MTHFD2 suppresses glioblastoma progression via the inhibition of ERK1/2 phosphorylation

Huang, Mei‐Hui; Xue, Jiajian; Chen, Zhiming; Zhou, Xiao; Chen, Mantong; Sun, Jianhong; Xu, Zhennan; Wang, Shaohong; Xu, Haixiong; Du, Zhifeng; Liu, Mingfa

doi:10.1139/bcb-2022-0291

Cited by 4 publications

(2 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…CKAP4 is an essential regulator of carcinogenesis, and its upregulation promotes the malignant progression of human gliomas and is associated with poor patient survival 40 . The potential tumour‐suppressor role of MTHFD2 has recently been reported in GBM cells via the inhibition of ERK1/2 phosphorylation 41 . Dysregulated chemokines in TME have been reported to favour the growth of tumours, including GBM 42 .…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Identification of T‐cell exhaustion‐related gene signature for predicting prognosis in glioblastoma multiforme

Liu,

Jin,

Liu

2023

J Cellular Molecular Medi

View full text Add to dashboard Cite

Glioblastoma multiforme (GBM) is a highly malignant primary brain tumour with a poor prognosis in adults. Identifying biomarkers that can aid in the molecular classification and risk stratification of GBM is critical. Here, we conducted a transcriptional profiling analysis of T‐cell immunity in the tumour microenvironment of GBM patients and identified two novel T cell exhaustion (TEX)‐related GBM subtypes (termed TEX‐C1 and TEX‐C2) using the consensus clustering. Our multi‐omics analysis revealed distinct immunological, molecular and clinical characteristics for these two subtypes. Specifically, the TEX‐C1 subtype had higher infiltration levels of immune cells and expressed higher levels of immune checkpoint molecules than the TEX‐C2 subtype. Functional analysis revealed that upregulated genes in the TEX‐C1 subtype were significantly enriched in immune response and signal transduction pathways, and upregulated genes in the TEX‐C2 subtype were predominantly associated with cell fate and nervous system development pathways. Notably, patients with activated T‐cell activity status in the TEX‐C1 subgroup demonstrated a significantly worse prognosis than those with severe T cell exhaustion status in the TEX‐C2 subgroup. Finally, we proposed a machine‐learning‐derived novel gene signature comprising 12 TEX‐related genes (12TexSig) to indicate tumour subtyping. In the TCGA cohort, the 12TexSig demonstrated the ability to accurately predict the prognosis of GBM patients, and this prognostic value was further confirmed in two independent external cohorts. Taken together, our results suggest that the TEX‐derived subtyping and gene signature has the potential to serve as a clinically helpful biomarker for guiding the management of GBM patients, pending further prospective validation.

show abstract

Section: Discussionmentioning

confidence: 99%

“…40 The potential tumour-suppressor role of MTHFD2 has recently been reported in GBM cells via the inhibition of ERK1/2 phosphorylation. 41 Dysregulated chemokines in TME have been reported to favour the growth of tumours, including GBM.…”

Section: Discussionmentioning

confidence: 99%

Identification of T‐cell exhaustion‐related gene signature for predicting prognosis in glioblastoma multiforme

Liu,

Jin,

Liu

2023

J Cellular Molecular Medi

View full text Add to dashboard Cite

show abstract

Exploring the Anticancer Potential of Furanpydone A: A Computational Study on its Inhibition of MTHFD2 Across Diverse Cancer Cell Lines

Alhawarri

2024

Cell Biochem Biophys

View full text Add to dashboard Cite

Identification of EMT-related gene signatures in glioblastoma to aid immunotherapy and prognosis

Diao

Ning

et al. 2023

Preprint

View full text Add to dashboard Cite

Objective: Glioblastoma (GBM) is the most aggressive malignancy of the central nervous system, with the lowest survival rate of malignant brain tumors at approximately 6%. Epithelial mesenchymal transition (EMT) is closely associated with tumor metastasis and drug resistance. Therefore, it is necessary to construct models consisting of EMT-related genes (ERGs) to forecast prognosis and benefit from immunotherapy in GBM patients. Methods: To identify differentially expressed genes (DEGs) for GBM by TCGA, CGGC, and GEO databases. To collect ERGs, databases called dbEMT2 and MSigDB were employed. Weighted gene co-expression network analysis (WGCNA) was used to find the core differentially expressed EMT-related genes (CDEERGs) at the junction of DEGs and ERGs. We developed the CDEERGs prognosis model (CPM) using a variety of bioinformatics analysis methods. The CPM was employed to determine risk scores for patients in TCGA-GBM dataset, and CGGC-325 and CGGC-693 datasets were utilized to externally validate the CPM's predictive ability. The differences in immunological traits and immunotherapeutic indicators between different groups were compared. Finally, it was evaluated if CPM may be helpful for determining whether immunotherapy would be appropriate for oncology patients. Results:IGFBP2, RGS4, AGTR1, CCL5, and LOXL1 were the five risk factors and ACTL6A and MTHFD2 were the two protective factors in the CPM. Patients with GBM were separated into high-risk and low-risk subgroups according on median risk scores. Low-risk subgroup in the TCGA-GBM dataset was significantly better than that in high-risk subgroup, and prognosis of patients in CGGC-325 and CGGC-693 datasets remained consistent with that in TCGA-GBM dataset. Risk scores and the expression of CD274 and PDCD1 were positively associated, and CD274 expression was higher in high-risk subgroup than in low-risk subgroup. The expression of numerous immunotherapy markers was different in high-risk and low-risk subgroups. Compared to the other prognostic models, the CPM has greater predictive power. We discovered that patients with low-risk scores may be better candidates for immunotherapy by calculating the risk scores of patients in the IMvigor210 dataset. Conclusion: The present study constructs CPMs that could be used to predict the prognosis of GBM patients as well as to screen for patients who can benefit from immunotherapy and to screen for CDEERGs that may provide new therapeutic targets for the treatment of GBM patients.

show abstract

MTHFD2 suppresses glioblastoma progression via the inhibition of ERK1/2 phosphorylation

Cited by 4 publications

References 39 publications

Identification of T‐cell exhaustion‐related gene signature for predicting prognosis in glioblastoma multiforme

Identification of T‐cell exhaustion‐related gene signature for predicting prognosis in glioblastoma multiforme

Exploring the Anticancer Potential of Furanpydone A: A Computational Study on its Inhibition of MTHFD2 Across Diverse Cancer Cell Lines

Identification of EMT-related gene signatures in glioblastoma to aid immunotherapy and prognosis

Contact Info

Product

Resources

About