Machine-learning derived identification of prognostic signature to forecast head and neck squamous cell carcinoma prognosis and drug response
IntroductionHead and neck squamous cell carcinoma (HNSCC), a highly heterogeneous malignancy is often associated with unfavorable prognosis. Due to its unique anatomical position and the absence of effective early inspection methods, surgical intervention alone is frequently inadequate for achieving...
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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.2024.1469895/full |
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| author | Sha-Zhou Li Hai-Ying Sun Yuan Tian Liu-Qing Zhou Tao Zhou |
| author_facet | Sha-Zhou Li Hai-Ying Sun Yuan Tian Liu-Qing Zhou Tao Zhou |
| author_sort | Sha-Zhou Li |
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| description | IntroductionHead and neck squamous cell carcinoma (HNSCC), a highly heterogeneous malignancy is often associated with unfavorable prognosis. Due to its unique anatomical position and the absence of effective early inspection methods, surgical intervention alone is frequently inadequate for achieving complete remission. Therefore, the identification of reliable biomarker is crucial to enhance the accuracy of screening and treatment strategies for HNSCC.MethodTo develop and identify a machine learning-derived prognostic model (MLDPM) for HNSCC, ten machine learning algorithms, namely CoxBoost, elastic network (Enet), generalized boosted regression modeling (GBM), Lasso, Ridge, partial least squares regression for Cox (plsRcox), random survival forest (RSF), stepwise Cox, supervised principal components (SuperPC), and survival support vector machine (survival-SVM), along with 81 algorithm combinations were utilized. Time-dependent receiver operating characteristics (ROC) curves and Kaplan-Meier analysis can effectively assess the model’s predictive performance. Validation was performed through a nomogram, calibration curves, univariate and multivariate Cox analysis. Further analyses included immunological profiling and gene set enrichment analyses (GSEA). Additionally, the prediction of 50% inhibitory concentration (IC50) of potential drugs between groups was determined.ResultsFrom analyses in the HNSCC tissues and normal tissues, we found 536 differentially expressed genes (DEGs). Subsequent univariate-cox regression analysis narrowed this list to 18 genes. A robust risk model, outperforming other clinical signatures, was then constructed using machine learning techniques. The MLDPM indicated that high-risk scores showed a greater propensity for immune escape and reduced survival rates. Dasatinib and 7 medicine showed the superior sensitivity to the high-risk NHSCC, which had potential to the clinical.ConclusionsThe construction of MLDPM effectively eliminated artificial bias by utilizing 101 algorithm combinations. This model demonstrated high accuracy in predicting HNSCC outcomes and has the potential to identify novel therapeutic targets for HNSCC patients, thus offering significant advancements in personalized treatment strategies. |
| format | Article |
| id | doaj-art-d1fd34047f4b4c09b7af1d021a68d018 |
| institution | DOAJ |
| issn | 1664-3224 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Immunology |
| spelling | doaj-art-d1fd34047f4b4c09b7af1d021a68d0182025-08-20T02:52:20ZengFrontiers Media S.A.Frontiers in Immunology1664-32242024-12-011510.3389/fimmu.2024.14698951469895Machine-learning derived identification of prognostic signature to forecast head and neck squamous cell carcinoma prognosis and drug responseSha-Zhou Li0Hai-Ying Sun1Yuan Tian2Liu-Qing Zhou3Tao Zhou4Department of Otorhinolaryngology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, ChinaDepartment of Otorhinolaryngology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, ChinaDepartment of Geriatrics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, ChinaDepartment of Otorhinolaryngology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, ChinaDepartment of Otorhinolaryngology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, ChinaIntroductionHead and neck squamous cell carcinoma (HNSCC), a highly heterogeneous malignancy is often associated with unfavorable prognosis. Due to its unique anatomical position and the absence of effective early inspection methods, surgical intervention alone is frequently inadequate for achieving complete remission. Therefore, the identification of reliable biomarker is crucial to enhance the accuracy of screening and treatment strategies for HNSCC.MethodTo develop and identify a machine learning-derived prognostic model (MLDPM) for HNSCC, ten machine learning algorithms, namely CoxBoost, elastic network (Enet), generalized boosted regression modeling (GBM), Lasso, Ridge, partial least squares regression for Cox (plsRcox), random survival forest (RSF), stepwise Cox, supervised principal components (SuperPC), and survival support vector machine (survival-SVM), along with 81 algorithm combinations were utilized. Time-dependent receiver operating characteristics (ROC) curves and Kaplan-Meier analysis can effectively assess the model’s predictive performance. Validation was performed through a nomogram, calibration curves, univariate and multivariate Cox analysis. Further analyses included immunological profiling and gene set enrichment analyses (GSEA). Additionally, the prediction of 50% inhibitory concentration (IC50) of potential drugs between groups was determined.ResultsFrom analyses in the HNSCC tissues and normal tissues, we found 536 differentially expressed genes (DEGs). Subsequent univariate-cox regression analysis narrowed this list to 18 genes. A robust risk model, outperforming other clinical signatures, was then constructed using machine learning techniques. The MLDPM indicated that high-risk scores showed a greater propensity for immune escape and reduced survival rates. Dasatinib and 7 medicine showed the superior sensitivity to the high-risk NHSCC, which had potential to the clinical.ConclusionsThe construction of MLDPM effectively eliminated artificial bias by utilizing 101 algorithm combinations. This model demonstrated high accuracy in predicting HNSCC outcomes and has the potential to identify novel therapeutic targets for HNSCC patients, thus offering significant advancements in personalized treatment strategies.https://www.frontiersin.org/articles/10.3389/fimmu.2024.1469895/fullmachine learningHNSCCDEGstumor microenvironmentimmunotherapy |
| spellingShingle | Sha-Zhou Li Hai-Ying Sun Yuan Tian Liu-Qing Zhou Tao Zhou Machine-learning derived identification of prognostic signature to forecast head and neck squamous cell carcinoma prognosis and drug response Frontiers in Immunology machine learning HNSCC DEGs tumor microenvironment immunotherapy |
| title | Machine-learning derived identification of prognostic signature to forecast head and neck squamous cell carcinoma prognosis and drug response |
| title_full | Machine-learning derived identification of prognostic signature to forecast head and neck squamous cell carcinoma prognosis and drug response |
| title_fullStr | Machine-learning derived identification of prognostic signature to forecast head and neck squamous cell carcinoma prognosis and drug response |
| title_full_unstemmed | Machine-learning derived identification of prognostic signature to forecast head and neck squamous cell carcinoma prognosis and drug response |
| title_short | Machine-learning derived identification of prognostic signature to forecast head and neck squamous cell carcinoma prognosis and drug response |
| title_sort | machine learning derived identification of prognostic signature to forecast head and neck squamous cell carcinoma prognosis and drug response |
| topic | machine learning HNSCC DEGs tumor microenvironment immunotherapy |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2024.1469895/full |
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