Salivary transcriptomic biomarkers for detection of ovarian cancer: for serous papillary adenocarcinoma

Yu Hsiang Lee, Jae Hoon Kim, Hui Zhou, Bo Wook Kim, David T. Wong

Research output: Contribution to journalArticle

59 Citations (Scopus)

Abstract

Ovarian cancer is the most lethal gynecological cancer due to lack of clear symptom and reliable screening biomarker in the early stage. The capability to detect the initiation of malignancy with a sensitive and effective approach is one of the most desirable goals for ovarian cancer therapy. In this study, we spearheaded noninvasive detection of ovarian cancer by salivary transcriptomic biomarkers, and evaluated the clinical utilities of discovered biomarkers using a clinical case-control study. To find salivary mRNA biomarkers, salivary transcriptomes in 11 ovarian cancer patients and 11 matched controls were profiled by Affymetrix HG-U133-Plus-2.0 array. The biomarker candidates selected from the microarray results were then subjected to clinical validation by RT-qPCR using an independent sample cohort including 21 ovarian cancer patients and 35 healthy controls. Seven downregulated mRNA biomarkers were validated. The logistic regression model revealed the combination of five validated biomarkers (AGPAT1, B2M, BASP2, IER3, and IL1B) can significantly discriminate ovarian cancer patients (n=21) from the healthy controls (n=35), yielding a receiver operating characteristic plot, area under the curve value of 0.909 with 85.7% sensitivity and 91.4% specificity. In summary, we have demonstrated that the RNA signatures in saliva could serve as biomarkers for detection of ovarian cancer with high sensitivity and specificity. This emerging approach with high-throughput, noninvasive, and effective advantages provides a feasible means for detection of systemic cancer, and opens a new avenue for early disease detection.

Original languageEnglish
Pages (from-to)427-434
Number of pages8
JournalJournal of Molecular Medicine
Volume90
Issue number4
DOIs
Publication statusPublished - 2012 Apr

All Science Journal Classification (ASJC) codes

  • Molecular Medicine
  • Drug Discovery
  • Genetics(clinical)

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