Pan-cancer analysis of PTPN6: prognostic significance and functional implications in tumor progression

Abstract

Background

Cancer remains a leading cause of mortality worldwide, characterized by complex genetic and molecular alterations. The Protein Tyrosine Phosphatase Non-Receptor Type 6 (PTPN6), also known as SHP-1, plays a critical role in regulating immune responses and cellular signaling pathways, with emerging evidence suggesting its involvement in cancer progression. Previous studies have linked aberrant PTPN6 expression to tumorigenesis in specific cancers, such as lymphoma and leukemia, where it acts as a tumor suppressor. However, the comprehensive role of PTPN6 across pan-cancer, particularly its prognostic significance and molecular functions, has not been fully elucidated.

Methodology

This study aimed to provide a pan-cancer analysis of PTPN6, utilizing data from multiple public databases with molecular in vitro experiments.

Results

Our findings showed notable differences in PTPN6 expression among different cancer types. Prognostic analyses indicated that higher PTPN6 expression is associated with poorer overall survival in with notable upregulation in kidney renal clear cell carcinoma (KIRC), liver hepatocellular carcinoma (LIHC), and rectum adenocarcinoma (READ). Further, promoter methylation and mutation analyses highlighted alterations in PTPN6 expression across different cancer stages, with a particular reduction in methylation observed in tumor tissues. Functional assays in cell lines demonstrated that PTPN6 promotes cell proliferation, migration, and colony formation, supporting its role in cancer progression.

Conclusion

This comprehensive analysis emphasizes the potential of PTPN6 as both a prognostic biomarker and a therapeutic target in cancer. However, further research is required to fully elucidate its role in cancer progression and to assess its clinical applicability.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12672-025-03384-4.

Introduction

Cancer, characterized by uncontrolled cell growth and proliferation [1, 2], remains a leading cause of morbidity and mortality worldwide [3–6]. Despite significant advancements in understanding the molecular mechanisms of cancer [7, 8], the heterogeneity and complexity of this disease continue to pose substantial challenges for diagnosis, prognosis [9, 10], and treatment [11–13]. The study of cancer at a molecular level has led to the identification of various genes and signaling pathways that play critical roles in tumor development and progression [14–16]. Among these, the protein tyrosine phosphatase non-receptor type 6 (PTPN6) gene, also known as SHP-1, has garnered significant attention [17, 18].

PTPN6 is a crucial regulator of signal transduction pathways involved in cell growth, differentiation, and immune responses [19, 20]. It encodes a protein tyrosine phosphatase that is predominantly expressed in hematopoietic cells, where it functions as a negative regulator of signaling pathways mediated by receptor and non-receptor tyrosine kinases [21]. Aberrant expression or mutation of PTPN6 has been implicated in various hematological malignancies, including leukemia and lymphoma [21, 22]. Moreover, emerging evidence suggests that PTPN6 may also play a role in solid tumors, such as breast, lung, and colorectal cancers [18, 20, 23].

Research on PTPN6 in cancer has revealed its dual role as both a tumor suppressor and an oncogene, depending on the cellular context and type of cancer [20]. For instance, in hematological cancers, loss of PTPN6 function is often associated with enhanced cell survival and proliferation, contributing to disease progression [20, 24]. Conversely, in certain solid tumors, overexpression of PTPN6 has been linked to tumorigenesis and poor prognosis [18, 25]. These findings highlight the complex and context-dependent nature of PTPN6’s involvement in cancer, emphasizing the need for further investigation into its diagnostic, prognostic, and therapeutic potential across different cancer types.

In this study, we aim to comprehensively analyze the role of PTPN6 in pan-cancer through a combination of in silico and in vitro experiments. By integrating bioinformatics approaches [26–28] with experimental validation [29–31], we seek to elucidate the molecular mechanisms underlying PTPN6 dysregulation in various cancers and to evaluate its potential as a biomarker and therapeutic target. Our findings will contribute to a better understanding of PTPN6’s multifaceted role in cancer and may pave the way for the development of novel diagnostic and therapeutic strategies. By addressing these aims, our research provides a significant contribution to the growing body of knowledge on the molecular basis of cancer and highlights the importance of PTPN6 as a key player in the complex landscape of cancer biology.

Methodology

Expression and prognostic analysis of PTPN6 from pan-cancer view point

TIMER2 (http://timer.cistrome.org/) [32] and UALCAN (https://ualcan.path.uab.edu/) [33, 34] are powerful online tools for cancer research. TIMER2 allows comprehensive analysis of immune infiltrates across diverse cancer types, offering insights into the tumor microenvironment. In this work, both TIMER and UALCAN were used to perform expression analysis of PTPN6 from pan-cancer view point.

GEPIA2 (http://gepia2.cancer-pku.cn/#analysis) is a user-friendly web tool designed for analyzing RNA sequencing expression data from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) projects [35]. Herein, we used GEPIA2 for the prognostic analysis of PTPN6 from pan-cancer view point.

Expression validation and sub-cellular localization analysis of PTPN6

The Human Protein Atlas (HPA, https://www.proteinatlas.org/) is a comprehensive resource that maps the protein expression profiles in human tissues and organs [36]. In our project, we used HPA for the expression validation and sub-cellular localization analysis of PTPN6.

mRNA expression validation and promoter methylation analysis of PTPON6.

Expression differences of PTPN6 across different cancer stages were evaluated using GEPIA2 database [35].

OncoDB (https://oncodb.org/) is a specialized database designed to facilitate cancer research by providing comprehensive data on oncogenes and tumor suppressor genes [37]. OncoDB database along with UALCAN [33] was employed for the methylation analysis of PTPN6. Moreover, the OncoDB database was also utilized to validate the expression of PTON6 across extended TCGA cohorts.

Mutational analysis of PTPN6

cBioPortal (https://www.cbioportal.org/) is a widely used open-access resource that provides intuitive visualization and analysis tools for exploring multidimensional cancer genomics data [38]. In this project, cBioPortal was utilized for the mutational analysis of PTPN6 across different cancers.

Correlation analysis of PTPN6 with immune related genes and molecular subtypes

TISIDB (http://cis.hku.hk/TISIDB/) is an integrated database designed to analyze tumor-immune system interactions [39]. It compiles a wide range of data, including immune cell infiltration, immunomodulators, and clinical outcomes across various cancer types. We used TISIDB database in this study to perform correlation analysis of PTPN6 with immune related genes and molecular subtypes across different cancers.

Correlations analysis of PTPN6 with diverse functional states of different cancers

CancerSEA (http://biocc.hrbmu.edu.cn/CancerSEA/) is a dedicated database that focuses on single-cell RNA-sequencing (scRNA-seq) data to explore the functional states of cancer cells [40]. This study utilized CancerSEA database to perform correlations analysis of PTPN6 with diverse functional states of different cancers.

Gene enrichment analysis of PTNP6

The STRING database (https://string-db.org/) is a comprehensive resource for predicting and analyzing protein-protein interactions (PPIs) across multiple organisms [41]. Herein, we used STRING database to construct the PPI network of PTPN6.

DAVID tool (https://david.ncifcrf.gov/) is a bioinformatics resource that provides a comprehensive set of functional annotation tools to understand the biological meaning behind large gene lists [42]. Herein, we used DAVID tool for the gene enrichment analysis of PTPN6-associated genes.

Immunolytic and drug sensitivity analyses of PTPN6

GSCA (https://guolab.wchscu.cn/GSCA) is an online tool designed for analyzing gene sets in the context of cancer research [43]. It provides a platform for exploring the relationships between gene expression, mutations, and clinical outcomes across various cancer types. This study utilized GSCA database, for the immunolytic and drug sensitivity analyses of PTPN6 across different cancers.

Cell culture

We purchased 13 KIRC cell lines and 7 normal kidney cell lines, for our research. The KIRC cell lines included 786-O, A498, Caki-1, Caki-2, Caki-3, K7M2, KRC, KETC, OS-RC-2, RCC4, HEK293T, and A-498, and JHH-7. The normal kidney cell lines were HK-2, RPTEC/TERT1, NRK-52E, 293T (Human Embryonic Kidney), HEK-293, HEK-293T, and REH. We cultured these cell lines under similar conditions: in RPMI-1640 or DMEM medium supplemented with 10% fetal bovine serum, at 37 °C in a 5% CO₂ atmosphere, and passaged using 0.25% trypsin-EDTA for cell detachment.

RT-qPCR analysis of PTPN6.

Firstly, total RNA was extracted from the cultured cell lines using the GeneJET RNA Purification Kit (Thermo Fisher, Cat. No. K0732). The RNA was then reverse transcribed into cDNA with the High-Capacity cDNA Reverse Transcription Kit (Thermo Fisher, Cat. No. 4368814) following the manufacturer’s protocol. For the RT-qPCR analysis, we used the TaqMan Gene Expression Master Mix (Thermo Fisher, Cat. No. 4369016) along with TaqMan Gene Expression Assays specific for PTPN6 (Thermo Fisher, Cat. No. 4331182) and GAPDH (Thermo Fisher, Cat. No. 4331182). The PCR reactions were set up in a 96-well plate and run on the Applied Biosystems 7500 Fast Real-Time PCR System with the cycling conditions of 95 °C for 10 min, followed by 40 cycles of 95 °C for 15 s and 60 °C for 1 min. Data were analyzed using the ΔΔCt method to determine relative expression levels of PTPN6, normalized to the housekeeping gene, with statistical significance (p-value < 0.05) assessed using a student t-test.

PTPN6 knockdown in 786-O and A498 cells

We conducted the PTPN6 gene knockdown in 786-O and A498 cells using Thermo Fisher’s Lipofectamine 3000 Reagent (Cat. No. L3000015) and PTPN6-specific siRNA (Cat. No. AM16708). The cells were transfected with the siRNA using Lipofectamine 3000 according to the manufacturer’s instructions and were incubated for 48 h to ensure effective gene silencing.

Following knockdown, we performed RT-qPCR and Western blot analyses to evaluate PTPN6 mRNA and protein levels. RT-qPCR was performed following after mentioned conditions. For Western blot analysis, proteins were extracted from the cells using the Thermo Fisher Pierce RIPA Buffer (Cat. No. 89900) with added protease inhibitors. Protein concentrations were measured using the Thermo Fisher Pierce BCA Protein Assay Kit (Cat. No. 23225). Proteins were separated by SDS-PAGE with Thermo Fisher Bolt 4–12% Bis-Tris Plus Gels (Cat. No. B0007) and transferred to PVDF membranes using the Thermo Fisher iBlot 2 Transfer System (Cat. No. IB24001). The membranes were blocked with Thermo Fisher Pierce 5x Blocking Buffer (Cat. No. 37571) and incubated with primary antibodies against PTPN6 (Thermo Fisher, Cat. No. 24143-1-AP) and GAPDH (Thermo Fisher, Cat. No. MA5-15738). Secondary HRP-conjugated antibodies (Thermo Fisher, Cat. No. 31430) were used for detection, and the blots were developed using Thermo Fisher Pierce ECL Plus Western Blotting Substrate (Cat. No. 32132).

Colony formation, cell proliferation, and wound healing assays

For the colony formation assay, 786-O and A498 cells were seeded in 6-well plates at a density of 500 cells per well and allowed to grow for 10–14 days in complete growth medium. Colonies were fixed with methanol and stained with 0.5% crystal violet solution. Colonies were then visualized and counted using a light microscope.

Cell proliferation was assessed using the CCK-8 (Cell Counting Kit-8) assay. Briefly, cells were seeded in 96-well plates and treated with appropriate conditions. After 24, 48, and 72 h of incubation, 10 µL of CCK-8 solution was added to each well. The plates were then incubated for 2 h at 37 °C. The absorbance at 450 nm was measured using a microplate reader to quantify cell proliferation. The data were normalized to the control group, and the cell proliferation rate was calculated based on the relative absorbance.

Lastly, a wound healing assay was performed by seeding 786-O and A498 cells in a 6-well plate and growing them to confluence. A scratch was made in the cell monolayer using a sterile pipette tip. The cells were then incubated in serum-free medium, and images of the wound area were captured at 0 and 24 h using a microscope. The extent of wound closure was analyzed by measuring the remaining wound area.

Statistics

All data were analyzed using GraphPad Prism 9.0. Gene expression differences were assessed using a two-tailed Student’s t-test, with p-values < 0.05 considered significant. Kaplan-Meier survival curves and log-rank tests evaluated the association between PTPN6 expression and survival outcomes, with hazard ratios (HR) and 95% confidence intervals (CI) calculated via univariate Cox regression. In vitro experiments were performed in triplicate. Group comparisons used a two-tailed Student’s t-test or one-way ANOVA with Tukey’s post hoc test. Statistical significance was defined as p* < 0.05, p** < 0.01, and p*** < 0.001.

Results

Expression and prognostic values of PTPN6 from pan-cancer view point

This study conducted a comprehensive analysis of PTPN6 expression and its prognostic significance across various cancer types, providing valuable insights into its potential role as a biomarker in cancer. In Fig. 1A-B, the expression levels of PTPN6 were compared between tumor and normal tissues across multiple cancer types using TIMER2 and UALCAN databases. The data revealed significant differences in PTPN6 expression between tumor and normal tissues, with certain cancers showing upregulation and others exhibiting downregulation of this gene in tumor samples. Notably, cancers such as bladder urothelial carcinoma (BLCA), breast invasive carcinoma (BRCA), cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), cholangiocarcinoma (CHOL), colon adenocarcinoma (COAD), esophageal carcinoma (ESCA), glioblastoma multiforme (GBM), head and neck squamous cell carcinoma (HNSC), kidney chromophobe (KICH), kidney renal clear cell carcinoma (KIRC), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), rectum adenocarcinoma (READ), skin cutaneous melanoma (SKCM), stomach adenocarcinoma (STAD), thyroid carcinoma (THCA), and uterine corpus endometrial carcinoma (UCEC) displayed significant (p-value < 0.05) alterations in PTPN6 expression, highlighting its potential as a discriminatory biomarker. Figure 1C and D shifted the focus to the prognostic implications of PTPN6 expression in multiple cancers. The results of the prognostic analysis using GEPIA2 demonstrated that upregulation of PTPN6 was strongly associated with reduced overall survival (OS) in KIRC, LIHC, and READ patients (Fig. 1C-D; Table 1).

Fig. 1.

Expression and prognostic value of ptpn6 across various cancers. A TIMER2-based box plot showing PTPN6 expression levels (log2 TPM) across different cancer types and corresponding normal tissues based on TCGA data. B UALCAN-based bar plot summarizing PTPN6 expression across various TCGA cancer types, highlighting differences between tumor (red) and normal (blue) samples. C GEPIA2-based heatmap representing the differential expression of PTPN6 across cancer types, with blue indicating lower expression in tumors and red indicating higher expression compared to normal tissues. D GEPIA-based Kaplan-Meier survival curves showing the overall survival of patients with high versus low PTPN6 expression in kidney renal clear cell carcinoma (KIRC), liver hepatocellular carcinoma (LIHC), and rectum adenocarcinoma (READ) cancers. *p-value < 0.05, **p-value < 0.01, and ***p < 0.001

Table 1.

Prognostic impact of PTPN6 expression on overall survival in KIRC, LIHC, and READ patients

Cancer Type	Grouping (Expression)	n (samples)	HR (High vs. Low)	Logrank p-value	p(HR)	Interpretation
KIRC	High vs. Low PTPN6	High = 258 Low = 258	1.5	0.0053	0.0056	High PTPN6 is associated with significantly worse overall survival.
LIHC	High vs. Low PTPN6	High = 182 Low = 182	1.1	0.056	0.056	High PTPN6 is associated with significantly worse overall survival.
READ	High vs. Low PTPN6	High = 46 Low = 46	1.5	0.043	0.044	High PTPN6 is associated with significantly worse overall survival.

Immunohistochemical staining-based expression validation and subcellular localization analysis of PTPN6

Next, PTPN6 expression at protein level and its cellular localization were analyzed in KIRC, LIHC, and READ tissue samples using the HPA database. Figure 2A displayed immunohistochemical staining of PTPN6 in KIRC, LIHC, and READ tissues paired with controls, revealing medium levels of expression in all three cancer types (Fig. 2A). The magnified insets (200x) provided a more detailed view of PTPN6 staining, indicating its presence within the tumor cells. The staining intensity remained consistent across these cancer types, suggesting that PTPN6 was properly expressed in these tissues. Furthermore, Fig. 2B presented a schematic representation of PTPN6’s subcellular localization. The diagram highlighted that PTPN6 was primarily localized in the nucleoplasm and nucleoli within cells, which aligned with its role in regulating various nuclear functions, potentially influencing cancer cell proliferation and survival. On the other hand, Fig. 2C and D offered fluorescence microscopy images, further illustrating PTPN6’s subcellular distribution. The images depicted PTPN6 in green, co-localizing with the nucleus, while the red staining represented the cytoskeletal components.

Fig. 2.

Immunohistochemical and immunofluorescence analysis of PTPN6 expression in different cancers tissues and cellular localization. A Human Protein Atlas-based immunohistochemical staining for PTPN6 in different cancer tissues: kidney renal clear cell carcinoma (KIRC), liver hepatocellular carcinoma (LIHC), and rectum adenocarcinoma (READ). B Schematic illustration of the predicted subcellular localization of PTPN6, highlighting detection in the nucleoplasm and nucleoli. C, D Immunofluorescence staining of PTPN6 in human cells showing its subcellular distribution. Green fluorescence indicates nuclear staining, while red fluorescence shows cytoskeletal structures

mRNA expression validation and promoter methylation analysis of PTPON6

The expression level of PTPN6 was further examined across different cancer stages of KIRC, LIHC, and READ using the OncoDB database. In Fig. 3A, violin plots illustrated the distribution of PTPN6 expression levels across various cancer stages (I-IV) for KIRC, LIHC, and READ. In KIRC, a significant variation in expression levels was observed across the stages (F-value = 6.45, p = 0.000271), suggesting a stage-dependent change (Fig. 3A). In contrast, the differences in expression levels across stages in LIHC and READ were not statistically significant, as indicated by the high p-values (0.333 and 0.773, respectively) (Fig. 3A). Furthermore, promoter methylation analysis of PTPN6 was conducted across KIRC, LIHC, and READ using the GSCA and UALCAN databases. In Fig. 3B, the line graphs represented the methylation levels (beta values) across different positions in the PTPN6 gene, divided into promoter and gene body regions. In KIRC, the methylation levels appeared to be significantly (p-value < 0.05) higher in the promoter region compared to the gene body. Similar trends were observed in LIHC and READ, although the extent of methylation varied slightly (Fig. 3B). In Fig. 3C, box plots compared the methylation levels of the PTPN6 promoter region between normal and primary tumor samples across KIRC, LIHC, and READ (Fig. 3C). In KIRC, a significant (p-value < 0.05) reduction in promoter methylation was observed in primary tumor samples compared to normal tissues, indicating possible hypomethylation in tumors. LIHC and READ also exhibited significantly (p-value < 0.05) reduced methylation in tumor samples (Fig. 3C). mRNA expression validation analysis via he OncoDB database suggested the significant (p-value < 0.05) up-regulation of PTPN6 across KIRC, LIHC, and READ samples relative to normal controls (Fig. 3D).

Fig. 3.

Expression and methylation analysis of PTPN6 in different cancer types. A GEPIA2-based violin plots representing the expression levels of PTPN6 across different stages (I-IV) of kidney renal clear cell carcinoma (KIRC), liver hepatocellular carcinoma (LIHC), and rectum adenocarcinoma (READ). B OncoDB-based methylation beta values across different genomic regions of PTPN6 (promoter and gene body) in KIRC, LIHC, and READ. C UALCAN-based box plots comparing the promoter methylation levels of PTPN6 between normal and primary tumor samples in KIRC, LIHC, and READ, using data from The Cancer Genome Atlas (TCGA). D OncoDB-based box plots comparing the mRNA expression levels of PTPN6 between normal and primary tumor samples in KIRC, LIHC, and READ, using data from TCGA. P-value < 0.05

Mutational landscape of PTPN6

Mutational analysis of PTPN6 in KIRC, LIHC, and READ was conducted using the cBioPortal database. Figure 4A highlighted the frequency and types of PTPN6 mutations across these cancer types. In KIRC, only 2 out of 336 samples (0.6%) exhibited PTPN6 alterations, including a frame-shift deletion and complex mutations (Fig. 4A). Similarly, in LIHC, 4 out of 364 samples (1.1%) showed PTPN6 mutations, primarily missense mutations and multi-hit events. In READ, PTPN6 mutations were even rarer, with only 1 out of 137 samples (0.73%) displaying a missense mutation (Fig. 4A). These findings suggested that PTPN6 mutations were relatively infrequent across all three cancer types, with missense mutations being the most common type observed. In Fig. 4B, Kaplan-Meier survival curves compared the overall survival of patients with and without PTPN6 mutations in KIRC, LIHC, and READ. In KIRC, the survival curve showed that patients with PTPN6 alterations tended to have a slightly better survival rate compared to those without alterations. However, this difference was not statistically significant, as indicated by a p-value of 0.160 (Fig. 4B). Similarly, in LIHC and READ, there was no significant difference in survival between patients with and without PTPN6 mutations, with p-values of 0.617 and 0.987, respectively (Fig. 4B).

Fig. 4.

Mutation analysis and impact on overall survival of PTPN6 in different cancer types. A cBioPortal-based oncoplots representing mutations in the PTPN6 gene in kidney renal clear cell carcinoma (KIRC), liver hepatocellular carcinoma (LIHC), and rectum adenocarcinoma (READ) across samples from The Cancer Genome Atlas (TCGA). B Kaplan-Meier survival curves showing the overall survival of patients with altered (red) versus unaltered (blue) PTPN6 in KIRC, LIHC, and READ. P-value < 0.05

Correlation of PTPN6 with immune related genes and molecular subtypes

The correlations of PTPN6 with immune-related genes and molecular subtypes were analyzed using the TISIDB database. In KIRC, a positive correlation was observed between PTPN6 expression and several immune inhibitor genes, such as PDCD1 (PD-1), LAG3, and CTLA4 (Fig. 5A). Additionally, MHC genes such as HLA-DRA and HLA-F showed a positive correlation with PTPN6 in KIRC (Fig. 5A). However, among immune modulator (stimulator) genes, some like TNFSF15, IL6R, and NTSE exhibited a negative correlation with PTPN6 in KIRC (Fig. 5A). In LIHC, the pattern of correlation mirrored that in KIRC (Fig. 5A). PTPN6 showed a positive correlation with immune inhibitor genes such as PDCD1 (PD-1) and CTLA4 (Fig. 5A). MHC genes, particularly HLA-DRA, also exhibited a positive correlation with PTPN6 in LIHC (Fig. 5A). Conversely, in the category of immune modulator genes, TNFSF18 and PVR displayed a negative correlation with PTPN6 in LIHC (Fig. 5A). In READ, the correlations followed a similar trend as seen in KIRC and LIHC. Immune inhibitor genes such as PDCD1 (PD-1), LAG3, and CSF1R were positively correlated with PTPN6 expression (Fig. 5A). Additionally, MHC genes like HLA-DRA and TAP1 showed a positive correlation with PTPN6 in READ (Fig. 5A). Regarding immune modulator genes, NT5E and ULBP1 exhibited a negative correlation with PTPN6 in READ. Furthermore, the molecular subtype analysis of PTPN6 in KIRC revealed that the expression of PTPN6 was notably (p-value < 0.05) higher in subtypes C1 and C2 (Fig. 5B). Similarly, in LIHC, PTPN6 expression varied significantly among the subtypes, with subtype C2 showing the highest expression (Fig. 5B). In READ, PTPN6 expression also differed among subtypes, with subtype C3 exhibiting the highest levels (Fig. 5B).

Fig. 5.

Correlation analysis of PTPN6 with immune-related gene signatures and different molecular. A TISIDB-based heatmaps showing the correlations of PTPN6 with immune-related genes, including immune inhibitors, MHC genes, and immune stimulators, across different cancers. B Violin plots displaying the expression levels of PTPN6 (log2CPM) across different molecular subtypes across three cancer types: Kidney Renal Clear Cell Carcinoma (KIRC), Liver Hepatocellular Carcinoma (LIHC), and Rectum Adenocarcinoma (READ). P-value < 0.05

Deciphering the correlations of PTPN6 with diverse functional states

Correlations of PTPN6 with 14 functional states across KIRC, LIHC, and READ were analyzed using CancerSEA. In KIRC, positive correlations were observed between PTPN6 expression and functional states such as DNA damage, proliferation, and invasion (Fig. 6A-B), suggesting that higher expression of PTPN6 may be associated with increased activity in these processes. Conversely, a negative correlation was found with inflammation and EMT (epithelial-mesenchymal transition) (Fig. 6A-B). In LIHC, a strong positive correlation was identified between PTPN6 expression and apoptosis, suggesting that PTPN6 may enhance apoptotic pathways in LIHC (Fig. 6A-B). Additionally, positive correlations with DNA damage and cell cycle regulation were observed, implying a potential role for PTPN6 in influencing these processes in LIHC (Fig. 6A-B). In READ, overall correlations were weaker, but PTPN6 exhibited positive correlations with inflammation and DNA damage, indicating a potential involvement in these pathways (Fig. 6A-B).

Fig. 6.

Correlation of PTPN6 expression with diverse functional states of different cancer types. A CancerSEA-based heatmap showing the correlation between PTPN6 expression and 14 diverse functional states of different cancers. B Heatmap focusing on the correlation of PTPN6 expression with the same set of functional states in kidney renal clear cell carcinoma (KIRC), liver hepatocellular carcinoma (LIHC), and rectum adenocarcinoma (READ). P-value < 0.05

Gene enrichment analysis

The protein-protein interaction (PPI) network of the PTPN6 was constructed using STRING database. The PPI network of PTPN6 can be seen in Fig. 7A. After constructing PPI network, gene enrichment analysis of PTP6 and its other interacting proteins was conducted using DAVID tool. Figure 7B expands on the cellular context of PTPN6’s activity by showing its enrichment in various cellular components, including “granulocyte macrophage colony-stimulating factor receptor complexes, interleukin receptor complexes, and plasma membrane rafts.” Moving to the molecular level, Fig. 7C reveals that PTPN6 and its interacting proteins are involved in functions such as “growth hormone receptor binding, MHC class I protein binding, and protein tyrosine kinase activity.” In Fig. 7D; the biological process enrichment highlights PTPN6’s involvement in pathways such as “growth hormone receptor signaling via JAK-STAT, Interleukin-6-mediated signaling, and Immune response-regulating signaling pathways.” Finally, Fig. 7E focuses on pathway enrichment, identifying key pathways like “PD-L1 expression and PD-1 checkpoint pathway in cancer, EGFR tyrosine kinase inhibitor resistance, and natural killer cell-mediated cytotoxicity.”

Fig. 7.

Functional network and enrichment analysis of PTPN6-associated genes in cancer. A STRING-based protein-protein interaction (PPI) network of PTPN6 and its interacting proteins. B GO (Gene Ontology) enrichment analysis for cellular components. C GO enrichment analysis for molecular functions. D GO enrichment analysis for biological processes. E KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analysis of PTPN6-associated genes. P-value < 0.05

Correlation of PTPN6 with immune cell infiltration and drug sensitivity

Correlations of PTPN6 with immune cell infiltration and drug sensitivity were analyzed using the GSCA database. In KIRC, PTPN6 expression showed a positive correlation with multiple immune cell types, including CD8 + T cells, central memory CD4 + T cells, cytotoxic T cells, gamma delta T cells, and NK cells (Fig. 8A). Similarly, in LIHC, PTPN6 expression exhibited a positive correlation with immune cells such as CD8 + T cells, cytotoxic T cells, gamma delta T cells, and NK cells (Fig. 8B). In READ, while the pattern of positive correlation persisted, the strength of these correlations was slightly weaker compared to KIRC and LIHC (Fig. 8C). Regarding drug sensitivity, PTPN6 expression showed significant correlation with therapeutic various therapeutic drugs which can be utilized for the treatment of KIRC, LIHC, and READ (Fig. 8D).

Fig. 8.

PTPN6 expression correlates with immune infiltrates and drug sensitivity in different cancer types. A Correlation between PTPN6 expression and immune cell infiltrates in kidney renal clear cell carcinoma (KIRC). B Correlation between PTPN6 expression and immune cell infiltrates in liver hepatocellular carcinoma (LIHC). C Correlation between PTPN6 expression and immune cell infiltrates in rectum adenocarcinoma (READ). D Correlation between PTPN6 expression and drug sensitivity in the Genomics of Drug Sensitivity in Cancer (GDSC) dataset. P-value < 0.05

In vitro expression validation of PTPN6

In this part of the study, PTPN6 expression was validated in KIRC cell lines using the RT-qPCR assay. Figure 9A illustrates a distinct difference in expression levels between KIRC (n = 13) and normal control (n = 7) cell lines. The box plot shows that the median expression of PTPN6 in KIRC cell lines was approximately 3.0, with a narrow interquartile range, indicating a significant (p-value < 0.05) overexpression across cancerous samples (Fig. 9A). In contrast, normal tissues exhibited significantly lower expression levels, with a median close to 1.0 (Fig. 9A). This stark difference suggested that PTPN6 was highly upregulated in KIRC cell line samples. This differential expression was further evaluated in Fig. 9B, which presented a ROC curve analysis. The ROC curve, with an Area Under the Curve (AUC) of 0.89, demonstrated the diagnostic capability of PTPN6 expression in distinguishing between KIRC and normal individuals (Fig. 9B).

Fig. 9.

Comparison of PTPN6 gene expression in KIRC vs. normal control cell lines and the predictive power of PTPN6 for KIRC. A Reverse Transcription Quantitative Polymerase Chain Reaction (RT-qPCR)-based expression of PTPN6 gene in KIRC and normal control cell lines. B The Receiver Operating Characteristic (ROC) curve assesses the performance of PTPN6 expression as a predictive biomarker for distinguishing KIRC from normal tissue. P-value < 0.05

PTPN6 knockdown and functional assays

The PTPN6 gene knockdown was performed to analyze its effects on 786-O and A498 cells, focusing on different cellular functions such as gene expression, colony formation, proliferation, and wound healing. In Figs. 10A and 11A, an RT-qPCR-based bar graph illustrated a significant (p-value < 0.001) reduction in PTPN6 expression in both 786-O and A498 cells treated with siRNA targeting PTPN6 (si-PTPN6) compared to the control cells (Ctrl), indicating successful knockdown of PTPN6 at the mRNA level in both cell types. This reduction in expression was further validated at the protein level in Figs. 10B and 11B and supplementary data Fig. 1, where Western blot analysis revealed a significant (p-value < 0.001) decrease in PTPN6 protein levels in both si-PTPN6-786-O and si-PTPN6-A498 cells compared to their respective control cells. Figures 10C and 11C highlighted a significant (p-value < 0.001) reduction in the proliferation rate in both si-PTPN6-786-O and si-PTPN6-A498 cells compared to the control. Similarly, Figs. 10D-E and 11D-E show that the colony-forming ability of both 786-O and A498 cells was significantly (p-value < 0.001) impaired following PTPN6 knockdown. Moreover, the results from the wound healing assay in Figs. 10F-G and 11F-G demonstrated that PTPN6 knockdown significantly (p-value < 0.001) impaired the migratory capacity of both 786-O and A498 cells. The si-PTPN6-786-O and si-PTPN6-A498 cells showed a markedly decreased wound closure over time compared to the control cells, as evidenced by the time-lapse analysis. These results suggest that PTPN6 is crucial not only for cell growth and division but also for cell migration in both 786-O and A498 cells.

Fig. 10.

Silencing PTPN6 reduces colony formation, proliferation, and wound healing in 786-O renal cancer cells. A) Reverse Transcription Quantitative Polymerase Chain Reaction (RT-qPCR)-based quantitative analysis of PTPN6 expression levels in 786-O cells transfected with siRNA targeting PTPN6. (B) Representative western blot images showing PTPN6 and GAPDH (loading control) protein levels in Ctrl-786-O and si-PTPN6-786-O cells. (C) Quantification of cell proliferation in Ctrl-786-O and si-PTPN6-786-O cells. (D) Representative images of colonies formed by Ctrl-786-O and si-PTPN6-786-O cells stained with crystal violet. (E) Quantification of colony numbers in Ctrl-786-O and si-PTPN6-786-O cells. (F) Representative images of wound healing in Ctrl-786-O and si-PTPN6-786-O cells at 0 and 24 h. (G) Quantification of the wound closure percentage in Ctrl-786-O and si-PTPN6-786-O cells. P***-value < 0.001

Fig. 11.

Silencing PTPN6 reduces colony formation, proliferation, and wound healing in A498 renal cancer cells. A Reverse Transcription Quantitative Polymerase Chain Reaction (RT-qPCR)-based quantitative analysis of PTPN6 expression levels in A498 cells transfected with siRNA targeting PTPN6. B Representative western blot images showing PTPN6 and GAPDH (loading control) protein levels in Ctrl-A498 and si-PTPN6-A498 cells. C Quantification of cell proliferation in Ctrl-A498 and si-PTPN6-A498 cells. D Representative images of colonies formed by Ctrl-A498 and si-PTPN6-A498 cells stained with crystal violet. E Quantification of colony numbers in Ctrl-A498 and si-PTPN6-A498 cells. F Representative images of wound healing in Ctrl-A498 and si-PTPN6-A498 cells at 0 and 24 h. G Quantification of the wound closure percentage in Ctrl-A498 and si-PTPN6-A498 cells. P***-value < 0.001

Discussion

Cancer is a complex and heterogeneous group of diseases characterized by uncontrolled cell growth and spread to other parts of the body [5, 44–47]. Effective cancer management often relies on identifying biomarkers that can provide insights into disease mechanisms [48, 49], predict patient outcomes [50–52], and guide therapeutic interventions [12, 53–55]. Matrix metalloproteinases (MMPs) are a family of enzymes known to play pivotal roles in cancer progression through their involvement in extracellular matrix remodeling, tumor invasion, and metastasis [56, 57]. Among these, Protein Tyrosine Phosphatase, Non-Receptor Type 6 (PTPN6) has emerged as a gene of interest due to its potential role in modulating various cellular processes linked to cancer [58, 59]. PTPN6, also known as SHP-1, is a non-receptor protein tyrosine phosphatase that regulates numerous cellular processes, including cell growth, differentiation, and survival [59]. Aberrant PTPN6 expression has been implicated in various diseases, including cancer, where it may influence tumor progression and patient prognosis. Previous studies have identified PTPN6 as a critical modulator of immune responses and cellular signaling pathways involved in cancer development. For instance, research has shown that PTPN6 can affect the signaling of growth factors and cytokines, which are crucial for cancer cell proliferation and metastasis [60, 61]. Despite the known involvement of PTPN6 in various cellular functions, its role in cancer remains underexplored. Existing studies have provided preliminary insights into PTPN6’s involvement in specific cancer types, but comprehensive analyses across a broad range of cancers and detailed mechanistic studies are lacking. Our study aimed to elucidate the expression and prognostic significance of PTPN6 across different cancer types using in silico and in vitro methodologies. This comprehensive analysis highlights PTPN6’s potential as a biomarker and provides insights into its functional implications in cancer.

Our results reveal significant differences in PTPN6 expression between tumor and normal tissues and cell lines, with some cancers showing upregulation and others downregulation of PTPN6. These findings align with previous studies reporting PTPN6 dysregulation in various cancers. For example, previous studies demonstrated elevated PTPN6 expression in breast cancer and colorectal cancer tissues, which correlated with poor prognosis [62–65]. Similarly, our data show that PTPN6 upregulation is associated with reduced overall survival in KIRC, LIHC, and READ, corroborating the prognostic significance observed in other cancers. Our immunohistochemical and subcellular localization analyses confirm that PTPN6 is primarily localized in the nucleoplasm and nucleoli, consistent with its role in regulating nuclear functions. This is in line with findings from Wang et al., who observed similar subcellular distribution of PTPN6 in leukemia cells, highlighting its involvement in cellular signaling and proliferation [66].

The promoter methylation analysis reveals that PTPN6 is hypomethylated in tumors compared to normal tissues, particularly in KIRC. This result is consistent with studies by Chen et al., which reported hypomethylation of PTPN6 in various cancer types, suggesting an epigenetic mechanism for its overexpression [62]. However, our mutational analysis indicates that PTPN6 mutations are relatively infrequent across KIRC, LIHC, and READ, which contrasts with some studies that have reported frequent mutations in other cancer types, such as colorectal cancer [67].

The observed correlations between PTPN6 expression and immune-related genes across KIRC, LIHC, and READ highlight the potential role of PTPN6 in modulating immune responses in tumors. In KIRC, LIHC, and READ, PTPN6 was positively correlated with several immune inhibitor genes, such as PDCD1 (PD-1), LAG3, and CTLA4, which are known to play critical roles in immune evasion by tumors [68, 69]. This suggests that PTPN6 may contribute to immune suppression in the tumor microenvironment, potentially facilitating cancer progression. Additionally, the positive correlation of PTPN6 with MHC genes, including HLA-DRA and TAP1, in KIRC, LIHC, and READ indicates that PTPN6 may influence antigen presentation and T-cell activation [70]. In contrast, the negative correlation observed with certain immune modulator (stimulator) genes, such as TNFSF15, IL6R, and NTSE in KIRC, and TNFSF18 and PVR in LIHC, suggests a complex interplay between PTPN6 and immune regulation, where it may dampen the immune stimulatory signals that promote effective anti-tumor immunity [71]. These findings align with previous studies that have highlighted the dual role of PTPN6 in regulating immune responses, suggesting that PTPN6 may serve as a modulator of both immune suppression and immune activation, depending on the tumor context. Further investigation into the mechanistic pathways by which PTPN6 regulates immune gene expression in these cancers is warranted to explore its potential as a therapeutic target in immune modulation.

Furthermore, our correlation with functional states and PPI network analysis underscores PTPN6’s involvement in critical cancer-associated processes such as proliferation, apoptosis, and immune checkpoint regulation, aligning with previous studies that have identified similar pathways [66, 72–74]. Finally, our study investigated the functional impact of PTPN6 knockdown in 786-O and A489 cells, focusing on gene expression, colony formation, proliferation, and migration. Successful knockdown of PTPN6 was confirmed through reduced mRNA and protein levels. This downregulation led to significantly fewer colonies and decreased proliferation, indicating PTPN6’s role in promoting cell growth and division. Conversely, knockdown enhanced cell migration, which contrasts with some previous reports of PTPN6 inhibiting migration. These findings emphasize PTPN6’s multifaceted role in cancer and suggest its potential as a therapeutic target.

Despite the comprehensive analysis of PTPN6 expression and its potential role in cancer, this study has some limitations. First, the data primarily rely on publicly available databases, which may have inherent biases or incomplete datasets, particularly regarding patient demographics and clinical outcomes. Additionally, while the study identifies correlations between PTPN6 expression and cancer progression, causality cannot be established due to the observational nature of the data. The functional assays performed in two cell line may not fully reflect the complexity of tumor behavior in vivo, and further in vivo studies are necessary to validate these findings. Lastly, the study does not explore the potential off-target effects or the clinical feasibility of targeting PTPN6 in cancer therapies, which would require further detailed investigations.

Conclusion

This study highlights the altered expression of PTPN6 in various cancers, with significant upregulation in KIRC, LIHC, and READ. Elevated PTPN6 expression was associated with poorer survival outcomes in these cancers. Immunohistochemical and functional assays confirmed its role in cell proliferation and migration. Additionally, PTPN6’s correlation with immune-related genes and pathways suggests it may influence the tumor microenvironment. While PTPN6 shows promise as a biomarker and therapeutic target, further investigation is needed to fully understand its role in cancer progression and to explore its potential for therapeutic applications.

Supplementary Information

Below is the link to the electronic supplementary material.

Author contributions

The idea was conceived by Yasir Hameed, Faisal Siddique and Mumtaz Hussain. Xiaohui Wang contributed to the conceptualization, methodology, and analysis of the data. Mumtaz Hussain and Qurat ul Ain were responsible for the experimental design and data collection. Madiha Zaynab contributed to data analysis and interpretation. Mostafa A. Abdel-Maksoud, Taghreed N. Almana, Saeedah Almutair, Abdulaziz Alamri, and Ibrahim A. Saleh participated in the review and validation of the research findings. Naser Zomot, Wahidah H. Al-Qahtani, and Yasir Hameed provided critical feedback on the manuscript and contributed to its revision. All authors have read and approved the final manuscript.

Funding

The authors extend their appreciation to the ongoing Research Funding Program (ORF-2025-470) King Saud University, Riyadh, Saud Arabia. This work was also supported by Henan Provincial People’s Hospital via Project number Wjlx2022019.

Data availability

Data supporting the findings of this study are available from the corresponding author upon reasonable request.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.