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. 2023 Oct 26;13(1):18372.
doi: 10.1038/s41598-023-44892-z.

Pan-cancer analysis revealing that PTPN2 is an indicator of risk stratification for acute myeloid leukemia

Xuanyu Wang #  1 , Sanyun Wu #  2 , Le Sun #  1 , Peipei Jin  2 , Jianmin Zhang  2 , Wen Liu  2 , Zhuo Zhan  2 , Zisong Wang  3 , Xiaoping Liu  4 , Li He  5   6
Affiliations

Pan-cancer analysis revealing that PTPN2 is an indicator of risk stratification for acute myeloid leukemia

Xuanyu Wang et al. Sci Rep. .

Abstract

The non-receptor protein tyrosine phosphatases gene family (PTPNs) is involved in the tumorigenesis and development of many cancers, but the role of PTPNs in acute myeloid leukemia (AML) remains unclear. After a comprehensive evaluation on the expression patterns and immunological effects of PTPNs using a pan-cancer analysis based on RNA sequencing data obtained from The Cancer Genome Atlas, the most valuable gene PTPN2 was discovered. Further investigation of the expression patterns of PTPN2 in different tissues and cells showed a robust correlation with AML. PTPN2 was then systematically correlated with immunological signatures in the AML tumor microenvironment and its differential expression was verified using clinical samples. In addition, a prediction model, being validated and compared with other models, was developed in our research. The systematic analysis of PTPN family reveals that the effect of PTPNs on cancer may be correlated to mediating cell cycle-related pathways. It was then found that PTPN2 was highly expressed in hematologic diseases and bone marrow tissues, and its differential expression in AML patients and normal humans was verified by clinical samples. Based on its correlation with immune infiltrates, immunomodulators, and immune checkpoint, PTPN2 was found to be a reliable biomarker in the immunotherapy cohort and a prognostic predictor of AML. And PTPN2'riskscore can accurately predict the prognosis and response of cancer immunotherapy. These findings revealed the correlation between PTPNs and immunophenotype, which may be related to cell cycle. PTPN2 was differentially expressed between clinical AML patients and normal people. It is a diagnostic biomarker and potentially therapeutic target, providing targeted guidance for clinical treatment.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Pan-cancer analysis of PTPNs. (a) Differential expression of PTPNs. (b) The constitute of the Heterozygous/Homozygous CNV of PTPNs in pan-cancer. (c) The mutation distribution of the top 10 mutated genes in PTPNs and a SNV classification of SNV types. (d) The percentage of cancers in which PTPNs expression has potential effect (FDR <  = 0.05) on pathway activity. (e) Priority of PTPNs among four immunosuppressive indices, including the T-cell dysfunction levels, ICB response outcome, phenotypes in CRISPR screens, and T-cell exclusion cell types. (f) Correlation between PTPNs expression and drug IC50.
Figure 2
Figure 2
Pan-cancer analysis of PTPN2. (a) Expression of PTPN2 in normal and tumor tissues. (b) Expression analysis of PTPN2 in pan-cancer. (c) Alteration frequency of PTPN2. (d–f) Correlation between PTPN2 and methyltransferases, modification regulators, stemness score, and tumor heterogeneity. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001.
Figure 3
Figure 3
Association of PTPN2 with cancer pathways and immune processes. (a) Immunophenotypes Enrichment analysis for metabolism pathway and cancer signaling between high and low PTPN2 expression. (b) Correlation between PTPN2 and ICP. (c) Correlation between PTPN2 expression and immune infiltration in pan-cancer. *P < 0.05.
Figure 4
Figure 4
Prognostic value and biomarker potential of PTPN2. (a) Effect of PTPN2 on cancer prognosis. (b) Correlation between PTPN2 expression and overall survival in the TCGA and TARGET cohort. (c) A Comparison of PTPN2 expression before and after ICB treatments across different tumor models in vivo. (d) Ability of PTPN2 to predict response outcome and overall survival in immunotherapy cohorts. (e) The top 12 drugs positively correlated with PTPN2 expression in the CellMiner database.
Figure 5
Figure 5
Prognostic value and biomarker potential of PTPN2. (a, b) The expression of PTPN2 in different cell types. (c) Expression level of PTPN2 in the bone marrow samples of AML patients and normal donors. (d) The prognostic role of PTPN2 in the TCGA-LAML cohort. (e, f) Correlation between PTPN2 expression and cancer microenvironment-related signatures in TCGA-LAML. *P < 0.05, **P < 0.01, ***P < 0.001.
Figure 6
Figure 6
Drug discovery of PTPN2 in AML. (a) Drug screening in patients with high PTPN2 expression in CTRP and PRISM. (b) CMAP analysis between high and low PTPN2 expression groups.
Figure 7
Figure 7
Construction and characterization of PTPRS. (a) Development of PTPRS in metaX training set and the predictive accuracy of PTPRS for survival. Validation of the PTPRS in metaX validation set and five external independent sets, including GSE71014, GSE37642, GSE106291, GSE12417 and GSE10358. (b, c) ROC-AUC value and C-index in different risk scoring systems. (d) Heatmap for infiltration of immune based on CIBERSORT, CIBERSORT-ABS, QUANTISEQ, MCPCOUNTER, XCELL, and EPIC algorithms among high-risk and low-risk group. (e–g) Functional and signaling pathway analysis of these differential genes in AML according to GO, Hallmarks, and KEGG pathway. *P < 0.05, **P < 0.01, ***P < 0.001.
Figure 8
Figure 8
Clinical association of PTPRS and construction of nomogram. (a) The difference in clinicopathologic features and pathological stages of AML between high-risk and low-risk group. (b) Nomogram predicting the 1-, 2-, and 3-year OS in patients with AML. (c) The calibration curves for predicting patient OS at a 1-, 2-, and 3-year. (d, e) DCA curves of the nomogram, PTPRS and other pooled models for predicting 1-, 2-, and 3-year OS. (f) Time-dependent AUC values of nomogram and PTPRS for the prediction of OS.
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References

    1. Grimwade D, Ivey A, Huntly BJ. Molecular landscape of acute myeloid leukemia in younger adults and its clinical relevance. Blood. 2016;127:29–41. doi: 10.1182/blood-2015-07-604496. - DOI - PMC - PubMed
    1. Kayser S, Levis MJ. Updates on targeted therapies for acute myeloid leukaemia. Br. J. Haematol. 2022;196:316–328. doi: 10.1111/bjh.17746. - DOI - PubMed
    1. Newell LF, Cook RJ. Advances in acute myeloid leukemia. BMJ. 2021;375:n2026. doi: 10.1136/bmj.n2026. - DOI - PubMed
    1. Lewis DR, Siembida EJ, Seibel NL, Smith AW, Mariotto AB. Survival outcomes for cancer types with the highest death rates for adolescents and young adults, 1975–2016. Cancer-Am. Cancer Soc. 2021;127:4277–4286. - PubMed
    1. Tang X, Qi C, Zhou H, Liu Y. Critical roles of PTPN family members regulated by non-coding RNAs in tumorigenesis and immunotherapy. Front. Oncol. 2022;12:972906. doi: 10.3389/fonc.2022.972906. - DOI - PMC - PubMed

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