Genomic analysis and clinical implications of immune cell infiltration in gastric cancer.

Abstract

The immune infiltration of patients with gastric cancer (GC) is closely associated with clinical prognosis. However, previous studies failed to explain the different subsets of immune cells involved in immune responses and diverse functions. The present study aimed to uncover the differences in immunophenotypes in a tumor microenvironment (TME) between adjacent and tumor tissues and to explore their therapeutic targets. In our study, the relative proportion of immune cells in 229 GC tumor samples and 22 paired matched tissues was evaluated with a Cell type Identification By Estimating Relative Subsets Of known RNA Transcripts (CIBERSORT) algorithm. The correlation between immune cell infiltration and clinical information was analyzed. The proportion of 22 immune cell subsets was assessed to determine the correlation between each immune cell type and clinical features. Three molecular subtypes were identified with 'CancerSubtypes' R-package. Functional enrichment was analyzed in each subtype. The profiles of immune infiltration in the GC cohort from The Cancer Genome Atlas (TCGA) varied significantly between the 22 paired tissues. TNM stage was associated with M1 macrophages and eosinophils. Follicular helper T cells were activated at the late stage. Monocytes were associated with radiation therapy. Three clustering processes were obtained via the 'CancerSubtypes' R-package. Each cancer subtype had a specific molecular classification and subtype-specific characterization. These findings showed that the CIBERSOFT algorithm could be used to detect differences in the composition of immune-infiltrating cells in GC samples, and these differences might be an important driver of GC progression and treatment response.

• Authors: Ming Wu, Yadong Wang, Hang Liu, Jukun Song, Jie Ding
• Journal: Bioscience reports (Biosci Rep) Vol. 40 Issue 5 (05 29 2020) ISSN: 1573-4935 [Electronic] England
• PMID: 32338286 (Publication Type: Journal Article, Research Support, Non-U.S. Gov't)
• Copyright: © 2020 The Author(s).

Topics

• Algorithms
• Computational Biology
• Databases, Genetic
• Eosinophils (classification, immunology)
• Gene Expression Profiling
• Genomics
• Humans
• Immunophenotyping
• Lymphocytes, Tumor-Infiltrating (classification, immunology)
• Phenotype
• Stomach Neoplasms (genetics, immunology, therapy)
• Transcriptome
• Tumor Microenvironment
• Tumor-Associated Macrophages (classification, immunology)