Dataset Information

Ferroptosis-associated DNA methylation signature predicts overall survival in patients with head and neck squamous cell carcinoma.

ABSTRACT:

Background

Head and neck squamous cell carcinoma (HNSCC) is a common cancer characterized by late diagnosis and poor prognosis. The aim of this study was to identify a novel ferroptosis-related DNA methylation signature as an alternative diagnosis index for patients with HNSCC.

Methods

Methylome and transcriptome data of 499 HNSCC patients, including 275 oral squamous cell carcinoma (OSCC) samples, were obtained from The Cancer Genome Atlas (TCGA). An additional independent methylation dataset of 50 OSCC patients from the NCBI Gene Expression Omnibus (GEO) database was used for validation. As an index of ferroptosis activity, the ferroptosis score (FS) of each patient was inferred from the transcriptome data using single-sample gene set enrichment analysis. Univariate, multivariate, and LASSO Cox regression analyses were used to select CpG sites for the construction of a ferroptosis-related DNA methylation signature for diagnosis of patients.

Results

We initially inferred the FS of each TCGA HNSCC patient and divided the samples into high- and low-FS subgroups. Results showed that the high-FS subgroup displayed poor overall survival. Moreover, 378 differentially methylated CpG sites (DMCs) were identified between the two HNSCC subgroups, with 16 selected to construct a 16-DNA methylation signature for risk prediction in HNSCC patients using the LASSO and multivariate Cox regression models. Relative operating characteristic (ROC) curve analysis showed great predictive efficiency for 1-, 3-, and 5-year HNSCC survival using the 16-DNA methylation signature. Its predictive efficiency was also observed in OSCC patients from the TCGA and GEO databases. In addition, we found that the signature was associated with the fractions of immune types in the tumor immune microenvironment (TIME), suggesting potential interactions between ferroptosis and TIME in HNSCC progression.

Conclusions

We established a novel ferroptosis-related 16-DNA methylation signature that could be applied as an alternative tool to predict prognosis outcome in patients with HNSCC, including OSCC.

SUBMITTER: Xu Y

PROVIDER: S-EPMC8767683 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

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.

Project description:Background: Ferroptosis is an iron-dependent programmed cell death (PCD) form that plays a crucial role in tumorigenesis and might affect the antitumor effect of radiotherapy and immunotherapy. This study aimed to investigate distinct ferroptosis-related genes, their prognostic value and their relationship with immunotherapy in patients with head and neck squamous cell carcinoma (HNSCC). Methods: The differentially expressed ferroptosis-related genes in HNSCC were filtered based on multiple public databases. To avoid overfitting and improve clinical practicability, univariable, least absolute shrinkage and selection operator (LASSO) and multivariable Cox algorithms were performed to construct a prognostic risk model. Moreover, a nomogram was constructed to forecast individual prognosis. The differences in tumor mutational burden (TMB), immune infiltration and immune checkpoint genes in HNSCC patients with different prognoses were investigated. The correlation between drug sensitivity and the model was firstly analyzed by the Pearson method. Results: Ten genes related to ferroptosis were screened to construct the prognostic risk model. Kaplan-Meier (K-M) analysis showed that the prognosis of HNSCC patients in the high-risk group was significantly lower than that in the low-risk group (P < 0.001), and the area under the curve (AUC) of the 1-, 3- and 5-year receiver operating characteristic (ROC) curve increased year by year (0.665, 0.743, and 0.755). The internal and external validation further verified the accuracy of the model. Then, a nomogram was build based on the reliable model. The C-index of the nomogram was superior to a previous study (0.752 vs. 0.640), and the AUC (0.729 vs. 0.597 at 1 year, 0.828 vs. 0.706 at 3 years and 0.853 vs. 0.645 at 5 years), calibration plot and decision curve analysis (DCA) also shown the satisfactory predictive capacity. Furthermore, the TMB was revealed to be positively correlated with the risk score in HNSCC patients (R = 0.14; P < 0.01). The differences in immune infiltration and immune checkpoint genes were significant (P < 0.05). Pearson analysis showed that the relationship between the model and the sensitivity to antitumor drugs was significant (P < 0.05). Conclusion: Our findings identified potential novel therapeutic targets, providing further potential improvement in the individualized treatment of patients with HNSCC.