Skip to main content

Advertisement

Log in

Integration of quantitative phosphoproteomics and transcriptomics revealed phosphorylation-mediated molecular events as useful tools for a potential patient stratification and personalized treatment of human nonfunctional pituitary adenomas

  • Research
  • Published:
EPMA Journal Aims and scope Submit manuscript

Abstract

Background

Invasiveness is a very challenging clinical problem in nonfunctional pituitary adenomas (NFPAs), and currently, there are no effective invasiveness-related molecular biomarkers. The post-neurosurgery treatment is much different as for invasive and noninvasive NFPAs. The aim of this study was to integrate phosphoproteomics and transcriptomics data to reveal phosphorylation-mediated molecular events for invasive characteristics of NFPAs to achieve a potential tool for patient stratification, and prognostic/predictive assessment to discriminate invasive from noninvasive NFPAs for personalized attitude.

Methods

The 6-plex tandem mass tag (TMT) labeling reagents coupled with TiO2 enrichment of phosphopeptides and liquid chromatography-tandem mass spectrometry (LC-MS/MS) were used to identify and quantify each phosphoprotein and phosphosite in NFPAs and controls. Differentially expressed genes (DEGs) between invasive NFPA and control tissues were obtained from the Gene Expression Omnibus (GEO) database. The overlapping analysis was performed between phosphoprotiens and invasive DEGs. Gene Ontology (GO) enrichment, the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and protein–protein interaction (PPI) analyses were used to analyze these overlapped molecules.

Results

In total, 1035 phosphoproteins with 2982 phosphorylation sites were identified in NFPAs vs. controls, and 2751 DEGs were identified in invasive NFPAs vs. controls. Overlapping analysis of these phosphoproteins and DEGs exposed 130 overlapped molecules (phosphoproteins; invasive DEGs). GO enrichment and KEGG pathway analyses of 130 overlapped molecules revealed multiple biological processes and signaling pathway network alterations, including cell–cell adhesion, platelet activation, GTPase signaling pathway, protein kinase signaling, calcium signaling pathway, estrogen signaling pathway, glucagon signaling pathway, cGMP–PKG signaling pathway, GnRH signaling pathway, inflammatory mediator regulation of TRP channels, vascular smooth muscle contraction, and Fc gamma R-mediated phagocytosis, which were obviously associated with tumor invasive characteristics. For 130 overlapped molecules, PPI network-based molecular complex detection (MCODE) identified 10 hub molecules, namely SLC2A4, TSC2, AKT1, SCG3, ALB, APOL1, ACACA, SPARCL1, CHGB, and IGFBP5. These hub molecules are involved in multiple signaling pathways and represent potential predictive/prognostic markers in NFPA patients as well as they represent potential therapeutic targets.

Conclusions

This study provided the first large-scale phosphoprotein profiling and phosphorylation-related signaling pathway network alterations in human NFPA tissues. Further, overlapping analysis of phosphoproteins and invasive DEGs revealed the phosphorylation-mediated signaling pathway network changes in invasive NFPAs. These findings are the precious resource for in-depth insight into the molecular mechanisms of NFPAs, as well as for the discovery of effective phosphoprotein biomarkers and therapeutic targets for invasive NFPAs.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Abbreviations

ACACA:

acetyl-CoA carboxylase 1

ACN:

acetonitrile

AGC:

automatic gain control

AKT:

protein kinase B

AKT1:

RAC-alpha serine/threonine-protein kinase

ALB:

serum albumin

AMPK:

AMP-activated protein kinase

APOL1:

apolipoprotein L1

ATF2:

activating transcription factor 2

BP:

biological process

CC:

cellular component

cGMP:

cyclic nucleotide cGMP

CHGB:

secretogranin-1

DEG:

differentially expressed gene

DEP:

differentially expressed protein

DTT:

dithiothreitol

r-ERG channel:

rat ether-à-go-go-related (ERG) channel

ERK:

extracellular signal–regulated kinase

ESCRT:

endosomal sorting complex required for transport

ESI-TRAP:

electrospray ionization-ion trap

FC:

fold change

FDR:

false discovery rate

FGF-2:

fibroblast growth factor-2

FPA:

functional pituitary adenoma

GEO:

Gene Expression Omnibus

GH:

growth hormone

GH3:

pituitary growth hormone 3

GnRH:

gonadotropin-releasing hormone

GO:

Gene Ontology

HCD:

high-energy collision dissociation

HIF-1a:

hypoxia-inducible factor-1a

HPLC:

high-performance liquid chromatography

IGF-1:

insulin growth factor-1

IGFBP5:

insulin-like growth factor-binding protein 5

KEGG:

Kyoto Encyclopedia of Genes and Genomes

LC:

liquid chromatography

MAPK:

mitogen-activated protein kinase

MCODE:

molecular complex detection

MF:

molecular function

MMPs:

matrix metalloproteinases

MS/MS:

tandem mass spectrometry

mTOR:

mammalian target of rapamycin

NCBI:

National Center for Biotechnology Information

NFκB:

nuclear factor kappa-B

NFPA:

nonfunctional pituitary adenoma

NMR:

nuclear magnetic resonance

PACAP:

pituitary adenylyl cyclase activating polypeptide

PI3K:

phosphatidylinositol 3 kinase

PKG:

protein kinase G

PPI:

protein–protein interaction

PTM:

post-translational modification

PTTG:

pituitary tumor-transforming gene

RSK:

ribosomal S6 kinase

SCG3:

secretogranin-3

Ser or S:

serine

SLC2A4:

solute carrier family 2 facilitated glucose transporter member 4

S/N:

signal-to-noise

SNAP:

soluble N-ethylmaleimide-sensitive fusion attachment protein

SNARE:

soluble N-ethylmaleimide-sensitive factor attachment protein receptor

SPARCL1:

SPARC-like protein 1

TFA:

trifluoroacetic acid

Thr or T:

threonine

TMT:

tandem mass tag

TSC2:

Tuberin

TRH:

thyrotropin-releasing hormone

Tyr or Y:

tyrosine

VEGF:

vascular endothelial growth factor

References

  1. Melmed S. Mechanisms for pituitary tumorigenesis: the plastic pituitary. J Clin Invest. 2003;112:1603–18. https://doi.org/10.1172/JCI20401.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Melmed S. Pathogenesis of pituitary tumors. Nat Rev Endocrinol. 2011;7:257–66. https://doi.org/10.1038/nrendo.2011.40.

    Article  CAS  PubMed  Google Scholar 

  3. Melmed S. Pituitary tumors. Endocrinol Metab Clin N Am. 2015;44:1–9. https://doi.org/10.1016/j.ecl.2014.11.004.

    Article  Google Scholar 

  4. Zhan X, Desiderio DM. Editorial: Molecular network study of pituitary adenomas. Front Endocrinol. 2020;11:26. https://doi.org/10.3389/fendo.2020.00026.

    Article  Google Scholar 

  5. Cheng T, Wang Y, Lu M, Zhan X, Zhou T, Li B, et al. Quantitative analysis of proteome in non-functional pituitary adenomas: clinical relevance and potential benefits for the patient. Front Endocrinol. 2019;10:854. https://doi.org/10.3389/fendo.2019.00854.

    Article  Google Scholar 

  6. Wang Y, Cheng T, Lu M, Mu Y, Li B, Li X, et al. TMT-based quantitative proteomics revealed follicle-stimulating hormone (FSH)-related molecular characterizations for potentially prognostic assessment and personalized treatment of FSH-positive non-functional pituitary adenomas. EPMA J. 2019;10:395–414. https://doi.org/10.1007/s13167-019-00187-w.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Zhan X, Desiderio DM, Wang X, Zhan X, Guo T, Li M, et al. Identification of the proteomic variations of invasive relative to noninvasive nonfunctional pituitary adenomas. Electrophoresis. 2014;35(15):2184–94.

    CAS  PubMed  Google Scholar 

  8. Losa M, Mortini P, Barzaghi R, Ribotto P, Terreni MR, Marzoli SB, et al. Early results of surgery in patients with nonfunctioning pituitary adenoma and analysis of the risk of tumor recurrence. J Neurosurg. 2008;108(3):525–32. https://doi.org/10.3171/JNS/2008/108/3/0525.

    Article  PubMed  Google Scholar 

  9. Meij BP, Lopes MB, Ellegala DB, Alden TD, Laws ER Jr. The long-term significance of microscopic dural invasion in 354 patients with pituitary adenomas treated with transsphenoidal surgery. J Neurosurg. 2002;96(2):195–208. https://doi.org/10.3171/jns.2002.96.2.0195.

    Article  PubMed  Google Scholar 

  10. Selman WR, Laws ER Jr, Scheithauer BW, Carpenter SM. The occurrence of dural invasion in pituitary adenomas. J Neurosurg. 1986;64(3):402–7. https://doi.org/10.3171/jns.1986.64.3.0402.

    Article  CAS  PubMed  Google Scholar 

  11. Cheng T, Zhan X. Pattern recognition for predictive, preventive, and personalized medicine in cancer. EPMA J. 2017;8:51–60. https://doi.org/10.1007/s13167-017-0083-9.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Grech G, Zhan X, Yoo BC, Bubnov R, Hagan S, Danesi R, et al. EPMA position paper in cancer: current overview and future perspectives. EPMA J. 2015;6:9. https://doi.org/10.1186/s13167-015-0030-6.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Zhan X, Desiderio DM. The use of variations in proteomes to predict, prevent, personalize treatment for clinically non-functional pituitary adenomas. EPMA J. 2010;1:439–59. https://doi.org/10.1007/s13167-010-0028-z.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Hu R, Wang X, Zhan X. Multi-parameter systematic strategy for predictive, preventive, and personalized medicine in cancer. EPMA J. 2013;4:2. https://doi.org/10.1186/1878-5085-4-2.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Lu M, Zhan X. The crucial role of multiomic approach in cancer research and clinically relevant outcomes. EPMA J. 2018;9(1):77–102. https://doi.org/10.1007/s13167-018-0128-8.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Zhan X, Long Y, Lu M. Exploration of variations in proteome and metabolome for predictive diagnostics and personalised treatment algorithms: innovative approach and examples for potential clinical application. J Proteome. 2018;188:30–40. https://doi.org/10.1016/j.jprot.2017.08.020.

    Article  CAS  Google Scholar 

  17. Zhan X, Li B, Zhan X, Schlüter H, Jungblut PR, Coorssen JR. Innovating the concept and practice of two-dimensional gel electrophoresis in the analysis of proteomes at the proteoform level. Proteomes. 2019;7(4):36. https://doi.org/10.3390/proteomes704003.

    Article  CAS  PubMed Central  Google Scholar 

  18. Guo T, Wang X, Li M, Yang H, Li L, Peng F, et al. Identification of glioblastoma phosphotyrosine-containing proteins with two-dimensional Western blotting and tandem mass spectrometry. Biomed Res Int. 2015;2015:134050.

    PubMed  PubMed Central  Google Scholar 

  19. Singh V, Ram M, Kumar R, Prasad R, Roy BK, Singh KK. Phosphorylation: implications in cancer. Protein J. 2017;36:1–6. https://doi.org/10.1007/s10930-017-9696-z.

    Article  CAS  PubMed  Google Scholar 

  20. Golden RJ, Chen B, Li T, Braun J, Manjunath H, Chen X, et al. An argonaute phosphorylation cycle promotes microRNA-mediated silencing. Nature. 2017;542:197–202. https://doi.org/10.1038/nature21025.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Tsai CF, Wang YT, Yen HY, Tsou CC, Ku WC, Lin PY, et al. Large-scale determination of absolute phosphorylation stoichiometries in human cells by motif-targeting quantitative proteomics. Nat Commun. 2015;6:6622. https://doi.org/10.1038/ncomms7622.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Sergina NV, Rausch M, Wang D, Blair J, Hann B, Shokat KM, et al. Escape from HER-family tyrosine kinase inhibitor therapy by the kinase-inactive HER3. Nature. 2007;445(7126):437–41.

    Article  CAS  Google Scholar 

  23. Shah KN, Bhatt R, Rotow J, Rohrberg J, Olivas V, Wang VE, et al. Aurora kinase A drives the evolution of resistance to third-generation EGFR inhibitors in lung cancer. Nat Med. 2019;25(1):111–8. https://doi.org/10.1038/s41591-018-0264-7.

    Article  CAS  PubMed  Google Scholar 

  24. Kreuzer J, Edwards A, Haas W. Multiplexed quantitative phosphoproteomics of cell line and tissue samples. Methods Enzymol. 2019;626:41–65. https://doi.org/10.1016/bs.mie.2019.07.027.

    Article  PubMed  Google Scholar 

  25. Li Z, Li M, Li X, Xin J, Wang Y, Shen QW, et al. Quantitative phosphoproteomic analysis among muscles of different color stability using tandem mass tag labeling. Food Chem. 2018;249:8–15. https://doi.org/10.1016/j.foodchem.2017.12.047.

    Article  CAS  PubMed  Google Scholar 

  26. Carretero L, Llavona P, López-Hernández A, Casado P, Cutillas PR, de la Peña P, et al. ERK and RSK are necessary for TRH-induced inhibition of r-ERG potassium currents in rat pituitary GH3 cells. Cell Signal. 2015;27(9):1720–30. https://doi.org/10.1016/j.cellsig.2015.05.014.

    Article  CAS  PubMed  Google Scholar 

  27. Zhao S, Feng J, Li C, Gao H, Lv P, Li J, et al. Phosphoproteome profiling revealed abnormally phosphorylated AMPK and ATF2 involved in glucose metabolism and tumorigenesis of GH-PAs. J Endocrinol Investig. 2019;42(2):137–48. https://doi.org/10.1007/s40618-018-0890-4.

    Article  CAS  Google Scholar 

  28. Delcourt N, Thouvenot E, Chanrion B, Galéotti N, Jouin P, Bockaert J, et al. PACAP type I receptor transactivation is essential for IGF-1 receptor signalling and antiapoptotic activity in neurons. EMBO J. 2007;26(6):1542–51.

    Article  CAS  Google Scholar 

  29. Beranova-Giorgianni S, Zhao Y, Desiderio DM, Giorgianni F. Phosphoproteomic analysis of the human pituitary. Pituitary. 2006;9(2):109–20.

    Article  CAS  Google Scholar 

  30. Long Y, Lu M, Cheng T, Zhan X, Zhan X. Multiomics-based signaling pathway network alterations in human non-functional pituitary adenomas. Front Endocrinol. 2019;10:835. https://doi.org/10.3389/fendo.2019.00835.

    Article  Google Scholar 

  31. Ota M, Gonja H, Koike R, Fukuchi S. Multiple-localization and hub proteins. PLoS One. 2016;11:e0156455. https://doi.org/10.1371/journal.pone.0156455.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Zhan X, Li N, Zhan X, Qian S. Revival of 2DE-LC/MS in proteomics and its potential for large-scale study of human proteoforms. Med One. 2018;3:e180008. https://doi.org/10.20900/mo.20180008.

    Article  Google Scholar 

  33. Zhan X, Yang H, Peng F, Li J, Mu Y, Long Y, et al. How many proteins can be identified in a 2-DE gel spot within an analysis of a complex human cancer tissue proteome? Electrophoresis. 2018;39:965–80. https://doi.org/10.1002/elps.201700330.

    Article  CAS  PubMed  Google Scholar 

  34. Aebersold R, Agar JN, Amster IJ, Baker MS, Bertozzi CR, Boja ES, et al. How many human proteoforms are there? Nat Chem Biol. 2018;14(3):206–14. https://doi.org/10.1038/nchembio.2576.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Smith LM, Kelleher NL. Proteoforms as the next proteomics currency. Science. 2018;359(6380):1106–7. https://doi.org/10.1126/science.aat1884.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Broncel M, Treeck M. Label-based mass spectrometry approaches for robust quantification of the phosphoproteome and total proteome in Toxoplasma gondii. Methods Mol Biol. 2020;2071:453–68. https://doi.org/10.1007/978-1-4939-9857-9_23.

    Article  CAS  PubMed  Google Scholar 

  37. Serioli S, Doglietto F, Fiorindi A, Biroli A, Mattavelli D, Buffoli B, et al. Pituitary adenomas and invasiveness from anatomo-surgical, radiological, and histological perspectives: a systematic literature review. Cancers (Basel). 2019;11(12). https://doi.org/10.3390/cancers11121936.

  38. Zheng X, Li S, Zhang W, Zang Z, Hu J, Yang H. Current biomarkers of invasive sporadic pituitary adenomas. Ann Endocrinol (Paris). 2016;77(6):658–67. https://doi.org/10.1016/j.ando.2016.02.004.

    Article  Google Scholar 

  39. Øystese KA, Evang JA, Bollerslev J. Non-functioning pituitary adenomas: growth and aggressiveness. Endocrine. 2016;53(1):28–34. https://doi.org/10.1007/s12020-016-0940-7.

    Article  CAS  PubMed  Google Scholar 

  40. Yang Q, Li X. Molecular network basis of invasive pituitary adenoma: a review. Front Endocrinol. 2019;10:7. https://doi.org/10.3389/fendo.2019.00007.

    Article  Google Scholar 

  41. Zhan X, Desiderio DM. Editorial: Systems biological aspects of pituitary tumors. Front Endocrinol. 2016;7:86. https://doi.org/10.3389/fendo.2016.00086.

    Article  Google Scholar 

  42. Zhan X, Long Y. Exploration of molecular network variations in different subtypes of human nonfunctional pituitary adenomas. Front Endocrinol. 2016;7:13. https://doi.org/10.3389/fendo.2016.00013.

    Article  Google Scholar 

  43. Zhan X, Long Y, Zhan X, Mu Y. Consideration of statistical vs. biological significances for omics data-based pathway network analysis. Med One. 2017;1:e170002. https://doi.org/10.20900/mo.20170002.

    Article  Google Scholar 

  44. Seifirad S, Haghpanah V. Inappropriate modeling of chronic and complex disorders: how to reconsider the approach in the context of predictive, preventive and personalized medicine, and translational medicine. EPMA J. 2019;10(3):195–209. https://doi.org/10.1007/s13167-019-00176-z.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Janssens JP, Schuster K, Voss A. Preventive, predictive, and personalized medicine for effective and affordable cancer care. EPMA J. 2018;9(2):113–23. https://doi.org/10.1007/s13167-018-0130-1.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Zhan X, Desiderio DM, editors. Molecular network study of pituitary adenomas. Lausanne: Frontiers Media SA; 2020. ISBN: 978-2-88963-602-0. https://doi.org/10.3389/978-2-88963-602-0.

    Book  Google Scholar 

  47. Banerjee S, Saxena N, Sengupta K, Banerjee SK. 17alpha-Estradiol-induced VEGF-A expression in rat pituitary tumor cells is mediated through ER independent but PI3K-Akt dependent signaling pathway. Biochem Biophys Res Commun. 2003;300(1):209–15. https://doi.org/10.1016/s0006-291x(02)02830-9.

    Article  CAS  PubMed  Google Scholar 

  48. Wang Z, Jiang C, Ganther H, Lü J. Antimitogenic and proapoptotic activities of methylseleninic acid in vascular endothelial cells and associated effects on PI3K-AKT, ERK, JNK and p38 MAPK signaling. Cancer Res. 2001;61(19):7171–8.

    CAS  PubMed  Google Scholar 

  49. Smyth LM, Zhou Q, Nguyen B, Yu C, Lepisto EM, Arnedos M, et al. Characteristics and outcome of AKT1 E17K-mutant breast cancer defined through AACR Project GENIE, a clinicogenomic registry. Cancer Discov. 2020;10(4):526–35. https://doi.org/10.1158/2159-8290.CD-19-1209.

    Article  PubMed  Google Scholar 

  50. Iida M, Harari PM, Wheeler DL, Toulany M. Targeting AKT/PKB to improve treatment outcomes for solid tumors. Mutat Res. 2020;819-820:111690. https://doi.org/10.1016/j.mrfmmm.2020.111690.

    Article  CAS  PubMed  Google Scholar 

  51. Hunkeler M, Hagmann A, Stuttfeld E, Chami M, Guri Y, Stahlberg H, et al. Structural basis for regulation of human acetyl-CoA carboxylase. Nature. 2018;558(7710):470–4. https://doi.org/10.1038/s41586-018-0201-4.

    Article  CAS  PubMed  Google Scholar 

  52. Stoiber K, Nagło O, Pernpeintner C, Zhang S, Koeberle A, Ulrich M, et al. Targeting de novo lipogenesis as a novel approach in anti-cancer therapy. Br J Cancer. 2018;118(1):43–51. https://doi.org/10.1038/bjc.2017.374.

    Article  CAS  PubMed  Google Scholar 

  53. Fang W, Cui H, Yu D, Chen Y, Wang J, Yu G. Increased expression of phospho-acetyl-CoA carboxylase protein is an independent prognostic factor for human gastric cancer without lymph node metastasis. Med Oncol. 2014;31(7):15. https://doi.org/10.1007/s12032-014-0015-7.

    Article  CAS  PubMed  Google Scholar 

  54. Alkharusi A, Lesma E, Ancona S, Chiaramonte E, Nyström T, Gorio A, et al. Role of prolactin receptors in lymphangioleiomyomatosis. PLoS One. 2016;11(1):e0146653. https://doi.org/10.1371/journal.pone.0146653.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Zhao SJ, Jiang YQ, Xu NW, Li Q, Zhang Q, Wang SY, et al. SPARCL1 suppresses osteosarcoma metastasis and recruits macrophages by activation of canonical WNT/β-catenin signaling through stabilization of the WNT-receptor complex. Oncogene. 2018;37(8):1049–61. https://doi.org/10.1038/onc.2017.403.

    Article  CAS  PubMed  Google Scholar 

  56. Ma Y, Xu Y, Li L. SPARCL1 suppresses the proliferation and migration of human ovarian cancer cells via the MEK/ERK signaling. Exp Ther Med. 2018;16(4):3195–201. https://doi.org/10.3892/etm.2018.6575.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Aruleba RT, Adekiya TA, Oyinloye BE, Kappo AP. Structural studies of predicted ligand binding sites and molecular docking analysis of Slc2a4 as a therapeutic target for the treatment of cancer. Int J Mol Sci. 2018;19(2):386. https://doi.org/10.3390/ijms19020386.

    Article  CAS  PubMed Central  Google Scholar 

  58. Wang J, Ding N, Li Y, Cheng H, Wang D, Yang Q, et al. Insulin-like growth factor binding protein 5 (IGFBP5) functions as a tumor suppressor in human melanoma cells. Oncotarget. 2015;6(24):20636–49. https://doi.org/10.18632/oncotarget.4114.

    Article  PubMed  PubMed Central  Google Scholar 

  59. Duan C, Allard JB. Insulin-like growth factor binding protein-5 in physiology and disease. Front Endocrinol. 2020;11:100. https://doi.org/10.3389/fendo.2020.00100.

    Article  Google Scholar 

  60. Güllü G, Karabulut S, Akkiprik M. Functional roles and clinical values of insulin-like growth factor-binding protein-5 in different types of cancers. Chin J Cancer. 2012;31(6):266–80. https://doi.org/10.5732/cjc.011.10405.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Lloyd RV, Jin L. Analysis of chromogranin/secretogranin messenger RNAs in human pituitary adenomas. Diagn Mol Pathol. 1994;3(1):38–45. https://doi.org/10.1097/00019606-199403010-00007.

    Article  CAS  PubMed  Google Scholar 

  62. Lloyd RV, Jin L, Kulig E, Fields K. Molecular approaches for the analysis of chromogranins and secretogranins. Diagn Mol Pathol. 1992;1(1):2–15. https://doi.org/10.1097/00019606-199203000-00002.

    Article  CAS  PubMed  Google Scholar 

  63. Jin L, Chandler WF, Smart JB, England BG, Lloyd RV. Differentiation of human pituitary adenomas determines the pattern of chromogranin/secretogranin messenger ribonucleic acid expression. J Clin Endocrinol Metab. 1993;76(3):728–35. https://doi.org/10.1210/jcem.76.3.7680355.

    Article  CAS  PubMed  Google Scholar 

  64. d'Herbomez M, Do Cao C, Vezzosi D, Borzon-Chasot F, Baudin E, groupe des tumeurs endocrines (GTE France). Chromogranin A assay in clinical practice. Ann Endocrinol (Paris). 2010;71(4):274–80. https://doi.org/10.1016/j.ando.2010.04.004 Epub 2010 Jun 9.

    Article  CAS  Google Scholar 

  65. Cruz-Topete D, Christensen B, Sackmann-Sala L, Okada S, Jorgensen JO, Kopchick JJ. Serum proteome changes in acromegalic patients following transsphenoidal surgery: novel biomarkers of disease activity. Eur J Endocrinol. 2011;164(2):157–67. https://doi.org/10.1530/EJE-10-0754.

    Article  CAS  PubMed  Google Scholar 

  66. Tang KT, Yang HJ, Choo KB, Lin HD, Fang SL, Braverman LE. A point mutation in the albumin gene in a Chinese patient with familial dysalbuminemic hyperthyroxinemia. Eur J Endocrinol. 1999;141(4):374–8. https://doi.org/10.1530/eje.0.1410374.

    Article  CAS  PubMed  Google Scholar 

  67. Liu X, Zheng W, Wang W, Shen H, Liu L, Lou W, et al. A new panel of pancreatic cancer biomarkers discovered using a mass spectrometry-based pipeline. Br J Cancer. 2017;117(12):1846–54. https://doi.org/10.1038/bjc.2017.365.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Lin X, Hong S, Huang J, Chen Y, Chen Y, Wu Z. Plasma apolipoprotein A1 levels at diagnosis are independent prognostic factors in invasive ductal breast cancer. Discov Med. 2017;23(127):247–58.

    PubMed  Google Scholar 

  69. Hu CA, Klopfer EI, Ray PE. Human apolipoprotein L1 (ApoL1) in cancer and chronic kidney disease. FEBS Lett. 2012;586(7):947–55. https://doi.org/10.1016/j.febslet.2012.03.002.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Zhan X, Desiderio DM. Heterogeneity analysis of the human pituitary proteome. Clin Chem. 2003;49(10):1740–51. https://doi.org/10.1373/49.10.1740.

    Article  CAS  PubMed  Google Scholar 

  71. Moreno CS, Evans CO, Zhan X, Okor M, Desiderio DM, Oyesiku NM. Novel molecular signaling and classification of human clinically nonfunctional pituitary adenomas identified by gene expression profiling and proteomic analyses. Cancer Res. 2005;65(22):10214–22. https://doi.org/10.1158/0008-5472.CAN-05-0884.

    Article  CAS  PubMed  Google Scholar 

  72. Zhan X, Wang X, Long Y, Desiderio DM. Heterogeneity analysis of the proteomes in clinically nonfunctional pituitary adenomas. BMC Med Genet. 2014;7:69. https://doi.org/10.1186/s12920-014-0069-6.

    Article  Google Scholar 

  73. Golubnitschaja O, Costigliola V, EPMA. General report & recommendations in predictive, preventive and personalised medicine 2012: white paper of the European Association for Predictive, Preventive and Personalised Medicine. EPMA J. 2012;3(1):14. https://doi.org/10.1186/1878-5085-3-14.

    Article  PubMed  PubMed Central  Google Scholar 

  74. Hu R, Wang X, Zhan X. Multi-parameter systematic strategies for predictive, preventive and personalised medicine in cancer. EPMA J. 2013;4(1):2. https://doi.org/10.1186/1878-5085-4-2.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgments

The authors also acknowledge Professor Xuejun Li from Xiangya Hospital and Professor Dominic M. Desiderio from University of Tennessee Health Science Center to assist in obtaining human tissues.

Funding

This work was supported by the Shandong First Medical University Talent Introduction Funds (to X.Z.), the Hunan Provincial Hundred Talent Plan (to X.Z.), the SCIBP supported project (No. SCIBP2019090006), and China “863” Plan Project (Grant No. 2014AA020610-1 to XZ).

Author information

Authors and Affiliations

Authors

Contributions

D.L. analyzed data, prepared figures and tables, and wrote the manuscript draft. J.J., N.L., M.L., and S.W. participated in partial data analysis and bioinformatics. X.Z. conceived the concept, designed and instructed the experiments, analyzed the data, obtained the phosphoproteomic data, supervised the results, coordinated, wrote and critically revised the manuscript, and was responsible for its financial supports and the corresponding works. All authors approved the final manuscript.

Corresponding author

Correspondence to Xianquan Zhan.

Ethics declarations

Competing interests

The authors declare that they have no conflict of interest.

Ethical approval

Four pituitary adenoma tissue samples, obtained from the Department of Neurosurgery of Xiangya Hospital, Central South University, were approved by the Xiangya Hospital Medical Ethics Committee of Central South University. Post-mortem control pituitary tissue samples, obtained from the Memphis Regional Medical Center (n = 5), were approved by the University of Tennessee Health Science Center Internal Review Board.

Additional information

Abbreviations for all particular genes and proteins can be found in the Supplemental Table 1 and the UniProtKB database at the following link: https://www.expasy.org/.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

ESM 1

(PPT 1739 kb)

ESM 2

(PDF 802 kb)

ESM 3

(XLS 68 kb)

ESM 4

(XLS 55 kb)

ESM 5

(XLS 72 kb)

ESM 6

(XLS 436 kb)

ESM 7

(XLS 28 kb)

ESM 8

(XLS 26 kb)

ESM 9

(XLS 27 kb)

ESM 10

(XLS 66 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, D., Li, J., Li, N. et al. Integration of quantitative phosphoproteomics and transcriptomics revealed phosphorylation-mediated molecular events as useful tools for a potential patient stratification and personalized treatment of human nonfunctional pituitary adenomas. EPMA Journal 11, 419–467 (2020). https://doi.org/10.1007/s13167-020-00215-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13167-020-00215-0

Keywords

Navigation