<i>RRM2</i> promotes the progression of human glioblastoma

Li, Chao; Zheng, Junnian; Chen, Si; Huang, Bin; Li, Guosheng; Feng, Zichao; Wang, Jiwei; Xu, Shujun

doi:10.1002/jcp.26529

Cited by 51 publications

(45 citation statements)

References 24 publications

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“…The high level of RRM2 expression has received extensive attention in several cancers (38). It has been proven that the overexpression of RRM2 can promote the proliferation, migration and invasion of GBM cells and inhibit apoptosis in cell experiments (39). The same results were observed in animal experiments, which may explain its influence on OS.…”

Section: Discussionmentioning

confidence: 75%

Prognostic prediction model for glioblastoma: a metabolic gene signature and independent external validation

Lei

Chen

Wang

et al. 2020

Preprint

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Background. Glioblastoma (GBM) is the most common primary malignant intracranial tumor and is closely related to metabolic alterations. However, few accepted prognostic models are currently available, especially models based on metabolic genes. Methods . Transcriptome data were obtained for all patients diagnosed with GBM from the Gene Expression Omnibus (GEO) (training cohort, n=369) and The Cancer Genome Atlas (TCGA) (validation cohort, n=152) with the following variables: age at diagnosis, sex, follow-up and overall survival (OS). Metabolic genes according to Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were filtered, and a Lasso regression model was constructed. Survival was assessed by univariate or multivariate Cox proportional hazards regression and Kaplan-Meier analysis, and we also conducted an independent external validation to examine the model. Results. There were 341 metabolic genes that showed significant differences between normal brain tissues and GBM tissues in both the training and validation cohorts, among which 56 genes were significantly correlated with the OS of patients. Lasso regression revealed that the metabolic prognostic model was composed of 18 genes, including COX10 , COMT , and GPX2 , with protective effects, as well as OCRL and RRM2 , with unfavorable effects. Patients classified as high-risk by the risk score from this model had markedly shorter OS than low-risk patients ( P <0.0001), and this significant result was also observed in the independent external validation cohort ( P <0.001). Conclusions . The prognosis of GBM was dramatically related to metabolic pathways, and our metabolic prognostic model had high accuracy and application value in predicting the OS of GBM patients. Background. Glioblastoma (GBM) is the most common primary malignant intracranial tumor and is closely related to metabolic alterations. However, few accepted prognostic models are currently available, especially models based on metabolic genes. Methods . Transcriptome data were obtained for all patients diagnosed with GBM from the Gene Expression Omnibus (GEO) (training cohort, n=369) and The Cancer Genome Atlas (TCGA) (validation cohort, n=152) with the following variables: age at diagnosis, sex, follow-up and overall survival (OS). Metabolic genes according to Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were filtered, and a Lasso regression model was constructed. Survival was assessed by univariate or multivariate Cox proportional hazards regression and Kaplan-Meier analysis, and we also conducted an independent external validation to examine the model. Results. There were 341 metabolic genes that showed significant differences between normal brain tissues and GBM tissues in both the training and validation cohorts, among which 56 genes were significantly correlated with the OS of patients. Lasso regression revealed that the metabolic prognostic model was composed of 18 genes, including COX10 , COMT , and GPX2 , with protective effects, as well as OCRL and RRM2 , with unfavorable effects. Patients classified as high-risk by the risk score from this model had markedly shorter OS than low-risk patients ( P <0.0001), and this significant result was also observed in the independent external validation cohort ( P <0.001).Conclusions . The prognosis of GBM was dramatically related to metabolic pathways, and our metabolic prognostic model had high accuracy and application value in predicting the OS of GBM patients.

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Section: Discussionmentioning

confidence: 75%

Prognostic prediction model for glioblastoma: a metabolic gene signature and independent external validation

Lei

Chen

Wang

et al. 2020

Preprint

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“…A recent study indicated that RRM2 upregulation occurred in multiple myeloma tumors and that RRM2 knockdown inhibited multiple myeloma cell proliferation 12 . Li et al illustrated that RRM2 was overexpressed in human glioblastoma cells, and promoted proliferation, migration, and invasion of human glioblastoma cells 34 . Suppression of RRM2 inhibits cell proliferation, causes cell cycle arrest, and promotes the apoptosis of human neuroblastoma cells 35 .…”

Section: Discussionmentioning

confidence: 99%

Independent prognostic implications of RRM2 in lung adenocarcinoma

Luo

Cao

et al. 2020

J. Cancer

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Background: Ribonucleoside-diphosphate reductase subunit M2 ( RRM2 ) is the catalytic subunit of ribonucleotide reductase and modulates the enzymatic activity, which is essential for DNA replication and repair. However, the role of RRM2 in lung adenocarcinoma (LUAD) remains unclear. Methods: In this study, we explored the expression pattern and prognostic value of RRM2 in LUAD across TCGA, GEO, Oncomine, UALCAN, PrognoScan, and Kaplan-Meier Plotter, and confirmed its independent prognostic value via Cox analyses. LinkedOmics and GEPIA2 were applied to investigate co-expression and functional networks associated with RRM2 . Besides, we used TIMER to assess the correlation between RRM2 and the main six types of tumor-infiltrating immune cells. Lastly, the correlations between immune signatures of immunomodulators, chemokines, and 28 tumor-infiltrating lymphocytes (TILs) and RRM2 were examined by tumor purity-corrected partial Spearman's rank correlation coefficient through TIMER portal. Results: RRM2 was found upregulated in tumor tissues in TCGA-LUAD, and validated in multiple independent cohorts. Moreover, whether in TCGA or other cohorts, high RRM2 expression was found to be associated with poor survival. Cox analyses showed that high RRM2 expression was an independent risk factor for overall survival, disease-specific survival, and progression-free survival of LUAD. Functional network analysis suggested that RRM2 regulates RNA transport, oocyte meiosis, spliceosome, ribosome biogenesis in eukaryotes, and cellular senescence signaling through pathways involving multiple cancer-related kinases and E2F family. Also, RRM2 expression correlated with infiltrating levels of B cells, CD4+ T cells, and neutrophils. Subsequent analysis found that B cells and dendritic cells could predict the outcome of LUAD. B cells were identified as an independent risk factor among six types of immune cells through Cox analyses. At last, the correlation analysis showed RRM2 correlated with 67.68% (624/922) of the immune signatures we performed. Conclusion: Our research showed that RRM2 could independently predict the prognosis of LUAD and was associated with immune infiltration. In particular, the tight relationship between RRM2 and B cell marker genes are the potential epicenter of the immune response and one of the critical factors affecting the prognosis. Our findings laid the foundation for further research on the immunomodulatory role of RRM2 in LUAD.

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“…Another example was RRM2 and BRIP1, which was significantly correlated with each other in expression ( Figure 6C, R = 0.93 and p-value < 0.001). RRM2 had been found to promote the progression of human GBM and was a potential prognostic biomarker in glioma (Li et al, 2018a;Sun et al, 2019). BRIP1 was found to be an independent signature, which was correlated with worse prognosis in glioma (de Sousa et al, 2017).…”

Section: Rna-binding Protein Regulatory Network During Glioma Progresmentioning

confidence: 93%

Systematically Dissecting the Function of RNA-Binding Proteins During Glioma Progression

Wang

Hou

2020

Front. Genet.

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RNA-binding proteins (RBPs) play important roles in regulating gene expression and dysregulation of RBPs have been observed in various types of cancer. However, the role of RBPs during glioma progression, and particular in Chinese patients, is only starting to be unveiled. Here, we systematically analyzed the somatic mutation, gene expression patterns of 2949 RBPs during glioma progression. Our comprehensive study reveals several of highly mutated genes (such as ATRX, TTN and SETD2) and differentially expressed genes (such as KIF4A, TTK and CEP55). Integration of the expression of RBPs and genes, we constructed a regulatory network in glioma and revealed the functional links between RBPs and cancer-related genes. Moreover, we identified the prognosis spectrum of RBPs during glioma progression. The expression of a number of RBPs, such as SNRPN and IGF2BP3, are significantly associated with overall survival of patients in all grades. Taken together, our analyses provided a valuable RBP resource during glioma progression, and revealed several candidates that potentially contribute to development of therapeutic targets for glioma.

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RRM2 promotes the progression of human glioblastoma

Cited by 51 publications

References 24 publications

Prognostic prediction model for glioblastoma: a metabolic gene signature and independent external validation

Prognostic prediction model for glioblastoma: a metabolic gene signature and independent external validation

Independent prognostic implications of RRM2 in lung adenocarcinoma

Systematically Dissecting the Function of RNA-Binding Proteins During Glioma Progression

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