scholarly journals A novel model based on liquid‐liquid phase separation–Related genes correlates immune microenvironment profiles and predicts prognosis of lung squamous cell carcinoma

2021 ◽  
Author(s):  
Lingdun Zhuge ◽  
Kun Zhang ◽  
Zeliang Zhang ◽  
Wentao Guo ◽  
Yang Li ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Phase Separation ◽  
Liquid Phase ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Lung Squamous Cell Carcinoma ◽  
Liquid Phase Separation ◽  
Immune Microenvironment ◽  
Novel Model ◽  
Liquid Liquid Phase Separation

scholarly journals Prognostic Impact of Immune Microenvironment in Lung Squamous Cell Carcinoma

2015 ◽  
Vol 10 (9) ◽  
pp. 1301-1310 ◽  
Author(s):  
Kyuichi Kadota ◽  
Jun-ichi Nitadori ◽  
Hideki Ujiie ◽  
Daniel H. Buitrago ◽  
Kaitlin M. Woo ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Lung Squamous Cell Carcinoma ◽  
Prognostic Impact ◽  
Immune Microenvironment

scholarly journals Immune Infiltration Landscape in Lung Squamous Cell Carcinoma Implications

2020 ◽  
Vol 2020 ◽  
pp. 1-21
Author(s):  
Jungang Zhao ◽  
Wenming Bao ◽  
Weiyang Cai
Keyword(s):  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Clustering Analysis ◽  
Immune Cell ◽  
Lung Squamous Cell Carcinoma ◽  
Principal Component ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Immune Microenvironment

Intrinsic cancer cells and the tumor-infiltrating immune cells (TIICs) recruited to the immune microenvironment define the malignant phenotype of lung squamous cell carcinoma (LUSC). Understanding more about the immune microenvironment of LUSC enables the selection of high-risk patients who would derive benefit from immunotherapy. Based on large public LUSC cohorts obtained from TCGA and GEO datasets, 22 types of infiltrating immune cell subgroups were evaluated by CIBERSORT. Meta-analysis, principal component analysis (PCA), single-sample gene set enrichment analysis (ssGSEA), and hierarchical clustering analysis were used to evaluate specific immune responses of LUSC. The distribution of TIICs of LUSC was entirely different from normal. TIIC subpopulations were also found to be closely associated with clinical features and molecular subtypes. Unsupervised clustering analysis revealed that three distinct TIIC subgroups existed with different survival patterns. TIICs are extensively implicated in the pathogenesis and development of LUSC. Characterizing the composition of TIICs influences the metabolism, pathological stage, and survival of tumor patients. It is hoped that this immune landscape could provide a more accurate understanding of the development and immunotherapy of LUSC.

scholarly journals P2.01-064 Molecular Context of Immune Microenvironment in Early-Stage Lung Squamous Cell Carcinoma

2017 ◽  
Vol 12 (1) ◽  
pp. S825-S826
Author(s):  
Esther Conde ◽  
Alejandra Caminoa ◽  
Carolina Dominguez ◽  
Stefan Walter ◽  
Marta Alonso ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Early Stage ◽  
Lung Squamous Cell Carcinoma ◽  
Immune Microenvironment ◽  
Molecular Context

scholarly journals m5C RNA Methylation Regulators Predict Prognosis and Regulate the Immune Microenvironment in Lung Squamous Cell Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Junfan Pan ◽  
Zhidong Huang ◽  
Yiquan Xu
Keyword(s):  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Epigenetic Modification ◽  
Lung Squamous Cell Carcinoma ◽  
Normal Lung ◽  
Immune Microenvironment ◽  
Genetic Characteristics ◽  
Clinical Prognosis ◽  
Rna Methylation

RNA methylation is a novel epigenetic modification that can be used to evaluate tumor prognosis. However, the underlying mechanisms are unclear. This study aimed to investigate the genetic characteristics of 5-methylcytosine (m5C) and N1-methyladenosine (m1A) regulators in lung squamous cell carcinoma (LUSC) and the prognostic value and immune-related effects of m5C regulators. To this end, we selected the public LUSC dataset from the Cancer Genome Atlas and Gene Expression Omnibus. The least absolute shrinkage and selection operator regression model was used to identify prognostic risk signatures. We used the UALCAN and Human Protein Atlas databases to study the expression of target gene mRNA/protein expression. Furthermore, the Tumor Immune Single Cell Hub and the Tumor Immune Estimation Resource were used to evaluate the degree of immune cell infiltration. Most of the m5C and m1A regulators showed significantly different expression between LUSC and normal samples. The m5C regulators were associated with poor prognosis. In addition, a prognostic risk signature was developed based on two m5C regulators, NOP2/Sun RNA methyltransferase 3 (NSUN3), and NOP2/Sun RNA methyltransferase 4 (NSUN4). Compared with normal lung tissues, the expression of NSUN3 and NSUN4 in the LUSC TCGA dataset was increased, which was related to clinicopathological characteristics and survival. NSUN3 and NSUN4 were related to the infiltration of six major immune cells; especially NSUN3, which was closely related to CD8+ T cells, while NSUN4 was closely related to neutrophils. Our findings suggest that m5C regulators can predict the clinical prognosis risk and regulate the tumor immune microenvironment in LUSC.

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