ECE2024 Eposter Presentations Endocrine-Related Cancer (90 abstracts)
In silico and ex vivo identification of gonadotropin-related genes involved in ovarian cancer
Clara Lazzaretti 1 , Beatrice Melli 2,3,4 , Samantha Sperduti 1 , Carmela Perri 1,3 , Lara Baschieri 1,3 , Vincenza Ylenia Cusenza 2 , Sandra Martinelli 2 , Davide Nicoli 2 , Andrea Palicelli 5 , Dario Vincenzo Mandato 4 , Lorenzo Aguzzoli 4 , Manuela Simoni 1,6 & Livio Casarini 1
1Unit of Endocrinology - University of Modena and Reggio Emilia, Department of Biomedical, Metabolic and Neural Sciences, Modena, Italy; 2Unit of Molecular Pathology - Azienda USL-IRCCS di Reggio Emilia, Reggio Emilia, Italy; 3International PhD School in Clinical and Experimental Medicine (CEM) - University of Modena and Reggio Emilia, Modena, Italy; 4Unit of Obstetrics and Oncological Gynecology - Azienda USL-IRCCS di Reggio Emilia, Reggio Emilia, Italy; 5Unit of Pathology, Azienda USL-IRCCS di Reggio Emilia, Reggio Emilia, Italy; 6Azienda Ospedaliero-Universitaria di Modena, Department of Medical Specialties, Modena, Italy
Serous ovarian cancer is one of the leading causes of death among all gynaecological malignancies. Even though a hereditary component has been suggested, most ovarian carcinomas are sporadic and markers for a diagnosis at an early stage of the disease are still to be identified. Since gonadotropins may stimulate the growth of certain cancer cells, we aim to identify tumour markers linked to gonadotropin receptor-mediated pathways. Through an in silico and ex vivo approach, we characterized high-grade serous ovarian cancer features. A network of genes associated with gonadotropin-dependent regulatory pathways of ovarian follicle development was identified. Gene expression data of 60 genes from two groups of high-grade serous ovarian cancer patients, as well as two control groups, were obtained from the GEO-NCBI database. Principal component analysis (PCA) performed on the datasets revealed that the 60 genes have a differential expression pattern in cases vs controls. PCA was repeated considering 7 genes better discriminating healthy individuals vs patients. These genes described 50-70% of the total variance in each dataset, resulting in a clear clusterization of cases vs controls. PCA was also performed on a group of housekeeping genes serving as a negative control, which did not discriminate between cases vs controls. To further confirm the differential expression pattern of the genes identified through in silico analysis, RNA samples were extracted from high-grade serous ovarian cancer and healthy tissue of 30 women to perform a gene expression analysis by digital droplet PCR. In pathological tissues, results revealed upregulation of proliferative genes together with those associated with gonadotropin-dependent signalling pathways. In conclusion, we found a set of genes linked to gonadotropin functions which may be involved in ovarian cancer progression, as potential targets for specific pharmacological approaches.
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Endocrine Abstracts
ISSN 1470-3947 (print) | ISSN 1479-6848 (online)
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