Transcriptomic landscape of endothelial cells: Key tumor microenvironment components indicating variable clinical outcomes in pancreatic ductal adenocarcinoma

Endothelial cells (ECs) present in the tumor microenvironment (TME) exhibit significant diversity that may impact the efficacy of anti‐tumor treatments. Thus, our study sought to elucidate the various clusters of ECs present in pancreatic ductal adenocarcinoma (PDAC) and explore their possible inter...

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Bibliographic Details
Published in:Environmental toxicology 2024-02, Vol.39 (2), p.572-582
Main Authors: Xie, Peng, Tan, Si‐Yuan, Li, Hai‐Feng, Tang, Hao‐Dong, Zhou, Jia‐hua
Format: Article
Language:English
Subjects:
Abundance
Adenocarcinoma
Bioinformatics
Biomarkers
Carcinoma, Pancreatic Ductal - drug therapy
Carcinoma, Pancreatic Ductal - genetics
Carcinoma, Pancreatic Ductal - metabolism
Cell interactions
Clinical outcomes
Clusters
Cohorts
Communication
Endothelial cells
Endothelial Cells - metabolism
Endothelial Cells - pathology
Gelatinase A
Gene Expression Profiling - methods
Heterogeneity
Humans
immune microenvironment
Immunology
Metabolism
Neoplasms
Pancreas
Pancreatic cancer
pancreatic ductal adenocarcinoma
Pancreatic Neoplasms - drug therapy
Pancreatic Neoplasms - genetics
Patients
Prognosis
Relative abundance
Transcriptome
Transcriptomes
Transcriptomics
Tumor microenvironment
Tumor Microenvironment - genetics
Tumors
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Summary:Endothelial cells (ECs) present in the tumor microenvironment (TME) exhibit significant diversity that may impact the efficacy of anti‐tumor treatments. Thus, our study sought to elucidate the various clusters of ECs present in pancreatic ductal adenocarcinoma (PDAC) and explore their possible interactions and influence on clinical outcomes. We obtained single‐cell transcriptome data from 24 PDAC tumors and 11 normal pancreases, minimizing any batch effects between samples. Next, we compared the relative abundance of various ECs clusters across distinct sample types. Pseudo‐time analysis was employed to investigate the differentiation origin of ECs. A variety of bioinformatics methods were used to investigate potential communication between ECs and malignant cells, as well as assess metabolic changes, pathway alterations, and immune‐related markers expression within distinct EC clusters. Lastly, we investigated the impact of particular ECs clusters on patient prognosis in bulk transcriptome data. Our study identified seven distinct clusters of ECs, denoted as CA4+ ECs, MMP2+ ECs, SPP1+ ECs, MT1F+ ECs, CCL5+ ECs, RGS5+ ECs, and TYROBP+ ECs. Pseudo‐time analysis suggested that the loss of CA4+ ECs and MT1F+ ECs may promote malignant progression. Cell communication elucidated that MT1F+ ECs exhibited the strongest outgoing interaction strength, whereas RGS5+ ECs displayed the strongest incoming interaction strength. Furthermore, TYROBP+ ECs exhibited greater metabolic activity, and notably, CCL5+ ECs displayed increased expression of immune‐related molecules. Lastly, across cohorts of bulk transcriptome levels, CA4+ ECs, MT1F+ ECs, and RGS5+ ECs consistently demonstrated prognostic indicative effects. PDAC patients exhibit the presence of seven distinct EC clusters, each demonstrating significant metabolic and immunological heterogeneity. Targeted therapeutic approaches directed toward CA4+ ECs and MT1F+ ECs may prove advantageous in addressing challenges associated with PDAC treatment. Additionally, variations in the relative abundance of CA4+ ECs, MT1F+ ECs, and RGS5+ ECs were indicated as predictive of patient prognosis.
ISSN:1520-4081
1522-7278
DOI:10.1002/tox.23881