Abstract
PURPOSE: This study endeavors to build a deep learning (DL)-based model for predicting disease progression in head and neck squamous cell carcinoma (HNSCC) patients by integrating multi-omics data. METHODS: RNA sequencing, miRNA sequencing, and methylation data from The Cancer Genome Atlas (TCGA) were used as input for autoencoder, a DL approach. An autoencoder-based prognosis model for PFS was built by SVM algorithm and tested in three confirmation sets. Predictive performance of the model was compared to two alternative approaches. Differential expression analysis for mRNAs, microRNAs (miRNA) and methylation was conducted. Moreover, functional annotation of differentially expressed genes (DEGs) was achieved through function enrichment analysis. RESULT: The DL-based prognosis model identified two subgroups of patients with significantly different PFS, and showcased a good model fitness (C-index = 0.73). The two identified PFS subtypes were successfully validated in three confirmation sets. The DL-based model was more accurate and efficient than principal component analysis (PCA) or individual Cox-PH-based models. There were 348 DEGs, 23 differentially expressed miRNAs and 55 differentially methylated genes between the two PFS subtypes. These genes were significantly involved in several immune-related biological processes and primary immunodeficiency, cell adhesion molecules (CAMs), B cell receptor signaling and leukocyte transendothelial migration pathways. CONCLUSION: The DL-based model introduced in this study is reliable and robust in predicting disease progression in HNSCC patients. A number of pathways and genes targets are unraveled to be implicated in cancer progression. Utility of this model would facilitate development of more individualized therapy for HNSCC patients and improve prognosis.