Identification and validation of HOXC6 as a diagnostic biomarker for Ewing sarcoma: insights from machine learning algorithms and in vitro experiments
IntroductionEarly diagnosis of Ewing sarcoma (ES) is critical for improving patient prognosis. However, the accurate diagnosis of ES remains challenging, underscoring the need for novel diagnostic biomarkers to enhance diagnostic precision and reliability. This study aimed to identify potential gene...
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Frontiers Media S.A.
2025-04-01
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| Series: | Frontiers in Immunology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2025.1449355/full |
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| author | Yonghua Pang Jiahui Liang Jiahui Liang Yakai Deng Weinan Chen Yunyan Shen Jing Li Xin Wang Zhiyao Ren |
| author_facet | Yonghua Pang Jiahui Liang Jiahui Liang Yakai Deng Weinan Chen Yunyan Shen Jing Li Xin Wang Zhiyao Ren |
| author_sort | Yonghua Pang |
| collection | DOAJ |
| description | IntroductionEarly diagnosis of Ewing sarcoma (ES) is critical for improving patient prognosis. However, the accurate diagnosis of ES remains challenging, underscoring the need for novel diagnostic biomarkers to enhance diagnostic precision and reliability. This study aimed to identify potential gene expression-based biomarkers for the diagnosis of ES.MethodsWe selected the GSE17679, GSE45544, and GSE68776 datasets from the Gene Expression Omnibus (GEO) database. After correcting for batch effects, we combined ES and normal tissue samples from the GSE17679 and GSE45544 datasets to create a combined cohort. Two-thirds of both the tumor and normal samples from the combined cohort were randomly selected for the training cohort, while the remaining one-third served as the internal validation cohort. Additionally, the GSE68776 dataset was used for external validation. To identify key diagnostic genes, we applied three machine learning algorithms: least absolute shrinkage and selection operator (LASSO), support vector machine recursive feature elimination (SVM-RFE), and random forest (RF).ResultsHOXC6 was identified as a key diagnostic biomarker for ES. It demonstrated strong diagnostic performance across all cohorts, with area under the curve (AUC) values of 0.956 (95% CI: 0.909−0.990) in the training cohort, 0.995 (95% CI: 0.977−1.000) in the internal validation cohort, and 0.966 (95% CI: 0.910−0.999) in the external validation cohort. Functional validation through HOXC6 knockdown in the RD-ES cell line revealed that its suppression significantly inhibited cell proliferation and migration. Furthermore, transcriptome sequencing suggested potential oncogenic mechanisms underlying HOXC6 function.DiscussionThese findings highlight HOXC6 as a promising diagnostic biomarker for ES, demonstrating robust performance across multiple datasets. Additionally, its functional role suggests potential as a therapeutic target. |
| format | Article |
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| institution | DOAJ |
| issn | 1664-3224 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Immunology |
| spelling | doaj-art-aa633b8c2f7e4c5ea7ce7b07fb12d7c42025-08-20T03:07:28ZengFrontiers Media S.A.Frontiers in Immunology1664-32242025-04-011610.3389/fimmu.2025.14493551449355Identification and validation of HOXC6 as a diagnostic biomarker for Ewing sarcoma: insights from machine learning algorithms and in vitro experimentsYonghua Pang0Jiahui Liang1Jiahui Liang2Yakai Deng3Weinan Chen4Yunyan Shen5Jing Li6Xin Wang7Zhiyao Ren8Department of Orthopedics, The 904th Hospital of the Joint Logistics Support Force, People's Liberation Army of China, Wuxi, Jiangsu, ChinaDepartment of Breast Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, ChinaDepartment of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, ChinaDepartment of Orthopedics, The 904th Hospital of the Joint Logistics Support Force, People's Liberation Army of China, Wuxi, Jiangsu, ChinaDepartment of Orthopedics, The 904th Hospital of the Joint Logistics Support Force, People's Liberation Army of China, Wuxi, Jiangsu, ChinaDepartment of Orthopedics, The 904th Hospital of the Joint Logistics Support Force, People's Liberation Army of China, Wuxi, Jiangsu, ChinaDepartment of Orthopedics, Linyi People's Hospital, Linyi, Shandong, ChinaFaculty of Medicine and Health Sciences, Ghent University, Ghent, BelgiumFaculty of Medicine and Health Sciences, Ghent University, Ghent, BelgiumIntroductionEarly diagnosis of Ewing sarcoma (ES) is critical for improving patient prognosis. However, the accurate diagnosis of ES remains challenging, underscoring the need for novel diagnostic biomarkers to enhance diagnostic precision and reliability. This study aimed to identify potential gene expression-based biomarkers for the diagnosis of ES.MethodsWe selected the GSE17679, GSE45544, and GSE68776 datasets from the Gene Expression Omnibus (GEO) database. After correcting for batch effects, we combined ES and normal tissue samples from the GSE17679 and GSE45544 datasets to create a combined cohort. Two-thirds of both the tumor and normal samples from the combined cohort were randomly selected for the training cohort, while the remaining one-third served as the internal validation cohort. Additionally, the GSE68776 dataset was used for external validation. To identify key diagnostic genes, we applied three machine learning algorithms: least absolute shrinkage and selection operator (LASSO), support vector machine recursive feature elimination (SVM-RFE), and random forest (RF).ResultsHOXC6 was identified as a key diagnostic biomarker for ES. It demonstrated strong diagnostic performance across all cohorts, with area under the curve (AUC) values of 0.956 (95% CI: 0.909−0.990) in the training cohort, 0.995 (95% CI: 0.977−1.000) in the internal validation cohort, and 0.966 (95% CI: 0.910−0.999) in the external validation cohort. Functional validation through HOXC6 knockdown in the RD-ES cell line revealed that its suppression significantly inhibited cell proliferation and migration. Furthermore, transcriptome sequencing suggested potential oncogenic mechanisms underlying HOXC6 function.DiscussionThese findings highlight HOXC6 as a promising diagnostic biomarker for ES, demonstrating robust performance across multiple datasets. Additionally, its functional role suggests potential as a therapeutic target.https://www.frontiersin.org/articles/10.3389/fimmu.2025.1449355/fullEwing sarcomabioinformaticsmachine learningHOXC6diagnostic biomarker |
| spellingShingle | Yonghua Pang Jiahui Liang Jiahui Liang Yakai Deng Weinan Chen Yunyan Shen Jing Li Xin Wang Zhiyao Ren Identification and validation of HOXC6 as a diagnostic biomarker for Ewing sarcoma: insights from machine learning algorithms and in vitro experiments Frontiers in Immunology Ewing sarcoma bioinformatics machine learning HOXC6 diagnostic biomarker |
| title | Identification and validation of HOXC6 as a diagnostic biomarker for Ewing sarcoma: insights from machine learning algorithms and in vitro experiments |
| title_full | Identification and validation of HOXC6 as a diagnostic biomarker for Ewing sarcoma: insights from machine learning algorithms and in vitro experiments |
| title_fullStr | Identification and validation of HOXC6 as a diagnostic biomarker for Ewing sarcoma: insights from machine learning algorithms and in vitro experiments |
| title_full_unstemmed | Identification and validation of HOXC6 as a diagnostic biomarker for Ewing sarcoma: insights from machine learning algorithms and in vitro experiments |
| title_short | Identification and validation of HOXC6 as a diagnostic biomarker for Ewing sarcoma: insights from machine learning algorithms and in vitro experiments |
| title_sort | identification and validation of hoxc6 as a diagnostic biomarker for ewing sarcoma insights from machine learning algorithms and in vitro experiments |
| topic | Ewing sarcoma bioinformatics machine learning HOXC6 diagnostic biomarker |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2025.1449355/full |
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