Development and validation of a GRGPI model for predicting the prognostic and treatment outcomes in head and neck squamous cell carcinoma
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...
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Frontiers Media S.A.
2023-01-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2022.972215/full |
| _version_ | 1797955306248994816 |
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| author | Fei Han Hong-Zhi Wang Min-Jing Chang Min-Jing Chang Yu-Ting Hu Li-Zhong Liang Shuai Li Feng Liu Pei-Feng He Xiao-Tang Yang Feng Li |
| author_facet | Fei Han Hong-Zhi Wang Min-Jing Chang Min-Jing Chang Yu-Ting Hu Li-Zhong Liang Shuai Li Feng Liu Pei-Feng He Xiao-Tang Yang Feng Li |
| author_sort | Fei Han |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-10T23:32:09Z |
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| id | doaj.art-bb7df01d1e114d35b2ec3a750b013577 |
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| language | English |
| last_indexed | 2024-04-10T23:32:09Z |
| publishDate | 2023-01-01 |
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| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj.art-bb7df01d1e114d35b2ec3a750b0135772023-01-12T05:51:40ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2023-01-011210.3389/fonc.2022.972215972215Development and validation of a GRGPI model for predicting the prognostic and treatment outcomes in head and neck squamous cell carcinomaFei Han0Hong-Zhi Wang1Min-Jing Chang2Min-Jing Chang3Yu-Ting Hu4Li-Zhong Liang5Shuai Li6Feng Liu7Pei-Feng He8Xiao-Tang Yang9Feng Li10Department of Head and Neck Surgery, Shanxi Province Tumor Hospital, Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences, Affiliated Tumor Hospital of Shanxi Medical University, Taiyuan, ChinaDepartment of Anesthesiology, Shanxi Province Tumor Hospital, Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences, Affiliated Tumor Hospital of Shanxi Medical University, Taiyuan, ChinaMinistry of Education, Key Laboratory of Cellular Physiology at Shanxi Medical University, Taiyuan, ChinaShanxi Key Laboratory of Big Data for Clinical Decision, Shanxi Medical University, Taiyuan, ChinaMinistry of Education, Key Laboratory of Cellular Physiology at Shanxi Medical University, Taiyuan, ChinaMinistry of Education, Key Laboratory of Cellular Physiology at Shanxi Medical University, Taiyuan, ChinaMinistry of Education, Key Laboratory of Cellular Physiology at Shanxi Medical University, Taiyuan, ChinaDepartment of Head and Neck Surgery, Shanxi Province Tumor Hospital, Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences, Affiliated Tumor Hospital of Shanxi Medical University, Taiyuan, ChinaMedical Data Sciences, Shanxi Medical University, Taiyuan, ChinaDepartment of Radiology, Shanxi Province Tumor Hospital, Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences, Affiliated Tumor Hospital of Shanxi Medical University, Taiyuan, ChinaDepartment of Cell biology, Shanxi Province Tumor Hospital, Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences, Affiliated Tumor Hospital of Shanxi Medical University, Taiyuan, ChinaBackgroundHead 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.https://www.frontiersin.org/articles/10.3389/fonc.2022.972215/fullpredictionglycolysis prognosis modelhead and neck squamos cell carcinomaimmune microenviromentchemothearapeutic responses |
| spellingShingle | Fei Han Hong-Zhi Wang Min-Jing Chang Min-Jing Chang Yu-Ting Hu Li-Zhong Liang Shuai Li Feng Liu Pei-Feng He Xiao-Tang Yang Feng Li Development and validation of a GRGPI model for predicting the prognostic and treatment outcomes in head and neck squamous cell carcinoma Frontiers in Oncology prediction glycolysis prognosis model head and neck squamos cell carcinoma immune microenviroment chemothearapeutic responses |
| title | Development and validation of a GRGPI model for predicting the prognostic and treatment outcomes in head and neck squamous cell carcinoma |
| title_full | Development and validation of a GRGPI model for predicting the prognostic and treatment outcomes in head and neck squamous cell carcinoma |
| title_fullStr | Development and validation of a GRGPI model for predicting the prognostic and treatment outcomes in head and neck squamous cell carcinoma |
| title_full_unstemmed | Development and validation of a GRGPI model for predicting the prognostic and treatment outcomes in head and neck squamous cell carcinoma |
| title_short | Development and validation of a GRGPI model for predicting the prognostic and treatment outcomes in head and neck squamous cell carcinoma |
| title_sort | development and validation of a grgpi model for predicting the prognostic and treatment outcomes in head and neck squamous cell carcinoma |
| topic | prediction glycolysis prognosis model head and neck squamos cell carcinoma immune microenviroment chemothearapeutic responses |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2022.972215/full |
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