标题： 五基因表达公式准确检测肝细胞癌肿瘤 显示英文标题

标题： Five-Gene Expression Formula Accurately Detects Hepatocellular Carcinoma Tumors

摘要： 肝细胞癌（HCC）是全球癌症相关死亡的主要原因之一。 几种诊断方法，如成像技术以及 血清甲胎蛋白（AFP）检测，已被用于HCC的检测；然而，它们的有效性仅限于疾病的后期阶段。 相比之下，活检样本的转录组分析在早期检测中显示出前景。 虽然机器学习技术已被应用于转录组数据进行癌症检测，但由于跨不同数据集的泛化能力差、缺乏可解释性以及计算复杂度高等挑战，其临床应用仍然有限。 为了解决这些限制，我们开发了一种新的HCC检测预测公式，使用了Kolmogorov-Arnold网络（KAN）。 该公式基于五个基因的表达水平：VIPR1、CYP1A2、FCN3、ECM1和LIFR。 该公式来源于GSE25097数据集，为HCC识别提供了一种简单、可解释、高效且易于获取的方法。 它在GSE25097测试集上实现了99%的准确率，并在六个额外的独立数据集上表现出稳健的性能，在所有情况下准确率均超过90%。 这些发现突显了这五个基因在HCC检测中的关键作用，作为生物标志物，为未来的研究和临床应用提供了基础，以改进HCC的诊断方法。 显示英文摘要

摘要： Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related deaths worldwide. Several diagnostic methods, such as imaging modalities and Serum Alpha-Fetoprotein (AFP) testing, have been used for HCC detection; however, their effectiveness is limited to later stages of the disease. In contrast, transcriptomic analysis of biposy samples has shown promise for early detection. While machine learning techniques have been applied to transcriptomic data for cancer detection, their clinical adoption remains limited due to challenges such as poor generalizability across different datasets, lack of interpretability, and high computational complexity. To address these limitations, we developed a novel predictive formula for HCC detection using the Kolmogorov-Arnold Network (KAN). This formula is based on the expression levels of five genes: VIPR1, CYP1A2, FCN3, ECM1, and LIFR. Derived from the GSE25097 dataset, the formula offers a simple, interpretable, efficient, and accessible approach for HCC identification. It achieves 99% accuracy on the GSE25097 test set and demonstrates robust performance on six additional independent datasets, achieving accuracies of above 90% in all cases. These findings highlight the critical role of these five genes as biomarkers for HCC detection, offering a foundation for future research and clinical applications to improve HCC diagnostic approaches.