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Integrated multi-omics profiling of nonfunctioning pituitary adenomas

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Abstract

Purpose

Genetic and epigenetic alterations are involved in pituitary adenoma pathogenesis, however the molecular basis of proliferative nonfunctioning pituitary adenomas (NFPAs) remains unclear. Here, we analyzed integrated multi-omics profiling including copy number variation (CNV), DNA methylation and gene expression of 8 NFPAs.

Methods

We collected 4 highly proliferative (hpNFPA, Ki-67 ≥ 3) and 4 lowly proliferative (Ki-67 ≤ 1) NFPAs, and comprehensively assessed CNV, DNA methylation, and gene expression by Illumina HumanMethylation450 BeadChip and Affymetrix GeneChip PrimeView Human Gene Expression Array. We performed Ingenuity Pathway Analysis (IPA) for differentially expressed genes to illustrate aberrant pathways and delineated protein–protein networks of selected key genes in dysregulated pathways.

Results

Aberrant arm level CNV, dysregulated DNA methylation, and associated impacts on gene expressions were observed in 2 early occurring hpNFPAs. Chromosomal losses were associated with attenuated expression of DNA methyltransferases, further altering global methylation in these 2 samples. Correlation analysis between DNA methylation and gene expression in 8 NFPAs indicates methylation in promoter and gene body regions are both involved in gene regulation. IPA showed PPARα/RXRα, dopamine receptor signaling, cAMP-mediated signaling, and calcium signaling were all activated, while p38 MAPK and ERK5 signaling were inhibited in hpNFPAs. Moreover, selected key gene networks in hpNFPAs exhibited concurrent methylation status and expression levels of adenylate cyclase genes, G protein subunits, HLA genes, CXCL12, and CCL2.

Conclusion

This study presents comprehensive multi-omics views of CNV, DNA methylation, and gene expression in 8 NFPAs. Pathway analysis and network maps of key genes provide clues to elucidate the molecular basis of hpNFPA.

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Fig. 1
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adapted from Fig. 4e. The relative β values of methylation sites were scaled in 8 samples for each specific gene context

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Data availability

The authors confirm that the data supporting the findings of this study are available within the article and its supplementary materials. Additional data that support the findings of this study are available from the corresponding author, ZW and RW, upon reasonable request.

References

  1. Melmed S (2020) Pituitary-tumor endocrinopathies. N Engl J Med 382(10):937–950. https://doi.org/10.1056/NEJMra1810772

    Article  CAS  PubMed  Google Scholar 

  2. Raverot G, Jouanneau E, Trouillas J (2014) Management of endocrine disease: clinicopathological classification and molecular markers of pituitary tumours for personalized therapeutic strategies. Eur J Endocrinol 170(4):R121-132. https://doi.org/10.1530/EJE-13-1031

    Article  CAS  PubMed  Google Scholar 

  3. Buchfelder M, Schlaffer S (2014) Imaging of pituitary pathology. Handb Clin Neurol 124:151–166. https://doi.org/10.1016/B978-0-444-59602-4.00011-3

    Article  PubMed  Google Scholar 

  4. Nammour GM, Ybarra J, Naheedy MH, Romeo JH, Aron DC (1997) Incidental pituitary macroadenoma: a population-based study. Am J Med Sci 314(5):287–291. https://doi.org/10.1097/00000441-199711000-00003

    Article  CAS  PubMed  Google Scholar 

  5. Neou M, Villa C, Armignacco R, Jouinot A, Raffin-Sanson ML, Septier A, Letourneur F, Diry S, Diedisheim M, Izac B, Gaspar C, Perlemoine K, Verjus V, Bernier M, Boulin A, Emile JF, Bertagna X, Jaffrezic F, Laloe D, Baussart B, Bertherat J, Gaillard S, Assie G (2020) Pangenomic classification of pituitary neuroendocrine tumors. Cancer Cell 37(1):123–134. https://doi.org/10.1016/j.ccell.2019.11.002

    Article  CAS  PubMed  Google Scholar 

  6. Dai C, Feng M, Liu X, Ma S, Sun B, Bao X, Yao Y, Deng K, Wang Y, Xing B, Lian W, Zhong D, Ma W, Wang R (2016) Refractory pituitary adenoma: a novel classification for pituitary tumors. Oncotarget 7(50):83657–83668. https://doi.org/10.18632/oncotarget.13274

    Article  PubMed  PubMed Central  Google Scholar 

  7. Kirschner LS, Carney JA, Pack SD, Taymans SE, Giatzakis C, Cho YS, Cho-Chung YS, Stratakis CA (2000) Mutations of the gene encoding the protein kinase A type I-alpha regulatory subunit in patients with the Carney complex. Nat Genet 26(1):89–92. https://doi.org/10.1038/79238

    Article  CAS  PubMed  Google Scholar 

  8. Marx SJ (2005) Molecular genetics of multiple endocrine neoplasia types 1 and 2. Nat Rev Cancer 5(5):367–375. https://doi.org/10.1038/nrc1610

    Article  CAS  PubMed  Google Scholar 

  9. Vierimaa O, Georgitsi M, Lehtonen R, Vahteristo P, Kokko A, Raitila A, Tuppurainen K, Ebeling TM, Salmela PI, Paschke R, Gundogdu S, De Menis E, Makinen MJ, Launonen V, Karhu A, Aaltonen LA (2006) Pituitary adenoma predisposition caused by germline mutations in the AIP gene. Science 312(5777):1228–1230. https://doi.org/10.1126/science.1126100

    Article  CAS  PubMed  Google Scholar 

  10. Vandeva S, Jaffrain-Rea ML, Daly AF, Tichomirowa M, Zacharieva S, Beckers A (2010) The genetics of pituitary adenomas. Best Pract Res Clin Endocrinol Metab 24(3):461–476. https://doi.org/10.1016/j.beem.2010.03.001

    Article  CAS  PubMed  Google Scholar 

  11. Melmed S (2011) Pathogenesis of pituitary tumors. Nat Rev Endocrinol 7(5):257–266. https://doi.org/10.1038/nrendo.2011.40

    Article  CAS  PubMed  Google Scholar 

  12. Tichomirowa MA, Lee M, Barlier A, Daly AF, Marinoni I, Jaffrain-Rea ML, Naves LA, Rodien P, Rohmer V, Faucz FR, Caron P, Estour B, Lecomte P, Borson-Chazot F, Penfornis A, Yaneva M, Guitelman M, Castermans E, Verhaege C, Wemeau JL, Tabarin A, Fajardo Montanana C, Delemer B, Kerlan V, Sadoul JL, Cortet Rudelli C, Archambeaud F, Zacharieva S, Theodoropoulou M, Brue T, Enjalbert A, Bours V, Pellegata NS, Beckers A (2012) Cyclin-dependent kinase inhibitor 1B (CDKN1B) gene variants in AIP mutation-negative familial isolated pituitary adenoma kindreds. Endocr Relat Cancer 19(3):233–241. https://doi.org/10.1530/ERC-11-0362

    Article  CAS  PubMed  Google Scholar 

  13. Farrell WE (2014) Epigenetics of pituitary tumours: an update. Curr Opin Endocrinol Diabetes Obes 21(4):299–305. https://doi.org/10.1097/MED.0000000000000078

    Article  CAS  PubMed  Google Scholar 

  14. Bi WL, Horowitz P, Greenwald NF, Abedalthagafi M, Agarwalla PK, Gibson WJ, Mei Y, Schumacher SE, Ben-David U, Chevalier A, Carter S, Tiao G, Brastianos PK, Ligon AH, Ducar M, MacConaill L, Laws ER Jr, Santagata S, Beroukhim R, Dunn IF (2016) Landscape of genomic alterations in pituitary adenomas. Clin Cancer Res 23(7):1841–1851. https://doi.org/10.1158/1078-0432.CCR-16-0790

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Reincke M, Sbiera S, Hayakawa A, Theodoropoulou M, Osswald A, Beuschlein F, Meitinger T, Mizuno-Yamasaki E, Kawaguchi K, Saeki Y, Tanaka K, Wieland T, Graf E, Saeger W, Ronchi CL, Allolio B, Buchfelder M, Strom TM, Fassnacht M, Komada M (2015) Mutations in the deubiquitinase gene USP8 cause Cushing’s disease. Nat Genet 47(1):31–38. https://doi.org/10.1038/ng.3166

    Article  CAS  PubMed  Google Scholar 

  16. Ma ZY, Song ZJ, Chen JH, Wang YF, Li SQ, Zhou LF, Mao Y, Li YM, Hu RG, Zhang ZY, Ye HY, Shen M, Shou XF, Li ZQ, Peng H, Wang QZ, Zhou DZ, Qin XL, Ji J, Zheng J, Chen H, Wang Y, Geng DY, Tang WJ, Fu CW, Shi ZF, Zhang YC, Ye Z, He WQ, Zhang QL, Tang QS, Xie R, Shen JW, Wen ZJ, Zhou J, Wang T, Huang S, Qiu HJ, Qiao ND, Zhang Y, Pan L, Bao WM, Liu YC, Huang CX, Shi YY, Zhao Y (2015) Recurrent gain-of-function USP8 mutations in Cushing’s disease. Cell Res 25(3):306–317. https://doi.org/10.1038/cr.2015.20

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Song ZJ, Reitman ZJ, Ma ZY, Chen JH, Zhang QL, Shou XF, Huang CX, Wang YF, Li SQ, Mao Y, Zhou LF, Lian BF, Yan H, Shi YY, Zhao Y (2016) The genome-wide mutational landscape of pituitary adenomas. Cell Res 26(11):1255–1259. https://doi.org/10.1038/cr.2016.114

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Valimaki N, Demir H, Pitkanen E, Kaasinen E, Karppinen A, Kivipelto L, Schalin-Jantti C, Aaltonen LA, Karhu A (2015) Whole-genome sequencing of growth hormone (GH)-secreting pituitary adenomas. J Clin Endocrinol Metab 100(10):3918–3927. https://doi.org/10.1210/jc.2015-3129

    Article  CAS  PubMed  Google Scholar 

  19. Woloschak M, Yu A, Post KD (1997) Frequent inactivation of the p16 gene in human pituitary tumors by gene methylation. Mol Carcinog 19(4):221–224. https://doi.org/10.1002/(sici)1098-2744(199708)19:4%3c221::aid-mc1%3e3.0.co;2-f

    Article  CAS  PubMed  Google Scholar 

  20. Simpson DJ, Bicknell JE, McNicol AM, Clayton RN, Farrell WE (1999) Hypermethylation of the p16/CDKN2A/MTSI gene and loss of protein expression is associated with nonfunctional pituitary adenomas but not somatotrophinomas. Genes Chromosomes Cancer 24(4):328–336

    Article  CAS  PubMed  Google Scholar 

  21. Simpson DJ, Clayton RN, Farrell WE (2002) Preferential loss of Death Associated Protein kinase expression in invasive pituitary tumours is associated with either CpG island methylation or homozygous deletion. Oncogene 21(8):1217–1224. https://doi.org/10.1038/sj.onc.1205195

    Article  CAS  PubMed  Google Scholar 

  22. Bahar A, Bicknell JE, Simpson DJ, Clayton RN, Farrell WE (2004) Loss of expression of the growth inhibitory gene GADD45gamma, in human pituitary adenomas, is associated with CpG island methylation. Oncogene 23(4):936–944. https://doi.org/10.1038/sj.onc.1207193

    Article  CAS  PubMed  Google Scholar 

  23. Qian ZR, Sano T, Yoshimoto K, Yamada S, Ishizuka A, Mizusawa N, Horiguchi H, Hirokawa M, Asa SL (2005) Inactivation of RASSF1A tumor suppressor gene by aberrant promoter hypermethylation in human pituitary adenomas. Lab Invest 85(4):464–473. https://doi.org/10.1038/labinvest.3700248

    Article  CAS  PubMed  Google Scholar 

  24. Farrell WE (2006) A novel apoptosis gene identified in the pituitary gland. Neuroendocrinology 84(4):217–221. https://doi.org/10.1159/000097486

    Article  CAS  PubMed  Google Scholar 

  25. Ling C, Pease M, Shi L, Punj V, Shiroishi MS, Commins D, Weisenberger DJ, Wang K, Zada G (2014) A pilot genome-scale profiling of DNA methylation in sporadic pituitary macroadenomas: association with tumor invasion and histopathological subtype. PLoS ONE 9(4):e96178. https://doi.org/10.1371/journal.pone.0096178

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Gu Y, Zhou X, Hu F, Yu Y, Xie T, Huang Y, Zhao X, Zhang X (2016) Differential DNA methylome profiling of nonfunctioning pituitary adenomas suggesting tumour invasion is correlated with cell adhesion. J Neurooncol 129(1):23–31. https://doi.org/10.1007/s11060-016-2139-4

    Article  CAS  PubMed  Google Scholar 

  27. Morris TJ, Butcher LM, Feber A, Teschendorff AE, Chakravarthy AR, Wojdacz TK, Beck S (2013) ChAMP: 450k chip analysis methylation pipeline. Bioinformatics 30(3):428–430. https://doi.org/10.1093/bioinformatics/btt684

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Feber A, Guilhamon P, Lechner M, Fenton T, Wilson GA, Thirlwell C, Morris TJ, Flanagan AM, Teschendorff AE, Kelly JD, Beck S (2014) Using high-density DNA methylation arrays to profile copy number alterations. Genome Biol 15(2):R30. https://doi.org/10.1186/gb-2014-15-2-r30

    Article  PubMed  PubMed Central  Google Scholar 

  29. Irizarry RA, Ladd-Acosta C, Wen B, Wu Z, Montano C, Onyango P, Cui H, Gabo K, Rongione M, Webster M, Ji H, Potash J, Sabunciyan S, Feinberg AP (2009) The human colon cancer methylome shows similar hypo- and hypermethylation at conserved tissue-specific CpG island shores. Nat Genet 41(2):178–186. https://doi.org/10.1038/ng.298

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Bestor TH (2000) The DNA methyltransferases of mammals. Hum Mol Genet 9(16):2395–2402. https://doi.org/10.1093/hmg/9.16.2395

    Article  CAS  PubMed  Google Scholar 

  31. Zhu X, Mao X, Hurren R, Schimmer AD, Ezzat S, Asa SL (2008) Deoxyribonucleic acid methyltransferase 3B promotes epigenetic silencing through histone 3 chromatin modifications in pituitary cells. J Clin Endocrinol Metab 93(9):3610–3617. https://doi.org/10.1210/jc.2008-0578

    Article  CAS  PubMed  Google Scholar 

  32. Ma HS, Wang EL, Xu WF, Yamada S, Yoshimoto K, Qian ZR, Shi L, Liu LL, Li XH (2018) Overexpression of DNA (Cytosine-5)-methyltransferase 1 (DNMT1) and DNA (Cytosine-5)-methyltransferase 3A (DNMT3A) is associated with aggressive behavior and hypermethylation of tumor suppressor genes in human ppituitary adenomas. Med Sci Monit 24:4841–4850. https://doi.org/10.12659/MSM.910608

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Singh V, Miranda TB, Jiang W, Frankel A, Roemer ME, Robb VA, Gutmann DH, Herschman HR, Clarke S, Newsham IF (2004) DAL-1/4.1B tumor suppressor interacts with protein arginine N-methyltransferase 3 (PRMT3) and inhibits its ability to methylate substrates in vitro and in vivo. Oncogene 23(47):7761–7771. https://doi.org/10.1038/sj.onc.1208057

    Article  CAS  PubMed  Google Scholar 

  34. Swiercz R, Cheng D, Kim D, Bedford MT (2007) Ribosomal protein rpS2 is hypomethylated in PRMT3-deficient mice. J Biol Chem 282(23):16917–16923. https://doi.org/10.1074/jbc.M609778200

    Article  CAS  PubMed  Google Scholar 

  35. Yan H, Parsons DW, Jin G, McLendon R, Rasheed BA, Yuan W, Kos I, Batinic-Haberle I, Jones S, Riggins GJ, Friedman H, Friedman A, Reardon D, Herndon J, Kinzler KW, Velculescu VE, Vogelstein B, Bigner DD (2009) IDH1 and IDH2 mutations in gliomas. N Engl J Med 360(8):765–773. https://doi.org/10.1056/NEJMoa0808710

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Turcan S, Rohle D, Goenka A, Walsh LA, Fang F, Yilmaz E, Campos C, Fabius AW, Lu C, Ward PS, Thompson CB, Kaufman A, Guryanova O, Levine R, Heguy A, Viale A, Morris LG, Huse JT, Mellinghoff IK, Chan TA (2012) IDH1 mutation is sufficient to establish the glioma hypermethylator phenotype. Nature 483(7390):479–483. https://doi.org/10.1038/nature10866

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Lowdon RF, Jang HS, Wang T (2016) Evolution of epigenetic regulation in vertebrate genomes. Trends Genet 32(5):269–283. https://doi.org/10.1016/j.tig.2016.03.001

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Kass SU, Landsberger N, Wolffe AP (1997) DNA methylation directs a time-dependent repression of transcription initiation. Curr Biol 7(3):157–165. https://doi.org/10.1016/s0960-9822(97)70086-1

    Article  CAS  PubMed  Google Scholar 

  39. Moore LD, Le T, Fan G (2013) DNA methylation and its basic function. Neuropsychopharmacology 38(1):23–38. https://doi.org/10.1038/npp.2012.112

    Article  CAS  PubMed  Google Scholar 

  40. Jones PA (1999) The DNA methylation paradox. Trends Genet 15(1):34–37

    Article  CAS  PubMed  Google Scholar 

  41. Ball MP, Li JB, Gao Y, Lee JH, LeProust EM, Park IH, Xie B, Daley GQ, Church GM (2009) Targeted and genome-scale strategies reveal gene-body methylation signatures in human cells. Nat Biotechnol 27(4):361–368. https://doi.org/10.1038/nbt.1533

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Yang X, Han H, De Carvalho DD, Lay FD, Jones PA, Liang G (2014) Gene body methylation can alter gene expression and is a therapeutic target in cancer. Cancer Cell 26(4):577–590. https://doi.org/10.1016/j.ccr.2014.07.028

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Wahli W, Michalik L (2012) PPARs at the crossroads of lipid signaling and inflammation. Trends Endocrinol Metab 23(7):351–363. https://doi.org/10.1016/j.tem.2012.05.001

    Article  CAS  PubMed  Google Scholar 

  44. Tachibana K, Yamasaki D, Ishimoto K, Doi T (2008) The role of PPARs in cancer. PPAR Res 2008:102737. https://doi.org/10.1155/2008/102737

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Peters JM, Shah YM, Gonzalez FJ (2012) The role of peroxisome proliferator-activated receptors in carcinogenesis and chemoprevention. Nat Rev Cancer 12(3):181–195. https://doi.org/10.1038/nrc3214

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Gao J, Yuan S, Jin J, Shi J, Hou Y (2015) PPARalpha regulates tumor progression, foe or friend? Eur J Pharmacol 765:560–564. https://doi.org/10.1016/j.ejphar.2015.09.027

    Article  CAS  PubMed  Google Scholar 

  47. Heaney AP, Fernando M, Melmed S (2003) PPAR-gamma receptor ligands: novel therapy for pituitary adenomas. J Clin Invest 111(9):1381–1388. https://doi.org/10.1172/JCI16575

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Konig B, Rauer C, Rosenbaum S, Brandsch C, Eder K, Stangl GI (2009) Fasting upregulates PPARalpha target genes in brain and influences pituitary hormone expression in a PPARalpha dependent manner. PPAR Res 2009:801609. https://doi.org/10.1155/2009/801609

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Polidoro MA, Rotondi S, Morace R, Rostomyan L, Colapietro A, Arcella A, Ventura L, Angelucci A, Giangaspero F, Esposito V, Beckers A, Jaffrain-Rea ML (2018) Expression of peroxisome proliferator-activated receptor alpha (PPARalpha) in non-somatotroph pituitary tumours and the effects of PPARalpha agonists on MMQ cells. Horm Metab Res 50(8):640–647. https://doi.org/10.1055/a-0633-2706

    Article  CAS  PubMed  Google Scholar 

  50. Sanno N, Sugawara A, Teramoto A, Abe Y, Yen PM, Chin WW, Osamura RY (1997) Immunohistochemical expression of retinoid X receptor isoforms in human pituitaries and pituitary adenomas. Neuroendocrinology 65(4):299–306. https://doi.org/10.1159/000127188

    Article  CAS  PubMed  Google Scholar 

  51. Formosa R, Vassallo J (2014) cAMP signalling in the normal and tumorigenic pituitary gland. Mol Cell Endocrinol 392(1–2):37–50. https://doi.org/10.1016/j.mce.2014.05.004

    Article  CAS  PubMed  Google Scholar 

  52. Ben-Shlomo A, Deng N, Ding E, Yamamoto M, Mamelak A, Chesnokova V, Labadzhyan A, Melmed S (2020) DNA damage and growth hormone hypersecretion in pituitary somatotroph adenomas. J Clin Invest. https://doi.org/10.1172/JCI138540

    Article  PubMed  PubMed Central  Google Scholar 

  53. Beninger RJ, Miller R (1998) Dopamine D1-like receptors and reward-related incentive learning. Neurosci Biobehav Rev 22(2):335–345. https://doi.org/10.1016/s0149-7634(97)00019-5

    Article  CAS  PubMed  Google Scholar 

  54. Missale C, Nash SR, Robinson SW, Jaber M, Caron MG (1998) Dopamine receptors: from structure to function. Physiol Rev 78(1):189–225. https://doi.org/10.1152/physrev.1998.78.1.189

    Article  CAS  PubMed  Google Scholar 

  55. Boyd KN, Mailman RB (2012) Dopamine receptor signaling and current and future antipsychotic drugs. Handb Exp Pharmacol 212:53–86. https://doi.org/10.1007/978-3-642-25761-2_3

    Article  CAS  Google Scholar 

  56. Chun LS, Free RB, Doyle TB, Huang XP, Rankin ML, Sibley DR (2013) D1–D2 dopamine receptor synergy promotes calcium signaling via multiple mechanisms. Mol Pharmacol 84(2):190–200. https://doi.org/10.1124/mol.113.085175

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Banihashemi B, Albert PR (2002) Dopamine-D2S receptor inhibition of calcium influx, adenylyl cyclase, and mitogen-activated protein kinase in pituitary cells: distinct Galpha and Gbetagamma requirements. Mol Endocrinol 16(10):2393–2404. https://doi.org/10.1210/me.2001-0220

    Article  CAS  PubMed  Google Scholar 

  58. Liu JC, Baker RE, Sun C, Sundmark VC, Elsholtz HP (2002) Activation of Go-coupled dopamine D2 receptors inhibits ERK1/ERK2 in pituitary cells: a key step in the transcriptional suppression of the prolactin gene. J Biol Chem 277(39):35819–35825. https://doi.org/10.1074/jbc.M202920200

    Article  CAS  PubMed  Google Scholar 

  59. Choo SY (2007) The HLA system: genetics, immunology, clinical testing, and clinical implications. Yonsei Med J 48(1):11–23. https://doi.org/10.3349/ymj.2007.48.1.11

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Farid NR, Buehler S, Russell NA, Maroun FB, Allerdice P, Smyth HS (1980) Prolactinomas in familial multiple endocrine neoplasia syndrome type I. Relationship to HLA and carcinoid tumors. Am J Med 69(6):874–880. https://doi.org/10.1016/s0002-9343(80)80013-1

    Article  CAS  PubMed  Google Scholar 

  61. Rossi ML, Jones NR, Esiri MM, Havas L, al Izzi M, Coakham HB (1990) Mononuclear cell infiltrate and HLA-Dr expression in 28 pituitary adenomas. Tumori 76(6):543–547

    Article  CAS  PubMed  Google Scholar 

  62. Richardson TE, Shen ZJ, Kanchwala M, Xing C, Filatenkov A, Shang P, Barnett S, Abedin Z, Malter JS, Raisanen JM, Burns DK, White CL, Hatanpaa KJ (2017) Aggressive behavior in silent subtype III pituitary adenomas may depend on suppression of local immune response: a whole transcriptome analysis. J Neuropathol Exp Neurol 76(10):874–882. https://doi.org/10.1093/jnen/nlx072

    Article  CAS  PubMed  Google Scholar 

  63. Marques P, Barry S, Carlsen E, Collier D, Ronaldson A, Awad S, Dorward N, Grieve J, Mendoza N, Muquit S, Grossman AB, Balkwill F, Korbonits M (2019) Chemokines modulate the tumour microenvironment in pituitary neuroendocrine tumours. Acta Neuropathol Commun 7(1):172. https://doi.org/10.1186/s40478-019-0830-3

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

This work was supported by Guidance Plan of Natural Fund of Liaoning Province to Dr. Zhenqing Wei (No. 2019-ZD-0912).

Funding

This work was supported by Guidance Plan of Natural Fund of Liaoning Province to Dr. Zhenqing Wei (No. 2019-ZD-0912).

Author information

Author notes

  1. Zhenqing Wei, Cuiqi Zhou, and Minghui Li have contributed equally to this publication.

Authors and Affiliations

  1. Department of Neurosurgery, Peking Union Medical College Hospital, Beijing, China

    Zhenqing Wei & Renzhi Wang

  2. Department of Neurosurgery, The First Hospital Affiliated to Dalian Medical University, Dalian, China

    Zhenqing Wei

  3. Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, USA

    Cuiqi Zhou & Stephen Shen

  4. Sinotech Genomics Co., Ltd., Shenzhen, China

    Minghui Li & Ruocheng Huang

  5. Shanghai Biotechnology Corporation, Shanghai, China

    Hongjuan Deng

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  1. Zhenqing Wei
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Contributions

ZW, CZ and RW designed and supervised the study; ZW and RW recruited patients, collected specimens and clinical information; ZW, ML, RH, HD carried out experiments; ZW, CZ, ML, RH, HD, RW analyzed data and prepared figures; CZ and SS wrote the paper with discussion input from all authors.

Corresponding authors

Correspondence to Zhenqing Wei or Renzhi Wang.

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The authors declare that they have no conflicts of interest.

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The study was approved by the Institutional Review Board of Peking Union Medical College Hospital.

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Informed consent was obtained from all patients included in the study.

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11102_2020_1109_MOESM1_ESM.tif

Supplemental Figure S1. Representative immunohistochemical staining of Pit-1. Left: hpNFPA-3 (Pit-1 positive). Right: NFPA-5 (Pit-1 negative). Brown: Pit-1 staining. Blue: Nuclear. Picture: 200 X. Supplementary file1 (TIF 4283 KB)

11102_2020_1109_MOESM2_ESM.jpg

Supplemental Figure S2. Impact of copy numbers on DNA methylation and gene expression. (A) Chromosomal level copy number plot displayed dramatic arm-level changes in hpNFPA-3 and hpNFPA-4. Green: gain; Red: loss. (B) Correlation of copy number gains (Green) and losses (Red) to DMP in hpNFPA-3. The vertical axis indicates different methylation levels (Δβ value). The hypomethylated probes were under horizon. (C-D) Correlation of copy number gains (Green) and losses (Red) to differentially expressed genes in hpNFPA-3 (C) and hpNFPA-4 (D). The number of downregulated genes (shown under the horizon) are significantly increased in the regions of copy number losses (Red). Supplementary file1 (TIF 4283 KB)

11102_2020_1109_MOESM3_ESM.jpg

Supplemental Figure S3. Heat map of the DMP between NFPAs and hpNFPAs. DMP is calculated by Δβ≥0.2, p<0.01. Supplementary file1 (TIF 4283 KB)

11102_2020_1109_MOESM4_ESM.tif

Supplemental Figure S4. Gene body methylation positively correlates with gene expression in 3 representative examples. (A) CACNA2D4 gene, (B) EBF1 gene, and (C) PCDH9 gene. Left expression scale shows gene expression levels (log2 signal density). All 8 specimens are sorted by expression levels. Methylation levels are colored from green to red, indicating levels from low to high. Methylation probes are arranged according to the chromosomal positions, from TSS, 1st Exon, 5’ UTR, gene body to 3’UTR. Correlation between probe methylation and gene expression is exhibited on the top by color scale and the blue line. Blue line above 0 indicates positive correlation. Supplementary file4 (TIF 8855 KB)

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Supplemental Figure S5. Assessment of selected gene expressions by real-time PCR. A) Validating gene expressions of DDR2, HHATL, CCNJ, KIF22, GNAO1, PRKAR2B, SLC17A5 and HSF4 by real-time PCR in hpNFPAs (n=4) and NFPAs (n=4). B) Expressions of DDR2, HHATL, KIF22, GNAO1, PRKAR2B, SLC17A5 in expanded hpNFPA (n=26) and NFPA (n=32) specimens. Expression levels are presented relative to GAPDH. Two-sided Student’s t-test was used for statistical testing. *=p<0.05; *=p<0.01. Supplementary file5 (TIF 186 KB)

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Wei, Z., Zhou, C., Li, M. et al. Integrated multi-omics profiling of nonfunctioning pituitary adenomas. Pituitary 24, 312–325 (2021). https://doi.org/10.1007/s11102-020-01109-0

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