Proteomic Discovery of Biomarkers to Predict Prognosis of High-Grade Serous Ovarian Carcinoma

被引:18
作者
Kim, Se Ik [1 ]
Jung, Minsun [2 ]
Dan, Kisoon [3 ]
Lee, Sungyoung [4 ]
Lee, Cheol [2 ]
Kim, Hee Seung [1 ]
Chung, Hyun Hoon [1 ]
Kim, Jae-Weon [1 ]
Park, Noh Hyun [1 ]
Song, Yong-Sang [1 ]
Han, Dohyun [3 ]
Lee, Maria [1 ]
机构
[1] Seoul Natl Univ, Dept Obstet & Gynecol, Coll Med, Seoul 03080, South Korea
[2] Seoul Natl Univ, Dept Pathol, Coll Med, Seoul 03080, South Korea
[3] Seoul Natl Univ Hosp, Biomed Res Inst, Prote Core Facil, Seoul 03082, South Korea
[4] Seoul Natl Univ Hosp, Ctr Precis Med, Seoul 03080, South Korea
关键词
ovarian neoplasms; high-grade serous carcinoma; proteomics; immunohistochemistry; prognosis; VASCULAR ADHESION PROTEIN-1; EPITHELIAL OVARIAN; PROTEOGENOMIC CHARACTERIZATION; COMPUTATIONAL PLATFORM; CANCER; EXPRESSION; SURVIVAL; LUNG;
D O I
10.3390/cancers12040790
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
Initial identification of biomarkers predicting the exact prognosis of high-grade serous ovarian carcinoma (HGSOC) is important in precision cancer medicine. This study aimed to investigate prognostic biomarkers of HGSOC through proteomic analysis. We conducted label-free liquid chromatography-mass spectrometry using chemotherapy-naive, fresh-frozen primary HGSOC specimens, and compared the results between a favorable prognosis group (progression-free survival (PFS) >= 18 months, n = 6) and a poor prognosis group (PFS < 18 months, n = 6). Among 658 differentially expressed proteins, 288 proteins were upregulated in the favorable prognosis group and 370 proteins were upregulated in the poor prognosis group. Using hierarchical clustering, we selected alpha 1-antitrypsin (AAT), nuclear factor-kappa B (NFKB), phosphomevalonate kinase (PMVK), vascular adhesion protein 1 (VAP1), fatty acid-binding protein 4 (FABP4), platelet factor 4 (PF4), apolipoprotein A1 (APOA1), and alpha 1-acid glycoprotein (AGP) for further validation via immunohistochemical (IHC) staining in an independent set of chemotherapy-naive primary HGSOC samples (n = 107). Survival analyses revealed that high expression of AAT, NFKB, and PMVK were independent biomarkers for favorable PFS. Conversely, high expression of VAP1, FABP4, and PF4 were identified as independent biomarkers for poor PFS. Furthermore, we constructed models predicting the 18-month PFS by combining clinical variables and IHC results. Through leave-one-out cross-validation, the optimal model was based on initial serum CA-125, germline BRCA1/2 mutations, residual tumors after surgery, International Federation of Gynecology and Obstetrics (FIGO) stage, and expression levels of the six proteins. The present results elucidate the proteomic landscape of HGSOC and six protein biomarkers to predict the prognosis of HGSOC.
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页数:16
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共 40 条
  • [1] Analyse multiple disease subtypes and build associated gene networks using genome-wide expression profiles
    Aibar, Sara
    Fontanillo, Celia
    Droste, Conrad
    Roson-Burgo, Beatriz
    Campos-Laborie, Francisco J.
    Hernandez-Rivas, Jesus M.
    Rivas, Javier De Las
    [J]. BMC GENOMICS, 2015, 16
  • [2] Systematic pan-cancer analysis of tumour purity
    Aran, Dvir
    Sirota, Marina
    Butte, Atul J.
    [J]. NATURE COMMUNICATIONS, 2015, 6
  • [3] Survival effect of maximal cytoreductive surgery for advanced ovarian carcinoma during the platinum era: A meta-analysis
    Bristow, RE
    Tomacruz, RS
    Armstrong, DK
    Trimble, EL
    Montz, FJ
    [J]. JOURNAL OF CLINICAL ONCOLOGY, 2002, 20 (05) : 1248 - 1259
  • [4] Cancer of the ovary
    Cannistra, SA
    [J]. NEW ENGLAND JOURNAL OF MEDICINE, 2004, 351 (24) : 2519 - 2529
  • [5] Improved optimal cytoreduction rates for stages IIIC and IV epithelial ovarian, fallopian tube, and primary peritoneal cancer: a change in surgical approach
    Chi, DS
    Franklin, CC
    Levine, DA
    Akselrod, F
    Sabbatini, P
    Jarnagin, WR
    DeMatteo, R
    Poynor, EA
    Abu-Rustum, NR
    Barakat, RR
    [J]. GYNECOLOGIC ONCOLOGY, 2004, 94 (03) : 650 - 654
  • [6] Ovarian Cancer
    Cho, Kathleen R.
    Shih, Ie-Ming
    [J]. ANNUAL REVIEW OF PATHOLOGY-MECHANISMS OF DISEASE, 2009, 4 : 287 - 313
  • [7] Predicting Response to Bevacizumab in Ovarian Cancer: A Panel of Potential Biomarkers Informing Treatment Selection
    Collinson, Fiona
    Hutchinson, Michelle
    Craven, Rachel A.
    Cairns, David A.
    Zougman, Alexandre
    Wind, Tobias C.
    Gahir, Narinder
    Messenger, Michael P.
    Jackson, Sharon
    Thompson, Douglas
    Adusei, Cybil
    Ledermann, Jonathan A.
    Hall, Geoffrey
    Jayson, Gordon C.
    Selby, Peter J.
    Banks, Rosamonde E.
    [J]. CLINICAL CANCER RESEARCH, 2013, 19 (18) : 5227 - 5239
  • [8] Integrative proteomic profiling of ovarian cancer cell lines reveals precursor cell associated proteins and functional status
    Coscia, F.
    Watters, K. M.
    Curtis, M.
    Eckert, M. A.
    Chiang, C. Y.
    Tyanova, S.
    Montag, A.
    Lastra, R. R.
    Lengyel, E.
    Mann, M.
    [J]. NATURE COMMUNICATIONS, 2016, 7
  • [9] Andromeda: A Peptide Search Engine Integrated into the MaxQuant Environment
    Cox, Juergen
    Neuhauser, Nadin
    Michalski, Annette
    Scheltema, Richard A.
    Olsen, Jesper V.
    Mann, Matthias
    [J]. JOURNAL OF PROTEOME RESEARCH, 2011, 10 (04) : 1794 - 1805
  • [10] Machine Learning-based Classification of Diffuse Large B-cell Lymphoma Patients by Their Protein Expression Profiles
    Deeb, Sally J.
    Tyanova, Stefka
    Hummel, Michael
    Schmidt-Supprian, Marc
    Cox, Juergen
    Mann, Matthias
    [J]. MOLECULAR & CELLULAR PROTEOMICS, 2015, 14 (11) : 2947 - 2960