Identification of genes associated with cancer stem cell characteristics in head and neck squamous cell carcinoma through co-expression network analysis.

Abstract

Head and neck squamous cell carcinoma (HNSCC) is a type of invasive malignancy and the seventh most common cancer in the worldwide. Cancer stem cells (CSCs) are self-renewal cells in tumors and can produce heterogeneous tumor cells, which play an important role in the development of HNSCC. In our research, we aimed to identify genes related to the CSCs characteristics in HNSCC. Messenger RNA (mRNA) expression-based stiffness index (mRNAsi) can be used as a quantitative characterization of CSCs. We used one-class logistic regression machine learning algorithm (OCLR) to calculate the mRNAsi and investigate the relationship between mRNAsi and clinical characteristics of HNSCC. Then, a weighted gene co-expression network analysis (WGCNA) and protein-protein interaction (PPI) network was constructed to screen hub genes related to mRNAsi of HNSCC. The results indicated that the score of mRNAsi in HNSCC tissues is higher than in paracancer tissues, while the mRNAsi was not statistically correlated with the prognosis and clinical characteristics of HNSCC. Six positive and six negative hub genes related to mRNAsi of HNSCC were selected, which may act as therapeutic targets for inhibiting CSCs within HNSCC. In conclusion, our research selected 12 hub genes related to mRNAsi of HNSCC through weighted gene co-expression network analysis. These genes may become therapeutic targets to inhibit the CSCs of HNSCC in the future.