Dataset Information

Construction and validation of a prognostic nine-gene signature associated with radiosensitivity in head and neck squamous cell carcinoma.

ABSTRACT:

Background

Radiotherapy is an effective treatment for head and neck squamous cell carcinoma (HNSCC), however how to predict the prognosis is not clear.

Methods

Here we collected 262 radiosensitivity-associated genes, screened and constructed a prognostic nine-gene risk model through univariate COX, lasso regression, stepwise regression and multivariate COX analysis for transcriptome and clinical information of HNSCC patients obtained from the cancer genome atlas (TCGA) and gene expression omnibus (GEO) databases.

Results

The reliability and robustness of the risk model were verified by receiver operating characteristic (ROC) curves, risk maps, and Kaplan-Meier (KM) curves analysis. Differences in immune cell infiltration and immune-related pathway enrichment between high-risk and low-risk subgroups were determined by multiple immune infiltration analyses. Meanwhile, the mutation map and the responses to immunotherapy were also differentiated by the prognostic nine-gene signature associated with radiosensitivity. These nine genes expression in HNSCC was verified in the Human Protein Atlas (HPA) database. After that, these nine genes expression was verified to be related to radiation resistance through in-vitro cell experiments.

Conclusions

All results showed that the nine-gene signature associated with radiosensitivity is a potential prognostic indicator for HNSCC patients after radiotherapy and provides potential gene targets for enhancing the efficacy of radiotherapy.

SUBMITTER: Lu C

PROVIDER: S-EPMC10579965 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature

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Publications

Construction and validation of a prognostic nine-gene signature associated with radiosensitivity in head and neck squamous cell carcinoma.

Lu Congxian C Sun Qi Q Guo Ying Y Han Xiao X Zhang Mingjun M Liu Jiahui J Wang Yaqi Y Mou Yakui Y Li Yumei Y Song Xicheng X

Clinical and translational radiation oncology 20231002

<h4>Background</h4>Radiotherapy is an effective treatment for head and neck squamous cell carcinoma (HNSCC), however how to predict the prognosis is not clear.<h4>Methods</h4>Here we collected 262 radiosensitivity-associated genes, screened and constructed a prognostic nine-gene risk model through univariate COX, lasso regression, stepwise regression and multivariate COX analysis for transcriptome and clinical information of HNSCC patients obtained from the cancer genome atlas (TCGA) and gene ex ...[more]

PMID: 37854672

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Project description:BackgroundHead and neck squamous cell carcinoma (HNSCC) is a malignancy of epithelial origin and with poor prognosis. Exploring the biomarkers and prognostic models that can contribute to early tumor detection is meaningful. A comprehensive analysis was conducted according to the stage-related signature genes of HNSCC, and a prognostic model was developed to validate their ability to predict the prognosis.MethodsThe transcriptome profiles and clinical information of HNSCC patients were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) respectively. mRNA expressions of differentially expressed genes (DEGs) were analyzed in stage I-II patients and stage III-IV patients from TCGA by R packages. A protein-protein interaction (PPI) network and core-gene network map were constructed, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to examine pathway enrichment. Kaplan-Meier, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression were applied to establish a stage-associated signature model. A Spearman analysis was conducted to examine the correlations between the characteristic genes and immune cell infiltration. Kaplan-Meier analysis and a receiver operating characteristic (ROC) curve were used to test the effectiveness of the model. Univariate multivariate Cox regression analyses were used to assess whether the risk score was an independent prognostic indicator for HNSCC.ResultsIn TCGA cohort, 5 genes (i.e., BRINP1, IL17A, ALB, FOXA2, and ZCCHC12) in the constructed prognostic risk model were associated with prognosis. Patients in the low-risk group had a better prognosis outcome than those in the high-risk group. The predictive power was good because all the area under the curve (AUC) of the risk score was higher than 0.6. Risk score [hazard ratio (HR) =1.985; P<0.001] was an independent risk factor for the prognosis of HNSCC. The results in the GEO cohort were consistent with those in the TCGA cohort.ConclusionsWe constructed and verified a prognostic risk model of stage-related signature genes for HNSCC based on the GEO and TCGA data. Due to the good predictive accuracy of this model, the prognosis of and the tumor immune cell infiltration with patients can be estimated.

Project description:Endoplasmic reticulum stress (ERS) occurs when misfolded or unfolded proteins accumulate in the endoplasmic reticulum (ER), and it is often observed in tumors, including head and neck squamous cell carcinoma (HNSCC). Relevant studies have demonstrated the prognostic significance of ERS-related long non-coding RNAs (lncRNAs) in various cancers. However, the relationship between ERS and lncRNAs in HNSCC has received limited attention in previous studies. In this study, we aimed to develop an ERS-related lncRNAs prognostic model using correlation analysis, Cox regression analysis, least absolute shrinkage, and selection operator (LASSO) regression analysis based on data from The Cancer Genome Atlas (TCGA) database. The survival and predictive ability of this model were evaluated using Kaplan-Meier analysis and time-dependent receiver operating characteristics (ROC), while nomograms and calibration curves were constructed. Then, functional enrichment analyses, tumor mutation burden (TMB), tumor infiltration of immune cells, single sample Gene Set Enrichment Analysis (ssGSEA), and drug sensitivity analysis were performed. Additionally, we conducted a consensus cluster analysis to compare differences between subtypes of tumors. Finally, we validated the expression of the ERS-related lncRNAs that constructed prognostic risk score model in HNSCC tissues through quantitative real-time PCR (qRT-PCR). We developed a prognostic signature based on seven ERS-related lncRNAs, which showed better predictive performance than other clinicopathological features. The high-risk poor prognosis group had a poorer prognosis in comparison to the low-risk good prognosis. The area under the ROC curve (AUC) predicted by this model for 3-year survival rates of HNSCC patients was 0.805. Enrichment analysis revealed that the differentially expressed genes were primarily enriched in pathways related to immune responses and signal transduction. Low-risk patients had lower TMB, more immune cell infiltrations, and enhanced anti-tumor immunity. Cluster analysis indicated that cluster 3 may have a better prognosis and immunotherapy effect. In addition, the result of qRT-PCR was consistent with our analysis. This prognostic model based on seven ERS-related lncRNAs is a promising tool for risk stratification, survival prediction, and immune cell infiltration status assessment.

Project description:Background: Head and neck squamous cell carcinoma (HNSC) is a prevalent and heterogeneous malignancy with poor prognosis and high mortality rates. There is significant evidence of alternative splicing (AS) contributing to tumor development, suggesting its potential in predicting prognosis and therapeutic efficacy. This study aims to establish an AS-based prognostic signature in HNSC patients. Methods: The expression profiles and clinical information of 486 HNSC patients were downloaded from the TCGA database, and the AS data were downloaded from the TCGA SpliceSeq database. The survival-associated AS events were identified by conducting a Cox regression analysis and utilized to develop a prognostic signature by fitting into a LASSO-regularized Cox regression model. Survival analysis, univariate and multivariate Cox regression analysis, and receiver operating characteristic (ROC) curve analysis were performed to evaluate the signature and an independent cohort was used for validation. The immune cell function and infiltration were analyzed by CIBERSORT and the ssGSEA algorithm. Results: Univariate Cox regression analysis identified 2726 survival-associated AS events from 1714 genes. The correlation network reported DDX39B, PRPF39, and ARGLU1 as key splicing factors (SF) regulating these AS events. Eight survival-associated AS events were selected and validated by LASSO regression to develop a prognostic signature. It was confirmed that this signature could predict HNSC outcomes independent of other variables via multivariate Cox regression analysis. The risk score AUC was more than 0.75 for 3 years, highlighting the signature's prediction capability. Immune infiltration analysis reported different immune cell distributions between the two risk groups. The immune cell content was higher in the high-risk group than in the low-risk group. The correlation analysis revealed a significant correlation between risk score, immune cell subsets, and immune checkpoint expression. Conclusion: The prognostic signature developed from survival-associated AS events could predict the prognosis of HNSC patients and their clinical response to immunotherapy. However, this signature requires further research and validation in larger cohort studies.

Dataset Information

Construction and validation of a prognostic nine-gene signature associated with radiosensitivity in head and neck squamous cell carcinoma.

Background

Methods

Results

Conclusions

Publications

Construction and validation of a prognostic nine-gene signature associated with radiosensitivity in head and neck squamous cell carcinoma.

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