Abstract
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.
Highlights
Metabolic reprogramming is critical for maintaining the survival of cancer cells and defined as a hallmark of cancer, which might be the consequence of oncogenic mutations [1]
We focused on the amino acid metabolism and immune status which was explored by single-sample Gene Sets Enrichment Analysis (ssGSEA) and ESTIMATE and confirmed the differential expression of genes in gliomas
weighted gene co-expression network analysis (WGCNA) was performed to identify amino acid metabolism and immuno-related gene modules based on the data from The Cancer Genome Atlas (TCGA), and an amino acid metabolism and immune signature was constructed by least absolute shrinkage and selection operator (LASSO) Cox regression model
Summary
Metabolic reprogramming is critical for maintaining the survival of cancer cells and defined as a hallmark of cancer, which might be the consequence of oncogenic mutations [1]. Amino acid metabolism emerges as an important role in the metabolic reprogramming of cancer cells because of its function in redox balance, energy regulation, biosynthesis support, and so on [2]. The metabolism of amino acid is varied in tumors and plays a significant role in the biological process of tumor cells and in the tumor microenvironment, the modulation of the immune. All these indicate a better understanding of the metabolism of amino acids, which will offer potentially effective targets for cancer therapy [8]
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