Dataset Information

Immune cell landscape analysis reveals prognostic immune cells and its potential mechanism in squamous cell lung carcinoma.

ABSTRACT: Background:Squamous cell lung carcinoma (LUSC) was closely associated with smoking which was known to have a distant immunosuppression effect. In this study, we aimed to explore the relationship between immune cells and clinical outcomes of LUSC patients with smoking history. Methods:The immune cell infiltration and RNA expression profiles of LUSC patients were collected from The Cancer Genome Atlas (TCGA). Then, the correlation between immune cell infiltration and clinical characteristics was explored. According to the level of immune cell infiltration, LUSC patients with smoking history were divided into high or low group to screen the differentially expressed lncRNAs and mRNAs. The prediction of target genes was performed by miRanda. Finally, the prognostic value of a certain signature was confirmed in an independent dataset. Results:Higher abundance of tumor-infiltrating T follicular helper (Tfh) cells together with a lower abundance of resting memory CD4 T cells had been found in LUSC current reformed smokers for ?15 years and current smoking patients. Moreover, Tfh cell infiltration was not only associated with better overall survival (OS) but also varied from different degrees of TNM stage. Low expression of lncRNA PWRN1 and its potential regulating genes DMRTB1, PIRT, APOBEC1, and ZPBP2 were associated with better OS. Combining PWRN1 and four regulating genes as a signature, patients with higher-level expression of the signature had shorter survival time in not only the TCGA but also in the GEO dataset. Conclusions:It was found that Tfh cells presented higher infiltration in LUSC current reformed smokers for ?15 years and current smokers, while resting memory CD4 T cells had lower infiltration. The signature consisting of PWRN1 as well as its predicted targeted mRNAs was dysregulated in different levels of Tfh cell infiltration and might indicate patients' OS.

SUBMITTER: Wang Y

PROVIDER: S-EPMC7543728 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Immune cell landscape analysis reveals prognostic immune cells and its potential mechanism in squamous cell lung carcinoma.

Wang Yongyong Y Guo Jianji J

PeerJ 20201005

<h4>Background</h4>Squamous cell lung carcinoma (LUSC) was closely associated with smoking which was known to have a distant immunosuppression effect. In this study, we aimed to explore the relationship between immune cells and clinical outcomes of LUSC patients with smoking history.<h4>Methods</h4>The immune cell infiltration and RNA expression profiles of LUSC patients were collected from The Cancer Genome Atlas (TCGA). Then, the correlation between immune cell infiltration and clinical charac ...[more]

PMID: 33083119

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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:BackgroundNumerous studies have shown that the aging microenvironment played a huge impact on tumor progression. However, the clinical prognostic value of aging-related risk signatures and their effects on the tumor immune microenvironment (TIME) in head and neck squamous cell carcinoma (HNSCC) remains largely unclear. This study aimed to identify novel prognostic signatures based on aging-related genes (AGs) and reveal the landscape of the TIME in HNSCC.MethodsDifferentially expressed AGs were identified using the gene set enrichment analysis (GSEA). The prognostic risk model of AGs was established by univariate and multivariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses. The independent prognostic value of the risk model and the correlations of the prognostic signature with immune score, tumor immune cell infiltration, and immune checkpoints were systematically analyzed.ResultsA prognostic risk model of four AGs (BAK1, DKK1, CDKN2A, and MIF) was constructed and validated in the training and testing datasets. Kaplan-Meier curves and time-dependent receiver operating characteristic (ROC) curve analysis confirmed that the four-AG risk signature possessed an accurate predictive value for the prognosis of patients with HNSCC. Correlation analysis revealed that the risk score was negatively associated with immune score and immune cell infiltration level while positively correlated with immune checkpoint blockade (ICB) response score. Patients of the high-risk subtype contained higher infiltration levels of resting natural killer (NK) cells, M0 macrophages, M2 macrophages, and resting mast cells while having lower infiltration levels of memory B cells, CD8+ T cells, follicular helper T cells, regulatory T cells (Tregs), and activated mast cells than did those of the low-risk subtype. The expressions of CTLA4, PDCD1, and TIGIT were downregulated while the PDCD1LG2 expression was upregulated in the high-risk subtype compared to those in the low-risk subtype. Furthermore, the four selected AGs in the risk model were demonstrated to possess important functions in immune cell infiltration and ICB response of HNSCC.ConclusionsThe aging-related risk signature is a reliable prognostic model for predicting the survival of HNSCC patients and provides potential targets for improving outcomes of immunotherapy.

Project description:BackgroundCancer stem cells (CSCs) refer to cells with self-renewal capability in tumors. CSCs play important roles in proliferation, metastasis, recurrence, and tumor heterogeneity. This study aimed to identify immune-related gene-prognostic models based on stemness index (mRNAsi) in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), respectively.MethodsX-tile software was used to determine the best cutoff value of survival data in LUAD and LUSC based on mRNAsi. Tumor purity and the scores of infiltrating stromal and immune cells in lung cancer tissues were predicted with ESTIMATE R package. Differentially expressed immune-related genes (DEIRGs) between higher- and lower-mRNAsi subtypes were used to construct prognostic models.ResultsmRNAsi was negatively associated with StromalScore, ImmuneScore, and ESTIMATEScore, and was positively associated with tumor purity. LUAD and LUSC samples were divided into higher- and lower-mRNAsi groups with X-title software. The distribution of immune cells was significantly different between higher- and lower-mRNAsi groups in LUAD and LUSC. DEIRGs between those two groups in LUAD and LUSC were enriched in multiple cancer- or immune-related pathways. The network between transcriptional factors (TFs) and DEIRGs revealed potential mechanisms of DEIRGs in LUAD and LUSC. The eight-gene-signature prognostic model (ANGPTL5, CD1B, CD1E, CNTFR, CTSG, EDN3, IL12B, and IL2)-based high- and low-risk groups were significantly related to overall survival (OS), tumor microenvironment (TME) immune cells, and clinical characteristics in LUAD. The five-gene-signature prognostic model (CCL1, KLRC3, KLRC4, CCL23, and KLRC1)-based high- and low-risk groups were significantly related to OS, TME immune cells, and clinical characteristics in LUSC. These two prognostic models were tested as good ones with principal components analysis (PCA) and univariate and multivariate analyses. Tumor T stage, pathological stage, or metastasis status were significantly correlated with DEIRGs contained in prognostic models of LUAD and LUSC.ConclusionCancer stemness was not only an important biological process in cancer progression but also might affect TME immune cell infiltration in LUAD and LUSC. The mRNAsi-related immune genes could be potential biomarkers of LUAD and LUSC. Evaluation of integrative characterization of multiple immune-related genes and pathways could help to understand the association between cancer stemness and tumor microenvironment in lung cancer.

Dataset Information

Immune cell landscape analysis reveals prognostic immune cells and its potential mechanism in squamous cell lung carcinoma.

Publications

Immune cell landscape analysis reveals prognostic immune cells and its potential mechanism in squamous cell lung carcinoma.

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