Six-gene signature for predicting survival in patients with head and neck squamous cell carcinoma.
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ABSTRACT:
SUBMITTER: Wang J
PROVIDER: S-EPMC6977678 | biostudies-literature | 2020 Jan
REPOSITORIES: biostudies-literature
Publications
Six-gene signature for predicting survival in patients with head and neck squamous cell carcinoma.
Wang Juncheng J Chen Xun X Tian Yuxi Y Zhu Gangcai G Qin Yuexiang Y Chen Xuan X Pi Leiming L Wei Ming M Liu Guancheng G Li Zhexuan Z Chen Changhan C Lv Yunxia Y Cai Gengming G
Aging 20200112 1
The prognosis of head and neck squamous cell carcinoma (HNSCC) patients remains poor. High-throughput sequencing data have laid a solid foundation for identifying genes related to cancer prognosis, but a gene marker is needed to predict clinical outcomes in HNSCC. In our study, we downloaded RNA Seq, single nucleotide polymorphism, copy number variation, and clinical follow-up data from TCGA. The samples were randomly divided into training and test. In the training set, we screened genes and use ...[more]
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Project description:Infection with high-risk types of human papilloma virus (HPV) is currently the best-established prognostic marker for head and neck squamous cell carcinoma (HNSCC), one of the most common and lethal human malignancies worldwide. Clinical trials have been launched to address the concept of treatment de-escalation for HPV-positive HNSCC with the final aim to reduce treatment related toxicity and debilitating long-term impacts on the quality of life. However, HPV-related tumors are mainly restricted to oropharyngeal SCC (OPSCC) and there is an urgent need to establish reliable biomarkers for all patients at high risk for treatment failure. A patient cohort (n = 295) with mainly non-OPSCC (72.9%) and a low prevalence of HPV16-related tumors (8.8%) was analyzed by MassARRAY to determine a previously established prognostic methylation score (MS). Kaplan-Meier revealed a highly significant correlation between a high MS and a favorable survival for OPSCC (P = 0.0004) and for non-OPSCC (P<0.0001), which was confirmed for all HNSCC by multivariate Cox regression models (HR: 9.67, 95% CI [4.61-20.30], P<0.0001). Next, we established a minimal methylation signature score (MMSS), which consists of ten most informative of the originally 62 CpG units used for the MS. The prognostic value of the MMSS was confirmed by Kaplan-Meier analysis for all HNSCC (P<0.0001) and non-OPSCC (P = 0.0002), and was supported by multivariate Cox regression models for all HNSCC (HR: 2.15, 95% CI [1.36-3.41], P = 0.001). In summary, the MS and the MMSS exhibit an excellent performance as prognosticators for survival, which is not limited by the anatomical site, and both could be implemented in future clinical trials.
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Project description:Background and objectiveHead and neck cancer is a malignant tumor that begins in the head and neck region, and has the sixth highest incidence worldwide. Previous studies have indicated several prognostic markers for head and neck squamous cell carcinoma (HNSCC), but due to poor accuracy and sensitivity of these clinical characteristic markers attention has been gradually switched to molecular biomarkers. This study aimed to sort out the mRNAs correlated with patient survival time to establish an mRNA combination prognostic biomarker model for HNSCC patient risk stratification, providing optimal therapeutic regimens and improving patient prognosis.MethodsClinical data and transcriptome sequencing data of HNSCC were retrieved from TCGA database and were allocated into training and validation datasets. The prognostic model was established using the mRNAs, which were sorted out from training dataset by a significant correlation with survival time. Eventually, the prediction property of the model was evaluated by Kaplan-Meier survival analysis and receiver operating characteristic (ROC) curve.ResultsAn optimal prognostic model by the combination of six mRNAs was established. Kaplan-Meier survival analysis revealed effective risk stratification by this model for patients in the two datasets. The area under ROC curve (AUC) was >?0.65 for training and validation datasets, indicating good sensitivity and specificity of this model. Moreover, prominent superiority of this model to investigate prognostic biomarkers was demonstrated.ConclusionOur model provided effective prognostication in terms of death risk stratification and evaluation in HNSCC patients. Combination of this prognostic model with current treatment measures is expected to greatly improve the patients' prognosis.
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Project description:Background: Head and neck squamous cell carcinoma (HNSCC) is a highly heterogeneous tumor with a high incidence and poor prognosis. Therefore, effective predictive models are needed to evaluate patient outcomes and optimize treatment. Methods: Robust Rank Aggregation (RRA) method was used to identify highly robust differentially-expressed genes (DEGs) between HNSCC and normal tissue in 9 GEO and TCGA datasets. Univariate Cox regression analysis and Lasso Cox regression analysis were performed to identify DEGs related to the Overall survival (OS) and to construct a prognostic gene signature (HNSCCSig). External validation was performed using GSE65858 dataset. Moreover, comprehensive bioinformatics analyses were used to identify the association between HNSCCSig and tumor immune environment. Results: A total of 257 reliable DEGs were identified by differentially analysis result of TCGA and GSE65858 datasets. The HNSCCSig including 7 mRNAs (SLURP1, SCARA5, CLDN10, MYH11, CXCL13, HLF, and ITGA3) were developed and validated to identify high-risk group who had a worse OS than low-risk group in TCGA and GSE65858 datasets. Cox regression analysis showed that the HNSCCSig could independently predict OS in both the TCGA and the GSE65858 datasets. Further research demonstrated that the infiltration bundance of CD8 + T cells, B cells, neutrophils, and NK cells were significantly lower in the high-risk group. A nomogram was also constructed by combining the HNSCCSig and clinical characters. Conclusion: We established and validated the HNSCCSig consisting of SLURP1, SCARA5, CLDN10, MYH11, CXCL13, HLF, and ITGA3. A nomogram combining HNSCCSig and some clinical parameters was constructed to identify high-risk HNSCC-patients with poor prognosis.
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Project description:PurposeThe inflammatory response was associated with the prognosis of head and neck squamous cell carcinoma (HNSCC). This study aimed to perform a novel prognostic signature based on inflammation-related genes (IRGs) for a better understanding of the prognosis of HNSCC.Patients and methodsIRGs were obtained from The Cancer Genome Atlas (TCGA) database and the Gene Expression Omnibus (GEO) database. Functional enrichment analysis was performed to explore potential pathways. Univariate and multivariate Cox regression as well as the Least Absolute Shrinkage and Selection Operator (LASSO) were utilized to construct an IRGs-based prognostic model on TCGA database and the GEO database was utilized for outcome validation. The nomogram model was constructed based on independent prognostic factors after univariate and multivariate Cox regression. The immune cell infiltration level was analyzed via the Tumor Immune Estimation Resource (TIMER) database.ResultsIn this study, we confirmed that 60% IRGs were abnormally expressed in HNSCC samples, and these were associated with important oncobiology. Then, a prognostic signature comprising 7 hub genes was generated based on TCGA database. The results were validated in 97 patients from GSE41613. A nomogram comprising risk score, age, M stage and N stage was generated to improve the accuracy of prognosis evaluation. The immune cell infiltration analysis suggested that 5 hub genes (ADGRE1, OLR1, TIMP1, GPR132 and CCR7) were negatively correlated with tumor purity and positively correlated with the infiltration of immune cells.ConclusionOur study established a novel signature consisting of 7 hub genes for the prognostic prediction in patients with HNSCC.
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Project description:BackgroundImmune-related genes (IRGs) were linked to the prognosis of head and neck squamous cell carcinoma (HNSCC). This study aimed to identify the effects of an immune-related gene signature (IRGS) that can predict the of HNSCC prognosis.MethodsThe expression data of 770 HNSCC patients from the TCGA database and the GEO database were used. To explore a predictive model, the Cox proportional hazards model was applied. The Kaplan-Meier survival analysis, as well as univariate and multivariate analyses were performed to evaluate the independent predictive value of IRGS. To explore biological functions of IRGS, enrichment analyses and pathway annotation for differentially expressed genes (DEGs) in different immune groups were applied, as well as the immune infiltration.ResultsA prognostic signature comprising 27 IRGs was generated. IRGS significantly stratified HNSCC patients into high and low immune risk groups in regard to overall survival in the training cohort (HR = 3.69, 95% CI 2.73-4.98, P < 0.001). Likewise, IRGS could be linked to the prognosis of HNSCC in patients of the validation cohort (HR = 1.84, 95% CI 1.21-2.81, P < 0.01). Even after adjusting for TNM stage, IRGS was maintained as an independent predictor in the multivariate analysis (HR = 3.62, 95% CI 2.58-5.09, P < 0.001), and in the validation cohort (HR = 1.73, 95% CI 1.12-2.67, P = 0.014). The IFN-α response, the IFN-γ response, IL-2/STAT5 signaling, and IL-6/JAK/STAT3 signaling were all negatively correlated with the immune risk (P < 0.01). Immune infiltration of the high-risk group was significantly lower than that of the low-risk group (P < 0.01). Most notably, the infiltration of CD8 T cells, memory-activated CD4 T cells, and regulatory T cells was strongly upregulated in the low immune risk groups, while memory resting CD4 T cell infiltration was downregulated (P < 0.01).ConclusionOur analysis provides a comprehensive prognosis of the immune microenvironments and outcomes for different individuals. Further studies are needed to evaluate the clinical application of this signature.
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Project description:Cancer stem cells (CSCs) have been characterized by several exclusive features that include differentiation, self-renew, and homeostatic control, which allows tumor maintenance and spread. Recurrence and therapeutic resistance of head and neck squamous cell carcinomas (HNSCC) have been identified to be attributed to CSCs. However, the biomarkers led to the development of HNSCC stem cells remain less defined. In this study, we quantified cancer stemness by mRNA expression-based stemness index (mRNAsi), and found that mRNAsi indices were higher in HNSCC tissues than that in normal tissue. A significantly higher mRNAsi was observed in HPV positive patients than HPV negative patients, as well as in male patients than in female patients. The 8-mRNAsi signature was identified from the genes in two modules which were mostly related to mRNAsi screened by weighted gene co-expression network analysis. In this prognostic signatures, high expression of RGS16, LYVE1, hnRNPC, ANP32A, and AIMP1 focus in promoting cell proliferation and tumor progression. While ZNF66, PIK3R3, and MAP2K7 are associated with a low risk of death. The riskscore of eight signatures have a powerful capacity for 1-, 3-, 5-year of overall survival prediction (5-year AUC 0.77, 95% CI 0.69-0.85). These findings based on stemness indices may provide a novel understanding of target therapy for suppressing HNSCC stem cells.
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Project description:BackgroundHead and neck squamous cell carcinoma (HNSCC) is a common malignancy that emanates from the lips, mouth, paranasal sinuses, oropharynx, larynx, nasopharynx, and from other pharyngeal cancers. The availability of high-throughput expression data has made it possible to use global gene expression data to analyze the relationship between metabolic-related gene expression and clinical outcomes in HNSCC patients.MethodIn this study, we used RNA sequencing (RNA-seq) data from the cancer genome atlas (TCGA), with validation in the GEO dataset to profile the metabolic microenvironment and define potential biomarkers for metabolic therapy.ResultsWe extracted data for 529 patients and 327 metabolic genes (198 upregulated and 129 downregulated genes) in the TCGA database. Carbonic anhydrase 9 (CA9) and CA6 had the largest logFCs in the upregulated and downregulated genes, respectively. Our Cox regression model data showed 51 prognostic-related genes with lysocardiolipin acyltransferase 1 (LCLAT1) and choline dehydrogenase (CHDH) being associated with the highest risk (HR = 1.144, 95% CI = 1.044 ~ 1.251) and the lowest risk (HR = 0.580, 95% CI = 0.400 ~ 0.839) in HNSCC, respectively. We next used the ROC curve to evaluate whether the differentially expressed metabolic-related genes could serve as early predictors of HNSCC. The findings showed an AUC of 0.745 and 0.618 in the TCGA and GEO analysis, respectively. Besides, the ability for the genes to predict clinicopathological HNSCC status was analyzed and the data showed that the AUC for age, gender, grade, stage, T, M, and N was 0.520, 0.495, 0.568, 0.606, 0.577, 0.476, and 0.673, respectively, in the TCGA dataset. On the other hand, the AUC for age, gender, stage, T, M, N, smoking, and HPV16-pos was 0.599, 0.531, 0.622, 0.606, 0.616, 0.550, 0.614, 0.519, and 0.397, respectively, in the GEO dataset.ConclusionTaken together, our study unearths a novel metabolic gene signature for the prediction of HNSCC prognosis based on the TCGA dataset. Our signature might point out the metabolic microenvironment disorders and provides potential treatment targets and prognostic biomarkers.
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Project description:ObjectivesTo develop a model that can assist in the diagnosis and prediction of prognosis for head and neck squamous cell carcinoma (HNSCC).Materials and methodsData from TCGA and GEO databases were used to generate normalized gene expression data. Consensus Cluster Plus was used for cluster analysis and the relationship between angiogenesis-associated gene (AAG) expression patterns, clinical characteristics and survival was examined. Support vector machine (SVM) and least absolute shrinkage and selection operator (LASSO) analyzes and multiple logistic regression analyzes were performed to determine the diagnostic model, and a prognostic nomogram was constructed using univariate and multivariate Cox regression analyses. ESTIMATE, XCELL, TIMER, QUANTISEQ, MCPCOUNTER, EPIC, CIBERSORT-ABS, CIBERSORT algorithms were used to assess the immune microenvironment of HNSCC patients. In addition, gene set enrichment analysis, treatment sensitivity analysis, and AAGs mutation studies were performed. Finally, we also performed immunohistochemistry (IHC) staining in the tissue samples.ResultsWe classified HNSCC patients into subtypes based on differences in AAG expression from TCGA and GEO databases. There are differences in clinical features, TME, and immune-related gene expression between two subgroups. We constructed a HNSCC diagnostic model based on nine AAGs, which has good sensitivity and specificity. After further screening, we constructed a prognostic risk signature for HNSCC based on six AAGs. The constructed risk score had a good independent prognostic significance, and it was further constructed into a prognostic nomogram together with age and stage. Different prognostic risk groups have differences in immune microenvironment, drug sensitivity, gene enrichment and gene mutation.ConclusionWe have constructed a diagnostic and prognostic model for HNSCC based on AAG, which has good performance. The constructed prognostic risk score is closely related to tumor immune microenvironment and immunotherapy response.
| S-EPMC10861726 | biostudies-literature
Project description:Background:The prognosis of patients with head and neck squamous cell carcinoma (HNSCC) is still poor due to the lack of effective prognostic biomarkers. lncRNA is an important survival prognostic indicator and has important biological functions in tumorigenesis. Methods:RNA-seq was re-annotated, and comprehensive clinical information was obtained from the GEO database. Univariate and multivariate Cox regression analyses were used to construct the lncRNA prognosis signature. Gene set enrichment analysis (GSEA) enrichment analysis method is used to explore the possible mechanism of the selected lncRNA influencing HNSCC development. The rms package was used to calculate the C-index to evaluate the overall prediction performance between different signature. PCR is used to detect the expression of selected lncRNA in cancer and adjacent tissues. Results:In the GSE65858 training cohort, 124 probes significantly related to prognosis were identified, 11 significant lncRNAs were further selected by rbsurv dimensionality reduction analysis. Finally, 4-lncRNA signature was constructed by multivariate Cox analysis. This signature was associated with tumor-associated pathway and is an independent factor of the patient's prognosis. 4-lncRNA signature has strong robustness and can exert stable prediction performance in different cohorts. A nomogram comprising the prognostic model to predict the overall survival was established. The 4-lncRNA signature was significantly upregulated in HNSCC samples. Conclusion:The predictive model and nomogram will enable patients to be more accurately managed in trials and clinical practices and could be applied as a new prognostic model for predicting survival of HNSCC patients.
| S-EPMC7457573 | biostudies-literature
Project description:For TP53-mutated head and neck squamous cell carcinomas (HNSCCs), the codon and specific amino acid sequence change resulting from a patient's mutation can be prognostic. Thus, developing a framework to predict patient survival for specific mutations in TP53 would be valuable. There are many bioinformatics and functional methods for predicting the phenotypic impact of genetic variation, but their overall clinical value remains unclear. Here, we assess the ability of 15 different methods to predict HNSCC patient survival from TP53 mutation, using TP53 mutation and clinical data from patients enrolled in E4393 by the Eastern Cooperative Oncology Group (ECOG), which investigated whether TP53 mutations in surgical margins were predictive of disease recurrence. These methods include: server-based computational tools SIFT, PolyPhen-2, and Align-GVGD; our in-house POSE and VEST algorithms; the rules devised in Poeta et al. with and without considerations for splice-site mutations; location of mutation in the DNA-bound TP53 protein structure; and a functional assay measuring WAF1 transactivation in TP53-mutated yeast. We assessed method performance using overall survival (OS) and progression-free survival (PFS) from 420 HNSCC patients, of whom 224 had TP53 mutations. Each mutation was categorized as "disruptive" or "non-disruptive". For each method, we compared the outcome between the disruptive group vs. the non-disruptive group. The rules devised by Poeta et al. with or without our splice-site modification were observed to be superior to others. While the differences in OS (disruptive vs. non-disruptive) appear to be marginally significant (Poeta rules + splice rules, P = 0.089; Poeta rules, P = 0.053), both algorithms identified the disruptive group as having significantly worse PFS outcome (Poeta rules + splice rules, P = 0.011; Poeta rules, P = 0.027). In general, prognostic performance was low among assessed methods. Further studies are required to develop and validate methods that can predict functional and clinical significance of TP53 mutations in HNSCC patients.
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