Department of Medicine, Lankenau Medical Center, Philadelphia, PA, USA.
Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA.
FASEB J. 2020 Sep;34(9):13022-13032. doi: 10.1096/fj.202001412R. Epub 2020 Aug 10.
Currently, there is no sensitive molecular test for identifying transformation-prone actinic keratoses (AKs) and aggressive squamous cell carcinoma (SCC) subtypes. Biomarker-based molecular testing represents a promising tool for risk stratifying these lesions. We evaluated the utility of a panel of ultraviolet (UV) radiation-biomarker genes in distinguishing between benign and transformation-prone AKs and SCCs. The expression of the UV-biomarker genes in 31 SCC and normal skin (NS) pairs and 10 AK/NS pairs was quantified using the NanoString nCounter system. Biomarker testing models were built using logistic regression models with leave-one-out cross validation in the training set. The best model to classify AKs versus SCCs (area under curve (AUC) 0.814, precision score 0.833, recall 0.714) was constructed using a top-ranked set of 13 UV-biomarker genes. Another model based on a 15-gene panel was developed to differentiate histologically concerning from less concerning SCCs (AUC 1, precision score 1, recall 0.714). Finally, 12 of the UV-biomarker genes were differentially expressed between AKs and SCCs, while 10 genes were uniquely expressed in the more concerning SCCs. UV-biomarker gene subsets demonstrate dynamic utility as molecular tools to classify and risk stratify AK and SCC lesions, which will complement histopathologic diagnosis to guide treatment of high-risk patients.
目前,尚无敏感的分子检测方法可用于识别易发生转化的光化性角化病(AK)和侵袭性鳞状细胞癌(SCC)亚型。基于生物标志物的分子检测代表了一种有前途的风险分层工具,可以用于这些病变。我们评估了一组紫外线(UV)辐射生物标志物基因在区分良性和易发生转化的 AK 和 SCC 中的效用。使用 NanoString nCounter 系统定量测定 31 对 SCC 和正常皮肤(NS)以及 10 对 AK/NS 配对中的 UV 生物标志物基因的表达。使用训练集中的逻辑回归模型和留一交叉验证构建了生物标志物测试模型。用于区分 AK 与 SCC 的最佳模型(曲线下面积(AUC)为 0.814,精度评分为 0.833,召回率为 0.714)是使用排名最高的 13 个 UV 生物标志物基因构建的。另一个基于 15 个基因面板的模型用于区分组织学上令人关注和不太关注的 SCC(AUC 为 1,精度评分为 1,召回率为 0.714)。最后,12 个 UV 生物标志物基因在 AK 和 SCC 之间表达差异,而 10 个基因在更令人关注的 SCC 中特异性表达。UV 生物标志物基因子集作为用于分类和风险分层 AK 和 SCC 病变的分子工具具有动态效用,将补充组织病理学诊断以指导高危患者的治疗。