bioRxivImmunologyPreprint
Pan-cancer analysis identified inflamed microenvironment associated multi-omics signatures
Abstract
BackgroundImmunotherapy has revolutionized cancer therapy. However, responses are not universal. The inflamed tumor microenvironment has been reported to correlate with response in tumor patients. However, how different tumors shape their tumor microenvironment remains a critical unsolved problem. A deeper insight into the molecular characteristics of inflamed tumor microenvironment may be needed. Materials and methodsHere, based on single-cell RNA sequencing technology and TCGA pan-cancer cohort, we investigated multi-omics molecular features of tumor microenvironment phenotypes. Based on single-cell RNA-seq analysis, we classified pan-cancer tumor samples into inflamed or non-inflamed tumor and identified molecular features of these tumors. Analysis of integrating identified gene signatures with a drug-genomic perturbation database identified multiple drugs which may be helpful for converting non-inflamed tumors to inflamed tumors. ResultsOur results revealed several inflamed/non-inflamed tumor microenvironments-specific molecular characteristics. For example, inflamed tumors highly expressed miR-650 and lncRNA including MIR155HG and LINC00426, these tumors showed activated cytokines-related signaling pathways. Interestingly, non-inflamed tumors tended to express several genes related to neurogenesis. Multi-omics analysis demonstrated the neuro phenotype transformation may be induced by hypomethylated promoters of these genes and down-regulated miR-650. Drug discovery analysis revealed histone deacetylase inhibitors may be a potential choice for helping favorable tumor microenvironment phenotype transformation and aiding current immunotherapy. ConclusionOur results provide a comprehensive molecular-level understanding of tumor cell-immune cell interaction and may have profound clinical implications.
- organism: human
- assay: scRNA-seq
- assay: DNA methylation
- research focus: cell type annotation
- research focus: differential expression
- research focus: drug response / drug discovery
- approach: deep learning
- approach: statistical model
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