Comprehensive Landscape of Ovarian Cancer Immune Microenvironment Based on Integrated Multi-Omics Analysis

Shen, Jiacheng; Liu, Tingwei; Bei, Qiaoli; Xu, Shaohua

doi:10.3389/fonc.2021.685065

Cited by 5 publications

(1 citation statement)

References 49 publications

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“…Besides, KEGG enrichment analysis showed that the DEGs were significantly enriched in the hematopoietic cell lineage, the cytokine-cytokine receptor interaction, the primary immunodeficiency, and allograft rejection. These results indicated that DEGs played a potential role in regulating the TIME and greatly supported that the TIME was involved in OV malignant progression, consistent with previous studies (Westergaard et al, 2020;Shen et al, 2021). Moreover, it provided supporting evidence that both the Immune Class and the non-Immune Class existed in the OV immune microenvironment and differed significantly in their biological functions.…”

Section: Discussionsupporting

confidence: 89%

Identification of the Immune Signatures for Ovarian Cancer Based on the Tumor Immune Microenvironment Genes

Shen

et al. 2022

Front. Cell Dev. Biol.

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Ovarian cancer (OV) is a deadly gynecological cancer. The tumor immune microenvironment (TIME) plays a pivotal role in OV development. However, the TIME of OV is not fully known. Therefore, we aimed to provide a comprehensive network of the TIME in OV. Gene expression data and clinical information from OV patients were obtained from the Cancer Genome Atlas Program (TCGA) database. Non-negative Matrix Factorization, NMFConsensus, and nearest template prediction algorithms were used to perform molecular clustering. The biological functions of differentially expressed genes (DEGs) were identified using Metascape, gene set enrichment analysis (GSEA), gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. The copy number variations (CNVs), single nucleotide polymorphisms (SNPs) and tumor mutation burden were analyzed using Gistic 2.0, R package maftools, and TCGA mutations, respectively. Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data and CIBERSORT were utilized to elucidate the TIME. Moreover, external data from the International Cancer Genome Consortium (ICGC) and ArrayExpress databases were used to validate the signature. All 361 samples from the TCGA OV dataset were classified into Immune Class and non-Immune Class with immune signatures. By comparing the two classes, we identified 740 DEGs that accumulated in immune-related, cancer-related, inflammation-related biological functions and pathways. There were significant differences in the CNVs between the Immune and non-Immune Classes. The Immune Class was further divided into immune-activated and immune-suppressed subtypes. There was no significant difference in the top 20 genes in somatic SNPs among the three groups. In addition, the immune-activated subtype had significantly increased proportions of CD4 memory resting T cells, T cells, M1 macrophages, and M2 macrophages than the other two groups. The qRT-PCR results indicated that the mRNA expression levels of RYR2, FAT3, MDN1 and RYR1 were significantly down-regulated in OV compared with normal tissues. Moreover, the signatures of the TIME were validated using ICGC cohort and the ArrayExpress cohort. Our study clustered the OV patients into an immune-activated subtype, immune-suppressed subtype, and non-Immune Class and provided potential clues for further research on the molecular mechanisms and immunotherapy strategies of OV.

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Section: Discussionsupporting

confidence: 89%