Dataset Information

Development and validation of a predictive model based on β-Klotho for head and neck squamous cell carcinoma.

ABSTRACT: Head and neck epithelial tissue tumors may be identified as head and neck squamous cell carcinoma (HNSC). Numerous malignancies are encouraged by dysregulation of the FGF19-β-Klotho (KLB) axis in the tumor microenvironment. Using protein databases and RT-qPCR, we examined KLB expression in HNSC. In HNSC, higher KLB expression was linked to longer survival times and better prognoses. Furthermore, variations in drug susceptibility and immunological infiltration were noted according to KLB expression levels. These results underscore the importance of KLB in the course and management of HNSC by indicating that it may function as a possible prognostic marker and influence immunological and therapeutic responses in these individuals. Further study on HNSC is necessary to investigate KLB's potential as a therapeutic target and prognostic indicator.

SUBMITTER: Wang X

PROVIDER: S-EPMC11269606 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature

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Development and validation of a predictive model based on β-Klotho for head and neck squamous cell carcinoma.

Wang XiangXiu X Liu HongWei H Wu Gang G Lu Yan Y Cui Ying Y

Scientific reports 20240724 1

Head and neck epithelial tissue tumors may be identified as head and neck squamous cell carcinoma (HNSC). Numerous malignancies are encouraged by dysregulation of the FGF19-β-Klotho (KLB) axis in the tumor microenvironment. Using protein databases and RT-qPCR, we examined KLB expression in HNSC. In HNSC, higher KLB expression was linked to longer survival times and better prognoses. Furthermore, variations in drug susceptibility and immunological infiltration were noted according to KLB expressi ...[more]

PMID: 39048709

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Project description:Necroptosis is a new regulated cell-death mechanism that plays a critical role in various cancers. However, few studies have considered necroptosis-related genes (NRGs) as prognostic indexes for cancer. As one of the most common cancers in the world, head and neck squamous cell carcinoma (HNSCC) lacks effective diagnostic strategies at present. Hence, a series of novel prognostic indexes are required to support clinical diagnosis. Recently, many studies have confirmed that necroptosis was a key regulated mechanism in HNSCC, but no systematic study has ever studied the correlation between necroptosis-related signatures and the prognosis of HNSCC. Thus, in the current study, we aimed to construct a risk model of necroptosis-related signatures for HNSCC. We acquired 159 NRGs from the Kyoto Encyclopedia of Genes and Genomes (KEGG) and compared them with samples of normal tissue downloaded from The Cancer Genome Atlas (TCGA), ultimately screening 38 differentially expressed NRGs (DE-NRGs). Then, by Cox regression analysis, we successfully identified 7 NRGs as prognostic factors. We next separated patients into high- and low-risk groups via the prognostic model consisting of 7 NRGs. Individuals in the high-risk group had much shorter overall survival (OS) times than their counterparts. Furthermore, using Cox regression analysis, we confirmed the necroptosis-related prognostic model to be an independent prognostic factor for HNSCC. Receiver operating characteristic (ROC) curve analysis proved the predictive ability of this model. Finally, Gene Expression Omnibus (GEO) data sets (GSE65858, GSE4163) were used as independent databases to verify the model's predictive ability, and similar results obtained from two data sets confirmed our conclusion. Collectively, in this study, we first referred to necroptosis-related signatures as an independent prognostic model for cancer via bioinformatics measures, and the necroptosis-related prognostic model we constructed could precisely forecast the OS time of patients with HNSCC. Utilizing the model may significantly improve the diagnostic rate and provide a series of new targets for treatment in the future.

Project description:BackgroundHead and neck squamous cell carcinoma (HNSCC) is among the most lethal and most prevalent malignant tumors. Glycolysis affects tumor growth, invasion, chemotherapy resistance, and the tumor microenvironment. Therefore, we aimed at identifying a glycolysis-related prognostic model for HNSCC and to analyze its relationship with tumor immune cell infiltrations.MethodsThe mRNA and clinical data were obtained from The Cancer Genome Atlas (TCGA), while glycolysis-related genes were obtained from the Molecular Signature Database (MSigDB). Bioinformatics analysis included Univariate cox and least absolute shrinkage and selection operator (LASSO) analyses to select optimal prognosis-related genes for constructing glycolysis-related gene prognostic index(GRGPI), as well as a nomogram for overall survival (OS) evaluation. GRGPI was validated using the Gene Expression Omnibus (GEO) database. A predictive nomogram was established based on the stepwise multivariate regression model. The immune status of GRGPI-defined subgroups was analyzed, and high and low immune groups were characterized. Prognostic effects of immune checkpoint inhibitor (ICI) treatment and chemotherapy were investigated by Tumor Immune Dysfunction and Exclusion (TIDE) scores and half inhibitory concentration (IC50) value. Reverse transcription-quantitative PCR (RT-qPCR) was utilized to validate the model by analyzing the mRNA expression levels of the prognostic glycolysis-related genes in HNSCC tissues and adjacent non-tumorous tissues.ResultsFive glycolysis-related genes were used to construct GRGPI. The GRGPI and the nomogram model exhibited robust validity in prognostic prediction. Clinical correlation analysis revealed positive correlations between the risk score used to construct the GRGPI model and the clinical stage. Immune checkpoint analysis revealed that the risk model was associated with immune checkpoint-related biomarkers. Immune microenvironment and immune status analysis exhibited a strong correlation between risk score and infiltrating immune cells. Gene set enrichment analysis (GSEA) pathway enrichment analysis showed typical immune pathways. Furthermore, the GRGPIdel showed excellent predictive performance in ICI treatment and drug sensitivity analysis. RT-qPCR showed that compared with adjacent non-tumorous tissues, the expressions of five genes were significantly up-regulated in HNSCC tissues.ConclusionThe model we constructed can not only be used as an important indicator for predicting the prognosis of patients but also had an important guiding role for clinical treatment.

Dataset Information

Development and validation of a predictive model based on β-Klotho for head and neck squamous cell carcinoma.

Publications

Development and validation of a predictive model based on β-Klotho for head and neck squamous cell carcinoma.

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