Single-Cell RNA-Sequencing Shift in the Interaction Pattern Between Glioma Stem Cells and Immune Cells During Tumorigenesis

Single-cell and spatial transcriptome assays reveal heterogeneity in gliomas through stress responses and pathway alterations

Background

Glioma is a highly heterogeneous malignancy of the central nervous system. This heterogeneity is driven by various molecular processes, including neoplastic transformation, cell cycle dysregulation, and angiogenesis. Among these biomolecular events, inflammation and stress pathways in the development and driving factors of glioma heterogeneity have been reported. However, the mechanisms of glioma heterogeneity under stress response remain unclear, especially from a spatial aspect.

Methods

This study employed single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) to explore the impact of oxidative stress response genes in oligodendrocyte precursor cells (OPCs). Our analysis identified distinct pathways activated by oxidative stress in two different types of gliomas: high- and low- grade (HG and LG) gliomas.

Results

In HG gliomas, oxidative stress induced a metabolic shift from oxidative phosphorylation to glycolysis, promoting cell survival by preventing apoptosis. This metabolic reprogramming was accompanied by epithelial-to-mesenchymal transition (EMT) and an upregulation of stress response genes. Furthermore, SCENIC (Single-Cell rEgulatory Network Inference and Clustering) analysis revealed that oxidative stress activated the AP1 transcription factor in HG gliomas, thereby enhancing tumor cell survival and proliferation.

Conclusion

Our findings provide a novel perspective on the mechanisms of oxidative stress responses across various grades of gliomas. This insight enhances our comprehension of the evolutionary processes and heterogeneity within gliomas, potentially guiding future research and therapeutic strategies.

Single-Nucleus Landscape of Glial Cells and Neurons in Alzheimer's Disease

Alzheimer's disease (AD) is a neurodegenerative disease with a projected significant increase in incidence. Therefore, this study analyzed single-nucleus AD data to provide a theoretical basis for the clinical development and treatment of AD. We downloaded AD-related monocyte data from the Gene Expression Omnibus database, annotated cells, compared cell abundance between groups, and investigated glial and neuronal cell biological processes and pathways through functional enrichment analysis. Furthermore, we constructed a global regulatory network for AD based on cell communication and ecological analyses. Our findings revealed increased abundance of Capping Protein Regulator And Myosin 1 linker 1 (CARMIL1)+ astrocytes (AST), Immunoglobulin Superfamily Member 21 (IGSF21)+ microglia (MIC), SRY-Box Transcription Factor 6 (SOX6)+ inhibitory neurons (InNeu), and laminin alpha-2 chain (LAMA2)+ oligodendrocytes (OLI) cell subgroups in tissues of patients with AD, while prostaglandin D2 synthase (PTGDS)+ AST, Src Family Tyrosine Kinase (FYN)+ MIC, and Proteolipid Protein 1 (PLP1)+ InNeu subgroups specifically decreased. We found that the cell phenotype of patients with AD shifted from a simpler to a more complex state compared to the control group. Cell communication analysis revealed strong communication between MIC and NEU. Furthermore, AST, MIC, NEU, and OLI were involved in oxidative stress- and inflammation-related pathways, potentially contributing to disease development. This study provides a theoretical basis for further exploring the specific mechanisms underlying AD.

Glioma stem cells remodel immunotolerant microenvironment in GBM and are associated with therapeutic advancements

Glioma is the most common primary tumor of the central nervous system (CNS). Glioblastoma (GBM) is incurable with current treatment strategies. Additionally, the treatment of recurrent GBM (rGBM) is often referred to as terminal treatment, necessitating hospice-level care and management. The presence of the blood-brain barrier (BBB) gives GBM a more challenging or "cold" tumor microenvironment (TME) than that of other cancers and gloma stem cells (GSCs) play an important role in the TME remodeling, occurrence, development and recurrence of giloma. In this review, our primary focus will be on discussing the following topics: niche-associated GSCs and macrophages, new theories regarding GSC and TME involving pyroptosis and ferroptosis in GBM, metabolic adaptations of GSCs, the influence of the cold environment in GBM on immunotherapy, potential strategies to transform the cold GBM TME into a hot one, and the advancement of GBM immunotherapy and GBM models.

Development of stemness-related signature to optimize prognosis prediction and identify XMD8-85 as a novel therapeutic compound for glioma

Glioma is a highly invasive and aggressive type of brain cancer with poor treatment response. Stemness-related transcription factors form a regulatory network that sustains the malignant phenotype of gliomas. We conducted an integrated analysis of stemness-related transcription factors using The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) datasets, established the characteristics of stemness-related transcription factors, including Octamer-Binding Protein 4 (OCT4), Meis Homeobox 1 (MEIS1), E2F Transcription Factor 1 (E2F1), Transcription Factor CP2 Like 1 (TFCP2L1), and RUNX Family Transcription Factor 1 (RUNX1). The characteristic of stemness-related transcription factors was identified as an independent prognostic factor for glioma patients. Patients in the high-risk group have a worse prognosis than those in the low-risk group. The glioma microenvironment in the high-risk group exhibited a more active immune status. Single-cell level analysis revealed that stem cell-like cells exhibited stronger intercellular communication than glioma cells. Meanwhile, patients in different risk stratification exhibited varying sensitivities to immunotherapy and small molecule drug therapy. XMD8-85 was more effective in the high-risk group, and its antitumor effects were validated both in vivo and in vitro. Our results indicate that this prognostic feature will assist clinicians in predicting the prognosis of glioma patients, guiding immunotherapy and personalized treatment, as well as the potential clinical application of XMD8-85 in glioma treatment, and helping to develop effective treatment strategies.

Targeting EZH2 regulates the biological characteristics of glioma stem cells via the Notch1 pathway

Glioma is the most common malignant brain tumor, and its behavior is closely related to the presence of glioma stem cells (GSCs). We found that the enhancer of zeste homolog 2 (EZH2) is highly expressed in glioma and that its expression is correlated with the prognosis of glioblastoma multiforme (GBM) in two databases: The Cancer Genome Atlas and the Chinese Glioma Genome Atlas. Additionally, EZH2 is known to regulate the stemness-associated gene expression, proliferation, and invasion ability of GSCs, which may be achieved through the activation of the STAT3 and Notch1 pathways. Furthermore, we demonstrated the effect of the EZH2-specific inhibitor GSK126 on GSCs; these results not only corroborate our hypothesis, but also provide a potential novel treatment approach for glioma.

Comprehensive Analysis to Identify Rh Family C Glycoprotein (RHCG) as the Causative Gene for Psoriasis and Search for Alternative Treatment Modalities

Background

Psoriasis is a complex autoimmune disease. Frequent interactions between epidermal and immune cells are likely to be responsible for the strong heterogeneity of psoriasis. Therefore, our work aims to build on current knowledge and further search for new molecular mechanisms related to psoriasis pathogenesis in order to develop new targeted drugs.

Methods

Data from psoriasis samples were obtained from the Gene Expression Omnibus (GEO) database, and batch effects were corrected using the "Combat" algorithm in the "SVA" package. Functional annotation of differential genes in psoriasis was performed by Gene set enrichment analysis (GSEA). Core functional modules were identified using the Multiscale Embedded Gene Co-Expression Network Analysis (MEGENA) algorithm for selection from the differential gene interaction network. The expression and potential function of Rh Family C Glycoprotein (RHCG) was predicted in single cell data by the "Seurat" package and validated in psoriasis samples by multiplex immunofluorescence. In addition, the regulatory function of HOP Homeobox (HOPX) on RHCG in keratinocytes was confirmed using RNA interference. Using immune infiltration analysis, RHCG and DC cells were analyzed for their association. Finally, the molecular mechanisms of treatment of psoriasis using Tripterygii Radix (TR) and Cinnamomi Ramulus (CR) were explored through network pharmacology and experimental validation.

Results

Immune response (represented by C1_2) and collagen matrix formation (represented by C1_3) were identified as two important pathogenic factors in psoriasis and helped to define new biological subtypes of psoriasis. One important psoriasis hub gene, RHCG, was obtained and found to be closely associated with keratinocyte differentiation as well as DC cell maturation. And RHCG was regulated by HOPX in keratinocytes. In addition, the mechanism of action of CR and TR in the treatment of psoriasis was tentatively confirmed to be related to TRPV3, NFKB2, and YAP1.

Conclusions

Our study identifies a new causal disease gene (RHCG) and offers potential alternatives for the treatment of psoriasis.

Cancer stem cells in brain tumors: From origin to clinical implications

Malignant brain tumors are highly heterogeneous tumors with a poor prognosis and a high morbidity and mortality rate in both children and adults. The cancer stem cell (CSC, also named tumor-initiating cell) model states that tumor growth is driven by a subset of CSCs. This model explains some of the clinical observations of brain tumors, including the almost unavoidable tumor recurrence after initial successful chemotherapy and/or radiotherapy and treatment resistance. Over the past two decades, strategies for the identification and characterization of brain CSCs have improved significantly, supporting the design of new diagnostic and therapeutic strategies for brain tumors. Relevant studies have unveiled novel characteristics of CSCs in the brain, including their heterogeneity and distinctive immunobiology, which have provided opportunities for new research directions and potential therapeutic approaches. In this review, we summarize the current knowledge of CSCs markers and stemness regulators in brain tumors. We also comprehensively describe the influence of the CSCs niche and tumor microenvironment on brain tumor stemness, including interactions between CSCs and the immune system, and discuss the potential application of CSCs in brain-based therapies for the treatment of brain tumors.

Machine learning-based identification of lower grade glioma stemness subtypes discriminates patient prognosis and drug response

Glioma stem cells (GSCs) remodel their tumor microenvironment to sustain a supportive niche. Identification and stratification of stemness related characteristics in patients with glioma might aid in the diagnosis and treatment of the disease. In this study, we calculated the mRNA stemness index in bulk and single-cell RNA-sequencing datasets using machine learning methods and investigated the correlation between stemness and clinicopathological characteristics. A glioma stemness-associated score (GSScore) was constructed using multivariate Cox regression analysis. We also generated a GSC cell line derived from a patient diagnosed with glioma and used glioma cell lines to validate the performance of the GSScore in predicting chemotherapeutic responses. Differentially expressed genes (DEGs) between GSCs with high and low GSScores were used to cluster lower-grade glioma (LGG) samples into three stemness subtypes. Differences in clinicopathological characteristics, including survival, copy number variations, mutations, tumor microenvironment, and immune and chemotherapeutic responses, among the three LGG stemness-associated subtypes were identified. Using machine learning methods, we further identified genes as subtype predictors and validated their performance using the CGGA datasets. In the current study, we identified a GSScore that correlated with LGG chemotherapeutic response. Through the score, we also identified a novel classification of the LGG subtype and associated subtype predictors, which might facilitate the development of precision therapy.

Applying single cell multi-omic analyses to understand treatment resistance in pediatric high grade glioma

Despite improvements in cancer patient outcomes seen in the past decade, tumor resistance to therapy remains a major impediment to achieving durable clinical responses. Intratumoral heterogeneity related to genetic, epigenetic, transcriptomic, proteomic, and metabolic differences between individual cancer cells has emerged as a driver of therapeutic resistance. This cell to cell heterogeneity can be assessed using single cell profiling technologies that enable the identification of tumor cell clones that exhibit similar defining features like specific mutations or patterns of DNA methylation. Single cell profiling of tumors before and after treatment can generate new insights into the cancer cell characteristics that confer therapeutic resistance by identifying intrinsically resistant sub-populations that survive treatment and by describing new cellular features that emerge post-treatment due to tumor cell evolution. Integrative, single cell analytical approaches have already proven advantageous in studies characterizing treatment-resistant clones in cancers where pre- and post-treatment patient samples are readily available, such as leukemia. In contrast, little is known about other cancer subtypes like pediatric high grade glioma, a class of heterogeneous, malignant brain tumors in children that rapidly develop resistance to multiple therapeutic modalities, including chemotherapy, immunotherapy, and radiation. Leveraging single cell multi-omic technologies to analyze naïve and therapy-resistant glioma may lead to the discovery of novel strategies to overcome treatment resistance in brain tumors with dismal clinical outcomes. In this review, we explore the potential for single cell multi-omic analyses to reveal mechanisms of glioma resistance to therapy and discuss opportunities to apply these approaches to improve long-term therapeutic response in pediatric high grade glioma and other brain tumors with limited treatment options.

Reprogramming the Canine Glioma Microenvironment with Tumor Vaccination plus Oral Losartan and Propranolol Induces Objective Responses

Purpose

Malignant gliomas have a highly immune suppressive tumor microenvironment (TME) which renders them largely unresponsive to conventional therapeutics. Therefore, the present study evaluated a therapeutic protocol designed overcome the immune barrier by combining myeloid cell targeted immunotherapy with tumor vaccination.

Experimental Design

We utilized a spontaneously occurring canine glioma model to investigate an oral TME modifying immunotherapy in conjunction with cancer stem cell (CSC) vaccination. Dogs were treated daily with losartan (monocyte migration inhibitor) and propranolol (myeloid-derived suppressor cell depleting agent) plus anti-CSC vaccination on a bi-weekly then monthly schedule. Tumor volume was monitored by MRI and correlated with patient immune responses.

Results

Ten dogs with histologically confirmed gliomas were enrolled into a prospective, open-label clinical trial to evaluate the immunotherapy protocol. Partial tumor regression was observed in 2 dogs, while 6 dogs experienced stable disease, for an overall clinical benefit rate of 80%. Overall survival times (median = 351 days) and progression-free intervals (median = 163 days) were comparable to prior studies evaluating surgical debulking followed by immunotherapy. Dogs with detectable anti-CSC antibody responses had an increased overall survival time relative to dogs that did not generate antibody responses (vaccine responder MST = 500 days; vaccine non-responder MST = 218 days; p = 0.02).

Conclusions

These findings suggest that combining myeloid cell targeted oral immunotherapy with tumor vaccination can generate objective tumor responses, even in the absence of conventional therapy. Overall, this approach has promise as a readily implemented therapeutic strategy for use in brain cancer patients.

Construction of an immune-related ceRNA network to screen for potential diagnostic markers for autism spectrum disorder

Purpose: The diagnosis of autism spectrum disorder (ASD) is reliant on evaluation of patients' behavior. We screened the potential diagnostic and therapeutic targets of ASD through bioinformatics analysis. Methods: Four ASD-related datasets were downloaded from the Gene Expression Omnibus database. The "limma" package was employed to analyze differentially expressed messenger (m)RNAs, long non-coding (lnc)RNAs, and micro (mi)RNAs between ASD patients and healthy volunteers (HVs). We constructed a competing endogenous-RNA (ceRNA) network. Enrichment analyses of key genes were undertaken using the Gene Ontology database and Kyoto Encyclopedia of Genes and Genomes database. The ImmucellAI database was used to analyze differences in immune-cell infiltration (ICI) in ASD and HV samples. Synthetic analyses of the ceRNA network and ICI was done to obtain a diagnostic model using LASSO regression analysis. Analyses of receiver operating characteristic (ROC) curves were done for model verification. Results: The ceRNA network comprised 49 lncRNAs, 30 miRNAs, and 236 mRNAs. mRNAs were associated with 41 cellular components, 208 biological processes, 39 molecular functions, and 35 regulatory signaling pathways. Significant differences in the abundance of 10 immune-cell species between ASD patients and HVs were noted. Using the ceRNA network and ICI results, we constructed a diagnostic model comprising five immune cell-associated genes: adenosine triphosphate-binding cassette transporter A1 (ABCA1), DiGeorge syndrome critical region 2 (DGCR2), glucose-fructose oxidoreductase structural domain gene 1 (GFOD1), glutaredoxin (GLRX), and SEC16 homolog A (SEC16A). The diagnostic performance of our model was revealed by an area under the ROC curve of 0.923. Model verification was done using the validation dataset and serum samples of patients. Conclusion: ABCA1, DGCR2, GFOD1, GLRX, and SEC16A could be diagnostic biomarkers and therapeutic targets for ASD.

Prognostic value of γ‐aminobutyric acidergic synapse-associated signature for lower-grade gliomas

Background

Synapse-associated proteins (SAPs) play important roles in central nervous system (CNS) tumors. Recent studies have reported that γ-aminobutyric acidergic (GABAergic) synapses also play critical roles in the development of gliomas. However, biomarkers of GABAergic synapses in low-grade gliomas (LGGs) have not yet been reported.

Methods

mRNA data from normal brain tissue and gliomas were obtained from the Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) databases, respectively. A validation dataset was also obtained from the Chinese Glioma Genome Atlas (CGGA) database. The expression patterns of GABAergic synapse-related genes (GSRGs) were evaluated with difference analysis in LGGs. Then, a GABAergic synapse-related risk signature (GSRS) was constructed with least absolute shrinkage and selection operator (LASSO) Cox regression analysis. According to the expression value and coefficients of identified GSRGs, the risk scores of all LGG samples were calculated. Univariate and multivariate Cox regression analyses were conducted to evaluate related risk scores for prognostic ability. Correlations between characteristics of the tumor microenvironment (TME) and risk scores were explored with single-sample gene set enrichment analysis (ssGSEA) and immunity profiles in LGGs. The GSRS-related pathways were investigated by gene set variation analysis (GSVA). Real-time PCR and the Human Protein Atlas (HPA) database were applied to explore related expression of hub genes selected in the GSRS.

Results

Compared with normal brain samples, 25 genes of 31 GSRGs were differentially expressed in LGG samples. A constructed five-gene GSRS was related to clinicopathological features and prognosis of LGGs by the LASSO algorithm. It was shown that the risk score level was positively related to the infiltrating level of native CD4 T cells and activated dendritic cells. GSVA identified several cancer-related pathways associated with the GSRS, such as P53 pathways and the JAK-STAT signaling pathway. Additionally, CA2, PTEN, OXTR, and SLC6A1 (hub genes identified in the GSRS) were regarded as the potential predictors in LGGs.

Conclusion

A new five-gene GSRS was identified and verified by bioinformatics methods. The GSRS provides a new perspective in LGG that may contribute to more accurate prediction of prognosis of LGGs.

Analysis on heterogeneity of hepatocellular carcinoma immune cells and a molecular risk model by integration of scRNA-seq and bulk RNA-seq

Background

Studies have shown that hepatocellular carcinoma (HCC) heterogeneity is a main cause leading to failure of treatment. Technology of single-cell sequencing (scRNA) could more accurately reveal the essential characteristics of tumor genetics.

Methods

From the Gene Expression Omnibus (GEO) database, HCC scRNA-seq data were extracted. The FindCluster function was applied to analyze cell clusters. Autophagy-related genes were acquired from the MSigDB database. The ConsensusClusterPlus package was used to identify molecular subtypes. A prognostic risk model was built with the Least Absolute Shrinkage and Selection Operator (LASSO)-Cox algorithm. A nomogram including a prognostic risk model and multiple clinicopathological factors was constructed.

Results

Eleven cell clusters labeled as various cell types by immune cell markers were obtained from the combined scRNA-seq GSE149614 dataset. ssGSEA revealed that autophagy-related pathways were more enriched in malignant tumors. Two autophagy-related clusters (C1 and C2) were identified, in which C1 predicted a better survival, enhanced immune infiltration, and a higher immunotherapy response. LASSO-Cox regression established an eight-gene signature. Next, the HCCDB18, GSA14520, and GSE76427 datasets confirmed a strong risk prediction ability of the signature. Moreover, the low-risk group had enhanced immune infiltration and higher immunotherapy response. A nomogram which consisted of RiskScore and clinical features had better prediction ability.

Conclusion

To precisely assess the prognostic risk, an eight-gene prognostic stratification signature was developed based on the heterogeneity of HCC immune cells.

Role of the TSPO–NOX4 axis in angiogenesis in glioblastoma

Objective: Angiogenesis is a pathological feature of glioblastoma. Nicotinamide adenine dinucleotide phosphate oxidase 4 (NOX4) is a vital source of reactive oxygen species (ROS) related to angiogenesis. However, signaling pathways correlated with the isoform oxidase are unknown. The aim of this study was to elucidate the detailed mechanism of the role of NOX4 in angiogenesis in glioblastoma.

Methods: Public datasets were searched for studies on immunohistochemistry and western blotting to evaluate NOX4 expression in glioma. The location of NOX4 expression was detected by immunofluorescence. We conducted conditional deletion of the translocator protein (TSPO) targeting the protein with the synthetic ligand XBD173 in the glioblastoma mouse model. NOX4 downregulation was conducted with the NOX4 inhibitor GLX351322, and ROS production and angiogenesis were detected in glioma tissues.

Results: Clinical samples and public datasets showed that NOX4 was upregulated and associated with the prognosis. NOX4 is mainly expressed in endothelial cells of glioblastoma. Both TSPO and NOX4 promoted angiogenesis in an ROS-dependent manner, suggesting that TSPO triggered ROS production in glioblastoma via NOX4.

Conclusion: These results showed that TSPO is an upstream target of NOX4-derived mitochondrial ROS, which is indispensable for NOX4-derived mitochondrial ROS-induced angiogenesis in glioblastoma. TSPO–NOX4 signaling could serve as a molecular target for therapeutic strategies for glioblastoma.

Identification of Diagnostic Genes and Effective Drugs Associated with Osteoporosis Treatment by Single-Cell RNA-Seq Analysis and Network Pharmacology

Background

Osteoporosis is a common bone metabolic disease with increased bone fragility and fracture rate. Effective diagnosis and treatment of osteoporosis still need to be explored due to the increasing incidence of disease.

Methods

Single-cell RNA-seq was acquired from GSE147287 dataset. Osteoporosis-related genes were obtained from chEMBL. Cell subpopulations were identified and characterized by scRNA-seq, t-SNE, clusterProfiler, and other computational methods. “limma” R packages were used to identify all differentially expressed genes. A diagnosis model was build using rms R packages. Key drugs were determined by proteins-proteins interaction and molecular docking.

Results

Firstly, 15,577 cells were obtained, and 12 cell subpopulations were identified by clustering, among which 6 cell subpopulations belong to CD45+ BM-MSCs and the other subpopulations were CD45-BM-MSCs. CD45- BM-MSCs_6 and CD45+ BM-MSCs_5 were consider as key subpopulations. Furthermore, we found 7 genes were correlated with above two subpopulations, and F9 gene had highest AUC. Finally, five compounds were identified, among which DB03742 bound well to F9 protein.

Conclusions

This work discovered that 7 genes were correlated with CD45-BM-MSCs_6 and CD45+ BM-MSCs_5 subpopulations in osteoporosis, among which F9 gene had better research value. Moreover, compound DB03742 was a potential inhibitor of F9 protein.

A systematic evaluation of the computational tools for ligand-receptor-based cell-cell interaction inference

Cell-cell interactions (CCIs) are essential for multicellular organisms to coordinate biological processes and functions. One classical type of CCI interaction is between secreted ligands and cell surface receptors, i.e. ligand-receptor (LR) interactions. With the recent development of single-cell technologies, a large amount of single-cell ribonucleic acid (RNA) sequencing (scRNA-Seq) data has become widely available. This data availability motivated the single-cell-resolution study of CCIs, particularly LR-based CCIs. Dozens of computational methods and tools have been developed to predict CCIs by identifying LR-based CCIs. Many of these tools have been theoretically reviewed. However, there is little study on current LR-based CCI prediction tools regarding their performance and running results on public scRNA-Seq datasets. In this work, to fill this gap, we tested and compared nine of the most recent computational tools for LR-based CCI prediction. We used 15 well-studied scRNA-Seq samples that correspond to approximately 100K single cells under different experimental conditions for testing and comparison. Besides briefing the methodology used in these nine tools, we summarized the similarities and differences of these tools in terms of both LR prediction and CCI inference between cell types. We provided insight into using these tools to make meaningful discoveries in understanding cell communications.

Integrative multi-omics approach to targeted therapy for glioblastoma

This review describes recent technological advances applied to glioblastoma (GBM), a brain tumor with dismal prognosis. International consortial efforts suggest the presence of molecular subtypes within histologically identical GBM tumors. This emphasizes that future treatment decisions should no longer be made based solely on morphological analyses, but must now take into consideration such molecular and cellular heterogeneity. The use of single-cell technologies has advanced our understanding and assignation of functional subtypes revealing therapeutic vulnerabilities. Our team has developed stratification approaches in the past few years, and we have been able to identify patient cohorts enriched for various signaling pathways. Importantly, our Glioportal brain tumor resource has been established under the National Neuroscience Institute Tissue Bank in 2021. This resource offers preclinical capability to validate working hypotheses established from patient clinical datasets. This review highlights recent developments with the ultimate goal of assigning functional meaning to molecular subtypes, revealing therapeutic vulnerabilities.

Clinically relevant orthotopic xenograft models of patient-derived glioblastoma in zebrafish

An accurate prediction of the intracranial infiltration tendency and drug response of individual glioblastoma (GBM) cells is essential for personalized prognosis and treatment for this disease. However, the clinical utility of mouse patient-derived orthotopic xenograft (PDOX) models remains limited given current technical constraints, including difficulty in generating sufficient sample numbers from small tissue samples and a long latency period for results. To overcome these issues, we established zebrafish GBM xenografts of diverse origin, which can tolerate intracranial engraftment and maintain their unique histological features. Subsequent single-cell RNA-sequencing (scRNA-seq) analysis confirmed significant transcriptional identity to that of invading GBM microtumors observed in the proportionally larger brains of model animals and humans. Endothelial scRNA-seq confirmed that the zebrafish blood–brain barrier is homologous to the mammalian blood–brain barrier. Finally, we established a rapid and efficient zebrafish PDOX (zPDOX) model, which can predict long-term outcomes of GBM patients within 20 days. The zPDOX model provides a novel avenue for precision medicine of GBM, especially for the evaluation of intracranial infiltration tendency and prediction of individual drug sensitivity.

Editor's choice: We established zebrafish glioblastoma (GBM) xenograft models that can be used to perform genetic and biological analysis of GBMs, identify blood–brain barrier-penetrating drugs and predict clinical sensitivity to temozolomide in GBM patients.

DNA Damage Repair in Brain Tumor Immunotherapy

With the gradual understanding of tumor development, many tumor therapies have been invented and applied in clinical work, and immunotherapy has been widely concerned as an emerging hot topic in the last decade. It is worth noting that immunotherapy is nowadays applied under too harsh conditions, and many tumors are defined as “cold tumors” that are not sensitive to immunotherapy, and brain tumors are typical of them. However, there is much evidence that suggests a link between DNA damage repair mechanisms and immunotherapy. This may be a breakthrough for the application of immunotherapy in brain tumors. Therefore, in this review, first, we will describe the common pathways of DNA damage repair. Second, we will focus on immunotherapy and analyze the mechanisms of DNA damage repair involved in the immune process. Third, we will review biomarkers that have been or may be used to evaluate immunotherapy for brain tumors, such as TAMs, RPA, and other molecules that may provide a precursor assessment for the rational implementation of immunotherapy for brain tumors. Finally, we will discuss the rational combination of immunotherapy with other therapeutic approaches that have an impact on the DNA damage repair process in order to open new pathways for the application of immunotherapy in brain tumors, to maximize the effect of immunotherapy on DNA damage repair mechanisms, and to provide ideas and guidance for immunotherapy in brain tumors.

Development of a T-cell activation-related module with predictive value for the prognosis and immune checkpoint blockade therapy response in glioblastoma

Background

Despite the rise in the use of immune checkpoint blockade drugs (ICBs) in recent years, there are no ICB drugs that are currently approved or under large-scale clinical trials for glioblastoma (GBM). T-cells, which mainly mediate adaptive immunity, are an important part of the tumor immune microenvironment. The activation of T-cells in tumors plays a key role in evaluating the sensitivity of patients to immunotherapy. Therefore, we applied bioinformatics approaches to construct a T-cell activation related risk score to study the effect of the activation of T-cells on the prognosis and ICB response of patients with GBM.

Materials and Methods

This study collected TCGA, CGGA, and GSE16011 glioma cohorts, as well as the IMvigor210 immunotherapy dataset, with complete mRNA expression profiles and clinical information. GraphPad Prism 8 and R 3.6.3 were used for bioinformatics analysis and plotting.

Results

The activation of T-cells in patients with GBM is characterized by obvious heterogeneity. We established a T-cell activation-related risk score based on five univariate Cox regression prognostic genes (CD276, IL15, SLC11A1, TNFSF4, and TREML2) in GBM. The risk score was an independent risk factor for poor prognosis. The overall survival time of patients in the high-risk group was significantly lower than in the low-risk group. Moreover, the high-risk score was accompanied by a stronger immune response and a more complex tumor immune microenvironment. “Hot tumors” were mainly enriched in the high-risk group, and high-risk group patients highly expressed inhibitory immune checkpoints (PD1, PD-L1, TIM3 etc.). By combining the risk and priming scores we obtained the immunotherapy score, which was shown to be a good evaluation index for sensitivity to GBM immunotherapy.

Conclusions

As an independent risk factor for poor prognosis, the T-cell activation-related risk score, combined with other clinical characteristics, could efficiently evaluate the survival of patients with GBM. The immunotherapy score obtained by combining the risk and priming scores could evaluate the ICB response of patients with GBM, providing treatment opportunities.

Unravelling glioblastoma heterogeneity by means of single-cell RNA sequencing

Glioblastoma (GBM) is the most invasive and deadliest brain cancer in adults. Its inherent heterogeneity has been designated as the main cause of treatment failure. Thus, a deeper understanding of the diversity that shapes GBM pathobiology is of utmost importance. Single-cell RNA sequencing (scRNA-seq) technologies have begun to uncover the hidden composition of complex tumor ecosystems. Herein, a semi-systematic search of reference literature databases provided all existing publications using scRNA-seq for the investigation of human GBM. We compared and discussed findings from these works to build a more robust and unified knowledge base. All aspects ranging from inter-patient heterogeneity to intra-tumoral organization, cancer stem cell diversity, clonal mosaicism, and the tumor microenvironment (TME) are comprehensively covered in this report. Tumor composition not only differs across patients but also involves a great extent of heterogeneity within itself. Spatial and cellular heterogeneity can reveal tumor evolution dynamics. In addition, the discovery of distinct cell phenotypes might lead to the development of targeted treatment approaches. In conclusion, scRNA-seq expands our knowledge of GBM heterogeneity and helps to unravel putative therapeutic targets.

Clinical characterization and immunosuppressive regulation of CD161 (KLRB1) in glioma through 916 samples

Background

Glioblastoma is a paradigm of cancer‐associated immunosuppression, limiting the effects of immunotherapeutic strategies. Thus, identifying the molecular mechanisms underlying immune surveillance evasion is critical. Recently, the preferential expression of inhibitory natural killer (NK) cell receptor CD161 on glioma‐infiltrating cytotoxic T cells was identified. Focusing on the molecularly annotated, large‐scale clinical samples from different ethnic origins, the data presented here provide evidence of this immune modulator's essential roles in brain tumor biology.

Methods

Retrospective RNA‐seq data analysis was conducted in a cohort of 313 patients with glioma in the Chinese Glioma Genome Atlas (CGGA) database and 603 patients in The Cancer Genome Atlas (TCGA) database. In addition, single‐cell sequencing data from seven surgical specimens of glioblastoma patients and a model in which patient‐derived glioma stem cells were cocultured with peripheral lymphocytes, were used to analyze the molecular evolution process during gliomagenesis.

Results

CD161 was enriched in high‐grade gliomas and isocitrate dehydrogenase (IDH)‐wildtype glioma. CD161 acted as a potential biomarker for the mesenchymal subtype of glioma and an independent prognostic factor for the overall survival (OS) of patients with glioma. In addition, CD161 played an essential role in inhibiting the cytotoxicity of T cells in glioma patients. During the process of gliomagenesis, the expression of CD161 on different lymphocytes dynamically evolved.

Conclusion

The expression of CD161 was closely related to the pathology and molecular pathology of glioma. Meanwhile, CD161 promoted the progression and evolution of gliomas through its unique effect on T cell dysfunction. Thus, CD161 is a promising novel target for immunotherapeutic strategies in glioma treatment.

Single-Cell Transcriptomic Analysis Revealed a Critical Role of SPP1/CD44-Mediated Crosstalk Between Macrophages and Cancer Cells in Glioma

High-grade glioma is one of the most lethal human cancers characterized by extensive tumor heterogeneity. In order to identify cellular and molecular mechanisms that drive tumor heterogeneity of this lethal disease, we performed single-cell RNA sequencing analysis of one high-grade glioma. Accordingly, we analyzed the individual cellular components in the ecosystem of this tumor. We found that tumor-associated macrophages are predominant in the immune microenvironment. Furthermore, we identified five distinct subpopulations of tumor cells, including one cycling, two OPC/NPC-like and two MES-like cell subpopulations. Moreover, we revealed the evolutionary transition from the cycling to OPC/NPC-like and MES-like cells by trajectory analysis. Importantly, we found that SPP1/CD44 interaction plays a critical role in macrophage-mediated activation of MES-like cells by exploring the cell-cell communication among all cellular components in the tumor ecosystem. Finally, we showed that high expression levels of both SPP1 and CD44 correlate with an increased infiltration of macrophages and poor prognosis of glioma patients. Taken together, this study provided a single-cell atlas of one high-grade glioma and revealed a critical role of macrophage-mediated SPP1/CD44 signaling in glioma progression, indicating that the SPP1/CD44 axis is a potential target for glioma treatment.

A Systematic Review of the Tumor-Infiltrating CD8+ T-Cells/PD-L1 Axis in High-Grade Glial Tumors: Toward Personalized Immuno-Oncology

Based on preclinical findings, programmed death-ligand 1 (PD-L1) can substantially attenuate CD8⁺ T-cell-mediated anti-tumoral immune responses. However, clinical studies have reported controversial results regarding the significance of the tumor-infiltrating CD8⁺ T-cells/PD-L1 axis on the clinical picture and the response rate of patients with high-grade glial tumors to anti-cancer therapies. Herein, we conducted a systematic review according to the preferred reporting items for systematic reviews and meta-analyses (PRISMA) statements to clarify the clinical significance of the tumor-infiltrating CD8⁺ T-cells/PD-L1 axis and elucidate the impact of this axis on the response rate of affected patients to anti-cancer therapies. Indeed, a better understanding of the impact of this axis on the response rate of affected patients to anti-cancer therapies can provide valuable insights to address the futile response rate of immune checkpoint inhibitors in patients with high-grade glial tumors. For this purpose, we systematically searched Scopus, Web of Science, Embase, and PubMed to obtain peer-reviewed studies published before 1 January 2021. We have observed that PD-L1 overexpression can be associated with the inferior prognosis of glioblastoma patients who have not been exposed to chemo-radiotherapy. Besides, exposure to anti-cancer therapies, e.g., chemo-radiotherapy, can up-regulate inhibitory immune checkpoint molecules in tumor-infiltrating CD8⁺ T-cells. Therefore, unlike unexposed patients, increased tumor-infiltrating CD8⁺ T-cells in anti-cancer therapy-exposed tumoral tissues can be associated with the inferior prognosis of affected patients. Because various inhibitory immune checkpoints can regulate anti-tumoral immune responses, the single-cell sequencing of the cells residing in the tumor microenvironment can provide valuable insights into the expression patterns of inhibitory immune checkpoints in the tumor micromovement. Thus, administrating immune checkpoint inhibitors based on the data from the single-cell sequencing of these cells can increase patients’ response rates, decrease the risk of immune-related adverse events development, prevent immune-resistance development, and reduce the risk of tumor recurrence.

Research Progress About Glioma Stem Cells in the Immune Microenvironment of Glioma

Gliomas are the most common primary tumors of the central nervous system. Due to the existence of the blood-brain barrier and its unique regional immune characteristics, the study of the immune microenvironment of gliomas is particularly important. Glioma stem cells are an important cause of initiating glioma, promoting tumor progression and leading to tumor recurrence. Immunotherapeutic strategies targeting glioma stem cells have become the focus of current research. This paper will focus on the research progress of glioma stem cells in the immune microenvironment of glioma to provide the basis for the immunotherapy of glioma.

Single-cell RNA sequencing in cancer: Applications, advances, and emerging challenges

Cancer has become one of the greatest threats to human health, and new technologies are urgently needed to further clarify the mechanisms of cancer so that better detection and treatment strategies can be developed. At present, extensive genomic analysis and testing of clinical specimens shape the insights into carcinoma. Nevertheless, carcinoma of humans is a complex ecosystem of cells, including carcinoma cells and immunity-related and stroma-related subsets, with accurate characteristics obscured by extensive genome-related approaches. A growing body of research shows that sequencing of single-cell RNA (scRNA-seq) is emerging to be an effective way for dissecting human tumor tissue at single-cell resolution, presenting one prominent way for explaining carcinoma biology. This review summarizes the research progress of scRNA-seq in the field of tumors, focusing on the application of scRNA-seq in tumor circulating cells, tumor stem cells, tumor drug resistance, the tumor microenvironment, and so on, which provides a new perspective for tumor research.

Dual Targeting of Sorafenib-Resistant HCC-Derived Cancer Stem Cells

Sorafenib, an oral multi-tyrosine kinase inhibitor, has been the first-line therapy for the treatment of patients with advanced HCC, providing a survival benefit of only three months in approximately 30% of patients. Cancer stem cells (CSCs) are a rare tumour subpopulation with self-renewal and differentiation capabilities, and have been implicated in tumour growth, recurrence and drug resistance. The process of epithelial-to-mesenchymal transition (EMT) contributes to the generation and maintenance of the CSC population, resulting in immune evasion and therapy resistance in several cancers, including HCC. The aim of this study is to target the chemoresistant CSC population in HCC by assessing the effectiveness of a combination treatment approach with Sorafenib, an EMT inhibitor and an immune checkpoint inhibitor (ICI). A stem-cell-conditioned serum-free medium was utilised to enrich the CSC population from the human HCC cell lines Hep3B, PLC/PRF/5 and HepG2. The anchorage independent spheres were characterised for CSC features. The human HCC-derived spheres were assessed for EMT status and expression of immune checkpoint molecules. The effect of combination treatment with SB431542, an EMT inhibitor, and siRNA-mediated knockdown of programmed cell death protein ligand-1 (PD-L1) or CD73 along with Sorafenib on human HCC-derived CSCs was examined with cell viability and apoptosis assays. The three-dimensional spheres enriched from human HCC cell lines demonstrated CSC-like features. The human HCC-derived CSCs also exhibited the EMT phenotype along with the upregulation of immune checkpoint molecules. The combined treatment with SB431542 and siRNA-mediated PD-L1 or CD73 knockdown effectively enhanced the cytotoxicity of Sorafenib against the CSC population compared to Sorafenib alone, as evidenced by the reduced size and proliferation of spheres. Furthermore, the combination treatment of Sorafenib with SB431542 and PD-L1 or CD73 siRNA resulted in an increased proportion of an apoptotic population, as evidenced by flow cytometry analysis. In conclusion, the combined targeting of EMT and immune checkpoint molecules with Sorafenib can effectively target the CSC tumour subpopulation.

Molecular Subtypes Based on the Stemness Index Predict Prognosis in Glioma Patients

Glioma is the common histological subtype of malignancy in the central nervous system, with high morbidity and mortality. Glioma cancer stem cells (CSCs) play essential roles in tumor recurrence and treatment resistance. Thus, exploring the stem cell-related genes and subtypes in glioma is important. In this study, we collected the RNA-sequencing (RNA-seq) data and clinical information of glioma patients from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases. With the differentially expressed genes (DEGs) and weighted gene correlation network analysis (WGCNA), we identified 86 mRNA expression-based stemness index (mRNAsi)-related genes in 583 samples from TCGA RNA-seq dataset. Furthermore, these samples from TCGA database could be divided into two significantly different subtypes with different prognoses based on the mRNAsi corresponding gene, which could also be validated in the CGGA database. The clinical characteristics and immune cell infiltrate distribution of the two stemness subtypes are different. Then, functional enrichment analyses were performed to identify the different gene ontology (GO) terms and pathways in the two different subtypes. Moreover, we constructed a stemness subtype-related risk score model and nomogram to predict the prognosis of glioma patients. Finally, we selected one gene (ETV2) from the risk score model for experimental validation. The results showed that ETV2 can contribute to the invasion, migration, and epithelial-mesenchymal transition (EMT) process of glioma. In conclusion, we identified two distinct molecular subtypes and potential therapeutic targets of glioma, which could provide new insights for the development of precision diagnosis and prognostic prediction for glioma patients.