A Pilot Analysis of Whole Transcriptome of Human Cryopreserved Sperm.

Sperm cryopreservation is a procedure widely used to store gametes for later use, to preserve fertility in patients prior to gonadotoxic treatments or surgery, and for sperm donation programs. The purpose of the study was to assess the impact of cryopreservation on human sperm transcriptome. Semen samples were collected from 13 normospermic men. Each sample was divided into two aliquots. The total RNA was immediately extracted from one aliquot. The second aliquot was frozen and total RNA was extracted after a week of storage in liquid nitrogen. The RNA samples were randomized in four pools, each of six donors, and analyzed by microarrays. The paired Significance Analysis of Microarray was performed. We found 219 lower abundant transcripts and 28 higher abundant transcripts in cryopreserved sperm than fresh sperm. The gene ontology analysis disclosed that cryopreservation alters transcripts of pathways important for fertility (i.e., spermatogenesis, sperm motility, mitochondria function, fertilization, calcium homeostasis, cell differentiation, and early embryo development), although the increase of some transcripts involved in immune response can compensate for the harmful effects of freezing.

Machine Learning Methods for Gene Selection in Uveal Melanoma.

Uveal melanoma (UM) is the most common primary intraocular malignancy with a limited five-year survival for metastatic patients. Limited therapeutic treatments are currently available for metastatic disease, even if the genomics of this tumor has been deeply studied using next-generation sequencing (NGS) and functional experiments. The profound knowledge of the molecular features that characterize this tumor has not led to the development of efficacious therapies, and the survival of metastatic patients has not changed for decades. Several bioinformatics methods have been applied to mine NGS tumor data in order to unveil tumor biology and detect possible molecular targets for new therapies. Each application can be single domain based while others are more focused on data integration from multiple genomics domains (as gene expression and methylation data). Examples of single domain approaches include differentially expressed gene (DEG) analysis on gene expression data with statistical methods such as SAM (significance analysis of microarray) or gene prioritization with complex algorithms such as deep learning. Data fusion or integration methods merge multiple domains of information to define new clusters of patients or to detect relevant genes, according to multiple NGS data. In this work, we compare different strategies to detect relevant genes for metastatic disease prediction in the TCGA uveal melanoma (UVM) dataset. Detected targets are validated with multi-gene score analysis on a larger UM microarray dataset.