Identification of Precise Therapeutic Targets and Characteristic Prognostic Genes Based on Immune Gene Characteristics in Uveal Melanoma

Eosinophilic granulocytes as a potential prognostic marker for cancer progression and therapeutic response in malignant melanoma

The importance of eosinophilic granulocytes in cancer has been widely discussed in recent years. The current study reviews the evidence on the role of eosinophilic granulocytes in melanoma as a prognostic marker for cancer progression and the efficacy of treatment with modern immune checkpoint inhibitors. A total of 33 human clinical studies were included in the review, with heterogeneous data due to differences in patients populations, study design and inclusion of small study groups. However, 28 of the 33 studies suggested that eosinophilic granulocytes could be used as a prognostic biomarker for outcome and/or potential response to systemic treatment and/or occurrence of adverse events in melanoma patients. Nevertheless, the exact role of eosinophils remains to be elucidated. Further prospective, larger and better controlled studies are warranted to clarify the significance of eosinophilic granulocytes in patients with melanoma, in more details.

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.