A New Epigenetic Model to Stratify Glioma Patients According to Their Immunosuppressive State

Polano, Maurizio; Fabbiani, Emanuele; Adreuzzi, Eva; Cintio, Federica Di; Bedon, Luca; Gentilini, Davide; Mongiat, Maurizio; Ius, Tamara; Arcicasa, M.; Škrap, Miran; Bo, Michele Dal; Toffoli, Giuseppe

doi:10.3390/cells10030576

Cited by 9 publications

(6 citation statements)

References 69 publications

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“…There is a significant difference in epigenetic profiles within various grades of glioma as well. Promoter hypermethylation is usually associated with tumour suppresser gene inhibition while hypomethylation often activates oncogenes [39,40]. Hence, understanding epigenetic modifications in tumour cells can help in the early diagnosis and development of therapeutic strategies by influencing genes and transcription factors helping in stem cell maintenance and immune suppression.…”

Section: Discussionmentioning

confidence: 99%

Integrative Analysis to Identify Genes Associated with Stemness and Immune Infiltration in Glioblastoma

Warrier

Agarwal

Kumar

2021

Cells

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It is imperative to identify the mechanisms that confer stemness to the cancer cells for more effective targeting. Moreover, there are not many studies on the link between stemness characteristics and the immune response in tumours. Therefore, in the current study involving GBM, we started with the study of BIRC5 (one of the rare genes differentially expressed in normal and cancer cells) and CXCR4 (gene involved in the survival and proliferation of CSCs). Together, these genes have not been systematically explored. We used a set of 27 promoter methylated regions in GBM. Our analysis showed that four genes corresponding to these regions, namely EOMES, BDNF, HLA-A, and PECAM1, were involved with BIRC5 and CXCR4. Interestingly, we found EOMES to be very significantly involved in stemness and immunology and it was positively correlated to CXCR4. Additionally, BDNF, which was significant in methylation, was negatively correlated to BIRC5.

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Section: Discussionmentioning

confidence: 99%

Integrative Analysis to Identify Genes Associated with Stemness and Immune Infiltration in Glioblastoma

Warrier

Agarwal

Kumar

2021

Cells

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“…IL8 [11] Mixed effect: SNX10 [14,23] MARS, CHI3L1 [45,43] RANBP17 [45] M-effect: JMJD1A, OLIG2 [3] CBR1 [22] FPRL1 [26] SYTL2 [9] P2RY6, CCR1 [34] TLR2 [36] F3 [13] SECTM1 [35] GPRC5A [48] PIP4K2C [1] APOC2 [27] TKTL1 [16] ZNF135, PCYOX1 [8] LSP1, ZCCHC2, HPGD [31] Note: Those without citations are new genes needed to be further studied. of RANBP17, CHI3L1, and SNX10 are with prognosis of GBM and their methylation status are suggested to play an important role in GBM or other cancer.…”

Section: M-effectmentioning

confidence: 99%

Bayesian shrinkage models for integration and analysis of multiplatform high‐dimensional genomics data

Xue,

Chakraborty,

Dey

2024

Statistical Analysis

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With the increasing availability of biomedical data from multiple platforms of the same patients in clinical research, such as epigenomics, gene expression, and clinical features, there is a growing need for statistical methods that can jointly analyze data from different platforms to provide complementary information for clinical studies. In this paper, we propose a two‐stage hierarchical Bayesian model that integrates high‐dimensional biomedical data from diverse platforms to select biomarkers associated with clinical outcomes of interest. In the first stage, we use Expectation Maximization‐based approach to learn the regulating mechanism between epigenomics (e.g., gene methylation) and gene expression while considering functional gene annotations. In the second stage, we group genes based on the regulating mechanism learned in the first stage. Then, we apply a group‐wise penalty to select genes significantly associated with clinical outcomes while incorporating clinical features. Simulation studies suggest that our model‐based data integration method shows lower false positives in selecting predictive variables compared with existing method. Moreover, real data analysis based on a glioblastoma (GBM) dataset reveals our method's potential to detect genes associated with GBM survival with higher accuracy than the existing method. Moreover, most of the selected biomarkers are crucial in GBM prognosis as confirmed by existing literature.

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“…Several mutation-driver genes (Mut-driver-genes) have been characterized in cancer cells so far (e.g., KRAS , BRAF , MYC , TP53 ), however vsDPGs have gained attention in the process of carcinogenesis due to their tumorigenic potential [ 145 , 146 , 147 ]. VENTX and NANOG were found highly expressed in brain (glioma/glioblastoma) [ 148 , 149 , 150 ], pancreatic [ 151 , 152 , 153 ] renal [ 154 , 155 ], esophageal [ 156 , 157 ] and testicular cancers [ 28 , 158 , 159 ]. Interestingly, VENTX and NANOG share activity in hematopoiesis by repressing the genes responsible for terminal differentiation (e.g., TAL1 , KLF1 ) [ 160 , 161 ], as well as promoting leukemia [ 161 , 162 , 163 ].…”

Section: Developmental Potential Guardians In Human Diseasesmentioning

confidence: 99%

Vertebrate Cell Differentiation, Evolution, and Diseases: The Vertebrate-Specific Developmental Potential Guardians VENTX/NANOG and POU5/OCT4 Enter the Stage

Ducos

Bensimon

Scerbo

2022

Cells

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During vertebrate development, embryonic cells pass through a continuum of transitory pluripotent states that precede multi-lineage commitment and morphogenesis. Such states are referred to as “refractory/naïve” and “competent/formative” pluripotency. The molecular mechanisms maintaining refractory pluripotency or driving the transition to competent pluripotency, as well as the cues regulating multi-lineage commitment, are evolutionarily conserved. Vertebrate-specific “Developmental Potential Guardians” (vsDPGs; i.e., VENTX/NANOG, POU5/OCT4), together with MEK1 (MAP2K1), coordinate the pluripotency continuum, competence for multi-lineage commitment and morphogenesis in vivo. During neurulation, vsDPGs empower ectodermal cells of the neuro-epithelial border (NEB) with multipotency and ectomesenchyme potential through an “endogenous reprogramming” process, giving rise to the neural crest cells (NCCs). Furthermore, vsDPGs are expressed in undifferentiated-bipotent neuro-mesodermal progenitor cells (NMPs), which participate in posterior axis elongation and growth. Finally, vsDPGs are involved in carcinogenesis, whereby they confer selective advantage to cancer stem cells (CSCs) and therapeutic resistance. Intriguingly, the heterogenous distribution of vsDPGs in these cell types impact on cellular potential and features. Here, we summarize the findings about the role of vsDPGs during vertebrate development and their selective advantage in evolution. Our aim to present a holistic view regarding vsDPGs as facilitators of both cell plasticity/adaptability and morphological innovation/variation. Moreover, vsDPGs may also be at the heart of carcinogenesis by allowing malignant cells to escape from physiological constraints and surveillance mechanisms.

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A New Epigenetic Model to Stratify Glioma Patients According to Their Immunosuppressive State

Cited by 9 publications

References 69 publications

Integrative Analysis to Identify Genes Associated with Stemness and Immune Infiltration in Glioblastoma

Integrative Analysis to Identify Genes Associated with Stemness and Immune Infiltration in Glioblastoma

Bayesian shrinkage models for integration and analysis of multiplatform high‐dimensional genomics data

Vertebrate Cell Differentiation, Evolution, and Diseases: The Vertebrate-Specific Developmental Potential Guardians VENTX/NANOG and POU5/OCT4 Enter the Stage

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