Prognostic value of immune‐related genes and immune cell infiltration analysis in the tumor microenvironment of head and neck squamous cell carcinoma

Wang, Zizhuo; Yuan, Huangbo; Huang, Jia; Hu, Dianxing; Xu, Qin; Sun, Chaoyang; Chen, Gang; Wang, Beibei

doi:10.1002/hed.26474

Cited by 11 publications

(12 citation statements)

References 57 publications

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“…In our study, we found that mast cells resting are associated with good prognosis, which may be a target of immunotherapy. Chen et al reported that infiltrating mast cells promote renal cell carcinoma angiogenesis by modulating the PI3K-AKT-GSK-3b-AM signaling pathway (25). Some similar results were also found in triple-negative breast cancer and head and neck cancer.…”

Section: Discussionmentioning

confidence: 68%

Integrative Bioinformatics Analysis Demonstrates the Prognostic Value of Chromatin Accessibility Biomarkers in Clear Cell Renal Cell Carcinoma

et al. 2021

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Clear cell renal cell carcinoma (ccRCC) accounts for 75%–85% of renal cell carcinoma (RCC) and has a poor 5-year survival rate. In recent years, medical advancement has promoted the understanding of the histopathological and molecular characterization of ccRCC; however, the carcinogenesis and molecular mechanisms of ccRCC remain unclear. Chromatin accessibility is an essential determinant of cellular phenotype. This study aimed to explore the potential role of chromatin accessibility in the development and progression of ccRCC. By the combination of open-access genome-wide chromatin accessibility profiles and gene expression profiles in ccRCC, we obtained a total of 13,474 crucial peaks, corresponding to 5,120 crucial genes and 9,185 differentially expressed genes. Moreover, two potential function modules (P2 and G4) that contained 129 upregulated genes were identified via the weighted gene co-expression network analysis (WGCNA). Furthermore, we obtained five independent predictors (FSCN1, SLC17A9, ANKRD13B, ADCY2, and MAPT), and a prognostic model was established based on these genes through the least absolute shrinkage and selection operator-proportional hazards model (LASSO-Cox) analysis. This model can stratify the ccRCC samples into a high-risk and a low-risk group, from which the patients have distinct prognosis. Further analysis demonstrated a completely different immune cell infiltration pattern between these two risk groups. This study also suggested that mast cell resting is associated with the prognosis of ccRCC and could be a target of immunotherapy. Overall, this study indicated that chromatin accessibility plays an essential role in ccRCC. The five prognostic chromatin accessibility biomarkers and the prognostic immune cells can provide a new direction for the treatment of ccRCC.

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Section: Discussionmentioning

confidence: 68%

Integrative Bioinformatics Analysis Demonstrates the Prognostic Value of Chromatin Accessibility Biomarkers in Clear Cell Renal Cell Carcinoma

et al. 2021

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“…Other components of the tumor microenvironment, including the expression of genetic markers, and distribution of immune cells and molecules that regulate cellular function, have been studied for targeted immunotherapy [33,34]. Of 144 downregulated DEGs in our study, five were CD3D and CD3E as T cell markers, CD8+ T cellspecific marker CD8B, LCK, and ZAP70, which participates in primary immunodeficiency and the T cell-receptor signaling pathway in the KEGG pathway analysis (Table 2) [33].…”

Section: Discussionmentioning

confidence: 85%

Identification of differentially expressed genes and pathways for risk stratification in HPV-associated cancers governing different anatomical sites

Kwon¹,

Lee²,

Lee³

et al. 2022

Front. Biosci. (Landmark Ed)

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Background: Human papillomavirus (HPV) is the major cause of cervical cancer (CC) etiology; its contribution to head and neck cancer (HNC) incidence is steadily increasing. As individual patients' response to the treatment of HPV-associated cancer is variable, there is a pressing need for the identification of biomarkers for risk stratification that can help determine the intensity of treatment. Methods:We have previously reported a novel prognostic and predictive indicator (HPPI) scoring system in HPV-associated cancers regardless of anatomical location by analyzing The Cancer Genome Atlas and Gene Expression Omnibus databases. In the present study, we comprehensively investigated the association of group-specific expression patterns of common differentially expressed genes (DEGs) between high-and low-risk groups in HPV-associated CC and HNC, identifying molecular biomarkers and pathways for risk stratification. Results: Among the 174 identified DEGs, the expression of genes associated with extracellular matrix (ECM)-receptor interaction pathway (ITGA5, ITGB1, LAMB1, and LAMC1) was increased in high-risk groups in both HPV-associated CC and HNC, while the expression of genes associated with T-cell immunity (CD3D, CD3E, CD8B, LCK, and ZAP70) was decreased and vice versa. The individual genes showed significant prognostic impact on HPV-associated cancers but not on HPV-negative cancers. The expression levels of identified genes were similar between HPV-negative and HPV-associated high-risk groups with distinct expression patterns only in HPV-associated low-risk groups. Each group of genes showed negative correlations and distinct patterns of immune cell infiltration in tumor microenvironments. Conclusions: These results allowed us to identify molecular biomarkers and pathways for risk stratification in HPV-associated cancers regardless of anatomical location. The identified targets were found to be selectively working in only HPV-associated cancers and not in HPV-negative cancers, indicating the possibility of selective targets governing HPV-infective tumor microenvironments.

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“…However, these studies have failed to accurately identify key molecules for predicting the prognosis of HNSCC [ 9 , 10 , 37 ]. Accumulating evidence suggests that machine learning is a reliable and robust technique that can be used to quickly and accurately identify critical biomarkers [ 38 , 39 ]. ICIs may exert beneficial therapeutic effects in HNSCC, although the response rate to ICIs remains poor [ 15 ].…”

Section: Discussionmentioning

confidence: 99%

Identification of Novel Biomarkers for Predicting Prognosis and Immunotherapy Response in Head and Neck Squamous Cell Carcinoma Based on ceRNA Network and Immune Infiltration Analysis

Guo

Pan

Wang

et al. 2021

BioMed Research International

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Objectives. Patients with head and neck squamous cell carcinoma (HNSCC) have poor prognosis and show poor responses to immune checkpoint (IC) inhibitor (ICI) therapy. Competing endogenous RNA (ceRNA) networks, tumor-infiltrating immune cells (TIICs), and ICIs may influence tumor prognosis and response rates to ICI therapy. This study is aimed at identifying prognostic and IC-related biomarkers and key TIIC signatures to improve prognosis and ICI therapy response in HNSCC patients. Methods and Results. Ninety-five long noncoding RNAs (lncRNAs), microRNAs (miRNAs), and 1746 mRNAs were identified using three independent methods. We constructed a ceRNA network and estimated the proportions of 22 immune cell subtypes. Ten ceRNAs were related to prognosis according to Kaplan–Meier analysis. Two risk signatures based, respectively, on nine ceRNAs (ANLN, CFL2, ITGA5, KDELC1, KIF23, NFIA, PTX3, RELT, and TMC7) and three immune cell types (naïve B cells, neutrophils, and regulatory T cells) via univariate Cox regression, least absolute shrinkage and selection operator, and multivariate Cox regression analyses could accurately and independently predict the prognosis of HNSCC patients. Key mRNAs in the ceRNA network were significantly correlated with naïve B cells and regulatory T cells and with stage, grade, and immune and molecular subtype. Eight IC genes exhibited higher expression in tumor tissues and were correlated with eight key mRNAs in the ceRNA network in HNSCC patients with different HPV statuses according to coexpression and TIMER 2.0 analyses. Most drugs were effective in association with expression of these key signatures (ANLN, CFL2, ITGA5, KIF23, NFIA, PTX3, RELT, and TMC7) based on GSCALite analysis. The prognostic value of key biomarkers and associations between key ceRNAs and IC genes were validated using online databases. Eight key ceRNAs were confirmed to predict response to ICI in other cancers based on TIDE analysis. Conclusions. We constructed two risk signatures to accurately predict prognosis in HNSCC. Key IC-related signatures may be associated with response to ICI therapy. Combinations of ICIs with inhibitors of eight key mRNAs may improve survival outcomes of HNSCC patients.

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Prognostic value of immune‐related genes and immune cell infiltration analysis in the tumor microenvironment of head and neck squamous cell carcinoma

Cited by 11 publications

References 57 publications

Integrative Bioinformatics Analysis Demonstrates the Prognostic Value of Chromatin Accessibility Biomarkers in Clear Cell Renal Cell Carcinoma

Integrative Bioinformatics Analysis Demonstrates the Prognostic Value of Chromatin Accessibility Biomarkers in Clear Cell Renal Cell Carcinoma

Identification of differentially expressed genes and pathways for risk stratification in HPV-associated cancers governing different anatomical sites

Identification of Novel Biomarkers for Predicting Prognosis and Immunotherapy Response in Head and Neck Squamous Cell Carcinoma Based on ceRNA Network and Immune Infiltration Analysis

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