Integrated Bioinformatics Analysis of Differentially-Expressed Genes and Immune Cell Infiltration Characteristics in Esophageal Squamous Cell Carcinoma

2021 ◽  
Author(s):  
Zitong Feng ◽  
Jingge Qu ◽  
Xiao Liu ◽  
Jinghui Liang ◽  
Yongmeng Li ◽  
...  
Keyword(s):  
Gene Expression ◽  
Squamous Cell Carcinoma ◽  
Esophageal Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Differentially Expressed Genes ◽  
Squamous Cell ◽  
Bioinformatics Analysis ◽  
Immune Cell ◽  
Cell Infiltration ◽  
Immune Cell Infiltration

Abstract Esophageal squamous cell carcinoma (ESCC) is a life-threatening thoracic tumor with a poor prognosis. Identifying the best-targeted therapy, appropriate biomarkers and individual treatment for patients with ESCC remains a significant challenge. The present study aimed to elucidate key candidate genes and immune cell infiltration characteristics in ESCC by integrated bioinformatics analysis. We downloaded nine gene expression datasets from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between ESCC tissues and normal tissues in each dataset were identified by the “limma” R package, and a total of 152 robust DEGs were identified by robust rank aggregation (RRA) algorithm. Functional enrichment analyses of the robust DEGs showed that these genes were significantly associated with extracellular matrix related process. Immune cell infiltration analysis was also conducted by CIBERSORT algorithm. We found that M0 and M1 macrophages were increased dramatically in ESCC while M2 macrophages decreased. Nine hub genes were picked out from a protein-protein interaction (PPI) network used by the CytoHubba plugin in Cytoscape. According to the receiver operating characteristic (ROC) curves and Kaplan-Meier survival analysis, the genes PLAU, SPP1 and VCAN had high diagnostic and prognostic values for ESCC patients. Based on univariate and multivariate regression analyses, seven genes (IL18, PLAU, ANO1, SLCO1B3, CST1, NELL2 and MAGEA11) from the robust DEGs were used to construct a good prognostic model. A nomogram that incorporates seven genes signature was established to develop a quantitative method for ESCC prognosis. Our results might provide aid for exploring potential therapeutic targets and prognosis evaluation in ESCC.

scholarly journals Integrated bioinformatics analysis of differentially expressed genes and immune cell infiltration characteristics in Esophageal Squamous cell carcinoma

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zitong Feng ◽  
Jingge Qu ◽  
Xiao Liu ◽  
Jinghui Liang ◽  
Yongmeng Li ◽  
...  
Keyword(s):  
Gene Expression ◽  
Squamous Cell Carcinoma ◽  
Esophageal Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Differentially Expressed Genes ◽  
Bioinformatics Analysis ◽  
Immune Cell ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Hub Genes

AbstractEsophageal squamous cell carcinoma (ESCC) is a life-threatening thoracic tumor with a poor prognosis. The role of molecular alterations and the immune microenvironment in ESCC development has not been fully elucidated. The present study aimed to elucidate key candidate genes and immune cell infiltration characteristics in ESCC by integrated bioinformatics analysis. Nine gene expression datasets from the Gene Expression Omnibus (GEO) database were analysed to identify robust differentially expressed genes (DEGs) using the robust rank aggregation (RRA) algorithm. Functional enrichment analyses showed that the 152 robust DEGs are involved in multiple processes in the tumor microenvironment (TME). Immune cell infiltration analysis based on the 9 normalized GEO microarray datasets was conducted with the CIBERSORT algorithm. The changes in macrophages between ESCC and normal tissues were particularly obvious. In ESCC tissues, M0 and M1 macrophages were increased dramatically, while M2 macrophages were decreased. A robust DEG-based protein–protein interaction (PPI) network was used for hub gene selection with the CytoHubba plugin in Cytoscape. Nine hub genes (CDA, CXCL1, IGFBP3, MMP3, MMP11, PLAU, SERPINE1, SPP1 and VCAN) had high diagnostic efficiency for ESCC according to receiver operating characteristic (ROC) curve analysis. The expression of all hub genes except MMP3 and PLAU was significantly related to macrophage infiltration. Univariate and multivariate regression analyses showed that a 7-gene signature constructed from the robust DEGs was useful for predicting ESCC prognosis. Our results might facilitate the exploration of potential targeted TME therapies and prognostic evaluation in ESCC.

scholarly journals Characterization of the immune cell infiltration landscape in head and neck squamous cell carcinoma to aid immunotherapy

2020 ◽  
Author(s):  
Xinhai Zhang ◽  
Tielou Chen ◽  
Boxin Zhang
Keyword(s):  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Head And Neck ◽  
Signaling Pathway ◽  
Squamous Cell ◽  
Immune Cell ◽  
Neck Squamous Cell Carcinoma ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Stromal Component

Abstract Background: The tumor microenvironment chiefly consists of tumor cells, and tumor-infiltrating immune cells admixed with the stromal component. The recent clinical trial has shown that the tumor immune cell infiltration is correlated with the sensitivity to immunotherapy and the prognosis of head and neck squamous cell carcinoma (HNSC). However, to date, the immune infiltrative landscape of HNSC has not yet been elucidated. Methods: We proposed two computational algorithms to unravel the immune infiltration landscape of 1029 HNSC patients. The Boruta algorithm and principal component algorithms (PCA) were employed to quantify three immune cell infiltration gene subtypes categorized as per the immune cell infiltrations pattern. Results: The high ICI score subtype was characterized by a higher tumor mutation burden (TMB) and the immune-activated signaling pathway. However, a low ICI score subtype was categorized as per the activation of immunosuppressive signaling pathways such as TGF-BETA, WNT signaling pathway, and lower TMB. Two immunotherapy cohorts confirmed patients with higher ICI score demonstrated significant therapeutic advantages and clinical benefits.Conclusions: This demonstrated that the ICI score could serve as an effective prognostic biomarker and predictive indicator for immunotherapy. A comprehensive understanding of the HNSC immune landscape might help in tailoring immunotherapeutic strategies for different patients.

scholarly journals Prognostic Significance of STC2 and DNA methylation in Head and Neck Squamous Cell Carcinoma: A Study Based on the TCGA and GEO Databases

2021 ◽  
Author(s):  
Guangyao Li ◽  
Daquan Wu ◽  
Lei Zhou ◽  
Dan You ◽  
Xinsheng Huang
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Squamous Cell Carcinoma ◽  
Survival Rate ◽  
Cell Carcinoma ◽  
Head And Neck ◽  
Tp53 Mutation ◽  
Immune Cell ◽  
Cell Infiltration ◽  
Immune Cell Infiltration

Abstract Background: Head and neck squamous cell carcinoma (HNSC) is a popular malignancy type that brings about poor prognosis with a low survival rate worldwide. Stanniocalcin 2 (STC2) is a glycosylated peptide hormone and shows the potential to become a new biomarker for the evaluation of malignant tumors. The purpose of this study was to explore the prognostic implications of STC2 and DNA methylation in HNSC and the role of STC2 expression in immune cell infiltration.Methods: STC2 gene expression data were collected from Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA) databases. Univariate and multivariate analyses were employed to screen prognostic risk factors. The relationship between STC2 expression and TP53 mutation in HNSC was explored. TCGA data were utilized to analyze how STC2 expression affected immune cell infiltration in HNSC.Results: STC2 was highly expressed in HNSC patients (P < 0.01), especially those with a lower overall survival rate (P < 0.0001). TP53 mutation might be a risk factor of STC2 overexpression in HNSC (P = 0.0015). STC2 expression was negatively correlated with STC2 methylation (Spearman: -0.43, P < 0.001). Hypermethylation or hypomethylation at the eight CpG sites most related to STC2 expression was identified as independent factors for HNSC prognosis. STC2 was positively correlated with cancer-associated fibroblasts infiltration and associated with the infiltration of various immune cells.Conclusion: STC2 can be regarded as a vital prognostic biomarker of HNSC due to its essential roles in immune cell infiltration.

scholarly journals Identification of Immune-related lncRNA Panel for Predicting Immune Checkpoint Blockade and Prognosis in Head and Neck Squamous Cell Carcinoma

2021 ◽  
Author(s):  
Chongchang Zhou ◽  
Guowen Zhan ◽  
Zhisen Shen ◽  
Yi Shen ◽  
Hongxia Deng ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Head And Neck ◽  
Squamous Cell ◽  
Immune Cell ◽  
Risk Model ◽  
Low Risk ◽  
Neck Squamous Cell Carcinoma ◽  
Cell Infiltration ◽  
Immune Cell Infiltration

Abstract Immunotherapy is changing head and neck squamous cell carcinoma (HNSCC) treatment pattern. According to the Chinese Society of Clinical Oncology (CSCO) guidelines, immunotherapy has been deemed as first-line recommendation for recurrent/metastatic HNSCC, marking that advanced HNSCC has officially entered the era of immunotherapy. Long non-coding RNAs impact every step of cancer immunity. Therefore, reliable immune-lncRNA able to accurately predict the immune landscape and survival of HNSCC are crucial to clinical management. In the current study, we downloaded the transcriptomic and clinical data of HNSCC from The Cancer Genome Altas and identified differentially expressed immune-related lncRNAs (DEir-lncRNAs). Further then, Cox and least absolute shrinkage and selection operator (LASSO) regression analyses were performed to identify proper DEir-lncRNAs to construct optimal risk model. Low-risk and high-risk groups were classified based on the optimal cut-off value generated by the areas under curve for receiver operating characteristic curves (AUC), and Kaplan-Meier survival curves were utilized to validate the prediction model. We then evaluated the model based on the clinical factors, immune cell infiltration, chemotherapeutic and immunotherapeutic efficacy between two groups. Our results constructed a risk model consisted of 18 DEir-lncRNA pairs showing significantly association with survival of patients with HNSCC. Besides, HNSCC patients with low risk score significantly enriched of CD8+ T cell, and corelated with high chemosensitivity and immunotherapeutic sensitivity. In summary, our risk model could be served as a promising clinical prediction indicator, effective discoursing of the immune cell infiltration of HNSCC patients, and distinguishing patients who could benefit from chemotherapy and immunotherapy.

scholarly journals Characterization of Molecular Subtypes in Head and Neck Squamous Cell Carcinoma With Distinct Prognosis and Treatment Responsiveness

2021 ◽  
Vol 9 ◽  
Author(s):  
Pei Zhang ◽  
Shue Li ◽  
Tingting Zhang ◽  
Fengzhen Cui ◽  
Ji-Hua Shi ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Head And Neck ◽  
Squamous Cell ◽  
Immune Cell ◽  
Neck Squamous Cell Carcinoma ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Oncogenic Signaling ◽  
Mtorc1 Signaling

Head and neck squamous cell carcinoma (HNSCC) is one of the most aggressive malignancies with complex phenotypic, etiological, biological, and clinical heterogeneities. Previous studies have proposed different clinically relevant subtypes of HNSCC, but little is known about its corresponding prognosis or suitable treatment strategy. Here, we identified 101 core genes from three prognostic pathways, including mTORC1 signaling, unfold protein response, and UV response UP, in 124 pairs of tumor and matched normal tissues of HNSCC. Moreover, we identified three robust subtypes associated with distinct molecular characteristics and clinical outcomes using consensus clustering based on the gene expression profiles of 944 HNSCC patients from four independent datasets. We then integrated the genomic information of The Cancer Genome Atlas (TCGA) HNSCC cohort to comprehensively evaluate the molecular features of different subtypes and screen for potentially effective therapeutic agents. Cluster 1 had more arrested oncogenic signaling, the highest immune cell infiltration, the highest immunotherapy and chemotherapeutic responsiveness, and the best prognosis. By contrast, Cluster 3 showed more activated oncogenic signaling, the lowest immune cell infiltration, the lowest immunotherapy and chemotherapy responsiveness, and the worst prognosis. Our findings corroborate the molecular diversity of HNSCC tumors and provide a novel classification strategy that may guide for prognosis and treatment allocation.

scholarly journals Immune cell infiltration in head and neck squamous cell carcinoma and patient outcome: a retrospective study

2018 ◽  
Vol 57 (9) ◽  
pp. 1165-1172 ◽  
Author(s):  
Karolin Schneider ◽  
Etienne Marbaix ◽  
Caroline Bouzin ◽  
Marc Hamoir ◽  
Pierre Mahy ◽  
...  
Keyword(s):  
Patient Outcome ◽  
Squamous Cell Carcinoma ◽  
Retrospective Study ◽  
Cell Carcinoma ◽  
Head And Neck ◽  
Squamous Cell ◽  
Immune Cell ◽  
Neck Squamous Cell Carcinoma ◽  
Cell Infiltration ◽  
Immune Cell Infiltration

scholarly journals Potential Biomarkers for Predicting the Overall Survival of Lung squamous cell carcinoma: A analysis of Ferroptosis-Related lncRNAs

2021 ◽  
Author(s):  
zixuan Wu ◽  
Xuyan Huang ◽  
Min-jie Cai ◽  
Peidong Huang ◽  
Zunhui Guan
Keyword(s):  
Squamous Cell Carcinoma ◽  
High Risk ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Immune Cell ◽  
Lung Squamous Cell Carcinoma ◽  
Risk Groups ◽  
Low Risk ◽  
Cell Infiltration ◽  
Immune Cell Infiltration

Abstract Background In 502 Lung squamous cell carcinoma (LUSC) samples from The Cancer Genome Atlas (TCGA) datasets, the predictive significance of ferroptosis-related long non-coding RNAs (lncRNAs) was investigated. In LUSC, we meant to express how ferroptosis-associated lncRNAs interact with immune cell infiltration. Methods Gene expression enrichment was investigated using gene set enrichment analysis in the Kyoto Encyclopedia of Genes and Genomes. The prognostic model was constructed using Lasso regression. To better understand immune cell infiltration in different risk groups and its relationship to clinical outcome, researchers analyzed by modifications in the tumor microenvironment (TME) and immunological association. The expression of lncRNA was intimately connected to that of ferroptosis, according to co-expression analyses. Ferroptosis-related lncRNAs were shown to be partially overexpressed in high-risk patients in the absence of additional clinical signs, suggesting that they may be incorporated into a prediction model to predict LUSC prognosis. GSEA revealed the immunological and tumor-related pathways in the low-risk group. Results According to TCGA, CCR and inflammation-promoting genes were considered to be significantly different between the low-risk and high-risk groups. The expression of C10orf55, AC016924.1, AL161431.1, LUCAT1, AC104248.1, and MIR3945HG were likewise different in the two risk groups. Conclusion LncRNAs linked to ferroptosis are connected to the occurrence and development of LUSC. With the use of matching prognostic models, the prognosis of LUSC patients can be predicted. In LUSC, ferroptosis-related lncRNAs and immune cell infiltration in the TME might be novel therapeutic targets that should be investigated further.

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