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May 12, 2026Discover Oncology0 citationsOpen Access

Identification of biomarkers associated with diagnosis of gastroesophageal junction adenocarcinoma and their correlation with immune infiltration

JZJianfu ZhuXuzhou Medical CollegeAHAimin HeXuzhou Medical CollegeYZYujing ZhangXuzhou Medical College

Key Points

  • This study aims to identify biomarkers for gastroesophageal junction adenocarcinoma and investigate their correlation with immune cell infiltration.
  • Analyzed transcriptome data from gastroesophageal junction adenocarcinoma samples and matched normal controls using GEO.
  • Applied weighted gene co-expression network analysis (WGCNA) to identify hub genes from differentially expressed genes (DEGs).
  • Constructed a diagnostic prediction model using logistic regression and assessed immune cell composition with the CIBERSORT algorithm.
  • Identified 392 differentially expressed genes, with 47 overlap candidates selected through WGCNA.
  • Five hub genes (TPX2, CCNB2, BUB1, TOP2A, ASPM) were identified, forming a diagnostic model with an AUC of 0.9.
  • Immune infiltration analysis showed an inverse relationship between the hub genes and resting memory CD4+ T cells, and a positive relationship with activated memory CD4+ T cells.

Abstract

BACKGROUND: Gastroesophageal junction adenocarcinoma (GEJAC) is a highly lethal malignancy, and its molecular mechanisms are still not well understood. Reliable biomarkers for early diagnosis and immunotherapy are urgently needed. This study sought to identify hub genes linked to GEJAC by analyzing datasets from the Gene Expression Omnibus (GEO) and examining their correlation with immune cell infiltration. METHODS: Transcriptome data of GEJAC samples and matched normal controls were obtained from GEO. Differentially expressed genes were identified, followed by WGCNA to determine hub genes. Functional annotation was carried out through GO, KEGG, and PPI network analysis to elucidate their biological significance. A diagnostic prediction model was established using logistic regression, and its accuracy was validated through ROC curve analysis. Immune cell composition was assessed with the CIBERSORT algorithm, and the associations between hub genes and immune cell subsets were further investigated. RESULTS: A total of 392 genes with differential expression were identified, among which 47 overlapping candidates were screened by intersecting WGCNA modules with DEGs. Functional enrichment analysis revealed that these genes were involved in meiotic nuclear division, mitotic cell cycle checkpoint, and the p53 signaling pathway. Five hub genes (TPX2, CCNB2, BUB1, TOP2A, ASPM) were selected for the construction of a diagnostic model, which achieved strong predictive performance (AUC = 0.9). Immune infiltration analysis revealed an inverse relationship between all five hub genes and resting memory CD4 + T cells, as well as a positive relationship with activated memory CD4 + T cells. CONCLUSION: This study identified TPX2, CCNB2, BUB1, TOP2A, and ASPM as potential candidate diagnostic biomarkers for GEJAC at the transcriptomic level. These genes are closely associated with immune cell infiltration, providing new insights into GEJAC pathogenesis and potential targets for immunotherapy.

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Cite This Study

Zhu et al. (2026) studied this question.

synapsesocial.com/papers/6a02c2b9ce8c8c81e96403behttps://doi.org/10.1007/s12672-026-05067-0
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