Prognostic Value of CD200R1 mRNA Expression in Head and Neck Squamous Cell Carcinoma

Chang, Hyun; Lee, Yun Gyoo; Ko, Yoon Ho; Cho, Jae Yong; Choi, Jong Kwon; Park, Keon Uk; Kang, Eun Joo; Lee, Moon Hyoung; Lim, Sun Min; Lee, Hyun Woo; Kim, Min Kyoung; Hwang, In Gyu; Kim, Sang Woo; Nam, Byung Ho; Kim, Hye Ryun

doi:10.3390/cancers12071777

Cited by 11 publications

(6 citation statements)

References 50 publications

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“…The VIPR1 gene is downregulated at the level of LUAD cells and plays a key tumor suppressor role in the progression of LUAD [ 33 ]. The mRNA expression of CD2000R1 was a favorable prognostic factor in patients with non-small cell lung cancer [ 34 ]. The analysis in this study suggested that the VEGFC was a risk factor in LUAD, whereas the rest of the nine genes were protective factors, which aligned with the results shown in the mentioned previously reported research.…”

Section: Discussionmentioning

confidence: 99%

Intercellular Communication-Related Molecular Subtypes and a Gene Signature Identified by the Single-Cell RNA Sequencing Combined with a Transcriptomic Analysis

Guan

Cai

et al. 2022

Disease Markers

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Background. The tumor microenvironment (TME) of lung adenocarcinoma (LUAD) comprise various cell types that communicate with each other through ligand-receptor interactions. This study focused on the identification of cell types in LUAD by single-cell RNA sequencing (scRNA-seq) data and screening of intercellular communication-related genes. Methods. The Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo) provided the RNA-seq data of LUAD patients in the GSE149655, GSE31210, and GSE72094 datasets. Quality control of the scRNA-seq data in GSE149655 was performed by the Seurat package (http://seurat.r-forge.r-project.org) for identifying highly variable genes for principal component analysis (PCA) and cell clustering. The CellPhoneDB (http://www.cellphonedb.org) was used for filtering intercellular communication-related ligand-receptor pairs. According to ligand and receptor expressions, LUAD samples were clustered using ConsensusClusterPlus (https://www.bioconductor.org/packages/release/bioc/html/ConsensusClusterPlus). Additionally, the identification of prognosis-related ligand and receptor genes was conducted along with the development of a risk prediction model by the least absolute shrinkage and selection operator (LASSO) Cox regression analysis. Results. This study identified twelve cell types in 8170 cells of LUAD tissues along with 219 ligand and receptor genes. LUAD was classified into three different molecular subtypes, among which cluster 3 (C3) had the longest overall survival (OS) time and cluster (C1) had the shortest OS time. In comparison with the other two molecular subtypes, it was observed that C1 had a higher rate of somatic mutations and lower levels of infiltrating immune cells and immune scores. Ten genes were screened from the total ligand and receptor genes to construct a risk model, which showed a strong prediction power in the prognosis of patients with LUAD. Conclusion. The results of this study revealed cell types specific to LUAD, which were classified into different molecular subtypes according to intercellular communication-related genes. A novel prognostic risk model was developed in this study, providing new insights into prognostic assessment models for LUAD.

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Section: Discussionmentioning

confidence: 99%

Intercellular Communication-Related Molecular Subtypes and a Gene Signature Identified by the Single-Cell RNA Sequencing Combined with a Transcriptomic Analysis

Guan

Cai

et al. 2022

Disease Markers

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“…Yue Chen1, reported that they developed a prognostic marker to predict the prognosis of both conventional therapy and immunotherapy [25]. Hyun Chang, CD200R1, a variety of immune mechanisms, is related to an immunerich microenvironment with high immune cell estimates [12]. No matter how mutated the germline genetic information, it would reshape the tumor microenvironment functional mechanisms and modulatory genes process [29].…”

Section: Discussionmentioning

confidence: 99%

“…The key challenge was the selection of suitable patients and identi cation effective treatment regimens. To nd out better curative effect and explore more treatments methods, many researchers have reported that the expression of immune-related genes were different among immune cells and in uenced HNSCC patient prognosis [11,12]. Just like cancer-associated broblasts cell (CAFs), a common stromal cell, it has been reported play important roles in HNSCC progression and prognosis, such as participate in tumor cell proliferation, angiogenesis, and immunosuppression [13].…”

Section: Introductionmentioning

confidence: 99%

Development and Validation of an Individualized Immune Prognostic Signature in HNSCC

Sun

Zhao

et al. 2022

Preprint

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Head and neck cancer is the seventh most common type of cancer worldwide, and the development of immunotherapy is conducive to the preservation of function and the improvement of prognosis. Based on immune gene database ImmPort and InnateDB, we investigated prognostic value of 546 head and necks squamous cell carcinoma samples from The Cancer Genome Atlas. We intersected of the two databases, selected specific module by WGCNA, and then performed univariate and multivariate Cox regression analyses to screen their prognostic roles in HNSCC patients. Three prognostic immune genes were identified as prognostic gene, including DEFB1, PTX3and SEMA3G. According to the risk scores of patients, we divided HNSCC patients into low- and high-risk subgroups by the median value of risk scores in each dataset. In addition, we calculated immune cells and immune infiltration of the two groups and constructed K-M survival curve plot based on immune infiltration result. Enrichment analysis indicated that cytokines and cytokine receptors interaction was more common in the specific immune gene module. Moreover, we constructed a nomogram based on the results of multivariate cox analysis and clinical information and confirmed the survival of the two groups by using two GEO database. We also established a correlation analysis between immune prognostic signature genes and immune checkpoints. Finally, we believed that PTX3 was the key gene, and the expression levels of PTX3 in 30 clinical samples was detected by immunohistochemical method. In conclusion, PTX3 is a key immune gene, which may contribute to poor prognosis of HNSCC by affecting TME.

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“…However, many recent studies have reported TOX as a promoting factor for T cell exhaustion [29,30,48,49], while CD200R1 is a favorable prognostic factor [50], suggesting that those samples may show less exhaustion presumably. To further demonstrate that the CD200R1, TOX, and other well-studied genes indeed contribute to T cell exhaustion rather than due to certain diseases and technique factors, we reconducted the WGCNA analysis by removing the T1D dataset (anti-CD3 therapy) (S4 Fig) and confirmed the expressions of corresponding genes in each dataset (S5 Fig) . In detail, the WGCNA analysis revealed the highest positive correlation between exhaustion and the brown module.…”

Section: Plos Onementioning

confidence: 99%

Transcriptome analysis of Homo sapiens and Mus musculus reveals mechanisms of CD8+ T cell exhaustion caused by different factors

Zhang

Nishi

2022

PLoS ONE

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T cell exhaustion is a state of T cell dysfunction during chronic infection and cancer. Antibody-targeting immune checkpoint inhibitors to reverse T cell exhaustion is a promising approach for cancer immunotherapy. However, molecular mechanisms of T cell exhaustion remain incompletely understood. Here, we performed a transcriptome analysis by integrating seven exhaustion datasets caused by multiple diseases in both humans and mice. In this study, an overlap of 21 upregulated and 37 downregulated genes was identified in human and mouse exhausted CD8+ T cells. These genes were significantly enriched in exhaustion response-related pathways, such as signal transduction, immune system processes, and regulation of cytokine production. Gene expression network analysis revealed that the well-documented exhaustion genes were defined as hub genes in upregulated genes. In addition, a weighted gene co-expression analysis identified 175 overlapping genes that were significantly correlated with the exhaustion trait in both humans and mice. This study found that overlapping six genes were significantly upregulated and highly related to T cell exhaustion. Finally, we revealed that CD200R1 and ADGRG1, less described previously in exhaustion, contributed to T cell exhaustion. Overall, our findings reveal the mechanisms of T cell exhaustion and provide an important reference to the immunology community.

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Prognostic Value of CD200R1 mRNA Expression in Head and Neck Squamous Cell Carcinoma

Cited by 11 publications

References 50 publications

Intercellular Communication-Related Molecular Subtypes and a Gene Signature Identified by the Single-Cell RNA Sequencing Combined with a Transcriptomic Analysis

Intercellular Communication-Related Molecular Subtypes and a Gene Signature Identified by the Single-Cell RNA Sequencing Combined with a Transcriptomic Analysis

Development and Validation of an Individualized Immune Prognostic Signature in HNSCC

Transcriptome analysis of Homo sapiens and Mus musculus reveals mechanisms of CD8+ T cell exhaustion caused by different factors

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