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<p>Pancreatic adenocarcinoma (PAAD) is characterized by complex molecular alterations in cell adhesion molecules (CAMs) that influence tumor progression and treatment resistance. Identifying clinically relevant CAM signatures could improve patient stratification and therapeutic strategies. In this study, we integrated bulk RNA-seq data from TCGA and ICGC with single-cell RNA-seq data to identify survival-associated CAM genes and define molecular subtypes using consensus clustering. We identified 22 CAM genes associated with prognosis and established three molecular subgroups with distinct clinical and immune characteristics. An 11-gene CAM-based risk model (CDH3, CLDN16, CLDN3, CLDN15, ITGB1, NCAM1, SDC1, CD99L2, SLITRK2, LRRC4B, CD58) demonstrated strong prognostic performance across cohorts and effectively predicted chemotherapy and immunotherapy responses. High-risk patients exhibited extracellular matrix activation, immune evasion, and poor response to PD-1/PD-L1 blockade. Single-cell analysis revealed distinct expression patterns of CAM genes across malignant and epithelial compartments. Functional validation confirmed CLDN16 as a pro-tumorigenic factor enhancing invasiveness and reducing apoptosis. These findings highlight the potential of CAM-based signatures to classify PAAD subtypes, predict prognosis, and inform personalized therapeutic strategies.</p> Graphical Abstract <p></p>

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Multi-omics analysis reveals cell adhesion molecules as key regulators of immune cell infiltration and adverse outcomes and in vitro validation of CLDN16 in pancreatic adenocarcinoma

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月18日

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  • Zewei Zhuo,
  • Jianming Luo,
  • Guanpeng Liang,
  • Jiahao Li,
  • Qi Yang,
  • Lin Huang

摘要

Pancreatic adenocarcinoma (PAAD) is characterized by complex molecular alterations in cell adhesion molecules (CAMs) that influence tumor progression and treatment resistance. Identifying clinically relevant CAM signatures could improve patient stratification and therapeutic strategies. In this study, we integrated bulk RNA-seq data from TCGA and ICGC with single-cell RNA-seq data to identify survival-associated CAM genes and define molecular subtypes using consensus clustering. We identified 22 CAM genes associated with prognosis and established three molecular subgroups with distinct clinical and immune characteristics. An 11-gene CAM-based risk model (CDH3, CLDN16, CLDN3, CLDN15, ITGB1, NCAM1, SDC1, CD99L2, SLITRK2, LRRC4B, CD58) demonstrated strong prognostic performance across cohorts and effectively predicted chemotherapy and immunotherapy responses. High-risk patients exhibited extracellular matrix activation, immune evasion, and poor response to PD-1/PD-L1 blockade. Single-cell analysis revealed distinct expression patterns of CAM genes across malignant and epithelial compartments. Functional validation confirmed CLDN16 as a pro-tumorigenic factor enhancing invasiveness and reducing apoptosis. These findings highlight the potential of CAM-based signatures to classify PAAD subtypes, predict prognosis, and inform personalized therapeutic strategies.

Graphical Abstract