The combination of two‐dimensional and three‐dimensional analysis methods contributes to the understanding of glioblastoma spatial heterogeneity

Yang, Run-Wei; Guo, Jinglin; Lin, Zhiying; Song, Haimin; Feng, Zhanpeng; Ou, Yichao; Zhou, Mingfeng; Li, Yaomin; Yi, Guozhong; Li, Ké; Li, Kaishu; Guo, Manlan; Wang, Xiran; Huang, Guanglong; Liu, Zhifeng; Qi, Songtao; Liu, Yawei

doi:10.1002/jbio.201900196

Cited by 12 publications

(12 citation statements)

References 76 publications

(115 reference statements)

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“…We used GEPIA (Gene Expression Profiling Interactive Analysis) ( Yang et al, 2020 ) for DEMs, and OncomiR ( Wong et al, 2018 ) and UALCAN ( Chandrashekar et al, 2017 ) for DEMIs, which considers the RNAseq and miRNAseq data from TCGA to see if the DEMs and DEMIs in our triad have a significant impact on survival. The Kaplan–Meier method ( Nagy et al, 2018 ) was used to plot the overall survival curve for the DEMIs.…”

Section: Methodsmentioning

confidence: 99%

Cancer-Associated circRNA–miRNA–mRNA Regulatory Networks: A Meta-Analysis

Khan

Jha

Panda

et al. 2021

Front. Mol. Biosci.

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Recent advances in sequencing technologies and the discovery of non-coding RNAs (ncRNAs) have provided new insights in the molecular pathogenesis of cancers. Several studies have implicated the role of ncRNAs, including microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and recently discovered circular RNAs (circRNAs) in tumorigenesis and metastasis. Unlike linear RNAs, circRNAs are highly stable and closed-loop RNA molecules. It has been established that circRNAs regulate gene expression by controlling the functions of miRNAs and RNA-binding protein (RBP) or by translating into proteins. The circRNA–miRNA–mRNA regulatory axis is associated with human diseases, such as cancers, Alzheimer’s disease, and diabetes. In this study, we explored the interaction among circRNAs, miRNAs, and their target genes in various cancers using state-of-the-art bioinformatics tools. We identified differentially expressed circRNAs, miRNAs, and mRNAs on multiple cancers from publicly available data. Furthermore, we identified many crucial drivers and tumor suppressor genes in the circRNA–miRNA–mRNA regulatory axis in various cancers. Together, this study data provide a deeper understanding of the circRNA–miRNA–mRNA regulatory mechanisms in cancers.

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Section: Methodsmentioning

confidence: 99%

Cancer-Associated circRNA–miRNA–mRNA Regulatory Networks: A Meta-Analysis

Khan

Jha

Panda

et al. 2021

Front. Mol. Biosci.

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“…HE and IHC staining assays were performed to examine the histological structure and the protein expression in GBM samples and normal brain tissue. These assays were performed as previously reported 66, 67 . Briefly, specimen slices were obtained from paraffin-embedded tissue blocks with a microtome (Leica, EG1150H, Wetlzar, Germany), deparaffinized and rehydrated.…”

Section: Methodsmentioning

confidence: 99%

Genetic characterization of stem cell-like cancer cell in the peri-tumoral regions and proliferative lymphocyte in the peripheral blood of patients with glioblastoma

Yang

Huang

Guo

et al. 2020

Preprint

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Recurrence is almost inevitable in glioblastoma (GBM) patients even after aggressive multimodal therapies. Most recurrences occur at the resection margin that is outside GBM's contrast-enhancing region (C region) of magnetic resonance imaging (MRI), and always left behind after surgery. Current knowledge about GBM cells outside C region is limited and thus impedes the post-operative therapy. In this study, single cell RNA-sequencing (scRNA-seq) was performed to dissect twelve GBM samples of C regions and outside C regions from four patients. We found a cluster of peculiar GBM cells outside C region different from the GBM cells with typical copy number variations (CNVs), which had only fragmental CNVs in chromosomes 1, 10, 11, 12 and 19, and were termed CNV 4 in this study. Furthermore, transcription factor-cofactor (TF-TCF) interaction network was constructed to uncover the transcriptional regulatory mechanism of GBM cells. The core signature of TF-TCF interaction network of CNV 4 GBM cells shows prognostic significance in GBM patients. Besides, sub-cluster of these peculiar GBM cells possess stem cell-like properties and have potential of tumorigenesis as revealed by in silico and in vitro analyses. To explore whether these peculiar GBM cells exist in the circulation of GBM patients, additional peripheral blood mononuclear cells (PBMC) samples from four GBM patients, three epilepsy patients and three healthy volunteers were analyzed by scRNA-seq. GBM cells were not found in PBMC, but peculiar proliferative immune cells were discovered in the PBMC of GBM patients but epilepsy patients and healthy volunteers. Collectively, these results indicated that peculiar GBM cells 3 outside C region are responsible for recurrence, and proliferative immune cells are specific to the PBMC of GBM patients. This study sheds light on the hidden and neglected parts of GBM, providing a new perspective to understand the nature of GBM recurrence, to develop monitoring strategy or promising immune therapy against GBM.Notably, most GBM cells of CNV patterns 1, 2 and 3 (77.44%, 95.73%, and 98.90%, respectively), whose CNVs were similar with classical GBM hallmarks, came from C regions, whereas GBM cells of CNV pattern 4 were found mainly in P and N regions (59.43% and 35.05%, respectively) (Chi-square test, P < 0.0001) ( Figure 1E). CNV patterns contribute to transcriptomic status of GBM cells in limited extent and show potential transitional relationshipsClassification of GBM cells according to CNV profiles was based on transcriptomic information, so we next analyzed the relationship between CNV patterns and transcriptomic sub-clusters. Totally, 6, 715 GBM cells were divided into 11 sub-clusters ( Figure 2A). Interestingly, sub-cluster 3 accounted for 93.93% of CNV pattern 3, and sub-cluster 5 accounted for 94.31% of CNV pattern 2, while CNV pattern 1 and 4 were made up of diverse sub-clusters in relatively balanced role in four CNV patterns, and MYC in CNV patterns 2, 3 and 4, while TP53 and E2F1 were specific to CNV pattern 1, SMAD2 s...

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“…The study also revealed that while these core and periphery GSCs shared a common ancestry, they both differ in tumorigenic potential, growth kinetics, and phenotype, illustrating the functional range of stemness present within the same tumor ( 162 ). Yang et al, used a combination of optical tissue clearing methods (CUBIC and iDISCO + ) and deep tissue imaging with two-photon microscopy to also reveal intratumoral spatial heterogeneity in GBM in terms of GSC marker expression, microvasculature, and immune contexture ( 163 ).…”

Section: Geospatial Distribution Of Gscs and Association With The Imm...mentioning

confidence: 99%

Heterogeneity of glioblastoma stem cells in the context of the immune microenvironment and geospatial organization

et al. 2022

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Glioblastoma (GBM) is an extremely aggressive and incurable primary brain tumor with a 10-year survival of just 0.71%. Cancer stem cells (CSCs) are thought to seed GBM’s inevitable recurrence by evading standard of care treatment, which combines surgical resection, radiotherapy, and chemotherapy, contributing to this grim prognosis. Effective targeting of CSCs could result in insights into GBM treatment resistance and development of novel treatment paradigms. There is a major ongoing effort to characterize CSCs, understand their interactions with the tumor microenvironment, and identify ways to eliminate them. This review discusses the diversity of CSC lineages present in GBM and how this glioma stem cell (GSC) mosaicism drives global intratumoral heterogeneity constituted by complex and spatially distinct local microenvironments. We review how a tumor’s diverse CSC populations orchestrate and interact with the environment, especially the immune landscape. We also discuss how to map this intricate GBM ecosystem through the lens of metabolism and immunology to find vulnerabilities and new ways to disrupt the equilibrium of the system to achieve improved disease outcome.

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The combination of two‐dimensional and three‐dimensional analysis methods contributes to the understanding of glioblastoma spatial heterogeneity

Cited by 12 publications

References 76 publications

Cancer-Associated circRNA–miRNA–mRNA Regulatory Networks: A Meta-Analysis

Cancer-Associated circRNA–miRNA–mRNA Regulatory Networks: A Meta-Analysis

Genetic characterization of stem cell-like cancer cell in the peri-tumoral regions and proliferative lymphocyte in the peripheral blood of patients with glioblastoma

Heterogeneity of glioblastoma stem cells in the context of the immune microenvironment and geospatial organization

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