RESUMO
Background: Melanoma is a highly malignant form of skin cancer that exhibits remarkable metabolic adaptability. Melanoma cells exhibit the capacity to adapt to specific conditions of the tumor microenvironment through the utilization of diverse energy sources, thereby facilitating the growth and advancement of the tumor. One of the notable characteristics of metabolic reprogramming is the heightened rate of lipid synthesis. This review was conducted to illustrate how the integration of whole exom and transcriptome sequencing will enhance the detection of the effect of cholesterol metabolism in melanoma. Methods: The Cochrane database, Embase, PubMed, SCOPUS, Google Scholar, Ovid, and other databases were thoroughly searched for works addressing integrated whole exome and transcriptome sequencing in cholesterol metabolism in melanoma. Skin malignancy, melanoma progression, transcriptome sequencing, whole exome sequencing, transcriptome sequencing by RNA sequencing, and integrated transcriptome and whole exome sequencing were the key phrases employed. This article underwent a phased search for pertinent literature using a staged literature search methodology. Each section's relevant papers were identified and summarized independently. The results have been condensed and narratively given in the pertinent sections of this thorough assessment. Results: DNA-based analysis has proven to be ineffective in identifying numerous mutations that have an impact on splicing or gene expression. RNA-Sequencing, when combined with suitable bioinformatics, offers a reliable method for detecting supplementary mutations that aid in the genetic diagnosis of geno-dermatoses. Therefore, clinical RNA-Sequencing expands the scope of molecular diagnostics for rare genodermatoses, and it has the potential to serve as a dependable initial diagnostic method for expanding mutation databases in individuals with inheritable skin conditions. Conclusion: The integration of patient-specific tumor RNA-sequencing and tumor DNA whole-exome sequencing (WES) would potentially enhance mutation detection capabilities compared to relying solely on DNA-WES.