Validation of Novel Prognostic Biomarkers for Early-Stage Clear-Cell, Endometrioid and Mucinous Ovarian Carcinomas Using Immunohistochemistry.
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Engqvist H, Parris TZ, Kovács A, Rönnerman EW, Sundfeldt K, Karlsson P, Helou K
Front Oncol 10 (-) 162 [2020-02-18; online 2020-02-18]
Early-stage (I and II) ovarian carcinoma patients generally have good prognosis. Yet, some patients die earlier than expected. Thus, it is important to stratify early-stage patients into risk groups to identify those in need of more aggressive treatment regimens. The prognostic value of 29 histotype-specific biomarkers identified using RNA sequencing was evaluated for early-stage clear-cell (CCC), endometrioid (EC) and mucinous (MC) ovarian carcinomas (n = 112) using immunohistochemistry on tissue microarrays. Biomarkers with prognostic significance were further evaluated in an external ovarian carcinoma data set using the web-based Kaplan-Meier plotter tool. Here, we provide evidence of aberrant protein expression patterns and prognostic significance of 17 novel histotype-specific prognostic biomarkers [10 for CCC (ARPC2, CCT5, GNB1, KCTD10, NUP155, RPL13A, RPL37, SETD3, SMYD2, TRIO), three for EC (CECR1, KIF26B, PIK3CA), and four for MC (CHEK1, FOXM1, KIF23, PARPBP)], suggesting biological heterogeneity within the histotypes. Combined predictive models comprising the protein expression status of the validated CCC, EC and MC biomarkers together with established clinical markers (age, stage, CA125, ploidy) improved the predictive power in comparison with models containing established clinical markers alone, further strengthening the importance of the biomarkers in ovarian carcinoma. Further, even improved predictive powers were demonstrated when combining these models with our previously identified prognostic biomarkers PITHD1 (CCC) and GPR158 (MC). Moreover, the proteins demonstrated improved risk prediction of CCC-, EC-, and MC-associated ovarian carcinoma survival. The novel histotype-specific prognostic biomarkers may not only improve prognostication and patient stratification of early-stage ovarian carcinomas, but may also guide future clinical therapy decisions.
Bioinformatics Support for Computational Resources [Service]
NGI Stockholm (Genomics Applications) [Service]
NGI Stockholm (Genomics Production) [Service]
National Genomics Infrastructure [Service]
PubMed 32133296
DOI 10.3389/fonc.2020.00162
Crossref 10.3389/fonc.2020.00162