Dataset Information

Comprehensive analysis of metastatic gastric cancer tumour cells using single-cell RNA-seq.

ABSTRACT: Gastric cancer (GC) is a leading cause of cancer-induced mortality, with poor prognosis with metastasis. The mechanism of gastric carcinoma lymph node metastasis remains unknown due to traditional bulk-leveled approaches masking the roles of subpopulations. To answer questions concerning metastasis from the gastric carcinoma intratumoural perspective, we performed single-cell level analysis on three gastric cancer patients with primary cancer and paired metastatic lymph node cancer tissues using single-cell RNA-seq (scRNA-seq). The results showed distinct carcinoma profiles from each patient, and diverse microenvironmental subsets were shared across different patients. Clustering data showed significant intratumoural heterogeneity. The results also revealed a subgroup of cells bridging the metastatic group and primary group, implying the transition state of cancer during the metastatic process. In the present study, we obtained a more comprehensive picture of gastric cancer lymph node metastasis, and we discovered some GC lymph node metastasis marker genes (ERBB2, CLDN11 and CDK12), as well as potential gastric cancer evolution-driving genes (FOS and JUN), which provide a basis for the treatment of GC.

SUBMITTER: Wang B

PROVIDER: S-EPMC7806779 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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Comprehensive analysis of metastatic gastric cancer tumour cells using single-cell RNA-seq.

Wang Bin B Zhang Yingyi Y Qing Tao T Xing Kaichen K Li Jie J Zhen Timing T Zhu Sibo S Zhan Xianbao X

Scientific reports 20210113 1

Gastric cancer (GC) is a leading cause of cancer-induced mortality, with poor prognosis with metastasis. The mechanism of gastric carcinoma lymph node metastasis remains unknown due to traditional bulk-leveled approaches masking the roles of subpopulations. To answer questions concerning metastasis from the gastric carcinoma intratumoural perspective, we performed single-cell level analysis on three gastric cancer patients with primary cancer and paired metastatic lymph node cancer tissues using ...[more]

PMID: 33441952

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Project description:Thyroid hormones mediate a remarkable range of functions in many tissues and organ systems through the thyroid hormone receptors-THRA and THRB. Tissues and organs are composed of heterogeneous cells of different cell types. These different cell types have varying receptor expression abilities, which lead to variable responses in thyroid hormone regulation. The tissue-specific Thra and Thrb gene expression patterns help us understand the action of thyroid hormones at the tissue level. However, the situation becomes complicated if we wish to focus on tissues more closely to trace the responsive cells, which is a vital step in the process of understanding the molecular mechanism of diseases related to thyroid hormone regulation. Single-cell RNA sequencing technology is a powerful tool used to profile gene expression programs in individual cells. The Tabula Muris Consortium generates a single-cell transcriptomic atlas across the life span of Mus musculus that includes data from 23 tissues and organs. It provides an unprecedented opportunity to understand thyroid hormone regulation at the cell type resolution. We demonstrated the approaches that allow application of the single-cell RNA-Seq data generated by the Tabula Muris Consortium to trace responsive cells in tissues. First, employing the single-cell RNA-Seq data, we calculated the ability of different cell types to express Thra and Thrb, which direct us to the cell types sensitive to thyroid hormone regulation in tissues and organs. Next, using a cell clustering algorithm, we explored the subtypes with low Thra or Thrb expression within the different cell types and identified the potentially responsive cell subtypes. Finally, in the liver tissue treated with thyroid hormones, using the single-cell RNA-Seq data, we successfully traced the responsive cell types. We acknowledge that the computational predictions reported here need to be further validated using wet-lab experiments. However, we believe our results provide powerful information and will be beneficial for wet lab researchers.

Dataset Information

Comprehensive analysis of metastatic gastric cancer tumour cells using single-cell RNA-seq.

Publications

Comprehensive analysis of metastatic gastric cancer tumour cells using single-cell RNA-seq.

Similar Datasets

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