Abstract 446: Systematic discovery of biomarkers for ovarian cancer by using the Human Protein Atlas database
Authors: Shi Jianxiang , Sun Hao , Zhang Hongfei , Xing Mengtao , Li Jitian , Li Pei , Dai Liping , Luo Chenglin , Zhang Jian-Ying 
Published: July 2016

Purpose: The extensive accumulation of human protein expression datasets in normal and cancer tissues provides unique opportunities to perform systematic selection of tumor biomarkers. This study aims to discover biomarkers for ovarian cancer (OC) by data mining the Human Protein Atlas databases in a systematic manner.

Design methods: The Human Protein Atlas website (HPA, www.proteinatlas.org) is a publicly available database with spatial distribution of 20,356 proteins in 44 different normal human tissues and 20 different types of cancers. It is ideal for biomarker discovery. SAS software version 9.4 was used to data mine HPA database. All the potential biomarkers should meet the following criteria: the protein have ≥ 75% high or medium expression in OC but have low or no expression in other types of cancers, and the protein have low or no expression in at least one type of tissues of ovary included in the HPA database, the overall expression in normal tissues is ≤ 40%. Then ELISA was used to test the diagnostic performance of some of the selected biomarkers.

Results: Nineteen out of twenty different types of cancers included in the HPA database were studied in our study, carcinoid was excluded in the selection process due to the lack of proper normal control tissues. In general, PLAT might be a universal biomarker for 19 cancers. In total, 2,593 potential biomarkers for OC were selected. Ninety seven proteins out of 2,593 proteins might be OC-specific according to the available data. After an intensive manual search from the website, twelve proteins (STC1, MSLN, LSM3, WFDC2/HE4, ZNF2, CRIP3, XAF1, SLC35E2B, IFT88, ATP13A3, ZNF787 and TROAP) were selected for further research. Among them, WFDC2/HE4, MSLN have already been reported to have good diagnostic performance in OC by other researchers. ELISA was further used to test whether serum autoantibodies against PLAT or MSLN can distinguish 44 OC patients from 48 normal controls. The AUC of anti-PLAT and anti-MSLN were 0.783 (P &lt; 0.001) and 0.666 (P = 0.006), respectively.

Conclusions: HPA database have made systematic discovery of cancer biomarkers possible. PLAT and MSLN can distinguish OC from normal controls. Another 11 proteins have great potential to serve as OC biomarkers and further validation studies are warranted to test their diagnostic performance and whether these biomarkers are OC specific.

Keywords: ovarian cancer, tumor associated antigen, big data, data mining, systematic discovery

This study was supported by grants from the General Program of National Natural Science Foundation of China (No. 81172086 and No.81372371). We would also like to acknowledge the Border Biomedical Research Center (BBRC) Core facilities at The University of Texas at El Paso (UTEP) for their help, which were funded by NIH Grant (5G12MD007592).

Citation Format: Jianxiang Shi, Hao Sun, Hongfei Zhang, Mengtao Xing, Jitian Li, Pei Li, Liping Dai, Chenglin Luo, Jian-Ying Zhang. Systematic discovery of biomarkers for ovarian cancer by using the Human Protein Atlas database. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 446.