Going macro from micro – harnessing the power of serum microRNAs to predict surgical outcome for women with ovarian cancer — University of Technology
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Going macro from micro – harnessing the power of serum microRNAs to predict surgical outcome for women with ovarian cancer (14245)

Jaynish S Shah 1 , Greg Gard 2 , Patsy Soon 3 4 , Deborah J Marsh 1 5
  1. Kolling Institute of Medical Research, St Leonards, NSW, Australia
  2. Royal North Shore Hospital, St. Leonards, NSW, Australia
  3. Ingham Institute of Applied Medical Research, Liverpool, NSW, Australia
  4. University of New South Wales, Sydney, NSW, Australia
  5. University of Sydney, Sydney, NSW, Australia

Background

Optimal cytoreduction, complete surgical removal of the tumour, is one of the most important prognostic factors for women with ovarian cancer, yet remains challenging to predict prior to surgery. A molecular tool to assist in this prediction, in combination with other clinical factors, could help plan surgery and decide the best treatment options for individual patients. Short RNAs called microRNAs circulating in the blood are promising biomarkers due to their stability and disease-specific expression. However, interference from microRNAs released from erythrocytes poses a significant issue for the biomarker discovery process.
   
Aims:
1)    To test the sensitivity of existing methods to identify haemolysed serum samples.
2)    To identify a panel of microRNAs that could separate women with ovarian cancer from healthy women and further, to predict their surgical outcome.

Methods:
Healthy volunteer blood was haemolysed by sonication and serially diluted to obtain standards to be measured by a number of methods. Pre-surgical sera of fifteen and thirteen ovarian cancer patients with optimal cytoreduction and suboptimal cytoreduction, respectively, and fifteen age-matched healthy women were sourced from the Kolling Institute’s Tumour bank. Expression of 167 microRNAs was measured using Serum/Plasma focus panel (Exiqon, # 203843). Data were analysed using GenEx software (v 6.0, Exiqon).

Results and conclusions:
Samples predicted to have less than 0.25% haemolysis by spectrophotometric analysis, which could detect ≥ 0.0625% haemolysis, were used for microRNA profiling. Preliminary analysis identified sixteen differentially expressed microRNAs across the three test groups, including miR-210 known to be elevated in ovarian cancer and an important regulator of the hypoxic response. Additionally, microRNAs stably expressed across all three groups were identified and may have value for normalisation.   Candidate microRNAs are currently being validated in an independent cohort, data from which will be pooled with the serum glycoprotein biomarker CA-125 to assess for improved predictive power.

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