Dataset Information

High DKK3 expression related to immunosuppression was associated with poor prognosis in glioblastoma: machine learning approach.

ABSTRACT:

Background

Glioblastoma multiforme (GBM) is an aggressive malignant primary brain tumor. Wnt/β-catenin is known to be related to GBM stemness. Cancer stem cells induce immunosuppressive and treatment resistance in GBM. We hypothesized that Wnt/β-catenin-related genes with immunosuppression could be related to the prognosis in patients with GBM.

Methods

We obtained the clinicopathological data of 525 patients with GBM from the brain cancer gene database. The fraction of tumor-infiltrating immune cells was evaluated using in silico flow cytometry. Among gene sets of Wnt/β-catenin pathway, Dickkopf-3 (DKK3) gene related to the immunosuppressive response was found using machine learning. We performed gene set enrichment analysis (GSEA), network-based analysis, survival analysis and in vitro drug screening assays based on Dickkopf-3 (DKK3) expression.

Results

In analyses of 31 genes related to Wnt/β-catenin signaling, high DKK3 expression was negatively correlated with increased antitumoral immunity, especially CD8 + and CD4 + T cells, in patients with GBM. High DKK3 expression was correlated with poor survival and disease progression in patients with GBM. In pathway-based network analysis, DKK3 was directly linked to the THY1 gene, a tumor suppressor gene. Through in vitro drug screening, we identified navitoclax as an agent with potent activity against GBM cell lines with high DKK3 expression.

Conclusions

These results suggest that high DKK3 expression could be a therapeutic target in GBM. The results of the present study could contribute to the design of future experimental research and drug development programs for GBM.

SUBMITTER: Han MH

PROVIDER: S-EPMC9588473 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature

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Publications

High DKK3 expression related to immunosuppression was associated with poor prognosis in glioblastoma: machine learning approach.

Han Myung-Hoon MH Min Kyueng-Whan KW Noh Yung-Kyun YK Kim Jae Min JM Cheong Jin Hwan JH Ryu Je Il JI Won Yu Deok YD Koh Seong-Ho SH Myung Jae Kyung JK Park Ji Young JY Kwon Mi Jung MJ

Cancer immunology, immunotherapy : CII 20220523 12

<h4>Background</h4>Glioblastoma multiforme (GBM) is an aggressive malignant primary brain tumor. Wnt/β-catenin is known to be related to GBM stemness. Cancer stem cells induce immunosuppressive and treatment resistance in GBM. We hypothesized that Wnt/β-catenin-related genes with immunosuppression could be related to the prognosis in patients with GBM.<h4>Methods</h4>We obtained the clinicopathological data of 525 patients with GBM from the brain cancer gene database. The fraction of tumor-infil ...[more]

PMID: 35599254

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Dataset Information

High DKK3 expression related to immunosuppression was associated with poor prognosis in glioblastoma: machine learning approach.

Background

Methods

Results

Conclusions

Publications

High DKK3 expression related to immunosuppression was associated with poor prognosis in glioblastoma: machine learning approach.

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