Single-Cell RNA-Sequencing in Glioma. Curr Oncol Rep. 2018;20:42. [PMID: 29637300 DOI: 10.1007/s11912-018-0673-2]

Comprehensive characterization of the molecular feature of T cells in laryngeal cancer: evidence from integrated single-cell and bulk RNA sequencing data using multiple machine learning approaches

BACKGROUND

The clinical relevance of T cell-related molecules at single-cell resolution in laryngeal cancer (LC) has not been clarified.

MATERIALS AND METHODS

Three LC tissues and matching adjoining normal tissues from the hospital were used to perform 10X single-cell RNA sequencing. Hub T cell-related genes (TCRGs) were detected by applying ten machine learning (ML) techniques based TCGA and GEO databases, which were also utilized to create a prediction model (TCRG classifier) and a multicenter validation model. Lastly, we conducted a comprehensive analysis of the TCRG's correlation with immunological properties.

RESULTS

The analysis of single-cell RNA-seq data revealed that T cells are the primary components of the tumor microenvironment (TME), are significantly involved in cell differentiation pathways, and play a considerable role in intercellular communication. Based on 10 ML approaches, TCRG classifier were identified to develop and validate. The TCRG classifier exhibited excellent prognostic values with a mean C-index of 0.66 in six cohorts, serving as an independent risk factor (p < 0.01). Additionally, the TCRG exhibited a significant relationship with immune score, immune cell infiltration, immune-associated pathways, immune checkpoint inhibitors, human leukocyte antigen, and immunogenicity. Lastly, IPS, TCIC, TIDE, and IMvigor210 cohort analysis illustrated that the immunotherapy response may be accurately predicted using TCRG.

CONCLUSION

A TCRG classifier is an excellent resource for predicting a patient's prognosis, potentially guiding the preservation of laryngeal function, and identifying patients who may have a positive response to immunotherapy, which might have profound effects on therapeutic practice.

Integrative analysis of mitochondrial-related gene profiling identifies prognostic clusters and drug resistance mechanisms in low-grade glioma

Mitochondrial dysfunction has emerged as a critical factor in the progression and prognosis of low-grade glioma (LGG). In this study, we explored the role of mitochondrial-related genes through consensus clustering analysis using multi-omics data from the TCGA, CGGA, and other independent datasets. Patients were categorized into three clusters (Cluster A, B, and C), with Cluster B consistently associated with poorer prognosis. Mutation landscape analysis revealed distinct genetic alterations and copy number variations among clusters, particularly in Cluster B, which exhibited unique genetic signatures. Immune infiltration analysis showed that Cluster B had higher expression levels of immune checkpoint genes, stronger immune evasion activity, and greater immune cell infiltration, suggesting an immunosuppressive tumor microenvironment. Furthermore, we identified mitochondrial-related prognostic markers and developed a MITscore based on gene expression patterns, which stratified patients into high- and low-risk groups. High MITscore groups displayed stronger stemness characteristics, poorer survival outcomes, and differential responses to chemotherapy and immunotherapy. Cross-validation with drug sensitivity and immunotherapy cohorts indicated that high MITscore patients were more sensitive to certain chemotherapeutic agents and responded better to immunotherapy. Finally, using the SRGA method, we identified novel biomarkers (KDR, LRRK2, SQSTM1) closely associated with mitochondrial function, which may serve as potential targets for therapeutic intervention. These findings highlight the critical role of mitochondrial dysfunction in LGG prognosis, tumor microenvironment regulation, and treatment response, providing new avenues for precision oncology.

Identification of distinct profiles of glioblastoma through the immunocapture of extracellular vesicles from patient plasma

Glioblastoma (GBM), a primary brain tumor, exhibits intratumoral heterogeneity and dynamic spatial-temporal changes. GBM-derived extracellular vesicles (EVs), reflecting tumor characteristics, present potential as liquid-biopsy markers for early diagnosis and monitoring. This study aims to evaluate molecular signatures of plasma-derived EVs from GBM patients using a conventional flow cytometer. EVs have been isolated from glioma patients and healthy controls (HCs) plasma using density gradient ultracentrifugation (DGU). EVs were evaluated by bead-based multiplex analysis in a conventional flow cytometer. Principal component analysis (PCA), hierarchical clustering, and correlation analysis provided comprehensive insights into EV characteristics. EVs successfully isolated were visualized in transmission and scanning electron microscopy (STEM). Bead-based multiplex analysis in flow cytometer detected the level of 37 EV surface markers, including tumor-related, cancer stem cell, endothelial cell, and immune cell- specific antigens. PCA identified the EV surface markers that are most significant for differentiating the subjects, and hierarchical clustering revealed four distinct clusters based on EV surface marker levels. EV molecular signature demonstrated considerable heterogeneity across patient clusters. The presence of CD29 emerged not only as a defining factor for a cluster of patients, but also served as a marker to differentiate patients from HCs.

Exploring the role of Disulfidptosis in glioma progression: insights into tumor heterogeneity and therapeutic potential through single-cell RNA sequencing

BACKGROUND

Gliomas, particularly glioblastoma (GBM), are the most common and aggressive primary brain tumors in adults, characterized by high malignancy and frequent recurrence. Despite standard treatments, including surgery, radiotherapy, and chemotherapy, the prognosis for GBM remains poor, with a median survival of less than 15 months and a five-year survival rate below 10%. Tumor heterogeneity and resistance to treatment create significant challenges in controlling glioma progression. Therefore, there is an urgent need for new therapeutic targets and strategies.

OBJECTIVE

This study investigates the role of Disulfidptosis, a recently discovered form of programmed cell death, in gliomas. Unlike apoptosis and necrosis, Disulfidptosis is driven by the abnormal accumulation of intracellular disulfide bonds, leading to protein misfolding and cytoskeletal collapse, particularly in cancer cells with metabolic dysregulation. We aim to explore how glioma cells respond to Disulfidptosis and identify potential therapeutic targets by analyzing the heterogeneity of gliomas at the single-cell level using single-cell RNA sequencing (scRNA-seq).

METHODS

scRNA-seq data from glioma patients were analyzed to uncover differences in ferroptosis-related pathways, including iron metabolism and lipid peroxidation. Cellular subpopulations within gliomas were profiled to assess their sensitivity to Disulfidptosis and the underlying mechanisms. Survival analysis was conducted to evaluate the clinical relevance of Disulfidptosis-related gene expression.

RESULTS

Multiple cell subpopulations within gliomas exhibit varying sensitivities to Disulfidptosis, influenced by their metabolic properties. Dysregulated iron metabolism and antioxidant mechanisms were identified as key factors impacting Disulfidptosis sensitivity. Glioma microenvironment signaling pathways also play a role in regulating Disulfidptosis. These findings suggest that activating Disulfidptosis pathways may provide novel therapeutic strategies to overcome treatment resistance in gliomas.

CONCLUSION

This study offers new insights into the role of Disulfidptosis in glioma progression and highlights its potential as a therapeutic target. By leveraging single-cell sequencing data, the research uncovers tumor heterogeneity and identifies specific cell populations resistant to Disulfidptosis. These findings may pave the way for personalized treatment strategies to improve survival outcomes in glioma patients.

Construction and validation of a TAMRGs prognostic signature for gliomas by integrated analysis of scRNA and bulk RNA sequencing data

BACKGROUND

This study aimed to construct and validate a prognostic model based on tumor associated macrophage-related genes (TAMRGs) by integrating single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing (bulk RNA-seq) data.

METHODS

The scRNA-seq data of three inhouse glioma tissues were used to identify the tumor-associated macrophages (TAMs) marker genes, the DEGs from the The Cancer Genome Atlas (TCGA) - Genotype-Tissue Expression (GTEx) dataset were used to further select TAMs marker genes. Subsequently, a TAMRG-score was constructed by Least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression analysis in the TCGA dataset and validated in the Chinese Glioma Genome Atlas (CGGA) dataset.

RESULTS

We identified 186 TAMs marker genes, and a total of 6 optimal prognostic genes including CKS2, LITAF, CTSB, TWISTNB, PPIF and G0S2 were selected to construct a TAMRG-score. The high TAMRG-score was significantly associated with worse prognosis (log-rank test, P<0.001). Moreover, the TAMRG-score outperformed the other three models with AUC of 0.808. Immune cell infiltration, TME scores, immune checkpoints, TMB and drug susceptibility were significantly different between TAMRG-score groups. In addition, a nomogram were constructed by combing the TAMRG-score and clinical information (Age, Grade, IDH mutation and 1p19q codeletion) to predict the survival of glioma patients with AUC of 0.909 for 1-year survival.

CONCLUSION

The high TAMRG-score group was associated with a poor prognosis. A nomogram by incorporating TMARG-score could precisely predict glioma survival, and provide evidence for personalized treatment of glioma.

Extracellular vesicles in glioblastoma: a challenge and an opportunity

Glioblastoma is a highly heterogeneous tumor whose pathophysiological complexities dictate both the diagnosis of disease severity as well as response to therapy. Conventional diagnostic tools and standard treatment regimens have only managed to achieve limited success in the management of patients suspected of glioblastoma. Extracellular vesicles are an emerging liquid biopsy tool that has shown great promise in resolving the limitations presented by the heterogeneous nature of glioblastoma. Here we discuss the contrasting yet interdependent dual role of extracellular vesicles as communication agents that contribute to the progression of glioblastoma by creating a heterogeneous microenvironment and as a liquid biopsy tool providing an opportunity to accurately identify the disease severity and progression.

Single-cell RNA sequencing reveals pro-invasive cancer-associated fibroblasts in hypopharyngeal squamous cell carcinoma

BACKGROUND

Hypopharyngeal squamous cell carcinoma (HPSCC) has the worst prognosis among all head-and-neck cancers, and treatment options are limited. Tumor microenvironment (TME) analysis can help identify new therapeutic targets and combined treatment strategies.

METHODS

Six primary HPSCC tissues and two adjacent normal mucosae from six treatment-naïve patients with HPSCC were analyzed using scRNA-seq. Cell types were curated in detail, ecosystemic landscapes were mapped, and cell-cell interactions were inferred. Key results were validated with The Cancer Genome Atlas and cell biology experiments.

RESULTS

Malignant HPSCC epithelial cells showed significant intratumor heterogeneity. Different subtypes exhibited distinct histological features, biological behaviors, and spatial localization, all affecting treatment selection and prognosis. Extracellular matrix cancer-associated fibroblasts (mCAFs) expressing fibroblast activation protein were the dominant CAFs in HPSCC tumors. mCAFs, constituting an aggressive CAF subset, promoted tumor cell invasion, activated endothelial cells to trigger angiogenesis, and synergized with SPP1+ tumor associated macrophages to induce tumor progression, ultimately decreasing the overall survival of patients with HPSCC. Moreover, the LAMP3+ dendritic cell subset was identified in HPSCC and formed an immunosuppressive TME by recruiting Tregs and suppressing CD8+ T cell function.

CONCLUSIONS

mCAFs, acting as the communication center of the HPSCC TME, enhance the invasion ability of HPSCC cells, mobilizing surrounding cells to construct a tumor-favorable microenvironment. Inhibiting mCAF activation offers a new anti-HPSCC therapeutic strategy. Video Abstract.

Dual-Purpose 3D-Silica Nanostructure Matrix for Rapid Epigenetic Reprogramming of Tumor Cell to Cancer Stem Cell Spheroid

Cancer stem cells (CSCs), a rare subpopulation responsible for tumorigenesis and therapeutic resistance, are difficult to characterize and isolate. Conventional method of growing CSCs takes up to 2-8 weeks inhibiting the rate of research. Therefore, rapid reprogramming (RR) of tumor cells into CSCs is crucial to accelerate the stem cell oncology research. The current RR techniques cannot be utilized for CSC RR due to many limitations posed due to isolation requirements resulting in loss of vital data. Hence, a technique that can induce CSC RR without the need for isolation procedures is needed. Here, fabrication of a 3D-silica nanostructured extracellular matrix for RR and in situ monitoring is reported. The RR is tested using three preclinical cancer models. The 3D matrix and a zeta potential study confirm an intense material-cellular interaction resulting in the enhanced expressions of surface and epigenetic biomarkers. Cancer cells require only 3-day period to form CSC spheroids with 3D-silica extracellular matrix. Real-time single-cell monitoring of the methylene blue-induced photodynamic demonstrates the dual functionality. To the authors' knowledge, this is the first study to demonstrate a CSC epigenetic reprogramming using nanostructures. These findings may pave the path for accelerating the stem cell research in oncology.

Single-cell landscapes of primary glioblastomas and matched explants and cell lines show variable retention of inter- and intratumor heterogeneity

Glioblastomas (GBMs) are aggressive brain tumors characterized by extensive inter- and intratumor heterogeneity. Patient-derived models, such as organoids and explants, have recently emerged as useful models to study such heterogeneity, although the extent to which they can recapitulate GBM genomic features remains unclear. Here, we analyze bulk exome and single-cell genome and transcriptome profiles of 12 IDH wild-type GBMs, including two recurrent tumors, and of patient-derived explants (PDEs) and gliomasphere (GS) lines derived from these tumors. We find that PDEs are genetically similar to, and variably retain gene expression characteristics of, their parent tumors. Notably, PDEs appear to exhibit similar levels of transcriptional heterogeneity compared with their parent tumors, whereas GS lines tend to be enriched for cells in a more uniform transcriptional state. The approaches and datasets introduced here will provide a valuable resource to help guide experiments using GBM-derived models, especially in the context of studying cellular heterogeneity.

Prevention of tumor risk associated with the reprogramming of human pluripotent stem cells

Human pluripotent embryonic stem cells have two special features: self-renewal and pluripotency. It is important to understand the properties of pluripotent stem cells and reprogrammed stem cells. One of the major problems is the risk of reprogrammed stem cells developing into tumors. To understand the process of differentiation through which stem cells develop into cancer cells, investigators have attempted to identify the key factors that generate tumors in humans. The most effective method for the prevention of tumorigenesis is the exclusion of cancer cells during cell reprogramming. The risk of cancer formation is dependent on mutations of oncogenes and tumor suppressor genes during the conversion of stem cells to cancer cells and on the environmental effects of pluripotent stem cells. Dissecting the processes of epigenetic regulation and chromatin regulation may be helpful for achieving correct cell reprogramming without inducing tumor formation and for developing new drugs for cancer treatment. This review focuses on the risk of tumor formation by human pluripotent stem cells, and on the possible treatment options if it occurs. Potential new techniques that target epigenetic processes and chromatin regulation provide opportunities for human cancer modeling and clinical applications of regenerative medicine.

Genetic Hallmarks and Heterogeneity of Glioblastoma in the Single-Cell Omics Era

Glioblastoma multiforme is the most common and aggressive malignant primary brain tumor. As implied by its name, the disease displays impressive intrinsic heterogeneity. Among other complications, inter- and intratumoral diversity hamper glioblastoma research and therapy, typically leaving patients with little hope for long-term survival. Extensive genetic analyses, including omics, characterize several recurrent mutations. However, confounding factors mask crucial aspects of the pathology to conventional bulk approaches. In recent years, single-cell omics have made their first appearance in cancer research, and the methodology is about to reach its full potential for glioblastoma too. Here, recent glioblastoma single-cell omics investigations are reviewed, and most promising routes toward less grim prognoses and more efficient therapeutics are discussed.

Systematic expression analysis of ligand-receptor pairs reveals important cell-to-cell interactions inside glioma

Background

Glioma is the most commonly diagnosed malignant and aggressive brain cancer in adults. Traditional researches mainly explored the expression profile of glioma at cell-population level, but ignored the heterogeneity and interactions of among glioma cells.

Methods

Here, we firstly analyzed the single-cell RNA-seq (scRNA-seq) data of 6341 glioma cells using manifold learning and identified neoplastic and healthy cells infiltrating in tumor microenvironment. We systematically revealed cell-to-cell interactions inside gliomas based on corresponding scRNA-seq and TCGA RNA-seq data.

Results

A total of 16 significantly correlated autocrine ligand-receptor signal pairs inside neoplastic cells were identified based on the scRNA-seq and TCGA data of glioma. Furthermore, we explored the intercellular communications between cancer stem-like cells (CSCs) and macrophages, and identified 66 ligand-receptor pairs, some of which could significantly affect prognostic outcomes. An efficient machine learning model was constructed to accurately predict the prognosis of glioma patients based on the ligand-receptor interactions.

Conclusion

Collectively, our study not only reveals functionally important cell-to-cell interactions inside glioma, but also detects potentially prognostic markers for predicting the survival of glioma patients.

Electronic supplementary material

The online version of this article (10.1186/s12964-019-0363-1) contains supplementary material, which is available to authorized users.

Immune contexture defined by single cell technology for prognosis prediction and immunotherapy guidance in cancer

Tumor immune microenvironment is closely related to tumor initiation, prognosis, and response to immunotherapy. The immune landscapes, number of infiltrating immune cells, and the localization of lymphocytes in the tumor vary in across different types of tumors. The immune contexture in cancer, which is determined by the density, composition, functional state and organization of the leukocyte infiltrate of the tumor, can yield information relevant to the prediction of treatment response and patients’ prognosis. Better understanding of the immune atlas in human tumors have been achieved with the development and application of single-cell analysis technology, which has provided a reference for prognosis, and insights on new targets for immunotherapy. In this review, we summarized the different characteristics of immune contexture in cancer defined by a variety of single-cell techniques, which have enhanced our understanding on the pathophysiology of the tumor microenvironment. We believe that there are much more to be uncovered in this rapidly developing field of medicine, and they will predict the prognosis of cancer patients and guide the rational design of immunotherapies for success in cancer eradication.

Roles of Extracellular Vesicles in High-Grade Gliomas: Tiny Particles with Outsized Influence

High-grade gliomas, particularly glioblastomas (grade IV), are devastating diseases with dismal prognoses; afflicted patients seldom live longer than 15 months, and their quality of life suffers immensely. Our current standard-of-care therapy has remained essentially unchanged for almost 15 years, with little new therapeutic progress. We desperately need a better biologic understanding of these complicated tumors in a complicated organ. One area of rejuvenated study relates to extracellular vesicles (EVs)-membrane-enclosed nano- or microsized particles that originate from the endosomal system or are shed from the plasma membrane. EVs contribute to tumor heterogeneity (including the maintenance of glioma stem cells or their differentiation), the impacts of hypoxia (angiogenesis and coagulopathies), interactions amid the tumor microenvironment (concerning the survival of astrocytes, neurons, endothelial cells, blood vessels, the blood-brain barrier, and the ensuing inflammation), and influences on the immune system (both stimulatory and suppressive). This article reviews glioma EVs and the ways that EVs manifest themselves as autocrine, paracrine, and endocrine factors in proximal and distal intra- and intercellular communications. The reader should note that there is much controversy, and indeed confusion, in the field over the exact roles for EVs in many biological processes, and we will engage some of these difficulties herein.

Potential application of cell reprogramming techniques for cancer research

The ability to control the transition from an undifferentiated stem cell to a specific cell fate is one of the key techniques that are required for the application of interventional technologies to regenerative medicine and the treatment of tumors and metastases and of neurodegenerative diseases. Reprogramming technologies, which include somatic cell nuclear transfer, induced pluripotent stem cells, and the direct reprogramming of specific cell lineages, have the potential to alter cell plasticity in translational medicine for cancer treatment. The characterization of cancer stem cells (CSCs), the identification of oncogene and tumor suppressor genes for CSCs, and the epigenetic study of CSCs and their microenvironments are important topics. This review summarizes the application of cell reprogramming technologies to cancer modeling and treatment and discusses possible obstacles, such as genetic and epigenetic alterations in cancer cells, as well as the strategies that can be used to overcome these obstacles to cancer research.

Single-cell analysis of tumors: Creating new value for molecular biomarker discovery of cancer stem cells and tumor-infiltrating immune cells

Biomarker-driven individualized treatment in oncology has made tremendous progress through technological developments, new therapeutic modalities and a deeper understanding of the molecular biology for tumors, cancer stem cells and tumor-infiltrating immune cells. Recent technical developments have led to the establishment of a variety of cancer-related diagnostic, prognostic and predictive biomarkers. In this regard, different modern OMICs approaches were assessed in order to categorize and classify prognostically different forms of neoplasia. Despite those technical advancements, the extent of molecular heterogeneity at the individual cell level in human tumors remains largely uncharacterized. Each tumor consists of a mixture of heterogeneous cell types. Therefore, it is important to quantify the dynamic cellular variations in order to predict clinical parameters, such as a response to treatment and or potential for disease recurrence. Recently, single-cell based methods have been developed to characterize the heterogeneity in seemingly homogenous cancer cell populations prior to and during treatment. In this review, we highlight the recent advances for single-cell analysis and discuss the challenges and prospects for molecular characterization of cancer cells, cancer stem cells and tumor-infiltrating immune cells.

miR-17-5p-CXCL14 axis related transcriptome profile and clinical outcome in diffuse gliomas

GBM tissues are comprised of not only tumor cells but also tumor-associated nontumor cells, such as stromal cells and immune cells, which dilute the purity of glioma cells and function in glioma biology. However, the roles of miRNAs in modulating glioma purity are not clarified. In total, 838 glioma samples with transcriptome data, including 537 RNAseq data from TCGA project and 301 microarray data from Chinese Glioma Genome Atlas (CGGA project), were recruited into our investigation. Tumor purity, molecular subtypes and IDH status were also available. R language was employed as the main tool for statistical analysis and graphical work. Screening miRNA profiling and paired TCGA samples' transcriptome data demonstrates that miR-17-5p expression harbors the most significant positive correlation with glioma purity among all miRNAs. CXCL14 shows robust negative correlation with miR-17-5p expression in TCGA and CGGA dataset. miR-17-5p directly targets CXCL14 and functions as a tumor-suppressor of GBM. CXCL14 showed lower expression in proneural subtype and may contribute as a potential marker for proneural subtype in glioma. Genes markedly correlated with CXCL14 are involved in essential functions associated with anti-tumor immune process. CXCL14 has a strong correlation with immune(T cells, Monocytic lineage and Neutrophils) and Fibroblasts within glioma environment. miR-17-5p and CXCL14 exhibited predictive values for high-grade glioma(HGG) patients: Higher miR-17-5p indicated significantly longer survival while lower CXCL14 indicated longer survival. Our results highlight the importance of the miR-17-5p-CXCL14 axis in regulating key steps of anti-tumor immune process and may serve as potential targets of immune treatments for gliomas.

Using genomics to guide treatment for glioblastoma

Glioblastoma has been shown to have many different genetic mutations found both within and between tumor samples. Molecular testing and genomic sequencing has helped to classify diagnoses and clarify difficult to interpret histopathological specimens. Genomic information also plays a critical role in prognostication for patients, with IDH mutations and MGMT methylation having significant impact of the response to chemotherapy and overall survival of patients. Unfortunately, personalized medicine and targeted therapy against specific mutations have not been shown to improve patient outcomes. As technology continues to improve, exome and RNA sequencing will play a role in the design of clinical trials, classification of patient subgroups and identification of rare mutations that can be targeted by small-molecule inhibitors and biologic agents.