Qian Meng, Chen Ying, Wang Zhenxiang, Chen Ye, Zhang Yan, Wu Xiaofen, Sun Huihui, Xu Shuchang
Department of Gastroenterology, Tongji Institute of Digestive Disease, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China.
Department of Information, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China.
J Gene Med. 2025 Sep;27(9):e70044. doi: 10.1002/jgm.70044.
Cardia carcinoma (CC) is a highly heterogeneous cancer with an increasing incidence worldwide. Gastroesophageal reflux disease has been identified as a risk factor for CC, and patients with Sjögren's syndrome (SS) are often reported to have esophageal motility disorders. This study aimed to identify potential hub genes and molecular processes for CC with SS.
Four datasets were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) analysis and weighted gene coexpression network analysis (WGCNA) were conducted to identify shared genes between CC and SS. Functional enrichment analysis and protein-protein interaction (PPI) network construction were performed on these genes. Four machine learning algorithms, including random forest (RF), least absolute shrinkage and selection operator (LASSO), support vector machine-recursive feature elimination (SVM-RFE), and extreme gradient boosting (XGBoost), were applied to screen hub genes. Then, a nomogram predicting the risk of CC in SS patients was constructed and validated by the receiver operating characteristic (ROC) curve and calibration curve. Additionally, we analyzed the transcriptional regulatory relationships, coexpression networks, and correlations between the hub genes and immune infiltration.
By intersecting DEGs and module genes identified by WGCNA, we screened 60 shared genes that were mainly enriched in cell cycle, response to xenobiotic stimulus, and p53 signaling pathways. Based on machine learning algorithms, three hub genes were identified and used to construct a nomogram with high predictive performance (the AUC for the training cohort and validation cohort were 0.991 and 0.978, respectively). Furthermore, the immune infiltration results suggested that T cells, mast cells, macrophages, and B cells play an important role in both diseases, and the hub genes were significantly associated with T cells and B cells.
This study identified three hub genes (E2F3, CHIA, and SCNN1B) and established a nomogram that could effectively predict the risk of CC. The unbalanced immune response may be the common pathogenesis of these two diseases, which provides novel insights into the diagnosis and therapy of CC with SS.
贲门癌（CC）是一种高度异质性的癌症，在全球范围内发病率不断上升。胃食管反流病已被确定为CC的一个危险因素，并且经常有报道称干燥综合征（SS）患者存在食管动力障碍。本研究旨在确定合并SS的CC的潜在关键基因和分子过程。
从基因表达综合数据库（GEO）中获取了四个数据集。进行差异表达基因（DEG）分析和加权基因共表达网络分析（WGCNA）以确定CC和SS之间的共享基因。对这些基因进行功能富集分析和蛋白质-蛋白质相互作用（PPI）网络构建。应用包括随机森林（RF）、最小绝对收缩和选择算子（LASSO）、支持向量机递归特征消除（SVM-RFE）和极端梯度提升（XGBoost）在内的四种机器学习算法来筛选关键基因。然后，构建了一个预测SS患者CC风险的列线图，并通过受试者工作特征（ROC）曲线和校准曲线进行验证。此外，我们分析了关键基因之间的转录调控关系、共表达网络以及与免疫浸润的相关性。
通过将WGCNA鉴定的DEG与模块基因相交，我们筛选出60个共享基因，这些基因主要富集在细胞周期、对外源生物刺激的反应和p53信号通路中。基于机器学习算法，确定了三个关键基因，并用于构建具有高预测性能的列线图（训练队列和验证队列的AUC分别为0.991和0.978）。此外，免疫浸润结果表明T细胞、肥大细胞、巨噬细胞和B细胞在这两种疾病中均起重要作用，并且关键基因与T细胞和B细胞显著相关。
本研究确定了三个关键基因（E2F3、CHIA和SCNN1B）并建立了一个能够有效预测CC风险的列线图。免疫反应失衡可能是这两种疾病的共同发病机制，这为合并SS的CC的诊断和治疗提供了新的见解。
