Huang Heqing, Zhang Min, Lu Hong, Chen Yiling, Sun Weijie, Zhu Jinghan, Chen Zutao
Infectious Disease Department, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu Province, China.
BamRock Research Department, Suzhou BamRock Biotechnology Ltd., Suzhou, Jiangsu Province, China.
BMC Cancer. 2024 Dec 18;24(1):1552. doi: 10.1186/s12885-024-13332-0.
Non-invasive diagnostic methods, including medical imaging techniques and blood biomarkers such as alpha-fetoprotein (AFP), have been crucial in detecting hepatocellular carcinoma (HCC). However, imaging techniques are only effective for tumor size larger than 2 cm. AFP measurement remains unsatisfactory due to high rate of misdiagnosis and underdiagnosis. Therefore, new reliable biomarkers and better non-invasive diagnostic approach are necessary for HCC identification.
The differentially expressed genes were identified using multiple public RNA-seq data of liver tissues from healthy individuals and HCC patients including peritumoral and tumor tissues. The hub genes for HCC diagnosis were identified combining pathway enrichment analysis and protein-protein interaction network analysis. The performance of hub genes for non-invasive HCC diagnosis was analyzed in plasma of healthy individuals, HBV infected patients, and HCC patients based on exosomal RNA-seq data. A multi-layer perceptron (MLP) model based on exosomal hub genes was developed for non-invasive HCC diagnosis.
Through differential gene expression and pathway enrichment analysis on multiple public RNA-seq datasets, we first identified 30 dysregulated genes in HCC tissues. Protein-protein interaction analysis further narrowed down this list to 10 key genes: BRCA2, CDK1, MCM4, PLK1, DNA2, BLM, PCNA, POLD1, BRCA1 and FEN1. By further evaluation using additional public HCC tissue datasets, POLD1 and MCM4 were excluded from consideration as potential biomarkers due to their suboptimal performance. Notably, CDK1, FEN1, and PCNA gene were found to be significantly elevated in the plasma exosomes of HCC patients compared to non-HCC individuals, including those with HBV-infected hepatitis and healthy controls. The MLP model, based on three biomarkers, showed an area under the curve (AUC) of 0.85 and 0.84 in training and test dataset respectively, after adjusting for the covariates sex and age.
We identified three key genes, CDK1, FEN1, and PCNA, as exosomal biomarkers for non-invasive diagnosis of HCC. The MLP model utilizing three biomarkers showed good differentiation between non-HCC individuals and HCC patients, which exhibits promising potential as a non-invasive diagnostic tool for detecting HCC. Additional validation with a larger sample size is essential to thoroughly assess the reliability of the biomarkers and the model's performance.
包括医学成像技术和血液生物标志物如甲胎蛋白（AFP）在内的非侵入性诊断方法，在检测肝细胞癌（HCC）方面至关重要。然而，成像技术仅对大于2厘米的肿瘤有效。AFP测量由于误诊率和漏诊率高，仍然不尽人意。因此，需要新的可靠生物标志物和更好的非侵入性诊断方法来识别HCC。
利用来自健康个体以及HCC患者（包括癌旁组织和肿瘤组织）肝脏组织的多个公共RNA测序数据，鉴定差异表达基因。结合通路富集分析和蛋白质-蛋白质相互作用网络分析，确定用于HCC诊断的核心基因。基于外泌体RNA测序数据，在健康个体、乙肝感染患者和HCC患者的血浆中分析核心基因用于非侵入性HCC诊断的性能。开发了一种基于外泌体核心基因的多层感知器（MLP）模型用于非侵入性HCC诊断。
通过对多个公共RNA测序数据集进行差异基因表达和通路富集分析，我们首先在HCC组织中鉴定出30个失调基因。蛋白质-蛋白质相互作用分析进一步将该列表缩小至10个关键基因：BRCA2、CDK1、MCM4、PLK1、DNA2、BLM、PCNA、POLD1、BRCA1和FEN1。通过使用额外的公共HCC组织数据集进行进一步评估，由于POLD1和MCM4的性能欠佳，将它们排除在潜在生物标志物的考虑范围之外。值得注意的是，与非HCC个体（包括乙肝感染性肝炎患者和健康对照）相比，发现HCC患者血浆外泌体中的CDK1、FEN1和PCNA基因显著升高。在调整协变量性别和年龄后，基于三种生物标志物的MLP模型在训练数据集和测试数据集中的曲线下面积（AUC）分别为0.85和0.84。
我们确定了三个关键基因CDK1、FEN1和PCNA，作为用于HCC非侵入性诊断的外泌体生物标志物。利用三种生物标志物的MLP模型在非HCC个体和HCC患者之间显示出良好的区分度，作为检测HCC的非侵入性诊断工具具有广阔的潜力。使用更大样本量进行额外验证对于全面评估生物标志物的可靠性和模型的性能至关重要。
