Mario's Interview & Publication
SYNLAB Italy
Mario Zanfardino
SYNLAB IRCCS SDN - Naples
What inspired your research and what did it cover?
There is a growing desire and need to apply new and innovative methods in diagnostics using machine learning and artificial intelligence. Applying these methods and integrating them with classical bioinformatics enables us to identify possible biomarkers (in our case miRNAs) that might play a common role in female cancers, such as breast, ovarian and uterine cancers.
Looking at the potential of your findings, what difference can they make?
Our results could have a significant impact on personalised medicine by increasing knowledge of specific biomarkers. These could be used, for example, in molecular diagnostics of different female cancers and as novel molecular targets for the development of new diagnostic panels. In addition, our work seeks to contribute to advancing the application of machine learning methods in diagnostics and molecular medicine.
Publication
Discovering Common miRNA Signatures Underlying Female-Specific Cancers via a Machine Learning Approach Driven by the Cancer Hallmark ERBB
National Library of Medicine – https://pubmed.ncbi.nlm.nih.gov/35740327/
In this work, a machine learning approach was applied to TCGA (The Cancer Genome Atlas) miRNA profiles of breast, endometrial and ovarian carcinomas to identify common miRNA signatures that distinguish tumour and normal states. In vitro validations revealed that five miRNA signatures were consistent between breast, ovarian and endometrial carcinoma lines compared to normal tumours.