Original ArticleCirculating miRNA landscape identifies miR-1246 as promising diagnostic biomarker in high-grade serous ovarian carcinoma: A validation across two independent cohorts
Introduction
The most common histological subtype of epithelial ovarian cancer (EOC), the high-grade serous ovarian carcinoma (HGSOC), is generally diagnosed late, when multiple synchronous lesions localised to the ovary, as well as in other anatomical sites within the peritoneal cavity. The five-year survival rate is less than 30%, as patients, despite an initial response to platinum agents, become progressively resistant and die from incurable disease. It is becoming evident that efforts to optimize patients' clinical outcome should be focused either on improving the therapeutic armamentarium or on enhancing early disease detection [1]. The often asymptomatic nature of HGSOC has hampered till now any attempt to improve early diagnosis.
Results from a recently published UK collaborative trial on ovarian cancer (UKCTOCS), among more than 200,000 postmenopausal women, showed the limits of currently known predictors of disease, like pelvic examination, CA-125 levels and transvaginal ultrasound [2].
Given the complex molecular nature lagging behind the single definition of HGSOC, future screening strategies aimed to define an individual's risk of EOC should take into consideration this biologic heterogeneity [3]. One of the most promising field of research is based on the analysis of microRNA (miRNA) expression profile in liquid biopsies, as serum or plasma.
In the clinical practice, the identification of novel biomarkers in tumor tissues suffers of at least two main limitations. Firstly, tissue samples are difficult to acquire during patients' follow up, therefore assessing the prognostic role of tumor biomarkers in longitudinal analysis is limited. Secondly, since HGSOC is a systemic disease, molecular portraits obtained at the ovary not necessarily reflect those obtained from synchronous lesions in other anatomical sites [4]. Liquid biopsies are now becoming a new source to develop novel biomarkers with diagnostic and prognostic purposes. Previous studies have confirmed the potential use of miRNA profiling as a novel non invasive biomarker for diagnosis of ovarian cancer [5], [6], [7].
The focus of this study was the detection of the levels of circulating miRNAs in tissues and sera from patients with HGSOC as a first step in the evaluation process of their role as diagnostic biomarkers.
Section snippets
Methods-patients
We retrospectively evaluated a cohort of 233 serum samples gathered together from two independent Italian serum collections. The first, collected at the Division of Obstetrics & Gynecology, ASST Spedali Civili, University of Brescia, Brescia, Italy, between 2003 and 2013 consists of serum samples from 110 patients with stage III-IV HGSOC and from 52 healthy individuals of comparable age. The second, collected at the Division of Gynecologic Oncology of Policlinico A. Gemelli, Catholic
Cohort description and study design
Table 1 shows clinico-pathological features of HGSOC patients and healthy controls from whom sera were selected for the study. Samples were gathered together from two independent Italian tumor tissue collections and subdivided into a training set and a validation set. Serum samples were withdrawn at diagnosis, before any treatment. The median age at diagnosis was 61 and 58 years for the training and the validation set, respectively. The vast majority of women were in postmenopausal status (77%
Discussion
By exploiting different miRNA expression technologies and a dedicated computational approach, we have identified miR-1246 as significantly up-regulated in the serum of patients with diagnosis of HGSOC, compared to healthy individuals. According to its specificity and sensitivity, our data suggest that miR-1246 is worthy to be investigated in future studies as potential diagnostic biomarker for HGSOC.
Considering the lack of ovarian cancer screening tests able to significantly reduce the
Conflict of interest
The authors declare no potential conflicts of interest.
Acknowledgments
We would like to thank Professor Michele Samaja (University of Milan, Italy) for his essential support to the project. We wish to thank all the physicians and the nurses working in the Department of Obstetrics and Gynecology, ASST Spedali Civili, University of Brescia and in particular Mrs Maria Teresa Pedretti for the assistance in collecting blood samples. We are grateful to “Cloud4CARE” project for providing computational resources for data analysis, Nerina and Mario Mattioli Foundation,
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