{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T19:24:33Z","timestamp":1792697073069,"version":"4.5.0"},"reference-count":51,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2025,9,25]],"date-time":"2025-09-25T00:00:00Z","timestamp":1758758400000},"content-version":"vor","delay-in-days":24,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["The Journal of Gene Medicine"],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:title>ABSTRACT<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>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 Sj\u00f6gren'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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>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\u2013protein 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\u2013recursive feature elimination (SVM\u2010RFE), 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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec>","DOI":"10.1002\/jgm.70044","type":"journal-article","created":{"date-parts":[[2025,9,25]],"date-time":"2025-09-25T22:59:12Z","timestamp":1758841152000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Exploring Potential Hub Genes and Molecular Mechanisms Linking Cardia Carcinoma With Sj\u00f6gren's Syndrome Based on Comprehensive Bioinformatics Analysis and Machine Learning"],"prefix":"10.1002","volume":"27","author":[{"given":"Meng","family":"Qian","sequence":"first","affiliation":[{"name":"Department of Gastroenterology, Tongji Institute of Digestive Disease, Tongji Hospital, School of Medicine Tongji University  Shanghai 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