The Integrative Analysis Identifies Three Cancer Subtypes and Stemness Features in Cutaneous Melanoma - Wang, Xiaoran; Wan, Qi; Jin, Lin; Liu, Chengxiu; Liu, Chang; Cheng, Yaqi; Wang, Zhichong (Frontiers in Molecular Biosciences, 2021)
Background: With the growing uncovering of drug resistance in melanoma treatment, personalized cancer therapy and cancer stem cells are potential therapeutic targets for this aggressive skin cancer. Methods: Multi-omics data of cutaneous melanoma were obtained from the The Cancer Genome Atlas (TCGA) database. Then, these melanoma patients were classified into different subgroups by performing "CancerSubtypes" method. The difference of stemness indices (mRNAsi and mDNAsi) and tumor microenvironment indices (immune score, stromal score and tumor purity) among subtypes was investigated. Moreover, the Least Absolute Shrinkage and Selection Operator (LASSO) and Support Vector Machine-Recursive Feature Elimination (SVM-RFE) Algorithms were performed to identify a cancer cell stemness feature and the likelihood of immuno/chemotherapeutic response was further explored. Results: Totally, 3 specific subtypes of melanoma with different survival outcomes were identified from TCGA. we found subtype 2 of melanoma with higher immune score, stromal score and lower mRNAsi, tumor purity score, which has the best survival time than other subtypes. By performing Kaplan–Meier survival analysis, we found that mRNAsi significantly associated with overall survival time of melanomas in subtype 2. Correlation analysis indicated surprising associations between stemness indices and subsets of tumor-infiltrating immune cells. Besides, we developed and validated a prognostic stemness related genes feature which can divide melanoma patients into high and low risk subgroups by applied risk score system. The high risk of group has a significantly shorter survival time than the low risk subgroup. The low risk of melanomas is more sensitive to CTLA-4 immune therapy. Finally, 16 compounds were screen out in the Connectivity Map database may potential therapeutic drugs for melanomas. Conclusions: Thus, our finding provides a new framework for classification and finds some potential targets for treatment of melanoma.