Comprehensive analysis of single-cell and bulk RNA sequencing data unveils antigen-presenting and processing fibroblasts and establishes a predictive model in gastric cancer
Abstract Background Antigen-presenting and processing fibroblasts (APPFs) have emerged as pivotal regulators of antitumor immunity. However, the predictive value of APPF-related genes (APPFRGs) in the prognosis and tumor immune status of gastric cancer (GC) remains largely unexplored. Methods Bioinf...
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BMC
2025-06-01
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| Online Access: | https://doi.org/10.1186/s12935-025-03878-9 |
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| author | Chenggang Zhang Fangqi Chen Jie Li Yixuan He Juan Sun Zicheng Zheng Guanmo Liu Yihua Wang Weiming Kang Xin Ye |
| author_facet | Chenggang Zhang Fangqi Chen Jie Li Yixuan He Juan Sun Zicheng Zheng Guanmo Liu Yihua Wang Weiming Kang Xin Ye |
| author_sort | Chenggang Zhang |
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| description | Abstract Background Antigen-presenting and processing fibroblasts (APPFs) have emerged as pivotal regulators of antitumor immunity. However, the predictive value of APPF-related genes (APPFRGs) in the prognosis and tumor immune status of gastric cancer (GC) remains largely unexplored. Methods Bioinformatics analysis was conducted using single-cell and bulk RNA sequencing datasets of GC retrieved from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. The APPFs were identified using AUCell algorithm based on APP-associated genes obtained from the InnateDB database. CellChat algorithm was utilized to evaluate interactions between cells. The non-negative matrix factorization (NMF) clustering analysis was performed to identify APPF-related subgroups based on TCGA‑stomach adenocarcinoma cohort. LASSO and multivariate Cox regression analysis were conducted to establish the predictive model. Immunohistochemistry of GC tissue microarrays was performed to validate the model. Results Compared to non-APPFs, APPFs exhibited more interactions with myeloid cells, endothelial cells, and lymphocytes via MHC-II signaling network. The two APPF-related subgroups clustered by NMF demonstrated significant differences in prognosis and immune cell infiltration. Five APPFRGs (CPVL, ZNF331, TPP1, LGALS9, TNFAIP2) were identified to establish the predictive model and stratify GC patients based on risk score. The prognosis was significantly different between the two risk groups and was validated using GEO datasets. A nomogram that efficiently predicted the overall survival of GC patients was established by integrating the risk score with age, T stage, N stage, and M stage. Furthermore, 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. Finally, 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. Additionally, higher risk score in GC samples was relevant with poor differentiation, positive nerve invasion, advanced T and TNM stages, and higher expression of FOXP3. Conclusions APPFs may play an important role in the regulation of tumor immune microenvironment in GC and warrant further exploration. The predictive model based on APPFRGs effectively predicts the prognosis and tumor immune status of GC. |
| format | Article |
| id | doaj-art-2297ff4e9b1841b4a03eceecda60245f |
| institution | Kabale University |
| issn | 1475-2867 |
| language | English |
| publishDate | 2025-06-01 |
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| series | Cancer Cell International |
| spelling | doaj-art-2297ff4e9b1841b4a03eceecda60245f2025-08-20T03:47:17ZengBMCCancer Cell International1475-28672025-06-0125112310.1186/s12935-025-03878-9Comprehensive analysis of single-cell and bulk RNA sequencing data unveils antigen-presenting and processing fibroblasts and establishes a predictive model in gastric cancerChenggang Zhang0Fangqi Chen1Jie Li2Yixuan He3Juan Sun4Zicheng Zheng5Guanmo Liu6Yihua Wang7Weiming Kang8Xin Ye9Department of General Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical CollegeHospital for Skin Diseases, Institute of Dermatology, Chinese Academy of Medical Sciences and Peking Union Medical CollegeDepartment of General Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical CollegeDepartment of General Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical CollegeDepartment of General Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical CollegeDepartment of General Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical CollegeDepartment of General Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical CollegeDepartment of General Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical CollegeDepartment of General Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical CollegeDepartment of General Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical CollegeAbstract Background Antigen-presenting and processing fibroblasts (APPFs) have emerged as pivotal regulators of antitumor immunity. However, the predictive value of APPF-related genes (APPFRGs) in the prognosis and tumor immune status of gastric cancer (GC) remains largely unexplored. Methods Bioinformatics analysis was conducted using single-cell and bulk RNA sequencing datasets of GC retrieved from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. The APPFs were identified using AUCell algorithm based on APP-associated genes obtained from the InnateDB database. CellChat algorithm was utilized to evaluate interactions between cells. The non-negative matrix factorization (NMF) clustering analysis was performed to identify APPF-related subgroups based on TCGA‑stomach adenocarcinoma cohort. LASSO and multivariate Cox regression analysis were conducted to establish the predictive model. Immunohistochemistry of GC tissue microarrays was performed to validate the model. Results Compared to non-APPFs, APPFs exhibited more interactions with myeloid cells, endothelial cells, and lymphocytes via MHC-II signaling network. The two APPF-related subgroups clustered by NMF demonstrated significant differences in prognosis and immune cell infiltration. Five APPFRGs (CPVL, ZNF331, TPP1, LGALS9, TNFAIP2) were identified to establish the predictive model and stratify GC patients based on risk score. The prognosis was significantly different between the two risk groups and was validated using GEO datasets. A nomogram that efficiently predicted the overall survival of GC patients was established by integrating the risk score with age, T stage, N stage, and M stage. Furthermore, 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. Finally, 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. Additionally, higher risk score in GC samples was relevant with poor differentiation, positive nerve invasion, advanced T and TNM stages, and higher expression of FOXP3. Conclusions APPFs may play an important role in the regulation of tumor immune microenvironment in GC and warrant further exploration. The predictive model based on APPFRGs effectively predicts the prognosis and tumor immune status of GC.https://doi.org/10.1186/s12935-025-03878-9Gastric cancerFibroblastAntigen presentation and processingSingle-cell sequencingPredictive modelBioinformatics analysis |
| spellingShingle | Chenggang Zhang Fangqi Chen Jie Li Yixuan He Juan Sun Zicheng Zheng Guanmo Liu Yihua Wang Weiming Kang Xin Ye 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 Gastric cancer Fibroblast Antigen presentation and processing Single-cell sequencing Predictive model Bioinformatics analysis |
| title | Comprehensive analysis of single-cell and bulk RNA sequencing data unveils antigen-presenting and processing fibroblasts and establishes a predictive model in gastric cancer |
| title_full | Comprehensive analysis of single-cell and bulk RNA sequencing data unveils antigen-presenting and processing fibroblasts and establishes a predictive model in gastric cancer |
| title_fullStr | Comprehensive analysis of single-cell and bulk RNA sequencing data unveils antigen-presenting and processing fibroblasts and establishes a predictive model in gastric cancer |
| title_full_unstemmed | Comprehensive analysis of single-cell and bulk RNA sequencing data unveils antigen-presenting and processing fibroblasts and establishes a predictive model in gastric cancer |
| title_short | Comprehensive analysis of single-cell and bulk RNA sequencing data unveils antigen-presenting and processing fibroblasts and establishes a predictive model in gastric cancer |
| title_sort | comprehensive analysis of single cell and bulk rna sequencing data unveils antigen presenting and processing fibroblasts and establishes a predictive model in gastric cancer |
| topic | Gastric cancer Fibroblast Antigen presentation and processing Single-cell sequencing Predictive model Bioinformatics analysis |
| url | https://doi.org/10.1186/s12935-025-03878-9 |
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