通过其多种优点，最近非生物标记依赖的分子检测已经展现出了癌症筛查的良好前景，但其临床应用受到可类比生物标志物的可衡量标准的短缺的阻碍。在这里，我们报告了一种数字生物标志物，作为基于SERS生物传感器和深度神经网络“数字视网膜”发现的新概念血清生物标志物，用于可视化和明确定义光谱指纹的数字生物标志物的方法。我们通过对来自正常个体和HCC病例的光谱的无监督聚类验证了发现的数字生物标志物（血清SERS光谱中10个特征峰的集合）；验证结果分别显示聚类准确度为95.71%和100.00%。此外，我们发现HCC的数字生物标志物与三种在临床应用的血清生物标志物共享了一些共同峰，这意味着它可以传达类似于这些生物标志物的基本生物分子信息。因此，我们提出了一种利用数字生物标志物的智能方法进行早期HCC检测，其具有与生物标志物类似的特征。应用数字生物标志物，我们可以使用线性分类器准确地分层HCC，乙肝和正常人群，展示出超过92%的准确率和大于0.93的接受者工作特征曲线值。预计这种非生物标记依赖的分子检测方法将促进大规模癌症筛查。版权所有©2023 Elsevier B.V.。保留所有权利。

By its many virtues, non-biomarker-reliant molecular detection has recently shown bright prospects for cancer screening but its clinical application is hindered by the shortage of measurable criteria that are analogous to biomarkers. Here, we report a digital biomarker, as a new-concept serum biomarker, of hepatocellular carcinoma (HCC) found with SERS-based biosensors and a deep neural network "digital retina" for visualizing and explicitly defining spectral fingerprints. We validate the discovered digital biomarker (a collection of 10 characteristic peaks in the serum SERS spectra) with unsupervised clustering of spectra from an independent sample batch comprised normal individuals and HCC cases; the validation results show clustering accuracies of 95.71% and 100.00%, respectively. Furthermore, we find that the digital biomarker of HCC shares a few common peaks with three clinically applied serum biomarkers, which means it could convey essential biomolecular information similar to these biomarkers. Accordingly, we present an intelligent method for early HCC detection that leverages the digital biomarker with similar traits as biomarkers. Employing the digital biomarker, we could accurately stratify HCC, hepatitis B, and normal populations with linear classifiers, exhibiting accuracies over 92% and area under the receiver operating curve values above 0.93. It is anticipated that this non-biomarker-reliant molecular detection method will facilitate mass cancer screening.Copyright © 2023 Elsevier B.V. All rights reserved.