Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2022 Oct 7;14(19):4913.
doi: 10.3390/cancers14194913.

Metabolic Biomarkers Affecting Cell Proliferation and Prognosis in Polycythemia Vera

Ziqing Wang  1   2 , Yan Lv  1 , Erpeng Yang  1   3 , Yujin Li  1   3 , Dehao Wang  1   2 , Guang Hu  3   4 , Yumeng Li  1   3 , Mingjing Wang  1 , Weiyi Liu  1 , Mingqian Sun  4 , Xiaomei Hu  1
Affiliations

Metabolic Biomarkers Affecting Cell Proliferation and Prognosis in Polycythemia Vera

Ziqing Wang et al. Cancers (Basel). .

Abstract

Polycythemia vera (PV) is a malignant clonal hematological disease of hematopoietic stem cells characterized by the proliferation of peripheral blood cells, and JAK2 mutation is one of the main causes of PV peripheral blood cell proliferation. Abnormal cell metabolism is a new feature of malignant proliferation of tumor cells, but the role of metabolism in the pathogenesis and prognosis of PV remains unclear. We analyzed metabolic differences of peripheral blood sera between 32 PV patients and 20 healthy controls (HCs) by liquid chromatography-mass spectrometry (LC-MS) to investigate their relationship with cell proliferation and to screen for prognosis-related metabolic biomarkers. Compared to HC, 33 endogenous metabolites were significantly changed in PV and were involved in fatty acid metabolism, glucose metabolism, sphingolipid metabolism, and amino acid metabolism pathways. Among them, seven metabolites were closely associated with JAK2 mutations, 2 of which may contribute to the proliferation of peripheral blood cells in PV patients. A set of potential prognostic metabolic biomarkers containing four metabolites was identified by a receiver operating characteristic (ROC) curve according to the risk stratification of the PV patients and their combined AUC value of 0.952, with a sensitivity of 90.905% and specificity of 90.909% at the optimal cutoff point. Metabonomics is an important tool for the study of the pathogenesis of PV and the relationship between JAK2 gene mutation. Furthermore, the potential biomarkers of this study may provide a reference for the prognosis of PV.

Keywords: cell proliferation; metabolic biomarkers; metabolomics; polycythemia vera; prognosis.

PubMed Disclaimer

Conflict of interest statement

The authors have no relevant financial or non-financial interests to disclose.

Figures

Figure 1
Figure 1
Score plots of serum metabolic profiles of the PV and HC groups. (A) PLS-DA score plots for the PV and HC groups in positive mode. (B) PLS-DA score plots for the PV and HC groups in negative mode.
Figure 2
Figure 2
Metabolic pathways enrichment analysis of potential biomarkers in PV patients. 1. D-glutamine and D-glutamate metabolism. 2. Sphingolipid metabolism. 3. Pyruvate metabolism. 4. Alanine, aspartate and glutamate metabolism. 5. Arginine and proline metabolism. 6. Tryptophan metabolism. 7. Lysine degradation. 8. Arginine biosynthesis. 9. Glycolysis/gluconeogenesis.
Figure 3
Figure 3
Correlation of JAK2 mutations and mutational burden with blood cell counts. Levels of PLT (A) and HCT (B) in JAK2-mutated patients compared with non-JAK2-mutated patients. (E) WBC levels in JAK2 high mutational burden, low burden, and unmutated groups. Mean value ± standard error of the mean (SEM). Mann–Whitney U test, * p < 0.05, ** p < 0.01, ns: no significance (p > 0.05). Scatter plot of the correlation between WBC (C) and PLT (D) levels and JAK2 mutational burden. Spearman’s correlation test.
Figure 4
Figure 4
Specific metabolic profile of PV patients according to the mutational status of JAK2. (A) PLS-DA score plots for the JAK2-mutated group and non-JAK2-mutated group in positive ion mode. (B) PLS-DA score plots for the JAK2-mutated group and non-JAK2-mutated group in negative ion mode. (C) Levels of Cer(d18:2/22:6-2OH(7S, 17S)), SM(d18:0/PGF1α), CerP(d18:1/16:0), glutamic acid, lactic acid, melibiose and xylose in JAK2-mutated patients compared with non-JAK2-mutated patients. Mean value ± SEM. Mann–Whitney U test, * p < 0.05, ** p < 0.01.
Figure 5
Figure 5
Scatter plot of the correlation between Cer(d18:2/22:6-2OH(7S, 17S)) (A) and SM(d18:0/PGF1α) (B) levels and JAK2 mutational burden. Spearman’s correlation test.
Figure 6
Figure 6
Correlation of Cer(d18:2/22:6-2OH(7S, 17S)) and SM(d18:0/PGF1α) with blood cell counts. Spearman’s correlation test. (A) Scatter plot of the correlation between Cer(d18:2/22:6-2OH(7S, 17S)) and WBC levels. (B) Scatter plot of the correlation between SM(d18:0/PGF1α) and WBC levels. (C) Scatter plot of the correlation between SM(d18:0/PGF1α) and PLT levels.
Figure 7
Figure 7
Potential biomarkers related to the prognosis of PV patients. (A) Levels of Cer(d18:2/22:6-2OH(7S, 17S)), SM(d18:0/PGF1α), CerP(d18:1/16:0), octadec-13-enoylcarnitine, glutamic acid, lactic acid, melibiose and 1-pyrroline-5-carboxylic acid in intermediate/high-risk patients compared with low-risk patients. Mean value ± SEM. Mann–Whitney U test, * p < 0.05, ** p < 0.01. (B) ROC curve analysis for the combination of 4 biomarkers.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

References

    1. Tefferi A., Barbui T. Polycythemia vera and essential thrombocythemia: 2021 update on diagnosis, risk-stratification and management. Am. J. Hematol. 2020;95:1599–1613. doi: 10.1002/ajh.26008. - DOI - PubMed
    1. Srour S.A., Devesa S.S., Morton L.M., Check D.P., Curtis R.E., Linet M.S., Dores G. Incidence and patient survival of myeloproliferative neoplasms and myelodysplastic/myeloproliferative neoplasms in the United States, 2001–2012. Br. J. Haematol. 2016;174:382–396. doi: 10.1111/bjh.14061. - DOI - PMC - PubMed
    1. Baxter E.J., Scott L.M., Campbell P.J., East C., Fourouclas N., Swanton S., Vassiliou G.S., Bench A.J., Boyd E.M., Curtin N., et al. Acquired mutation of the tyrosine kinase JAK2 in human myeloproliferative disorders. Lancet. 2005;365:1054–1061. doi: 10.1016/S0140-6736(05)71142-9. - DOI - PubMed
    1. Vainchenker W., Kralovics R. Genetic basis and molecular pathophysiology of classical myeloproliferative neoplasms. Blood. 2017;129:667–679. doi: 10.1182/blood-2016-10-695940. - DOI - PubMed
    1. Hanahan D., Weinberg R.A. Hallmarks of cancer: The next generation. Cell. 2011;144:646–674. doi: 10.1016/j.cell.2011.02.013. - DOI - PubMed

LinkOut - more resources