Application of Machine Learning in Developing a Novelty Five-Pseudogene Signature to Predict Prognosis of Head and Neck Squamous Cell Carcinoma: A New Aspect of "Junk Genes" in Biomedical Practice.

Head and neck squamous cell carcinoma (HNSCC) is the sixth malignancy, which is characterized by poor prognosis or high mortality because of the lack of predicting markers. Aberrant cancer pseudogenes have been found predictive for prognosis. We aim to identify a pseudogene-based prognosis signature for HNSCC by machine learning. RNA-seq data were downloaded from The Cancer Genome Atlas, and 700 differentially-expressed pseudogenes were identified. The survival-related pseudogenes were screened through COX-regression analysis, which includes univariate regression, least absolute shrinkage and selection operator regression, and multivariate regression, and a five-pseudogene signature was constructed. The value of prediction for the signature was validated in multiple subgroups in terms of survival. Gene set enrichment analysis (GSEA) and coexpression analysis were used to determine the underlying biological functions. Seven hundred dysregulated pseudogenes were identified, and the five-pseudogene signature can distinguish the low-risk and high-risk patients for both training and testing sets and predicted prognosis with high sensitivity and specificity. Furthermore, the signature was applicable to patients of different genders, ages, stages, and grades. Coexpression analysis revealed that the five-pseudogene is associated with immune system. GSEA showed cancer-related biological process and pathways the five-pseudogene involved in. The five-pseudogene signature is not only a novel marker for prognosis but also a promising signature for monitoring therapeutic schedule. Therefore, our findings may have potential clinical significance.