Functional implications of aging-related lncRNAs for predicting prognosis and immune status in glioma patients

Guangying Zhang, Yanyan Li, Na Li, Liangfang Shen, Zhanzhan Li
2021 · doi:10.1101/2021.12.26.21268427
preprint OA: closed
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Abstract

Glioma, is the most prevalent intracranial tumor with high recurrence and mortality rate. Long noncoding RNAs (lncRNAs) play a critical role in the occurrence and progression of tumors as well as in aging regulation. Our study aimed to establish a new glioma prognosis model by integrating aging-related lncRNAs expression profiles and clinical parameters in glioma patients from the Chinese Glioma Genome Atlas (CGGA) and the Cancer Genome Atlas (TCGA) datasets. The Pearson correlation analysis (|R|> 0.6, P<0.001) was performed to explore the aging-related lncRNAs, and univariate cox tregresion and least absolute shrinkage and selection operator (LASSO) regression were used to screening prognostic signature in glioma patients. Based on the fifteen lncRNAs, we can divide glioma patients into three subtypes, and developed a prognostic model. Kaplan-Meier survival curve analysis showed that low-risk patients had longer survival time than high-risk group. Principal component analysis indicated that aging-related lncRNAs signature had a clear distinction between high- and low-risk groups. We also found that fifteen target lncRNAs were closely correlated with 119 genes by establishing a co-expression network. In addition, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis displayed different function and pathways enrichment in high-and low-risk groups. The different missense mutations were observed in two groups, and the most frequent variant types were single nucleotide polymorphism (SNP). This study demonstrated that the novel aging-related lncRNAs signature had an important prognosis prediction and may contribute to individual treatment for glioma.

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