Molecular subtyping of glioblastoma based on immune-related genes for prognosis

Chen, Xueran; Fan, Xiaoqing; Zhao, Chenggang; Zhao, Zhiyang; Hu, Lizhu; Wang, Delong; Wang, Ruiting; Fang, Zhiyou

doi:10.1038/s41598-020-72488-4

Cited by 17 publications

(15 citation statements)

References 45 publications

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“…The IRGs, typically represented by programmed death 1/programmed cell death-ligand 1 (PD1/PD-L1), have attracted much attention in tumor immunotherapy in BC [ 6 ]. In addition, the progression of BC has been implied to be closely related to the tumor immunophenotype [ 7 ]. Hence, it is not a stretch to infer that a robust, reliable, and individualized IRG-based classifier can be highly valuable for predicting BC outcomes.…”

Section: Introductionmentioning

confidence: 99%

Development of a prognostic signature based on immune-related genes and the correlation with immune microenvironment in breast cancer

Dong

Cui

Wang

et al. 2022

Aging

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Breast cancer (BC) is an inflammatory tumor caused by a variety of pathological factors, and is still the most common malignant tumor in women. Immune-related genes (IRGs) play a prominent role in the oncogenesis and progression of BC, and are of tumor-specific expression patterns that would benefit the prognosis evaluation. However, there were no systematic studies concerning the possibilities of IRGs in BC prognosis. In this study, the Cancer Genome Atlas (TCGA) database was used to integrate the expression profiles of IRG with the overall survival (OS) rate of 1039 breast cancer patients. The Cox regression analysis was used to predict the survival-related IRGs in BC. Then, we successfully screened a total of 6 IRGs, including PSME2, ULBP2, IGHE, SCG2, SDC1, and SSTR1, and accordingly constructed a prognosis prediction model of BC. Based on the IRG-related model, the BC patients were divided into high- and low-risk groups, and the association between the prognostic model and tumor immune microenvironment (TME) was further explored. The prognostic model reflected the infiltration of various immune cells. Moreover, the low-risk group was found to be with higher immunophenoscore and distinct mutation signatures compared with the high-risk group. The histological validation showed that SDC1, as well as M2 macrophage biomarker CD206, were both of higher abundance in BC samples of high-risk patients, compared with those of low-risk patients. Our results identify the clinically significant IRGs and demonstrate the importance of the IRG-based immune prognostic model in BC monitoring, prognosis prediction, and therapy.

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Section: Introductionmentioning

confidence: 99%

Development of a prognostic signature based on immune-related genes and the correlation with immune microenvironment in breast cancer

Dong

Cui

Wang

et al. 2022

Aging

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“…The prognosis of GBM is very poor; only about 5 percent GBM patients could survive more than 5 years (Delgado-Lopez and Corrales-Garcia, 2016). Currently, therapeutic options for GBM include surgery alone or adjuvant radio/chemotherapy in combination with surgical resection (Chen et al, 2020). Surgical resection is ineffective because cancer cells may have infiltrated the surrounding tissues or metastasized.…”

Section: Discussionmentioning

confidence: 99%

A Novel Extracellular Matrix Gene-Based Prognostic Model to Predict Overall Survive in Patients With Glioblastoma

et al. 2022

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Background: Glioblastoma (GBM), one of the most prevalent brain tumor types, is correlated with an extremely poor prognosis. The extracellular matrix (ECM) genes could activate many crucial pathways that facilitate tumor development. This study aims to provide online models to predict GBM survival by ECM genes.Methods: The associations of ECM genes with the prognosis of GBM were analyzed, and the significant prognosis-related genes were used to develop the ECM index in the CGGA dataset. Furthermore, the ECM index was then validated on three datasets, namely, GSE16011, TCGA-GBM, and GSE83300. The prognosis difference, differentially expressed genes, and potential drugs were obtained. Multiple machine learning methods were selected to construct the model to predict the survival status of GBM patients at 6, 12, 18, 24, 30, and 36 months after diagnosis.Results: Five ECM gene signatures (AEBP1, F3, FLNC, IGFBP2, and LDHA) were recognized to be associated with the prognosis. GBM patients were divided into high– and low–ECM index groups with significantly different overall survival rates in four datasets. High–ECM index patients exhibited a worse prognosis than low–ECM index patients. Four small molecules (podophyllotoxin, lasalocid, MG-262, and nystatin) that might reduce GBM development were predicted by the Cmap dataset. In the independent dataset (GSE83300), the maximum values of prediction accuracy at 6, 12, 18, 24, 30, and 36 months were 0.878, 0.769, 0.748, 0.720, 0.705, and 0.868, respectively. These machine learning models were provided on a publicly accessible, open-source website (https://ospg.shinyapps.io/OSPG/).Conclusion: In summary, our findings indicated that ECM genes were prognostic indicators for patient survival. This study provided an online server for the prediction of survival curves of GBM patients.

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“…However, in principle, the host’s innate and adapted immune system shield against pro-carcinogen immunological activities. Interestingly, glioma patients seem to reveal a systematic immunodeficiency [ 106 ], and glioma itself can induce a vast immune response [ 107 ]. The prognosis for glioma is associated with the presence of immune cells in the TME [ 108 ]; for example, regulatory T cells (Tregs) are reported to be associated with a poor prognosis in cancer [ 107 ].…”

Section: The Tme Immune-state Is Receptive To the Molecular Heterogen...mentioning

confidence: 99%

“…Interestingly, glioma patients seem to reveal a systematic immunodeficiency [ 106 ], and glioma itself can induce a vast immune response [ 107 ]. The prognosis for glioma is associated with the presence of immune cells in the TME [ 108 ]; for example, regulatory T cells (Tregs) are reported to be associated with a poor prognosis in cancer [ 107 ]. Nevertheless, previous infections seem to aid glioma occurrence, as gliomas, but not normal brain tissue of the same patients, are associated with human cytomegalic virus nucleic acids and proteins [ 109 ].…”

Section: The Tme Immune-state Is Receptive To the Molecular Heterogen...mentioning

confidence: 99%

Glioma: molecular signature and crossroads with tumor microenvironment

Barthel¹,

Hadamitzky²,

Dammann³

et al. 2021

Cancer Metastasis Rev

107

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In patients with glioblastoma, the average survival time with current treatments is short, mainly due to recurrences and resistance to therapy. This insufficient treatment success is, in large parts, due to the tremendous molecular heterogeneity of gliomas, which affects the overall prognosis and response to therapies and plays a vital role in gliomas’ grading. In addition, the tumor microenvironment is a major player for glioma development and resistance to therapy. Active communication between glioma cells and local or neighboring healthy cells and the immune environment promotes the cancerogenic processes and contributes to establishing glioma stem cells, which drives therapy resistance. Besides genetic alterations in the primary tumor, tumor-released factors, cytokines, proteins, extracellular vesicles, and environmental influences like hypoxia provide tumor cells the ability to evade host tumor surveillance machinery and promote disease progression. Moreover, there is increasing evidence that these players affect the molecular biological properties of gliomas and enable inter-cell communication that supports pro-cancerogenic cell properties. Identifying and characterizing these complex mechanisms are inevitably necessary to adapt therapeutic strategies and to develop novel measures. Here we provide an update about these junctions where constant traffic of biomolecules adds complexity in the management of glioblastoma. Graphical abstract

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Molecular subtyping of glioblastoma based on immune-related genes for prognosis

Cited by 17 publications

References 45 publications

Development of a prognostic signature based on immune-related genes and the correlation with immune microenvironment in breast cancer

Development of a prognostic signature based on immune-related genes and the correlation with immune microenvironment in breast cancer

A Novel Extracellular Matrix Gene-Based Prognostic Model to Predict Overall Survive in Patients With Glioblastoma

Glioma: molecular signature and crossroads with tumor microenvironment

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