<HashMap><database>biostudies-literature</database><scores/><additional><omics_type>Unknown</omics_type><volume>12</volume><submitter>Zhao G</submitter><pubmed_abstract>&lt;h4>Introduction&lt;/h4>Pancreatic adenocarcinoma (PAAD) is a fatal disease characterized by promoting connective tissue proliferation in the stroma. Activated cancer-associated fibroblasts (CAFs) play a key role in fibrogenesis in PAAD. CAF-based tumor typing of PAAD has not been explored.&lt;h4>Methods&lt;/h4>We extracted single-cell sequence transcriptomic data from GSE154778 and CRA001160 datasets from Gene Expression Omnibus or Tumor Immune Single-cell Hub to collect CAFs in PAAD. On the basis of Seurat packages and new algorithms in machine learning, CAF-related subtypes and their top genes for PAAD were analyzed and visualized. We used CellChat package to perform cell-cell communication analysis. In addition, we carried out functional enrichment analysis based on clusterProfiler package. Fin</pubmed_abstract><journal>Frontiers in oncology</journal><pagination>1045477</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC9762551</full_dataset_link><repository>biostudies-literature</repository><pubmed_title>The novel subclusters based on cancer-associated fibroblast for pancreatic adenocarcinoma.</pubmed_title><pmcid>PMC9762551</pmcid><pubmed_authors>Zhang W</pubmed_authors><pubmed_authors>Yang H</pubmed_authors><pubmed_authors>Wang C</pubmed_authors><pubmed_authors>Zhao G</pubmed_authors><pubmed_authors>Jiao J</pubmed_authors></additional><is_claimable>false</is_claimable><name>The novel subclusters based on cancer-associated fibroblast for pancreatic adenocarcinoma.</name><description>&lt;h4>Introduction&lt;/h4>Pancreatic adenocarcinoma (PAAD) is a fatal disease characterized by promoting connective tissue proliferation in the stroma. Activated cancer-associated fibroblasts (CAFs) play a key role in fibrogenesis in PAAD. CAF-based tumor typing of PAAD has not been explored.&lt;h4>Methods&lt;/h4>We extracted single-cell sequence transcriptomic data from GSE154778 and CRA001160 datasets from Gene Expression Omnibus or Tumor Immune Single-cell Hub to collect CAFs in PAAD. On the basis of Seurat packages and new algorithms in machine learning, CAF-related subtypes and their top genes for PAAD were analyzed and visualized. We used CellChat package to perform cell-cell communication analysis. In addition, we carried out functional enrichment analysis based on clusterProfiler package. Fin</description><dates><release>2022-01-01T00:00:00Z</release><publication>2022</publication><modification>2025-05-29T20:28:52.349Z</modification><creation>2024-11-07T01:34:33.828Z</creation></dates><accession>S-EPMC9762551</accession><cross_references><pubmed>36544710</pubmed><doi>10.3389/fonc.2022.1045477</doi></cross_references></HashMap>