Integrating machine learning, bioinformatics and experimental verification to identify a novel prognostic marker associated with tumor immune microenvironment in head and neck squamous carcinoma
Head and neck squamous carcinoma (HNSC), characterized by a high degree of malignancy, develops in close association with the tumor immune microenvironment (TIME). Therefore, identifying effective targets related to HNSC and TIME is of paramount importance. Here, we employed the ESTIMATE algorithm t...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-12-01
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| Series: | Frontiers in Immunology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2024.1501486/full |
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| author | Xiaoxia Zeng Dunhui Yang Jin Zhang Kang Li Xijia Wang Fang Ma Xianqin Liao Zhen Wang Xianhai Zeng Peng Zhang |
| author_facet | Xiaoxia Zeng Dunhui Yang Jin Zhang Kang Li Xijia Wang Fang Ma Xianqin Liao Zhen Wang Xianhai Zeng Peng Zhang |
| author_sort | Xiaoxia Zeng |
| collection | DOAJ |
| description | Head and neck squamous carcinoma (HNSC), characterized by a high degree of malignancy, develops in close association with the tumor immune microenvironment (TIME). Therefore, identifying effective targets related to HNSC and TIME is of paramount importance. Here, we employed the ESTIMATE algorithm to compute immune and stromal cell scores for HNSC samples from the TCGA database and identified differentially expressed genes (DEGs) based on these scores. Subsequently, we utilized four machine learning algorithms to identify four key genes: ITM2A, FOXP3, WIPF1, and RSPO1 from DEGs. Through a comprehensive pan-cancer analysis, our study identified aberrant expression of ITM2A across various tumor types, with a significant association with the TIME. Specifically, ITM2A expression was markedly reduced and correlated with poor prognosis in HNSC. Functional enrichment analysis revealed that ITM2A is implicated in multiple immune-related pathways, including immune-infiltrating cells, immune checkpoints, and immunotherapeutic responses. ITM2A expression was observed in various immune cell populations through single-cell analysis. Furthermore, we showed that ITM2A overexpression inhibited the growth of HNSC cells. Our results suggest that ITM2A may be a novel prognostic marker associated with TIME. |
| format | Article |
| id | doaj-art-2f11de91f93a44f0823782191f724b9f |
| institution | Kabale University |
| issn | 1664-3224 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Immunology |
| spelling | doaj-art-2f11de91f93a44f0823782191f724b9f2024-12-10T06:33:48ZengFrontiers Media S.A.Frontiers in Immunology1664-32242024-12-011510.3389/fimmu.2024.15014861501486Integrating machine learning, bioinformatics and experimental verification to identify a novel prognostic marker associated with tumor immune microenvironment in head and neck squamous carcinomaXiaoxia Zeng0Dunhui Yang1Jin Zhang2Kang Li3Xijia Wang4Fang Ma5Xianqin Liao6Zhen Wang7Xianhai Zeng8Peng Zhang9Department of Otolaryngology, Longgang Otolaryngology hospital & Shenzhen Key Laboratory of Otolaryngology, Shenzhen Institute of Otolaryngology, Shenzhen, Guangdong, ChinaDepartment of Otolaryngology, Longgang Otolaryngology hospital & Shenzhen Key Laboratory of Otolaryngology, Shenzhen Institute of Otolaryngology, Shenzhen, Guangdong, ChinaDepartment of Otolaryngology, The Second People’s Hospital of Yibin, Yibin, Sichuan, ChinaDepartment of Otolaryngology, Longgang Otolaryngology hospital & Shenzhen Key Laboratory of Otolaryngology, Shenzhen Institute of Otolaryngology, Shenzhen, Guangdong, ChinaDepartment of Otolaryngology, Longgang Otolaryngology hospital & Shenzhen Key Laboratory of Otolaryngology, Shenzhen Institute of Otolaryngology, Shenzhen, Guangdong, ChinaDepartment of Otolaryngology, Longgang Otolaryngology hospital & Shenzhen Key Laboratory of Otolaryngology, Shenzhen Institute of Otolaryngology, Shenzhen, Guangdong, ChinaDepartment of Otolaryngology, Longgang Otolaryngology hospital & Shenzhen Key Laboratory of Otolaryngology, Shenzhen Institute of Otolaryngology, Shenzhen, Guangdong, ChinaDepartment of Otolaryngology, Longgang Otolaryngology hospital & Shenzhen Key Laboratory of Otolaryngology, Shenzhen Institute of Otolaryngology, Shenzhen, Guangdong, ChinaDepartment of Otolaryngology, Longgang Otolaryngology hospital & Shenzhen Key Laboratory of Otolaryngology, Shenzhen Institute of Otolaryngology, Shenzhen, Guangdong, ChinaDepartment of Otolaryngology, Longgang Otolaryngology hospital & Shenzhen Key Laboratory of Otolaryngology, Shenzhen Institute of Otolaryngology, Shenzhen, Guangdong, ChinaHead and neck squamous carcinoma (HNSC), characterized by a high degree of malignancy, develops in close association with the tumor immune microenvironment (TIME). Therefore, identifying effective targets related to HNSC and TIME is of paramount importance. Here, we employed the ESTIMATE algorithm to compute immune and stromal cell scores for HNSC samples from the TCGA database and identified differentially expressed genes (DEGs) based on these scores. Subsequently, we utilized four machine learning algorithms to identify four key genes: ITM2A, FOXP3, WIPF1, and RSPO1 from DEGs. Through a comprehensive pan-cancer analysis, our study identified aberrant expression of ITM2A across various tumor types, with a significant association with the TIME. Specifically, ITM2A expression was markedly reduced and correlated with poor prognosis in HNSC. Functional enrichment analysis revealed that ITM2A is implicated in multiple immune-related pathways, including immune-infiltrating cells, immune checkpoints, and immunotherapeutic responses. ITM2A expression was observed in various immune cell populations through single-cell analysis. Furthermore, we showed that ITM2A overexpression inhibited the growth of HNSC cells. Our results suggest that ITM2A may be a novel prognostic marker associated with TIME.https://www.frontiersin.org/articles/10.3389/fimmu.2024.1501486/fullITM2Aimmune microenvironmentHNSCmachine learningprognostic |
| spellingShingle | Xiaoxia Zeng Dunhui Yang Jin Zhang Kang Li Xijia Wang Fang Ma Xianqin Liao Zhen Wang Xianhai Zeng Peng Zhang Integrating machine learning, bioinformatics and experimental verification to identify a novel prognostic marker associated with tumor immune microenvironment in head and neck squamous carcinoma Frontiers in Immunology ITM2A immune microenvironment HNSC machine learning prognostic |
| title | Integrating machine learning, bioinformatics and experimental verification to identify a novel prognostic marker associated with tumor immune microenvironment in head and neck squamous carcinoma |
| title_full | Integrating machine learning, bioinformatics and experimental verification to identify a novel prognostic marker associated with tumor immune microenvironment in head and neck squamous carcinoma |
| title_fullStr | Integrating machine learning, bioinformatics and experimental verification to identify a novel prognostic marker associated with tumor immune microenvironment in head and neck squamous carcinoma |
| title_full_unstemmed | Integrating machine learning, bioinformatics and experimental verification to identify a novel prognostic marker associated with tumor immune microenvironment in head and neck squamous carcinoma |
| title_short | Integrating machine learning, bioinformatics and experimental verification to identify a novel prognostic marker associated with tumor immune microenvironment in head and neck squamous carcinoma |
| title_sort | integrating machine learning bioinformatics and experimental verification to identify a novel prognostic marker associated with tumor immune microenvironment in head and neck squamous carcinoma |
| topic | ITM2A immune microenvironment HNSC machine learning prognostic |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2024.1501486/full |
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