Integrated multiomics analysis identifies PHLDA1+ fibroblasts as prognostic biomarkers and mediators of biological functions in pancreatic cancer
BackgroundPancreatic cancer (PC) is marked by extensive heterogeneity, posing significant challenges to effective treatment. The tumor microenvironment (TME), particularly cancer-associated fibroblasts (CAFs), plays a critical role in driving PC progression. However, the prognostic and functional co...
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Frontiers Media S.A.
2025-07-01
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| Series: | Frontiers in Immunology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2025.1592416/full |
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| author | Rui Wang Rui Wang Guan-Hua Qin Guan-Hua Qin Yifei Jiang Fu-Xiang Chen Fu-Xiang Chen Zi-Han Wang Zi-Han Wang Lin-Ling Ju Lin Chen Da Fu En-Yu Liu Su-Qing Zhang Wei-Hua Cai |
| author_facet | Rui Wang Rui Wang Guan-Hua Qin Guan-Hua Qin Yifei Jiang Fu-Xiang Chen Fu-Xiang Chen Zi-Han Wang Zi-Han Wang Lin-Ling Ju Lin Chen Da Fu En-Yu Liu Su-Qing Zhang Wei-Hua Cai |
| author_sort | Rui Wang |
| collection | DOAJ |
| description | BackgroundPancreatic cancer (PC) is marked by extensive heterogeneity, posing significant challenges to effective treatment. The tumor microenvironment (TME), particularly cancer-associated fibroblasts (CAFs), plays a critical role in driving PC progression. However, the prognostic and functional contributions of distinct CAF subtypes remain inadequately understood. Here, we introduce a novel 7-gene risk model that not only robustly stratifies PC patients but also unveils the unique role of PHLDA1 as a key mediator in tumor-stroma crosstalk.MethodsBy integrating single-cell RNA sequencing (scRNA-seq), spatial transcriptomics, and bulk RNA sequencing data, we comprehensively characterized the heterogeneity of CAFs in PC. We identified five CAF subtypes and focused on matrix CAFs (mCAFs), which were strongly associated with poor prognosis. A 7-gene mCAF-associated risk model was constructed using advanced machine learning algorithms, and the biological significance of PHLDA1 was validated through co-culture experiments and pan-cancer analyses.ResultsOur multiomics analysis revealed that the novel 7-gene model (comprising USP36, KLF5, MT2A, KDM6B, PHLDA1, REL, and DDIT4) accurately predicts patient survival, immunotherapy response, and TME status. Notably, PHLDA1 was uniquely overexpressed in CAFs and correlated with the activation of key protumorigenic pathways, including EMT, KRAS, and TGF-β, underscoring its central role in modulating the crosstalk between CAFs and malignant ductal cells. Pan-cancer analysis further supported PHLDA1’s prognostic and immunomodulatory significance across multiple tumor types.ConclusionOur study presents a novel 7-gene prognostic model that significantly enhances risk stratification in PC and identifies PHLDA1+ CAFs as promising prognostic biomarkers and therapeutic targets. These findings provide new insights into the TME of PC and open avenues for personalized treatment strategies. |
| format | Article |
| id | doaj-art-60bfeaa7440f439eae08bff543d054c8 |
| institution | DOAJ |
| issn | 1664-3224 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Immunology |
| spelling | doaj-art-60bfeaa7440f439eae08bff543d054c82025-08-20T03:16:10ZengFrontiers Media S.A.Frontiers in Immunology1664-32242025-07-011610.3389/fimmu.2025.15924161592416Integrated multiomics analysis identifies PHLDA1+ fibroblasts as prognostic biomarkers and mediators of biological functions in pancreatic cancerRui Wang0Rui Wang1Guan-Hua Qin2Guan-Hua Qin3Yifei Jiang4Fu-Xiang Chen5Fu-Xiang Chen6Zi-Han Wang7Zi-Han Wang8Lin-Ling Ju9Lin Chen10Da Fu11En-Yu Liu12Su-Qing Zhang13Wei-Hua Cai14Department of Hepatobiliary Surgery, Affiliated Nantong Hospital 3 of Nantong University, Nantong, Jiangsu, ChinaMedical School of Nantong University, Nantong, Jiangsu, ChinaMedical School of Nantong University, Nantong, Jiangsu, ChinaDepartment of Hepatobiliary and Pancreatic Surgery, Affiliated Tumor Hospital of Nantong University, Nantong, Jiangsu, ChinaDepartment of Nuclear Medicine, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, ChinaMedical School of Nantong University, Nantong, Jiangsu, ChinaDepartment of Hepatobiliary and Pancreatic Surgery, Affiliated Tumor Hospital of Nantong University, Nantong, Jiangsu, ChinaDepartment of Hepatobiliary Surgery, Affiliated Nantong Hospital 3 of Nantong University, Nantong, Jiangsu, ChinaMedical School of Nantong University, Nantong, Jiangsu, ChinaNantong Institute of Liver Disease, Affiliated Nantong Hospital 3 of Nantong University, Nantong, Jiangsu, ChinaNantong Institute of Liver Disease, Affiliated Nantong Hospital 3 of Nantong University, Nantong, Jiangsu, ChinaDepartment of General Surgery, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, ChinaDepartment of General Surgery, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, ChinaDepartment of Hepatobiliary and Pancreatic Surgery, Affiliated Tumor Hospital of Nantong University, Nantong, Jiangsu, ChinaDepartment of Hepatobiliary Surgery, Affiliated Nantong Hospital 3 of Nantong University, Nantong, Jiangsu, ChinaBackgroundPancreatic cancer (PC) is marked by extensive heterogeneity, posing significant challenges to effective treatment. The tumor microenvironment (TME), particularly cancer-associated fibroblasts (CAFs), plays a critical role in driving PC progression. However, the prognostic and functional contributions of distinct CAF subtypes remain inadequately understood. Here, we introduce a novel 7-gene risk model that not only robustly stratifies PC patients but also unveils the unique role of PHLDA1 as a key mediator in tumor-stroma crosstalk.MethodsBy integrating single-cell RNA sequencing (scRNA-seq), spatial transcriptomics, and bulk RNA sequencing data, we comprehensively characterized the heterogeneity of CAFs in PC. We identified five CAF subtypes and focused on matrix CAFs (mCAFs), which were strongly associated with poor prognosis. A 7-gene mCAF-associated risk model was constructed using advanced machine learning algorithms, and the biological significance of PHLDA1 was validated through co-culture experiments and pan-cancer analyses.ResultsOur multiomics analysis revealed that the novel 7-gene model (comprising USP36, KLF5, MT2A, KDM6B, PHLDA1, REL, and DDIT4) accurately predicts patient survival, immunotherapy response, and TME status. Notably, PHLDA1 was uniquely overexpressed in CAFs and correlated with the activation of key protumorigenic pathways, including EMT, KRAS, and TGF-β, underscoring its central role in modulating the crosstalk between CAFs and malignant ductal cells. Pan-cancer analysis further supported PHLDA1’s prognostic and immunomodulatory significance across multiple tumor types.ConclusionOur study presents a novel 7-gene prognostic model that significantly enhances risk stratification in PC and identifies PHLDA1+ CAFs as promising prognostic biomarkers and therapeutic targets. These findings provide new insights into the TME of PC and open avenues for personalized treatment strategies.https://www.frontiersin.org/articles/10.3389/fimmu.2025.1592416/fullpancreatic cancerPHLDA1prognostic biomarkertumor microenvironment (TME)spatial transcriptomics |
| spellingShingle | Rui Wang Rui Wang Guan-Hua Qin Guan-Hua Qin Yifei Jiang Fu-Xiang Chen Fu-Xiang Chen Zi-Han Wang Zi-Han Wang Lin-Ling Ju Lin Chen Da Fu En-Yu Liu Su-Qing Zhang Wei-Hua Cai Integrated multiomics analysis identifies PHLDA1+ fibroblasts as prognostic biomarkers and mediators of biological functions in pancreatic cancer Frontiers in Immunology pancreatic cancer PHLDA1 prognostic biomarker tumor microenvironment (TME) spatial transcriptomics |
| title | Integrated multiomics analysis identifies PHLDA1+ fibroblasts as prognostic biomarkers and mediators of biological functions in pancreatic cancer |
| title_full | Integrated multiomics analysis identifies PHLDA1+ fibroblasts as prognostic biomarkers and mediators of biological functions in pancreatic cancer |
| title_fullStr | Integrated multiomics analysis identifies PHLDA1+ fibroblasts as prognostic biomarkers and mediators of biological functions in pancreatic cancer |
| title_full_unstemmed | Integrated multiomics analysis identifies PHLDA1+ fibroblasts as prognostic biomarkers and mediators of biological functions in pancreatic cancer |
| title_short | Integrated multiomics analysis identifies PHLDA1+ fibroblasts as prognostic biomarkers and mediators of biological functions in pancreatic cancer |
| title_sort | integrated multiomics analysis identifies phlda1 fibroblasts as prognostic biomarkers and mediators of biological functions in pancreatic cancer |
| topic | pancreatic cancer PHLDA1 prognostic biomarker tumor microenvironment (TME) spatial transcriptomics |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2025.1592416/full |
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