The Identification of Stemness-Related Genes in the Risk of Head and Neck Squamous Cell Carcinoma
ObjectivesThis study aimed to identify genes regulating cancer stemness of head and neck squamous cell carcinoma (HNSCC) and evaluate the ability of these genes to predict clinical outcomes.Materials and MethodsThe stemness index (mRNAsi) was obtained using a one-class logistic regression machine le...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-06-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2021.688545/full |
| _version_ | 1798035174024282112 |
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| author | Guanying Feng Guanying Feng Feifei Xue Yingzheng He Tianxiao Wang Hua Yuan Hua Yuan |
| author_facet | Guanying Feng Guanying Feng Feifei Xue Yingzheng He Tianxiao Wang Hua Yuan Hua Yuan |
| author_sort | Guanying Feng |
| collection | DOAJ |
| description | ObjectivesThis study aimed to identify genes regulating cancer stemness of head and neck squamous cell carcinoma (HNSCC) and evaluate the ability of these genes to predict clinical outcomes.Materials and MethodsThe stemness index (mRNAsi) was obtained using a one-class logistic regression machine learning algorithm based on sequencing data of HNSCC patients. Stemness-related genes were identified by weighted gene co-expression network analysis and least absolute shrinkage and selection operator analysis (LASSO). The coefficient of LASSO was applied to construct a diagnostic risk score model. The Cancer Genome Atlas database, the Gene Expression Omnibus database, Oncomine database and the Human Protein Atlas database were used to validate the expression of key genes. Interaction network analysis was performed using String database and DisNor database. The Connectivity Map database was used to screen potential compounds. The expressions of stemness-related genes were validated using quantitative real‐time polymerase chain reaction (qRT‐PCR).ResultsTTK, KIF14, KIF18A and DLGAP5 were identified. Stemness-related genes were upregulated in HNSCC samples. The risk score model had a significant predictive ability. CDK inhibitor was the top hit of potential compounds.ConclusionStemness-related gene expression profiles may be a potential biomarker for HNSCC. |
| first_indexed | 2024-04-11T20:53:34Z |
| format | Article |
| id | doaj.art-4128437740334e76859979483fc67c06 |
| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-04-11T20:53:34Z |
| publishDate | 2021-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj.art-4128437740334e76859979483fc67c062022-12-22T04:03:45ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2021-06-011110.3389/fonc.2021.688545688545The Identification of Stemness-Related Genes in the Risk of Head and Neck Squamous Cell CarcinomaGuanying Feng0Guanying Feng1Feifei Xue2Yingzheng He3Tianxiao Wang4Hua Yuan5Hua Yuan6Jiangsu Key Laboratory of Oral Diseases, Nanjing Medical University, Nanjing, ChinaDepartment of Oral and Maxillofacial Surgery, Affiliated Hospital of Stomatology, Nanjing Medical University, Nanjing, ChinaJiangsu Key Laboratory of Oral Diseases, Nanjing Medical University, Nanjing, ChinaJiangsu Key Laboratory of Oral Diseases, Nanjing Medical University, Nanjing, ChinaJiangsu Key Laboratory of Oral Diseases, Nanjing Medical University, Nanjing, ChinaJiangsu Key Laboratory of Oral Diseases, Nanjing Medical University, Nanjing, ChinaDepartment of Oral and Maxillofacial Surgery, Affiliated Hospital of Stomatology, Nanjing Medical University, Nanjing, ChinaObjectivesThis study aimed to identify genes regulating cancer stemness of head and neck squamous cell carcinoma (HNSCC) and evaluate the ability of these genes to predict clinical outcomes.Materials and MethodsThe stemness index (mRNAsi) was obtained using a one-class logistic regression machine learning algorithm based on sequencing data of HNSCC patients. Stemness-related genes were identified by weighted gene co-expression network analysis and least absolute shrinkage and selection operator analysis (LASSO). The coefficient of LASSO was applied to construct a diagnostic risk score model. The Cancer Genome Atlas database, the Gene Expression Omnibus database, Oncomine database and the Human Protein Atlas database were used to validate the expression of key genes. Interaction network analysis was performed using String database and DisNor database. The Connectivity Map database was used to screen potential compounds. The expressions of stemness-related genes were validated using quantitative real‐time polymerase chain reaction (qRT‐PCR).ResultsTTK, KIF14, KIF18A and DLGAP5 were identified. Stemness-related genes were upregulated in HNSCC samples. The risk score model had a significant predictive ability. CDK inhibitor was the top hit of potential compounds.ConclusionStemness-related gene expression profiles may be a potential biomarker for HNSCC.https://www.frontiersin.org/articles/10.3389/fonc.2021.688545/fullhead and neck squamous cell carcinomacancer stemnessriskmachine learningcompounds |
| spellingShingle | Guanying Feng Guanying Feng Feifei Xue Yingzheng He Tianxiao Wang Hua Yuan Hua Yuan The Identification of Stemness-Related Genes in the Risk of Head and Neck Squamous Cell Carcinoma Frontiers in Oncology head and neck squamous cell carcinoma cancer stemness risk machine learning compounds |
| title | The Identification of Stemness-Related Genes in the Risk of Head and Neck Squamous Cell Carcinoma |
| title_full | The Identification of Stemness-Related Genes in the Risk of Head and Neck Squamous Cell Carcinoma |
| title_fullStr | The Identification of Stemness-Related Genes in the Risk of Head and Neck Squamous Cell Carcinoma |
| title_full_unstemmed | The Identification of Stemness-Related Genes in the Risk of Head and Neck Squamous Cell Carcinoma |
| title_short | The Identification of Stemness-Related Genes in the Risk of Head and Neck Squamous Cell Carcinoma |
| title_sort | identification of stemness related genes in the risk of head and neck squamous cell carcinoma |
| topic | head and neck squamous cell carcinoma cancer stemness risk machine learning compounds |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2021.688545/full |
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