Integrating multiple-level molecular data to infer the distinctions between glioblastoma and lower-grade glioma

Multiomics Profiling and Clustering of Low-Grade Gliomas Based on the Integrated Stress Status

Background

Although the prognosis of low-grade glioma is better than that of glioblastoma, there are still some groups with poor prognosis. The integrated stress response contributes to the malignant progress of tumors. As there had limited research focused on the integrated stress status in LGG, it is urgent to profile and reclassify LGG based on the integrated stress response.

Methods

Information of glioma patients was obtained from the Chinese Glioma Genome Atlas, The Cancer Genome Atlas, and the GSE16011 cohorts. Statistical analyses were conducted using GraphPad Prism 8 and R language.

Results

We summarized and quantified four types of integrated stress responses. Relationships between these four types of stress states and the clinical characteristics were analyzed in low-grade glioma. We then reclassified the patients based on these four scores and found that cluster 2 had the worst prognosis, while cluster 1 had the best prognosis. We also established an accurate integrated stress response risk signature for predicting cluster 2. We found that immune response and suppressive immune cell components were more enriched in the high-risk group. We also profiled the genomic differences between the low- and high-risk groups, including the nonmissense mutation of driver genes and the copy number variations.

Conclusion

Low-grade glioma patients were divided into three clusters based on the integrated stress status, with cluster 2 exhibiting malignant transformation trends. The signature adequately reflected the traits of cluster 2, while a high risk score indicated a worse prognosis and an enriched inhibitory immune microenvironment.

Comprehensive Transcriptomic Analysis and Experimental Validation Identify lncRNA HOXA-AS2/miR-184/COL6A2 as the Critical ceRNA Regulation Involved in Low-Grade Glioma Recurrence

Purpose

The recurrence and metastasis of glioma are closely related to complex regulatory networks among protein-coding genes, lncRNAs and microRNAs. The aim of this study was to investigate core genes, lncRNAs, miRNAs and critical ceRNA regulatory mechanisms, which are involved in lower-grade glioma (LGG) recurrence.

Materials and Methods

We employed multiple datasets from Chinese Glioma Genome Atlas (CGGA) database and The Cancer Genome Atlas (TCGA) to perform comprehensive transcriptomic analysis. Further in vitro experiments including cell proliferation assay, luciferase reporter assay, and Western blot were performed to validate our results.

Results

Recurrent LGG and glioblastoma (GBM) showed different transcriptome characteristics with less overlap of differentially expressed protein-coding genes (DEPs), lncRNAs (DELs) and miRNAs (DEMs) compared with primary samples. There were no overlapping gene in ontology (GO) terms related to GBM recurrence in the TCGA and CGGA databases, but there were overlaps associated with LGG recurrence. GO analysis and protein–protein interaction (PPI) network analysis identified three core genes: TIMP1, COL1A1 and COL6A2. By hierarchical cluster analysis of them, LGGs could be clustered as Low_risk and High_risk group. The High_risk group with high expression of TIMP1, COL1A1, and COL6A2 showed worse prognosis. By coexpression networks analysis, competing endogenous RNA (ceRNA) network analysis, cell proliferation assay and luciferase reporter assay, we confirmed that lncRNA HOXA-AS2 functioned as a ceRNA for miR-184 to regulate expression of COL6A2, which induced cell proliferation of low-grade glioma.

Conclusion

In this study, we revealed a 3-hub protein-coding gene signature to improve prognostic prediction in LGG, and identified a critical ceRNA regulation involved in LGG recurrence.

Overexpression of PSAT1 Gene is a Favorable Prognostic Marker in Lower-Grade Gliomas and Predicts a Favorable Outcome in Patients with IDH1 Mutations and Chromosome 1p19q Codeletion

Patients with lower-grade gliomas (LGGs) have highly diverse clinical outcomes. Although histological features and molecular markers have been used to predict prognosis, the identification of new biomarkers for the accurate prediction of patient outcomes is still needed. The serine synthesis pathway (SSP) is important in cancer metabolism. There are three key regulators, including phosphoglycerate dehydrogenase (PHGDH), phosphoserine phosphatase (PSPH), and phosphoserine aminotransferase 1 (PSAT1), in SSP. However, their clinical importance in LGGs is still unknown. In this study, we used the bioinformatics tool in the Gene Expression Profiling Interactive Analysis (GEPIA) website to examine the prognostic significance of PHGDH, PSPH, and PSAT1 genes in LGGs. PSAT1 gene expression was then identified as a potential biomarker candidate for LGGs. Datasets from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) were further used to explore the prognostic role of PSAT1 gene. Our results demonstrated that PSAT1 overexpression is a favorable prognostic marker of LGGs and significantly correlated with patient age ≤40, and a lower WHO histological grade, as well as mutations in IDH1, TP53 and ATRX, but not with chromosome 1p19q codeletions. More importantly, LGG patients with isocitrate dehydrogenase 1 (IDH1) mutations, chromosome 1p19q codeletions, and PSAT1 overexpression may have the best overall survival (five-year survival rate: 100%). Finally, we observed a coordinated biological reaction between IDH1 mutations and PSAT1 overexpression, and suggested overexpression of PSAT1 might enhance the function of mutant IDH1 to promote a favorable outcome in LGG patients. In conclusion, our study confirmed the importance of identifying the overexpression of PSAT1 as a favorable prognostic marker of LGGs, which may compensate for the limitation of IDH1 mutations and chromosome 1p19q codeletion in the prognostication of LGGs.