Dataset Information

Development of an m6A-Related lncRNAs Signature Predicts Tumor Stemness and Prognosis for Low-Grade Glioma Patients.

ABSTRACT:

Background

Growing evidence has revealed that m6A modification of long noncoding RNAs (lncRNAs) dynamically controls tumor stemness and tumorigenesis-related processes. However, the prognostic significance of m6A-related lncRNAs and their associations with stemness in low-grade glioma (LGG) remain to be clarified.

Methods

A multicenter transcriptome analysis of lncRNA expression in 1,247 LGG samples was performed in this study. The stemness landscape of LGG tumors was presented and associations with clinical features were revealed. The m6A-related lncRNAs were identified between stemness groups and were further prioritized via least absolute shrinkage and selection operator Cox regression analysis. A risk score model based on m6A-related lncRNAs was constructed and validated in external LGG datasets.

Results

Based on the expression of LINC02984, PFKP-DT, and CRNDE, a risk model and nomogram were constructed; they successfully predicted the survival of patients and were extended to external datasets. Significant correlations were observed between the risk score and tumor stemness. Moreover, patients in different risk groups exhibited distinct tumor immune microenvironments and immune signatures. We finally provided several potential compounds suitable for specific risk groups, which may aid in LGG treatment.

Conclusions

This novel signature presents noteworthy value in the prediction of prognosis and stemness status for LGG patients and will foster future research on the development of clinical regimens.

SUBMITTER: Xu D

PROVIDER: S-EPMC10791469 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature

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Publications

Development of an m6A-Related lncRNAs Signature Predicts Tumor Stemness and Prognosis for Low-Grade Glioma Patients.

Xu Dahua D Li Peihu P Zhang Chunrui C Shen Yutong Y Cai Jiale J Wei Qingchen Q Cao Meng M Xu Zhizhou Z Wu Deng D Wang Hong H Bi Xiaoman X Wang Bo B Li Kongning K

Stem cells international 20240109

<h4>Background</h4>Growing evidence has revealed that m6A modification of long noncoding RNAs (lncRNAs) dynamically controls tumor stemness and tumorigenesis-related processes. However, the prognostic significance of m6A-related lncRNAs and their associations with stemness in low-grade glioma (LGG) remain to be clarified.<h4>Methods</h4>A multicenter transcriptome analysis of lncRNA expression in 1,247 LGG samples was performed in this study. The stemness landscape of LGG tumors was presented an ...[more]

PMID: 38229597

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Project description:BackgroundN6-methyladenosine (m6A) and 5-methylcytosine (m5C) are the main RNA methylation modifications involved in the oncogenesis of cancer. However, it remains obscure whether m6A/m5C-related long non-coding RNAs (lncRNAs) affect the development and progression of low grade gliomas (LGG).MethodsWe summarized 926 LGG tumor samples with RNA-seq data and clinical information from The Cancer Genome Atlas and Chinese Glioma Genome Atlas. 105 normal brain samples with RNA-seq data from the Genotype Tissue Expression project were collected for control. We obtained a molecular classification cluster from the expression pattern of sreened lncRNAs. The least absolute shrinkage and selection operator Cox regression was employed to construct a m6A/m5C-related lncRNAs prognostic signature of LGG. In vitro experiments were employed to validate the biological functions of lncRNAs in our risk model.ResultsThe expression pattern of 14 sreened highly correlated lncRNAs could cluster samples into two groups, in which various clinicopathological features and the tumor immune microenvironment were significantly distinct. The survival time of cluster 1 was significantly reduced compared with cluster 2. This prognostic signature is based on 8 m6A/m5C-related lncRNAs (GDNF-AS1, HOXA-AS3, LINC00346, LINC00664, LINC00665, MIR155HG, NEAT1, RHPN1-AS1). Patients in the high-risk group harbored shorter survival times. Immunity microenvironment analysis showed B cells, CD4 + T cells, macrophages, and myeloid-derived DC cells were significantly increased in the high-risk group. Patients in high-risk group had the worse overall survival time regardless of followed TMZ therapy or radiotherapy. All observed results from the TCGA-LGG cohort could be validated in CGGA cohort. Afterwards, LINC00664 was found to promote cell viability, invasion and migration ability of glioma cells in vitro.ConclusionOur study elucidated a prognostic prediction model of LGG by 8 m6A/m5C methylated lncRNAs and a critical lncRNA regulation function involved in LGG progression. High-risk patients have shorter survival times and a pro-tumor immune microenvironment.

Project description:BackgroundAs immunotherapy has received attention as new treatments for brain cancer, the role of inflammation in the process of glioma is of particular importance. Increasing studies have further shown that long non-coding RNAs (lncRNAs) are important factors that promote the development of glioma. However, the relationship between inflammation-related lncRNAs and the prognosis of glioma patients remains unclear. The purpose of this study is to construct and validate an inflammation-related lncRNA prognostic signature to predict the prognosis of low-grade glioma patients.MethodsBy downloading and analyzing the gene expression data and clinical information of the Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) patients with low-grade gliomas, we could screen for inflammatory gene-related lncRNAs. Furthermore, through Cox and the Least Absolute Shrinkage and Selection Operator regression analyses, we established a risk model and divided patients into high- and low-risk groups based on the median value of the risk score to analyze the prognosis. In addition, we analyzed the tumor mutation burden (TMB) between the two groups based on somatic mutation data, and explored the difference in copy number variations (CNVs) based on the GISTIC algorithm. Finally, we used the MCPCounter algorithm to study the relationship between the risk model and immune cell infiltration, and used gene set enrichment analysis (GSEA), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses to explore the enrichment pathways and biological processes of differentially expressed genes between the high- and low-risk groups.ResultsA novel prognostic signature was constructed including 11 inflammatory lncRNAs. This risk model could be an independent prognostic predictor. The patients in the high-risk group had a poor prognosis. There were significant differences in TMB and CNVs for patients in the high- and low-risk groups. In the high-risk group, the immune system was activated more significantly, and the expression of immune checkpoint-related genes was also higher. The GSEA, GO, and KEGG analyses showed that highly expressed genes in the high-risk group were enriched in immune-related processes, while lowly expressed genes were enriched in neuromodulation processes.ConclusionThe risk model of 11 inflammation-related lncRNAs can serve as a promising prognostic biomarker for low-grade gliomas patients.

Project description:BackgroundTyrosine protein tyrosine kinase binding protein (TYROBP) binds non-covalently to activated receptors on the surface of various immune cells, and mediates signal transduction and cellular activation. It is dysregulated in various malignancies, although little is known regarding its role in low-grade glioma. The aim of this study is to explore the clinicopathological significance, prognostic value and immune signature of TYROBP expression in low-grade glioma (LGG).MethodsThe differentially expressed genes (DEGs) between glioma samples and normal tissues were identified from two GEO microarray datasets using the limma package. The DEGs overlapping across both datasets were functionally annotated by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. STRING database was used to establish the protein-protein interaction (PPI) of the DEGs. The PPI network was visualized by Cytoscape and cytoHubba, and the core module and hub genes were identified. The expression profile of TYROBP and patient survival were validated in the Oncomine, GEPIA2 and CGGA databases. The correlation between TYROBP expression and the clinicopathologic characteristics were evaluated. Gene Set Enrichment Analysis (GSEA) and single-sample GSEA (ssGSEA) were performed by R based on the LGG data from TCGA. The TIMER2.0 database was used to determine the correlation between TYROBP expression and tumor immune infiltrating cells in the LGG patients. Univariate and multivariate Cox regression analyses were performed to determine the prognostic impact of clinicopathological factors via TCGA database.ResultsSixty-two overlapping DEGs were identified in the 2 datasets, and were mainly enriched in the response to wounding, focal adhesion, GTPase activity and Parkinson disease pathways. TYROBP was identified through the PPI network and cytoHubba. TYROBP expression levels were significantly higher in the LGG tissues compared to the normal tissues, and was associated with worse prognosis and poor clinicopathological parameters. In addition, GSEA showed that TYROBP was positively correlated to neutrophil chemotaxis, macrophage activation, chemokine signaling pathway, JAK-STAT signaling pathway, and negatively associated with gamma aminobutyric acid signaling pathway, neurotransmitter transport, neuroactive ligand receptor intersection etc. TIMER2.0 and ssGSEA showed that TYROBP expression was significantly associated with the infiltration of neutrophils, macrophages, myeloid dendritic cells and monocytes. The infiltration of the M2 phenotype macrophages, cancer-associated fibroblasts and myeloid dendritic cells correlated to worse prognosis in LGG patients. Finally, multivariate analysis showed that elevated TYROBP expression is an independent risk factor for LGG.ConclusionTYROBP is dysregulated in LGG and correlates with immune infiltration. It is a potential therapeutic target and prognostic marker for LGG.

Project description:Low-grade glioma (LGG), a common primary tumor, mainly originates from astrocytes and oligodendrocytes. Increasing evidence has shown that peroxisomes function in the regulation of tumorigenesis and development of cancer. However, the prognostic value of peroxisome-related genes (PRGs) in LGG has not been reported. Therefore, it is necessary to construct a prognostic risk model for LGG patients based on the expression profiles of peroxisome-related genes. Our study mainly concentrated on developing a peroxisome-related gene signature for overall survival (OS) prediction in LGG patients. First, according to these peroxisome-related genes, all LGG patients from The Cancer Genome Atlas (TCGA) database could be divided into two subtypes. Univariate Cox regression analysis was used to find prognostic peroxisome-related genes in TCGA_LGG dataset, and least absolute shrinkage and selection operator Cox regression analysis was employed to establish a 14-gene signature. The risk score based on the signature was positively associated with unfavorable prognosis. Then, multivariate Cox regression incorporating additional clinical characteristics showed that the 14-gene signature was an independent predictor of LGG. Time-dependent ROC curves revealed good performance of this prognostic signature in LGG patients. The performance about predicting OS of LGG was validated using the GSE107850 dataset derived from the Gene Expression Omnibus (GEO) database. Furethermore, we constructed a nomogram model based on the gene signature and age, which showed a better prognostic power. Gene ontology (GO) and Kyoto Encylopedia of Genes and Genomes (KEGG) analyses showed that neuroactive ligand-receptor interaction and phagosome were enriched and that the immune status was decreased in the high-risk group. Finally, cell counting kit-8 (CCK8) were used to detect cell proliferation of U251 and A172 cells. Inhibition of ATAD1 (ATPase family AAA domain-containing 1) and ACBD5 (Acyl-CoA binding-domain-containing-5) expression led to significant inhibition of U251 and A172 cell proliferation. Flow cytometry detection showed that ATAD1 and ACBD5 could induce apoptosis of U251 and A172 cells. Therefore, through bioinformatics methods and cell experiments, our study developed a new peroxisome-related gene signature that migh t help improve personalized OS prediction in LGG patients.

Dataset Information

Development of an m6A-Related lncRNAs Signature Predicts Tumor Stemness and Prognosis for Low-Grade Glioma Patients.

Background

Methods

Results

Conclusions

Publications

Development of an m6A-Related lncRNAs Signature Predicts Tumor Stemness and Prognosis for Low-Grade Glioma Patients.

Similar Datasets

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