An integrative microenvironment approach for laryngeal carcinoma: the role of immune/methylation/autophagy signatures on disease clinical prognosis and single-cell genotypes

Kang, Xueran; Chen, Yisheng; Yi, Bin; Xin, Yan; Jiang, Chenyan; Chen, Bin; Lu, Lixing; Sun, Yuxing; Shi, Ruihan

doi:10.7150/jca.58076

Cited by 20 publications

(15 citation statements)

References 103 publications

(112 reference statements)

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“…Gene set variation analysis (GSVA) was used to quantify the expression of pathways in each sample, completed by using the “GSVA” R package [ 22 – 24 ]. Single cells play an important role in revealing the heterogeneity of tissue and gene expression [ 25 , 26 ]. Single-cell expression profile data of MyD88 in bronchial cells were obtained from Tabula Muris ( https://tabula-muris.ds.czbiohub.org/ ), a single-cell transcriptome profile for Mus mucus [ 27 – 29 ].…”

Section: Methodsmentioning

confidence: 99%

Bioinformatic Analysis and Cellular Assays Identify Substance P Influencing Th17/Treg Differentiation via the MyD88 Pathway as a Potential Contributor to the Progression of Asthma and Allergic Rhinitis

Liu

et al. 2022

Disease Markers

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Objective. This study is aimed at investigating the role of substance P (SP) in the development of asthma. Methods. The Gene Expression Omnibus (GEO) database was used to characterize SP expression in allergic rhinitis (AR) and asthma. Peripheral blood was collected from patients with asthma or AR. The expression of relevant cytokines and neuropeptides was measured. Enzyme-linked immunosorbent assay (ELISA) was also performed. The mast cell line LAD2 and the lung bronchial epithelial cell line BEAS-2B were treated with different concentrations of SP concentration. Then, the qRT-PCR method was used to determine the mRNA expression. Furthermore, p38 and p65 and their associated phosphorylated proteins (p-p38 and p-p65) were further validated by western blotting. Result. Clinical and GSE75011 data analysis suggested that MyD88 expression was upregulated in AR and asthma. Through the gene set variation analysis (GSVA), MyD88-related pathways were noticed and further investigated. ELISA results suggested that the SP expression was significantly increased in AR and asthma and IL-10 expression was decreased, whereas the expression of IL-6, IL-17A, IL-23, and TGF-β expressions increased. The mast cell line LAD2 was treated with different SP concentrations, and ELISA results showed that the expression of IL-6, IL-17A, IL-23, and TGF-β in the cell supernatant gradually increased with increasing SP concentrations, whereas that of IL-10 decreased. The lung bronchial epithelial cell line BEAS-2B was treated with different SP concentrations, and the expression of myeloid differentiation factor 88 (MyD88) and its related proteins was elevated. The expression of p38 and p-p38 proteins was elevated after SP treatment, and their expression levels elevated as SP concentrations increased. Finally, MyD88 expression at the single-cell level was also demonstrated. Conclusion. SP may affect the cytokine expression through the MyD88 pathway, thereby influencing Th17/Treg differentiation and eventually participating in the pathological process of asthma and AR. There are many pathological similarities between allergic rhinitis (AR) and bronchial asthma. In the present study, SP was found to possibly activate downstream inflammatory signaling pathways via MyD88, thereby affecting Th17/Treg differentiation and ultimately participating in the pathological process of asthma and AR.

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Section: Methodsmentioning

confidence: 99%

Bioinformatic Analysis and Cellular Assays Identify Substance P Influencing Th17/Treg Differentiation via the MyD88 Pathway as a Potential Contributor to the Progression of Asthma and Allergic Rhinitis

Liu

et al. 2022

Disease Markers

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“…Ligand-receptor analysis is a good way to understand the state of interaction between different types of cells in the microenvironment [ 15 , 22 , 23 ]. To systematically analyze the cell-cell communication network, we used CellPhoneDB, a public knowledge base of ligands, receptors, and their interactions, for the annotation of membrane proteins, secreted proteins, and peripheral proteins clustered at different time points and for the calculation of their significance means and cell communication significance [ 24 ].…”

Section: Methodsmentioning

confidence: 99%

The Neuronal Transcription Factor Creb3l1 Potential Upregulates Ntrk2 in the Hypertensive Microenvironment to Promote Vascular Smooth Muscle Cell-Neuron Interaction and Prevent Neurons from Ferroptosis: A Bioinformatic Research of scRNA-seq Data

Zhao

Yang

2022

Disease Markers

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Background. There is still a lack of knowledge regarding the association between hypertension and ferroptosis. A single-cell approach was used to study the changes in neuropeptide expression as they might contribute to the mechanisms leading to ferroptosis in a hypertensive microenvironment. Methods. We analyzed 11798 cells from the SHR group and 12589 cells from the WKY group of mouse arterial cells. CellPhoneDB was used for cell communication analysis, and the SCENIC method was used to identify key transcription factors in neurons. The correlation between Ntrk2 and ferroptosis-related genes was further analyzed and validated via quantitative polymerase chain reaction. Results. The arterial cells were clustered into six cell types. Ligand-receptor analysis suggested that Ngf, Ntf3, Cxcr4, and Ntrk2 were key neuropeptide-related genes involved in the communication between vascular smooth muscle cells and neural cells. In the hypertensive microenvironment, the neuronal transcription factor Creb3l1 appears to play a key role in the upregulation of Ntrk2 to promote the interaction between neurons and vascular smooth muscle cells. An association between Ntrk2 and the ferroptosis death inhibitor Gpx4 was suggested. RT-qPCR experiments confirmed that Ntrk2 downregulation in neural cells was followed by downregulated expression of Gpx4. Conclusions. Creb3l1, a key transcription factor in vascular neurons, may upregulate Ntrk2 to promote vascular smooth muscle cell-neuron interaction and thereby potentially prevent ferroptosis in neurons.

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“…Quality control was carried out as described previously [ 8 ]. Similar to previous studies, the single-cell analysis was completed using the Seurat package [ 5 , 9 ]. Briefly, the “Find neighbors” and “Find Clusters” functions from R software were utilized to perform principal component analysis and cell clustering [ 10 ].…”

Section: Methodsmentioning

confidence: 99%

“…These controversies revolve around whether neural development is induced or transformed [ 3 , 4 ]. Recent studies now include the single-cell transcriptome that provides a way to precisely map the transcriptional heterogeneity of cells across time [ 5 , 6 ]. As a result, CNS research has made tremendous progress in recent years due to the rapid advances in single-cell histology techniques [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Identification of KLF6/PSGs and NPY-Related USF2/CEACAM Transcriptional Regulatory Networks via Spinal Cord Bulk and Single-Cell RNA-Seq Analysis

2021

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Background. To further understand the development of the spinal cord, an exploration of the patterns and transcriptional features of spinal cord development in newborn mice at the cellular transcriptome level was carried out. Methods. The mouse single-cell sequencing (scRNA-seq) dataset was downloaded from the GSE108788 dataset. Single-cell RNA-Seq (scRNA-Seq) was conducted on cervical and lumbar spinal V2a interneurons from 2 P0 neonates. Single-cell analysis using the Seurat package was completed, and marker mRNAs were identified for each cluster. Then, pseudotemporal analysis was used to analyze the transcription changes of marker mRNAs in different clusters over time. Finally, the functions of these marker mRNAs were assessed by enrichment analysis and protein-protein interaction (PPI) networks. A transcriptional regulatory network was then constructed using the TRRUST dataset. Results. A total of 949 cells were screened. Single-cell analysis was conducted based on marker mRNAs of each cluster, which revealed the heterogeneity of neonatal mouse spinal cord neuronal cells. Functional analysis of pseudotemporal trajectory-related marker mRNAs suggested that pregnancy-specific glycoproteins (PSGs) and carcinoembryonic antigen cell adhesion molecules (CEACAMs) were the core mRNAs in cluster 3. GSVA analysis then demonstrated that the different clusters had differences in pathway activity. By constructing a transcriptional regulatory network, USF2 was identified to be a transcriptional regulator of CEACAM1 and CEACAM5, while KLF6 was identified to be a transcriptional regulator of PSG3 and PSG5. This conclusion was then validated using the Genotype-Tissue Expression (GTEx) spinal cord transcriptome dataset. Conclusions. This study completed an integrated analysis of a single-cell dataset with the utilization of marker mRNAs. USF2/CEACAM1&5 and KLF6/PSG3&5 transcriptional regulatory networks were identified by spinal cord single-cell analysis.

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An integrative microenvironment approach for laryngeal carcinoma: the role of immune/methylation/autophagy signatures on disease clinical prognosis and single-cell genotypes

Cited by 20 publications

References 103 publications

Bioinformatic Analysis and Cellular Assays Identify Substance P Influencing Th17/Treg Differentiation via the MyD88 Pathway as a Potential Contributor to the Progression of Asthma and Allergic Rhinitis

Bioinformatic Analysis and Cellular Assays Identify Substance P Influencing Th17/Treg Differentiation via the MyD88 Pathway as a Potential Contributor to the Progression of Asthma and Allergic Rhinitis

The Neuronal Transcription Factor Creb3l1 Potential Upregulates Ntrk2 in the Hypertensive Microenvironment to Promote Vascular Smooth Muscle Cell-Neuron Interaction and Prevent Neurons from Ferroptosis: A Bioinformatic Research of scRNA-seq Data

Identification of KLF6/PSGs and NPY-Related USF2/CEACAM Transcriptional Regulatory Networks via Spinal Cord Bulk and Single-Cell RNA-Seq Analysis

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