The role of epigenetic regulation in pancreatic ductal adenocarcinoma progression and drug response: an integrative genomic and pharmacological prognostic prediction model
BackgroundPancreatic ductal adenocarcinoma (PDAC) is a highly lethal malignancy with poor prognosis. Epigenetic dysregulation plays a crucial role in PDAC progression, but its comprehensive landscape and clinical implications remain unclear.MethodsWe integrated single-cell RNA sequencing, bulk RNA s...
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Frontiers Media S.A.
2024-11-01
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| Series: | Frontiers in Pharmacology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fphar.2024.1498031/full |
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| author | Kang Fu Junzhe Su Yiming Zhou Xiaotong Chen Xiao Hu |
| author_facet | Kang Fu Junzhe Su Yiming Zhou Xiaotong Chen Xiao Hu |
| author_sort | Kang Fu |
| collection | DOAJ |
| description | BackgroundPancreatic ductal adenocarcinoma (PDAC) is a highly lethal malignancy with poor prognosis. Epigenetic dysregulation plays a crucial role in PDAC progression, but its comprehensive landscape and clinical implications remain unclear.MethodsWe integrated single-cell RNA sequencing, bulk RNA sequencing, and clinical data from multiple public databases. Single-cell analysis was performed using Seurat and hdWGCNA packages to reveal cell heterogeneity and epigenetic features. Weighted gene co-expression network analysis (WGCNA) identified key epigenetic modules. A machine learning-based prognostic model was constructed using multiple algorithms, including Lasso and Random Survival Forest. We further analyzed mutations, immune microenvironment, and drug sensitivity associated with the epigenetic risk score.ResultsSingle-cell analysis revealed distinct epigenetic patterns across different cell types in PDAC. WGCNA identified key modules associated with histone modifications and DNA methylation. Our machine learning model, based on 17 epigenetic genes, showed robust prognostic value (AUC >0.7 for 1-, 3-, and 5-year survival) and outperformed existing models. High-risk patients exhibited distinct mutation patterns, including higher frequencies of KRAS and TP53 mutations. Low-risk patients showed higher immune and stromal scores, with increased infiltration of CD8+ T cells and M2 macrophages. Drug sensitivity analysis revealed differential responses to various therapeutic agents between high- and low-risk groups, with low-risk patients showing higher sensitivity to EGFR and MEK inhibitors.ConclusionOur study provides a comprehensive landscape of epigenetic regulation in PDAC at single-cell resolution and establishes a robust epigenetics-based prognostic model. The integration of epigenetic features with mutation profiles, immune microenvironment, and drug sensitivity offers new insights into PDAC heterogeneity and potential therapeutic strategies. These findings pave the way for personalized medicine in PDAC management and highlight the importance of epigenetic regulation in cancer research. |
| format | Article |
| id | doaj-art-8d5f886d48e447fcab5c5ebb3c441c21 |
| institution | OA Journals |
| issn | 1663-9812 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Pharmacology |
| spelling | doaj-art-8d5f886d48e447fcab5c5ebb3c441c212025-08-20T02:13:01ZengFrontiers Media S.A.Frontiers in Pharmacology1663-98122024-11-011510.3389/fphar.2024.14980311498031The role of epigenetic regulation in pancreatic ductal adenocarcinoma progression and drug response: an integrative genomic and pharmacological prognostic prediction modelKang FuJunzhe SuYiming ZhouXiaotong ChenXiao HuBackgroundPancreatic ductal adenocarcinoma (PDAC) is a highly lethal malignancy with poor prognosis. Epigenetic dysregulation plays a crucial role in PDAC progression, but its comprehensive landscape and clinical implications remain unclear.MethodsWe integrated single-cell RNA sequencing, bulk RNA sequencing, and clinical data from multiple public databases. Single-cell analysis was performed using Seurat and hdWGCNA packages to reveal cell heterogeneity and epigenetic features. Weighted gene co-expression network analysis (WGCNA) identified key epigenetic modules. A machine learning-based prognostic model was constructed using multiple algorithms, including Lasso and Random Survival Forest. We further analyzed mutations, immune microenvironment, and drug sensitivity associated with the epigenetic risk score.ResultsSingle-cell analysis revealed distinct epigenetic patterns across different cell types in PDAC. WGCNA identified key modules associated with histone modifications and DNA methylation. Our machine learning model, based on 17 epigenetic genes, showed robust prognostic value (AUC >0.7 for 1-, 3-, and 5-year survival) and outperformed existing models. High-risk patients exhibited distinct mutation patterns, including higher frequencies of KRAS and TP53 mutations. Low-risk patients showed higher immune and stromal scores, with increased infiltration of CD8+ T cells and M2 macrophages. Drug sensitivity analysis revealed differential responses to various therapeutic agents between high- and low-risk groups, with low-risk patients showing higher sensitivity to EGFR and MEK inhibitors.ConclusionOur study provides a comprehensive landscape of epigenetic regulation in PDAC at single-cell resolution and establishes a robust epigenetics-based prognostic model. The integration of epigenetic features with mutation profiles, immune microenvironment, and drug sensitivity offers new insights into PDAC heterogeneity and potential therapeutic strategies. These findings pave the way for personalized medicine in PDAC management and highlight the importance of epigenetic regulation in cancer research.https://www.frontiersin.org/articles/10.3389/fphar.2024.1498031/fullpancreatic ductal adenocarcinomaepigenetic regulationsingle-cell RNA sequencingmachine learningprognostic modeltumor microenvironment |
| spellingShingle | Kang Fu Junzhe Su Yiming Zhou Xiaotong Chen Xiao Hu The role of epigenetic regulation in pancreatic ductal adenocarcinoma progression and drug response: an integrative genomic and pharmacological prognostic prediction model Frontiers in Pharmacology pancreatic ductal adenocarcinoma epigenetic regulation single-cell RNA sequencing machine learning prognostic model tumor microenvironment |
| title | The role of epigenetic regulation in pancreatic ductal adenocarcinoma progression and drug response: an integrative genomic and pharmacological prognostic prediction model |
| title_full | The role of epigenetic regulation in pancreatic ductal adenocarcinoma progression and drug response: an integrative genomic and pharmacological prognostic prediction model |
| title_fullStr | The role of epigenetic regulation in pancreatic ductal adenocarcinoma progression and drug response: an integrative genomic and pharmacological prognostic prediction model |
| title_full_unstemmed | The role of epigenetic regulation in pancreatic ductal adenocarcinoma progression and drug response: an integrative genomic and pharmacological prognostic prediction model |
| title_short | The role of epigenetic regulation in pancreatic ductal adenocarcinoma progression and drug response: an integrative genomic and pharmacological prognostic prediction model |
| title_sort | role of epigenetic regulation in pancreatic ductal adenocarcinoma progression and drug response an integrative genomic and pharmacological prognostic prediction model |
| topic | pancreatic ductal adenocarcinoma epigenetic regulation single-cell RNA sequencing machine learning prognostic model tumor microenvironment |
| url | https://www.frontiersin.org/articles/10.3389/fphar.2024.1498031/full |
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