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Table of Contents
Year : 2022  |  Volume : 5  |  Issue : 1  |  Page : 12-19

Glioma stem cells and their microenvironment: A narrative review on docking and transformation

1 Department of Neurosurgery, The Affiliated Suzhou Science and Technology Town Hospital of Nanjing Medical University, Suzhou, Jiangsu Province, China
2 Department of Neurosurgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province, China
3 Department of Neurosurgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
4 Department of Neurosurgery and Brain Tumor Research Laboratory, Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu Province, China

Date of Submission30-Jan-2022
Date of Decision10-Feb-2022
Date of Acceptance21-Feb-2022
Date of Web Publication30-Mar-2022

Correspondence Address:
Dr. Yaodong Zhao
No. 650 New Songjiang Road, Songjiang District, Shanghai 201600; Prof. Qiang Huang, No. 1055 Sanxiang Road, Gusu District, Suzhou 215004, Jiangsu Province
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/glioma.glioma_5_22

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Significant new progress was made 10 years ago in the hypothesis that neuroglial cells, neural stem cells, and glioma stem cells (GSCs) depend on the tumor microenvironment (TME) transformation: (1) Because GSCs also have heterogeneity, they are a state, not an entity. (2) The importance of the border niche among many tumor niches is emphasized because it is a shelter for tumor resistance to radiotherapy and chemotherapy. (3) The plasticity of GSCs and TME cells allows TME cells to become GSC-initiating cells. (4) Future development will entail a close interaction between high-throughput molecular biology and artificial intelligence. In this review, we summarize recent advances in GSCs and their microenvironment from the following three aspects: the constantly updated of concept of stem cells, the concept of TME and niche, and the plasticity of GSCs and TME cells.

Keywords: Cytomegalovirus infects glioma, embryonic stem cell, epithelial-stromal transformation of glioma, glioblastoma, glioma, glioma microenvironment, glioma stem cells, glioma stem cell niche, mesenchymal stem cells, remodeling of glioma microenvironment

How to cite this article:
Zhu W, Chen H, Yan K, Wu J, Zhao Y, Huang Q. Glioma stem cells and their microenvironment: A narrative review on docking and transformation. Glioma 2022;5:12-9

How to cite this URL:
Zhu W, Chen H, Yan K, Wu J, Zhao Y, Huang Q. Glioma stem cells and their microenvironment: A narrative review on docking and transformation. Glioma [serial online] 2022 [cited 2022 Dec 1];5:12-9. Available from: http://www.jglioma.com/text.asp?2022/5/1/12/341378

  Introduction Top

The neuro-oncology community has paid much attention to glioma stem cell (GSC) and tumor microenvironment (TME) studies since GSCs were successfully cloned from fresh solid tumor tissues,[1],[2],[3],[4] and the number of these studies has grown rapidly in China.[5] Our studies of the glioma system[6] showed that although gliomas are derived from neural stem cells (NSCs), they are completely different from NSCs at the differentiation level and are always in a de-differentiation state.[2] This viewpoint was contrary to the report by Singh et al.[7] at that time. In the current view,[8] this situation is associated with insufficient understanding of the intersections between neuroscience and glioma biology; to be precise, it is caused by insufficient understanding of the TME. Ten years ago, we proposed the hypothesis that the transformation of neuroglial cells (NGCs), NSCs, and NGCs depended on the TME.[9] Although this transformation could also be interpreted in terms of intersections, much progress has been made to date. This article explains three aspects of this progress.

  Retrieval Strategy Top

Electronic articles were collected from PubMed and GeenMedical databases according to the keywords of this paper. The first is to screen the titles of more than 1000 articles collected and browse the abstracts for those that are far related or obviously inconsistent with the topic. A total of 400 articles were included that met the title or abstract. Then, the full texts of the included articles were allocated to three authors to read, and the articles were divided into three subtopics for centralized synthesis: (1) The constantly updated concept of stem cells; (2) The concept of the TME and niche; (3) The plasticity of GSCs and TME cells. Then summarized by the first author, and comprehensively reviewed and cooperated by the corresponding author. A total of 1013 articles from 2002 to 2022 were finally adopted, and the literature in the past 5 years accounted for 51% from 2017 to 2021.

  Continuous Update on the Concept of Stem Cells Top

The presence of stem cells is a state rather than an entity. The theory of TSCs can be traced backed to the mid-19th century. After several changes in this theory, the presence and characteristics of TSCs were confirmed in 2012.[10] However, the results of numerous in-depth studies prompted us to amend this concept.[11] It should be started from embryonic stem cells (ESCs). The study of Efroni et al.[12] indicated that mouse ESCs had chromatin remodeling, abnormally high expression levels of genes and transcribed proteins, and low expression levels of tissue-specific genes and differentiation genes; in addition, the gene expression profile became obviously discontinuous in differentiated cells. Importantly, these features exhibited very strong randomness. In addition, there was a very unique wrapping feature, and DNA from differentiated cells was present in open chromatin. Furthermore, the DNA of ESCs had enriched epigenetic signature proteins that were associated with high gene expression, making chromatin overly dense.[13],[14] These characteristics were discovered in subsequent in vitro cultures of hematopoietic stem/progenitor cells and were mainly associated with the stem cell antigen (Sca-1) and the cell environment.[15],[16],[17] Therefore, ESCs are only a cell state and cannot be defined as solid cells with stable phenotypes. Transcription factors that were considered specific to ECSs, such as pluripotent Nanog, the so-called stemness protein, usually fluctuated between high and low levels of expression and thus exhibited high heterogeneity and were sometimes present with genes in other subpopulations[18],[19],[20] rather than being fixed and unchanged. A detailed study of Nanog showed that pluripotency seemed to rely on random fluctuations in the expression of a group of genes and that regulation of the network function might be the reason for this heterogeneity.[21] In addition to Nanog, SOX1 and Sox2, both of which belong to GSCs stemness genes, were reported by Kanwore et al.[22] and Wang et al.,[23] respectively: they are highly expressed in glioblastoma cells and can promote GSC proliferation and invasion under hypoxic conditions, HIF1α/HIF2α through Sox2 Induction of dedifferentiation of glioma cells into GSCs. Overall, ESCs have generalized and highly random gene expression and their observed heterogeneity is the result of combinations of homogeneous subgroups that should have been separated. However, these heterogeneous subgroups have varying differentiation and dedifferentiation tendencies.

All of the above findings contradict the TSC theory that prevailed in the early 21st century. At that time, precisely regulated stem cells were thought to only express a few specific genes in a homogeneous way and to respond to differentiation signals in a homogeneous way. Singh et al.[7] reported that GSCs could only differentiate into terminal cells, similar to a cluster of differentiation (CD) 133+ NSCs; that report was representative of this view. In fact, in addition to ESC studies, the energy metabolism theory does not support that hypothesis. A series of experiments indicated that pluripotency has many links with the metabolic activities of cells.[24],[25] For example, glycolysis stimulated by chemical agents or hypoxia[26],[27] in cell cultures could dedifferentiate differentiated cells into pluripotent cells; of these, the highly dynamic chromatin state was associated with chromatin modification through the regulation of cell metabolism by acetyl-CoA, S-adenosyl methionine, or α-ketoglutarate.[28],[29] Sharma et al.[30] also recently reported that glioma cell metabolism is associated with NAMPT, a key cofactor for multiple biological processes including cellular redox reactions, energy metabolism, and biosynthesis. Sørensen and Kristensen[31] proposes that tumor-associated CD204+ microglia/macrophages are enriched in perivascular and peri-necrotic niches and correlate with the interleukin-6-rich inflammatory signature in glioblastoma. Overall, the permissive chromatin state in stem cells means that they cannot be clearly defined at a single-cell level. Functional pluripotency is spontaneously produced by dynamic variations that are intrinsically linked to the pluripotent state. This variability is temporally and spatially controlled during development in the organism. However, these limitations are not present in in vitro cultured cells. From this angle, the highly variable genome in stem cells is expected to provide multiple developmental options. In contrast, once the constraints are locked during differentiation, GSCs can develop in a benign direction, consistent with our previous hypothesis[5] showing that GSCs could differentiate into NGSs in a specific TME.

We support the view by Suva and Tirosh[32] that the GSC definition needs to continue to be updated, because: (1) GSCs, defined by their self-renewal and tumor proliferation core functions, are a feature shared by many cancer cells, blurring the distinction between the malignant phenotype of cancer cells and the GSC signature; (2) most of these definitions have been tested in animal models, but may not fit perfectly with clinical cases; (3) current functional definition methods cannot be distinguished between genetic alterations and epigenetic states; (4) the plethora of surface markers used to isolate GSCs is likely to identify subpopulations of cells rather than GSCs that are upstream of differentiation. It must be noted that the classification of GBM into proneural, neural, classical, and mesenchymal concepts as defined in the Cancer Genome Atlas, and it has been challenged to consider each tumor as belonging to one specific subtype.[33] While the GSC concept was further updated to (1) neural progenitor cell-like, (2) oligodendrocyte progenitor cell (OPC)-like, (3) astrocyte like, and (4) mesenchymal-like states by informatics studies such as scRNA Seq.

  The Concept of Tumor Microenvironment and Niche Top

The concept of the niche was first used to describe the location of NSCs in the ventricular subependymal zone.[34] With the development of GSC research, the niche has been found to be closely associated with TME. New research results continue to emerge, and the new concept of the border niche has been developed. There are already many reports regarding the GSC niche. It is generally believed that tumors are derived from TSCs, and TSCs rely on the secretion of cytokines by cells, such as vascular endothelial cells, to maintain their stem cell-like state. Therefore, this niche is generally called the perivascular niche.[35],[36],[37],[38] Hide et al.[39],[40],[41] proposed a novel perspective on niches at the tumor border, describing a phenomenon in which despite the complete resection of lesions revealed by enhanced magnetic resonance imaging, tumor recurrence usually occurs, and cells often accumulate in the white matter around the residual cavity, including oligodendroglioma-like cells (OLCs) such as OPCs and macrophages (MPs)/microglia. Glioblastoma multiforme (GBM) can still recur after total surgical resection, radiotherapy, and chemotherapy because cells in the border niche not only protect the presence of TSCs but also promote regeneration. This article mainly described the role of the following cells in the border niche.

Bone marrow-derived oligodendrocytes from OPCs maintain the density of OPCs through local proliferation in the brain, which plays a role in the regulation of neuronal activities and in the regulation of axonal metabolism through myelination.[42],[43] In general, under physiological conditions, the myelin exchange rate of OPCs is very high and very stable in the white matter that makes up the blood-brain barrier. However, during the development of GBM, relevant cells extensively and rapidly migrate to the white matter to create a new butterfly-shaped mass, which is evidence of the invasion of GBM cells to the contralateral hemisphere through the commissural fibers of the corpus callosum. Therefore, GBM cells preferentially distribute along myelinated axons of OPCs and use them as pathways and scaffolds to form new shelters for TSCs and defend them from many treatments. Furthermore, OPCs have been reported to be the source of GBM cells.[44],[45] However, the potential for OPCs to support GBM cells was first reported by Hide et al.[41] They showed that the spreading mode of GBM was not always consistent with the vascular network and that there was a border niche that was particularly associated with OPCs.

In fact, there are other supporting cells in the GBM microenvironment; for example, microglia (MG), MPs, astrocytes (ASs), pericytes, and T-cells all play a role in the promotion of GBM proliferation, migration, and recurrence.[35],[36],[37],[46] However, the TME formed by these cells is mainly inside the tumor and is not in the border area. The relationships between these cells and border niches are still not clear; thus, these cells only represent candidate cells in border niches. The analysis is presented below.


MG-derived factors can influence the attractiveness, proliferation, differentiation, and myelination of OPCs. In addition, MG can enhance the differentiation of neural stem/progenitor cells into OLCs.[47],[48],[49] Under normal conditions, MG only migrates and proliferates in the brain. However, under pathological conditions, MPs invade the brain parenchyma through the damaged blood-brain barrier and produce inflammatory responses that are functionally similar to those of resident MG. GBM is also a type of inflammation. Myeloid MPs and resident MG are called glioma-associated MPs/MG and account for 85% of inflammatory cells; of these, resident MG account for approximately 15%.[50] It should be noted that bone marrow-derived MPs preferentially localize to perivascular areas and resident MG mainly localizes to peritumoral regions.[50],[51] Since border niches are present in the peritumoral regions and because tumorous abnormal blood vessels are not fully developed, glioma-associated MPs/MG in border niches are mainly MG; however, the specific mechanism underlying this phenomenon is still not clear.


ASs in TME promote GBM cell proliferation, migration, and drug resistance.[52],[53] It should be noted that the messenger RNA (mRNA) expression profile of GBM-associated ASs (tumor-associated ASs) is different from that of normal Ass.[54] In biopsies from GBMs, ASs surrounding secret chemokine (C-C motif) ligand 2 and chemokine (C-C motif) ligand 2 attract MPs and regulatory T cells to reduce immune responses.[55] Furthermore, ASs seem to have indirect roles in the formation of border niches because they affect OPC proliferation and remyelination[56],[57] but not OLCs. However, ASs have low proliferation rates and low potential to migrate to lesions.[58] Compared to ASs, OPCs and MG play a direct role in lesions. These data indicate that compared to ASs, OPCs, and MG, OLCs play a more critical role in the formation of border niches.


Neuronal activity promotes mitosis in OPCs and increases myelination in cortical and subcortical deep white matter.[59] The study of Mitew et al.[60] showed that noninvasive pharmacogenetic stimulation of neuronal activity robustly increased the proliferation and differentiation of OPCs in the corpus callosum, and decreased neuronal activity could reduce myelination. Therefore, neuronal activity directly influences OPC migration and proliferation and directly promotes GBM cell survival. The neuronal regulation-dependent synaptic adhesion molecule neuroligin-3 also participates in molecular regulation. It can promote GBM proliferation through the PI3K-mTOR pathway.[61],[62] These data indicate that neurons and molecules involved in the neuron-regulated proliferation of OPCs can promote GBM survival and recurrence. Circulating glioma cells (circulating tumor cells [CTCs]): CTCs have been discovered in the peripheral blood[63],[64] and cerebrospinal fluid[65],[66] of GBM patients in recent years. CTCs exhibit stem cell-like properties and have high tumorigenicity in transgenic mouse models. It has been confirmed[67] that CTCs are localized in the border area of the primary tumor to cause local micrometastasis and GBM heterogeneity. It is undeniable that the anatomical structure of blood vessels is one determining factor in identifying the precipitation location of CTCs. However, the influence of border niches formed by OPCs and MC cannot be ignored because niches are very important to CTC precipitation and amplification.

Based on the importance of TME, Cheng et al.[68] reviewed new strategies for modifying the TME in the treatment of malignant tumors. Unfortunately, the TME of GSC is different from other cancers. The proposed changes in a hypoxic environment, angiogenesis and cellular immunity, and bevacizumab, PD1/PDL1 are unsatisfactory and need further study. It is worth pointing out that Parmigiani proposed that in the TME, nonmalignant cells can acquire a senescent phenotype and then promote tumor progression, which should be paid attention to.[69]

  Plasticity of Glioma Stem Cells and Tumor Microenvironment Cells Top

The central component of cellular plasticity theory in the review of Elshamy and Duhé[70] in 2013 is the change of tumor cell differentiation from a unidirectional process into a plastic (bidirectional) process. During this process, tumor cells can dedifferentiate into more primitive, stem-like phenotypes. However, this is only a dedifferentiation process. It differs from our previous hypothesis that tumor stem cells (TSCs) and NGCs can transform into one another.[5],[9] In fact, research has shown that in addition to the presence of plastic TSCs in the TME, OPCs and MPs can also be changed into TSCs in a process that can be considered a transformation between NGCs and TSCs. The details are described below.

Liu et al.[44] and Galvao et al.[45] used the Cre transgenic model to induce p53 and neurofibromatosis 1 (NF1) mutations in OPCs to develop glioma. By tracking dynamic changes in these cells in the pretransformation stage, the results showed that transformation was a multistep process. The proliferation status of OPCs was closely associated with whether they could develop malignant transformation. Although all adult OPCs retained proliferation potential, the proliferation rate was very slow. Sixty-day-old mice only had one division within 36 days, and 6-day-old mice had one division every 4 days.[71],[72] In addition, the expression levels of cell cycle genes in adult OPCs greatly decreased;[73],[74] therefore, the cells did not proliferate frequently. The first condition for malignant transformation is the reactivation of OPCs in the quiescent state, and p53/NF1 mutations in OPCs are the prerequisite for reactivation. Galvao et al.[45] also used the mammalian target of rapamycin (mTOR) inhibitor in animal experiments to confirm that mTOR signals were necessary for the early activation of mutated OPCs.[75] Liu et al.[44] used mosaic analysis with double markers[76] to analyze this model. They showed that for NSCs after p53/MF1 mutations and all of their daughter cells in the pretransformation stage, OPCs but not NSCs presented overexpansion and aberrant growth. The researchers considered that OPCs were still the cell origin of GBM, even though p53/NF1 mutations occurred in NSCs.

Our group[77],[78],[79],[80] stably transfected the red fluorescent protein (RFP) gene into the human GSC cell line SU3[4] and then implanted the green fluorescent protein (GFP) gene into the brains of nude mice under a stereotactic device. The results of this GFP/RFP tracing model[81] showed that some GFP+ cells cloned from tumor tissues had unlimited proliferation capacity and expressed the OPC marker C-type natriuretic peptide and the MP maker CD68. The results of chromosomal karyotype and fluorescent in situ hybridization analyses were consistent with characteristics of mouse-derived cells. The results of the platelet-derived growth factor receptor-α phosphorylation inhibition experiment confirmed that the PDGF/PDGFR signal regulated the malignant transformation of normal gliocytes in the TME of SU3 xenograft tumors. Lei et al.[80] and Piperi et al.[82] also showed that malignant transformation of MPs in in vitro cultured cells could be regulated by the STAT3 pathway. These results indicated that the malignant transformation of normal OPCs and MPs in the TME could be regulated by different signaling pathways; however, the mechanisms require further studies. Innes et al.[83] used a barcoding approach of glioma initiating cells to generate clonal populations over multiple passages, combined with phenotypic analysis using established stem cell markers CD133, CD15, CD44, and A2B5, which further confirmed glioma initiating cells have strong plasticity for TME cells. However, the complex mechanisms under what conditions and how plasticity arises remain unexplained.

Another characteristic of plasticity is epithelial-to-mesenchymal transition. Cancers are characterized by a lack of completeness of epithelial-to-mesenchymal transition, as it opposes the process of mesenchymal to epithelial transition, resulting in a mixed epithelial/mesenchymal phenotype that exhibits marked cellular plasticity. Majc et al.[84] reported epithelial-mesenchymal transition is a driver of altered cancer and glioblastoma microenvironments. Zhu et al.[85] reported that human cytomegalovirus infection enhances the invasive and migratory abilities of glioblastoma cells through the epithelial-to-mesenchymal transition. In fact, glioblastoma does not belong to the origin of epithelial cells. It is controversial whether some of the mesenchymal transition phenomena seen are also assigned the name epithelial-mesenchymal transition. We believe that the epithelioid-mesenchymal transition is more accurate.

  Summary, Outlook, and Limitations Top

In summary, it was previously thought[86] that the TME, which is composed of GSCs in the tumor bed and different types of immune cells, cytokines, and other factors, played an important role in the development and progression of GBM. However, since the concept of tumor border niche[39],[40],[41] was proposed, its role in the TME cannot be ignored, although it has been reported[87] that accumulation of myeloid MPs in the center of lesions in the TME is also very important. Furthermore, the report of Behnan et al.[88] on mesenchymal signatures in tumors showed that GSCs plasticity is associated with the status of the TME. The Cancer Genome Atlas network initially identified four subtypes of GBM, i.e., proneural, neural, mesenchymal, and classical. Further studies investigated GSCs and identified three subtypes, i.e., proneural, mesenchymal, and classical. Therefore, high-throughput molecular bioinformatics methods are very important for GSCs studies.

With the close connection between high-throughput molecular biology and artificial intelligence, studies of GBM and TME are entering a new stage. In addition to developments from genomics to proteomics,[89] integrated proteomics and molecular network,[90] and integrated proteomics and metabolomics,[91] single-cell sequencing,[92],[93],[94],[95],[96] and radiomics[97],[98],[99],[100],[101],[102],[103] have also been used in more in-depth studies. It is expected that research on GBM and its TME will show that relevant cells can develop malignant progression through remodeling (including mesenchymal-epithelial transition and self-malignant transformation) and can be returned to the physiological status with “newly developed successful interference measures” (precision diagnosis and precision treatment). Therefore, our previous hypothesis that transformation among NGCs, NSCs, and GSCs depends on the TME adheres more closely to reality [Figure 1].
Figure 1: In addition to being driven by TAM in the tumor microenvironment, the transformation of NSCs, NGCs, and TSCs along a counterclockwise or clockwise direction in GBM is also affected by astrocytes and oligodendrocytes. In addition, niches in tumor tissues and tumor borders play an important role in tumor development, progression, invasion, and treatment resistance. ASTRO: Astrocytes, NGCs: Neuroglial cells, NSCs: Neural stem cells, Oligo: Oligodendrocyte, TAM: Tumor-associated macrophages, TSCs: Tumor stem cells

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Although GBM/TME may be able to return to the physiological status from the pathological status, this is still a hypothesis. However, compared to our previous hypothesis, it is much closer to reality. For example, the four best papers[104],[105],[106],[107] selected by Smaïl-Tabbone et al.[108] from 636 papers in the field of bioinformatics and translational informatics in 2018 benefited from artificial intelligence and deep learning[109],[110] help.

The first paper screened the sensitivity of tumors to 160 chemotherapy drugs using multiomics data (genome-wide gene expression profiles). The second paper predicted the survival of patients with gliomas using the neural network approach. The third paper adopted a pancancer approach (with a pancancer cohort of more than 6500 tumors) to use multiomics data for drug repurposing. The fourth paper used a graph-based semisupervised method[111] for accurate phenotype classification. In the future, further data mining, experiments, and clinical validation will achieve a high level of precision in diagnosis and treatment.[112]

As a relevant study of GSCs with TME, Akter et al.[113] suggested that establishing preclinical animal models, which are both possible and challenging, would move in two different directions towards large animal dogs and small animal zebrafish. The advantage of the former is that the data observed by imaging are very close to clinical patients; the latter is transparent, and tumors and TME can dynamically observe the whole proliferation process in animal life only by the naked eye. Admittedly, the limitation of this article is that the narrative is mostly benign to malignant transformation, but the malignant to benign transformation is less, which needs to be confirmed by more studies.



Financial support and sponsorship

This work was supported by the Medical-Health Science-Technology Plan of Suzhou New District, China (No. 2018Q010).

Conflicts of interest

There are no conflicts of interest.

  References Top

Singh SK, Hawkins C, Clarke ID, Squire JA, Bayani J, Hide T, et al. Identification of human brain tumour initiating cells. Nature 2004;432:396-401.  Back to cited text no. 1
Zhang QB, Ji XY, Huang Q, Dong J, Zhu YD, Lan Q. Differentiation profile of brain tumor stem cells: A comparative study with neural stem cells. Cell Res 2006;16:909-15.  Back to cited text no. 2
Huang Q, Zhang QB, Dong J, Wu YY, Shen YT, Zhao YD, et al. Glioma stem cells are more aggressive in recurrent tumors with malignant progression than in the primary tumor, and both can be maintained long-term in vitro. BMC Cancer 2008;8:304.  Back to cited text no. 3
Wan Y, Fei XF, Wang ZM, Jiang DY, Chen HC, Yang J, et al. Expression of miR-125b in the new, highly invasive glioma stem cell and progenitor cell line SU3. Chin J Cancer 2012;31:207-14.  Back to cited text no. 4
Zhao YD, Zhang QB, Chen H, Fei XF, Shen YT, Ji XY, et al. Research on human glioma stem cells in China. Neural Regen Res 2017;12:1918-26.  Back to cited text no. 5
[PUBMED]  [Full text]  
Huang Q, Dong J, Wang ZM. Neuro-Oncology. 1st ed. Beijing: People's Health Press; 2011.  Back to cited text no. 6
Singh SK, Clarke ID, Hide T, Dirks PB. Cancer stem cells in nervous system tumors. Oncogene 2004;23:7267-73.  Back to cited text no. 7
Jung E, Alfonso J, Osswald M, Monyer H, Wick W, Winkler F. Emerging intersections between neuroscience and glioma biology. Nat Neurosci 2019;22:1951-60.  Back to cited text no. 8
Huang Q, DU ZW. A hypothesis: Neural glial cells, neural stem cells and tumor stem cells transform each other depending on the micro-ecological environment. Zhonghua Zhong Liu Za Zhi 2010;32:76-8.  Back to cited text no. 9
Baker M. Cancer stem cells tracked. Nature 2012;488:13-4.  Back to cited text no. 10
Capp JP. Cancer stem cells: From historical roots to a new perspective. J Oncol 2019;2019:5189232.  Back to cited text no. 11
Efroni S, Duttagupta R, Cheng J, Dehghani H, Hoeppner DJ, Dash C, et al. Global transcription in pluripotent embryonic stem cells. Cell Stem Cell 2008;2:437-47.  Back to cited text no. 12
Meshorer E, Yellajoshula D, George E, Scambler PJ, Brown DT, Misteli T. Hyperdynamic plasticity of chromatin proteins in pluripotent embryonic stem cells. Dev Cell 2006;10:105-16.  Back to cited text no. 13
Spivakov M, Fisher AG. Epigenetic signatures of stem-cell identity. Nat Rev Genet 2007;8:263-71.  Back to cited text no. 14
Terskikh AV, Miyamoto T, Chang C, Diatchenko L, Weissman IL. Gene expression analysis of purified hematopoietic stem cells and committed progenitors. Blood 2003;102:94-101.  Back to cited text no. 15
Chang HH, Hemberg M, Barahona M, Ingber DE, Huang S. Transcriptome-wide noise controls lineage choice in mammalian progenitor cells. Nature 2008;453:544-7.  Back to cited text no. 16
Grün D, Kester L, van Oudenaarden A. Validation of noise models for single-cell transcriptomics. Nat Methods 2014;11:637-40.  Back to cited text no. 17
Chambers I, Silva J, Colby D, Nichols J, Nijmeijer B, Robertson M, et al. Nanog safeguards pluripotency and mediates germline development. Nature 2007;450:1230-4.  Back to cited text no. 18
Hayashi K, de Sousa Lopes SM, Tang F, Lao K, Surani MA. Dynamic equilibrium and heterogeneity of mouse pluripotent stem cells with distinct functional and epigenetic states. Cell Stem Cell 2008;3:391-401.  Back to cited text no. 19
Trott J, Hayashi K, Surani A, Babu MM, Martinez-Arias A. Dissecting ensemble networks in ES cell populations reveals micro-heterogeneity underlying pluripotency. Mol Biosyst 2012;8:744-52.  Back to cited text no. 20
Kalmar T, Lim C, Hayward P, Muñoz-Descalzo S, Nichols J, Garcia-Ojalvo J, et al. Regulated fluctuations in nanog expression mediate cell fate decisions in embryonic stem cells. PLoS Biol 2009;7:e1000149.  Back to cited text no. 21
Kanwore K, Guo XX, Abdulrahman AA, Kambey PA, Nadeem I, Gao D. SOX1 is a backup gene for brain neurons and glioma stem cell protection and proliferation. Mol Neurobiol 2021;58:2634-42.  Back to cited text no. 22
Wang P, Gong S, Liao B, Pan J, Wang J, Zou D, et al. HIF1α/HIF2α induces glioma cell dedifferentiation into cancer stem cells through Sox2 under hypoxic conditions. J Cancer 2022;13:1-14.  Back to cited text no. 23
Paldi A. Effects of the in vitro manipulation of stem cells: Epigenetic mechanisms as mediators of induced metabolic fluctuations. Epigenomics 2013;5:429-37.  Back to cited text no. 24
Perestrelo T, Correia M, Ramalho-Santos J, Wirtz D. Metabolic and mechanical cues regulating pluripotent stem cell fate. Trends Cell Biol 2018;28:1014-29.  Back to cited text no. 25
Zhu S, Li W, Zhou H, Wei W, Ambasudhan R, Lin T, et al. Reprogramming of human primary somatic cells by OCT4 and chemical compounds. Cell Stem Cell 2010;7:651-5.  Back to cited text no. 26
Yoshida Y, Takahashi K, Okita K, Ichisaka T, Yamanaka S. Hypoxia enhances the generation of induced pluripotent stem cells. Cell Stem Cell 2009;5:237-41.  Back to cited text no. 27
Gut P, Verdin E. The nexus of chromatin regulation and intermediary metabolism. Nature 2013;502:489-98.  Back to cited text no. 28
Reid MA, Dai Z, Locasale JW. The impact of cellular metabolism on chromatin dynamics and epigenetics. Nat Cell Biol 2017;19:1298-306.  Back to cited text no. 29
Sharma P, Xu J, Williams K, Easley M, Elder JB, Lonser R, et al. Inhibition of nicotinamide phosphoribosyltransferase (NAMPT), the rate-limiting enzyme of the nicotinamide adenine dinucleotide (NAD) salvage pathway, to target glioma heterogeneity through mitochondrial oxidative stress. Neuro Oncol 2022;24:229-44.  Back to cited text no. 30
Sørensen MD, Kristensen BW. Tumour-associated CD204+microglia/macrophages accumulate in perivascular and perinecrotic niches and correlate with an interleukin-6-enriched inflammatory profile in glioblastoma. Neuropathol Appl Neurobiol 2022;48:e12772.  Back to cited text no. 31
Suvà ML, Tirosh I. The glioma stem cell model in the era of single-cell genomics. Cancer Cell 2020;37:630-6.  Back to cited text no. 32
Neftel C, Laffy J, Filbin MG, Hara T, Shore ME, Rahme GJ, et al. An integrative model of cellular states, plasticity, and genetics for glioblastoma. Cell 2019;178:835-49.e21.  Back to cited text no. 33
Zhang GL, Wang CF, Qian C, Ji YX, Wang YZ. Role and mechanism of neural stem cells of the subventricular zone in glioblastoma. World J Stem Cells 2021;13:877-93.  Back to cited text no. 34
Schiffer D, Annovazzi L, Casalone C, Corona C, Mellai M. Glioblastoma: Microenvironment and niche concept. Cancers (Basel) 2018;11:5.  Back to cited text no. 35
Diksin M, Smith SJ, Rahman R. The molecular and phenotypic basis of the glioma invasive perivascular niche. Int J Mol Sci 2017;18:2342.  Back to cited text no. 36
Schiffer D, Mellai M, Bovio E, Bisogno I, Casalone C, Annovazzi L. Glioblastoma niches: From the concept to the phenotypical reality. Neurol Sci 2018;39:1161-8.  Back to cited text no. 37
Ho IA, Shim WS. Contribution of the microenvironmental niche to glioblastoma heterogeneity. Biomed Res Int 2017;2017:9634172.  Back to cited text no. 38
Hide T, Komohara Y, Miyasato Y, Nakamura H, Makino K, Takeya M, et al. Oligodendrocyte progenitor cells and macrophages/microglia produce glioma stem cell niches at the tumor border. EBioMedicine 2018;30:94-104.  Back to cited text no. 39
Hide T, Makino K, Nakamura H, Yano S, Anai S, Takezaki T, et al. New treatment strategies to eradicate cancer stem cells and niches in glioblastoma. Neurol Med Chir (Tokyo) 2013;53:764-72.  Back to cited text no. 40
Hide T, Shibahara I, Kumabe T. Novel concept of the border niche: Glioblastoma cells use oligodendrocytes progenitor cells (GAOs) and microglia to acquire stem cell-like features. Brain Tumor Pathol 2019;36:63-73.  Back to cited text no. 41
Bercury KK, Macklin WB. Dynamics and mechanisms of CNS myelination. Dev Cell 2015;32:447-58.  Back to cited text no. 42
Kaller MS, Lazari A, Blanco-Duque C, Sampaio-Baptista C, Johansen-Berg H. Myelin plasticity and behaviour-connecting the dots. Curr Opin Neurobiol 2017;47:86-92.  Back to cited text no. 43
Liu C, Sage JC, Miller MR, Verhaak RG, Hippenmeyer S, Vogel H, et al. Mosaic analysis with double markers reveals tumor cell of origin in glioma. Cell 2011;146:209-21.  Back to cited text no. 44
Galvao RP, Kasina A, McNeill RS, Harbin JE, Foreman O, Verhaak RG, et al. Transformation of quiescent adult oligodendrocyte precursor cells into malignant glioma through a multistep reactivation process. Proc Natl Acad Sci U S A 2014;111:E4214-23.  Back to cited text no. 45
Hosono J, Morikawa S, Ezaki T, Kawamata T, Okada Y. Pericytes promote abnormal tumor angiogenesis in a rat RG2 glioma model. Brain Tumor Pathol 2017;34:120-9.  Back to cited text no. 46
Butovsky O, Ziv Y, Schwartz A, Landa G, Talpalar AE, Pluchino S, et al. Microglia activated by IL-4 or IFN-gamma differentially induce neurogenesis and oligodendrogenesis from adult stem/progenitor cells. Mol Cell Neurosci 2006;31:149-60.  Back to cited text no. 47
Shigemoto-Mogami Y, Hoshikawa K, Goldman JE, Sekino Y, Sato K. Microglia enhance neurogenesis and oligodendrogenesis in the early postnatal subventricular zone. J Neurosci 2014;34:2231-43.  Back to cited text no. 48
Miron VE. Microglia-driven regulation of oligodendrocyte lineage cells, myelination, and remyelination. J Leukoc Biol 2017;101:1103-8.  Back to cited text no. 49
Chen Z, Feng X, Herting CJ, Garcia VA, Nie K, Pong WW, et al. Cellular and molecular identity of tumor-associated macrophages in glioblastoma. Cancer Res 2017;77:2266-78.  Back to cited text no. 50
Chen Z, Hambardzumyan D. Immune microenvironment in glioblastoma subtypes. Front Immunol 2018;9:1004.  Back to cited text no. 51
Guan X, Hasan MN, Maniar S, Jia W, Sun D. Reactive astrocytes in glioblastoma multiforme. Mol Neurobiol 2018;55:6927-38.  Back to cited text no. 52
Brandao M, Simon T, Critchley G, Giamas G. Astrocytes, the rising stars of the glioblastoma microenvironment. Glia 2019;67:779-90.  Back to cited text no. 53
Katz AM, Amankulor NM, Pitter K, Helmy K, Squatrito M, Holland EC. Astrocyte-specific expression patterns associated with the PDGF-induced glioma microenvironment. PLoS One 2012;7:e32453.  Back to cited text no. 54
Barcia C Jr., Gómez A, Gallego-Sanchez JM, Perez-Vallés A, Castro MG, Lowenstein PR, et al. Infiltrating CTLs in human glioblastoma establish immunological synapses with tumorigenic cells. Am J Pathol 2009;175:786-98.  Back to cited text no. 55
Lundgaard I, Osório MJ, Kress BT, Sanggaard S, Nedergaard M. White matter astrocytes in health and disease. Neuroscience 2014;276:161-73.  Back to cited text no. 56
Moore CS, Abdullah SL, Brown A, Arulpragasam A, Crocker SJ. How factors secreted from astrocytes impact myelin repair. J Neurosci Res 2011;89:13-21.  Back to cited text no. 57
Bardehle S, Krüger M, Buggenthin F, Schwausch J, Ninkovic J, Clevers H, et al. Live imaging of astrocyte responses to acute injury reveals selective juxtavascular proliferation. Nat Neurosci 2013;16:580-6.  Back to cited text no. 58
Gibson EM, Purger D, Mount CW, Goldstein AK, Lin GL, Wood LS, et al. Neuronal activity promotes oligodendrogenesis and adaptive myelination in the mammalian brain. Science 2014;344:1252304.  Back to cited text no. 59
Mitew S, Gobius I, Fenlon LR, McDougall SJ, Hawkes D, Xing YL, et al. Pharmacogenetic stimulation of neuronal activity increases myelination in an axon-specific manner. Nat Commun 2018;9:306.  Back to cited text no. 60
Venkatesh HS, Johung TB, Caretti V, Noll A, Tang Y, Nagaraja S, et al. Neuronal activity promotes glioma growth through neuroligin-3 secretion. Cell 2015;161:803-16.  Back to cited text no. 61
Venkatesh HS, Tam LT, Woo PJ, Lennon J, Nagaraja S, Gillespie SM, et al. Targeting neuronal activity-regulated neuroligin-3 dependency in high-grade glioma. Nature 2017;549:533-7.  Back to cited text no. 62
Müller C, Holtschmidt J, Auer M, Heitzer E, Lamszus K, Schulte A, et al. Hematogenous dissemination of glioblastoma multiforme. Sci Transl Med 2014;6:247ra101.  Back to cited text no. 63
Macarthur KM, Kao GD, Chandrasekaran S, Alonso-Basanta M, Chapman C, Lustig RA, et al. Detection of brain tumor cells in the peripheral blood by a telomerase promoter-based assay. Cancer Res 2014;74:2152-9.  Back to cited text no. 64
Gahoi N, Malhotra D, Moiyadi A, Varma SG, Gandhi MN, Srivastava S. Multi-pronged proteomic analysis to study the glioma pathobiology using cerebrospinal fluid samples. Proteomics Clin Appl 2018;12:e1700056.  Back to cited text no. 65
Dasgupta B, Yi Y, Hegedus B, Weber JD, Gutmann DH. Cerebrospinal fluid proteomic analysis reveals dysregulation of methionine aminopeptidase-2 expression in human and mouse neurofibromatosis 1-associated glioma. Cancer Res 2005;65:9843-50.  Back to cited text no. 66
Liu T, Xu H, Huang M, Ma W, Saxena D, Lustig RA, et al. Circulating glioma cells exhibit stem cell-like properties. Cancer Res 2018;78:6632-42.  Back to cited text no. 67
Cheng YQ, Wang SB, Liu JH, Jin L, Liu Y, Li CY, et al. Modifying the tumour microenvironment and reverting tumour cells: New strategies for treating malignant tumours. Cell Prolif 2020;53:e12865.  Back to cited text no. 68
Parmigiani E, Scalera M, Mori E, Tantillo E, Vannini E. Old stars and new players in the brain tumor microenvironment. Front Cell Neurosci 2021;15:709917.  Back to cited text no. 69
Elshamy WM, Duhé RJ. Overview: Cellular plasticity, cancer stem cells and metastasis. Cancer Lett 2013;341:2-8.  Back to cited text no. 70
Psachoulia K, Jamen F, Young KM, Richardson WD. Cell cycle dynamics of NG2 cells in the postnatal and ageing brain. Neuron Glia Biol 2009;5:57-67.  Back to cited text no. 71
Young KM, Psachoulia K, Tripathi RB, Dunn SJ, Cossell L, Attwell D, et al. Oligodendrocyte dynamics in the healthy adult CNS: Evidence for myelin remodeling. Neuron 2013;77:873-85.  Back to cited text no. 72
Belachew S, Aguirre AA, Wang H, Vautier F, Yuan X, Anderson S, et al. Cyclin-dependent kinase-2 controls oligodendrocyte progenitor cell cycle progression and is downregulated in adult oligodendrocyte progenitors. J Neurosci 2002;22:8553-62.  Back to cited text no. 73
Lin G, Mela A, Guilfoyle EM, Goldman JE. Neonatal and adult O4(+) oligodendrocyte lineage cells display different growth factor responses and different gene expression patterns. J Neurosci Res 2009;87:3390-402.  Back to cited text no. 74
Hill RA, Patel KD, Medved J, Reiss AM, Nishiyama A. NG2 cells in white matter but not gray matter proliferate in response to PDGF. J Neurosci 2013;33:14558-66.  Back to cited text no. 75
Visvader JE. Cells of origin in cancer. Nature 2011;469:314-22.  Back to cited text no. 76
Chen Y, Wang Z, Dai X, Fei X, Shen Y, Zhang M, et al. Glioma initiating cells contribute to malignant transformation of host glial cells during tumor tissue remodeling via PDGF signaling. Cancer Lett 2015;365:174-81.  Back to cited text no. 77
Wang A, Dai X, Cui B, Fei X, Chen Y, Zhang J, et al. Experimental research of host macrophage canceration induced by glioma stem progenitor cells. Mol Med Rep 2015;11:2435-42.  Back to cited text no. 78
Dai X, Chen H, Chen Y, Wu J, Wang H, Shi J, et al. Malignant transformation of host stromal fibroblasts derived from the bone marrow traced in a dual-color fluorescence xenograft tumor model. Oncol Rep 2015;34:2997-3006.  Back to cited text no. 79
Lei H, Ma J, Zhu H, Cai H, Dong J, Ming L, et al. STAT3 signaling pathway regulates glioma stem cells induced host macrophage malignance. Transl Cancer Res 2016;5:805-16.  Back to cited text no. 80
Lan Q, Chen Y, Dai C, Li S, Fei X, Dong J, et al. Novel enhanced GFP-positive congenic inbred strain establishment and application of tumor-bearing nude mouse model. Cancer Sci 2020;111:3626-38.  Back to cited text no. 81
Piperi C, Papavassiliou KA, Papavassiliou AG. Pivotal role of STAT3 in shaping glioblastoma immune microenvironment. Cells 2019;8:1398.  Back to cited text no. 82
Innes JA, Lowe AS, Fonseca R, Aley N, El-Hassan T, Constantinou M, et al. Phenotyping clonal populations of glioma stem cell reveals a high degree of plasticity in response to changes of microenvironment. Lab Invest 2022;102:172-84.  Back to cited text no. 83
Majc B, Sever T, Zarić M, Breznik B, Turk B, Lah TT. Epithelial-to-mesenchymal transition as the driver of changing carcinoma and glioblastoma microenvironment. Biochim Biophys Acta Mol Cell Res 2020;1867:118782.  Back to cited text no. 84
Zhu X, Hu B, Hu M, Qian D, Wang B. Human cytomegalovirus infection enhances invasiveness and migration of glioblastoma cells by epithelial-to-mesenchymal transition. Int J Clin Exp Pathol 2020;13:2637-47.  Back to cited text no. 85
Ma Q, Long W, Xing C, Chu J, Luo M, Wang HY, et al. Cancer stem cells and immunosuppressive microenvironment in glioma. Front Immunol 2018;9:2924.  Back to cited text no. 86
Cai B, Ji TT, Wang N, Li XB, He RX, Liu W, et al. A microfluidic platform utilizing anchored water-in-oil-in-water double emulsions to create a niche for analyzing single non-adherent cells. Lab Chip 2019;19:422-31.  Back to cited text no. 87
Behnan J, Finocchiaro G, Hanna G. The landscape of the mesenchymal signature in brain tumours. Brain 2019;142:847-66.  Back to cited text no. 88
Buser DP, Ritz MF, Moes S, Tostado C, Frank S, Spiess M, et al. Quantitative proteomics reveals reduction of endocytic machinery components in gliomas. EBioMedicine 2019;46:32-41.  Back to cited text no. 89
Wang J, Wang F, Li Q, Wang Q, Li J, Wang Y, et al. Proteomics and molecular network analyses reveal that the interaction between the TAT-DCF1 peptide and TAF6 induces an antitumor effect in glioma cells. Mol Omics 2020;16:73-82.  Back to cited text no. 90
Li M, Ren T, Lin M, Wang Z, Zhang J. Integrated proteomic and metabolomic profiling the global response of rat glioma model by temozolomide treatment. J Proteomics 2020;211:103578.  Back to cited text no. 91
Venteicher AS, Tirosh I, Hebert C, Yizhak K, Neftel C, Filbin MG, et al. Decoupling genetics, lineages, and microenvironment in IDH-mutant gliomas by single-cell RNA-seq. Science 2017;355:eaai8478.  Back to cited text no. 92
Tirosh I, Suvà ML. Dissecting human gliomas by single-cell RNA sequencing. Neuro Oncol 2018;20:37-43.  Back to cited text no. 93
Johnson E, Dickerson KL, Connolly ID, Hayden Gephart M. Single-cell RNA-sequencing in glioma. Curr Oncol Rep 2018;20:42.  Back to cited text no. 94
Saurty-Seerunghen MS, Bellenger L, El-Habr EA, Delaunay V, Garnier D, Chneiweiss H, et al. Capture at the single cell level of metabolic modules distinguishing aggressive and indolent glioblastoma cells. Acta Neuropathol Commun 2019;7:155.  Back to cited text no. 95
Bagley SJ, Hwang WT, Brem S, Linette GP, O'Rourke DM, Desai AS. RNA-seq for identification of therapeutically targetable determinants of immune activation in human glioblastoma. J Neurooncol 2019;141:95-102.  Back to cited text no. 96
Aerts HJ. The potential of radiomic-based phenotyping in precision medicine: A review. JAMA Oncol 2016;2:1636-42.  Back to cited text no. 97
Fang N, Wu Z, Wang X, Lin Y, Li L, Huang Z, et al. Quantitative assessment of microenvironment characteristics and metabolic activity in glioma via multiphoton microscopy. J Biophotonics 2019;12:e201900136.  Back to cited text no. 98
Lagerweij T, Dusoswa SA, Negrean A, Hendrikx EM, de Vries HE, Kole J, et al. Optical clearing and fluorescence deep-tissue imaging for 3D quantitative analysis of the brain tumor microenvironment. Angiogenesis 2017;20:533-46.  Back to cited text no. 99
Li D, Chen X, Wang H, Liu J, Zheng M, Fu Y, et al. Cetuximab-conjugated nanodiamonds drug delivery system for enhanced targeting therapy and 3D Raman imaging. J Biophotonics 2017;10:1636-46.  Back to cited text no. 100
Alban TJ, Alvarado AG, Sorensen MD, Bayik D, Volovetz J, Serbinowski E, et al. Global immune fingerprinting in glioblastoma patient peripheral blood reveals immune-suppression signatures associated with prognosis. JCI Insight 2018;3:122264.  Back to cited text no. 101
Castiglioni I, Gallivanone F, Soda P, Avanzo M, Stancanello J, Aiello M, et al. AI-based applications in hybrid imaging: How to build smart and truly multi-parametric decision models for radiomics. Eur J Nucl Med Mol Imaging 2019;46:2673-99.  Back to cited text no. 102
Fang N, Wu Z, Wang X, Kang D, Li L, Chen Y, et al. Automatic and label-free identification of blood vessels in gliomas using the combination of multiphoton microscopy and image analysis. J Biophotonics 2019;12:e201900006.  Back to cited text no. 103
Lee SI, Celik S, Logsdon BA, Lundberg SM, Martins TJ, Oehler VG, et al. A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia. Nat Commun 2018;9:42.  Back to cited text no. 104
Mobadersany P, Yousefi S, Amgad M, Gutman DA, Barnholtz-Sloan JS, Velázquez Vega JE, et al. Predicting cancer outcomes from histology and genomics using convolutional networks. Proc Natl Acad Sci U S A 2018;115:E2970-9.  Back to cited text no. 105
Sengupta S, Sun SQ, Huang KL, Oh C, Bailey MH, Varghese R, et al. Integrative omics analyses broaden treatment targets in human cancer. Genome Med 2018;10:60.  Back to cited text no. 106
Doostparast Torshizi A, Petzold LR. Graph-based semi-supervised learning with genomic data integration using condition-responsive genes applied to phenotype classification. J Am Med Inform Assoc 2018;25:99-108.  Back to cited text no. 107
Smaïl-Tabbone M, Rance B, Section Editors for the IMIA Yearbook Section on Bioinformatics and Translational Informatics. Contributions from the 2018 literature on bioinformatics and translational informatics. Yearb Med Inform 2019;28:190-3.  Back to cited text no. 108
Moscatelli M, Manconi A, Pessina M, Fellegara G, Rampoldi S, Milanesi L, et al. An infrastructure for precision medicine through analysis of big data. BMC Bioinformatics 2018;19:351.  Back to cited text no. 109
Krempel R, Kulkarni P, Yim A, Lang U, Habermann B, Frommolt P. Integrative analysis and machine learning on cancer genomics data using the Cancer Systems Biology Database (CancerSysDB). BMC Bioinformatics 2018;19:156.  Back to cited text no. 110
Lee E, Chuang HY, Kim JW, Ideker T, Lee D. Inferring pathway activity toward precise disease classification. PLoS Comput Biol 2008;4:e1000217.  Back to cited text no. 111
Ormondroyd E, Mackley MP, Blair E, Craft J, Knight JC, Taylor JC, et al. “Not pathogenic until proven otherwise”: Perspectives of UK clinical genomics professionals toward secondary findings in context of a Genomic Medicine Multidisciplinary Team and the 100,000 Genomes Project. Genet Med 2018;20:320-8.  Back to cited text no. 112
Akter F, Simon B, de Boer NL, Redjal N, Wakimoto H, Shah K. Pre-clinical tumor models of primary brain tumors: Challenges and opportunities. Biochim Biophys Acta Rev Cancer 2021;1875:188458.  Back to cited text no. 113


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