SUMOylation Regulator-Related Molecules Can Be Used as Prognostic Biomarkers for Glioblastoma

Li, Xiaozhi; Meng, Yutong

doi:10.3389/fcell.2021.658856

Cited by 9 publications

(6 citation statements)

References 32 publications

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“…We found that RBM8A-related candidate mRNAs were strongly associated with prognosis of GBM patients, especially ANK1 and MYO15A. These results echo previous studies linking these two mRNAs to GBM prognosis (Serão et al, 2011;Li and Meng, 2021). Methylation of the ANK1 gene changes during development, and this is important for regulating formation of the nervous system (Gasparoni et al, 2018).…”

Section: Discussionsupporting

confidence: 86%

Molecular Insights and Prognosis Associated With RBM8A in Glioblastoma

Wei

Zou

Chen

et al. 2022

Front. Mol. Biosci.

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Background: Glioblastoma (GBM) is the most invasive brain tumors, and it is associated with high rates of recurrence and mortality. The purpose of this study was to investigate the expression of RBM8A in GBM and the potential influence of its expression on the disease.Methods: Levels of RBM8A mRNA in GBM patients and controls were examined in The Cancer Genome Atlas (TCGA), GSE16011 and GSE90604 databases. GBM samples in TCGA were divided into RBM8Ahigh and RBM8Alow groups. Differentially expressed genes (DEGs) between GBM patients and controls were identified, as were DEGs between RBM8Ahigh and RBM8Alow groups. DEGs common to both of these comparisons were analyzed for coexpression and regression analyses. In addition, we identified potential effects of RBM8A on competing endogenous RNAs, immune cell infiltration, methylation modifications, and somatic mutations.Results: RBM8A is expressed at significantly higher levels in GBM than control samples, and its level correlates with tumor purity. We identified a total of 488 mRNAs that differed between GBM and controls as well as between RBM8Ahigh and RBM8Alow groups, which enrichment analysis revealed to be associated mainly with neuroblast proliferation, and T cell immune responses. We identified 174 mRNAs that gave areas under the receiver operating characteristic curve >0.7 among coexpression module genes, of which 13 were significantly associated with overall survival of GBM patients. We integrated 11 candidate mRNAs through LASSO algorithm, then nomogram, risk score, and decision curve analyses were analyzed. We found that RBM8A may compete with DLEU1 for binding to miR-128-1-5p, and aberrant RBM8A expression was associations with tumor infiltration by immune cells. Some mRNAs associated with GBM prognosis also appear to be methylated or mutated.Conclusions: Our study strongly links RBM8A expression to GBM pathobiology and patient prognosis. The candidate mRNAs identified here may lead to therapeutic targets against the disease.

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Section: Discussionsupporting

confidence: 86%

Molecular Insights and Prognosis Associated With RBM8A in Glioblastoma

Wei

Zou

Chen

et al. 2022

Front. Mol. Biosci.

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“…However, high expression has been associated with better prognosis in some cancer types. Downregulated expression levels in cancer tissues have consistently been reported for SENP2 [52][53][54][55][56][57] and PIAS2 [58][59][60]. The prognostic value of a single SUMO machinery component can also vary between cancer (sub)types, and, e.g., the value for PC2 [61,62] and PIAS4 [63,64] as prognostic biomarkers in breast cancer and for PIAS3 [65,66] in mesothelioma remains ambiguous (Table 1).…”

Section: Altered Expression and Prognostic Significance Of Sumo Pathway In Cancermentioning

confidence: 99%

Therapeutic Potential of Targeting the SUMO Pathway in Cancer

Kukkula

Ojala

Mendez

et al. 2021

Cancers

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SUMOylation is a dynamic and reversible post-translational modification, characterized more than 20 years ago, that regulates protein function at multiple levels. Key oncoproteins and tumor suppressors are SUMO substrates. In addition to alterations in SUMO pathway activity due to conditions typically present in cancer, such as hypoxia, the SUMO machinery components are deregulated at the genomic level in cancer. The delicate balance between SUMOylation and deSUMOylation is regulated by SENP enzymes possessing SUMO-deconjugation activity. Dysregulation of SUMO machinery components can disrupt the balance of SUMOylation, contributing to the tumorigenesis and drug resistance of various cancers in a context-dependent manner. Many molecular mechanisms relevant to the pathogenesis of specific cancers involve SUMO, highlighting the potential relevance of SUMO machinery components as therapeutic targets. Recent advances in the development of inhibitors targeting SUMOylation and deSUMOylation permit evaluation of the therapeutic potential of targeting the SUMO pathway in cancer. Finally, the first drug inhibiting SUMO pathway, TAK-981, is currently also being evaluated in clinical trials in cancer patients. Intriguingly, the inhibition of SUMOylation may also have the potential to activate the anti-tumor immune response. Here, we comprehensively and systematically review the recent developments in understanding the role of SUMOylation in cancer and specifically focus on elaborating the scientific rationale of targeting the SUMO pathway in different cancers.

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“…To improve prognostic and predictive survival power in GBM patients, researchers [ 69 , 91 – 92 , 94 – 95 , 97 , 109 , 131 , 137 – 138 , 144 , 147 , 172 , 186 , 189 , 195 , 197 , 200 , 222 , 242 , 251 , 253 ], identified a panel of 2, 3, 4, 6,7, 8, 13, or 14 genes using mRNA expression datasets. They established a risk score model that performed well in survival prediction.…”

Section: Discussionmentioning

confidence: 99%

mRNA markers for survival prediction in glioblastoma multiforme patients: a systematic review with bioinformatic analyses

Azimi,

Yazdanian,

Ahmadiani

2024

BMC Cancer

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Background Glioblastoma multiforme (GBM) is a type of fast-growing brain glioma associated with a very poor prognosis. This study aims to identify key genes whose expression is associated with the overall survival (OS) in patients with GBM. Methods A systematic review was performed using PubMed, Scopus, Cochrane, and Web of Science up to Journey 2024. Two researchers independently extracted the data and assessed the study quality according to the New Castle Ottawa scale (NOS). The genes whose expression was found to be associated with survival were identified and considered in a subsequent bioinformatic study. The products of these genes were also analyzed considering protein-protein interaction (PPI) relationship analysis using STRING. Additionally, the most important genes associated with GBM patients’ survival were also identified using the Cytoscape 3.9.0 software. For final validation, GEPIA and CGGA (mRNAseq_325 and mRNAseq_693) databases were used to conduct OS analyses. Gene set enrichment analysis was performed with GO Biological Process 2023. Results From an initial search of 4104 articles, 255 studies were included from 24 countries. Studies described 613 unique genes whose mRNAs were significantly associated with OS in GBM patients, of which 107 were described in 2 or more studies. Based on the NOS, 131 studies were of high quality, while 124 were considered as low-quality studies. According to the PPI network, 31 key target genes were identified. Pathway analysis revealed five hub genes (IL6, NOTCH1, TGFB1, EGFR, and KDR). However, in the validation study, only, the FN1 gene was significant in three cohorts. Conclusion We successfully identified the most important 31 genes whose products may be considered as potential prognosis biomarkers as well as candidate target genes for innovative therapy of GBM tumors.

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SUMOylation Regulator-Related Molecules Can Be Used as Prognostic Biomarkers for Glioblastoma

Cited by 9 publications

References 32 publications

Molecular Insights and Prognosis Associated With RBM8A in Glioblastoma

Molecular Insights and Prognosis Associated With RBM8A in Glioblastoma

Therapeutic Potential of Targeting the SUMO Pathway in Cancer

mRNA markers for survival prediction in glioblastoma multiforme patients: a systematic review with bioinformatic analyses

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