Abstract 849: Gene expression profiling identifies molecular signatures associated to survival of melanoma patients from the TCGA database
Authors: Figueroa Stephanie , Tiwari Raj , Geliebter Jan , Reyes Niradiz 
Published: September 2020

Abstract
Background: Although melanoma accounts for less than 5 percent of the diagnosed cutaneous malignancies, it is responsible for the majority of skin cancer-related deaths due to its high metastatic potential and therapeutic resistance. Therefore, research is needed to find suitable biomarkers that could improve early diagnosis, prognosis and/or therapy of cutaneous melanoma. Aside from contributing to our understanding of tumor biology and immunology, data mining of large-scale databases is a powerful tool to identify molecular signatures associated to different aspects of cancer. Previous research has shown the potential of gene expression signatures to predict survival of cancer patients. In the present study, we aim to identify gene expression signatures associated with survival of melanoma patients, using bioinformatics analysis of The Cancer Genome Atlas (TCGA) database. Methods: To identify the most significant survival-associated genes in melanoma patients from the TCGA database we used the bioinformatics online platform GEPIA (Gene Expression Profiling Interactive Analysis). A list of survival associated genes was generated by GEPIA. This gene dataset was subjected to pathway and process enrichment analysis with the Metascape bioinformatics online tool (www.metascape.org) that uses several ontology sources: KEGG Pathway, GO Biological Processes, Reactome Gene Sets, Canonical Pathways and CORUM. Genes were clustered according to their pathways and processes. Relationships between genes were identified using the network map generated from the Metascape tool and visualized with Cytoscape. We also used Metascape to perform Protein-protein Interaction (PPI) Enrichment Analysis in different protein interaction databases (BioGrid, InWeb_IM, and OmniPath) and the Molecular complex detection (MCODE) algorithm was applied to identify closely related proteins from the PPI network. Results: Using the online GEPIA tool, we identified 369 genes associated to survival of melanoma patients. Metascape analysis of the most significant survival-associated genes identified the top 20 most significantly enriched clusters. From these 20 clusters, the five most significantly enriched were: lymphocyte activation, cytokine-mediated signaling pathway, cytokine production, adaptive immune response, and response to interferon-gamma. Functional enrichment analysis revealed that genes associated with survival of melanoma patients were predominantly those expressed both by innate and adaptive immune cells, reflecting the interplay between melanoma cells and non-malignant cells present in the tumor microenvironment. Conclusions: This study enabled the detection of genes associated with stromal and infiltrating immune/inflammatory cells confirming an immune response signature associated with favorable prognosis of melanoma. Data mining analysis of the TCGA database using GEPIA tool followed by Metascape analysis, allowed the identification of gene expression signatures related to survival of melanoma patients. The genes and pathways identified may serve as predictors of response to immunotherapy in patients with melanoma.
Keywords: survival, melanoma, immune response, gene expression.
Citation Format: Stephanie Figueroa, Raj Tiwari, Jan Geliebter, Niradiz Reyes. Gene expression profiling identifies molecular signatures associated to survival of melanoma patients from the TCGA database [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 849.