Integrative analysis of single-cell transcriptomic and multilayer signaling networks in glioma reveal tumor progression stage

Introduction

Tumor microenvironments (TMEs) encompass complex ecosystems of cancer cells, infiltrating immune cells, and diverse cell types. Intercellular and intracellular signals within the TME significantly influence cancer progression and therapeutic outcomes. Although computational tools are available to study TME interactions, explicitly modeling tumor progression across different cancer types remains a challenge.

Methods

This study introduces a comprehensive framework utilizing single-cell RNA sequencing (scRNA-seq) data within a multilayer network model, designed to investigate molecular changes across glioma progression stages. The heterogeneous, multilayered network model replicates the hierarchical structure of biological systems, from genetic building blocks to cellular functions and phenotypic manifestations.

Results

Applying this framework to glioma scRNA-seq data allowed complex network analysis of different cancer stages, revealing significant ligand‒receptor interactions and key ligand‒receptor-transcription factor (TF) axes, along with their associated biological pathways. Differential network analysis between grade III and grade IV glioma highlighted the most critical nodes and edges involved in interaction rewiring. Pathway enrichment analysis identified four essential genes-PDGFA (ligand), PDGFRA (receptor), CREB1 (TF), and PLAT (target gene)-involved in the Receptor Tyrosine Kinases (RTK) signaling pathway, which plays a pivotal role in glioma progression from grade III to grade IV.

Discussion

These genes emerged as significant features for machine learning in predicting glioma progression stages, achieving 87% accuracy and 93% AUC in a 3-year survival prediction through Kaplan-Meier analysis. This framework provides deeper insights into the cellular machinery of glioma, revealing key molecular relationships that may inform prognosis and therapeutic strategies.

Macrophage polarization-related gene signature for risk stratification and prognosis of survival in gliomas

Macrophage polarization plays an essential role in tumour immune cell infiltration and tumour growth. In this study, we selected a series of genes distinguishing between M1 and M2 macrophages and explored their prognostic value in gliomas. A total of 170 genes were included in our study. The CGGA database was used as the training cohort and the TCGA database as the validation cohort. The biological processes and functions were identified by GO and KEGG analysis. Kaplan-Meier analysis was used to compare survival differences between groups. Importantly, we built a risk score model using Cox regression analysis based on the CGGA and verified it in the TCGA database and our sequencing data. Patients with gliomas in the high-risk group were associated with high pathologic grade, IDH WT status, MGMT promoter unmethylation, 1p19q non-codeletion and prone to have a poor outcome. GEPIA results revealed that CD300C, CNRIP1 and MYO1F are the most related genes of immune infiltrations. The differential expression of these genes between low-grade gliomas and glioblastomas was confirmed by q-RT-PCR. Macrophage polarization-related gene signatures can predict the malignancy and outcome of patients with gliomas and might act as a promising target for glioma immunotherapy in the future.

Single-cell RNA-seq reveals the transcriptional program underlying tumor progression and metastasis in neuroblastoma

Neuroblastoma (NB) is one of the most common childhood malignancies. Sixty percent of patients present with widely disseminated clinical signs at diagnosis and exhibit poor outcomes. However, the molecular mechanisms triggering NB metastasis remain largely uncharacterized. In this study, we generated a transcriptomic atlas of 15 447 NB cells from eight NB samples, including paired samples of primary tumors and bone marrow metastases. We used time-resolved analysis to chart the evolutionary trajectory of NB cells from the primary tumor to the metastases in the same patient and identified a common 'starter' subpopulation that initiates tumor development and metastasis. The 'starter' population exhibited high expression levels of multiple cell cycle-related genes, indicating the important role of cell cycle upregulation in NB tumor progression. In addition, our evolutionary trajectory analysis demonstrated the involvement of partial epithelial-to-mesenchymal transition (p-EMT) along the metastatic route from the primary site to the bone marrow. Our study provides insights into the program driving NB metastasis and presents a signature of metastasis-initiating cells as an independent prognostic indicator and potential therapeutic target to inhibit the initiation of NB metastasis.

Phagocytosis Checkpoints in Glioblastoma: CD47 and Beyond

Glioblastoma multiforme (GBM) is one of the deadliest human cancers with very limited treatment options available. The malignant behavior of GBM is manifested in a tumor which is highly invasive, resistant to standard cytotoxic chemotherapy, and strongly immunosuppressive. Immune checkpoint inhibitors have recently been introduced in the clinic and have yielded promising results in certain cancers. GBM, however, is largely refractory to these treatments. The immune checkpoint CD47 has recently gained attention as a potential target for intervention as it conveys a "don't eat me" signal to tumor-associated macrophages (TAMs) via the inhibitory SIRP alpha protein. In preclinical models, the administration of anti-CD47 monoclonal antibodies has shown impressive results with GBM and other tumor models. Several well-characterized oncogenic pathways have recently been shown to regulate CD47 expression in GBM cells and glioma stem cells (GSCs) including Epidermal Growth Factor Receptor (EGFR) beta catenin. Other macrophage pathways involved in regulating phagocytosis including TREM2 and glycan binding proteins are discussed as well. Finally, chimeric antigen receptor macrophages (CAR-Ms) could be leveraged for greatly enhancing the phagocytosis of GBM and repolarization of the microenvironment in general. Here, we comprehensively review the mechanisms that regulate the macrophage phagocytosis of GBM cells.

NDC80/HEC1 promotes macrophage polarization and predicts glioma prognosis via single-cell RNA-seq and in vitro experiment

INTRODUCTION

Glioma is the most frequent and lethal form of primary brain tumor. The molecular mechanism of oncogenesis and progression of glioma still remains unclear, rendering the therapeutic effect of conventional radiotherapy, chemotherapy, and surgical resection insufficient. In this study, we sought to explore the function of HEC1 (highly expressed in cancer 1) in glioma; a component of the NDC80 complex in glioma is crucial in the regulation of kinetochore.

METHODS

Bulk RNA and scRNA-seq analyses were used to infer HEC1 function, and in vitro experiments validated its function.

RESULTS

HEC1 overexpression was observed in glioma and was indicative of poor prognosis and malignant clinical features, which was confirmed in human glioma tissues. High HEC1 expression was correlated with more active cell cycle, DNA-associated activities, and the formation of immunosuppressive tumor microenvironment, including interaction with immune cells, and correlated strongly with infiltrating immune cells and enhanced expression of immune checkpoints. In vitro experiments and RNA-seq further confirmed the role of HEC1 in promoting cell proliferation, and the expression of DNA replication and repair pathways in glioma. Coculture assay confirmed that HEC1 promotes microglial migration and the transformation of M1 phenotype macrophage to M2 phenotype.

CONCLUSION

Altogether, these findings demonstrate that HEC1 may be a potential prognostic marker and an immunotherapeutic target in glioma.

Tumor biomarkers for diagnosis, prognosis and targeted therapy

Tumor biomarkers, the substances which are produced by tumors or the body's responses to tumors during tumorigenesis and progression, have been demonstrated to possess critical and encouraging value in screening and early diagnosis, prognosis prediction, recurrence detection, and therapeutic efficacy monitoring of cancers. Over the past decades, continuous progress has been made in exploring and discovering novel, sensitive, specific, and accurate tumor biomarkers, which has significantly promoted personalized medicine and improved the outcomes of cancer patients, especially advances in molecular biology technologies developed for the detection of tumor biomarkers. Herein, we summarize the discovery and development of tumor biomarkers, including the history of tumor biomarkers, the conventional and innovative technologies used for biomarker discovery and detection, the classification of tumor biomarkers based on tissue origins, and the application of tumor biomarkers in clinical cancer management. In particular, we highlight the recent advancements in biomarker-based anticancer-targeted therapies which are emerging as breakthroughs and promising cancer therapeutic strategies. We also discuss limitations and challenges that need to be addressed and provide insights and perspectives to turn challenges into opportunities in this field. Collectively, the discovery and application of multiple tumor biomarkers emphasized in this review may provide guidance on improved precision medicine, broaden horizons in future research directions, and expedite the clinical classification of cancer patients according to their molecular biomarkers rather than organs of origin.

Tutorial on survival modeling with applications to omics data

MOTIVATION

Identification of genomic, molecular and clinical markers prognostic of patient survival is important for developing personalized disease prevention, diagnostic and treatment approaches. Modern omics technologies have made it possible to investigate the prognostic impact of markers at multiple molecular levels, including genomics, epigenomics, transcriptomics, proteomics and metabolomics, and how these potential risk factors complement clinical characterization of patient outcomes for survival prognosis. However, the massive sizes of the omics datasets, along with their correlation structures, pose challenges for studying relationships between the molecular information and patients' survival outcomes.

RESULTS

We present a general workflow for survival analysis that is applicable to high-dimensional omics data as inputs when identifying survival-associated features and validating survival models. In particular, we focus on the commonly used Cox-type penalized regressions and hierarchical Bayesian models for feature selection in survival analysis, which are especially useful for high-dimensional data, but the framework is applicable more generally.

AVAILABILITY AND IMPLEMENTATION

A step-by-step R tutorial using The Cancer Genome Atlas survival and omics data for the execution and evaluation of survival models has been made available at https://ocbe-uio.github.io/survomics.

Microglia in Glioma

Myeloid cells are fundamental constituents of the brain tumor microenvironment. In this chapter, we describe the state-of-the-art knowledge on the role of microglial cells in the cross-talk with the most common and aggressive brain tumor, glioblastoma. We report in vitro and in vivo studies related to glioblastoma patients and glioma models to outline the symbiotic interactions that microglia develop with tumoral cells, highlighting the heterogeneity of microglial functions in shaping the brain tumor microenvironment.

Multiscale modeling of drug resistance in glioblastoma with gene mutations and angiogenesis

Drug resistance is a prominent impediment to the efficacy of targeted therapies across various cancer types, including glioblastoma (GBM). However, comprehending the intricate intracellular and extracellular mechanisms underlying drug resistance remains elusive. Empirical investigations have elucidated that genetic aberrations, such as gene mutations, along with microenvironmental adaptation, notably angiogenesis, act as pivotal drivers of tumor progression and drug resistance. Nonetheless, mathematical models frequently compartmentalize these factors in isolation. In this study, we present a multiscale agent-based model of GBM, encompassing cellular dynamics, intricate signaling pathways, gene mutations, angiogenesis, and therapeutic interventions. This integrative framework facilitates an exploration of the interplay between genetic mutations and the vascular microenvironment in shaping the dynamic evolution of tumors during treatment with tyrosine kinase inhibitor. Our simulations unveil that mutations influencing the migration and proliferation of tumor cells expedite the emergence of phenotype heterogeneity, thereby exacerbating tumor invasion under both treated and untreated conditions. Moreover, angiogenesis proximate to the tumor fosters a protumoral milieu, augmenting mutation-induced drug resistance by increasing the survival rate of tumor cells. Collectively, our findings underscore the dual roles of intrinsic genetic mutations and extrinsic microenvironmental adaptations in steering tumor growth and drug resistance. Finally, we substantiate our model predictions concerning the impact of gene mutations and angiogenesis on the responsiveness of targeted therapies by integrating single-cell RNA-seq, spatial transcriptomics, bulk RNA-seq, and clinical data from GBM patients. The multidimensional approach enhances our understanding of the complexities governing drug resistance in glioma and offers insights into potential therapeutic strategies.

ESICCC as a systematic computational framework for evaluation, selection, and integration of cell-cell communication inference methods

Cell-cell communication (CCC) is critical for determining cell fates and functions in multicellular organisms. With the advent of single-cell RNA-sequencing (scRNA-seq) and spatial transcriptomics (ST), an increasing number of CCC inference methods have been developed. Nevertheless, a thorough comparison of their performances is yet to be conducted. To fill this gap, we developed a systematic benchmark framework called ESICCC to evaluate 18 ligand-receptor (LR) inference methods and five ligand/receptor-target inference methods using a total of 116 data sets, including 15 ST data sets, 15 sets of cell line perturbation data, two sets of cell type-specific expression/proteomics data, and 84 sets of sampled or unsampled scRNA-seq data. We evaluated and compared the agreement, accuracy, robustness, and usability of these methods. Regarding accuracy evaluation, RNAMagnet, CellChat, and scSeqComm emerge as the three best-performing methods for intercellular ligand-receptor inference based on scRNA-seq data, whereas stMLnet and HoloNet are the best methods for predicting ligand/receptor-target regulation using ST data. To facilitate the practical applications, we provide a decision-tree-style guideline for users to easily choose best tools for their specific research concerns in CCC inference, and develop an ensemble pipeline CCCbank that enables versatile combinations of methods and databases. Moreover, our comparative results also uncover several critical influential factors for CCC inference, such as prior interaction information, ligand-receptor scoring algorithm, intracellular signaling complexity, and spatial relationship, which may be considered in the future studies to advance the development of new methodologies.

In silico resources help combat cancer drug resistance mediated by target mutations

Drug resistance causes catastrophic cancer treatment failures. Mutations in target proteins with altered drug binding indicate a main mechanism of cancer drug resistance (CDR). Global research has generated considerable CDR-related data and well-established knowledge bases and predictive tools. Unfortunately, these resources are fragmented and underutilized. Here, we examine computational resources for exploring CDR caused by target mutations, analyzing these tools based on their functional characteristics, data capacity, data sources, methodologies and performance. We also discuss their disadvantages and provide examples of how potential inhibitors of CDR have been discovered using these resources. This toolkit is designed to help specialists explore resistance occurrence effectively and to explain resistance prediction to non-specialists easily.

Bioinformatics toolbox for exploring target mutation-induced drug resistance

Drug resistance is increasingly among the main issues affecting human health and threatening agriculture and food security. In particular, developing approaches to overcome target mutation-induced drug resistance has long been an essential part of biological research. During the past decade, many bioinformatics tools have been developed to explore this type of drug resistance, and they have become popular for elucidating drug resistance mechanisms in a low cost, fast and effective way. However, these resources are scattered and underutilized, and their strengths and limitations have not been systematically analyzed and compared. Here, we systematically surveyed 59 freely available bioinformatics tools for exploring target mutation-induced drug resistance. We analyzed and summarized these resources based on their functionality, data volume, data source, operating principle, performance, etc. And we concisely discussed the strengths, limitations and application examples of these tools. Specifically, we tested some predictive tools and offered some thoughts from the clinician's perspective. Hopefully, this work will provide a useful toolbox for researchers working in the biomedical, pesticide, bioinformatics and pharmaceutical engineering fields, and a good platform for non-specialists to quickly understand drug resistance prediction.

The Evolution of Single-Cell RNA Sequencing Technology and Application: Progress and Perspectives

As an emerging sequencing technology, single-cell RNA sequencing (scRNA-Seq) has become a powerful tool for describing cell subpopulation classification and cell heterogeneity by achieving high-throughput and multidimensional analysis of individual cells and circumventing the shortcomings of traditional sequencing for detecting the average transcript level of cell populations. It has been applied to life science and medicine research fields such as tracking dynamic cell differentiation, revealing sensitive effector cells, and key molecular events of diseases. This review focuses on the recent technological innovations in scRNA-Seq, highlighting the latest research results with scRNA-Seq as the core technology in frontier research areas such as embryology, histology, oncology, and immunology. In addition, this review outlines the prospects for its innovative application in traditional Chinese medicine (TCM) research and discusses the key issues currently being addressed by scRNA-Seq and its great potential for exploring disease diagnostic targets and uncovering drug therapeutic targets in combination with multiomics technologies.

Predicting Microenvironment in CXCR4- and FAP-Positive Solid Tumors-A Pan-Cancer Machine Learning Workflow for Theranostic Target Structures

(1) Background: C-X-C Motif Chemokine Receptor 4 (CXCR4) and Fibroblast Activation Protein Alpha (FAP) are promising theranostic targets. However, it is unclear whether CXCR4 and FAP positivity mark distinct microenvironments, especially in solid tumors. (2) Methods: Using Random Forest (RF) analysis, we searched for entity-independent mRNA and microRNA signatures related to CXCR4 and FAP overexpression in our pan-cancer cohort from The Cancer Genome Atlas (TCGA) database-representing n = 9242 specimens from 29 tumor entities. CXCR4- and FAP-positive samples were assessed via StringDB cluster analysis, EnrichR, Metascape, and Gene Set Enrichment Analysis (GSEA). Findings were validated via correlation analyses in n = 1541 tumor samples. TIMER2.0 analyzed the association of CXCR4 / FAP expression and infiltration levels of immune-related cells. (3) Results: We identified entity-independent CXCR4 and FAP gene signatures representative for the majority of solid cancers. While CXCR4 positivity marked an immune-related microenvironment, FAP overexpression highlighted an angiogenesis-associated niche. TIMER2.0 analysis confirmed characteristic infiltration levels of CD8+ cells for CXCR4-positive tumors and endothelial cells for FAP-positive tumors. (4) Conclusions: CXCR4- and FAP-directed PET imaging could provide a non-invasive decision aid for entity-agnostic treatment of microenvironment in solid malignancies. Moreover, this machine learning workflow can easily be transferred towards other theranostic targets.

Immune-related gene signature to predict TACE refractoriness in patients with hepatocellular carcinoma based on artificial neural network

Background: Transarterial chemoembolization (TACE) is the standard treatment option for intermediate-stage hepatocellular carcinoma (HCC), while response varies among patients. This study aimed to identify novel immune-related genes (IRGs) and establish a prediction model for TACE refractoriness in HCC patients based on machine learning methods. Methods: Gene expression data were downloaded from GSE104580 dataset of Gene Expression Omnibus (GEO) database, differential analysis was first performed to screen differentially expressed genes (DEGs). The least absolute shrinkage and selection operator (LASSO) regression analysis was performed to further select significant DEGs. Weighted gene co-expression network analysis (WGCNA) was utilized to build a gene co-expression network and filter the hub genes. Final signature genes were determined by the intersection of LASSO analysis results, WGCNA results and IRGs list. Based on the above results, the artificial neural network (ANN) model was constructed in the training cohort and verified in the validation cohort. Receiver operating characteristics (ROC) analysis was used to assess the prediction accuracy. Correlation of signature genes with tumor microenvironment scores, immune cells and immune checkpoint molecules were further analyzed. The tumor immune dysfunction and exclusion (TIDE) score was used to evaluate the response to immunotherapy. Results: One hundred and forty-seven samples were included in this study, which was randomly divided into the training cohort (n = 103) and validation cohort (n = 44). In total, 224 genes were identified as DEGs. Further LASSO regression analysis screened out 25 genes from all DEGs. Through the intersection of LASSO results, WGCNA results and IRGs list, S100A9, TREM1, COLEC12, and IFIT1 were integrated to construct the ANN model. The areas under the curves (AUCs) of the model were .887 in training cohort and .765 in validation cohort. The four IRGs also correlated with tumor microenvironment scores, infiltrated immune cells and immune checkpoint genes in various degrees. Patients with TACE-Response, lower expression of COLEC12, S100A9, TREM1 and higher expression of IFIT1 had better response to immunotherapy. Conclusion: This study constructed and validated an IRG signature to predict the refractoriness to TACE in patients with HCC, which may have the potential to provide insights into the TACE refractoriness in HCC and become the immunotherapeutic targets for HCC patients with TACE refractoriness.

Single-Cell and Transcriptome-Based Immune Cell-Related Prognostic Model in Clear Cell Renal Cell Carcinoma

Traditional studies mostly focus on the role of single gene in regulating clear cell renal cell carcinoma (ccRCC), while it ignores the impact of tumour heterogeneity on disease progression. The purpose of this study is to construct a prognostic risk model for ccRCC by analysing the differential marker genes related to immune cells in the single-cell database to provide help in clinical diagnosis and targeted therapy. Single-cell data and ligand-receptor relationship pair data were downloaded from related publications, and ccRCC phenotype and expression profile data were downloaded from TCGA and CPTAC. Based on the DEGs of each cluster acquired from single-cell data, immune cell marker genes, and ligand-receptor gene data, we constructed a multilayer network. Then, the genes in the network and the genes in TCGA were used to construct the WGCNA network, which screened out prognosis-associated genes for subsequent analysis. Finally, a prognostic risk scoring model was obtained, and CPTAC data showed that the effectiveness of this model was good. A nomogram based on the predictive model for predicting the overall survival was established, and internal validation was performed well. Our findings suggest that the predictive model built and based on the immune cell scRNA-seq will enable us to judge the prognosis of patients with ccRCC and provide more accurate directions for basic relevant research and clinical practice.

Computational Methods for the Analysis and Prediction of EGFR-Mutated Lung Cancer Drug Resistance: Recent Advances in Drug Design, Challenges and Future Prospects

Lung cancer is a major cause of cancer deaths worldwide, and has a very low survival rate. Non-small cell lung cancer (NSCLC) is the largest subset of lung cancers, which accounts for about 85% of all cases. It has been well established that a mutation in the epidermal growth factor receptor (EGFR) can lead to lung cancer. EGFR Tyrosine Kinase Inhibitors (TKIs) are developed to target the kinase domain of EGFR. These TKIs produce promising results at the initial stage of therapy, but the efficacy becomes limited due to the development of drug resistance. In this paper, we provide a comprehensive overview of computational methods, for understanding drug resistance mechanisms. The important EGFR mutants and the different generations of EGFR-TKIs, with the survival and response rates are discussed. Next, we evaluate the role of important EGFR parameters in drug resistance mechanism, including structural dynamics, hydrogen bonds, stability, dimerization, binding free energies, and signaling pathways. Personalized drug resistance prediction models, drug response curve, drug synergy, and other data-driven methods are also discussed. Recent advancements in deep learning; such as AlphaFold2, deep generative models, big data analytics, and the applications of statistics and permutation are also highlighted. We explore limitations in the current methodologies, and discuss strategies to overcome them. We believe this review will serve as a reference for researchers; to apply computational techniques for precision medicine, analyzing structures of protein-drug complexes, drug discovery, and understanding the drug response and resistance mechanisms in lung cancer patients.

Establishment and validation of a carbohydrate metabolism-related gene signature for prognostic model and immune response in acute myeloid leukemia

Introduction

The heterogeneity of treatment response in acute myeloid leukemia (AML) patients poses great challenges for risk scoring and treatment stratification. Carbohydrate metabolism plays a crucial role in response to therapy in AML. In this multicohort study, we investigated whether carbohydrate metabolism related genes (CRGs) could improve prognostic classification and predict response of immunity and treatment in AML patients.

Methods

Using univariate regression and LASSO-Cox stepwise regression analysis, we developed a CRG prognostic signature that consists of 10 genes. Stratified by the median risk score, patients were divided into high-risk group and low-risk group. Using TCGA and GEO public data cohorts and our cohort (1031 non-M3 patients in total), we demonstrated the consistency and accuracy of the CRG score on the predictive performance of AML survival.

Results

The overall survival (OS) was significantly shorter in high-risk group. Differentially expressed genes (DEGs) were identified in the high-risk group compared to the low-risk group. GO and GSEA analysis showed that the DEGs were mainly involved in immune response signaling pathways. Analysis of tumor-infiltrating immune cells confirmed that the immune microenvironment was strongly suppressed in high-risk group. The results of potential drugs for risk groups showed that inhibitors of carbohydrate metabolism were effective.

Discussion

The CRG signature was involved in immune response in AML. A novel risk model based on CRGs proposed in our study is promising prognostic classifications in AML, which may provide novel insights for developing accurate targeted cancer therapies.

Heterogeneity and Differentiation Trajectories of Infiltrating CD8+ T Cells in Lung Adenocarcinoma

Simple Summary

CD8+ T cells infiltrating the tumor microenvironment (TME) of lung adenocarcinoma (LUAD) play a crucial role in establishing anti-tumor immunotherapy. The number of CD8+ T cells affects the treatment response, but their functional status plays a more critical role, and this global landscape is still unclear. We divided CD8+ T cells into ten subsets by analyzing a LUAD single-cell dataset. The dynamic process of cell differentiation and functional exhaustion of CD8+ T cells was further discussed, and potential biomarkers in this process were screened. This study deepens the understanding of the heterogeneity of infiltrating CD8+ T cells in LUAD, and the prognostic marker provides a new target for targeted therapy and immunotherapy in LUAD patients.

Abstract

CD8+ T cells infiltrating the tumor microenvironment (TME) of lung adenocarcinoma (LUAD) are critical for establishing antitumor immunity. Nevertheless, the global landscape of their numbers, functional status, and differentiation trajectories remains unclear. In the single-cell RNA-sequencing (scRNA-seq) dataset GSE131907 of LUAD, the CD8+T cells were selected for TSNE clustering, and the results showed that they could be divided into ten subsets. The cell differentiation trajectory showed the presence of abundant transition-state CD8+ T cells during the differentiation of naive-like CD8+ T cells into cytotoxic CD8+ T cells and exhausted CD8+ T cells. The differentially expressed marker genes among subsets were used to construct the gene signature matrix, and the proportion of each subset was identified and calculated in The Cancer Genome Atlas (TCGA) samples. Survival analysis showed that the higher the proportion of the exhausted CD8+ T lymphocyte (ETL) subset, the shorter the overall survival (OS) time of LUAD patients (p = 0.0098). A total of 61 genes were obtained by intersecting the differentially expressed genes (DEGs) of the ETL subset, and the DEGs of the TCGA samples were divided into a high and a low group according to the proportion of the ETL subset. Through protein interaction network analysis and survival analysis, four hub genes that can significantly affect the prognosis of LUAD patients were finally screened, and RT-qPCR and Western blot verified the differential expression of the above four genes. Our study further deepens the understanding of the heterogeneity and functional exhaustion of infiltrating CD8+ T cells in LUAD. The screened prognostic marker genes provide potential targets for targeted therapy and immunotherapy in LUAD patients.

Uncovering the dynamic effects of DEX treatment on lung cancer by integrating bioinformatic inference and multiscale modeling of scRNA-seq and proteomics data

Lung cancer is one of the leading causes of cancer-related death, with a five-year survival rate of 18%. It is a priority for us to understand the underlying mechanisms affecting lung cancer therapeutics' implementation and effectiveness. In this study, we combine the power of Bioinformatics and Systems Biology to comprehensively uncover functional and signaling pathways of drug treatment using bioinformatics inference and multiscale modeling of both scRNA-seq data and proteomics data. Based on a time series of lung adenocarcinoma derived A549 cells after DEX treatment, we first identified the differentially expressed genes (DEGs) in those lung cancer cells. Through the interrogation of regulatory network of those DEGs, we identified key hub genes including TGFβ, MYC, and SMAD3 varied underlie DEX treatment. Further gene set enrichment analysis revealed the TGFβ signaling pathway as the top enriched term. Those genes involved in the TGFβ pathway and their crosstalk with the ERBB pathway presented a strong survival prognosis in clinical lung cancer samples. With the basis of biological validation and literature-based curation, a multiscale model of tumor regulation centered on both TGFβ-induced and ERBB-amplified signaling pathways was developed to characterize the dynamic effects of DEX therapy on lung cancer cells. Our simulation results were well matched to available data of SMAD2, FOXO3, TGFβ1, and TGFβR1 over the time course. Moreover, we provided predictions of different doses to illustrate the trend and therapeutic potential of DEX treatment. The innovative and cross-disciplinary approach can be further applied to other computational studies in tumorigenesis and oncotherapy. We released the approach as a user-friendly tool named BIMM (Bioinformatic Inference and Multiscale Modeling), with all the key features available at https://github.com/chenm19/BIMM.

Expression Analysis of Ligand-Receptor Pairs Identifies Cell-to-Cell Crosstalk between Macrophages and Tumor Cells in Lung Adenocarcinoma

Background

Lung adenocarcinoma is one of the most commonly diagnosed malignancies worldwide. Macrophage plays crucial roles in the tumor microenvironment, but its autocrine network and communications with tumor cell are still unclear.

Methods

We acquired single-cell RNA sequencing (scRNA-seq) (n = 30) and bulk RNA sequencing (n = 1480) samples of lung adenocarcinoma patients from previous literatures and publicly available databases. Various cell subtypes were identified, including macrophages. Differentially expressed ligand-receptor gene pairs were obtained to explore cell-to-cell communications between macrophages and tumor cells. Furthermore, a machine-learning predictive model based on ligand-receptor interactions was built and validated.

Results

A total of 159,219 single cells (18,248 tumor cells and 29,520 macrophages) were selected in this study. We identified significantly correlated autocrine ligand-receptor gene pairs in tumor cells and macrophages, respectively. Furthermore, we explored the cell-to-cell communications between macrophages and tumor cells and detected significantly correlated ligand-receptor signaling pairs. We determined that some of the hub gene pairs were associated with patient prognosis and constructed a machine-learning model based on the intercellular interaction network.

Conclusion

We revealed significant cell-to-cell communications (both autocrine and paracrine network) within macrophages and tumor cells in lung adenocarcinoma. Hub genes with prognostic significance in the network were also identified.

TPD: a web tool for tipping-point detection based on dynamic network biomarker

Tipping points or critical transitions widely exist during the progression of many biological processes. It is of great importance to detect the tipping point with the measured omics data, which may be a key to achieving predictive or preventive medicine. We present the tipping point detector (TPD), a web tool for the detection of the tipping point during the dynamic process of biological systems, and further its leading molecules or network, based on the input high-dimensional time series or stage course data. With the solid theoretical background of dynamic network biomarker (DNB) and a series of computational methods for DNB detection, TPD detects the potential tipping point/critical state from the input omics data and outputs multifarious visualized results, including a suggested tipping point with a statistically significant P value, the identified key genes and their functional biological information, the dynamic change in the DNB/leading network that may drive the critical transition and the survival analysis based on DNB scores that may help to identify ‘dark’ genes (nondifferential in terms of expression but differential in terms of DNB scores). TPD fits all current browsers, such as Chrome, Firefox, Edge, Opera, Safari and Internet Explorer. TPD is freely accessible at http://www.rpcomputationalbiology.cn/TPD.

Multi-modal optimization to identify personalized biomarkers for disease prediction of individual patients with cancer

Finding personalized biomarkers for disease prediction of patients with cancer remains a massive challenge in precision medicine. Most methods focus on one subnetwork or module as a network biomarker; however, this ignores the early warning capabilities of other modules with different configurations of biomarkers (i.e. multi-modal personalized biomarkers). Identifying such modules would not only predict disease but also provide effective therapeutic drug target information for individual patients. To solve this problem, we developed a novel model (denoted multi-modal personalized dynamic network biomarkers (MMPDNB)) based on a multi-modal optimization mechanism and personalized dynamic network biomarker (PDNB) theory, which can provide multiple modules of personalized biomarkers and unveil their multi-modal properties. Using the genomics data of patients with breast or lung cancer from The Cancer Genome Atlas database, we validated the effectiveness of the MMPDNB model. The experimental results showed that compared with other advanced methods, MMPDNB can more effectively predict the critical state with the highest early warning signal score during cancer development. Furthermore, MMPDNB more significantly identified PDNBs containing driver and biomarker genes specific to cancer tissues. More importantly, we validated the biological significance of multi-modal PDNBs, which could provide effective drug targets of individual patients as well as markers for predicting early warning signals of the critical disease state. In conclusion, multi-modal optimization is an effective method to identify PDNBs and offers a new perspective for understanding tumor heterogeneity in cancer precision medicine.

Interrogating glioma-M2 macrophage interactions identifies Gal-9/Tim-3 as a viable target against PTEN-null glioblastoma

Genomic alteration can reshape tumor microenvironment to drive tumor malignancy. However, how PTEN deficiency influences microenvironment-mediated cell-cell interactions in glioblastoma (GBM) remains unclear. Here, we show that PTEN deficiency induces a symbiotic glioma-M2 macrophage interaction to support glioma progression. Mechanistically, PTEN-deficient GBM cells secrete high levels of galectin-9 (Gal-9) via the AKT-GSK3β-IRF1 pathway. The secreted Gal-9 drives macrophage M2 polarization by activating its receptor Tim-3 and downstream pathways in macrophages. These macrophages, in turn, secrete VEGFA to stimulate angiogenesis and support glioma growth. Furthermore, enhanced Gal-9/Tim-3 expression predicts poor outcome in glioma patients. In GBM models, blockade of Gal-9/Tim-3 signaling inhibits macrophage M2 polarization and suppresses tumor growth. Moreover, α-lactose attenuates glioma angiogenesis by down-regulating macrophage-derived VEGFA, providing a novel antivascularization strategy. Therefore, our study suggests that blockade of Gal-9/Tim-3 signaling is effective to impair glioma progression by inhibiting macrophage M2 polarization, specifically for PTEN-null GBM.

New insights from the single-cell level: Tumor associated macrophages heterogeneity and personalized therapy

Tumor-associated macrophages (TAMs) are important immune cells in the tumor microenvironment, and their invasion in tumors is closely related to poor prognosis. Although TAMs are recognized as therapeutic targets, their heterogeneity makes studying tumor mechanism and developing drugs targeting TAMs difficult. The study of TAMs heterogeneity can be used to analyze the mechanism of tumor progression and drug resistance, and may provide possible treatment strategies for cancer patients. Single-cell RNA sequencing (scRNA-seq) can reveal the RNA expression profile for each TAM to distinguish heterogeneity, thereby providing a more efficient detection method and more accurate information for TAM-related studies. In this review, by summarizing the research progress in macrophage heterogeneity and other aspects of scRNA-seq over the past five years, we introduced the development of scRNA-seq technology and its application status in solid tumors, analyzed the advantages and selections of scRNA-seq in TAMs, and summarized the detailed specific research fields. To explore the mechanism of tumor progression and drug intervention from single cell level will provide new perspective for personalized treatment strategies targeting macrophages.

Single-cell sequencing: expansion, integration and translation

With the rapid advancement in sequencing technologies, the concept of omics has revolutionized our understanding of cellular behaviors. Conventional omics investigation approaches measure the averaged behaviors of multiple cells, which may easily hide signals represented by a small-cell cohort, urging for the development of techniques with enhanced resolution. Single-cell RNA sequencing, investigating cell transcriptomics at the resolution of a single cell, has been rapidly expanded to investigate other omics such as genomics, proteomics and metabolomics since its invention. The requirement for comprehensive understanding of complex cellular behavior has led to the integration of multi-omics and single-cell sequencing data with other layers of information such as spatial data and the CRISPR screening technique towards gained knowledge or innovative functionalities. The development of single-cell sequencing in both dimensions has rendered it a unique field that offers us a versatile toolbox to delineate complex diseases, including cancers.

Single-Cell Transcriptomics Revealed Subtype-Specific Tumor Immune Microenvironments in Human Glioblastomas

Human glioblastoma (GBM), the most aggressive brain tumor, comprises six major subtypes of malignant cells, giving rise to both inter-patient and intra-tumor heterogeneity. The interaction between different tumor subtypes and non-malignant cells to collectively shape a tumor microenvironment has not been systematically characterized. Herein, we sampled the cellular milieu of surgically resected primary tumors from 7 GBM patients using single-cell transcriptome sequencing. A lineage relationship analysis revealed that a neural-progenitor-2-like (NPC2-like) state with high metabolic activity was associated with the tumor cells of origin. Mesenchymal-1-like (MES1-like) and mesenchymal-2-like (MES2-like) tumor cells correlated strongly with immune infiltration and chronic hypoxia niche responses. We identified four subsets of tumor-associated macrophages/microglia (TAMs), among which TAM-1 co-opted both acute and chronic hypoxia-response signatures, implicated in tumor angiogenesis, invasion, and poor prognosis. MES-like GBM cells expressed the highest number of M2-promoting ligands compared to other cellular states while all six states were associated with TAM M2-type polarization and immunosuppression via a set of 10 ligand–receptor signaling pathways. Our results provide new insights into the differential roles of GBM cell subtypes in the tumor immune microenvironment that may be deployed for patient stratification and personalized treatment.

Single-cell sequencing reveals the heterogeneity and intratumoral crosstalk in human endometrial cancer

Background

Endometrial cancer (EC) is one of the most common gynecologic malignancies with increasing morbidity. Cell–cell and cell‐matrix interactions within the tumour microenvironment (TME) exert a powerful influence over the progression of EC. Therefore, a comprehensive exploration of heterogeneity and intratumoral crosstalk is essential to elucidate the mechanisms driving EC progression and develop novel therapeutic approaches.

Methods

4 EC and 2 normal endometrium samples were applied for single‐cell RNA sequencing (scRNA‐seq) analysis. In addition, we also included the public database to explore the clinical benefits of the single cell analysis.

Results

9 types of cells were identified with specific expression of maker genes. Both the malignant epithelial cells and cells comprising the immune microenvironment displayed a high degree of intertumoral heterogeneity. Notably, the proliferation T cells also showed an exhausted feature. Moreover, the malignant cells may induce an immunosuppressive microenvironment through TNF‐ICOS pair. Cancer‐associated fibroblasts (CAFs) were divided into four subsets with distinct characteristics and they maintained frequent communications with malignant cells which facilitating the progression of EC. We also found that the existence of vascular CAF (vCAF) may indicate a worse prognosis for EC patients through integrating TCGA database.

Conclusion

The TME of human EC remains highly heterogeneous. Out finding that malignant cells interact closely with immune cells and vCAFs identifies potential therapeutic targets.

Single-cell sequencing: a promising approach for uncovering the mechanisms of tumor metastasis

Single-cell sequencing (SCS) is an emerging high-throughput technology that can be used to study the genomics, transcriptomics, and epigenetics at a single cell level. SCS is widely used in the diagnosis and treatment of various diseases, including cancer. Over the years, SCS has gradually become an effective clinical tool for the exploration of tumor metastasis mechanisms and the development of treatment strategies. Currently, SCS can be used not only to analyze metastasis-related malignant biological characteristics, such as tumor heterogeneity, drug resistance, and microenvironment, but also to construct metastasis-related cell maps for predicting and monitoring the dynamics of metastasis. SCS is also used to identify therapeutic targets related to metastasis as it provides insights into the distribution of tumor cell subsets and gene expression differences between primary and metastatic tumors. Additionally, SCS techniques in combination with artificial intelligence (AI) are used in liquid biopsy to identify circulating tumor cells (CTCs), thereby providing a novel strategy for treating tumor metastasis. In this review, we summarize the potential applications of SCS in the field of tumor metastasis and discuss the prospects and limitations of SCS to provide a theoretical basis for finding therapeutic targets and mechanisms of metastasis.

Single-cell transcriptome highlights a multilayer regulatory network on an invasive trajectory within colorectal cancer progression

PURPOSE

Colorectal cancer (CRC) is one of the most common and fatal gastrointestinal malignancies, in which cancer stem cells (CSCs) were identified to enable tumor heterogeneity and initiate tumor formation. However, the process from CSCs to invasion cells is unconfirmed.

METHODS

Several bioinformatics methods, including clustering, pseudotime analysis, gene set variation analysis and gene ontology enrichment, were used to construct a path of gradual transformation of CSCs to invasive cells, called "stem-to-invasion path". A large amount of signaling interactions were collated to build the multilayer regulatory network. Kaplan-Meier curve and time-dependent ROC method were applied to reveal prognostic values.

RESULTS

We validated the heterogeneity of cells in the tumor microenvironment and revealed the presence of malignant epithelial cells with high invasive potential within primary colonic carcinomas. Next, the "stem-to-invasion path" was identified through constructing a branching trajectory with cancer cells arranged in order. A multilayer regulatory network considered as the vital factor involved in acquiring invasion characteristics underlying the path was built to elucidate the interactions between tumor cell and tumor-associated microenvironment. Then we further identified a novel combinatorial biomarker that can be used to assess the prognosis for CRC patients, and validated its predictive robustness on the independent dataset.

CONCLUSION

Our work provides new insights into the acquisition of invasive potential in primary tumor cells, as well as potential therapeutic targets for CRC invasiveness, which may be useful for the cancer research and clinical treatment.

Effects of microRNAs and long non-coding RNAs on chemotherapy response in glioma

Glioma is the most prevalent invasive primary tumor of the central nervous system. Glioma cells can spread and infiltrate into normal surrounding brain tissues. Despite the standard use of chemotherapy and radiotherapy after surgery in glioma patients, treatment resistance is still a problem, as the underlying mechanisms are still not fully understood. Non-coding RNAs are widely involved in tumor progression and treatment resistance mechanisms. In the present review, we discuss the pathways by which microRNAs and long non-coding RNAs can affect resistance to chemotherapy and radiotherapy, as well as offer potential therapeutic options for future glioma treatment.

Increased Expression of Homeobox 5 Predicts Poor Prognosis: A Potential Prognostic Biomarker for Glioma

Background

The homeobox gene 5 (HOXB5) encodes a transcription factor that regulates the embryonic development of the central nervous system. Notably, its expression pattern and prognostic role in glioma remain unelucidated.

Methods

This study identified the relationship between HOXB5 and glioma by investigating HOXB5 expression data from The Cancer Genome Atlas and the Genotype Tissue Expression databases and validating the obtained data using the Chinese Glioma Genome Atlas database. Western blots were used to identify HOXB5 expression levels in glioma cells and clinical samples. Kaplan-Meier and multivariate Cox regression analyses were performed to assess the prognostic value of HOXB5. The key functions and signaling pathways related to HOXB5 were analyzed using GO, KEGG, and GSEA. Immune infiltration was calculated using the microenvironment cell populations-counter, estimate the proportion of immune and cancer, and ESTIMATE algorithms.

Results

The expression of HOXB5 was upregulated in glioma and generally increased with malignancy. HOXB5 was an independent prognostic factor for glioma patients. A nomogram was further built that integrated HOXB5, and it showed stratifying prediction accuracy and efficiency. HOXB5 was associated with the regulation of cell growth, endothelial cell growth, and the IL-6/JAK-STAT3 pathway, and was determined to possibly promote stomatal specimen enrichment and angiogenesis.

Conclusion

HOXB5 protein is overexpressed in glioma and might serve as a good predictive factor of this disease.

Chimeric RNAs Discovered by RNA Sequencing and Their Roles in Cancer and Rare Genetic Diseases

Chimeric RNAs are transcripts that are generated by gene fusion and intergenic splicing events, thus comprising nucleotide sequences from different parental genes. In the past, Northern blot analysis and RT-PCR were used to detect chimeric RNAs. However, they are low-throughput and can be time-consuming, labor-intensive, and cost-prohibitive. With the development of RNA-seq and transcriptome analyses over the past decade, the number of chimeric RNAs in cancer as well as in rare inherited diseases has dramatically increased. Chimeric RNAs may be potential diagnostic biomarkers when they are specifically expressed in cancerous cells and/or tissues. Some chimeric RNAs can also play a role in cell proliferation and cancer development, acting as tools for cancer prognosis, and revealing new insights into the cell origin of tumors. Due to their abilities to characterize a whole transcriptome with a high sequencing depth and intergenically identify spliced chimeric RNAs produced with the absence of chromosomal rearrangement, RNA sequencing has not only enhanced our ability to diagnose genetic diseases, but also provided us with a deeper understanding of these diseases. Here, we reviewed the mechanisms of chimeric RNA formation and the utility of RNA sequencing for discovering chimeric RNAs in several types of cancer and rare inherited diseases. We also discussed the diagnostic, prognostic, and therapeutic values of chimeric RNAs.

Analysis of Gene Co-Expression Network to Identify the Role of CD8 + T Cell Infiltration-Related Biomarkers in High-Grade Glioma

Background

High-grade glioma is a type of heterogeneous lethal brain tumor most common in adults. At present, immune checkpoint inhibitors (ICIs) are being considered for first-line therapeutics for malignant GBM. Nonetheless, molecular markers for malignant GBM are unavailable at present. As a result, it is important to explore molecular markers related to immunity for GBM.

Materials and Methods

The present study adopted a deconvolution algorithm for quantifying immunocyte composition and measuring gene expression, and used weighted gene co-expression network analysis (WGCNA) to analyze GBM expression data obtained from Gene Expression Omnibus (GEO), Chinese Glioma Genome Atlas (CGGA), and the Cancer Genome Atlas Glioblastoma Multiforme (TCGA-GBM) databases. Thereafter, key CD8+ T cell infiltration-related genes and modules were identified, and database analysis was conducted to verify the therapeutic and immune features of the selected genes.

Results

From this study, CD8+ T cell-related modules were identified. By using consistent clustering analysis, two panels of genes (red and green) with the highest correlation with CD8+ T cells infiltration were used to construct high-, low-expression groups, silent and/or mixed group of T cell infiltrations. In the high and low CD8+ T cell infiltration groups, a total of 535 differential genes were obtained, of which ten genes (RPS5, RPS6, FAU, RPS19, RPS23, RPS15A, RPS29, RPS14, RPS16, RPS27A) were identified through protein–protein interactions and co-expression network analysis. Post Cox regression and Kaplan–Meier (K-M) survival analysis, RPS5, RPS6, and RPS16 were selected as candidate prognostic biomarkers related to CD8+ T cells.

Conclusion

The three associated genes RPS5, RPS6, and RPS16 were markedly related to degree of T cell infiltration and immune-related activated. We identified their potential biomarkers and therapeutic targets associated with the extent of CD8+ T cell infiltration in GBM.

Characteristics of alveolar macrophages in bronchioalveolar lavage fluids from active tuberculosis patients identified by single-cell RNA sequencing

Tuberculosis (TB), is an infectious disease caused by Mycobacterium tuberculosis (M. tuberculosis), and presents with high morbidity and mortality. Alveolar macrophages play an important role in TB pathogenesis although there is heterogeneity and functional plasticity. This study aimed to show the characteristics of alveolar macrophages from bronchioalveolar lavage fluid (BALF) in active TB patients. Single-cell RNA sequencing (scRNA-seq) was performed on BALF cells from three patients with active TB and additional scRNA-seq data from three healthy adults were established as controls. Transcriptional profiles were analyzed and compared by differential geneexpression and functional enrichment analysis. We applied pseudo-temporal trajectory analysis to investigate correlations and heterogeneity within alveolar macrophage subclusters. Alveolar macrophages from active TB patients at the single-cell resolution are described. We found that TB patients have higher cellular percentages in five macrophage subclusters. Alveolar macrophage subclusters with increased percentages were involved in inflammatory signaling pathways as well as the basic macrophage functions. The TB-increased alveolar macrophage subclusters might be derived from M1-like polarization state, before switching to an M2-like polarization state with the development ofM. tuberculosis infection. Cell-cell communications of alveolar macrophages also increased and enhanced in active TB patients. Overall, our study demonstrated the characteristics of alveolar macrophages from BALF in active TB patients by using scRNA-seq.

A Multiparametric Pharmacogenomic Strategy for Drug Repositioning predicts Therapeutic Efficacy for Glioblastoma Cell Lines

Background

Poor prognosis of glioblastoma patients and the extensive heterogeneity of glioblastoma at both the molecular and cellular level necessitates developing novel individualized treatment modalities via genomics-driven approaches.

Methods

This study leverages numerous pharmacogenomic and tissue databases to examine drug repositioning for glioblastoma. RNAseq of glioblastoma tumor samples from The Cancer Genome Atlas (TCGA, n=117) were compared to “normal” frontal lobe samples from Genotype-Tissue Expression Portal (GTEX, n=120) to find differentially expressed genes (DEGs). Using compound-gene expression data and drug activity data from the Library of Integrated Network-Based Cellular Signatures (LINCS, n=66,512 compounds) CCLE (71 glioma cell lines), and Chemical European Molecular Biology Laboratory (ChEMBL) platforms, we employed a summarized reversal gene expression metric (sRGES) to “reverse” the resultant disease signature for GBM and its subtypes. A multi-parametric strategy was employed to stratify compounds capable of blood brain barrier penetrance with a favorable pharmacokinetic profile (CNS-MPO).

Results

Significant correlations were identified between sRGES and drug efficacy in GBM cell lines in both ChEMBL(r=0.37,p<.001) and Cancer Therapeutic Response Portal (CTRP) databases (r=0.35, p<0.001). Our multiparametric algorithm identified two classes of drugs with highest sRGES and CNS-MPO: HDAC inhibitors (vorinostat and entinostat) and topoisomerase inhibitors suitable for drug repurposing.

Conclusions

Our studies suggest that reversal of glioblastoma disease signature correlates with drug potency for various GBM subtypes. This multiparametric approach may set the foundation for an early-phase personalized -omics clinical trial for glioblastoma by effectively identifying drugs that are capable of reversing the disease signature and have favorable pharmacokinetic and safety profiles.

Gene biomarker prediction in glioma by integrating scRNA-seq data and gene regulatory network

Background

Although great efforts have been made to study the occurrence and development of glioma, the molecular mechanisms of glioma are still unclear. Single-cell sequencing technology provides a new perspective for researchers to explore the pathogens of tumors to further help make treatment and prognosis decisions for patients with tumors.

Methods

In this study, we proposed an algorithm framework to explore the molecular mechanisms of glioma by integrating single-cell gene expression profiles and gene regulatory relations. First, since there were great differences among malignant cells from different glioma samples, we analyzed the expression status of malignant cells for each sample, and then tumor consensus genes were identified by constructing and analyzing cell-specific networks. Second, to comprehensively analyze the characteristics of glioma, we integrated transcriptional regulatory relationships and consensus genes to construct a tumor-specific regulatory network. Third, we performed a hybrid clustering analysis to identify glioma cell types. Finally, candidate tumor gene biomarkers were identified based on cell types and known glioma-related genes.

Results

We got six identified cell types using the method we proposed and for these cell types, we performed functional and biological pathway enrichment analyses. The candidate tumor gene biomarkers were analyzed through survival analysis and verified using literature from PubMed.

Conclusions

The results showed that these candidate tumor gene biomarkers were closely related to glioma and could provide clues for the diagnosis and prognosis of patients with glioma. In addition, we found that four of the candidate tumor gene biomarkers (NDUFS5, NDUFA1, NDUFA13, and NDUFB8) belong to the NADH ubiquinone oxidoreductase subunit gene family, so we inferred that this gene family may be strongly related to glioma.

Glial and myeloid heterogeneity in the brain tumour microenvironment

Brain cancers carry bleak prognoses, with therapeutic advances helping only a minority of patients over the past decade. The brain tumour microenvironment (TME) is highly immunosuppressive and differs from that of other malignancies as a result of the glial, neural and immune cell populations that constitute it. Until recently, the study of the brain TME was limited by the lack of methods to de-convolute this complex system at the single-cell level. However, novel technical approaches have begun to reveal the immunosuppressive and tumour-promoting properties of distinct glial and myeloid cell populations in the TME, identifying new therapeutic opportunities. Here, we discuss the immune modulatory functions of microglia, monocyte-derived macrophages and astrocytes in brain metastases and glioma, highlighting their disease-associated heterogeneity and drawing from the insights gained by studying these malignancies and other neurological disorders. Lastly, we consider potential approaches for the therapeutic modulation of the brain TME.

Cell type identification from single-cell transcriptomes in melanoma

BACKGROUND

Single-cell sequencing approaches allow gene expression to be measured at the single-cell level, providing opportunities and challenges to study the aetiology of complex diseases, including cancer.

METHODS

Based on single-cell gene and lncRNA expression levels, we proposed a computational framework for cell type identification that fully considers cell dropout characteristics. First, we defined the dropout features of the cells and identified the dropout clusters. Second, we constructed a differential co-expression network and identified differential modules. Finally, we identified cell types based on the differential modules.

RESULTS

The method was applied to single-cell melanoma data, and eight cell types were identified. Enrichment analysis of the candidate cell marker genes for the two key cell types showed that both key cell types were closely related to the physiological activities of the major histocompatibility complex (MHC); one key cell type was associated with mitosis-related activities, and the other with pathways related to ten diseases.

CONCLUSIONS

Through identification and analysis of key melanoma-related cell types, we explored the molecular mechanism of melanoma, providing insight into melanoma research. Moreover, the candidate cell markers for the two key cell types are potential therapeutic targets for melanoma.

Establishment of Prognosis Model in Acute Myeloid Leukemia Based on Hypoxia Microenvironment, and Exploration of Hypoxia-Related Mechanisms

Acute myeloid leukemia (AML) is a highly heterogeneous hematologic neoplasm with poor survival outcomes. However, the routine clinical features are not sufficient to accurately predict the prognosis of AML. The expression of hypoxia-related genes was associated with survival outcomes of a variety of hematologic and lymphoid neoplasms. We established an 18-gene signature-based hypoxia-related prognosis model (HPM) and a complex model that consisted of the HPM and clinical risk factors using machine learning methods. Both two models were able to effectively predict the survival of AML patients, which might contribute to improving risk classification. Differentially expressed genes analysis, Gene Ontology (GO) categories, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed to reveal the underlying functions and pathways implicated in AML development. To explore hypoxia-related changes in the bone marrow immune microenvironment, we used CIBERSORT to calculate and compare the proportion of 22 immune cells between the two groups with high and low hypoxia-risk scores. Enrichment analysis and immune cell composition analysis indicated that the biological processes and molecular functions of drug metabolism, angiogenesis, and immune cell infiltration of bone marrow play a role in the occurrence and development of AML, which might help us to evaluate several hypoxia-related metabolic and immune targets for AML therapy.

CancerSCEM: a database of single-cell expression map across various human cancers

With the proliferating studies of human cancers by single-cell RNA sequencing technique (scRNA-seq), cellular heterogeneity, immune landscape and pathogenesis within diverse cancers have been uncovered successively. The exponential explosion of massive cancer scRNA-seq datasets in the past decade are calling for a burning demand to be integrated and processed for essential investigations in tumor microenvironment of various cancer types. To fill this gap, we developed a database of Cancer Single-cell Expression Map (CancerSCEM, https://ngdc.cncb.ac.cn/cancerscem), particularly focusing on a variety of human cancers. To date, CancerSCE version 1.0 consists of 208 cancer samples across 28 studies and 20 human cancer types. A series of uniformly and multiscale analyses for each sample were performed, including accurate cell type annotation, functional gene expressions, cell interaction network, survival analysis and etc. Plus, we visualized CancerSCEM as a user-friendly web interface for users to browse, search, online analyze and download all the metadata as well as analytical results. More importantly and unprecedentedly, the newly-constructed comprehensive online analyzing platform in CancerSCEM integrates seven analyze functions, where investigators can interactively perform cancer scRNA-seq analyses. In all, CancerSCEM paves an informative and practical way to facilitate human cancer studies, and also provides insights into clinical therapy assessments.

CellCall: integrating paired ligand-receptor and transcription factor activities for cell-cell communication

With the dramatic development of single-cell RNA sequencing (scRNA-seq) technologies, the systematic decoding of cell-cell communication has received great research interest. To date, several in-silico methods have been developed, but most of them lack the ability to predict the communication pathways connecting the insides and outsides of cells. Here, we developed CellCall, a toolkit to infer inter- and intracellular communication pathways by integrating paired ligand-receptor and transcription factor (TF) activity. Moreover, CellCall uses an embedded pathway activity analysis method to identify the significantly activated pathways involved in intercellular crosstalk between certain cell types. Additionally, CellCall offers a rich suite of visualization options (Circos plot, Sankey plot, bubble plot, ridge plot, etc.) to present the analysis results. Case studies on scRNA-seq datasets of human testicular cells and the tumor immune microenvironment demonstrated the reliable and unique functionality of CellCall in intercellular communication analysis and internal TF activity exploration, which were further validated experimentally. Comparative analysis of CellCall and other tools indicated that CellCall was more accurate and offered more functions. In summary, CellCall provides a sophisticated and practical tool allowing researchers to decipher intercellular communication and related internal regulatory signals based on scRNA-seq data. CellCall is freely available at https://github.com/ShellyCoder/cellcall.

Single-Cell Transcriptome Analysis in Melanoma Using Network Embedding

Single-cell sequencing technology provides insights into the pathology of complex diseases like cancer. Here, we proposed a novel computational framework to explore the molecular mechanisms of cancer called melanoma. We first constructed a disease-specific cell–cell interaction network after data preprocessing and dimensionality reduction. Second, the features of cells in the cell–cell interaction network were learned by node2vec which is a graph embedding technology proposed previously. Then, consensus clusters were identified by considering different clustering algorithms. Finally, cell markers and cancer-related genes were further analyzed by integrating gene regulation pairs. We exploited our model on two independent datasets, which showed interesting results that the differences between clusters obtained by consensus clustering based on network embedding (CCNE) were observed obviously through visualization. For the KEGG pathway analysis of clusters, we found that all clusters are extremely related to MicroRNAs in cancer and HTLV-I infection, and the hub genes in cluster specific regulatory networks, i.e., ETS1, TP53, E2F1, and GATA3 are highly associated with melanoma. Furthermore, our method can also be extended to other scRNA-seq data.

Bioinformatics analysis identified MMP14 and COL12A1 as immune-related biomarkers associated with pancreatic adenocarcinoma prognosis

BACKGROUND

Pancreatic adenocarcinoma (PAAD) is one of the most common malignant tumors with high mortality rates and a poor prognosis. There is an urgent need to determine the molecular mechanism of PAAD tumorigenesis and identify promising biomarkers for the diagnosis and targeted therapy of the disease.

METHODS

Three GEO datasets (GSE62165, GSE15471 and GSE62452) were analyzed to obtain differentially expressed genes (DEGs). The PPI networks and hub genes were identified through the STRING database and MCODE plugin in Cytoscape software. GO and KEGG enrichment pathways were analyzed by the DAVID database. The GEPIA database was utilized to estimate the prognostic value of hub genes. Furthermore, the roles of MMP14 and COL12A1 in immune infiltration and tumor-immune interaction and their biological functions in PAAD were explored by TIMER, TISIDB, GeneMANIA, Metascape and GSEA.

RESULTS

A total of 209 common DEGs in the three datasets were obtained. GO function analysis showed that the 209 DEGs were significantly enriched in calcium ion binding, serine-type endopeptidase activity, integrin binding, extracellular matrix structural constituent and collagen binding. KEGG pathway analysis showed that DEGs were mainly enriched in focal adhesion, protein digestion and absorption and ECM-receptor interaction. The 14 genes with the highest degree of connectivity were defined as the hub genes of PAAD development. GEPIA revealed that PAAD patients with upregulated MMP14 and COL12A1 expression had poor prognoses. In addition, TIMER analysis revealed that MMP14 and COL12A1 were closely associated with the infiltration levels of macrophages, neutrophils and dendritic cells in PAAD. TISIDB revealed that MMP14 was strongly positively correlated with CD276, TNFSF4, CD70 and TNFSF9, while COL12A1 was strongly positively correlated with TNFSF4, CD276, ENTPD1 and CD70. GSEA revealed that MMP14 and COL12A1 were significantly enriched in epithelial mesenchymal transition, extracellular matrix receptor interaction, apical junction, and focal adhesion in PAAD development.

CONCLUSIONS

Our study revealed that overexpression of MMP14 and COL12A1 is significantly correlated with PAAD patient poor prognosis. MMP14 and COL12A1 participate in regulating tumor immune interactions and might become promising biomarkers for PAAD.

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.

Identification and analysis of genes associated with epithelial ovarian cancer by integrated bioinformatics methods

Background

Though considerable efforts have been made to improve the treatment of epithelial ovarian cancer (EOC), the prognosis of patients has remained poor. Identifying differentially expressed genes (DEGs) involved in EOC progression and exploiting them as novel biomarkers or therapeutic targets is of great value.

Methods

Overlapping DEGs were screened out from three independent gene expression omnibus (GEO) datasets and were subjected to Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analyses. The protein-protein interactions (PPI) network of DEGs was constructed based on the STRING database. The expression of hub genes was validated in GEPIA and GEO. The relationship of hub genes expression with tumor stage and overall survival and progression-free survival of EOC patients was investigated using the cancer genome atlas data.

Results

A total of 306 DEGs were identified, including 265 up-regulated and 41 down-regulated. Through PPI network analysis, the top 20 genes were screened out, among which 4 hub genes, which were not researched in depth so far, were selected after literature retrieval, including CDC45, CDCA5, KIF4A, ESPL1. The four genes were up-regulated in EOC tissues compared with normal tissues, but their expression decreased gradually with the continuous progression of EOC. Survival curves illustrated that patients with a lower level of CDCA5 and ESPL1 had better overall survival and progression-free survival statistically.

Conclusion

Two hub genes, CDCA5 and ESPL1, identified as probably playing tumor-promotive roles, have great potential to be utilized as novel therapeutic targets for EOC treatment.

Applications of single-cell sequencing in cancer research: progress and perspectives

Single-cell sequencing, including genomics, transcriptomics, epigenomics, proteomics and metabolomics sequencing, is a powerful tool to decipher the cellular and molecular landscape at a single-cell resolution, unlike bulk sequencing, which provides averaged data. The use of single-cell sequencing in cancer research has revolutionized our understanding of the biological characteristics and dynamics within cancer lesions. In this review, we summarize emerging single-cell sequencing technologies and recent cancer research progress obtained by single-cell sequencing, including information related to the landscapes of malignant cells and immune cells, tumor heterogeneity, circulating tumor cells and the underlying mechanisms of tumor biological behaviors. Overall, the prospects of single-cell sequencing in facilitating diagnosis, targeted therapy and prognostic prediction among a spectrum of tumors are bright. In the near future, advances in single-cell sequencing will undoubtedly improve our understanding of the biological characteristics of tumors and highlight potential precise therapeutic targets for patients.

Risk of Cardiac Adverse Events in Patients Treated With Immune Checkpoint Inhibitor Regimens: A Systematic Review and Meta-Analysis

Background

We performed a systematic review and meta-analysis to evaluate the risks of cardiac adverse events in solid tumor patients treated with monotherapy of immune checkpoint inhibitors (ICIs) or combined therapy of ICIs plus chemotherapy.

Methods

Eligible studies were selected through the following databases: PubMed, Embase and clinical trials (https://clinicaltrials.gov.) and included phase III/IV randomized controlled trials (RCTs) involving patients with the solid tumor treated with ICIs. The data was analyzed by using Review Manager (version5.3), Stata (version 15.1).

Results

Among 2,551 studies, 25 clinical trials including 20,244 patients were qualified for the meta-analysis. Compared with PD-1 inhibitor (nivolumab) or CTLA-4 inhibitor (ipilimumab), PD-1 inhibitor (nivolumab) plus CTLA-4 inhibitor (ipilimumab) combined therapy showed significant increase in grade 5 arrhythmology (OR 3.90, 95% CI: 1.08–14.06, p = 0.603). PD-1 inhibitor plus chemotherapy show significant increase in grades 1–5 myocardial disease (OR 5.09, 95% CI: 1.11–23.32, p = 1.000). Compared with chemotherapy, PD-1 inhibitor (nivolumab) or CTLA-4 inhibitor (ipilimumab), PD-1 inhibitor (nivolumab) plus CTLA-4 inhibitor (ipilimumab) combined therapy show significant increase in grades 1–5 arrhythmology (OR 2.49, 95% CI: 1.30–4.78, p = 0.289).

Conclusions

Our meta-analysis demonstrated that PD-1 inhibitor plus CTLA-4 inhibitor can result in a higher risk of grade 5 arrhythmology in comparison with PD-1/CTLA-4 inhibitor alone, and a higher risk of grade 5 arrhythmology in comparison with chemotherapy. PD-1 inhibitor plus chemotherapy treatment could increase the risk of all-grade myocardial disease compared with chemotherapy. However, in most cases, there was no significant increase of risks of cardiovascular toxicity in PD-1/PD-L1 inhibitor monotherapy or PD-1/PD-L1 inhibitor plus chemotherapy compared with chemotherapy alone.

What are the applications of single-cell RNA sequencing in cancer research: a systematic review

Single-cell RNA sequencing (scRNA-seq) is a tool for studying gene expression at the single-cell level that has been widely used due to its unprecedented high resolution. In the present review, we outline the preparation process and sequencing platforms for the scRNA-seq analysis of solid tumor specimens and discuss the main steps and methods used during data analysis, including quality control, batch-effect correction, normalization, cell cycle phase assignment, clustering, cell trajectory and pseudo-time reconstruction, differential expression analysis and gene set enrichment analysis, as well as gene regulatory network inference. Traditional bulk RNA sequencing does not address the heterogeneity within and between tumors, and since the development of the first scRNA-seq technique, this approach has been widely used in cancer research to better understand cancer cell biology and pathogenetic mechanisms. ScRNA-seq has been of great significance for the development of targeted therapy and immunotherapy. In the second part of this review, we focus on the application of scRNA-seq in solid tumors, and summarize the findings and achievements in tumor research afforded by its use. ScRNA-seq holds promise for improving our understanding of the molecular characteristics of cancer, and potentially contributing to improved diagnosis, prognosis, and therapeutics.

Artificial Intelligence in Bulk and Single-Cell RNA-Sequencing Data to Foster Precision Oncology

Artificial intelligence, or the discipline of developing computational algorithms able to perform tasks that requires human intelligence, offers the opportunity to improve our idea and delivery of precision medicine. Here, we provide an overview of artificial intelligence approaches for the analysis of large-scale RNA-sequencing datasets in cancer. We present the major solutions to disentangle inter- and intra-tumor heterogeneity of transcriptome profiles for an effective improvement of patient management. We outline the contributions of learning algorithms to the needs of cancer genomics, from identifying rare cancer subtypes to personalizing therapeutic treatments.