Wang Xiaoran, Wan Qi, Jin Lin, Liu Chengxiu, Liu Chang, Cheng Yaqi, Wang Zhichong
State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangzhou, China.
The First Affiliated Hospital of Shandong First Medical University, Shandong, China.
Front Mol Biosci. 2021 Feb 16;7:598725. doi: 10.3389/fmolb.2020.598725. eCollection 2020.
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. 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. 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. Thus, our finding provides a new framework for classification and finds some potential targets for the treatment of melanoma.
随着黑色素瘤治疗中耐药性的不断发现，个性化癌症治疗和癌症干细胞是这种侵袭性皮肤癌潜在的治疗靶点。从癌症基因组图谱（TCGA）数据库中获取皮肤黑色素瘤的多组学数据。然后，通过执行“癌症亚型”方法将这些黑色素瘤患者分为不同的亚组。研究了各亚型之间干性指数（mRNAsi和mDNAsi）和肿瘤微环境指数（免疫评分、基质评分和肿瘤纯度）的差异。此外，还运用最小绝对收缩和选择算子（LASSO）和支持向量机递归特征消除（SVM-RFE）算法来识别癌细胞干性特征，并进一步探索免疫/化疗反应的可能性。从TCGA中总共鉴定出3种具有不同生存结果的黑色素瘤特定亚型。我们发现黑色素瘤亚型2具有较高的免疫评分和基质评分以及较低的mRNAsi和肿瘤纯度评分，其生存时间比其他亚型最佳。通过进行Kaplan-Meier生存分析，我们发现mRNAsi与黑色素瘤亚型2的总生存时间显著相关。相关性分析表明干性指数与肿瘤浸润免疫细胞亚群之间存在惊人的关联。此外，我们开发并验证了一种与干性相关的预后基因特征，通过应用风险评分系统可将黑色素瘤患者分为高风险和低风险亚组。高风险组的生存时间明显短于低风险亚组，且对CTLA-4免疫治疗更敏感。最后，在连接图谱数据库中筛选出16种化合物，它们可能是黑色素瘤的潜在治疗药物。因此，我们的研究结果为分类提供了一个新框架，并找到了一些黑色素瘤治疗的潜在靶点。
