表皮鳞状细胞癌（cSCC）的转移是不常见的。当前的分期方法在转移预测中具有亚优性能。准确识别存在高转移风险肿瘤的患者对管理具有重要影响。本研究采用客观的全转录组发现性方法，开发了一个稳健且经验证的基因表达谱（GEP）标志，用于预测原发性cSCC的转移风险。从四个中心回顾性收集了237例免疫竞争患者（151例无转移和86例转移）的存档福尔马林固定石蜡包埋原发cSCC及其周围正常组织样本。采用TempO-seq探测整个转录组，并应用机器学习算法提取预测标志，在训练和测试数据集中采用了3:1的拆分比例。开发并验证了一个包含20个基因的预后模型，测试集的准确率为86.0％，敏感性为85.7％，特异性为86.1％，阳性预测值为78.3％。该模型提供了比病理分期系统更稳定、更准确的预测。还开发了一个线性预测器，与转移风险显著相关。这是一项回顾性的四中心研究，现在需要进行更大规模的前瞻性多中心研究。这个包含20个基因的标志预测是准确的，有潜力纳入cSCC的临床工作流程中。版权所有© 2023。Elsevier Inc.出版。

Metastasis of cutaneous squamous cell carcinoma (cSCC) is uncommon. Current staging methods are reported to have sub-optimal performances in metastasis prediction. Accurate identification of patients with tumours at high risk of metastasis would have a significant impact on management.To develop a robust and validated gene expression profile (GEP) signature for predicting primary cSCC metastatic risk using an unbiased whole transcriptome discovery-driven approach.Archival formalin-fixed paraffin-embedded primary cSCC with perilesional normal tissue from 237 immunocompetent patients (151 non-metastasising and 86 metastasising) were collected retrospectively from four centres. TempO-seq was used to probe the whole transcriptome and machine learning algorithms were applied to derive predictive signatures, with a 3:1 split for training and testing datasets.A 20-gene prognostic model was developed and validated, with an accuracy of 86.0%, sensitivity of 85.7%, specificity of 86.1%, and positive predictive value of 78.3% in the testing set, providing more stable, accurate prediction than pathological staging systems. A linear predictor was also developed, significantly correlating with metastatic risk.This was a retrospective 4-centre study and larger prospective multicentre studies are now required.The 20-gene signature prediction is accurate, with the potential to be incorporated into clinical workflows for cSCC.Copyright © 2023. Published by Elsevier Inc.