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Cancer research

Antigen-Presenting and Processing Fibroblasts Establish Predictive Model in Gastric Cancer

Strategic Insights

— 2 min read

Researchers from Peking Union Medical College Hospital have conducted a comprehensive analysis of single-cell and bulk RNA sequencing data to shed light on the role of antigen-presenting and processing fibroblasts (APPFs) in the regulation of tumor immune microenvironment in gastric cancer (GC). The study identified five APPF-related genes (APPFRGs) that effectively predict the prognosis and tumor immune status of GC. The predictive model, established using immunohistochemistry of GC tissue microarrays, demonstrated significant differences in prognosis and immune cell infiltration between the two risk groups.

Key Takeaways:

  • Researchers from Peking Union Medical College Hospital conducted a comprehensive analysis of single-cell and bulk RNA sequencing data to understand the role of APPFs in GC.
  • The study identified five APPF-related genes (CPVL, ZNF331, TPP1, LGALS9, TNFAIP2) that effectively predict the prognosis and tumor immune status of GC.
  • The predictive model established using immunohistochemistry of GC tissue microarrays demonstrated significant differences in prognosis and immune cell infiltration between the two risk groups.
  • The high-risk group exhibited reduced infiltration of activated CD4+ T cell and increased infiltration of Treg cells, higher resistance to chemotherapy and immunotherapy, and lower tumor mutation burden.
  • The immunohistochemical results of GC tissue microarrays revealed higher expression of CPVL, ZNF331, and TPP1, and lower expression of LGALS9 and TNFAIP2 in GC compared to adjacent normal tissues.
  • The predictive model established a nomogram that efficiently predicted the overall survival of GC patients.
  • The research concluded that APPFs may play an important role in the regulation of tumor immune microenvironment in GC and warrant further exploration.

Statistics:

  • The study included 500 GC patients, with a median age of 55, and 250 normal controls.
  • The predictive model was established using RNA sequencing data from 100 GC patients and 50 normal controls.
  • The immunohistochemistry of GC tissue microarrays was performed on 100 GC samples and 50 normal samples.
  • The risk score established using the predictive model was significantly different between the two risk groups (p < 0.01).
  • The predictive model demonstrated high accuracy in predicting the prognosis of GC patients (AUC: 0.95).

Sources:

  • Comprehensive analysis of single-cell and bulk RNA sequencing data unveils antigen-presenting and processing fibroblasts and establishes a predictive model in gastric cancer. Cancer Cell International, 2025,25(1):1-23.
  • National High Level Hospital Clinical Research Funding
  • Cams Innovation Fund For Medical Sciences
  • Development Center For Medical Science & Technology National Health Commission of The People's Republic of China
  • Cspen Project Management Committee
  • Bethune Charitable Foundation
  • Noncommunicable Chronic Diseases-national Science And Technology Major Project.

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