Integrative multi-omics analysis and experimental validation identify molecular subtypes, prognostic signature, and CA9 as a therapeutic target in oral squamous cell carcinoma
BackgroundOral squamous cell carcinoma (OSCC) is a challenging malignancy with poor prognosis despite therapeutic advancements. This study seeks to derive a precise molecular subtyping and prognostic model for personalized treatment strategies.MethodsMulti-omics data from TCGA cohort was analyzed us...
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Frontiers Media S.A.
2025-07-01
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| Series: | Frontiers in Cell and Developmental Biology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fcell.2025.1629683/full |
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| author | Yun Zhao Yun Zhao Jing Yang Yamei Jiang Jingbiao Wu |
| author_facet | Yun Zhao Yun Zhao Jing Yang Yamei Jiang Jingbiao Wu |
| author_sort | Yun Zhao |
| collection | DOAJ |
| description | BackgroundOral squamous cell carcinoma (OSCC) is a challenging malignancy with poor prognosis despite therapeutic advancements. This study seeks to derive a precise molecular subtyping and prognostic model for personalized treatment strategies.MethodsMulti-omics data from TCGA cohort was analyzed using consensus clustering algorithms for subtype classification. Based on the classification, a multi-omics cancer subtyping signature (MSCC) model was constructed using machine learning methods. The model’s clinical utility was assessed by evaluating immune features and immunotherapy response. Potential therapeutic agents were identified through drug sensitivity analysis.ResultsThree distinct OSCC subtypes with unique genetic and immunological profiles were identified. The MSCC model, developed using the StepCox [both]+plsRcox algorithm, demonstrated superior prognostic performance compared to existing models. High MSCC scores correlated with poor prognosis, reduced immune cell infiltration, and decreased likelihood of benefiting from immune checkpoint inhibitor therapy. Docetaxel and paclitaxel emerged as potential therapeutic candidates. In vitro experiments validated CA9 as a promising therapeutic target, with its knockdown significantly inhibiting OSCC cell proliferation and migration.ConclusionThis multi-omics analysis unveiled subtype-specific differences in OSCC and established an MSCC model for predicting prognosis and treatment response. These findings provide a foundation for early diagnosis, molecular subtyping, and personalized treatment strategies in OSCC. |
| format | Article |
| id | doaj-art-e1be46195ebc49499ac7880fe2992721 |
| institution | DOAJ |
| issn | 2296-634X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Cell and Developmental Biology |
| spelling | doaj-art-e1be46195ebc49499ac7880fe29927212025-08-20T03:17:24ZengFrontiers Media S.A.Frontiers in Cell and Developmental Biology2296-634X2025-07-011310.3389/fcell.2025.16296831629683Integrative multi-omics analysis and experimental validation identify molecular subtypes, prognostic signature, and CA9 as a therapeutic target in oral squamous cell carcinomaYun Zhao0Yun Zhao1Jing Yang2Yamei Jiang3Jingbiao Wu4Department of Stomatology, Affiliated Hospital of North Sichuan Medical College, Nanchong, ChinaDepartment of Stomatology, North Sichuan Medical College, Nanchong, ChinaDemonstration Center for Experimental Teaching in Biomedicine, Chengdu Medical College, Chengdu, ChinaDepartment of Stomatology, North Sichuan Medical College, Nanchong, ChinaDepartment of Stomatology, North Sichuan Medical College, Nanchong, ChinaBackgroundOral squamous cell carcinoma (OSCC) is a challenging malignancy with poor prognosis despite therapeutic advancements. This study seeks to derive a precise molecular subtyping and prognostic model for personalized treatment strategies.MethodsMulti-omics data from TCGA cohort was analyzed using consensus clustering algorithms for subtype classification. Based on the classification, a multi-omics cancer subtyping signature (MSCC) model was constructed using machine learning methods. The model’s clinical utility was assessed by evaluating immune features and immunotherapy response. Potential therapeutic agents were identified through drug sensitivity analysis.ResultsThree distinct OSCC subtypes with unique genetic and immunological profiles were identified. The MSCC model, developed using the StepCox [both]+plsRcox algorithm, demonstrated superior prognostic performance compared to existing models. High MSCC scores correlated with poor prognosis, reduced immune cell infiltration, and decreased likelihood of benefiting from immune checkpoint inhibitor therapy. Docetaxel and paclitaxel emerged as potential therapeutic candidates. In vitro experiments validated CA9 as a promising therapeutic target, with its knockdown significantly inhibiting OSCC cell proliferation and migration.ConclusionThis multi-omics analysis unveiled subtype-specific differences in OSCC and established an MSCC model for predicting prognosis and treatment response. These findings provide a foundation for early diagnosis, molecular subtyping, and personalized treatment strategies in OSCC.https://www.frontiersin.org/articles/10.3389/fcell.2025.1629683/fulloral squamous cell carcinomamulti-omics analysismachine learningimmunotherapyprognosisCA9 |
| spellingShingle | Yun Zhao Yun Zhao Jing Yang Yamei Jiang Jingbiao Wu Integrative multi-omics analysis and experimental validation identify molecular subtypes, prognostic signature, and CA9 as a therapeutic target in oral squamous cell carcinoma Frontiers in Cell and Developmental Biology oral squamous cell carcinoma multi-omics analysis machine learning immunotherapy prognosis CA9 |
| title | Integrative multi-omics analysis and experimental validation identify molecular subtypes, prognostic signature, and CA9 as a therapeutic target in oral squamous cell carcinoma |
| title_full | Integrative multi-omics analysis and experimental validation identify molecular subtypes, prognostic signature, and CA9 as a therapeutic target in oral squamous cell carcinoma |
| title_fullStr | Integrative multi-omics analysis and experimental validation identify molecular subtypes, prognostic signature, and CA9 as a therapeutic target in oral squamous cell carcinoma |
| title_full_unstemmed | Integrative multi-omics analysis and experimental validation identify molecular subtypes, prognostic signature, and CA9 as a therapeutic target in oral squamous cell carcinoma |
| title_short | Integrative multi-omics analysis and experimental validation identify molecular subtypes, prognostic signature, and CA9 as a therapeutic target in oral squamous cell carcinoma |
| title_sort | integrative multi omics analysis and experimental validation identify molecular subtypes prognostic signature and ca9 as a therapeutic target in oral squamous cell carcinoma |
| topic | oral squamous cell carcinoma multi-omics analysis machine learning immunotherapy prognosis CA9 |
| url | https://www.frontiersin.org/articles/10.3389/fcell.2025.1629683/full |
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