Xiaofeng Duan1, Xianlin Cheng1, Xinhai Yin1, Zhao Ke1, Jukun Song2. 1. Department of Oral and Maxillofacial Surgery, Guizhou Provincial People's Hospital, Guiyang, 550002, Guizhou, China. 2. Department of Oral and Maxillofacial Surgery, Guizhou Provincial People's Hospital, Guiyang, 550002, Guizhou, China. songjukun@163.com.
Abstract
OBJECTIVE: Dysregulation of RNA binding proteins (RBPs) plays an important role in controlling processes in cancer development. However, the function of RBPs has not been thoroughly and systematically documented in head and neck cancer. We aim to explore the role of RPB in the pathogenesis of HNSC. METHODS: We obtained HNSC gene expression data and corresponding clinical information from The Cancer Genome Atlas (TCGA) and the GEO databases, and identified aberrantly expressed RBPs between tumors and normal tissues. Meanwhile, we performed a series of bioinformatics to explore the function and prognostic value of these RBPs. RESULTS: A total of 249 abnormally expressed RBPs were identified, including 101 downregulated RBPs and 148 upregulated RBPs. Using least absolute shrinkage and selection operator (LASSO) and univariate Cox regression analysis, the 15 RPBs were identified as hub genes. With the 15 RPBS, the prognostic prediction model was constructed. Further analysis showed that the high-risk score of the patients expressed a better survival outcome. The prediction model was validated in another HNSC dataset, and similar findings were observed. CONCLUSIONS: Our results provide novel insights into the pathogenesis of HNSC. The fifteen RBP gene signature exhibited the predictive value of moderate HNSC prognosis, and have potential application value in clinical decision-making and individualized treatment.
OBJECTIVE: Dysregulation of RNA binding proteins (RBPs) plays an important role in controlling processes in cancer development. However, the function of RBPs has not been thoroughly and systematically documented in head and neck cancer. We aim to explore the role of RPB in the pathogenesis of HNSC. METHODS: We obtained HNSC gene expression data and corresponding clinical information from The Cancer Genome Atlas (TCGA) and the GEO databases, and identified aberrantly expressed RBPs between tumors and normal tissues. Meanwhile, we performed a series of bioinformatics to explore the function and prognostic value of these RBPs. RESULTS: A total of 249 abnormally expressed RBPs were identified, including 101 downregulated RBPs and 148 upregulated RBPs. Using least absolute shrinkage and selection operator (LASSO) and univariate Cox regression analysis, the 15 RPBs were identified as hub genes. With the 15 RPBS, the prognostic prediction model was constructed. Further analysis showed that the high-risk score of the patients expressed a better survival outcome. The prediction model was validated in another HNSC dataset, and similar findings were observed. CONCLUSIONS: Our results provide novel insights into the pathogenesis of HNSC. The fifteen RBP gene signature exhibited the predictive value of moderate HNSC prognosis, and have potential application value in clinical decision-making and individualized treatment.