High-throughput single-сell sequencing in cancer research

Applications of single-cell analysis in immunotherapy for lung cancer: Current progress, new challenges and expectations

BACKGROUND

Lung cancer is a prevalent form of cancer worldwide, presenting a substantial risk to human well-being. Lung cancer is classified into two main types: non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC). The advancement of tumor immunotherapy, specifically immune checkpoint inhibitors and adaptive T-cell therapy, has encountered substantial obstacles due to the rapid progression of SCLC and the metastasis, recurrence, and drug resistance of NSCLC. These challenges are believed to stem from the tumor heterogeneity of lung cancer within the tumor microenvironment.

AIM OF REVIEW

This review aims to comprehensively explore recent strides in single-cell analysis, a robust sequencing technology, concerning its application in the realm of tumor immunotherapy for lung cancer. It has been effectively integrated with transcriptomics, epigenomics, genomics, and proteomics for various applications. Specifically, these techniques have proven valuable in mapping the transcriptional activity of tumor-infiltrating lymphocytes in patients with NSCLC, identifying circulating tumor cells, and elucidating the heterogeneity of the tumor microenvironment.

KEY SCIENTIFIC CONCEPTS OF REVIEW

The review emphasizes the paramount significance of single-cell analysis in mapping the immune cells within NSCLC patients, unveiling circulating tumor cells, and elucidating the tumor microenvironment heterogeneity. Notably, these advancements highlight the potential of single-cell analysis to revolutionize lung cancer immunotherapy by characterizing immune cell fates, improving therapeutic strategies, and identifying promising targets or prognostic biomarkers. It is potential to unravel the complexities within the tumor microenvironment and enhance treatment strategies marks a significant step towards more effective therapies and improved patient outcomes.

Revisiting the CXCL13/CXCR5 axis in the tumor microenvironment in the era of single-cell omics: Implications for immunotherapy

As one of the important members of the family of chemokines and their receptors, the CXCL13/CXCR5 axis is involved in follicle formation in normal lymphoid tissues and the establishment of somatic cavity immunity under physiological conditions, as well as being associated with a wide range of infectious, autoimmune, and tumoral diseases. Here in this review, we focus on its role in tumors. Traditional studies have found the axis to be both pro- and anti-tumorigenic, involving a variety of immune cells, including the tumor cells themselves and those in the tumor microenvironment (TME), and the prognostic significance of this axis is clinical context-dependent. With the development of techniques at the single-cell level, we were able to explain in detail the status of the CXCL13/CXCR5 axis in the TME based on real clinical samples and found that it involves a range of crucial intrinsic anti-tumor immune processes in the TME and is therefore important in tumor immunotherapy. We summarize the cellular subsets, physiological functions, and prognostic significance associated with this axis in the most promising immune checkpoint inhibitor (ICI) therapies of the day and summarize possible therapeutic ideas based on this axis. As with any TME study, the most important takeaway is that the complexity of the CXCL13/CXCR5 axis in TME suggests the importance of personalized therapy in tumor therapy.

Improving tumor microenvironment assessment in chip systems through next-generation technology integration

The tumor microenvironment (TME) comprises a diverse array of cells, both cancerous and non-cancerous, including stromal cells and immune cells. Complex interactions among these cells play a central role in driving cancer progression, impacting critical aspects such as tumor initiation, growth, invasion, response to therapy, and the development of drug resistance. While targeting the TME has emerged as a promising therapeutic strategy, there is a critical need for innovative approaches that accurately replicate its complex cellular and non-cellular interactions; the goal being to develop targeted, personalized therapies that can effectively elicit anti-cancer responses in patients. Microfluidic systems present notable advantages over conventional in vitro 2D co-culture models and in vivo animal models, as they more accurately mimic crucial features of the TME and enable precise, controlled examination of the dynamic interactions among multiple human cell types at any time point. Combining these models with next-generation technologies, such as bioprinting, single cell sequencing and real-time biosensing, is a crucial next step in the advancement of microfluidic models. This review aims to emphasize the importance of this integrated approach to further our understanding of the TME by showcasing current microfluidic model systems that integrate next-generation technologies to dissect cellular intra-tumoral interactions across different tumor types. Carefully unraveling the complexity of the TME by leveraging next generation technologies will be pivotal for developing targeted therapies that can effectively enhance robust anti-tumoral responses in patients and address the limitations of current treatment modalities.

Identification of psoriasis-associated immune marker G3BP2 through single-cell RNA sequencing and meta analysis

Psoriasis is a chronic skin disease with an increasing prevalence each year. However, the mechanisms underlying its onset and progression remain unclear, and effective therapeutic targets are lacking. Therefore, we employs an innovative approach by combining single-cell RNA sequencing (scRNA-seq) with meta-analysis. This not only elucidates the potential mechanisms of psoriasis at the cellular level but also identifies immunoregulatory marker genes that play a statistically significant role in driving psoriasis progression through comprehensive analysis of multiple datasets. Skin tissue samples from 12 psoriasis patients underwent scRNA-seq, followed by quality control, filtering, PCA dimensionality reduction, and tSNE clustering analysis to identify T cell subtypes and differentially expressed genes (DEGs) in psoriatic skin tissue. Next, three psoriasis datasets were standardised and merged to identify differentially expressed genes (DEGs). Subsequently, weighted gene co-expression network analysis (WGCNA) was applied for clustering analysis of gene co-expression network modules and to assess the correlation between these modules and DEGs. Least absolute shrinkage and selection operator (LASSO) regression and receiver operating characteristic (ROC) curve analyses were conducted to select disease-specific genes and evaluate their diagnostic value. Single-cell data revealed nine cell types in psoriatic skin tissue, with seven T cell subtypes identified. Intersection analysis identified ADAM8 and G3BP2 as key genes. Through the integration of scRNA-seq and Meta analysis, we identified the immunoregulatory marker gene G3BP2, which is associated with the onset and progression of psoriasis and holds clinical significance. G3BP2 is speculated to promote the development of psoriasis by increasing the proportion of CD8+ T cells.

Abnormal Cellular Populations Shape Thymic Epithelial Tumor Heterogeneity and Anti-Tumor by Blocking Metabolic Interactions in Organoids

A variety of abnormal epithelial cells and immature and mature immune cells in thymic epithelial tumors (TETs) affect histopathological features, the degree of malignancy, and the response to treatment. Here, gene expression, trajectory inference, and T cell antigen receptor (TCR)-based lineage tracking are profiled in TETs at single-cell resolution. An original subpopulation of KRT14+ progenitor cells with a spindle cell phenotype is shown. An abnormal infiltration of immature T cells with a TCR hyper-rearrangement state is revealed, due to the lack of CCL21+ medullary epithelial cells. For thymic carcinoma, the novel biomarkers of MSLN, CCL20, and SLC1A5 are identified and observed an elevated expression of LAG3 and HAVCR2 in malignant tumorn-infiltrating mature T cells. These common features based on the single-cell populations may inform pathological reclassification of TETs. Meanwhile, it is found that macrophages (MACs) attract thymic tumor cells through the LGALS9-SLC1A5 axis, providing them with glutamine to elicit metabolic reprogramming. This MAC-based metabolic pattern can promote malignancy progression. Additionally, an interactive immune environment in TETs is identified that correlates with the infiltration of abnormal FOXI1+ CFTR- ionocytes. Collectively, the data broaden the knowledge of TET cellular ecosystems, providing a basis for tackling histopathological diagnosis and related treatment.

Genetic mutation and immune infiltration in embryonal tumor with multilayered rosettes

PURPOSE

Genetic mutations stand as pivotal factors leading to the occurrence of embryonal tumor with multilayered rosettes (ETMR). This study aims to identify improved treatment approaches by unraveling the genetic drivers and immune infiltration in ETMR.

METHODS

Two siblings with ETMR, treated at the General Hospital of Ningxia Medical University, were enrolled. Diagnosis involved MRI, Hematoxylin and Eosin (HE), and immunohistochemical (IHC) staining. Differentially expressed genes (DEGs) in ETMR were identified using GSE122077 and GSE14296 datasets. GO and KEGG analyses were used to determine ETMR-related pathways. Whole exome sequencing (WES) was utilized to annotate genetic variations in ETMR. Core genes, identified by protein-protein interaction (PPI), formed a diagnostic model evaluated by Logistic Regression. Single-sample Gene Set Enrichment Analysis (ssGSEA) assessed immune infiltration in ETMR, examining correlations between immune cells and core genes.

RESULTS

Two siblings were diagnosed with ETMR. In ETMR, 135 DEGs were identified, of which 25 genes were annotated with 28 mutation sites. Moreover, ETMR-related pathways included cell cycle, synaptic functions, and neurodegeneration. Three ETMR-related core genes (ALB, PSMD1, and PAK2) were screened by protein-protein interaction (PPI). The diagnostic model constructed using these genes demonstrated an AUC value of 0.901 (95% CI: 0.811-0.991) in the training set, indicating accurate predictions in ETMR. Enhanced ssGSEA scores for 16 immune cells in ETMR tissues suggested a strong immune response.

CONCLUSION

This study identifies diagnostic models associated with three core variant genes (ALB, PSMD1, PAK2) and enhanced immune cell activity in ETMR. It reveals crucial genetic features and significant immune responses in ETMR.

Anticancer effects of exercise: Insights from single-cell analysis

•Physical exercise can exert antitumorigenic effects; however, the molecular mechanisms are still poorly understood. •Single-cell analysis may help to characterize the molecular mechanisms underlying the effects of exercise on anticancer immune function as well as on the complex tumor microenvironment. •Recent research using single-cell analysis provides preliminary insights into the molecular mechanisms behind an improved antitumor immunity in response to exercise. Particularly, there is evidence for a “reprogramming” of several immune effectors towards a higher antitumoral toxicity.

Dynamic immunoediting by macrophages in homologous recombination deficiency-stratified pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is a lethal disease, notably resistant to existing therapies. Current research indicates that PDAC patients deficient in homologous recombination (HR) benefit from platinum-based treatments and poly-ADP-ribose polymerase inhibitors (PARPi). However, the effectiveness of PARPi in HR-deficient (HRD) PDAC is suboptimal, and significant challenges remain in fully understanding the distinct characteristics and implications of HRD-associated PDAC. We analyzed 16 PDAC patient-derived tissues, categorized by their homologous recombination deficiency (HRD) scores, and performed high-plex immunofluorescence analysis to define 20 cell phenotypes, thereby generating an in-situ PDAC tumor-immune landscape. Spatial phenotypic-transcriptomic profiling guided by regions-of-interest (ROIs) identified a crucial regulatory mechanism through localized tumor-adjacent macrophages, potentially in an HRD-dependent manner. Cellular neighborhood (CN) analysis further demonstrated the existence of macrophage-associated high-ordered cellular functional units in spatial contexts. Using our multi-omics spatial profiling strategy, we uncovered a dynamic macrophage-mediated regulatory axis linking HRD status with SIGLEC10 and CD52. These findings demonstrate the potential of targeting CD52 in combination with PARPi as a therapeutic intervention for PDAC.

BioM2: biologically informed multi-stage machine learning for phenotype prediction using omics data

Navigating the complex landscape of high-dimensional omics data with machine learning models presents a significant challenge. The integration of biological domain knowledge into these models has shown promise in creating more meaningful stratifications of predictor variables, leading to algorithms that are both more accurate and generalizable. However, the wider availability of machine learning tools capable of incorporating such biological knowledge remains limited. Addressing this gap, we introduce BioM2, a novel R package designed for biologically informed multistage machine learning. BioM2 uniquely leverages biological information to effectively stratify and aggregate high-dimensional biological data in the context of machine learning. Demonstrating its utility with genome-wide DNA methylation and transcriptome-wide gene expression data, BioM2 has shown to enhance predictive performance, surpassing traditional machine learning models that operate without the integration of biological knowledge. A key feature of BioM2 is its ability to rank predictor variables within biological categories, specifically Gene Ontology pathways. This functionality not only aids in the interpretability of the results but also enables a subsequent modular network analysis of these variables, shedding light on the intricate systems-level biology underpinning the predictive outcome. We have proposed a biologically informed multistage machine learning framework termed BioM2 for phenotype prediction based on omics data. BioM2 has been incorporated into the BioM2 CRAN package (https://cran.r-project.org/web/packages/BioM2/index.html).

Recent Innovations and Technical Advances in High-Throughput Parallel Single-Cell Whole-Genome Sequencing Methods

Single-cell whole-genome sequencing (scWGS) detects cell heterogeneity at the aspect of genomic variations, which are inheritable and play an important role in life processes such as aging and cancer progression. The recent explosive development of high-throughput single-cell sequencing methods has enabled high-performance heterogeneity detection through a vast number of novel strategies. Despite the limitation on total cost, technical advances in high-throughput single-cell whole-genome sequencing methods are made for higher genome coverage, parallel throughput, and level of integration. This review highlights the technical advancements in high-throughput scWGS in the aspects of strategies design, data efficiency, parallel handling platforms, and their applications on human genome. The experimental innovations, remaining challenges, and perspectives are summarized and discussed.

New HCC Subtypes Based on CD8 Tex-Related lncRNA Signature Could Predict Prognosis, Immunological and Drug Sensitivity Characteristics of Hepatocellular Carcinoma

Purpose

Hepatocellular carcinoma has become one of the severe diseases threatening human health. T cell exhaustion is deemed as a reason for immunotherapy resistance. However, little is known about the roles of CD8 Tex-related lncRNAs in HCC.

Materials and Methods

We processed single-cell RNA sequencing to identify CD8 Tex-related genes. CD8 Tex-related lncRNAs were identified based on their correlations with mRNAs. Unsupervised clustering approach was used to identify molecular clusters of CD8 Tex-related lncRNAs. Differences in prognosis and immune infiltration between the clusters were explored. Machine learning algorithms were used to construct a prognostic signature. Samples were classified as low- and high-risk groups based on their risk scores. We identified prognosis-related lncRNAs and constructed a ceRNA network. In vitro experiments were conducted to investigate the impacts of CD8 Tex-related lncRNAs on proliferation and apoptosis of HCC cells.

Results

We clarified cell types within two HCC single-cell datasets. We identified specific markers of CD8 Tex cells and analyzed their potential functions. Twenty-eight lncRNAs were identified as CD8 Tex-related. Based on CD8 Tex-related lncRNAs, samples were categorized into two distinct clusters, which exhibited significant differences in survival rates and immune infiltration. Ninety-six algorithm combinations were employed to establish a prognostic signature. RSF emerged as the one with the highest C-index. Patients in high- and low-risk groups exhibited marked differences in prognosis, enriched pathways, mutations and drug sensitivities. MCM3AP-AS1, MAPKAPK5-AS1 and PART1 were regarded as prognosis-related lncRNAs. A ceRNA network was constructed based on CD8 Tex-related lncRNAs and mRNAs. Experiments on cell lines and organoids indicated that downregulation of MCM3AP-AS1, MAPKAPK5-AS1 and PART1 suppressed cell proliferation and induced apoptosis.

Conclusion

CD8 Tex-related lncRNAs played crucial roles in HCC progression. Our findings provided new insights into the regulatory mechanisms of CD8 Tex-related lncRNAs in HCC.

Correlative multiscale 3D imaging of mouse primary and metastatic tumors by sequential light sheet and confocal fluorescence microscopy

Three-dimensional (3D) optical microscopy, combined with advanced tissue clearing, permits in situ interrogation of the tumor microenvironment (TME) in large volumetric tumors for preclinical cancer research. Light sheet (also known as ultramicroscopy) and confocal fluorescence microscopy are often used to achieve macroscopic and microscopic 3D images of optically cleared tumor tissues, respectively. Although each technique offers distinct fields of view (FOVs) and spatial resolution, the combination of these two optical microscopy techniques to obtain correlative multiscale 3D images from the same tumor tissues has not yet been explored. To establish correlative multiscale 3D optical microscopy, we developed a method for optically marking defined regions of interest (ROIs) within a cleared mouse tumor by employing a UV light-activated visible dye and Z-axis position-selective UV irradiation in a light sheet microscope system. By integrating this method with subsequent tissue processing, including physical ROI marking, reversal of tissue clearing, tissue macrosectioning, and multiplex immunofluorescence, we established a workflow that enables the tracking and 3D imaging of ROIs within tumor tissues through sequential light sheet and confocal fluorescence microscopy. This approach allowed for quantitative 3D spatial analysis of the immune response in the TME of a mouse mammary tumor following cancer immunotherapy at multiple spatial scales. The workflow also facilitated the direct localization of a metastatic lesion within a whole mouse brain. These results demonstrate that our ROI tracking method and its associated workflow offer a novel approach for correlative multiscale 3D optical microscopy, with the potential to provide new insights into tumor heterogeneity, metastasis, and response to therapy at various spatial levels.

Identification of eight genes associated with recurrent patellar dislocation

The inheritance of recurrent patellar dislocation (RPD) is known, but the susceptible gene remains unidentified. Here, we performed the first whole exome sequencing (WES) cohort study to identify the susceptible genes. The results showed eight genes were associated with this disease. Notably, the carboxypeptidase D (CPD) gene showed the highest relevance based on its gene function and tissue expression. Single-cell sequencing results indicate that the CPD gene is involved in the pathophysiological process of RPD through granulocytes. Implicated pathways include nuclear factor kappa B (NF-κB), mitogen-activated protein kinase (MAPK), and Wnt/β-catenin signaling, potentially influencing CPD's role in RPD pathogenesis. This study identified the susceptible gene and investigates the potential pathogenesis of RPD, which provided a new prospect for the understanding of RPD. Besides, it would offer the theoretical basis for disease prevention and genetic counseling.

Revealing molecular and cellular heterogeneity in hypopharyngeal carcinogenesis through single-cell RNA and TCR/BCR sequencing

Introduction

Hypopharyngeal squamous cell carcinoma (HSCC) is one of the malignant tumors with the worst prognosis in head and neck cancers. The transformation from normal tissue through low-grade and high-grade intraepithelial neoplasia to cancerous tissue in HSCC is typically viewed as a progressive pathological sequence typical of tumorigenesis. Nonetheless, the alterations in diverse cell clusters within the tissue microenvironment (TME) throughout tumorigenesis and their impact on the development of HSCC are yet to be fully understood.

Methods

We employed single-cell RNA sequencing and TCR/BCR sequencing to sequence 60,854 cells from nine tissue samples representing different stages during the progression of HSCC. This allowed us to construct dynamic transcriptomic maps of cells in diverse TME across various disease stages, and experimentally validated the key molecules within it.

Results

We delineated the heterogeneity among tumor cells, immune cells (including T cells, B cells, and myeloid cells), and stromal cells (such as fibroblasts and endothelial cells) during the tumorigenesis of HSCC. We uncovered the alterations in function and state of distinct cell clusters at different stages of tumor development and identified specific clusters closely associated with the tumorigenesis of HSCC. Consequently, we discovered molecules like MAGEA3 and MMP3, pivotal for the diagnosis and treatment of HSCC.

Discussion

Our research sheds light on the dynamic alterations within the TME during the tumorigenesis of HSCC, which will help to understand its mechanism of canceration, identify early diagnostic markers, and discover new therapeutic targets.

Single-Cell RNA Sequencing in Organ and Cell Transplantation

Single-cell RNA sequencing is a high-throughput novel method that provides transcriptional profiling of individual cells within biological samples. This method typically uses microfluidics systems to uncover the complex intercellular communication networks and biological pathways buried within highly heterogeneous cell populations in tissues. One important application of this technology sits in the fields of organ and stem cell transplantation, where complications such as graft rejection and other post-transplantation life-threatening issues may occur. In this review, we first focus on research in which single-cell RNA sequencing is used to study the transcriptional profile of transplanted tissues. This technology enables the analysis of the donor and recipient cells and identifies cell types and states associated with transplant complications and pathologies. We also review the use of single-cell RNA sequencing in stem cell implantation. This method enables studying the heterogeneity of normal and pathological stem cells and the heterogeneity in cell populations. With their remarkably rapid pace, the single-cell RNA sequencing methodologies will potentially result in breakthroughs in clinical transplantation in the coming years.

Single-cell sequencing: A promising approach for uncovering the characteristic of pancreatic islet cells in type 2 diabetes

Single-cell sequencing is a novel and rapidly advancing high-throughput technique that can be used to investigating genomics, transcriptomics, and epigenetics at a single-cell level. Currently, single-cell sequencing can not only be used to draw the pancreatic islet cells map and uncover the characteristics of cellular heterogeneity in type 2 diabetes, but can also be used to label and purify functional beta cells in pancreatic stem cells, improving stem cells and islet organoids therapies. In addition, this technology helps to analyze islet cell dedifferentiation and can be applied to the treatment of type 2 diabetes. In this review, we summarize the development and process of single-cell sequencing, describe the potential applications of single-cell sequencing in the field of type 2 diabetes, and discuss the prospects and limitations of single-cell sequencing to provide a new direction for exploring the pathogenesis of type 2 diabetes and finding therapeutic targets.

Toolkit for mapping the clonal landscape of tumor-infiltrating B cells

Our current understanding of whether B cell involvement in the tumor microenvironment benefits the patient or the tumor - in distinct cancers, subcohorts and individual patients - is quite limited. Both statements are probably true in most cases: certain clonal B cell populations contribute to the antitumor response, while others steer the immune response away from the desired mechanics. To step up to a new level of understanding and managing B cell behaviors in the tumor microenvironment, we need to rationally discern these roles, which are cumulatively defined by B cell clonal functional programs, specificities of their B cell receptors, specificities and isotypes of the antibodies they produce, and their spatial interactions within the tumor environment. Comprehensive analysis of these characteristics of clonal B cell populations is now becoming feasible with the development of a whole arsenal of advanced technical approaches, which include (1) methods of single-cell and spatial transcriptomics, genomics, and proteomics; (2) methods of massive identification of B cell specificities; (3) methods of deep error-free profiling of B cell receptor repertoires. Here we overview existing techniques, summarize their current application for B cells studies and propose promising future directions in advancing B cells exploration.

Resolving the heterogeneous tumour microenvironment in cardiac myxoma through single-cell and spatial transcriptomics

BACKGROUND

Cardiac myxoma (CM) is the most common (58%-80%) type of primary cardiac tumours. Currently, there is a need to develop medical therapies, especially for patients not physically suitable for surgeries. However, the mechanisms that shape the tumour microenvironment (TME) in CM remain largely unknown, which impedes the development of targeted therapies. Here, we aimed to dissect the TME in CM at single-cell and spatial resolution.

METHODS

We performed single-cell transcriptomic sequencing and Visium CytAssist spatial transcriptomic (ST) assays on tumour samples from patients with CM. A comprehensive analysis was performed, including unsupervised clustering, RNA velocity, clonal substructure inference of tumour cells and cell-cell communication.

RESULTS

Unsupervised clustering of 34 759 cells identified 12 clusters, which were assigned to endothelial cells (ECs), mesenchymal stroma cells (MSCs), and tumour-infiltrating immune cells. Myxoma tumour cells were found to encompass two closely related phenotypic states, namely, EC-like tumour cells (ETCs) and MSC-like tumour cells (MTCs). According to RNA velocity, our findings suggest that ETCs may be directly differentiated from MTCs. The immune microenvironment of CM was found to contain multiple factors that promote immune suppression and evasion, underscoring the potential of using immunotherapies as a treatment option. Hyperactive signals sent primarily by tumour cells were identified, such as MDK, HGF, chemerin, and GDF15 signalling. Finally, the ST assay uncovered spatial features of the subclusters, proximal cell-cell communication, and clonal evolution of myxoma tumour cells.

CONCLUSIONS

Our study presents the first comprehensive characterisation of the TME in CM at both single-cell and spatial resolution. Our study provides novel insight into the differentiation of myxoma tumour cells and advance our understanding of the TME in CM. Given the rarity of cardiac tumours, our study provides invaluable datasets and promotes the development of medical therapies for CM.

Aggrephagy-related gene signature correlates with survival and tumor-associated macrophages in glioma: Insights from single-cell and bulk RNA sequencing

BACKGROUND

Aggrephagy is a lysosome-dependent process that degrades misfolded protein condensates to maintain cancer cell homeostasis. Despite its importance in cellular protein quality control, the role of aggrephagy in glioma remains poorly understood.

OBJECTIVE

To investigate the expression of aggrephagy-related genes (ARGs) in glioma and in different cell types of gliomas and to develop an ARGs-based prognostic signature to predict the prognosis, tumor microenvironment, and immunotherapy response of gliomas.

METHODS

ARGs were identified by searching the Reactome database. We developed the ARGs-based prognostic signature (ARPS) using data from the Cancer Genome Atlas (TCGA, n = 669) by Lasso-Cox regression. We validated the robustness of the signature in clinical subgroups and CGGA cohorts (n = 970). Gene set enrichment analysis (GSEA) was used to identify the pathways enriched in ARPS subgroups. The correlations between ARGs and macrophages were also investigated at single cell level.

RESULTS

A total of 44 ARGs showed heterogeneous expression among different cell types of gliomas. Five ARGs (HSF1, DYNC1H1, DYNLL2, TUBB6, TUBA1C) were identified to develop ARPS, an independent prognostic factor. GSEA showed gene sets of patients with high-ARPS were mostly enriched in cell cycle, DNA replication, and immune-related pathways. High-ARPS subgroup had higher immune cell infiltration states, particularly macrophages, Treg cells, and neutrophils. APRS had positive association with tumor mutation burden (TMB) and immunotherapy response predictors. At the single cell level, we found ARGs correlated with macrophage development and identified ARGs-mediated macrophage subtypes with distinct communication characteristics with tumor cells. VIM+ macrophages were identified as pro-inflammatory and had higher interactions with malignant cells.

CONCLUSION

We identified a novel signature based on ARGs for predicting glioma prognosis, tumor microenvironment, and immunotherapy response. We highlight the ARGs-mediated macrophages in glioma exhibit classical features.

Comprehensive analysis reveals TSPEAR as a prognostic biomarker in colorectal cancer

Background: Colorectal cancer (CRC) is one of the most common malignant tumors and has high morbidity and mortality rates. Previous studies have shown that TSPEAR mutations are involved in the development and progression of gastric cancer and liver cancer. However, the role of TSPEAR in CRC is still unclear. Methods: In The Cancer Genome Atlas (TCGA) database, 590 CRC patients with complete survival information were analyzed. We assessed TSPEAR expression in a pan-cancer dataset from the TCGA database. Cox regression analysis was performed to evaluate factors associated with prognosis. Enrichment analysis via the R package "clusterProfiler" was used to explore the potential function of TSPEAR. The single-sample GSEA (ssGSEA) method from the R package "GSVA" and the TIMER database were used to investigate the association between the immune infiltration level and TSPEAR expression in CRC. The R package "maftools" was used to explore the association between tumour mutation burden (TMB) and TSPEAR expression in CRC. CCK-8 assays and cell invasion assays were used to detect the effect of TSPEAR and TGIF2 on the biological behavior of CRC cells. Results: Pan-cancer analysis revealed that TSPEAR was upregulated in CRC tissues compared to normal tissues and that high TSPEAR expression was associated with poorer overall survival (OS) (p=0.0053). The expression of TSPEAR increased with increasing TNM stage, T stage, N stage, and M stage. The nomogram constructed with TSPEAR, age, and TNM stage showed better predictive value than TSPEAR, age, or TNM stage alone. Immune cell infiltration analysis revealed that high expression of TSPEAR was associated with lower immune cell infiltration. Tumor mutation burden (TMB) analysis indicated that high expression of TSPEAR was associated with lower TMB (p=0.005), and high TMB was associated with shorter OS (p=0.02). CCK-8 assays and cell invasion assays indicated that in vitro knockdown of TSPEAR inhibited the proliferation, migration, and invasion of CRC cells. In addition, TSPEAR expression may be regulated by the upstream transcription factor TGIF2. Conclusion: TSPEAR expression was higher in CRC tissues than in normal tissues. Its upregulation was significantly associated with a poor prognosis. Additionally, TSPEAR plays a significant role in tumor immunity and the biological behavior of CRC cells. Thus, TSPEAR may become a promising prognostic biomarker and therapeutic target for CRC patients.

A model for predicting clinical prognosis based on brain metastasis-related genes in patients with breast cancer

Background

Brain metastasis (BM) is a clinically relevant cause of death in patients with breast cancer (BRCA). This study was designed to develop a clinical model capable of predicting BRCA patients' prognostic outcomes according to the expression of BM-related genes (BMRGs).

Methods

The public Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases served as data sources. BMRGs of BRCA were selected from previous literature. Differences among BRCA molecular subtypes were compared using R 'limma' package. The impact of BM-related differentially expressed genes (BM_DEGs) on BRCA patients' outcomes was explored with a risk score model, after which the relationship between these risk scores and immune cell infiltration was examined. Risk scores were also used to judge the predicted efficacy of immunotherapeutic interventions. The utility of risk scores in combination with clinicopathological characteristics was evaluated as a predictor of patient's survival through univariate and multivariate analyses.

Results

The R limma package was used to explore differential gene expression, after which 12 BM_DEGs were incorporated into a risk scoring model. The resultant risk scores were able to predict immunotherapeutic treatment efficacy. In addition, a nomogram incorporating risk scores, stage, and age was established. The nomogram was able to reliably predict the overall survival (OS) of BRCA patients, yielding predictive outcomes that aligned well with actual observations.

Conclusions

In summary, a predictive clinical model for BRCA patients was successfully established in this study, providing a valuable tool that may be particularly helpful for the assessment of patients facing a risk of BM development.

Review: therapeutic approaches for circadian modulation of the glioma microenvironment

High-grade gliomas are malignant brain tumors that are characteristically hard to treat because of their nature; they grow quickly and invasively through the brain tissue and develop chemoradiation resistance in adults. There is also a distinct lack of targeted treatment options in the pediatric population for this tumor type to date. Several approaches to overcome therapeutic resistance have been explored, including targeted therapy to growth pathways (ie. EGFR and VEGF inhibitors), epigenetic modulators, and immunotherapies such as Chimeric Antigen Receptor T-cell and vaccine therapies. One new promising approach relies on the timing of chemotherapy administration based on intrinsic circadian rhythms. Recent work in glioblastoma has demonstrated temporal variations in chemosensitivity and, thus, improved survival based on treatment time of day. This may be due to intrinsic rhythms of the glioma cells, permeability of the blood brain barrier to chemotherapy agents, the tumor immune microenvironment, or another unknown mechanism. We review the literature to discuss chronotherapeutic approaches to high-grade glioma treatment, circadian regulation of the immune system and tumor microenvironment in gliomas. We further discuss how these two areas may be combined to temporally regulate and/or improve the effectiveness of immunotherapies.

Systems immunology spanning tumors, lymph nodes, and periphery

The immune system defines a complex network of tissues and cell types that orchestrate responses across the body in a dynamic manner. The local and systemic interactions between immune and cancer cells contribute to disease progression. Lymphocytes are activated in lymph nodes, traffic through the periphery, and impact cancer progression through their interactions with tumor cells. As a result, therapeutic response and resistance are mediated across tissues, and a comprehensive understanding of lymphocyte dynamics requires a systems-level approach. In this review, we highlight experimental and computational methods that can leverage the study of leukocyte trafficking through an immunomics lens and reveal how adaptive immunity shapes cancer.

Single-cell RNA sequencing in osteoarthritis

Osteoarthritis is a progressive and heterogeneous joint disease with complex pathogenesis. The various phenotypes associated with each patient suggest that better subgrouping of tissues associated with genotypes in different phases of osteoarthritis may provide new insights into the onset and progression of the disease. Recently, single-cell RNA sequencing was used to describe osteoarthritis pathogenesis on a high-resolution view surpassing traditional technologies. Herein, this review summarizes the microstructural changes in articular cartilage, meniscus, synovium and subchondral bone that are mainly due to crosstalk amongst chondrocytes, osteoblasts, fibroblasts and endothelial cells during osteoarthritis progression. Next, we focus on the promising targets discovered by single-cell RNA sequencing and its potential applications in target drugs and tissue engineering. Additionally, the limited amount of research on the evaluation of bone-related biomaterials is reviewed. Based on the pre-clinical findings, we elaborate on the potential clinical values of single-cell RNA sequencing for the therapeutic strategies of osteoarthritis. Finally, a perspective on the future development of patient-centred medicine for osteoarthritis therapy combining other single-cell multi-omics technologies is discussed. This review will provide new insights into osteoarthritis pathogenesis on a cellular level and the field of applications of single-cell RNA sequencing in personalized therapeutics for osteoarthritis in the future.

Revolutionizing cancer immunotherapy in solid tumor: CAR engineering and single-cell sequencing insights

The global increase in cancer incidence presents significant economic and societal challenges. While chimeric antigen receptor-modified T cell (CAR-T) therapy has demonstrated remarkable success in hematologic malignancies and has earned FDA approval, its translation to solid tumors encounters faces significant obstacles, primarily centered around identifying reliable tumor-associated antigens and navigating the complexities of the tumor microenvironment. Recent developments in single-cell RNA sequencing (scRNA-seq) have greatly enhanced our understanding of tumors by offering high-resolution, unbiased analysis of cellular heterogeneity and molecular patterns. These technologies have revolutionized our comprehension of tumor immunology and have led to notable progress in cancer immunotherapy. This mini-review explores the progress of chimeric antigen receptor (CAR) cell therapy in solid tumor treatment and the application of scRNA-seq at various stages following the administration of CAR cell products into the body. The advantages of scRNA-seq are poised to further advance the investigation of the biological characteristics of CAR cells in vivo, tumor immune evasion, the impact of different cellular components on clinical efficacy, the development of clinically relevant biomarkers, and the creation of new targeted drugs and combination therapy approaches. The integration of scRNA-seq with CAR therapy represents a promising avenue for future innovations in cancer immunotherapy. This synergy holds the potential to enhance the precision and efficacy of CAR cell therapies while expanding their applications to a broader range of malignancies.

A pan-cancer analysis of the MAPK family gene and their association with prognosis, tumor microenvironment, and therapeutic targets

The mitogen-activated protein kinases family of genes plays a crucial role in a wide range of inflammatory responses in the human body. The MAPK family of genes includes ERK, ERK5, JNK, P-38 mitogen-activated protein kinases. However, the correlation between MAPK family gene expression and pan-cancer prognosis, as well as the tumor microenvironment, has not been extensively studied. This study integrated multiple bioinformatics analysis methods to assess the expression and prognostic value of MAPK family genes, as well as their relationship with tumor microenvironment in patients with pan-cancer. The results showed that ERK, JNK, and P-38 MAPK expression were found to be significantly upregulated in rectum adenocarcinoma (READ), colon adenocarcinoma/rectum adenocarcinoma esophageal carcinoma (COADREAD), and kidney renal clear cell carcinoma (KIRC), and significantly downregulated in acute myeloid leukemia. And the results revealed good prognostic results for ERK, JNK, and P-38 MAPK in READ, COADREAD, and KIRC. We observed significant positive correlation between MAPK family gene expression and immune scores especially dendritic cells in READ, COADREAD, and KIRC. And we observed that the expression levels of MAPK family genes were significantly correlated with the expression of immune-related genes, such as CXCL1, CXCL2, CXCL8, CXCR1, CXCR2, CTLA-4, CD80, CD86, and CD28, suggesting their important role in regulating immune infiltrates and tumor progression. Therefore, our study suggested that MAPK family gene plays an important role in regulating immune infiltrates and tumor progression.

SALL1 promotes proliferation and metastasis and activates phosphorylation of p65 and JUN in colorectal cancer cells

BACKGROUND

Colorectal cancer (CRC) is one of the most usual malignant tumors, and its incidence continues to rise. Our purpose was to explore the function and potential regulatory mechanisms of SALL1, a differentially methylated gene in CRC, in vivo and in vitro.

METHODS

Firstly, methylation differential gene SALL1 in CRC was screened and validated. SALL1 overexpression plasmids or SALL1 siRNAs were transfected in HT-29 and SW480 cells. Moreover, 10 μM T-5224 was added in SALL1-overexpressed CRC cells. CCK-8, flow cytometry and transwell assays were utilized to assess cell proliferation, cycle, migration, and invasion, respectively. Then CRC organoids were cultured. Next, HT-29 and SW480 cells transfected with SALL1 overexpression lentivirus were analyzed by transcriptome sequencing. Finally, in vivo tumorigenesis was used to analyze the effect of SALL1 overexpression on subcutaneous tumorigenesis in nude mice.

RESULTS

The methylation level of CpG island in SALL1 promoter was increased in CRC tissues and could distinguish tumor tissues. Overexpression of SALL1 accelerated proliferation, migration and invasion of HT-29 and SW480 cells, and silencing of SALL1 attenuated proliferation, migration and invasion of HT-29 and SW480 cells. Through analysis and validation, we found that overexpression of SALL1 also could upregulate p-p65 and p-JUN expressions. Besides, c-Fos/activator protein (AP)- 1 inhibitor (T-5224) could reverse the induction of CRC progression by SALL1 overexpression. In vivo, we also proved that overexpression of SALL1 significantly increased tumor volume, tumor weight, and p-JUN expression.

CONCLUSIONS

SALL1 could promote the proliferation, migration, and invasion of CRC cells and activate phosphorylation of p65 and JUN.

Microfluidic design in single-cell sequencing and application to cancer precision medicine

Single-cell sequencing (SCS) is a crucial tool to reveal the genetic and functional heterogeneity of tumors, providing unique insights into the clonal evolution, microenvironment, drug resistance, and metastatic progression of cancers. Microfluidics is a critical component of many SCS technologies and workflows, conferring advantages in throughput, economy, and automation. Here, we review the current landscape of microfluidic architectures and sequencing techniques for single-cell omics analysis and highlight how these have enabled recent applications in oncology research. We also discuss the challenges and the promise of microfluidics-based single-cell analysis in the future of precision oncology.

Dissecting the tumor ecosystem of liver cancers in the single-cell era

Primary liver cancers (PLCs) are a broad class of malignancies that include HCC, intrahepatic cholangiocarcinoma, and combined hepatocellular and intrahepatic cholangiocarcinoma. PLCs are often associated with a poor prognosis due to their high relapse and low therapeutic response rates. Importantly, PLCs exist within a dynamic and complex tumor ecosystem, which includes malignant, immune, and stromal cells. It is critical to dissect the PLC tumor ecosystem to uncover the underlying mechanisms associated with tumorigenesis, relapse, and treatment resistance to facilitate the discovery of novel therapeutic targets. Single-cell and spatial multi-omics sequencing techniques offer an unprecedented opportunity to elucidate spatiotemporal interactions among heterogeneous cell types within the complex tumor ecosystem. In this review, we describe the latest advances in single-cell and spatial technologies and review their applications with respect to dissecting liver cancer tumor ecosystems.

Advances in single-cell RNA sequencing and its applications in cancer research

Cancers are a group of heterogeneous diseases characterized by the acquisition of functional capabilities during the transition from a normal to a neoplastic state. Powerful experimental and computational tools can be applied to elucidate the mechanisms of occurrence, progression, metastasis, and drug resistance; however, challenges remain. Bulk RNA sequencing techniques only reflect the average gene expression in a sample, making it difficult to understand tumor heterogeneity and the tumor microenvironment. The emergence and development of single-cell RNA sequencing (scRNA-seq) technologies have provided opportunities to understand subtle changes in tumor biology by identifying distinct cell subpopulations, dissecting the tumor microenvironment, and characterizing cellular genomic mutations. Recently, scRNA-seq technology has been increasingly used in cancer studies to explore tumor heterogeneity and the tumor microenvironment, which has increased the understanding of tumorigenesis and evolution. This review summarizes the basic processes and development of scRNA-seq technologies and their increasing applications in cancer research and clinical practice.

Heterogeneity of the tumor immune microenvironment and clinical interventions

The tumor immune microenvironment (TIME) is broadly composed of various immune cells, and its heterogeneity is characterized by both immune cells and stromal cells. During the course of tumor formation and progression and anti-tumor treatment, the composition of the TIME becomes heterogeneous. Such immunological heterogeneity is not only present between populations but also exists on temporal and spatial scales. Owing to the existence of TIME, clinical outcomes can differ when a similar treatment strategy is provided to patients. Therefore, a comprehensive assessment of TIME heterogeneity is essential for developing precise and effective therapies. Facilitated by advanced technologies, it is possible to understand the complexity and diversity of the TIME and its influence on therapy responses. In this review, we discuss the potential reasons for TIME heterogeneity and the current approaches used to explore it. We also summarize clinical intervention strategies based on associated mechanisms or targets to control immunological heterogeneity.

Integrated single cell and bulk sequencing analysis identifies tumor reactive CXCR6+ CD8 T cells as a predictor of immune infiltration and immunotherapy outcomes in hepatocellular carcinoma

Background

Various immune cell types in the tumor microenvironment (TME) of hepatocellular carcinoma (HCC) have been identified as important parameters associated with prognosis and responsiveness to immunotherapy. However, how various factors influence immune cell infiltration remains incompletely understood. Hence, we investigated the single cell multi-omics landscape of immune infiltration in HCC, particularly key gene and cell subsets that influence immune infiltration, thus potentially linking the immunotherapy response and immune cell infiltration.

Methods

We grouped patients with HCC according to immune cell infiltration scores calculated by single sample gene set enrichment analysis (ssGSEA). Differential expression analysis, functional enrichment, clinical trait association, gene mutation analysis, tumor immune dysfunction and exclusion (TIDE) and prognostic model construction were used to investigate the immune infiltration landscape through multi-omics. Stepwise regression was further used to identify key genes regulating immune infiltration. Single cell analysis was performed to explore expression patterns of candidate genes and investigate associated cellular populations. Correlation analysis, ROC analysis, Immunotherapy cohorts were used to explore and confirm the role of key gene and cellular population in predicting immune infiltration state and immunotherapy response. Immunohistochemistry and multiplexed fluorescence staining were used to further validated our results.

Results

Patients with HCC were clustered into high and low immune infiltration groups. Mutations of CTNNB1 and TTN were significantly associated with immune infiltration and altered enrichment of cell populations in the TME. TIDE analysis demonstrated that T cell dysfunction and the T cell exclusion score were elevated in the high and low infiltration groups, respectively. Six risk genes and five risk immune cell types were identified and used to construct risk scores and a nomogram model. CXCR6 and LTA, identified by stepwise regression, were highly associated with immune infiltration. Single cell analysis revealed that LTA was expressed primarily in tumor infiltrating T lymphocytes and partial B lymphocytes, whereas CXCR6 was enriched predominantly in T and NK cells. Notably, CXCR6+ CD8 T cells were characterized as tumor enriched cells that may be potential predictors of high immune infiltration and the immune-checkpoint blockade response, and may serve as therapeutic targets.

Conclusion

We constructed a comprehensive single cell and multi-omics landscape of immune infiltration in HCC, and delineated key genes and cellular populations regulating immune infiltration and immunotherapy response, thus providing insights into the mechanisms of immune infiltration and future therapeutic control.

Respiratory epithelial cell types, states and fates in the era of single-cell RNA-sequencing

Standalone and consortia-led single-cell atlases of healthy and diseased human airways generated with single-cell RNA-sequencing (scRNA-seq) have ushered in a new era in respiratory research. Numerous discoveries, including the pulmonary ionocyte, potentially novel cell fates, and a diversity of cell states among common and rare epithelial cell types have highlighted the extent of cellular heterogeneity and plasticity in the respiratory tract. scRNA-seq has also played a pivotal role in our understanding of host-virus interactions in coronavirus disease 2019 (COVID-19). However, as our ability to generate large quantities of scRNA-seq data increases, along with a growing number of scRNA-seq protocols and data analysis methods, new challenges related to the contextualisation and downstream applications of insights are arising. Here, we review the fundamental concept of cellular identity from the perspective of single-cell transcriptomics in the respiratory context, drawing attention to the need to generate reference annotations and to standardise the terminology used in literature. Findings about airway epithelial cell types, states and fates obtained from scRNA-seq experiments are compared and contrasted with information accumulated through the use of conventional methods. This review attempts to discuss major opportunities and to outline some of the key limitations of the modern-day scRNA-seq that need to be addressed to enable efficient and meaningful integration of scRNA-seq data from different platforms and studies, with each other as well as with data from other high-throughput sequencing-based genomic, transcriptomic and epigenetic analyses.

Identification and Validation of a Metabolism-Related Prognostic Signature Associated with M2 Macrophage Infiltration in Gastric Cancer

High levels of M2 macrophage infiltration invariably contribute to poor cancer prognosis and can be manipulated by metabolic reprogramming in the tumor microenvironment. However, the metabolism-related genes (MRGs) affecting M2 macrophage infiltration and their clinical implications are not fully understood. In this study, we identified 173 MRGs associated with M2 macrophage infiltration in cases of gastric cancer (GC) using the TCGA and GEO databases. Twelve MRGs were eventually adopted as the prognostic signature to develop a risk model. In the high-risk group, the patients showed poorer survival outcomes than patients in the low-risk group. Additionally, the patients in the high-risk group were less sensitive to certain drugs, such as 5-Fluorouracil, Oxaliplatin, and Cisplatin. Risk scores were positively correlated with the infiltration of multiple immune cells, including CD8+ T cells and M2 macrophages. Furthermore, a difference was observed in the expression and distribution between the 12 signature genes in the tumor microenvironment through single-cell sequencing analysis. In vitro experiments proved that the M2 polarization of macrophages was suppressed by Sorcin-knockdown GC cells, thereby hindering the proliferation and migration of GC cells. These findings provide a valuable prognostic signature for evaluating clinical outcomes and corresponding treatment options and identifying potential targets for GC treatment.

Identification of Novel Targets for Treatment of Dilated Cardiomyopathy Based on the Ferroptosis and Immune Heterogeneity

Purpose

This study aimed to investigate the role of ferroptosis in dilated cardiomyopathy (DCM) and to identify new targets for treatment and diagnosis of DCM.

Methods

GSE116250 and GSE145154 were downloaded from the Gene Expression Omnibus database. Unsupervised consensus clustering of DCM patients was used to confirm the impact of ferroptosis. Ferroptosis-related hub genes were identified by WGCNA and single cell sequencing analyses. Finally, we established a DCM mouse model via injection of Doxorubicin to verify the expression level of OTUD1 and colocalization between cell markers and OTUD1 in DCM mouse heart.

Results

A total of 13 ferroptosis-related differentially expressed genes (DEGs) were identified. The DCM patients were divided into two clusters according to the expression of 13 DEGs. The DCM patients in different clusters showed discrepancies in immune infiltration. Four hub genes were further identified by WGCNA analysis. Single cell data analysis revealed that OTUD1 may regulate B cells and DC cells and then participate in immune infiltration discrepancy. The upregulation of OTUD1 and the colocalization of OTUD1 with CD19 (B cell maker) and CD11c (DCs markers) markers were confirmed in DCM mouse hearts.

Conclusion

Ferroptosis and the immune microenvironment are closely associated with DCM, and OTUD1 may play an important role through B cells and DCs.

Tumor Microenvironment Role in Pancreatic Cancer Stem Cells

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal malignancy with a majority of patients presenting with unresectable or metastatic disease, resulting in a poor 5-year survival rate. This, in turn, is due to a highly complex tumor microenvironment and the presence of cancer stem cells, both of which induce therapy resistance and tumor relapse. Therefore, understanding and targeting the tumor microenvironment and cancer stem cells may be key strategies for designing effective PDAC therapies. In the present review, we summarized recent advances in the role of tumor microenvironment in pancreatic neoplastic progression.

The non-coding competing endogenous RNAs in acute myeloid leukemia: biological and clinical implications

Acute myeloid leukemia (AML) is a hematologic carcinoma that has seen a considerable improvement in patient prognosis because of genetic diagnostics and molecularly-targeted therapies. Nevertheless, recurrence and drug resistance remain significant obstacles to leukemia treatment. It is critical to investigate the underlying molecular mechanisms and find solutions. Non-coding RNAs (ncRNAs), such as microRNAs (miRNAs), circular RNAs, long non-coding RNAs, and pseudogenes, have been found to be crucial components in driving cancer. The competing endogenous RNA (ceRNA) mechanism has expanded the complexity of miRNA-mediated gene regulation. A great deal of literature has shown that ncRNAs are essential to the biological functions of the ceRNA network (ceRNET). NcRNAs can compete for the same miRNA response elements to influence miRNA-target RNA interactions. Recent evidence suggests that ceRNA might be a potential biomarker and therapeutic strategy. So far, however, there have been no comprehensive studies on ceRNET about AML. What is not yet clear is the clinical application of ceRNA in AML. This study attempts to summarize the development of research on the related ceRNAs in AML and the roles of ncRNAs in ceRNET. We also briefly describe the mechanisms of ceRNA and ceRNET. What's more significant is that we explore the clinical value of ceRNAs to provide accurate diagnostic and prognostic biomarkers as well as therapeutic targets. Finally, limitations and prospects are considered.

Single-cell RNA-seq reveals intratumoral heterogeneity in osteosarcoma patients: A review

While primary bone malignancies make up just 0.2% of all cancers, osteosarcoma (OS) is the third most common cancer in adolescents. Due to its highly complex and heterogeneous tumor microenvironment (TME), OS has proven difficult to treat. There has been little to no improvement in therapy for this disease over the last 40 years. Even the recent success of immunotherapies in other blood-borne and solid malignancies has not translated to OS. With frequent recurrence and lung metastases continuing to pose a challenge in the clinic, recent advancements in molecular profiling, such as single-cell RNA sequencing (scRNA-seq), have proven useful in identifying novel biomarkers of OS tumors while providing new insight into this TME that could potentially lead to new therapeutic options. This review combines the analyses of over 150,000 cells from 18 lesions ranging from primary, recurrent, and metastatic OS lesions, revealing distinct cellular populations and gene signatures that exist between them. Here, we detail these previous findings and ultimately convey the intratumoral heterogeneity that exists within OS tumor specimens.

Single-cell profiling to explore pancreatic cancer heterogeneity, plasticity and response to therapy

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal cancer entity characterized by a heterogeneous genetic landscape and an immunosuppressive tumor microenvironment. Recent advances in high-resolution single-cell sequencing and spatial transcriptomics technologies have enabled an in-depth characterization of both malignant and host cell types and increased our understanding of the heterogeneity and plasticity of PDAC in the steady state and under therapeutic perturbation. In this Review we outline single-cell analyses in PDAC, discuss their implications on our understanding of the disease and present future perspectives of multimodal approaches to elucidate its biology and response to therapy at the single-cell level.

Targeting tumor microenvironment for non-small cell lung cancer immunotherapy

The tumor microenvironment (TME) is composed of different cellular and non-cellular elements. Constant interactions between tumor cells and the TME are responsible for tumor initiation, tumor progression, and responses to therapies. Immune cells in the TME can be classified into two broad categories, namely adaptive and innate immunity. Targeting these immune cells has attracted substantial research and clinical interest. Current research focuses on identifying key molecular players and developing targeted therapies. These approaches may offer more efficient ways of treating different cancers. In this review, we explore the heterogeneity of the TME in non-small cell lung cancer, summarize progress made in targeting the TME in preclinical and clinical studies, discuss the potential predictive value of the TME in immunotherapy, and highlight the promising effects of bispecific antibodies in the era of immunotherapy.

High-throughput sequencing approach for the identification of lncRNA biomarkers in hepatocellular carcinoma and revealing the effect of ZFAS1/miR-150-5p on hepatocellular carcinoma progression

Aims

To screen abnormal lncRNAs and diagnostic biomarkers in the progression of hepatocellular carcinoma through high-throughput sequencing and explore the underlying mechanisms of abnormal lncRNAs in the progression of hepatocellular carcinoma.

Methods

The transcriptome sequencing was used to analyze the RNA expression profile and identify differentially expressed RNAs. Hub lncRNAs were screened by combining (WGCNA, ceRNA regulatory network, PPI, GO and KEGG analyses, Kaplan-Meier curve analysis, Cox analysis, risk model construction and qPCR). Thereafter, the correlation between the expression of hub lncRNAs and tumor clinicopathological parameters was analyzed, and the hub lncRNAs were analyzed by GSEA. Finally, the effects of hub RNAs on the proliferation, migration and invasion of HepG2 cells were investigated in vitro.

Results

Compared with the control group, a total of 610 lncRNAs, 2,593 mRNAs and 26 miRNAs were screened in patients with hepatocellular carcinoma. Through miRNA target prediction and WGCNA, a ceRNA was constructed, comprising 324 nodes and 621 edges. Enrichment analysis showed that mRNAs in ceRNA were involved mainly in cancer development progression. Then, the ZFAS1/miR-150-5p interaction pair was screened out by Kaplan Meier curve analysis, Cox analysis and qPCR analysis. Its expression was related to tumor stage, TNM stage and patient age. ROC curve analysis showed that it has a good predictive value for the risk of hepatocellular carcinoma. GSEA showed that ZFAS1 was also enriched in the regulation of immune response, cell differentiation and proliferation. Loss-of-function experiments revealed that ZFAS1 inhibition could remarkably suppress HepG2 cell proliferation, migration and invasion in vitro. Bioinformatic analysis and luciferase reporter assays revealed that ZFAS1 directly interacted with miR-150-5p. Rescue experiments showed that a miR-150-5p inhibitor reversed the cell proliferation, migration and invasion functions of ZFAS1 knockdown in vitro.

Conclusion

ZFAS1 is associated with the malignant status and prognosis of patients with hepatocellular carcinoma, and the ZFAS1/miR-150-5p axis is involved in hepatocellular carcinoma progression.

A classification method of gastric cancer subtype based on residual graph convolution network

Background: Clinical diagnosis and treatment of tumors are greatly complicated by their heterogeneity, and the subtype classification of cancer frequently plays a significant role in the subsequent treatment of tumors. Presently, the majority of studies rely far too heavily on gene expression data, omitting the enormous power of multi-omics fusion data and the potential for patient similarities. Method: In this study, we created a gastric cancer subtype classification model called RRGCN based on residual graph convolutional network (GCN) using multi-omics fusion data and patient similarity network. Given the multi-omics data's high dimensionality, we built an artificial neural network Autoencoder (AE) to reduce the dimensionality of the data and extract hidden layer features. The model is then built using the feature data. In addition, we computed the correlation between patients using the Pearson correlation coefficient, and this relationship between patients forms the edge of the graph structure. Four graph convolutional network layers and two residual networks with skip connections make up RRGCN, which reduces the amount of information lost during transmission between layers and prevents model degradation. Results: The results show that RRGCN significantly outperforms other classification methods with an accuracy as high as 0.87 when compared to four other traditional machine learning methods and deep learning models. Conclusion: In terms of subtype classification, RRGCN excels in all areas and has the potential to offer fresh perspectives on disease mechanisms and disease progression. It has the potential to be used for a broader range of disorders and to aid in clinical diagnosis.

Transcriptomic Profiles of Normal Pituitary Cells and Pituitary Neuroendocrine Tumor Cells

The pituitary gland is one of the most cellularly diverse regions of the brain. Recent advancements in transcriptomic biology, such as single-cell RNA sequencing, bring an unprecedented glimpse into the molecular composition of the pituitary, both in its normal physiological state and in disease. Deciphering the normal pituitary transcriptomic signatures provides a better insight into the ontological origin and development of five types of endocrine cells, a process involving complex cascades of transcription factors that are still being established. In parallel with these observations about normal pituitary development, recent transcriptomic findings on pituitary neuroendocrine tumors (PitNETs) demonstrate both preservations and changes in transcription factor expression patterns compared to those seen during gland development. Furthermore, recent studies also identify differentially expressed genes that drive various tumor behaviors, including hormone hypersecretion and tumor aggression. Understanding the comprehensive multiomic profiles of PitNETs is essential in developing molecular profile-based therapies for PitNETs not curable with current treatment modalities and could eventually help align PitNETs with the breakthroughs being made in applying precision medicine to other tumors.

Data analysis guidelines for single-cell RNA-seq in biomedical studies and clinical applications

The application of single-cell RNA sequencing (scRNA-seq) in biomedical research has advanced our understanding of the pathogenesis of disease and provided valuable insights into new diagnostic and therapeutic strategies. With the expansion of capacity for high-throughput scRNA-seq, including clinical samples, the analysis of these huge volumes of data has become a daunting prospect for researchers entering this field. Here, we review the workflow for typical scRNA-seq data analysis, covering raw data processing and quality control, basic data analysis applicable for almost all scRNA-seq data sets, and advanced data analysis that should be tailored to specific scientific questions. While summarizing the current methods for each analysis step, we also provide an online repository of software and wrapped-up scripts to support the implementation. Recommendations and caveats are pointed out for some specific analysis tasks and approaches. We hope this resource will be helpful to researchers engaging with scRNA-seq, in particular for emerging clinical applications.

Mastering the use of cellular barcoding to explore cancer heterogeneity

Tumours are often composed of a multitude of malignant clones that are genomically unique, and only a few of them may have the ability to escape cancer therapy and grow as symptomatic lesions. As a result, tumours with a large degree of genomic diversity have a higher chance of leading to patient death. However, clonal fate can be driven by non-genomic features. In this context, new technologies are emerging not only to track the spatiotemporal fate of individual cells and their progeny but also to study their molecular features using various omics analysis. In particular, the recent development of cellular barcoding facilitates the labelling of tens to millions of cancer clones and enables the identification of the complex mechanisms associated with clonal fate in different microenvironments and in response to therapy. In this Review, we highlight the recent discoveries made using lentiviral-based cellular barcoding techniques, namely genetic and optical barcoding. We also emphasize the strengths and limitations of each of these technologies and discuss some of the key concepts that must be taken into consideration when one is designing barcoding experiments. Finally, we suggest new directions to further improve the use of these technologies in cancer research.

3D single cell migration driven by temporal correlation between oscillating force dipoles

Directional cell locomotion requires symmetry breaking between the front and rear of the cell. In some cells, symmetry breaking manifests itself in a directional flow of actin from the front to the rear of the cell. Many cells, especially in physiological 3D matrices do not show such coherent actin dynamics and present seemingly competing protrusion/retraction dynamics at their front and back. How symmetry breaking manifests itself for such cells is therefore elusive. We take inspiration from the scallop theorem proposed by Purcell for micro-swimmers in Newtonian fluids: self-propelled objects undergoing persistent motion at low Reynolds number must follow a cycle of shape changes that breaks temporal symmetry. We report similar observations for cells crawling in 3D. We quantified cell motion using a combination of 3D live cell imaging, visualization of the matrix displacement and a minimal model with multipolar expansion. We show that our cells embedded in a 3D matrix form myosin-driven force dipoles at both sides of the nucleus, that locally and periodically pinch the matrix. The existence of a phase shift between the two dipoles is required for directed cell motion which manifests itself as cycles with finite area in the dipole-quadrupole diagram, a formal equivalence to the Purcell cycle. We confirm this mechanism by triggering local dipolar contractions with a laser. This leads to directed motion. Our study reveals that these cells control their motility by synchronizing dipolar forces distributed at front and back. This result opens new strategies to externally control cell motion as well as for the design of micro-crawlers.

Construction of a Novel Clinical Stage-Related Gene Signature for Predicting Outcome and Immune Response in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) with high heterogeneity is one of the most frequent malignant tumors. However, there were no studies to create a clinical stage-related gene signature for HCC patients. Differentially expressed genes (DEGs) associated with clinical stage of HCC were analyzed based on TCGA datasets. Functional enrichment analysis was carried out by the use of stage-related DEGs. Then, the least absolute shrinkage and selection operator (LASSO) regression and univariate Cox regression were performed to reduce the overfit and the number of genes for further analysis. Next, survival and ROC assays were carried out to demonstrate the model using TCGA. Functional analysis and immune microenvironment analysis related to stage-related DEGs were performed. Reverse transcriptase polymerase chain reaction (RT-PCR) and Cell Counting Kit-8 (CCK-8) assays were applied to examine the expression and function of PNCK in HCC. In this research, there were 21 DEGs between HCC specimens with stage (I-II) and HCC specimens with stage (III-IV), including 20 increased genes and 1 decreased genes. A novel seven-gene signature (including PITX2, PNCK, GLIS1, SCNN1G, MMP1, ZNF488, and SHISA9) was created for the prediction of outcomes of HCC patients. The ROC curves confirmed the prognostic value of the new model. Cox assays demonstrated that the seven-gene signature can independently forecast overall survival. The immune analysis revealed that patients with low risk score exhibited more immune activities. Moreover, we confirmed that PNCK expressions were distinctly increased in HCC, and its silence suppressed the proliferation of HCC cells. Overall, our research offered a robust and reliable gene signature which displayed an important value in the prediction of overall survival of HCC patients and might deliver more effective personalized therapies.