scholarly journals Seven LncRNA-mRNA based risk score predicts the survival of head and neck squamous cell carcinoma

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Zhi-Li Zhang ◽  
Li-jing Zhao ◽  
Liang Chai ◽  
Shui-Hong Zhou ◽  
Feng Wang ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Head And Neck ◽  
Risk Score ◽  
Squamous Cell ◽  
Neck Squamous Cell Carcinoma

scholarly journals The Identification of Stemness-Related Genes in the Risk of Head and Neck Squamous Cell Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Guanying Feng ◽  
Feifei Xue ◽  
Yingzheng He ◽  
Tianxiao Wang ◽  
Hua Yuan
Keyword(s):  
Gene Expression ◽  
Squamous Cell Carcinoma ◽  
Network Analysis ◽  
Cell Carcinoma ◽  
Head And Neck ◽  
Risk Score ◽  
Squamous Cell ◽  
Neck Squamous Cell Carcinoma ◽  
Potential Biomarker ◽  
Score Model

ObjectivesThis study aimed to identify genes regulating cancer stemness of head and neck squamous cell carcinoma (HNSCC) and evaluate the ability of these genes to predict clinical outcomes.Materials and MethodsThe stemness index (mRNAsi) was obtained using a one-class logistic regression machine learning algorithm based on sequencing data of HNSCC patients. Stemness-related genes were identified by weighted gene co-expression network analysis and least absolute shrinkage and selection operator analysis (LASSO). The coefficient of LASSO was applied to construct a diagnostic risk score model. The Cancer Genome Atlas database, the Gene Expression Omnibus database, Oncomine database and the Human Protein Atlas database were used to validate the expression of key genes. Interaction network analysis was performed using String database and DisNor database. The Connectivity Map database was used to screen potential compounds. The expressions of stemness-related genes were validated using quantitative real‐time polymerase chain reaction (qRT‐PCR).ResultsTTK, KIF14, KIF18A and DLGAP5 were identified. Stemness-related genes were upregulated in HNSCC samples. The risk score model had a significant predictive ability. CDK inhibitor was the top hit of potential compounds.ConclusionStemness-related gene expression profiles may be a potential biomarker for HNSCC.

scholarly journals Gene Model Related to m6A Predicts the Prognostic Effect of Immune Infiltration on Head and Neck Squamous Cell Carcinoma

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yaping Deng ◽  
Kehua Li ◽  
Fengwu Tan ◽  
Hanbo Liu
Keyword(s):  
Immune Response ◽  
Squamous Cell Carcinoma ◽  
High Risk ◽  
Cell Carcinoma ◽  
Head And Neck ◽  
Risk Score ◽  
Squamous Cell ◽  
Immune Checkpoint ◽  
Neck Squamous Cell Carcinoma ◽  
Immune Microenvironment

Head and neck squamous cell carcinoma (HNSCC) is a highly aggressive solid tumor. Because most studies have focused on the intrinsic carcinogenic pathways of tumors, we focused on the relationship between N6-methyladenosine (m6A) and the prognosis of HNSCC in the tumor immune microenvironment. We downloaded RNA-seq data from the TCGA dataset and used univariate Cox regression to screen m6A-related lncRNAs. The expression value of LASSO-screened genes was the sum of LASSO regression coefficients. We then evaluated relationships between the risk score and cellular components or cellular immune response. Differences in immune response under various algorithms were visualized with heat maps. The GSVA package in R was used to analyze GO, BP, KEGG, and hallmark gene sets of immune checkpoint clusters and immune checkpoint scores. The GSEA analysis was performed with the cluster profile package, yielding 21 m6A genes. Related lncRNAs were screened with Pearson’s correlations, and the resulting 442 lncRNAs were screened using single-factor analysis. Eight lncRNAs closely related to prognosis were identified through survival random forest. Survival analysis showed that patients with a high risk score had a poor prognosis. Low- and high-risk-score groups differed significantly in m6A gene expression. Prognostic scores from different algorithms were significantly correlated with B cells, T cells, and memory cells in the immune microenvironment. Expression of immune checkpoints and signal pathways differed significantly across risk-score groups, suggesting that m6A could mediate lncRNA-induced immune system dysfunction and affect HNSCC development. A comprehensive study of tumor-cell immune characteristics should provide more insight into the complex immune microenvironment, thus contributing to the development of new immunotherapeutic agents.

scholarly journals A panel of Transcription factors identified by data mining can predict the prognosis of head and neck squamous cell carcinoma

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Boxin Zhang ◽  
Haihui Wang ◽  
Ziyan Guo ◽  
Xinhai Zhang
Keyword(s):  
Overall Survival ◽  
Squamous Cell Carcinoma ◽  
Transcription Factors ◽  
Cell Carcinoma ◽  
Head And Neck ◽  
Risk Score ◽  
Squamous Cell ◽  
Gene Set Enrichment Analysis ◽  
Neck Squamous Cell Carcinoma ◽  
Clinicopathological Parameters

Abstract Background Transcription factors (TFs) are responsible for the regulation of various activities related to cancer like cell proliferation, invasion, and migration. It is thought that, the measurement of TFs levels could assist in developing strategies for diagnosis and prognosis of cancer detection. However, due to lack of effective genome-wide tests, this cannot be carried out in clinical settings. Methods A complete assessment of RNA-seq data in samples of a head and neck squamous cell carcinoma (HNSCC) cohort in The Cancer Genome Atlas (TCGA) database was carried out. From the expression data of six TFs, a risk score model was developed and further validated in the GSE41613 and GSE65858 series. Potential functional roles were identified for the six TFs via gene set enrichment analysis. Results Based on our multi-TF signature, patients are stratified into high- and low-risk groups with significant variations in overall survival (OS) (median survival 2.416 vs. 5.934 years, log-rank test P < 0.001). The sensitivity and specificity evaluation of our multi-TF for 3-year OS in TCGA, GSE41613 and GSE65858 was 0.707, 0.679 and 0.605, respectively, demonstrating good reproducibility and robustness for predicting overall survival of HNSCC patients. Through multivariate Cox regression analyses (MCRA) and stratified analyses, we confirmed that the predictive capability of this risk score (RS) was not dependent on any of other factors like clinicopathological parameters. Conclusions With the help of a RS obtained from a panel of TFs expression signatures, effective OS prediction and stratification of HNSCC patients can be carried out.

scholarly journals An Immune Feature-Based, Three-Gene Scoring System for Prognostic Prediction of Head-and-Neck Squamous Cell Carcinoma

2022 ◽  
Vol 11 ◽  
Author(s):  
Yamin Zhang ◽  
Xiayan Luo ◽  
Jing Yu ◽  
Kejia Qian ◽  
Huiyong Zhu
Keyword(s):  
Squamous Cell Carcinoma ◽  
T Cells ◽  
Cell Carcinoma ◽  
Head And Neck ◽  
Candidate Genes ◽  
Risk Score ◽  
Squamous Cell ◽  
Scoring System ◽  
Cox Regression ◽  
Neck Squamous Cell Carcinoma

Head-and-neck squamous cell carcinoma (HNSCC) is characterized by a high frequency of neck lymph node metastasis (LNM), a key prognostic factor. Therefore, identifying the biological processes during LNM of HNSCC has significant clinical implications for risk stratification. This study performed Gene Ontology enrichment analysis of differentially expressed genes between tumors with LNM and those without LNM and identified the involvement of immune response in the lymphatic metastasis of HNSCC. We further identified greater infiltrations of CD8+ T cells in tumors than in adjacent normal tissues through immunochemistry in the patient cohort (n = 62), indicating the involvement of CD8+ T cells in the antitumor immunity. Hierarchical clustering analysis was conducted to initially identify the candidate genes relevant to lymphocyte-mediated antitumor response. The candidate genes were applied to construct a LASSO Cox regression analysis model. Three genes were eventually screened out as progression‐related differentially expressed candidates in HNSCC and a risk scoring system was established based on LASSO Cox regression model to predict the outcome in patients with HNSCC. The score was calculated using the formula: 0.0636 × CXCL11 − 0.4619 × CXCR3 + 0.2398 × CCR5. Patients with high scores had significantly worse overall survival than those with low scores (p &lt; 0.001). The risk score showed good performance in characterizing tumor-infiltrating lymphocytes and provided a theoretical basis for stratifying patients receiving immune therapies. Additionally, a nomogram including the risk score, age, and TNM stage was constructed. The prediction model displayed marginally better discrimination ability and higher agreement in predicting the survival of patients with HNSCC compared with the TNM stage.

scholarly journals Circulating Long Noncoding RNAs as Biomarkers for Predicting Head and Neck Squamous Cell Carcinoma

2018 ◽  
Vol 50 (4) ◽  
pp. 1429-1440 ◽  
Author(s):  
Yao Yao ◽  
Xinyuan Chen ◽  
Shuai Lu ◽  
Chao Zhou ◽  
Guolong Xu ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Head And Neck ◽  
Risk Score ◽  
Squamous Cell ◽  
Noncoding Rna ◽  
Expression Profiles ◽  
Neck Squamous Cell Carcinoma ◽  
Tissue Samples ◽  
Score Analysis

Background/Aims: The anatomical complexity of the head and neck region and the lack of sufficiently specific and sensitive biomarkers often lead to the diagnosis of head and neck squamous cell carcinoma (HNSCC) at advanced stages. To identify novel biomarkers for early diagnosis of primary HNSCC through a minimally invasive method, we investigated circulating long noncoding RNA (lncRNA) levels in plasma of HNSCC patients. Methods: The global lncRNA expression profiles of HNSCC patients were measured using microarray and next-generation RNA-sequencing (RNA-seq) data from both circulating and tissue samples. The diagnosis prediction model based on the lncRNA signatures and clinical features was evaluated by multi-stage validation and risk score analysis. Results: The data showed that 432 lncRNA transcripts were differentially expressed by fold changes of > 4 in circulating samples and 333 in tissues samples, respectively. Only 12 lncRNAs consistently emerged in these two kinds of samples. After the risk score analysis including a multistage validation, we identified three lncRNAs, namely, HOXA11-AS, LINC00964 and MALAT1, which were up-regulated in the plasma of HNSCC patients compared with those in healthy controls with merged areas under the curve (AUCs) in training and validation sets of 0.925 and 0.839, respectively. Conclusion: HOXA11-AS, LINC00964 and MALAT1 might be potential circulating biomarkers for the early detection of HNSCC in the future.

Three Autophagy-Related lncRNA are Prognostic Biomarkers for Patients with Head and Neck Squamous Cell Carcinoma

2021 ◽  
Author(s):  
Haoyue Xu ◽  
Xiangpu Wang ◽  
Zhien Feng ◽  
Renji Chen ◽  
Zhengxue Han
Keyword(s):  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Head And Neck ◽  
Risk Score ◽  
Squamous Cell ◽  
Cox Regression ◽  
Neck Squamous Cell Carcinoma ◽  
Regression Analyses ◽  
Good Ability ◽  
Non Coding Rnas

Abstract Background: Currently, no systematic analysis has been conducted to assess the potential of multiple autophagy-related long non-coding RNAs (lncRNA) to predict the prognosis of head and neck squamous cell carcinoma (HNSCC). we investigated the prognostic potential of autophagy-related long non-coding RNAs (lncRNA) in HNSCC patients. Methods: Patient information and Autophagy-associated genes were obtained from The Cancer Genome Atlas (TCGA) and Human Autophagy data resource. Autophagy-related lncRNAs were determined through Lasso and Cox regression analyses. Then, on the basis of autophagy- related lncRNAs, a risk score and a nomogram were constructed for estimation of prognostic outcomes for HNSCC patients. These models were verified internally using the TCGA and. Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) were used for gene functional analyses. Results: Three autophagy-related lncRNAs (AC002401.4, AC245041.2 and TMEM44-AS1) that are associated with HNSCC were identified. Univariate and multivariate Cox regression analyses revealed that the risk score is an independent prognostic indicator (p ≤ 0.001), with its ability to predict prognosis being higher than that of other clinicopathological indicators (AUC=0.732). Concordance index of the nomogram was 0.712, and AUC values for one-year, three-year and five-year survival rates were 0.730, 0.745 and 0.728, respectively. Internal verifications revealed that this nomogram had a good ability to predict prognosis. Functional analysis showed that the genes were mostly enriched in autophagy and tumor-related cascades. Conclusion: The autophagy-related lncRNAs model can predict the prognosis of patients with HNSCC.Trial registration: Prospective, Observational, Real-world Oral Malignant Tumors Study (POROMS), NCT02395367. Registered 23 March 2015, https://clinicaltrials.gov/ct2/show/NCT02395367

scholarly journals Autophagy-Related Signature for Head and Neck Squamous Cell Carcinoma

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Cheng Li ◽  
Zeng-hong Wu ◽  
Kun Yuan
Keyword(s):  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Head And Neck ◽  
Risk Score ◽  
Squamous Cell ◽  
Risk Group ◽  
Neck Squamous Cell Carcinoma ◽  
The Cancer Genome Atlas ◽  
Risk Scores ◽  
Kaplan Meier

Background. Head and neck squamous cell carcinoma (HNSCC) is one of the most common malignancies in the world, with low survival and poor quality of life. Autophagy-associated genes (ATGs) have been reported to be involved in the initiation and progression of malignancies. Here, we aimed to investigate the association between autophagy-associated genes and the outcomes in HNSCC patients. Methods. We obtained ATGs with prognostic values by analyzing the datasets from The Cancer Genome Atlas (TCGA) and Human Autophagy Database (HADb). The enrichment functions of autophagy differential genes were analyzed by Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). The Kaplan-Meier method was applied to the survival curve analysis. A prognostic autophagy-related gene signature was established, and its independence was verified. Results. We acquired a total of 529 samples and 232 ATGs; further, we identified 45 genes associated with prognosis and built a prognosis autophagy signature based on risk score of 15 genes. Patients were divided into two groups based on risk scores. The Kaplan-Meier curve illustrated that the survival rate of the high-risk group was significantly lower than that of the low-risk group in both the training group and validation group. The ROC curve revealed that the risk score had the highest AUC value in the 3rd and 5th years, reaching 0.703 and 0.724, which are higher than other risk factors such as gender, age, and TNM stage. The nomogram further confirmed its weight in the prognosis of HNSCC patients. Through KEGG and GO enrichment analyses, we observed that ATGs were involved in the tumorigenesis and invasion of tumor by various mediating pathways. We gained 3 hub genes (MAP1LC3B, FADD, and LAMP1) and further analyzed the survival curves, mutations, differential expressions, and their roles in tumors on the online websites. Conclusion. We identified a novel autophagy-related signature that may provide promising biomarker genes for the treatment and prognosis of HNSCC. We need to validate its prognostic value by applying it to the clinic.

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