Univariate and classification analysis reveals potential diagnostic biomarkers for early-stage ovarian cancer type 1 and type 2

ABSTRACT: 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.

INSTRUMENT(S): Orbitrap Fusion, Q Exactive

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Ovarian Cyst Fluid

DISEASE(S): Ovarian Cancer

SUBMITTER: Sahlgrenska Academy Proteomics Core  

LAB HEAD: Karin Sundfeldt

PROVIDER: PXD010939 | Pride | 2019-02-02

REPOSITORIES: Pride