Xiaoran Wang1, Qi Wan1, Lin Jin2, Chengxiu Liu3, Chang Liu1, Yaqi Cheng1, Zhichong Wang1. 1. State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangzhou, China. 2. The First Affiliated Hospital of Shandong First Medical University, Shandong, China. 3. Department of Ophthalmology, Affiliated Hospital of Qingdao University Medical College, Qingdao, China.
Abstract
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 Cancer Genome Atlas (TCGA) database. Then, these melanoma patients were classified into different subgroups by performing "CancerSubtypes" method. The differences of stemness indices (mRNAsi and mDNAsi) and tumor microenvironment indices (immune score, stromal score, and tumor purity) among subtypes were 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 the higher immune score and stromal score and lower mRNAsi and tumor purity score, which has the best survival time than the other subtypes. By performing Kaplan-Meier survival analysis, we found that mRNAsi was significantly associated with the 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 that can divide melanoma patients into high- and low-risk subgroups by applying risk score system. The high-risk group has a significantly shorter survival time than the low-risk subgroup, which is more sensitive to CTLA-4 immune therapy. Finally, 16 compounds were screened out in the Connectivity Map database which may be potential therapeutic drugs for melanomas. Conclusion: Thus, our finding provides a new framework for classification and finds some potential targets for the treatment of melanoma.
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 Cancer Genome Atlas (TCGA) database. Then, these melanomapatients were classified into different subgroups by performing "CancerSubtypes" method. The differences of stemness indices (mRNAsi and mDNAsi) and tumor microenvironment indices (immune score, stromal score, and tumor purity) among subtypes were 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 the higher immune score and stromal score and lower mRNAsi and tumor purity score, which has the best survival time than the other subtypes. By performing Kaplan-Meier survival analysis, we found that mRNAsi was significantly associated with the 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 that can divide melanomapatients into high- and low-risk subgroups by applying risk score system. The high-risk group has a significantly shorter survival time than the low-risk subgroup, which is more sensitive to CTLA-4 immune therapy. Finally, 16 compounds were screened out in the Connectivity Map database which may be potential therapeutic drugs for melanomas. Conclusion: Thus, our finding provides a new framework for classification and finds some potential targets for the treatment of melanoma.
Authors: Edward K Chow; Xue-Qing Zhang; Mark Chen; Robert Lam; Erik Robinson; Houjin Huang; Daniel Schaffer; Eiji Osawa; Andrei Goga; Dean Ho Journal: Sci Transl Med Date: 2011-03-09 Impact factor: 17.956
Authors: Gianluca Civenni; Anne Walter; Nikita Kobert; Daniela Mihic-Probst; Marie Zipser; Benedetta Belloni; Burkhardt Seifert; Holger Moch; Reinhard Dummer; Maries van den Broek; Lukas Sommer Journal: Cancer Res Date: 2011-03-10 Impact factor: 12.701
Authors: Wendy K Nevala; Celine M Vachon; Alexey A Leontovich; Christopher G Scott; Michael A Thompson; Svetomir N Markovic Journal: Clin Cancer Res Date: 2009-02-24 Impact factor: 12.531
Authors: Freddie Bray; Jacques Ferlay; Isabelle Soerjomataram; Rebecca L Siegel; Lindsey A Torre; Ahmedin Jemal Journal: CA Cancer J Clin Date: 2018-09-12 Impact factor: 508.702