Comparisons of forecasting for Survival Outcome for Head and Neck Squamous Cell Carcinoma by using Six Machine Learning Models Based on Multi-Omics

Background: Machine learning methods showed excellent predictive ability in a wide range of �elds. For the survival of head and neck squamous cell carcinoma (HNSC), its multi-omics in�uence is crucial. This study attempts to establish a variety of machine learning multi-omics models to predict the survival of HNSC and �nd the most suitable machine learning prediction method. Results: For omics of HNSC, the results of the six models all showed that the performance of multi-omics was better than each single-omic alone. Results were presented which showed that the BN model played a good prediction performance (area under the curve [AUC] 0.8250) in HNSC multi-omics data. The other machine learning models RF (AUC = 0.8002), NN (AUC = 0.7200), and GLM (AUC = 0.7145) also showed high predictive performance except for DT(AUC = 0.5149) and SVM(AUC = 0.6981). And the results of a vitro qPCR were consistent with the Random forest algorithm. Conclusion: Machine learning methods could better forecast the survival outcome of HNSC. Meanwhile, this study found that the Bayesian network was the most superior. Moreover, the forecast result of multi-omics was better than single-omic alone in HNSC. The results of in vitro veri�cation of the core genes selected by the random forest suggested that these genes could serve as therapeutic targets and poor prognostic factors for HNSC. At the same time, in the Cox proportional hazard regression and BN models, these genes indicated that they have a greater impact on the survival outcome of HNSC. Importantly, prior studies have noted TAC1wasa powerful epigenetic biomarker in HNSC 36 . Meanwhile, ZFR2,AQP5, and ACTN3 were found on the association between tumors such as cervical cancer, prostate cancer, acute myeloid leukemia, colorectal cancer, and breast cancer 37-41 . However, the MMP11 gene has not been studied in HNSC. The result of this study may suggest that MMP11could serve as a novel biomarker for the diagnosis in HNSC, while the matrix metalloproteases (MMP) family were related to pan-cancer, especially with HNSC 42 . These �ndings may be somewhat limited �rstly by the lack of predicting the combination of other personal factors such as smoking condition and alcohol condition with multi-omics data, and the lack of combined predictions of multi-omics data and data that fully describe the prognosis of cancer, such as TMN stage, radiotherapy, and chemotherapy. at the molecular level, we neglected to combine proteomics data with multi-omics to predict the survival outcome of HNSC. may be one of the reasons that the AUC that describes the prediction performance was lower than 0.8. Secondly, this paper did not conduct experimental veri�cation of the core genes screened. Despite these �aws, these results further support the idea of applying LASSO and BN combined model to multi-omics integration data to predict the survival outcome. And these results show that the use of machine learning methods, especially BN methods, is robust and accurate in predicting the survival of HNSC. to predict the affecting HNSC variables and survival outcome. This study set out to explore whether the LASSO and six machine learning models based on multi-omics integrated data could be robust in predicting the survival outcome of HNSC. However, this study had some limitations as it didn't combine other variables that may affect the survival progress of HNSC. And the further experiment of verifying the genes predicted by machine learning that affect the survival and development of HNSC didn't perform. Despite these limitations, the �ndings of this study are still valuable. The machine learning models especially the BN model are expected to become a practical prediction model for tumor survival and prognosis. The multi-omics integration data could bring more information about the molecular level to better predict survival outcome. And better clinical services may bring new ideas to the precise prognosis and treatment of tumors.