Use of Blood-based mRNA profiling to Identify Biomarkers for Ovarian Cancer ScreeningSamuel C Mok1*, Jae-Hoon Kim2, Steven J Skates3, John O Schorge3, Daniel W Cramer4, Karen H Lu1, Choong-Chin Liew4
- *Corresponding Author:
- Dr. Samuel C Mok
Department of Gynecologic Oncology and Reproductive Medicine
University of Texas M. D. Anderson Cancer Center T4.3908
1515 Holcombe Boulevard, Houston, TX 77030
E-mail: [email protected]
Received date: June 22, 2017; Accepted date: June 28, 2017; Published date: June 30, 2017
Citation: Mok CS, Kim JH, Skates SJ, Schorge JO, Cramer DW, et al. (2017) Use of Blood-based mRNA profiling to Identify Biomarkers for Ovarian Cancer Screening. Gynecol Obstet (Sunnyvale) 7:443. doi: 10.4172/2161-0932.1000443
Copyright: © 2017 Mok CS, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Purpose: To identify candidate genomic signatures for the early detection of ovarian cancer using whole bloodbased gene expression profiles.
Experimental Design: We performed Affymetrix U133Plus 2.0 GeneChip microarray analyses on whole blood RNA samples obtained from 14 ovarian cancer patients and 15 age-matched, healthy women. Genes differentially expressed were identified using a parametric Welch t-test. Real-time qRT-PCR analyses were performed on RNA prepared from 96 ovarian cancer patients and 83 age-matched healthy women, using primer sets specific for 14 genes. A Mann Whitney U test assessed individual gene significance. CA125 levels were determined in the same set of samples. We used logistic regression analyses and cross validation to assess the ability of linear combinations of specific transcripts combined with CA125 to distinguish cancer from controls.
Results: Microarray analyses showed that 9583 probes were significantly different in blood gene expression profiles from healthy women as compared with those from ovarian cancer patients (p<0.05). Real-time RT-PCR analyses on the 96 cases and 83 controls validated 7 genes, which showed significantly different expression levels in cases and controls. Logistic regression analyses and cross validation identified an optimal panel of markers including CA125, BRCA1, and KIAA0562, that could improve the sensitivity of CA125 alone to over 90% at 98% specificity in the detection of early stage ovarian cancer.
Conclusion: Circulating blood gene expression profiles identified RNA markers that can improve the sensitivity of CA125 in the detection of early stage ovarian cancer. Further validation is warranted to confirm the clinical usefulness of these biomarkers.