DataSheet_5_Development and Verification of the Amino Metabolism-Related and Immune-Associated Prognosis Signature in Gliomas.pdf (713.35 kB)
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DataSheet_5_Development and Verification of the Amino Metabolism-Related and Immune-Associated Prognosis Signature in Gliomas.pdf

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posted on 05.11.2021, 04:14 authored by Yang Xu, Liguo Ye, Rongxin Geng, Ping Hu, Qian Sun, Shiao Tong, Fanen Yuan, Qianxue Chen

Aberrant reprogramming of metabolism has been considered a hallmark in various malignant tumors. The metabolic changes of amino acid not only have dramatic effects in cancer cells but also influence their immune-microenvironment in gliomas. However, the features of the amino acid metabolism-related and immune-associated gene set have not been systematically described. The expression level of mRNA was obtained from The Cancer Genome Atlas database and the Chinese Glioma Genome Atlas database, which were used as training set and validation set, respectively. Different bioinformatics and statistical methods were combined to construct a robust amino metabolism-related and immune-associated risk signature for distinguishing prognosis and clinical pathology features. Constructing the nomogram enhanced risk stratification and quantified risk assessment based on our gene model. Besides this, the biological mechanism related to the risk score was investigated by gene set enrichment analysis. Hub genes of risk signature were identified by the protein–protein interaction network. The amino acid metabolism-related and immune-associated gene signature recognized high-risk patients, defined as an independent risk factor for overall survival. The nomogram exhibited a high accuracy in predicting the overall survival rate for glioma patients. Furthermore, the high risk score hinted an immunosuppressive microenvironment and a lower sensitivity of immune checkpoint blockade therapy and also identified PSMC5 and PSMD3 as novel biomarkers in glioma. In conclusion, a novel amino acid metabolism-related and immune-associated risk signature for predicting prognosis in glioma has been constructed and identified as two potential novel biomarkers.

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