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PXD010939

DataSet Summary

  • HostingRepository: PRIDE
  • AnnounceDate: 2019-02-02
  • AnnouncementXML: Submission_2019-02-11_04:53:21.xml
  • DigitalObjectIdentifier:
  • ReviewLevel: Peer-reviewed dataset
  • DatasetOrigin: Original data
  • RepositorySupport: Unsupported dataset by repository
  • PrimarySubmitter: Sahlgrenska Academy Proteomics Core
  • Title: Univariate and classification analysis reveals potential diagnostic biomarkers for early-stage ovarian cancer type 1 and type 2
  • Description: Biomarkers for early detection of ovarian tumors are urgently needed. Tumors of the ovary grow within cysts and most are benign.Surgical sampling is the only way to ensure accurate diagnosis, but often leads to morbidity and loss of female hormones. The present study explored the deep proteome in well-defined sets of ovarian tumors, FIGO stage I, Type 1 (low-grade serous, mucinous, endometrioid; n=9), Type 2 (high-grade serous; n=9), and benign serous (n=9) using TMT-LC-MS/MS. We evaluated new bioinformatics tools in the discovery phase. This innovative selection process involved different normalizations, a combination of univariate statistics, and logistic model tree and naïve Bayes tree classifiers. We identified 142 proteins by this combined approach. One biomarker panel and nine individual proteins were validated in cyst fluid and serum: transaldolase-1, fructose-bisphosphate aldolase A (ALDOA), transketolase, ceruloplasmin, mesothelin, clusterin, tenascin- XB, laminin subunit gamma-1, and mucin-16. Six of the proteins were found significant (p<0.05) in cyst fluid while ALDOA was the only protein significant in serum. The biomarker panel achieved ROC AUC 0.96 and 0.57 respectively. We conclude that classification algorithms complement traditional statistical methods by selecting combinations that may be missed by standard univariate tests.
  • SpeciesList: scientific name: Homo sapiens (Human); NCBI TaxID: 9606;
  • ModificationList: No PTMs are included in the dataset
  • Instrument: Orbitrap Fusion; Q Exactive

Dataset History

VersionDatetimeStatusChangeLog Entry
02018-08-30 01:23:21ID requested
12019-02-01 18:40:20announced
22019-02-11 04:53:22announcedUpdated publication reference for PubMed record(s): 30710757.

Publication List

  1. Marcišauskas S, Ulfenborg B, Kristjansdottir B, Waldemarson S, Sundfeldt K, Univariate and classification analysis reveals potential diagnostic biomarkers for early stage ovarian cancer Type 1 and Type 2. J Proteomics, 196():57-68(2019) [pubmed]

Keyword List

  1. curator keyword: Biomedical
  2. submitter keyword: Ovarian cancer, Cyst fluid, FIGO stage I type 1 and type 2, Proteomics, Biomarker, Q Exactive, Orbitrap Fusion

Contact List

    Karin Sundfeldt
    • contact affiliation: Professor, Department of Obstetrics and Gynecology at Institute of Clinical Sciences, Kvinnokliniken SU Östra 416 85 Gothenburg, Sweden
    • contact email: karin.sundfeldt@obgyn.gu.se
    • lab head:
    Sahlgrenska Academy Proteomics Core
    • contact affiliation: University of Gothenburg
    • contact email: egor.vorontsov@gu.se
    • dataset submitter:

Full Dataset Link List

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  2. PRIDE project URI
Repository Record List
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