Fei Hongjun, Chen Xiongming
Department of Reproductive Genetics, International Peace Maternity and Child Health Hospital, Shanghai Key Laboratory of Embryo Original Diseases, Shanghai Municipal Key Clinical Specialty, Shanghai Jiao Tong University School of Medicine Shanghai 200030, China.
Am J Cancer Res. 2021 Dec 15;11(12):5979-5991. eCollection 2021.
Existing staging system for prognosis evaluating for Skin Cutaneous Melanoma (SKCM) patients had defects of subjective, inaccuracy and inconsistently, therefore, to identify specific and applicable prognostic markers and promote personalized therapeutic interventions is urgently required. This study aims to build a robust autophagy-related genes (ARGs) signature for prognosis monitoring of SKCM patients. We determined 26 ARGs as differentially expressed autophagy-related genes (DEARGs) from 103 SKCM and 23 normal skin samples in GSE15605 and GSE3189 datasets. Optimal prognostic DEARGs composed the risk model were screened and verified in 458 SKCM patients in TCGA cohort as the training cohort and 209 patients in GSE65904 as the test cohort. Finally, 4 optimal independent prognostic DEARGs ( and ) were filtered out in the training cohort to establish the risk model. A prognostic nomogram was established for quantitative survival prediction. The risk model grouped high-risk SKCM cancer patients exhibited significantly shorter survival times in both training and test cohorts. The area under the ROC curve for risk score model was 0.788 and 0.627 in the training and test cohorts indicated the risk model was relatively accurate for prognosis monitoring. Clinical correlation analysis exhibited that risk score was an independent predictor for prognosis significantly associated with T/N classification. The prognostic value of the 4 risk genes formed the risk model was also validated respectively. We identified a novel autophagy-related signature for prognosis monitoring. It has the potential to be an independent prognostic indicator and can benefit targeted therapy.
现有的皮肤黑色素瘤（SKCM）患者预后评估分期系统存在主观性、不准确和不一致的缺陷，因此，迫切需要确定特异性和适用性强的预后标志物并推动个性化治疗干预。本研究旨在构建一个用于SKCM患者预后监测的强大自噬相关基因（ARGs）特征。我们从GSE15605和GSE3189数据集中的103例SKCM和23例正常皮肤样本中确定了26个ARGs作为差异表达的自噬相关基因（DEARGs）。在TCGA队列中的458例SKCM患者作为训练队列以及GSE65904中的209例患者作为测试队列中筛选并验证了构成风险模型的最佳预后DEARGs。最后，在训练队列中筛选出4个最佳独立预后DEARGs（ 和 ）以建立风险模型。建立了一个预后列线图用于定量生存预测。风险模型将高风险SKCM癌症患者分组，在训练和测试队列中均显示出明显更短的生存时间。训练和测试队列中风险评分模型的ROC曲线下面积分别为0.788和0.627，表明该风险模型对于预后监测相对准确。临床相关性分析表明，风险评分是与T/N分类显著相关的独立预后预测指标。还分别验证了构成风险模型的4个风险基因的预后价值。我们确定了一种用于预后监测的新型自噬相关特征。它有可能成为独立的预后指标并有益于靶向治疗。
