We are analyzing https://link.springer.com/article/10.1007/s12094-020-02435-2.

Title:
Immune cell infiltration features and related marker genes in lung cancer based on single-cell RNA-seq | Clinical and Translational Oncology
Description:
Purpose Immune cells in the immune microenvironment of lung cancer have a great impact on the development of lung cancer. Our purpose was to analyze the immune cell infiltration features and related marker genes for lung cancer. Methods Single cell RNA sequencing data of 11,485 lung cancer cells were retrieved from the Gene Expression Omnibus. After quality control and data normalization, cell clustering was performed using the Seurat package. Based on the marker genes of each cell type from the CellMarker database, each cell was divided into G1, G2M, and S phases. Then, differential expression and functional enrichment analyses were performed. CIBERSORT was used to reconstruct immune cell types. Results Following cell filtering, highly variable genes were identified for all cells. 14 cell types were clustered. Among them, CD4 + T cell, B cell, plasma cell, natural killer cell and cancer stem cell were the top five cell types. Up-regulated genes were mainly enriched in immune-related biological processes and pathways. Using CIBERSORT, we identified the significantly higher fractions of naïve B cell, memory CD4 + T cell, T follicular helper cell, T regulatory helper cell and M1 macrophage in lung cancer tissues compared to normal tissues. Furthermore, the fractions of resting NK cell, monocyte, M0 macrophage, resting mast cell, eosinophil and neutrophil were significantly lower in tumor tissues than normal tissues. Conclusion Our findings dissected the immune cell infiltration features and related marker genes for lung cancer, which might provide novel insights for the immunotherapy of lung cancer.
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pubmed, article, cell, google, scholar, cancer, lung, cas, central, immune, cells, data, genes, singlecell, zhang, wang, httpsdoiorgs, nonsmall, marker, chen, sequencing, nat, heterogeneity, content, related, zhong, microenvironment, rna, tumor, patients, oncol, privacy, cookies, infiltration, rnaseq, access, nature, information, publish, research, search, features, based, gene, expression, types, tissues, immunol, shanghai, springer,

Topics {✒️}

single-cell rna-seq reveals single-cell rna-seq data single-cell rna sequencing month download article/chapter single cell rna-seq single-cell rna-seq cytokine-induced killer cells tumor-infiltrating immune cells resting nk cell neoantigen-directed immune escape intra-tumoral immune composition small-cell lung cancer immune-related biological processes pd-1/pd-l1 blockade gene expression omnibus pd-l1-positive advanced full article pdf early tumor response microenvironment immune infiltration cell cycle tissue expression profiles related marker genes immune microenvironment heterogeneity gene ontology consortium tumor immune microenvironment translational oncology aims /d-r3 pathway privacy choices/manage cookies cancer stem cell lung cancer based lung cancer evolution hellmann md tumor-infiltrating lymphocytes natural killer cell cancer cell int 14 cell types cell types article clinical cancer\immunotherapy access hematopoietic stem cells cell death dis regulatory helper cell zh processed data highly variable genes differentially expressed genes follicular helper cell lung adenocarcinoma progression related subjects resting mast cell cell mol immunol

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         description:Immune cells in the immune microenvironment of lung cancer have a great impact on the development of lung cancer. Our purpose was to analyze the immune cell infiltration features and related marker genes for lung cancer. Single cell RNA sequencing data of 11,485 lung cancer cells were retrieved from the Gene Expression Omnibus. After quality control and data normalization, cell clustering was performed using the Seurat package. Based on the marker genes of each cell type from the CellMarker database, each cell was divided into G1, G2M, and S phases. Then, differential expression and functional enrichment analyses were performed. CIBERSORT was used to reconstruct immune cell types. Following cell filtering, highly variable genes were identified for all cells. 14 cell types were clustered. Among them, CD4 + T cell, B cell, plasma cell, natural killer cell and cancer stem cell were the top five cell types. Up-regulated genes were mainly enriched in immune-related biological processes and pathways. Using CIBERSORT, we identified the significantly higher fractions of naïve B cell, memory CD4 + T cell, T follicular helper cell, T regulatory helper cell and M1 macrophage in lung cancer tissues compared to normal tissues. Furthermore, the fractions of resting NK cell, monocyte, M0 macrophage, resting mast cell, eosinophil and neutrophil were significantly lower in tumor tissues than normal tissues. Our findings dissected the immune cell infiltration features and related marker genes for lung cancer, which might provide novel insights for the immunotherapy of lung cancer.
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            Cell cycle
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            Oncology
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