Dataset Information

Immune Infiltration Landscape in Lung Squamous Cell Carcinoma Implications.

ABSTRACT: 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.

SUBMITTER: Zhao J

PROVIDER: S-EPMC7569448 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Immune Infiltration Landscape in Lung Squamous Cell Carcinoma Implications.

Zhao Jungang J Bao Wenming W Cai Weiyang W

BioMed research international 20201010

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, prin ...[more]

PMID: 33102584

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Project description:BackgroundIncreasing evidence supports that immune cell infiltration (ICI) patterns play a key role in the tumor progression of lung squamous cell carcinoma (LUSC). However, to date, the immune infiltration picture of LUSC has not been elucidated.MethodTCGA was used to download multiomics data from LUSC samples. At the same time, we included two datasets on lung squamous cell carcinoma, GSE17710 and GSE157010. To reveal the landscape of tumor immune microenvironment (TIME), the ESTIMATE algorithm, ssGSEA approach, and CIBERSORT analysis are used. To quantify the ICI pattern in a single tumor, consistent clustering is used to determine the LUSC subtype based on the ICI pattern, and principal component analysis (PCA) is used to obtain the ICI score. The prognostic value of the Kaplan-Meier curves is confirmed. GSEA (Gene Set Enrichment Analysis) was used to perform functional annotation. To investigate the immunotherapeutic effects of the ICI score, the immunophenotyping score (IPS) is used. Finally, analyze the mutation data with the "maftools" R package.ResultsWe identified four different immune infiltration patterns with different prognosis and biological characteristics in 792 LUSC samples. The identification of ICI patterns in individual tumors developed under ICI-related characteristic genes based on the ICI score helps to analyze the biological process, clinical results, immune cell infiltration, immunotherapy effects, and genetic variation. Immune failure is indicated by a high ICI score subtype marked by immunosuppression. Patients with low ICI scores have an abundance of efficient immune cells, which corresponds to the immunological activation phenotype and may have therapeutic benefits. The immunophenotypic score was used as a surrogate indicator of immunotherapy results, and samples with low ICI scores obtained significantly higher immunophenotypic scores. Finally, the relationship between the ICI score and tumor mutation burden (TMB) was proven.ConclusionThis study fully clarified the indispensable role of the ICI model in the complexity and diversity of TIME. The quantitative identification of ICI patterns in a single tumor will help draw the picture of TIME and further optimize precision immunotherapy.

Project description:BackgroundImmunotherapy has achieved remarkable efficacy in treating oesophageal squamous cell carcinoma (ESCC). However, this treatment has limited efficacy in some patients. An increasing number of evidence suggested that immune cells within the tumour microenvironment (TME) are strongly related to immunotherapy response and patient prognosis. Thus, the landscape of immune cell infiltration (ICI) in ESCC needs to be mapped.MethodsIn the study, the ICI pattern in 206 cases of ESCC was characterised by two algorithms, namely, CIBERSORT and single-sample gene set enrichment analysis (ssGSEA). The ICI score of each specimen was calculated by principal component analysis (PCA) according to ICI signature genes A (ICISGA) and B (ICISGB). The prognostic difference was evaluated by using the Kaplan-Meier method. The related pathways of ICI score were investigated by applying gene set enrichment analysis (GSEA). The R packages of 'regplot', 'timeROC' and 'rms' were applied for the construction of nomogram model.ResultThree TME subtypes were identified with no prognostic implication. A total of 333 differentially expressed genes (DEGs) among immune subtypes were determined, among which ICISGA and ICISGB were identified. Finally, ICI scores were constructed, and the patients were grouped into high or low ICI score group. Compared with the low ICI score group, the high ICI score group had better prognosis. GSEA revealed that the high ICI score group referred to multiple signalling pathways, including B cell receptor, Fc gamma R-mediated phagocytosis, NOD-like receptor and TGF-β signalling pathways. In addition, the nomogram model was constructed to evaluate 1-, 3- and 5-year probability of death in an ESCC patient. The ROC and calibration curves indicated that the model has a good discrimination ability.ConclusionWe depicted a comprehensive ICI landscape in ESCC. ICI score may be used as a predictor of survival rate, which may be helpful for guiding immunotherapy in the future.

Project description:Regucalcin (RGN) is a potent inhibitory protein of calcium signaling and expresses in various tissues. However, the role of RGN in the tumor immunological microenvironment in lung squamous cell carcinoma (LUSC) remains unclear. This study identified the expression of RGN from public databases and immunohistochemistry with clinical specimen. The association between RGN and the tumor immune microenvironment (TIME) was investigated in LUSC by ESTIMATE and CIBERSORT algorithms. Similarly, the Tumor IMmune Estimation Resource (TIMER) database was used to identify the correlation between RGN and immune cells. The ceRNA network was established based on the data obtained from public databases. Finally, prediction of drug response to chemotherapy and immunotherapy was performed to evaluate clinical significance. This study found that RGN expression was significantly downregulated in tumor tissues and closely related to clinical factors and prognosis of LUSC patients. Differentially expressed genes (DEGs) grouped by the expression of RGN were mostly involved in immunobiological processes such as humoral immune response and leukocyte mediated immunity. RGN and its related miRNA (has-miR-203a-3p) and lncRNAs (ZNF876P and PSMG3-AS1) constructed the novel prognosis-related ceRNA network. Plasma cells, T cells CD4 memory resting, Macrophages M0, Macrophages M1, Mast cells resting, Mast cells activated and Neutrophils showed significantly different levels of infiltration between high and low RGN expression groups. The TIMER database showed that RGN expression was positively correlated with certain immune infiltrating cells. High RGN expression group showed a higher TIDE score, a higher dysfunction score and a lower MSI score, presenting a possible lower efficacy after accepting the immunotherapy than low RGN expression group. RGN expression was closely associated with prognosis of LUSC patients and played an important role in tumor microenvironment. This suggests that RGN could be a promising biomarker for assessing immunotherapy efficacy and prognosis.

Dataset Information

Immune Infiltration Landscape in Lung Squamous Cell Carcinoma Implications.

Publications

Immune Infiltration Landscape in Lung Squamous Cell Carcinoma Implications.

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