Por Eunice Carrasquinha (DEIO/FCUL).
Melanoma is the principal cause of death of all skin diseases, and its incidence is increasing faster than any other type of cancer. A successful treatment depends on early detection, as the metastatic form is resistant to therapies. Gene expression data are increasingly being used to establish a diagnosis and optimize treatment of oncological patients. In this work, we propose the analysis of gene expression data from metastatic melanoma as a tool to obtain potential genes that could be important targets for new therapies and treatment.
However, the high-dimensionality nature of the data brings many constraints, for which several approaches have been considered, with regularization techniques in the cutting-edge research front. Additionally, the network structure of gene expression data has fostered the development of network-based regularization techniques to convey data into a low-dimensional and interpretable level.
In this work, classical elastic net and two recently proposed network‐based methods, HubCox and OrphanCox, are applied to high-dimensional gene expression data, to model survival data. The melanoma transcriptomic dataset obtained from The Cancer Genome Atlas (TCGA) is used, considering patients' RNA-seq measurements as covariates. The application of sparsity-inducing techniques to the skcm dataset enabled the selection of relevant genes (CIITA, HLA-DQB1 and HLA-DQA1) over a range of parameters evaluated. Comparable results were obtained for the elastic net and the network-based OrphanCox regarding model performance and genes selected.
Short bio: E. Carrasquinha has worked as a researcher at several institutions, IDMEC, INESC-ID, Champalimaud Foundation and Instituto de Telecomunicações. She is currently an Invited Assistant Professor at the Faculty of Sciences of the University of Lisbon, in the Department of Statistics and Operations Research and a member of CEAUL (Center for Statistics and Applications of the University of Lisbon). Her research area is essentially in methodologies of dimensionality reduction of high dimension data, namely in survival models, where she has published several articles.
