PTPN1 is a prognostic biomarker related to cancer immunity and drug sensitivity: from pan-cancer analysis to validation in breast cancer

Abstract

Background: Protein tyrosine phosphatase non-receptor type 1 (PTPN1), a member of the protein tyrosine phosphatase superfamily, has been identified as an oncogene and therapeutic target in various cancers. However, its precise role in determining the prognosis of human cancer and immunological responses remains elusive. This study investigated the relationship between PTPN1 expression and clinical outcomes, immune infiltration, and drug sensitivity in human cancers, which will improve understanding regarding its prognostic value and immunological role in pan-cancer.

Methods: The PTPN1 expression profile was obtained from The Cancer Genome Atlas and Cancer Cell Line Encyclopedia databases. Kaplan-Meier, univariate Cox regression, and time-dependent receiver operating characteristic curve analyses were utilized to clarify the relationship between PTPN1 expression and the prognosis of pan-cancer patients. The relationships between PTPN1 expression and the presence of tumor-infiltrated immune cells were analyzed using Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data and Tumor Immune Estimation Resource. The cell counting kit-8 (CCK-8) assay was performed to examine the effects of PTPN1 level on the sensitivity of breast cancer cells to paclitaxel. Immunohistochemistry and immunoblotting were used to investigate the relationship between PTPN1 expression, immune cell infiltration, and immune checkpoint gene expression in human breast cancer tissues and a mouse xenograft model.

Results: The pan-cancer analysis revealed that PTPN1 was frequently up-regulated in various cancers. High PTPN1 expression was associated with poor prognosis in most cancers. Furthermore, PTPN1 expression correlated highly with the presence of tumor-infiltrating immune cells and the expression of immune checkpoint pathway marker genes in different cancers. Furthermore, PTPN1 significantly predicted the prognosis for patients undergoing immunotherapy. The results of the CCK-8 viability assay revealed that PTPN1 knockdown increased the sensitivity of MDA-MB-231 and MCF-7 cells to paclitaxel. Finally, our results demonstrated that PTPN1 was associated with immune infiltration and immune checkpoint gene expression in breast cancer.

Conclusion: PTPN1 was overexpressed in multiple cancer types and correlated with the clinical outcome and tumor immunity, suggesting it could be a valuable potential prognostic and immunological biomarker for pan-cancer.

Keywords: PTPN1; drug sensitivity; immunotherapy; pan-cancer; prognosis; tumor microenvironment.

Figure 1

Expression landscape of PTPN1 in pan-cancer. (A) Distribution of PTPN1 expression in various cancer types from TCGA database. (B) PTPN1 expression level in various cancer cell lines from the CCLE database. (C) Representative images of immunofluorescence staining of PTPN1 protein in the nucleus, endoplasmic reticulum (ER), and microtubules in A-431 and U-251 cell lines from the HPA database. *P < 0.05, **P < 0.01, ***P < 0.001 by Student’s t-test.

Figure 2

Relationship between PTPN1 expression and OS in pan-cancer. (A) The relationship between PTPN1 expression and OS in the cancer types indicated was analyzed using univariate Cox regression analyses. (B) The impact of PTPN1 on OS in the cancer types indicated was assessed using Kaplan-Meier survival curves. (C) Time-dependent ROC curve analysis was used to assess the performance of PTPN1 in predicting 1-, 3-, and 5-year OS for the cancer types indicated.

Figure 3

Relationship between PTPN1 expression and DSS in pan-cancer. (A) The relationship between PTPN1 expression and DSS in the cancer types indicated was analyzed using univariate Cox regression analysis. (B) Kaplan-Meier survival curves showing the impact of PTPN1 on DSS in the cancer types indicated. (C) The performance of PTPN1 in predicting the 1-, 3-, and 5-year DSS for the cancer types indicated was evaluated using time-dependent ROC curve analysis.

Figure 4

Relationship between PTPN1 expression and immune subtypes in cancers.

Figure 5

Correlation between PTPN1 level and immune infiltrates in various cancers. (A, B) Radar charts show the relationship between PTPN1 expression and stromal score (A) and immune score (B) in pan-cancer. (C) The heat map shows the correlation between the expression level of PTPN1 and the presence of immune infiltrates in pan-cancer.

Figure 6

Correlation between PTPN1 level and expression of immune checkpoint-related genes in various cancers. (A) The heat map shows the correlation between the expression levels of PTPN1 and immune checkpoint genes in pan-cancer. (B, C) Radar charts show the relationship between PTPN1 expression and TMB (B) and MSI (C) in pan-cancer. (D) Kaplan-Meier curve showing the impact of PTPN1 on OS of patients from the IMvigor210, GSE78220, and GSE91061 cohorts. ^* P < 0.05; ^** P < 0.01; ^*** P < 0.001.

Figure 7

PTPN1 expression correlated with immune infiltration in human breast cancer tissues. (A) Representative PTPN1 staining in human breast cancer tissues and adjacent normal tissues (scale bars: top, 200 μm; bottom, 50 μm). (B) The immunoreactivity scores of PTPN1 in normal breast tissues and breast cancer tissues were compared using the Mann-Whitney U test (**P < 0.01). (C) Representative IHC staining images for PTPN1, CD68, and CD163 expression in three serial sections of the same tumor from two primary human breast cancer specimens (scale bars: top, 200 μm; bottom, 50 μm). (D) Representative IHC staining images for PTPN1, CD8, and PD-L1 expression in three serial sections of the same tumor from two primary human breast cancer specimens (scale bars: top, 200 μm; bottom, 50 μm.). (E) Quantitative determination of CD68⁺ CD163⁺ cells in the PTPN1 high- and low-expression groups. (F) The number of CD8⁺ T cells in breast cancer tissues with high or low PTPN1 expression. (G) The correlation between the expression levels of PTPN1 and PD-L1 proteins was evaluated using Pearson’s correlation. Error bars, SEM. *P < 0.05, ***P < 0.001 using Student’s t-test.

Figure 8

PTPN1 expression correlated with tumor growth and tumor immune infiltration in vivo. (A) PTPN1 knockdown in 4T1 cells was confirmed using immunoblotting. β-actin was used as the internal loading control. (B) 4T1-shPTPN1 and 4T1-shCtrl cells were subcutaneously injected into BALB/c mice, and tumor growth was monitored for 28 days. (C) Representative images of tumors derived from mice. (D) Representative images of immunofluorescence staining of CD163 (green) and F4/80 (red) in BALB/c mouse tumor tissues from 4T1-shPTPN1 and 4T1-shCtrl cells (scale bars: 100 μm). (E) Quantitative determination of F4/80⁺CD163⁺ cells in tumor tissues of 4T1-shPTPN1 versus 4T1-shCtrl cells. (F) Representative immunofluorescence images of CD8 (green) and PD-L1 (red) expression in BALB/c mouse tumor tissues from 4T1-shPTPN1 and 4T1-shCtrl cells (scale bars: 200 μm). (G) The number of CD8⁺ T cells in tumor tissues of 4T1-shPTPN1 versus 4T1-shCtrl cells. (H) Quantitative determination of PD-L1 protein expression in tumor tissues of 4T1-shPTPN1 versus 4T1-shCtrl cells. Error bars, SEM. ^* P < 0.05, ^** P < 0.01, ^*** P < 0.001 using Student’s t-test.

Figure 9

Correlation between PTPN1 level and drug sensitivity in cancer cells. (A, B) The correlation between PTPN1 expression and chemotherapeutic drug sensitivity in cancer cells was investigated using the data from the CellMiner (A) and GDSC databases (B). (C) The mRNA level of PTPN1 in MDA-MB-231 and MCF-7 cells with PTPN1 knockdown was examined using qRT-PCR. (D) The protein level of PTPN1 in MDA-MB-231 and MCF-7 cells with PTPN1 knockdown was examined using immunoblotting. β-actin was used as the internal loading control. (E) The cell inhibition ratios of MDA-MB-231 and MCF-7 cells with PTPN1 knockdown were evaluated using the CCK-8 viability assay. Cells were treated with various concentrations of paclitaxel (0, 5, 10, 15, 20, 25, and 30 µM) for 72 h. Data are shown as the means of three independent experiments. Error bars indicate SEM. ^* P < 0.05, ^** P < 0.01, ^*** P < 0.001 using one-way ANOVA with post hoc intergroup comparisons.