State Key Laboratory of Virology, College of Life Sciences, Wuhan University, Wuhan, China.
Frontier Science Center for Immunology and Metabolism, Wuhan University, Wuhan, China.
Sci Rep. 2024 Sep 3;14(1):20441. doi: 10.1038/s41598-024-71495-z.
Liver cancer, classified as a malignant hepatic tumor, can be divided into two categories: primary, originating within the liver, and secondary, resulting from metastasis to the liver from other organs. Hepatocellular carcinoma (HCC) is the main form of primary liver cancer and the third leading cause of cancer-related deaths. The diagnosis and prognosis of HCC using current methods still face numerous challenges. This study aims to develop novel diagnostic and prognostic models while identifying new biomarkers for improved HCC treatment. Diagnostic and prognostic models for HCC were constructed using traditional binary classification methods and machine learning algorithms based on the TCGA database (Downloaded in August 2023). The mechanisms by which APLN (Apelin) affects HCC were investigated using single-cell sequencing data sourced from the GEO database (GSE149614). The diagnostic models yielded by various algorithms could effectively distinguished HCC samples from normal ones. The prognostic model, composed of four genes, was constructed using LASSO and Cox regression algorithms, demonstrating good performance in predicting the three-year survival rate of HCC patients. The HCC biomarker Apelin (APLN) was identified in this study. APLN in liver cancer tissues mainly comes from endothelial cells and is associated with the carcinogenesis of these cells. APLN expression is significantly upregulated in liver cancer tissues, marking it as a viable indicator of endothelial cell malignancy in HCC. Furthermore, APLN expression was determined to be an independent predictor of tumor endothelial cell carcinogenesis, unaffected by its modifications such as single nucleotide variation, copy number variation, and methylation. Additionally, liver cancers characterized by high APLN expression are likely to progress rapidly after T2 stage. Our study presents diagnostic and prognostic models for HCC with appreciably improved accuracy and reliability compared to previous reports. APLN is a reliable HCC biomarker and contributes to the establishment of our models.
肝癌，归类为一种恶性肝肿瘤，可以分为两类：原发的，源自于肝脏，和继发的，由其他器官转移至肝脏。肝细胞癌（HCC）是原发性肝癌的主要形式，也是癌症相关死亡的第三大原因。目前的方法在肝癌的诊断和预后方面仍然面临着许多挑战。本研究旨在开发新的诊断和预后模型，同时鉴定新的生物标志物，以改善 HCC 的治疗。基于 TCGA 数据库（2023 年 8 月下载），使用传统的二分类方法和机器学习算法构建 HCC 的诊断和预后模型。使用来自 GEO 数据库（GSE149614）的单细胞测序数据研究 APLN（Apelin）影响 HCC 的机制。各种算法生成的诊断模型能够有效地将 HCC 样本与正常样本区分开来。由四个基因组成的预后模型是使用 LASSO 和 Cox 回归算法构建的，该模型在预测 HCC 患者的三年生存率方面表现良好。本研究中鉴定出 HCC 生物标志物 Apelin（APLN）。肝癌组织中的 APLN 主要来自内皮细胞，与这些细胞的癌变有关。APLN 在肝癌组织中的表达显著上调，表明其是 HCC 内皮细胞恶性程度的一个可行指标。此外，APLN 表达被确定为肿瘤内皮细胞癌变的独立预测因子，不受其单核苷酸变异、拷贝数变异和甲基化等修饰的影响。此外，APLN 表达水平较高的肝癌在 T2 期后可能会迅速进展。与之前的报告相比，我们的研究为 HCC 提供了诊断和预后模型，具有显著提高的准确性和可靠性。APLN 是一种可靠的 HCC 生物标志物，有助于我们模型的建立。
