Investigation of genes related to oral cancer using time-to-event machine learning approaches

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Abstract
Background: Since cancer is one of the most common and deadly diseases, its early diagnosis is very important for treatment and prevents the irreparable physical, mental and social consequences of this disease. Oral cancer is also one of the most common cancers, and factors such as gender, age, and smoking influence the incidence of this disease. One of the most important factors affecting cancer is genetic factors. It is not enough to consider clinical factors for the treatment of this disease, and it is also very important to deal with the genes in people's bodies that are effective in their survival against cancer. Also, the survival of people with oral cancer in the early stages of the disease is 80%, so early detection is very important. Therefore, we are looking for a model to better investigate key and effective genes in this disease. Methods: A publicly available dataset of oral cancer (GSE26549) including information of 29096 genes expression profiles of 86 samples was used. A univariate cox regression was used for each gene’s expression to reduce the number of genes. Cox-Boost, Random Survival Forest and Support survival SVM (Recursive Feature Elimination) were used to identify related genes. Shared genes between three methods were discovered for calculating the prognostic score and the Kaplan-Meier curve. To do validation, common genes were selected from the validation dataset (GSE9844) to provide the ROC curve. Results: The univariate Cox regression models selected 945 significant genes. Four shared genes of RPL24, HTR3B, ASAH2B and TEX29 related to time-to-death in oral cancer patients were then identified by using the Cox-Boost, Random Survival Forest and Support survival SVM (Recursive Feature Elimination). The survival distributions of the high-risk and low-risk groups significantly differed. Conclusion: Common genes between three methods were RPL24, HTR3B, ASAH2B and TEX29 which all of them were significant in multiple Cox.

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