validate the prognostic value of PTPN2 in LGG, ovarian serous cystadenocarcinoma (OV), and skin cutaneous melanoma (SKCM), respectively. Among them, the continuous variable of PTPN2 expression data was used for univariate Cox regression analysis, and bivariate PTPN2 expression levels were performed for Kaplan-Meier curve analysis, whose cutoff was chosen by the “surv-cutpoint” function of the “survminer” R package. Data on the immune subtypes of LGG patients in TCGA were obtained from the paper published by Vesteinn Thorsson et al. [[48]18]. 2.2. Mutational analysis and pathway enrichment analysis of PTPN2 PanCancer Atlas Studies in the cBioPortal database ([49]http://cbioportal.org) were performed for PTPN2 genomic mutation analysis, including 10,967 samples. A protein-protein interaction network (PPI) with an interaction score of 0.400 was constructed using the STRING ([50]https://www.string-db.org/) online tool. Next, we performed Kyoto Gene and Genome Encyclopedia (KEGG) pathway enrichment analysis of PTPN2-related genes using the R package “clusterProfiler” and visualized them using the R package “ggplot2". 2.3. ESTIMATE score ESTIMATE ([51]https://bioinformatics.mdanderson.org/estimate/index.html) was used to download stromal and immune scores for each sample across all TCGA tumor types. The tumor purity is lower if the stromal and immune scores are higher. Here, the differential distribution of immune scores between the PTPN2-high and -low groups was assessed according to the median PTPN2 expression. 2.4. Single-cell analysis of PTPN2 Single-cell analysis of PTPN2 was performed using the TISCH online tool ([52]http://tisch.comp-genomics.org/), which aims to characterize the tumor microenvironment at single-cell resolution. The detailed procedure for single-cell analysis of PTPN2 in all cancers was as follows: Gene (PTPN2), Cell-type annotation (Major-lineage), and Cancer-type (All cancers). PTPN2 expression in each cancer type was quantified and visualized in the form of heat and scatter plots. 2.5. Immune cell infiltration analysis in TIMER2 The TIMER2.0 database ([53]http://timer.comp-genomics.org/) contains several algorithms for assessing the abundance of immune cell infiltration, including TIMER, CIBERSORT, quanTIseq, xCell, MCP-counter, and EPIC methods. We then downloaded data on immune cells from all cancer patients in TCGA and analyzed the correlation with PTPN2 expression levels. Among them, immune cells include CD4^+ T cells, cancer-associated fibroblasts (CAF), progenitors, endothelial cells (Endo), eosinophils (Eos), hematopoietic stem cells (HSC), follicular helper T cells (Tfh), gamma delta T cells (Tgd), natural killer T cells (NKT), regulatory T cells (Treg), mast cells, B cells, neutrophils, monocytes (Mono), macrophages (Macro), dendritic cells, natural killer cells (NK), and CD8^+ T cells. 2.6. Tumor heterogeneity and prediction of immunotherapy response Firstly, we downloaded the unified standardized dataset TCGA Pan-Cancer (PANCAN, n = 10535) from the UCSC database and extracted the expression data of PTPN2 in each sample. The tumor mutation burden (TMB) of each tumor was calculated using the R software package “MAfTools”. Data on microsatellite instability (MSI) scores for each tumor were obtained from the paper published by Russell Bonneville et al. [[54]19]. Data on tumor purity, tumor ploidy, homologous recombination deficiencies (HRD), and neoantigens were obtained from a paper published by Vesteinn Thorsson et al. [[55]18]. We then integrated the above tumor heterogeneity data and analyzed the correlation with PTPN2 expression levels. Finally, we adopted the TIDE algorithm to predict the efficacy of immunotherapy by using the RNAseq data (level3) of all tumors in the TCGA database and the corresponding patient clinical information, and further analyzed the distribution of TIDE scores in the PTPN2-high and -low groups according to the median PTPN2 expression. Patients with high TIDE scores may not respond to immunotherapy, while patients with low TIDE scores may benefit from immunotherapy. 2.7. Construction and evaluation of the Nomogram model Univariate and multivariate Cox regression analyses were conducted to screen independent risk factors for LGG patients. Then, PTPN expression and partial clinical data of LGG patients were jointly utilized to construct a Nomogram model. The construction of the Nomogram model and the plotting of the calibration curves were performed using the R package “rms” and the R package “survival”. Time-dependent receiver operating characteristic (ROC) curves were analyzed by the R package “timeROC” and visualized by the R package “ggplot2". 2.8. Cell culture and lentiviral packaging and infection All cell lines in this study were obtained from ATCC. HEK293T (ATCC, CRL-3216), melanoma cell line A375 (ATCC, CRL-1619), and glioblastoma cell line U87 (ATCC, HTB-14) were cultured in Dulbecco's modified Eagle's medium (DMEM, Gibco, USA) with 10% fetal bovine serum (FBS, Gibco, USA) and penicillin/streptomycin (Sigma, USA). Colon cancer cell line HCT116 (ATCC, CCL-247) and ovarian cancer cell line SKOV3 (ATCC, HTB-77) were cultured in RPMI 1640 medium (RPMI 1640, Gibco, USA) with 10% FBS and penicillin/streptomycin. All cell lines were cultured at 37 °C in a constant temperature incubator containing 5% CO[2]. HEK293T cells were used for lentiviral production. Lentiviral expression vector pLKO was used to construct the PTPN2 knockdown vector (shPTPN2). During preparation, 500 ng of target gene plasmid was added to 100 μl opti-MEM together with 50 ng VSVG, 500 ng pR8.74, and 3 μl transfection reagent PEI, mixed sufficiently and left for 15 min, and then the mixture was co-transfected into approximately 80% confluent HEK293T cells in 12-well plates. The supernatant containing lentivirus was harvested 72 h after transfection, filtered through a 0.45 mM PES filter, and then stored at −80 °C for backup. Subsequently, A375, HCT116, and U87 cells were seeded in six-well plates, and 24 h later 50 μl of viral solution and polybrene (1:1000) were added. 24 h after infection, the cell culture medium containing viral solution was replaced with a fresh complete cell culture medium with puromycin added. The target sequences of the shPTPN2 included: shPTPN2-1: CCGGGATGACCAAGAGATGCTGTTTCTCGAGAAACAGCATCTCTTGGTCATCTTTTT shPTPN2-2: CCGGTGCAAGATACAATGGAGGAGACTCGAGTCTCCTCCATTGTATCTTGCATTTTT shPTPN2-3: CCGGGAAGATGTGAAGTCGTATTATCTCGAGATAATACGACTTCACATCTTCTTTTT shPTPN2-4: CCGGGTGCAGTAGAATAGACATCAACTCGAGTTGATGTCTATTCTACTGCACTTTTT shPTPN2-5: CCGGCTCACTTTCATTATACTACCTCTCGAGAGGTAGTATAATGAAAGTGAGTTTTT. 2.9. Western blot and reverse transcription and quantitative real-time PCR (RT-qPCR) A375, HCT116, and U87 cells were collected separately in 1.5 ml EP tubes, RIPA lysis buffer containing protease inhibitors and phosphatase inhibitors was added, and cells were lysed sufficiently to obtain cellular proteins. Protein concentrations were determined using the BCA Protein Concentration Assay Kit (Thermo Fisher, Waltham, MA, USA) according to the manufacturer's instructions. Equal amounts of proteins were separated in 12% SDS-PAGE, then proteins were transferred to PVDF membranes, blocked with 5% skim milk powder for 1 h at room temperature, and then incubated with a 1:1000 dilution of protein primary antibody at 4 °C overnight. The membranes were washed three times with TBST, then incubated with a secondary antibody for 1 h at room temperature, and washed three more times with TBST. Total RNA was extracted using RNA-easy Isolation Reagent (Vazyme, China) according to the manufacturer's protocol. RNA concentration was quantified using NanoDrop ND2000 (Thermo Fisher, Waltham, MA, USA). 1 μg of RNA per sample was reverse transcribed into cDNA using the Tiangen Reverse Transcription Kit, and the obtained cDNA products were diluted to a final concentration of 10 ng/μl. Real-time PCR was performed using 2 × SYBR Green Premix Ex Taq (Takara, Shiga, Japan) on an ABI 7500 PCR system (Applied Biosystems, CA, USA). Primer pairs are listed below. Analyses were performed using the comparative cycle threshold (CT) method, and all samples were normalized for GAPDH expression. The sequences of primers used were as follows: GAPDH forward: GGAGCGAGATCCCTCCAAAAT GAPDH reverse: GGCTGTTGTCATACTTCTCATGG PTPN2 forward: GAAGAGTTGGATACTCAGCGTC PTPN2 reverse: TGCAGTTTAACACGACTGTGAT 2.10. Cell proliferation assay PTPN2 knockdown in A375, HCT116, and U87 cell lines to assay the proliferative capacity of the cells. The 96-well plates were seeded with 2 × 10^3 cells per well and incubated in an incubator at 37 °C with 5% CO[2]. Then, 10 μl of CCK-8 solution was added to each well, and the absorbance at 450 nm was measured using an enzyme marker after 0, 24, 48, 96, and 120 h, respectively. 2.11. Cell immunofluorescence Cell lines A375, HCT116, U87, and SKOV3 were utilized for immunofluorescence analysis. Cells were fixed and incubated with primary antibodies at a dilution of 1:100, fluorescence dye-conjugated secondary antibodies, and DAPI, according to standard protocols. Cells were examined using a Liver SR super-resolution turntable microscope (Live SR CSU W1, Nikon, Japan) with a 60 × oil immersion objective. 2.12. Detection of downstream targets of PTPN2 The cell lines A375, HCT116, and U87 were employed to analyze the downstream pathways regulated by PTPN2. Before the assay, one group of cells was treated with IFN-γ (11725-HNAS, Sino Biological) at a concentration of 100 ng/ml for 36 h, and the other group was treated with EGF (PHG0311, Invitrogen) at a concentration of 100 ng/ml for 30 min. The immunoblot assay was identical to the workflow described above. Antibody information was as follows: Antibodies against PTPN2 (58935, CST), JAK1 (3344, CST), STAT1(19994, CST), Phospho-STAT1(Y701) (7649, CST), PD-L1 (13684, CST), STAT3 (4904, CST), Phospho-STAT3 (Y705) (9145, CST), p44/42 MAPK (Erk1/2) (4695, CST), Phospho-p44/42 MAPK (Erk1/2) (4370, CST), and GAPDH (60004-1-Ig, Proteintech) were used. 2.13. Statistical analysis In this study, all statistical analyses were processed on R Studio software and P value < 0.05 was considered statistically significant. All analyses were preceded by a log 2 transformation of all RNA-seq data. Wilcoxon rank-sum test and Two-way repeated measures ANOVA were used for comparison between the two groups. Spearman's test was used for all correlation analyses. Univariate Cox regression analysis and log-rank test were employed to evaluate the prognostic value of PTPN2 in cancers. 3. Results 3.1. PTPN2 expression levels and the relationship with prognosis The overview of the process used in our study was shown in [56]Fig. 1. To investigate the expression of PTPN2 in human cancers, we integrated normal tissue data from the GTEx database with tumor tissue data from the TCGA database. PTPN2 was highly expressed in the majority of cancers, including bladder urothelial carcinoma (BLCA), cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), cholangiocarcinoma (CHOL), colon adenocarcinoma (COAD), diffuse large B-cell lymphoma (DLBC), esophageal carcinoma (ESCA), glioblastoma multiforme (GBM), head and neck squamous cell carcinoma (HNSC), kidney renal clear cell carcinoma (KIRC), kidney renal papillary cell carcinoma (KIRP), acute myeloid leukemia (LAML), LGG, pancreatic adenocarcinoma (PAAD), rectum adenocarcinoma (READ), stomach adenocarcinoma (STAD), testicular germ cell tumors (TGCT), thymoma (THYM), uterine corpus endometrial carcinoma (UCEC), and uterine carcinosarcoma (UCS), while it was down-regulated in adrenocortical carcinoma (ACC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), OV, prostate adenocarcinoma (PRAD), SKCM, and thyroid carcinoma (THCA) ([57]Fig. 2A). To gain further insight into the association between PTPN2 expression level and prognosis, we performed survival analyses for each cancer, including OS, DSS, and PFI. As shown in [58]Fig. 2B, PTPN2 exerted completely distinct roles in various cancer types and was associated with poor patient prognosis in most cancers, but was a protective factor in a small number of cancers. Two indicators, DSS and PFI, were associated with cancer patients’ treatment outcomes, in high agreement with the results of OS analysis. Specifically, univariate Cox regression analysis and Kaplan–Meier survival curves collectively showed that up-regulation of PTPN2 expression was a perilous factor that seriously affected the overall survival of patients with ACC, GBM, LGG, kidney chromophobe (KICH), and PAAD, but the opposite results were found in OV, SKCM, and THYM ([59]Fig. 2C–K). Therefore, we tentatively speculated that PTPN2 may exert carcinogenic effects in GBM, LGG, and PAAD while exhibiting tumor suppressive properties in OV and SKCM in combination with PTPN2 expression levels as well as prognosis. Fig. 1. [60]Fig. 1 [61]Open in a new tab Flow chart of this study. Fig. 2. [62]Fig. 2 [63]Open in a new tab The expression level of PTPN2 and the relationship with prognosis. (A) Comparison of PTPN2 mRNA expression between tumor and normal tissues in TCGA and GTEx databases. (B) The correlation between PTPN2 expression and overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI) was summarized based on univariate Cox regression and Kaplan-Meier survival curves. Red indicates PTPN2 is a risk factor and green indicates a protective factor. (C) Forest plot demonstrating the prognostic value of PTPN2 in human cancers based on univariate Cox regression analysis. Kaplan-Meier overall survival curves of PTPN2 in ACC (D), GBM (E), LGG (F), KICH (F), PAAD (H), OV (I), SKCM (J), and THYM (K). *P < 0.05, **P < 0.01, ***P < 0.001, and ****P < 0.0001. (For interpretation of the references to color in this