Zhi Peng, Liu Yue, Zhao Chenghui, He Kunlun
Chinese PLA Medical School, Chinese PLA General Hospital, Beijing 100853, China.
Medical Innovation Research Department of PLA General Hospital, Chinese PLA General Hospital, Beijing 100853, China.
Bioengineering (Basel). 2025 Mar 3;12(3):255. doi: 10.3390/bioengineering12030255.
Gastric cancer (GC) is a prevalent malignancy, and the discovery of biomarkers plays a crucial role in the diagnosis and prognosis of GC. However, current strategies for identifying GC biomarkers often focus on a single ribonucleic acid (RNA) class, neglecting the potential for multiple RNA types to collectively serve as biomarkers with improved predictive capabilities. To bridge this gap, our study introduces the GC biomarker relation graph convolution neural network (GCBRGCN) model which integrates the competing endogenous RNA (ceRNA) network with GC clinical informations and whole transcriptomics data, leveraging the relational graph convolutional network (RGCN) to predict GC biomarkers. It demonstrates exceptional performance, surpassing traditional machine learning and graph neural network algorithms with an area under the curve (AUC) of 0.8172 in the task of predicting GC biomarkers. Our study identified three unreported potential novel GC biomarkers: CCNG1, CYP1B1, and CITED2. Moreover, FOXC1 and LINC00324 were characterized as biomarkers with significance in both prognosis and diagnosis. Our work offers a novel framework for GC biomarker identification, highlighting the critical role of multiple types RNA interaction in oncological research.
胃癌（GC）是一种常见的恶性肿瘤，生物标志物的发现对胃癌的诊断和预后起着至关重要的作用。然而，目前识别胃癌生物标志物的策略通常只关注单一类型的核糖核酸（RNA），而忽略了多种RNA类型共同作为具有更高预测能力的生物标志物的潜力。为了弥补这一差距，我们的研究引入了胃癌生物标志物关系图卷积神经网络（GCBRGCN）模型，该模型将竞争性内源RNA（ceRNA）网络与胃癌临床信息和全转录组学数据相结合，利用关系图卷积网络（RGCN）来预测胃癌生物标志物。它表现出卓越的性能，在预测胃癌生物标志物的任务中，曲线下面积（AUC）为0.8172，超过了传统机器学习和图神经网络算法。我们的研究确定了三种未报道的潜在新型胃癌生物标志物：CCNG1、CYP1B1和CITED2。此外，FOXC1和LINC00324被确定为在预后和诊断方面均具有重要意义的生物标志物。我们的工作为胃癌生物标志物的识别提供了一个新的框架，突出了多种RNA相互作用在肿瘤学研究中的关键作用。
