These days we are collecting data in higher and higher dimensions, processing it, and developing tools that have strong descriptive and predictive powers. Especially in the field of cancer, the processing of data collected from patients has substantial potential in terms of discovering new biomarkers, developing personalized treatment methods, and better prognosticators. However, there are significant difficulties in utilizing and analyzing high-dimensional data. A good level of coding skills is required to bring the data together and apply different analysis methods. With the visual interfaces created in this study, we offer the opportunity to examine and analyze the high-dimensional data of thousands of cancer patients, which are open to the public through The Cancer Genome Atlas initiative, especially for bench scientists who has no prior coding expertise. The Cancer Genome Explorer, shortly TCGEx, is a robust bioinformatic tool that we developed to facilitate high-throughput cancer data analysis through several sophisticated algorithms. With special features like subset-specific analysis and comparative analysis by using multiple cancer data, TCGEx can contribute to the literature by accelerating the studies, especially in hypothesis-driven research. This study also describes a use-case scenario that demonstrates how hypothesis-driven research can be performed using TCGExplorer for melanoma. In melanoma, elucidating the interactions between the tumor and the immune system at the miRNA level is crucial for developing new therapeutics. In this study, we characterize the properties of potential therapeutic targets that act on tumor and immune cells, which we have identified using various statistical analysis methods including machine learning, dimensionality reduction, and survival modeling using the TCGEx portal.