Single-cell analysis of multiple cancer types reveals differences in endothelial cells between tumors and normal tissues

Zhang, Jiayu; Lü, Tong; Lu, Shiqi; Ma, Shuoyi; Han, Donghui; Zhang, Keying; Xu, Chao; Liu, Shaojie; Gan, Lunbiao; Wu, Xinjie; Fa, Yang; Wen, Weihong; Qin, Weijun

doi:10.1016/j.csbj.2022.12.049

Cited by 26 publications

(14 citation statements)

References 49 publications

(65 reference statements)

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“…21 The authors show that these ECs clusters exhibit significant metabolic and immunological heterogeneity, and that targeting specific clusters may hold promise as a therapeutic strategy. 21 The findings of this study are consistent with previous research indicating that the TME plays a crucial role in cancer progression and that communication between malignant and non-malignant cells is a key component of this process. While this approach allowed us to gain insight into the potential role of immune cells in the TME, further experimental validation is needed to confirm the presence and function of these cells.…”

Section: Discussionmentioning

confidence: 99%

“…Interestingly, we also found that MT1F + ECs exhibited the strongest outgoing interaction strength, whereas RGS5 + ECs displayed the strongest incoming interaction strength, indicating that these two clusters may play important roles in facilitating communication between malignant cells and the surrounding microenvironment. The study by Zhang et al contributes to our understanding of the complex interplay between ECs and cancers by identifying several distinct clusters of ECs and characterizing their functional properties 21 . The authors show that these ECs clusters exhibit significant metabolic and immunological heterogeneity, and that targeting specific clusters may hold promise as a therapeutic strategy 21 .…”

Section: Discussionmentioning

confidence: 99%

“…The study by Zhang et al contributes to our understanding of the complex interplay between ECs and cancers by identifying several distinct clusters of ECs and characterizing their functional properties 21 . The authors show that these ECs clusters exhibit significant metabolic and immunological heterogeneity, and that targeting specific clusters may hold promise as a therapeutic strategy 21 . The findings of this study are consistent with previous research indicating that the TME plays a crucial role in cancer progression and that communication between malignant and non‐malignant cells is a key component of this process.…”

Section: Discussionmentioning

confidence: 99%

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

Xie

Tan

et al. 2023

Environmental Toxicology

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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

Xie

Tan

et al. 2023

Environmental Toxicology

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“…Shiau et al performed snRNA-seq on ~15 000 endothelial cells from chemotherapy treated or treatment naïve PDAC patients and identified signatures associated with poor clinical outcomes 57. Zhang et al recently performed a computational analysis of scRNA-seq of endothelial cells from six different cancer types, including PDAC 58. They identified six endothelial subtypes and found that tip-like endothelial cells ( CXCR4+ESM1+ANGPT2+ ) were more abundant in tumours than normal tissue and were positively correlated with reduced patient survival.…”

Section: Introductionmentioning

confidence: 99%

It is better to light a candle than to curse the darkness: single-cell transcriptomics sheds new light on pancreas biology and disease

Cephas

Hwang

Maitra

et al. 2023

Gut

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Recent advances in single-cell RNA sequencing and bioinformatics have drastically increased our ability to interrogate the cellular composition of traditionally difficult to study organs, such as the pancreas. With the advent of these technologies and approaches, the field has grown, in just a few years, from profiling pancreas disease states to identifying molecular mechanisms of therapy resistance in pancreatic ductal adenocarcinoma, a particularly deadly cancer. Single-cell transcriptomics and related spatial approaches have identified previously undescribed epithelial and stromal cell types and states, how these populations change with disease progression, and potential mechanisms of action which will serve as the basis for designing new therapeutic strategies. Here, we review the recent literature on how single-cell transcriptomic approaches have changed our understanding of pancreas biology and disease progression.

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“…The metabolism of endothelial cells (EC) is gaining prominence as a target for anti-angiogenic therapy in the context of tumor angiogenesis and choroidal neovascularization. High proportion of tip-like EC is associated with poor prognosis in multiple cancers, including LUAD(Zhang et al 2023b). The interactions between NK cells, T cells, and endothelial cells in lung cancer and other cancers are complex and involve multiple aspects such as immune surveillance, cytotoxicity, and angiogenesis, fully understanding their role in lung cancer could help todevelop more effective treatment strategies.…”

mentioning

confidence: 99%

Biomarkers Exploration of Epithelial-Mesenchymal Transition and Ferroptosis in Lung Adenocarcinoma: Transcriptomic Analysis and Mendelian Randomization

Zhang,

Hou,

Chen

et al. 2024

Preprint

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Background:Studies have demonstrated the prognostic potential of Epithelial-mesenchymal transition (EMT)-related genes (ERGs) and ferroptosis-related genes (FRGs) for Lung adenocarcinoma (LUAD) patients, but their correlation and underlying molecular mechanisms remain unknown. Thus, this investigation employs transcriptomics, Mendelian randomization (MR) analysis and single-cell data to explore the relationship and prognosis of EMT and ferroptosis with LUAD. Methods:Transcriptomic data from training and validation sets, along with single-cell datasets, were utilized. Prognostic-related differentially expressed (DE)-FRGs and DE-ERGs were identified, and a risk model was constructed using MR analysis and machine learning techniques. Single-cell data were analyzed to characterize model genes’ expression and function. Results:Totally, 40 DE-FRGs and 222 DE-ERGs related to prognostic were identified in TCGA-LUAD. And there were 7 candidate genes by MR analysis. A total of 6 model genes (TFAM, ALOX15, GATA1, ALAD, PDCD4 and NDRG2) were selected to construct a risk model and the model was verified by GSE11969. Meanwhile, stage, N, T, and risk score were screened out as independent prognostic factors via univariate and multivariate cox regression and scores were counted to build a nomogram. Then, MR analysis results revealed that 6 model genes were significantly causally associated with LUAD. Meanwhile, model genes were mainly enriched in NK cells, T cells, Endothelial cell clusters, etc. by single-cell analysis. Conclusion:TFAM, ALOX15, GATA1, ALAD, PDCD4 and NDRG2 played crucial roles in LUAD prognosis, providing refresh directions for the treatment of LUAD.

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Single-cell analysis of multiple cancer types reveals differences in endothelial cells between tumors and normal tissues

Cited by 26 publications

References 49 publications

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

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

It is better to light a candle than to curse the darkness: single-cell transcriptomics sheds new light on pancreas biology and disease

Biomarkers Exploration of Epithelial-Mesenchymal Transition and Ferroptosis in Lung Adenocarcinoma: Transcriptomic Analysis and Mendelian Randomization

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