A five‐CpG signature of microRNA methylation in non‐G‐CIMP glioblastoma

Kang, Enming; Yin, Anan; He, Yalong; Chen, Weijun; Etcheverry, Amandine; Aubry, Marc; Barnholtz‐Sloan, Jill S.; Mosser, Jean; Zhang, Wei; Zhang, Xiang

doi:10.1111/cns.13133

Cited by 6 publications

(7 citation statements)

References 40 publications

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“…Abnormal epigenetic alterations contribute to tumorigenesis and progression, as reflected in the latest guidelines for glioma classification ( 27 ). DNA methylation has been found to regulate microRNAs and predict overall survival in glioma ( 28 , 29 ). Recurrence of LGGs occurs mainly within a few centimeters of the resected cavity, even in complete tumor resection and adjuvant chemotherapy ( 30 , 31 ).…”

Section: Discussionmentioning

confidence: 99%

Integrated analysis of genome-wide DNA methylation and cancer-associated fibroblasts identified prognostic biomarkers and immune checkpoint blockade in lower grade gliomas

et al. 2023

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BackgroundCancer-associated fibroblasts (CAFs) are vital components of prominent cellular components in lower-grade gliomas (LGGs) that contribute to LGGs’ progression, treatment resistance, and immunosuppression. Epigenetic modification and immunity have significant implications for tumorigenesis and development.MethodsWe combined aberrant methylation and CAFs abundances to build a prognostic model and the impact on the biological properties of LGGs. Grouping based on the median CAFs abundances score of samples in the TCGA-LGGs dataset, differentially expressed genes and aberrantly methylated genes were combined for subsequent analysis.ResultsWe identified five differentially methylated and expressed genes (LAT32, SWAP70, GSAP, EMP3, and SLC2A10) and established a prognostic gene signature validated in the CGGA-LGGs dataset. Immunohistochemistry (IHC) and in vitro tests were performed to verify these expressions. The high-risk group increased in tumor-promoting immune cells and tumor mutational burden. Notably, risk stratification had different ICB sensitivities in LGGs, and there were also significant sensitivity differences for temozolomide and the other three novel chemotherapeutic agents.ConclusionOur study reveals characteristics of CAFs in LGGs, refines the direct link between epigenetics and tumor stroma, and might provide clinical implications for guiding tailored anti-CAFs therapy in combination with immunotherapy for LGGs patients.

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Section: Discussionmentioning

confidence: 99%

Integrated analysis of genome-wide DNA methylation and cancer-associated fibroblasts identified prognostic biomarkers and immune checkpoint blockade in lower grade gliomas

et al. 2023

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“…Yin et al (2019) selected five genes from differentially expressed genes in glioblastoma to construct a prognostic model with potential in the prognosis prediction of GBM. Kang et al (2019) developed a 5-CpG signature of miRNA methylation with prognostic values in non-G-CIMP GBM patients. It is thus obvious that the genes within the majority of constructed prognostic models are selected by differential expression analysis.…”

Section: Discussionmentioning

confidence: 99%

Identification of a Tumor Microenvironment-Related Eight-Gene Signature for Predicting Prognosis in Lower-Grade Gliomas

Long

et al. 2019

Front. Genet.

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Lower-grade gliomas (LrGG), characterized by invasiveness and heterogeneity, remain incurable with current therapies. Clinicopathological features provide insufficient information to guide optimal individual treatment and cannot predict prognosis completely. Recently, an increasing amount of studies indicate that the tumor microenvironment plays a pivotal role in tumor malignancy and treatment responses. However, studies focusing on the tumor microenvironment (TME) of LrGG are still limited. In this study, taking advantage of the currently popular computational methods for estimating the infiltration of tumor-associated normal cells in tumor samples and using weighted gene co-expression network analysis, we screened the co-expressed gene modules associated with the TME and further identified the prognostic hub genes in these modules. Furthermore, eight prognostic hub genes (ARHGDIB, CLIC1, OAS3, PDIA4, PARP9, STAT1, TAP2, and TAGLN2) were selected to construct a prognostic risk score model using the least absolute shrinkage and selection operator method. Univariate and multivariate Cox regression analysis demonstrated that the risk score was an independent prognostic factor for LrGG. Moreover, time-dependent ROC curves indicated that our model had favorable efficiency in predicting both short- and long-term prognosis in LrGG patients, and the stratified survival analysis demonstrated that our model had prognostic value for different subgroups of LrGG patients. Additionally, our model had potential value for predicting the sensitivity of LrGG patients to radio- and chemotherapy. Besides, differential expression analysis showed that the eight genes were aberrantly expressed in LrGG compared to normal brain tissue. Correlation analysis revealed that the expression of the eight genes was significantly associated with the infiltration levels of six types of immune cells in LrGG. In summary, the TME-related eight-gene signature was significantly associated with the prognosis of LrGG patients. They might act as potential prognostic biomarkers for LrGG patients, offer better stratification for future clinical trials, and be candidate targets for immunotherapy, thus deserving further investigation.

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“…DNA methylation orchestrates a vital role in tumorigenesis and tumor development via epigenetic regulation. DNA methylation may act as one of the mostly potential prognostic and predictive value for GBM 22,23 . DNA methylation patterns were associated with the mutations in IDH1 or IDH2 in lower grade gliomas, and mutations in histone 3 in pediatric high‐grade gliomas 24 .…”

Section: Discussionmentioning

confidence: 99%

LRRFIP1, an epigenetically regulated gene, is a prognostic biomarker and predicts malignant phenotypes of glioma

Bao

Qian

et al. 2022

CNS Neurosci Ther

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Aims Glioblastoma (GBM) is the most common malignant brain tumor with an adverse prognosis in the central nervous system. Traditional histopathological diagnosis accompanied by subjective deviations cannot accurately reflect tumor characteristics for clinical guidance. DNA methylation plays a critical role in GBM genesis. The focus of this project was to identify an effective methylation point for the classification of gliomas, the interactions between DNA methylation and potential epigenetic targeted therapies for clinical treatments. Methods Three online (TCGA, CGGA, and REMBRANDT) databases were employed in this study. T‐test, Venn analysis, univariate cox analysis, and Pearson's correlation analysis were adopted to screen significant prognostic methylation genes. Clinical samples were collected to determine the distributions of LRRFIP1 (Leucine Rich Repeat of Flightless‐1 Interacting Protein) protein by immunohistochemistry assay. Kaplan–Meier survival and Cox analysis were adopted to evaluate the prognostic value of LRRFIP1. Nomogram model was used to construct a prediction model. GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway were performed to explore functions and related mechanisms of LRRFIP1 in gliomas. Results Our results showed that 16 genes were negatively connected with their methylation level and correlated with clinical prognosis of GBM patients. Among them, LRRFIP1 expression showed the highest correlation with its methylation level. LRRFIP1 was highly expressed in WHO IV, mesenchymal, and IDH wild‐type subtype. LRRFIP1 expression was an independent risk factor for OS (overall survival) in gliomas. Conclusion LRRFIP1 is an epigenetically regulated gene and a potential prognostic biomarker for glioma. Our research may be beneficial to evaluate clinical efficacy, assess the prognosis, and provide individualized treatment for gliomas.

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A five‐CpG signature of microRNA methylation in non‐G‐CIMP glioblastoma

Cited by 6 publications

References 40 publications

Integrated analysis of genome-wide DNA methylation and cancer-associated fibroblasts identified prognostic biomarkers and immune checkpoint blockade in lower grade gliomas

Integrated analysis of genome-wide DNA methylation and cancer-associated fibroblasts identified prognostic biomarkers and immune checkpoint blockade in lower grade gliomas

Identification of a Tumor Microenvironment-Related Eight-Gene Signature for Predicting Prognosis in Lower-Grade Gliomas

LRRFIP1, an epigenetically regulated gene, is a prognostic biomarker and predicts malignant phenotypes of glioma

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