Identification of purity and prognosis‐related gene signature by network analysis and survival analysis in brain lower grade glioma

Background. Glioma is the most common and lethal tumor in the central nervous system (CNS). More than 70% of WHO grade II/III gliomas were found to harbor isocitrate dehydrogenase (IDH) mutations which generated targetable metabolic vulnerabilities. Focusing on the metabolic vulnerabilities, some targeted therapies, such as NAMPT, have shown significant effects in preclinical and clinical trials. Methods. We explored the TCGA as well as CGGA database and analyzed the RNA-seq data of lower grade gliomas (LGG) with the method of weighted correlation network analysis (WGCNA). Differential expressed genes were screened, and coexpression relationships were grouped together by performing average linkage hierarchical clustering on the topological overlap. Clinical data were used to conduct Kaplan–Meier analysis. Results. In this study, we identified ACAA2 as a prognostic factor in IDH mutation lower grade glioma with the method of weighted correlation network analysis (WGCNA). The difference of ACAA2 gene expressions between the IDH wild-type (IDH-WT) group and the IDH mutant (IDH-MUT) group suggested that there may be different potential targeted therapies based on the fatty acid metabolic vulnerabilities, which promoted the personalized treatment for LGG patients.

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A Radiosensitivity Gene Signature and PD-L1 Status Predict Clinical Outcome of Patients With Lower Grade Glioma in the Cancer Genome Atlas Dataset

International Journal of Radiation Oncology*Biology*Physics ◽

10.1016/j.ijrobp.2017.06.786 ◽

2017 ◽

Vol 99 (2) ◽

pp. E82

Author(s):

B.S. Jang ◽

I.A. Kim

Keyword(s):

Clinical Outcome ◽

Gene Signature ◽

Cancer Genome ◽

The Cancer Genome Atlas ◽

Lower Grade ◽

Lower Grade Glioma ◽

Cancer Genome Atlas ◽

Genome Atlas

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A Novel Four-Gene Signature Associated With Immune Checkpoint for Predicting Prognosis in Lower-Grade Glioma

Frontiers in Oncology ◽

10.3389/fonc.2020.605737 ◽

2020 ◽

Vol 10 ◽

Author(s):

Youchao Xiao ◽

Gang Cui ◽

Xingguang Ren ◽

Jiaqi Hao ◽

Yu Zhang ◽

...

Keyword(s):

Overall Survival ◽

High Risk ◽

Risk Score ◽

Immune Checkpoint ◽

Risk Groups ◽

Gene Signature ◽

Low Risk ◽

Lower Grade ◽

Lower Grade Glioma ◽

Risk Patients

The overall survival of patients with lower grade glioma (LGG) varies greatly, but the current histopathological classification has limitations in predicting patients’ prognosis. Therefore, this study aims to find potential therapeutic target genes and establish a gene signature for predicting the prognosis of LGG. CD44 is a marker of tumor stem cells and has prognostic value in various tumors, but its role in LGG is unclear. By analyzing three glioma datasets from Gene Expression Omnibus (GEO) database, CD44 was upregulated in LGG. We screened 10 CD44-related genes via protein–protein interaction (PPI) network; function enrichment analysis demonstrated that these genes were associated with biological processes and signaling pathways of the tumor; survival analysis showed that four genes (CD44, HYAL2, SPP1, MMP2) were associated with the overall survival (OS) and disease-free survival (DFS)of LGG; a novel four-gene signature was constructed. The prediction model showed good predictive value over 2-, 5-, 8-, and 10-year survival probability in both the development and validation sets. The risk score effectively divided patients into high- and low- risk groups with a distinct outcome. Multivariate analysis confirmed that the risk score and status of IDH were independent prognostic predictors of LGG. Among three LGG subgroups based on the presence of molecular parameters, IDH-mutant gliomas have a favorable OS, especially if combined with 1p/19q codeletion, which further confirmed the distinct biological pattern between three LGG subgroups, and the gene signature is able to divide LGG patients with the same IDH status into high- and low- risk groups. The high-risk group possessed a higher expression of immune checkpoints and was related to the activation of immunosuppressive pathways. Finally, this study provided a convenient tool for predicting patient survival. In summary, the four prognostic genes may be therapeutic targets and prognostic predictors for LGG; this four-gene signature has good prognostic prediction ability and can effectively distinguish high- and low-risk patients. High-risk patients are associated with higher immune checkpoint expression and activation of the immunosuppressive pathway, providing help for screening immunotherapy-sensitive patients.

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Deconvolution and network analysis of IDH-mutant lower grade glioma predict recurrence and indicate therapeutic targets

Epigenomics ◽

10.2217/epi-2019-0137 ◽

2019 ◽

Vol 11 (11) ◽

pp. 1323-1333 ◽

Cited By ~ 3

Author(s):

Guangqi Li ◽

Yuanjun Jiang ◽

Xintong Lyu ◽

Yiru Cai ◽

Miao Zhang ◽

...

Keyword(s):

Network Analysis ◽

Characteristic Curve ◽

Therapeutic Targets ◽

Recurrence Risk ◽

The Cancer Genome Atlas ◽

High Recurrence Rate ◽

Lower Grade ◽

Cell Type ◽

Risk Area ◽

Lower Grade Glioma

Aim: IDH-mutant lower grade glioma (LGG) has been proven to have a good prognosis. However, its high recurrence rate has become a major therapeutic difficulty. Materials & methods: We combined epigenomic deconvolution and a network analysis on The Cancer Genome Atlas IDH-mutant LGG data. Results: Cell type compositions between recurrent and primary gliomas are significantly different, and the key cell type that determines the prognosis and recurrence risk was identified. A scoring model consisting of four gene expression levels predicts the recurrence risk (area under the receiver operating characteristic curve = 0.84). Transcription factor PPAR-α explains the difference between recurrent and primary gliomas. A cell cycle-related module controls prognosis in recurrent tumors. Conclusion: Comprehensive deconvolution and network analysis predict the recurrence risk and reveal therapeutic targets for recurrent IDH-mutant LGG.

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