Bioinformatics Analysis of Expression Profiles and Prognostic Values of the Signal Transducer and Activator of Transcription Family Genes in Glioma

胶质瘤中信号转导和转录激活因子家族基因表达谱及其预后价值的生物信息学分析

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Abstract

Signal transducer and activator of transcription (STAT) family genes-of which there are seven members: STAT1, STAT2, STAT3, STAT4, STAT5A, STAT5B, and STAT6-have been associated with the progression of multiple cancers. However, their prognostic values in glioma remain unclear. In this study, we systematically investigated the expression, the prognostic value, and the potential mechanism of the STAT family genes in glioma. The expression of STAT1/2/3/5A/6 members were significantly higher and positively correlated with IDH mutations, while the expression of STAT5B was lower and negatively correlated with IDH mutations in glioma. Survival analysis indicated that the upregulation of STAT1/2/3/5A/6 and downregulation of STAT5B expression was associated with poorer overall survival in glioma. Joint effects analysis of STAT1/2/3/5A/5B/6 expression suggested that the prognostic value of the group was more significant than that of each individual gene. Thus, we constructed a risk score model to predict the prognosis of glioma. The receiver operating characteristic curve and calibration curves showed good performance as prognostic indicators in both TCGA (The Cancer Genome Atlas) and the CGGA (Chinese Glioma Genome Atlas) databases. Furthermore, we analyzed the correlation between STAT expression with immune infiltration in glioma. The Protein-protein interaction network and enrichment analysis showed that STAT members and co-expressed genes mainly participated in signal transduction activity, Hepatitis B, the Jak-STAT signaling pathway, transcription factor activity, sequence-specific DNA binding, and the cytokine-mediated signaling pathway in glioma. In summary, our study analyzed the expression, prognostic values, and biological roles of the STAT gene family members in glioma, based on which we developed a new risk score model to predict the prognosis of glioma more precisely.

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