肝细胞癌（HCC）的早期发现对于改善患者预后和生存率非常重要。甲基化测序与神经网络相结合，可识别携带异常甲基化的游离 DNA (cfDNA)，为 HCC 检测提供了一种有吸引力的非侵入性方法。然而，传统的甲基化检测技术和模型存在一些局限性，这可能会阻碍其在HCC读取水平检测中的性能。我们开发了一种低DNA损伤和高保真甲基化检测方法，称为No End-repair Enzymatic Mmethyl-seq（ NEEM 序列）。我们进一步开发了一种名为 DeepTrace 的读取级神经检测模型，该模型可以通过预先训练和微调的神经网络更好地识别 HCC 衍生的测序读取。对 NEEM-seq 的 1100 万个读数进行预训练后，DeepTrace 使用降噪后来自肿瘤组织 DNA 的 120 万个 HCC 衍生读数和来自非肿瘤 cfDNA 的 270 万个非肿瘤读数进行微调。我们使用来自 130 名个体的数据对模型进行了验证，其 cfDNA 全基因组 NEEM-seq 深度约为 1.6 倍。NEEM-seq 通过避免在 cfDNA 中引入非甲基化错误，克服了传统酶促甲基化测序方法的缺点。 DeepTrace 在识别 HCC 衍生读数和检测 HCC 个体方面优于其他模型。基于 cfDNA 的全基因组 NEEM-seq 数据，我们的模型在由 62 名 HCC 患者、48 名肝病患者和 20 名健康人组成的验证队列中显示出 96.2% 的高精度、93.6% 的敏感性和 98.5% 的特异性个人。在 HCC 早期（BCLC 0/A 和 TNM I），DeepTrace 的敏感性分别为 89.6 和 89.5%，优于甲胎蛋白（AFP），后者在 BCLC 0/A（50.5%）和 TNM 中的敏感性均低得多I (44.7%)。通过将 NEEM-seq 的高保真甲基化数据与 DeepTrace 模型相结合，我们的方法在 HCC 早期检测方面具有巨大的潜力，具有高灵敏度和特异性，使其可能适合临床应用。 DeepTrace：https://github.com/Bamrock/DeepTrace.© 2023。作者。

Early detection of hepatocellular carcinoma (HCC) is important in order to improve patient prognosis and survival rate. Methylation sequencing combined with neural networks to identify cell-free DNA (cfDNA) carrying aberrant methylation offers an appealing and non-invasive approach for HCC detection. However, some limitations exist in traditional methylation detection technologies and models, which may impede their performance in the read-level detection of HCC.We developed a low DNA damage and high-fidelity methylation detection method called No End-repair Enzymatic Methyl-seq (NEEM-seq). We further developed a read-level neural detection model called DeepTrace that can better identify HCC-derived sequencing reads through a pre-trained and fine-tuned neural network. After pre-training on 11 million reads from NEEM-seq, DeepTrace was fine-tuned using 1.2 million HCC-derived reads from tumor tissue DNA after noise reduction, and 2.7 million non-tumor reads from non-tumor cfDNA. We validated the model using data from 130 individuals with cfDNA whole-genome NEEM-seq at around 1.6X depth.NEEM-seq overcomes the drawbacks of traditional enzymatic methylation sequencing methods by avoiding the introduction of unmethylation errors in cfDNA. DeepTrace outperformed other models in identifying HCC-derived reads and detecting HCC individuals. Based on the whole-genome NEEM-seq data of cfDNA, our model showed high accuracy of 96.2%, sensitivity of 93.6%, and specificity of 98.5% in the validation cohort consisting of 62 HCC patients, 48 liver disease patients, and 20 healthy individuals. In the early stage of HCC (BCLC 0/A and TNM I), the sensitivity of DeepTrace was 89.6 and 89.5% respectively, outperforming Alpha Fetoprotein (AFP) which showed much lower sensitivity in both BCLC 0/A (50.5%) and TNM I (44.7%).By combining high-fidelity methylation data from NEEM-seq with the DeepTrace model, our method has great potential for HCC early detection with high sensitivity and specificity, making it potentially suitable for clinical applications. DeepTrace: https://github.com/Bamrock/DeepTrace.© 2023. The Author(s).