Constructing a prognostic model for head and neck squamous cell carcinoma based on glucose metabolism related genes
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ABSTRACT:
SUBMITTER: Liu Y
PROVIDER: S-EPMC10591078 | biostudies-literature | 2023 Jan
REPOSITORIES: biostudies-literature
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Project description:The fatty acid metabolism (FAM) is known to impact tumorigenesis, tumor progression and treatment resistance via enhancing lipid synthesis, storage and catabolism. However, the role of FAM in head and neck squamous cell carcinoma (HNSCC) has remained elusive. In the present study, we obtained a total of 69 differentially expressed FAM-related genes between 502 HNSCC samples and 44 normal samples from The Cancer Genome Atlas (TCGA) database. The HNSCC samples were divided into 2 clusters according to 69 differentially expressed genes (DEGs) via cluster analysis. Then DEGs in the two clusters were found, and 137 prognostic DEGs were identified by univariate analysis. Subsequently, combined with the clinical information of 546 HNSCC patients from TCGA database, a 12-gene prognostic risk model was established (FEPHX3, SPINK7, FCRLA, MASP1, ZNF541, CD5, BEST2 and ZAP70 were down-regulation, ADPRHL1, DYNC1I1, KCNG1 and LINC00460 were up-regulation) using multivariate Cox regression and LASSO regression analysis. The risk scores of 546 HNSCC samples were calculated. According to the median risk score, 546 HNSCC patients were divided into the high- and low-risk (high- and low score) groups. The Kaplan-Meier survival analysis showed that the survival time of HNSCC patients was significantly shorter in the high-risk group than that in the low-risk group (p < 0.001). The same conclusion was obtained in the Gene Expression Omnibus (GEO) dataset. After that, the multivariate Cox regression analysis indicated that the risk score was an independent factor for patients with HNSCC in the TCGA cohort. In addition, single-sample gene set enrichment analysis (ssGSEA) indicated that the level of infiltrating immune cells was relatively low in the high-risk group compared with the low-risk group. In summary, FAM-related gene expression-based risk signature could predict the prognosis of HNSCC independently.
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Project description:The impact of the senescence related microenvironment on cancer prognosis and therapeutic response remains poorly understood. In this study, we investigated the prognostic significance of senescence related tumor microenvironment genes (PSTGs) and their potential implications for immunotherapy response. Using the Cancer Genome Atlas- head and neck squamous cell carcinoma (HNSC) data, we identified two subtypes based on the expression of PSTGs, acquired from tumor-associated senescence genes, tumor microenvironment (TME)-related genes, and immune-related genes, using consensus clustering. Using the LASSO, we constructed a risk model consisting of senescence related TME core genes (STCGs). The two subtypes exhibited significant differences in prognosis, genetic alterations, methylation patterns, and enriched pathways, and immune infiltration. Our risk model stratified patients into high-risk and low-risk groups and validated in independent cohorts. The high-risk group showed poorer prognosis and immune inactivation, suggesting reduced responsiveness to immunotherapy. Additionally, we observed a significant enrichment of STCGs in stromal cells using single-cell RNA transcriptome data. Our findings highlight the importance of the senescence related TME in HNSC prognosis and response to immunotherapy. This study contributes to a deeper understanding of the complex interplay between senescence and the TME, with potential implications for precision medicine and personalized treatment approaches in HNSC.
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Project description:Head and neck squamous cell carcinoma (HNSCC) is the most common malignant tumor in the epithelium of the head and neck. The role of the centrosome in malignant tumors is crucial. However, research on the centrosome in HNSCC remains largely unexplored. In this study, bioinformatics tools were utilized to analyze the expression and prognostic significance of centrosome-related genes (CRGs). CRGs exhibited a relatively high mutation frequency in HNSCC. Consensus unsupervised clustering analysis based on the expression profiles of CRGs revealed significant associations with clinical features, prognosis and immune microenvironment in HNSCC. Prognostic features were constructed using univariate and LASSO Cox regression, resulting in a centrosome-related model with eleven features. Patients were classified into high-risk and low-risk groups based on median risk scores. External validation using the GSE41613 dataset from the GEO database confirmed the reliability of the centrosome-related model. The model was associated with the prognosis of HNSCC patients, and centrosome-related features could impact tumor prognosis by influencing the tumor immune microenvironment. Finally, qPCR showed that CRGs were highly expressed in tumor tissues. This study developed a novel centrosome-related prognostic model, applicable for predicting the prognosis and immune landscape of HNSCC patients, offering potential targets for future HNSCC treatment.
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Project description:The relationship between oxidized lipid metabolism and the immunological function of cancer is well known. However, the functions and regulatory mechanisms of lncRNAs associated with oxidized lipid metabolism in head and neck squamous cell carcinoma (HNSCC) remain to be fully elucidated. In this study, we established an oxidized lipid metabolism-related lncRNA prognostic signature to assess the prognosis and immune infiltration of HNSCC patients. The HNSCC transcriptome was obtained from The Cancer Genome Atlas. The choice of the target genes with a relevance score greater than 10 was performed via a correlation analysis by GeneCards. Patients were categorized by risk score and generated with multivariate Cox regression, which was then validated and evaluated using the Kaplan-Meier analysis and time-dependent receiver operating characteristics (ROC). A nomogram was constructed by combining the risk score with the clinical data. We constructed a risk score with 24 oxidized lipid metabolism-related lncRNAs. The areas' 1-, 2-, and 3-year OS under the ROC curve (AUC) were 0.765, 0.724, and 0.724, respectively. Furthermore, the nomogram clearly distinguished the survival probabilities of patients in high- and low-risk groups, between which substantial variations were revealed by immune infiltration analysis. The results supported the fact that oxidized lipid metabolism-related lncRNAs might predict prognoses and assist with differentiating amid differences in immune infiltration in HNSCC.
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Project description:BackgroundThe purpose of this study was to identify the prognostic value of cuproptosis and copper metabolism-related genes, to clarify their molecular and immunological characteristics, and to elucidate their benefits in head and neck squamous cell carcinoma (HNSCC).MethodsThe details of human cuproptosis and copper metabolism-related genes were searched and filtered from the msigdb database and the latest literature. To identify prognostic genes associated with cuproptosis and copper metabolism, we used least absolute shrinkage and selection operator regression, and this coefficient was used to set up a prognostic risk score model. HNSCC samples were divided into two groups according to the median risk. Afterwards, the function and immune characteristics of these genes in HNSCC were analyzed.ResultsThe 14-gene signature was constructed to classify HNSCC patients into low-risk and high-risk groups according to the risk level. In the The Cancer Genome Atlas (TCGA) cohort, the overall survival (OS) rate of the high-risk group was lower than that of the low-risk group (P < 0.0001). The area under the curve of the time-dependent Receiver Operator Characteristic (ROC) curve assessed the good performance of the genetic signature in predicting OS and showed similar performance in the external validation cohort. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment assays and Protein-Protein Interaction (PPI) protein networks have been used to explore signaling pathways and potential mechanisms that were markedly active in patients with HNSCC. Furthermore, the 14 cuproptosis and copper metabolism-related genes were significantly correlated with the immune microenvironment, suggesting that these genes may be linked with the immune regulation and development of HNSCC.ConclusionsOur results emphasize the significance of cuproptosis and copper metabolism as a predictive biomarker for HNSCC, and its expression levels seem to be correlated with immune- related features; thus, they may be a possible biomarker for HNSCC prognosis.
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Project description:Human papillomavirus (HPV) positive HNSCC patients generally have a favorable survival and promising responsiveness to radiotherapy, chemoradiotherapy and checkpoint blockades. However, HPV negative patients, the majority of HNSCC patients, have been largely overlooked. Cell death has been involved in the therapeutic resistance of cancers. We constructed a cell death index (CDI), based on autophagy, apoptosis and pyroptosis related genes, to predict the prognosis for HNSCC using TCGA dataset, and validated in a cohort from Qilu Hospital of Shandong University. We performed RNA sequencing of 28 paraffin-embedded tissue from HNSCC patients and determined HPV status by immunohistochemistry of p16. We found that CDI was an independent prognostic indicator for overall survival. Notably, the prognostic value of CDI was more profound in HPV negative HNSCC patients compared with those with HPV positivity.
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Project description:The stratification of head and neck squamous cell carcinoma (HNSCC) patients based on prognostic differences is critical for therapeutic guidance. This study was designed to construct a predictive signature derived from T-cell receptor-related genes (TCRRGs) to forecast the clinical outcomes in HNSCC. We sourced gene expression profiles from The Cancer Genome Atlas (TCGA) HNSCC dataset, GSE41613, and GSE65858 datasets. Utilizing consensus clustering analysis, we identified two distinct HNSCC clusters according to TCRRG expression. A TCRRG-based signature was subsequently developed and validated across diverse independent HNSCC cohorts. Moreover, we established a nomogram model based on TCRRGs. We further explored differences in immune landscapes between high- and low-risk groups. The TCGA HNSCC dataset was stratified into two clusters, displaying marked variations in both overall survival (OS) and immune cell infiltration. Furthermore, we developed a robust prognostic signature based on TCRRG utilizing the TCGA HNSCC train cohort, and its prognostic efficacy was validated in the TCGA HNSCC test cohort, GSE41613, and GSE65858. Importantly, the high-risk group was characterized by a suppressive immune microenvironment, in contrast to the low-risk group. Our study successfully developed a robust TCRRG-based signature that accurately predicts clinical outcomes in HNSCC, offering valuable strategies for improved treatments.
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Project description:Metabolic reprogramming contributes to patient prognosis. Here, we aimed to reveal the comprehensive landscape in metabolism of head and neck squamous carcinoma (HNSCC), and establish a novel metabolism-related prognostic model to explore the clinical potential and predictive value on therapeutic response. We screened 4752 metabolism-related genes (MRGs) and then identified differentially expressed MRGs in HNSCC. A novel 10-MRGs risk model for prognosis was established by the univariate Cox regression analysis and the least absolute shrinkage and selection operator (Lasso) regression analysis, and then verified in both internal and external validation cohort. Kaplan-Meier analysis was employed to explore its prognostic power on the response of conventional therapy. The immune cell infiltration was also evaluated and we used tumor immune dysfunction and exclusion (TIDE) algorithm to estimate potential response of immunotherapy in different risk groups. Nomogram model was constructed to further predict patients' prognoses. We found the MRGs-related prognostic model showed good prediction performance. Survival analysis indicated that patients suffered obviously poorer survival outcomes in high-risk group (p < 0.001). The metabolism-related signature was further confirmed to be the independent prognostic value of HNSCC (HR = 6.387, 95% CI = 3.281-12.432, p < 0.001), the efficacy of predictive model was also verified by internal and external validation cohorts. We observed that HNSCC patients would benefit from the application of chemotherapy in the low-risk group (p = 0.029). Immunotherapy may be effective for HNSCC patients with high risk score (p < 0.01). Furthermore, we established a predictive nomogram model for clinical application with high performance. Our study constructed and validated a promising 10-MRGs signature for monitoring outcome, which may provide potential indicators for metabolic therapy and therapeutic response prediction in HNSCC.
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Project description:BackgroundThe human papillomavirus (HPV) is emerging as an important risk factor in head and neck squamous cell carcinoma (HNSCC) patients. This has been observed particularly in the case of HPV16. The HPV16+ HNSCC subtype has distinct pathological, clinical, molecular, and prognostic characteristics. This study aimed to identify potential microRNAs (miRNAs) and their roles in HPV16+ HNSCC progression.MethodmiRNA, mRNA and the clinical data of 519 HNSCC and 44 HNSCC-negative samples were obtained from The Cancer Genome Atlas (TCGA) database. Differentially expressed miRNAs (DEMs) in HPV16-related HNSCC tissues with prognostic value were selected. DEM levels were assessed based on clinicopathological parameters and overall survival (OS). Target genes were also predicted and functional analysis based on Gene Set Enrichment Analysis (GSEA) were then performed.ResultsIn HPV16+ HNSCC tissues, miR-99a-3p and miR-4746-5p were significantly upregulated. In contrast, miR-411-5p was shown to be downregulated. miR-99a-3phighmiR-411-5plowmiR-4746-5phigh expression could estimate improved OS and low frequent perineural invasion (PNI). Predicted target genes were enriched in cell growth, neuroepithelial cell differentiation, MAPK and FoxO signaling pathways. Epithelial mesenchymal transition (EMT) gene set and invasion related genes were downregulated in miR-99a-3phighmiR-411-5plowmiR-4746-5phigh HNSCC patients.ConclusionmiR-99a-3p, miR-411-5p and miR-4746-5p might participate in HPV16+ HNSCC progression through EMT related pathways and affect prognosis.
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Project description:The immune response within the tumor microenvironment plays a key role in tumorigenesis and determines the clinical outcomes of head and neck squamous cell carcinoma (HNSCC). However, to date, very limited robust and reliable immunological biomarkers have been developed that are capable of estimating prognosis in HNSCC patients. In this study, we aimed to identify the effects of novel immune-related gene signatures (IRGs) that can predict HNSCC prognosis. Based on gene expression profiles and clinical data of HNSCC patient cohorts from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database, a total of 439 highly variable expressed immune-related genes (including 239 upregulated and 200 downregulated genes) were identified by using differential gene expression analysis. Pathway enrichment analysis indicated that these immune-related differentially expressed genes were enriched in inflammatory functions. After process screening in the training TCGA cohort, six immune-related genes (PLAU, STC2, TNFRSF4, PDGFA, DKK1, and CHGB) were significantly associated with overall survival (OS) based on the LASSO Cox regression model. Integrating these genes with clinicopathological features, a multivariable model was built and suggested better performance in determining patients' OS in the testing cohort, and the independent validation cohort. In conclusion, a well-established model encompassing both immune-related gene signatures and clinicopathological factors would serve as a promising tool for the prognostic prediction of HNSCC.
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