Interfacing Seurat with the R tidy universe

The tidyomics ecosystem: enhancing omic data analyses

The growth of omic data presents evolving challenges in data manipulation, analysis and integration. Addressing these challenges, Bioconductor provides an extensive community-driven biological data analysis platform. Meanwhile, tidy R programming offers a revolutionary data organization and manipulation standard. Here we present the tidyomics software ecosystem, bridging Bioconductor to the tidy R paradigm. This ecosystem aims to streamline omic analysis, ease learning and encourage cross-disciplinary collaborations. We demonstrate the effectiveness of tidyomics by analyzing 7.5 million peripheral blood mononuclear cells from the Human Cell Atlas, spanning six data frameworks and ten analysis tools.

The tidyomics ecosystem: Enhancing omic data analyses

The growth of omic data presents evolving challenges in data manipulation, analysis, and integration. Addressing these challenges, Bioconductor1 provides an extensive community-driven biological data analysis platform. Meanwhile, tidy R programming2 offers a revolutionary standard for data organisation and manipulation. Here, we present the tidyomics software ecosystem, bridging Bioconductor to the tidy R paradigm. This ecosystem aims to streamline omic analysis, ease learning, and encourage cross-disciplinary collaborations. We demonstrate the effectiveness of tidyomics by analysing 7.5 million peripheral blood mononuclear cells from the Human Cell Atlas3, spanning six data frameworks and ten analysis tools.

Integration of Single-Cell RNA Sequencing and Bulk RNA Sequencing to Identify an Immunogenic Cell Death-Related 5-Gene Prognostic Signature in Hepatocellular Carcinoma

Introduction

Immunogenic cell death (ICD) can enhance the potency of immunotherapy in cancer treatment. Nevertheless, it is ambiguous how ICD-related genes (ICDRGs) contribute to hepatocellular carcinoma (HCC).

Methods

Single-cell RNA sequencing (scRNA-seq) data were used to distinguish malignant cells from normal cells in the HCC tumor microenvironment(TME). Bulk RNA sequencing data was employed to acquire the landscape of the 33 ICDRGs. Unsupervised clustering identified two ICD molecular subtypes. The cellular infiltration characteristics and biological behavior in different subtypes were analyzed by ssGSEA. Subsequently, differentially expressed genes (DEGs) between the two subtypes were determined, based on which patients were classified into three gene clusters. Then, the prognostic model was constructed by Lasso-Cox analysis. Finally, we investigated the expression of risk genes in cancer cell line encyclopedia (CCLE) and validated the function of NKX3-2 in vitro experiments.

Results

ICD scores and ICDRGs expression in malignant cells were significantly lower than in normal cells by scRNA-seq analysis. ICD-high subtype was characterized by ICD-related gene overexpression and high levels of immune infiltration abundance and immune checkpoints; Three DEGs-related gene clusters were likewise strongly linked to stromal and immunological activation. In the ICD-related prognostic model consisting of NKX3-2, CHODL, MMP1, NR0B1, and CTSV, the low-risk group patients had a better endpoint and displayed increased susceptibility to immunotherapy and chemotherapeutic drugs like 5-Fluorouracil, afatinib, bortezomib, cediratinib, lapatinib, dasatinib, gefitinib and crizotinib. Moreover, NKX3-2 amplification in HCC samples has been verified by experiments, and its disruption suppressed the proliferation and invasion of tumor cells.

Conclusion

Our study highlighted the potential of the ICDRGs risk score as a prognostic indicator to aid in the accurate diagnosis and immunotherapy sensitivity of HCC.

A multi-organ map of the human immune system across age, sex and ethnicity

Understanding tissue biology's heterogeneity is crucial for advancing precision medicine. Despite the centrality of the immune system in tissue homeostasis, a detailed and comprehensive map of immune cell distribution and interactions across human tissues and demographics remains elusive. To fill this gap, we harmonised data from 12,981 single-cell RNA sequencing samples and curated 29 million cells from 45 anatomical sites to create a comprehensive compositional and transcriptional healthy map of the healthy immune system. We used this resource and a novel multilevel modelling approach to track immune ageing and test differences across sex and ethnicity. We uncovered conserved and tissue-specific immune-ageing programs, resolved sex-dependent differential ageing and identified ethnic diversity in clinically critical immune checkpoints. This study provides a quantitative baseline of the immune system, facilitating advances in precision medicine. By sharing our immune map, we hope to catalyse further breakthroughs in cancer, infectious disease, immunology and precision medicine.

Single-cell sequencing analysis confirms the association of ANRIL with the increased smooth muscle cell proliferation and migration gene signatures in pulmonary artery hypertension in silico

PURPOSE

Smooth muscle cell (SMC) dysregulation is part of the pathological basis of pulmonary artery hypertension (PAH). We aimed to explore the heterogeneity of SMCs in PAH.

METHODS

The profile GSE210248 was obtained from NCBI Gene Expression Omnibus, containing the scRNA-seq data of pulmonary arteries (PA) from three patients with PAH and three healthy donors. After quality control, normalization, and dimension reduction, cell clustering analysis was performed. Differential expression analysis and functional enrichment analysis were carried out successively in smooth muscle cells (SMCs). The enrichment scores of cell cycle and cell migration gene sets in SMCs were calculated. Then, the Spearman correlation coefficients between antisense non-coding RNA in the INK4 locus (ANRIL) expression and two gene sets were computed.

RESULTS

Eight cell clusters were identified in PA from samples. The proportion of SMCs was increased in PAH samples. SMCs were divided into five subclusters with diverse biological functions. Muscle contraction-related SMC1 was decreased, while extracellular matrix organization-related SMC2, immune and inflammatory response-related SMC4 and SMC5 were increased in PAH samples compared with healthy donors. The enrichment scores of cell cycle and cell migration gene sets in SMCs were higher in PAH samples than in donors. ANRIL was down-regulated significantly in PAH samples and was negatively related to the scores of two gene sets.

CONCLUSION

SMCs exhibited significant heterogeneity in PAH. The altered abilities of SMC proliferation and migration in PAH were associated with ANRIL expression.

Prolonged exposure to lung-derived cytokines is associated with activation of microglia in patients with COVID-19

BACKGROUNDSurvivors of pneumonia, including SARS-CoV-2 pneumonia, are at increased risk for cognitive dysfunction and dementia. In rodent models, cognitive dysfunction following pneumonia has been linked to the systemic release of lung-derived pro-inflammatory cytokines. Microglia are poised to respond to inflammatory signals from the circulation, and their dysfunction has been linked to cognitive impairment in murine models of dementia and in humans.METHODSWe measured levels of 55 cytokines and chemokines in bronchoalveolar lavage fluid and plasma from 341 patients with respiratory failure and 13 healthy controls, including 93 unvaccinated patients with COVID-19 and 203 patients with other causes of pneumonia. We used flow cytometry to sort neuroimmune cells from postmortem brain tissue from 5 patients who died from COVID-19 and 3 patients who died from other causes for single-cell RNA-sequencing.RESULTSMicroglia from patients with COVID-19 exhibited a transcriptomic signature suggestive of their activation by circulating pro-inflammatory cytokines. Peak levels of pro-inflammatory cytokines were similar in patients with pneumonia irrespective of etiology, but cumulative cytokine exposure was higher in patients with COVID-19. Treatment with corticosteroids reduced expression of COVID-19-specific cytokines.CONCLUSIONProlonged lung inflammation results in sustained elevations in circulating cytokines in patients with SARS-CoV-2 pneumonia compared with those with pneumonia secondary to other pathogens. Microglia from patients with COVID-19 exhibit transcriptional responses to inflammatory cytokines. These findings support data from rodent models causally linking systemic inflammation with cognitive dysfunction in pneumonia and support further investigation into the role of microglia in pneumonia-related cognitive dysfunction.FUNDINGSCRIPT U19AI135964, UL1TR001422, P01AG049665, P01HL154998, R01HL149883, R01LM013337, R01HL153122, R01HL147290, R01HL147575, R01HL158139, R01ES034350, R01ES027574, I01CX001777, U01TR003528, R21AG075423, T32AG020506, F31AG071225, T32HL076139.

Development of single-cell transcriptomic atlas of human plasmacytoid dendritic cells from people with HIV-1

Many immunological treatment strategies for reducing the HIV-1 reservoir and enhancing adaptive immunity aim at activating the human plasmacytoid dendritic cells (pDCs). Here, we present a protocol for pDC enrichment, single-cell analysis, and development of a pDC transcriptomic database from healthy individuals and people with HIV-1 before and after Toll-like receptor 9 agonist treatment. For complete details on the use and execution of this protocol, please refer to Cham et al.1.

Exploration of immune cell heterogeneity by single-cell RNA sequencing and identification of secretory leukocyte protease inhibitor as an oncogene in pancreatic cancer

Clinical outcomes remain unsatisfactory in patients with pancreatic cancer (PAC). In this study, through single-cell sequencing, we identified eight cell subpopulations in the tumor microenvironment (TME). Redimensional clustering of epithelial cells, myeloid cells, and cancer-associated fibroblasts (CAFs) revealed heterogeneity in the TME of PAC. Intercellular communication analysis showed strong direct interactions between matrix CAFs, inflammatory CAFs, and epithelial cells. Additionally, we found that the SPP1-associated pathway was activated in monocytes, whereas the vascular endothelial growth factor-associated pathway was activated in epithelial cells. These results improve the understanding of the TME of pancreatic cancer and provide a foundation for further studies on intratumoral heterogeneity. In addition, differentially expressed gene secretory leukocyte protease inhibitor (SLPI) was identified in pancreatic cancer, and functional experiments showed that SLPI had a strong impact on cell viability and apoptosis, which offers a potential therapy target for pancreatic cancer.

Identification of TMZ resistance-associated histone post-translational modifications in glioblastoma using multi-omics data

BACKGROUD

Glioblastoma multiforme (GBM) is among the most aggressive cancers, with current treatments limited in efficacy. A significant hurdle in the treatment of GBM is the resistance to the chemotherapeutic agent temozolomide (TMZ). The methylation status of the MGMT promoter has been implicated as a critical biomarker of response to TMZ.

METHODS

To explore the mechanisms underlying resistance, we developed two TMZ-resistant GBM cell lines through a gradual increase in TMZ exposure. Transcriptome sequencing of TMZ-resistant cell lines revealed that alterations in histone post-translational modifications might be instrumental in conferring TMZ resistance. Subsequently, multi-omics analysis suggests a strong association between histone H3 lysine 9 acetylation (H3K9ac) levels and TMZ resistance.

RESULTS

We observed a significant correlation between the expression of H3K9ac and MGMT, particularly in the unmethylated MGMT promoter samples. More importantly, our findings suggest that H3K9ac may enhance MGMT transcription by facilitating the recruitment of the SP1 transcription factor to the MGMT transcription factor binding site. Additionally, by analyzing single-cell transcriptomics data from matched primary and recurrent GBM tumors treated with TMZ, we modeled the molecular shifts occurring upon tumor recurrence. We also noted a reduction in tumor stem cell characteristics, accompanied by an increase in H3K9ac, SP1, and MGMT levels, underscoring the potential role of H3K9ac in tumor relapse following TMZ therapy.

CONCLUSIONS

The increase in H3K9ac appears to enhance the recruitment of the transcription factor SP1 to its binding sites within the MGMT locus, consequently upregulating MGMT expression and driving TMZ resistance in GBM.

The levels of IL1RN is a factor influencing the onset of rheumatoid arthritis in non-alcoholic fatty liver disease

With the improvement of global dietary conditions, non-alcoholic fatty liver disease (NAFLD) has gradually become prevalent. As the number of NAFLD patients increases, the coexistence of diseases associated with it has come into focus. In this study, based on immune phenotypes, intercellular communication activities, and clinical manifestations of NAFLD patients, IL1RN was identified as a central pro-inflammatory factor. Subsequently, potential downstream biological pathways of IL1RN in liver tissues and various cell types were enriched to describe its functions. Transcription factors Nfkb1, Jun, and Sp1, significantly associated with these functions, were also enriched. Functional studies of IL1RN suggest its potential to trigger autoimmune diseases. Given this, Mendelian randomization analysis was used to explore the causal relationship between NAFLD and various autoimmune diseases, with IL1RN considered as an intermediary introduced into Mendelian randomization studies. The results indicate that IL1RN and its partially related proteins play a certain mediating role in the process of NAFLD inducing rheumatoid arthritis (RA). Finally, additional research results suggest that intrahepatic ALT levels may influence IL1RN levels, possibly through amino acid metabolism.

Exploration of neuron heterogeneity in human heart failure with dilated cardiomyopathy through single-cell RNA sequencing analysis

OBJECTIVE

We aimed to explore the heterogeneity of neurons in heart failure with dilated cardiomyopathy (DCM).

METHODS

Single-cell RNA sequencing (scRNA-seq) data of patients with DCM and chronic heart failure and healthy samples from GSE183852 dataset were downloaded from NCBI Gene Expression Omnibus, in which neuron data were extracted for investigation. Cell clustering analysis, differential expression analysis, trajectory analysis, and cell communication analysis were performed, and highly expressed genes in neurons from patients were used to construct a protein-protein interaction (PPI) network and validated by GSE120895 dataset.

RESULTS

Neurons were divided into six subclusters involved in various biological processes and each subcluster owned its specific cell communication pathways. Neurons were differentiated into two branches along the pseudotime, one of which was differentiated into mature neurons, whereas another tended to be involved in the immune and inflammation response. Genes exhibited branch-specific differential expression patterns. FLNA, ITGA6, ITGA1, and MDK interacted more with other gene-product proteins in the PPI network. The differential expression of FLNA between DCM and control was validated.

CONCLUSION

Neurons have significant heterogeneity in heart failure with DCM, and may be involved in the immune and inflammation response to heart failure.

Changes in cuproptosis-related gene expression in periodontitis: An integrated bioinformatic analysis

Periodontitis causes inflammatory destruction of tooth-supporting tissues; however, the complex mechanism underlying its etiology remains unclear. Cuproptosis is a type of cell death caused by an imbalance in intracellular copper homeostasis that leads to excess copper. However, changes in the expression and biological function of cuproptosis-related genes (CRGs) in periodontitis are not yet fully understood. This study investigated the comprehensive effects of differentially expressed CRGs (DE-CRGs) on periodontitis via bioinformatic analysis. Nine DE-CRGs were discovered using normal and periodontitis gingival samples, and single-cell RNA sequencing data were analyzed to identify them changes in diverse cell clusters. We then detected the correlation between DE-CRGs and immune infiltration, immune factors, mitochondrial dysfunction, diagnostic efficacy, and predicted drugs. Moreover, changes of DE-CRG in whole periodontitis tissue and a human gingival fibroblast cell line (HGF-1) were confirmed and copper content changes in HGF-1 cells were investigated. Most DE-CRG expression trends were reversed between the periodontal tissues and cell clusters, which may be related to the proportion of cell clusters changes caused periodontitis. Furthermore, most DE-CRG trends in periodontitis cell clusters were inconsistent with the effects of cuproptosis. In HGF-1 cells treated with Porphyromonas gingivalis lipopolysaccharide (Pg-LPS), the intracellular copper content increased by more than threefold, indicating that although some periodontitis cells had excess copper, the amount may not have been sufficient to trigger cuproptosis. Additionally, DE-CRGs were closely associated with multiple biological functions, antibiotic drugs, and natural herbal medicines. Our findings may provide an overview of DE-CRGs in the pathogenesis and treatment of periodontitis.

Multi-omics reveals the role of ENO1 in bladder cancer and constructs an epithelial-related prognostic model to predict prognosis and efficacy

α-Enolase (ENO1) is a crucial molecular target for tumor therapy and has emerged as a research hotspot in recent decades. Here, we aimed to explore the role of ENO1 in bladder cancer (BLCA) and then construct a signature to predict the prognosis and treatment response of BLCA. Firstly, we found ENO1 was highly expressed in BLCA tissues, as verified by IHC, and was associated with poor prognosis. The analysis of the tumor immune microenvironment by bulk RNA-seq and scRNA-seq showed that ENO1 was associated with CD8+ T-cell exhaustion. Additionally, the results in vitro showed that ENO1 could promote the proliferation and invasion of BLCA cells. Then, the analysis of epithelial cells (ECs) revealed that ENO1 might promote BLCA progression by metabolism, the cell cycle and some carcinogenic pathways. A total of 249 hub genes were obtained from differentially expressed genes between ENO1-related ECs, and we used LASSO analysis to construct a novel signature that not only accurately predicted the prognosis of BLCA patients but also predicted the response to treatment for BLCA. Finally, we constructed a nomogram to better guide clinical application. In conclusion, through multi-omics analysis, we found that ENO1 was overexpressed in bladder cancer and associated with poor prognosis, CD8+ T-cell exhaustion and epithelial heterogeneity. Moreover, the prognosis and treatment of patients can be well predicted by constructing an epithelial-related prognostic signature.

Multi-omics analysis reveals cuproptosis and mitochondria-based signature for assessing prognosis and immune landscape in osteosarcoma

Background

Osteosarcoma (OSA), the most common primary mesenchymal bone tumor, is a health threat to children and adolescents with a dismal prognosis. While cuproptosis and mitochondria dysfunction have been demonstrated to exert a crucial role in tumor progression and development, the mechanisms by which they are regulated in OSA still await clarification.

Methods

Two independent OSA cohorts containing transcriptome data and clinical information were collected from public databases. The heterogeneity of OSA were evaluated by single cell RNA (scRNA) analysis. To identify a newly molecular subtype, unsupervised consensus clustering was conducted. Cox relevant regression methods were utilized to establish a prognostic gene signature. Wet lab experiments were performed to confirm the effect of model gene in OSA cells.

Results

We determined 30 distinct cell clusters and assessed OSA heterogeneity and stemness scRNA analysis. Then, univariate Cox analysis identified 24 candidate genes which were greatly associated with the prognosis of OSA. Based on these prognostic genes, we obtained two molecular subgroups. After conducting step Cox regression, three model genes were selected to construct a signature showing a favorable performance to forecast clinical outcome. Our proposed signature could also evaluate the response to chemotherapy and immunotherapy of OSA cases.

Conclusion

We generated a novel risk model based on cuproptosis and mitochondria-related genes in OSA with powerful predictive ability in prognosis and immune landscape.

Identification of key biomarkers of the glomerulus in focal segmental glomerulosclerosis and their relationship with immune cell infiltration based on WGCNA and the LASSO algorithm

OBJECTIVE

The aim of our study was to identify key biomarkers of glomeruli in focal glomerulosclerosis (FSGS) and analyze their relationship with the infiltration of immune cells.

METHODS

The expression profiles (GSE108109 and GSE200828) were obtained from the GEO database. The differentially expressed genes (DEGs) were filtered and analyzed by gene set enrichment analysis (GSEA). MCODE module was constructed. Weighted gene coexpression network analysis (WGCNA) was performed to obtain the core gene modules. Least absolute shrinkage and selection operator (LASSO) regression was applied to identify key genes. ROC curves were employed to explore their diagnostic accuracy. Transcription factor prediction of the key biomarkers was performed using the Cytoscape plugin IRegulon. The analysis of the infiltration of 28 immune cells and their correlation with the key biomarkers were performed.

RESULTS

A total of 1474 DEGs were identified. Their functions were mostly related to immune-related diseases and signaling pathways. MCODE identified five modules. The turquoise module of WGCNA had significant relevance to the glomerulus in FSGS. TGFB1 and NOTCH1 were identified as potential key glomerular biomarkers in FSGS. Eighteen transcription factors were obtained from the two hub genes. Immune infiltration showed significant correlations with T cells. The results of immune cell infiltration and their relationship with key biomarkers implied that NOTCH1 and TGFB1 were enhanced in immune-related pathways.

CONCLUSION

TGFB1 and NOTCH1 may be strongly correlated with the pathogenesis of the glomerulus in FSGS and are new candidate key biomarkers. T-cell infiltration plays an essential role in the FSGS lesion process.

From multi-omics approaches to personalized medicine in myocardial infarction

Myocardial infarction (MI) is a prevalent cardiovascular disease characterized by myocardial necrosis resulting from coronary artery ischemia and hypoxia, which can lead to severe complications such as arrhythmia, cardiac rupture, heart failure, and sudden death. Despite being a research hotspot, the etiological mechanism of MI remains unclear. The emergence and widespread use of omics technologies, including genomics, transcriptomics, proteomics, metabolomics, and other omics, have provided new opportunities for exploring the molecular mechanism of MI and identifying a large number of disease biomarkers. However, a single-omics approach has limitations in understanding the complex biological pathways of diseases. The multi-omics approach can reveal the interaction network among molecules at various levels and overcome the limitations of the single-omics approaches. This review focuses on the omics studies of MI, including genomics, epigenomics, transcriptomics, proteomics, metabolomics, and other omics. The exploration extended into the domain of multi-omics integrative analysis, accompanied by a compilation of diverse online resources, databases, and tools conducive to these investigations. Additionally, we discussed the role and prospects of multi-omics approaches in personalized medicine, highlighting the potential for improving diagnosis, treatment, and prognosis of MI.

Role of NF-κB pathway in kidney renal clear cell carcinoma and its potential therapeutic implications

Kidney renal clear cell carcinoma (KIRC), a common malignant tumor of the urinary system, is the most aggressive renal tumor subtype. Since the discovery of nuclear factor kappa B (NF-κB) in 1986, many studies have demonstrated abnormal NF-κB signaling is associated with the development of various cancers, including kidney renal clear cell carcinoma. In this study, the relationship between NF-κB and kidney renal clear cell carcinoma was confirmed using bioinformatics analysis. First, we explored the differential expression of copy number variation (CNV), single nucleotide variant (SNV), and messenger RNA (mRNA) in NF-κB-related genes in different types of cancer, as well as the impact on cancer prognosis and sensitivity to common chemotherapy drugs. Then, we divided the mRNA expression levels of NF-κB-related genes in KIRC patients into three groups through GSVA cluster analysis and explored the correlation between the NF-κB pathway and clinical data of KIRC patients, classical cancer-related genes, common anticancer drug responsiveness, and immune cell infiltration. Finally, 11 tumor-related genes were screened using least absolute shrinkage and selection operator (LASSO) regression to construct a prognostic model. In addition, we used the UALCAN and HPA databases to verify the protein levels of three key NF-κB-related genes (CHUK, IKGGB, and IKBKG) in KIRC. In conclusion, our study established a prognostic survival model based on NF-κB-related genes, which can be used to predict the prognosis of patients with KIRC.

Single-cell transcriptomic data reveal the increase in extracellular matrix organization and antigen presentation abilities of fibroblasts and smooth muscle cells in patients with pelvic organ prolapse

INTRODUCTION AND HYPOTHESIS

We aimed to explore the cellular properties of fibroblasts and smooth muscle cells (SMCs), the two major cell types of the vagina wall, in pelvic organ prolapse (POP) to improve the knowledge of the underlying molecular mechanisms of POP.

METHODS

The single-cell RNA sequencing (scRNA-seq) profile GSE151202 was downloaded from NCBI Gene Expression Omnibus, in which vaginal wall tissues were harvested from patients with anterior vaginal wall prolapse and control subjects respectively. The scRNA-seq data of samples (5 POP and 5 controls) were adopted for analysis. Cluster analysis was performed to identify the cell subclusters. Trajectory analysis was applied to construct the differentiation trajectories of fibroblasts and SMCs. Cellular communication analysis was carried out to explore the ligand-receptor interactions between fibroblasts/SMCs and immune cells.

RESULTS

Ten subclusters were determined in both groups, among which fibroblasts and SMCs were the most abundant cell types. Compared with controls, fibroblasts increased whereas SMCs declined in POP. During the transition of fibroblasts and SMCs from a normal into a disease state, extracellular matrix organization and antigen presentation were heightened. The intercellular communications were altered in POP. Interactions between fibroblasts/SMCs and macrophages/natural killer/T cells were strengthened as more ligand-receptor pairs involved in antigen presentation pathways were gained in POP.

CONCLUSION

Extracellular matrix organization and antigen presentation abilities of fibroblasts and SMCs were enhanced in POP.

Integrated single-cell RNA sequencing analysis reveals a mesenchymal stem cell-associated signature for estimating prognosis and drug sensitivity in gastric cancer

BACKGROUND

Mesenchymal stem cells (MSCs) play an important role in regulating all stages of the immune response, angiogenesis, and transformation of matrix components in the tumor microenvironment. The aim of this study was to identify the prognostic value of MSC-related signatures in patients with gastric cancer (GC).

METHODS

MSC marker genes were identified by analyzing single-cell RNA sequencing (scRNA-seq) data for GC from the Gene Expression Omnibus (GEO) database. Using bulk sequencing data from the Cancer Genome Atlas-Stomach adenocarcinoma (TCGA-STAD), as a training cohort, and data from GEO, as a validation cohort, we developed a risk model consisting of MSC prognostic signature genes, and classified GC patients into high- and low-MSC risk subgroups. Multifactorial Cox regression was used to evaluate whether MSC prognostic signature was an independent prognostic factor. An MSC nomogram was constructed combining clinical information and risk grouping. Subsequently, we evaluated the effect of MSC prognostic signature on immune cell infiltration, antitumor drugs and immune checkpoints and verified the expression of MSC prognostic signature by in vitro cellular assays.

RESULTS

In this study, 174 MSC marker genes were identified by analyzing scRNA-seq data. We identified seven genes (POSTN, PLOD2, ITGAV, MMP11, SDC2, MARCKS, ANXA5) to construct MSC prognostic signature. MSC prognostic signature was an independent risk factor in the TCGA and GEO cohorts. GC patients in the high-MSC risk group had worse prognoses. In addition, the MSC nomogram has a high clinical application value. Notably, the MSC signature can induce the development of a poor immune microenvironment. GC patients in the high MSC-risk group were more sensitive to anticancer drugs and tended to have higher levels of immune checkpoint markers. In qRT-PCR assays, the MSC signature was more highly expressed in GC cell lines.

CONCLUSIONS

The MSC marker gene-based risk signature developed in this study can not only be used to predict the prognosis of GC patients, but also has the potential to reflect the efficacy of antitumor therapies.

The gut microbiota-induced kynurenic acid recruits GPR35-positive macrophages to promote experimental encephalitis

The intricate interplay between gut microbes and the onset of experimental autoimmune encephalomyelitis (EAE) remains poorly understood. Here, we uncover remarkable similarities between CD4+ T cells in the spinal cord and their counterparts in the small intestine. Furthermore, we unveil a synergistic relationship between the microbiota, particularly enriched with the tryptophan metabolism gene EC:1.13.11.11, and intestinal cells. This symbiotic collaboration results in the biosynthesis of kynurenic acid (KYNA), which modulates the recruitment and aggregation of GPR35-positive macrophages. Subsequently, a robust T helper 17 (Th17) immune response is activated, ultimately triggering the onset of EAE. Conversely, modulating the KYNA-mediated GPR35 signaling in Cx3cr1+ macrophages leads to a remarkable amelioration of EAE. These findings shed light on the crucial role of microbial-derived tryptophan metabolites in regulating immune responses within extraintestinal tissues.

sccomp: Robust differential composition and variability analysis for single-cell data

Cellular omics such as single-cell genomics, proteomics, and microbiomics allow the characterization of tissue and microbial community composition, which can be compared between conditions to identify biological drivers. This strategy has been critical to revealing markers of disease progression, such as cancer and pathogen infection. A dedicated statistical method for differential variability analysis is lacking for cellular omics data, and existing methods for differential composition analysis do not model some compositional data properties, suggesting there is room to improve model performance. Here, we introduce sccomp, a method for differential composition and variability analyses that jointly models data count distribution, compositionality, group-specific variability, and proportion mean-variability association, being aware of outliers. sccomp provides a comprehensive analysis framework that offers realistic data simulation and cross-study knowledge transfer. Here, we demonstrate that mean-variability association is ubiquitous across technologies, highlighting the inadequacy of the very popular Dirichlet-multinomial distribution. We show that sccomp accurately fits experimental data, significantly improving performance over state-of-the-art algorithms. Using sccomp, we identified differential constraints and composition in the microenvironment of primary breast cancer.

Integration of Single-Cell RNA Sequencing and Bulk RNA Sequencing Reveals That TAM2-Driven Genes Affect Immunotherapeutic Response and Prognosis in Pancreatic Cancer

Tumor-associated macrophages M2 (TAM2), which are highly prevalent infiltrating immune cells in the stroma of pancreatic cancer (PC), have been found to induce an immunosuppressive tumor microenvironment, thus enhancing tumor initiation and progression. However, the immune therapy response and prognostic significance of regulatory genes associated with TAM2 in PC are currently unknown. Based on TCGA transcriptomic data and single-cell sequencing data from the GEO database, we identified TAM2-driven genes using the WGCNA algorithm. Molecular subtypes based on TAM2-driven genes were clustered using the ConsensusClusterPlus algorithm. The study constructed a prognostic model based on TAM2-driven genes through Lasso-COX regression analysis. A total of 178 samples obtained by accessing TCGA were accurately categorized into two molecular subtypes, including the high-TAM2 infiltration (HMI) cluster and the low-TAM2 infiltration (LMI) cluster. The HMI cluster exhibits a poor prognosis, a malignant tumor phenotype, immune-suppressive immune cell infiltration, resistance to immunotherapy, and a high number of genetic mutations, while the LMI cluster is the opposite. The prognostic model composed of six hub genes from TAM2-driven genes exhibits a high degree of accuracy in predicting the prognosis of patients with PC and serves as an independent risk factor. The induction of TAM2 was employed as a means of verifying these six gene expressions, revealing the significant up-regulation of BCAT1, BST2, and MERTK in TAM2 cells. In summary, the immunophenotype and prognostic model based on TAM2-driven genes offers a foundation for the clinical management of PC. The core TAM2-driven genes, including BCAT1, BST2, and MERTK, are involved in regulating tumor progression and TAM2 polarization, which are potential targets for PC therapy.

Prognostic significance and mechanisms of CXCL genes in clear cell renal cell carcinoma

This study aimed to investigate the clinical significance, biological functions, and underlying mechanisms of CXCL genes in clear cell renal cell carcinoma (ccRcc) based on patient datasets and pan-cancer analysis. The interaction between CXCL genes in ccRcc and immune components, particularly in relation to neutrophil recruitment and polarization mechanisms, was also evaluated. Furthermore, a risk score was developed using a signature for neutrophil polarization. The role of CXCL2 was assessed through in vitro experiments. Results showed that five CXCL genes (CXCL 2, 5, 9, 10, and 11) were upregulated in renal cancer tissue, while seven genes (CXCL 1, 2, 3, 5, 8, 13, and 14) significantly impacted patient survival. Moreover, CXCL 1, 5, and 13 affected progression-free survival. Besides, differences in mRNA expression and immune components affected renal cancer outcomes. Furthermore, three pairs of CXCL gene-immune cell interactions (CXCL13-CD8+ T cells, CXCL9/10-M1 cells, CXCL1/2/3/8-neutrophils) were identified through single-cell and pan-cancer analysis. A TAN risk score with prognostic value for KIRC patients was constructed using 11 genes and a TAN signature. Neutrophil polarization significantly impacted survival. Notably, CXCL2 was involved in neutrophil recruitment and polarization, thus promoting ccRcc progression. In conclusion, seven prognostic CXCL genes (CXCL 1/2/3/5/8/13/14) for ccRcc patients and three pairs of CXCL gene-immune cell interactions were identified. Furthermore, results showed that CXCL 2 promotes ccRcc progression through neutrophil recruitment and polarization.

Prolonged exposure to lung-derived cytokines is associated with inflammatory activation of microglia in patients with COVID-19

Neurological impairment is the most common finding in patients with post-acute sequelae of COVID-19. Furthermore, survivors of pneumonia from any cause have an elevated risk of dementia1-4. Dysfunction in microglia, the primary immune cell in the brain, has been linked to cognitive impairment in murine models of dementia and in humans5. Here, we report a transcriptional response in human microglia collected from patients who died following COVID-19 suggestive of their activation by TNF-α and other circulating pro-inflammatory cytokines. Consistent with these findings, the levels of 55 alveolar and plasma cytokines were elevated in a cohort of 341 patients with respiratory failure, including 93 unvaccinated patients with COVID-19 and 203 patients with other causes of pneumonia. While peak levels of pro-inflammatory cytokines were similar in patients with pneumonia irrespective of etiology, cumulative cytokine exposure was higher in patients with COVID-19. Corticosteroid treatment, which has been shown to be beneficial in patients with COVID-196, was associated with lower levels of CXCL10, CCL8, and CCL2-molecules that sustain inflammatory circuits between alveolar macrophages harboring SARS-CoV-2 and activated T cells7. These findings suggest that corticosteroids may break this cycle and decrease systemic exposure to lung-derived cytokines and inflammatory activation of microglia in patients with COVID-19.

Identification of biomarkers and immune infiltration in acute myocardial infarction and heart failure by integrated analysis

The mortality of heart failure after acute myocardial infarction (AMI) remains high. The aim of the present study was to analyze hub genes and immune infiltration in patients with AMI and heart failure (HF). The study utilized five publicly available gene expression datasets from peripheral blood in patients with AMI who either developed or did not develop HF. The unbiased patterns of 24 immune cell were estimated by xCell algorithm. Single-cell RNA sequencing data were used to examine the immune cell infiltration in heart failure patients. Hub genes were validated by quantitative reverse transcription-PCR (RT-qPCR). In comparison with the coronary heart disease (CHD) group, immune infiltration analysis of AMI patients showed that macrophages M1, macrophages, monocytes, natural killer (NK) cells, and NKT cells were the five most highly activated cell types. Five common immune-related genes (S100A12, AQP9, CSF3R, S100A9, and CD14) were identified as hub genes associated with AMI. Using RT-qPCR, we confirmed FOS, DUSP1, CXCL8, and NFKBIA as the potential biomarkers to identify AMI patients at risk of HF. The study identified several transcripts that differentiate between AMI and CHD, and between HF and non-HF patients. These findings could improve our understanding of the immune response in AMI and HF, and allow for early identification of AMI patients at risk of HF.

Heterogeneity of cancer-associated fibroblasts in head and neck squamous cell carcinoma

Cancer-associated fibroblasts (CAFs) consist of heterogeneous cellular populations that contribute critical roles in head and neck squamous cell carcinoma (HNSCC). A series of computer-aided analyses were performed to determine various aspects of CAFs in HNSCC, including their cellular heterogeneity, prognostic value, relationship with immune suppression and immunotherapeutic response, intercellular communication, and metabolic activity. The prognostic significance of CKS2+ CAFs was verified using immunohistochemistry. Our findings revealed that fibroblasts group demonstrated prognostic significance, with the CKS2+ subset of inflammatory CAFs (iCAFs) exhibiting a significant correlation with unfavorable prognosis and being localized in close proximity to cancer cells. Patients with a high infiltration of CKS2+ CAFs had a poor overall survival rate. There is a negative correlation between CKS2+ iCAFs and cytotoxic CD8+ T cells and natural killer (NK) cells, while a positive correlation was found with exhausted CD8+ T cells. Additionally, patients in Cluster 3, characterized by a high proportion of CKS2+ iCAFs, and patients in Cluster 2, characterized by a high proportion of CKS2- iCAFs and CENPF-/MYLPF- myofibroblastic CAFs (myCAFs), did not exhibit significant immunotherapeutic responses. Moreover, close interactions was confirmed to exist between cancer cells and CKS2+ iCAFs/ CENPF+ myCAFs. Furthermore, CKS2+ iCAFs demonstrated the highest level of metabolic activity. In summary, our study enhances the understanding of the heterogeneity of CAFs and provided insights into improving the efficacy of immunotherapies and prognostic accuracy for HNSCC patients.

tidytof: a user-friendly framework for scalable and reproducible high-dimensional cytometry data analysis

Summary

While many algorithms for analyzing high-dimensional cytometry data have now been developed, the software implementations of these algorithms remain highly customized-this means that exploring a dataset requires users to learn unique, often poorly interoperable package syntaxes for each step of data processing. To solve this problem, we developed {tidytof}, an open-source R package for analyzing high-dimensional cytometry data using the increasingly popular 'tidy data' interface.

Availability and implementation

{tidytof} is available at https://github.com/keyes-timothy/tidytof and is released under the MIT license. It is supported on Linux, MS Windows and MacOS. Additional documentation is available at the package website (https://keyes-timothy.github.io/tidytof/).

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

Application of single-cell RNA sequencing analysis of novel breast cancer phenotypes based on the activation of ferroptosis-related genes

Ferroptosis is distinct from classic apoptotic cell death characterized by the accumulation of reactive oxygen species (ROS) and lipid peroxides on the cell membrane. Increasing findings have demonstrated that ferroptosis plays an important role in cancer development, but the exploration of ferroptosis in breast cancer is limited. In our study, we aimed to establish a ferroptosis activation-related model based on the differentially expressed genes between a group exhibiting high ferroptosis activation and a group exhibiting low ferroptosis activation. By using machine learning to establish the model, we verified the accuracy and efficiency of our model in The Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) set and gene expression omnibus (GEO) dataset. Additionally, our research innovatively utilized single-cell RNA sequencing data to systematically reveal the microenvironment in the high and low FeAS groups, which demonstrated differences between the two groups from comprehensive aspects, including the activation condition of transcription factors, cell pseudotime features, cell communication, immune infiltration, chemotherapy efficiency, and potential drug resistance. In conclusion, different ferroptosis activation levels play a vital role in influencing the outcome of breast cancer patients and altering the tumor microenvironment in different molecular aspects. By analyzing differences in ferroptosis activation levels, our risk model is characterized by a good prognostic capacity in assessing the outcome of breast cancer patients, and the risk score can be used to prompt clinical treatment to prevent potential drug resistance. By identifying the different tumor microenvironment landscapes between the high- and low-risk groups, our risk model provides molecular insight into ferroptosis in breast cancer patients.

Expression pattern analysis of m6A regulators reveals IGF2BP3 as a key modulator in osteoarthritis synovial macrophages

BACKGROUND

Disruption of N6 methyl adenosine (m6A) modulation hampers gene expression and cellular functions, leading to various illnesses. However, the role of m6A modification in osteoarthritis (OA) synovitis remains unclear. This study aimed to explore the expression patterns of m6A regulators in OA synovial cell clusters and identify key m6A regulators that mediate synovial macrophage phenotypes.

METHODS

The expression patterns of m6A regulators in the OA synovium were illustrated by analyzing bulk RNA-seq data. Next, we built an OA LASSO-Cox regression prediction model to identify the core m6A regulators. Potential target genes of these m6A regulators were identified by analyzing data from the RM2target database. A molecular functional network based on core m6A regulators and their target genes was constructed using the STRING database. Single-cell RNA-seq data were collected to verify the effects of m6A regulators on synovial cell clusters. Conjoint analyses of bulk and single-cell RNA-seq data were performed to validate the correlation between m6A regulators, synovial clusters, and disease conditions. After IGF2BP3 was screened as a potential modulator in OA macrophages, the IGF2BP3 expression level was tested in OA synovium and macrophages, and its functions were further tested by overexpression and knockdown in vitro.

RESULTS

OA synovium showed aberrant expression patterns of m6A regulators. Based on these regulators, we constructed a well-fitting OA prediction model comprising six factors (FTO, YTHDC1, METTL5, IGF2BP3, ZC3H13, and HNRNPC). The functional network indicated that these factors were closely associated with OA synovial phenotypic alterations. Among these regulators, the m6A reader IGF2BP3 was identified as a potential macrophage mediator. Finally, IGF2BP3 upregulation was verified in the OA synovium, which promoted macrophage M1 polarization and inflammation.

CONCLUSIONS

Our findings revealed the functions of m6A regulators in OA synovium and highlighted the association between IGF2BP3 and enhanced M1 polarization and inflammation in OA macrophages, providing novel molecular targets for OA diagnosis and treatment.

Integrated analysis reveals the microenvironment of non-small cell lung cancer and a macrophage-related prognostic model

Background

In the treatment of non-small cell lung cancer (NSCLC), recent advances in immunotherapy have heralded a new era. Despite the success of immune therapy, a subset of patients persistently fails to respond. Therefore, to better improve the efficacy of immunotherapy and achieve the purpose of precision therapy, the research and exploration of tumor immunotherapy biomarkers have received much attention.

Methods

Single-cell transcriptomic profiling was used to reveal tumor heterogeneity and the microenvironment in NSCLC. The Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm was utilized to speculate the relative fractions of 22 infiltration immunocyte types in NSCLC. Univariate Cox and least absolute shrinkage and selection operator (LASSO) regression analyses were used for the construction of risk prognostic models and predictive nomograms of NSCLC. Spearman's correlation analysis was employed to explore the relationship between risk score and tumor mutation burden (TMB) and immune checkpoint inhibitors (ICIs). Screening of chemotherapeutic agents in the high- and low-risk groups was performed with the "pRRophetic" package in R. Intercellular communication analysis was conducted using the "CellChat" package.

Results

We found that most tumor-infiltrating immune cells were T cells and monocytes. We also found that there was a significant difference in the tumor-infiltrating immune cells and ICIs across different molecular subtypes. Further analysis showed that M0 and M1 mononuclear macrophages were significantly different in different molecular subtypes. The risk prediction model was shown to have to ability to accurately predict the prognosis, immune cell infiltration, and chemotherapy efficacy of patients in the high and low-risk groups. Finally, we found that the carcinogenic effect of migration inhibitory factor (MIF) is mediated by binding to CD74, CXCR4, and CD44 receptors involved in MIF cell signaling.

Conclusions

We have revealed the tumor microenvironment (TME) of NSCLC through single-cell data analysis and constructed a prognosis model of macrophage-related genes. These results could provide new therapeutic targets for NSCLC.

Exploration of the heterogeneity and interaction of epithelial cells and NK/T-cells in Laryngeal Squamous Cell Carcinoma based on single-cell RNA sequencing data

OBJECTIVES

We aimed to explore the heterogeneity and differentiation trajectories of epithelial cells and NK/T-cells in Laryngeal Squamous Cell Carcinoma (LSCC).

METHODS

We downloaded the GSE150321 data set containing LSCC01 and LSCC02 samples single cell RNA data from Gene Expression Omnibus. The UMAP analysis was performed to identify the cell subpopulations and cell locations of subpopulations. Seurat package was used to analyze the differential expression of genes. The function of differential expression genes was analyzed using DAVID database. The monocle2 package was used to analyze differentiation trajectories. We used the CellChat package to observe the signaling pathways and ligand-receptor pairs for epithelial cells and NK/T-cells.

RESULTS

All the LSCC cells were divided into 16 subpopulation that included 7 epithelial cell subsets, 3 T-cell subsets. The function analysis indicated that epithelial cells and NK/T-cells mainly participated in different process, such as cell cycle, immune response, and cell migration. Then, the results of differentiation trajectory indicated that the ability of migration, and the activation of the immune system increases, while the ability of apoptosis, and glucose metabolic process decreases as pseudotime. Migration-related epithelial cells act on all T-cells via the CNTN2-CNTN2 ligand-receptor pair, which suggested that CNTN2 might be an important biomarker for regulating migration of epithelial cells.

CONCLUSIONS

Our study characterized the heterogeneity of LSCC, which provided novel insights into LSCC and identified a new mechanism and target for clinical LSCC threapies.

EVIDENCE

IV.

Construction of a ferroptosis scoring system and identification of LINC01572 as a novel ferroptosis suppressor in lung adenocarcinoma

Background: Ferroptosis is a novel process of programmed cell death driven by excessive lipid peroxidation that is associated with the development of lung adenocarcinoma. N6-methyladenosine (m6a) modification of multiple genes is involved in regulating the ferroptosis process, while the predictive value of N6-methyladenosine- and ferroptosis-associated lncRNA (FMRlncRNA) in the prognosis of patients remains with LUAD remains unknown. Methods: Unsupervised cluster algorithm was applied to generate subcluster in LUAD according to ferroptosis-associated lncRNA. Stepwise Cox analysis and LASSO algorithm were applied to develop a prognostic model. Cellular location was detected by single-cell analysis. Also, we conducted Gene set enrichment analysis (GSEA) enrichment, immune microenvironment and drug sensitivity analysis. In addition, the expression and function of the LINC01572 were investigated by several in vitro experiments including qRT-PCR, cell viability assays and ferroptosis assays. Results: A novel ferroptosis-associated lncRNAs-based molecular subtype containing two subclusters were determined in LUAD. Then, we successfully created a risk model according to five ferroptosis-associated lncRNAs (LINC00472, MBNL1-AS1, LINC01572, ZFPM2-AS1, and TMPO-AS1). Our nominated model had good stability and predictive function. The expression patterns of five ferroptosis-associated lncRNAs were confirmed by polymerase chain reaction (PCR) in LUAD cell lines. Knockdown of LINC01572 significantly inhibited cell viability and induced ferroptosis in LUAD cell lines. Conclusion: Our data provided a risk score system based on ferroptosis-associated lncRNAs with prognostic value in LUAD. Moreover, LINC01572 may serve as a novel ferroptosis suppressor in LUAD.

Identification and validation of a novel signature based on cell-cell communication in head and neck squamous cell carcinoma by integrated analysis of single-cell transcriptome and bulk RNA-sequencing

Background

The heterogeneous crosstalk between tumor cells and other cells in their microenvironment means a notable difference in clinical outcomes of head and neck squamous cell carcinoma (HNSCC). CD8+ T cells and macrophages are effector factors of the immune system, which have direct killing and phagocytosis effects on tumor cells. How the evolution of their role in the tumor microenvironment influences patients clinically remains a mystery. This study aims to investigate the complex communication networks in the HNSCC tumor immune microenvironment, elucidate the interactions between immune cells and tumors, and establish prognostic risk model.

Methods

20 HNSCC samples single-cell rna sequencing (scRNA-seq) data and bulk rna-seq data were derived from public databases. The "cellchat" R package was used to identify cell-to-cell communication networks and prognostic related genes, and then cell-cell communication (ccc) molecular subtypes were constructed by unsupervised clustering. Kaplan-Meier(K-M) survival analysis, clinical characteristics analysis, immune microenvironment analysis, immune cell infiltration analysis and CD8+T cell differentiation correlation analysis were performed. Finally, the ccc gene signature including APP, ALCAM, IL6, IL10 and CD6 was constructed based on univariate Cox analysis and multivariate Cox regression. Kaplan-Meier analysis and time-dependent receiver operating characteristic (ROC) analysis were used to evaluate the model in the train group and the validation group, respectively.

Results

With CD8+T cells from naive to exhaustion state, significantly decreased expression of protective factor (CD6 gene) is associated with poorer prognosis in patients with HNSCC. The role of macrophages in the tumor microenvironment has been identified as tumor-associated macrophage (TAM), which can promote tumor proliferation and help tumor cells provide more nutrients and channels to facilitate tumor cell invasion and metastasis. In addition, based on the strength of all ccc in the tumor microenvironment, we identified five prognostic ccc gene signatures (cccgs), which were identified as independent prognostic factors by univariate and multivariate analysis. The predictive power of cccgs was well demonstrated in different clinical groups in train and test cohorts.

Conclusion

Our study highlights the propensity for crosstalk between tumors and other cells and developed a novel signature on the basis of a strong association gene for cell communication that has a powerful ability to predict prognosis and immunotherapy response in patients with HNSCC. This may provide some guidance for developing diagnostic biomarkers for risk stratification and therapeutic targets for new therapeutic strategies.

Correlation of tumor mutational burden with prognosis and immune infiltration in lung adenocarcinoma

Background

Tumor mutational burden (TMB) plays an important role in the evaluation of immunotherapy efficacy in lung adenocarcinoma (LUAD).

Objective

To improve the clinical management of LUAD by investigating the prognostic value of TMB and the relationship between TMB and immune infiltration.

Methods

TMB scores were calculated from the mutation data of 587 LUAD samples from The Cancer Genome Atlas (TCGA), and patients were divided into low-TMB and high-TMB groups based on the quartiles of the TMB score. Differentially expressed genes (DEGs), immune cell infiltration and survival analysis were compared between the low-TMB and high-TMB groups. We queried the expression of genes in lung cancer tissues through the GEPIA online database and performed experimental validation of the function of aberrant genes expressed in lung cancer tissues.

Results

We obtained sample information from TCGA for 587 LUAD patients, and the results of survival analysis for the high- and low- TMB groups suggested that patients in the high-TMB group had lower survival rates than those in the low-TMB group. A total of 756 DEGs were identified in the study, and gene set enrichment analysis (GSEA) showed that DEGs in the low-TMB group were enriched in immune-related pathways. Among the differentially expressed genes obtained, 15 immune-related key genes were screened with the help of ImmPort database, including 5 prognosis-related genes (CD274, PDCD1, CTLA4, LAG3, TIGIT). No difference in the expression of PDCD1, CTLA4, LAG3, TIGIT in lung cancer tissues and differential expression of CD274 in lung cancer tissues.

Conclusions

The survival rate of LUAD patients with low TMB was better than that of LUAD patients with high TMB. CD274 expression was down regulated in human LUAD cell lines H1299, PC-9, A549 and SPC-A1, which inhibited malignant progression of A549 cells.

Single-Cell Transcriptomics Identifies Brain Endothelium Inflammatory Networks in Experimental Autoimmune Encephalomyelitis

BACKGROUND AND OBJECTIVES

Multiple sclerosis (MS) is a neuroinflammatory and neurodegenerative disease characterized by infiltration of immune cells in multifocal areas of the CNS. The specific molecular processes allowing autoreactive immune cells to enter the CNS compartment through the blood-brain barrier remain elusive.

METHODS

Using endothelial cell (EC) enrichment and single-cell RNA sequencing, we characterized the cells implicated in the neuroinflammatory processes in experimental autoimmune encephalomyelitis, an animal model of MS. Validations on human MS brain sections of the most differentially expressed genes in venous ECs were performed using immunohistochemistry and confocal microscopy.

RESULTS

We found an upregulation of genes associated with antigen presentation and interferon in most populations of CNS-resident cells, including ECs. Interestingly, instead of transcriptionally distinct profiles, a continuous gradient of gene expression separated the arteriovenous zonation of the brain vasculature. However, differential gene expression analysis presented more transcriptomic alterations on the venous side of the axis, suggesting a prominent role of venous ECs in neuroinflammation. Furthermore, analysis of ligand-receptor interactions identified important potential molecular communications between venous ECs and infiltrated immune populations. To confirm the relevance of our observation in the context of human disease, we validated the protein expression of the most upregulated genes (Ackr1 and Lcn2) in MS lesions.

DISCUSSION

In this study, we provide a landscape of the cellular heterogeneity associated with neuroinflammation. We also present important molecular insights for further exploration of specific cell processes that promote infiltration of immune cells inside the brain of experimental autoimmune encephalomyelitis mice.

Comprehensive bioinformatics analysis reveals biomarkers of DNA methylation-related genes in varicose veins

Background: Patients with Varicose veins (VV) show no obvious symptoms in the early stages, and it is a common and frequent clinical condition. DNA methylation plays a key role in VV by regulating gene expression. However, the molecular mechanism underlying methylation regulation in VV remains unclear. Methods: The mRNA and methylation data of VV and normal samples were obtained from the Gene Expression Omnibus (GEO) database. Methylation-Regulated Genes (MRGs) between VV and normal samples were crossed with VV-associated genes (VVGs) obtained by weighted gene co-expression network analysis (WGCNA) to obtain VV-associated MRGs (VV-MRGs). Their ability to predict disease was assessed using receiver operating characteristic (ROC) curves. Biomarkers were then screened using a random forest model (RF), support vector machine model (SVM), and generalized linear model (GLM). Next, gene set enrichment analysis (GSEA) was performed to explore the functions of biomarkers. Furthermore, we also predicted their drug targets, and constructed a competing endogenous RNAs (ceRNA) network and a drug target network. Finally, we verified their mRNA expression using quantitative real-time polymerase chain reaction (qRT-PCR). Results: Total three VV-MRGs, namely Wnt1-inducible signaling pathway protein 2 (WISP2), Cysteine-rich intestinal protein 1 (CRIP1), and Odd-skipped related 1 (OSR1) were identified by VVGs and MRGs overlapping. The area under the curves (AUCs) of the ROC curves for these three VV-MRGs were greater than 0.8. RF was confirmed as the optimal diagnostic model, and WISP2, CRIP1, and OSR1 were regarded as biomarkers. GSEA showed that WISP2, CRIP1, and OSR1 were associated with oxidative phosphorylation, extracellular matrix (ECM), and respiratory system functions. Furthermore, we found that lncRNA MIR17HG can regulate OSR1 by binding to hsa-miR-21-5p and that PAX2 might treat VV by targeting OSR1. Finally, qRT-PCR results showed that the mRNA expression of the three genes was consistent with the results of the datasets. Conclusion: This study identified WISP2, CRIP1, and OSR1 as biomarkers of VV through comprehensive bioinformatics analysis, and preliminary explored the DNA methylation-related molecular mechanism in VV, which might be important for VV diagnosis and exploration of potential molecular mechanisms.

The Risk Correlation between N7-Methylguanosine Modification-Related lncRNAs and Survival Prognosis of Oral Squamous Cell Carcinoma Based on Comprehensive Bioinformatics Analysis

Objective. N7-methylguanosine modification-related lncRNAs (m7G-related lncRNAs) are involved in progression of many diseases. This study was aimed at revealing the risk correlation between N7-methylguanosine modification-related lncRNAs and survival prognosis of oral squamous cell carcinoma. Methods. In the present study, coexpression network analysis and univariate Cox analysis were used to obtained 31 m7G-related mRNAs and 399 m7G-related lncRNAs. And the prognostic risk score model of OSCC patients was evaluated and optimized through cross-validation. Results. Through the coexpression analysis and risk assessment analysis of m7G-related prognostic mRNAs and lncRNAs, it was found that six m7G-related prognostic lncRNAs (AC005332.6, AC010894.1, AC068831.5, AL035446.1, AL513550.1, and HHLA3) were high-risk lncRNAs. Three m7G-related prognostic lncRNAs (AC007114.1, HEIH, and LINC02541) were protective lncRNAs. Then, survival curves were drawn by comparing the survival differences between patients with high and low expression of each m7G-related prognostic lncRNA in the prognostic risk score model. Further, risk curves, scatter plots, and heat maps were drawn by comparing the survival differences between high-risk and low-risk OSCC patients in the prognostic model. Finally, forest maps and the ROC curve were generated to verify the predictive power of the prognostic risk score model. Our results will help to find early and accurate prognostic risk markers for OSCC, which could be used for early prediction and early clinical intervention of survival, prognosis, and disease risk of OSCC patients in the future.

A ubiquitin-related gene signature for predicting prognosis and constructing molecular subtypes in osteosarcoma

Background: Ubiquitination is medicated by three classes of enzymes and has been proven to involve in multiple cancer biological processes. Moreover, dysregulation of ubiquitination has received a growing body of attention in osteosarcoma (OS) tumorigenesis and treatment. Therefore, our study aimed to identify a ubiquitin-related gene signature for predicting prognosis and immune landscape and constructing OS molecular subtypes.

Methods: Therapeutically Applicable Research to Generate Effective Treatments (TARGET) was regarded as the training set through univariate Cox regression, Lasso Cox regression, and multivariate Cox regression. The GSE21257 and GSE39055 served as the validation set to verify the predictive value of the signature. CIBERSORT was performed to show immune infiltration and the immune microenvironment. The NMF algorithm was used to construct OS molecular subtypes.

Results: In this study, we developed a ubiquitin-related gene signature including seven genes (UBE2L3, CORO6, DCAF8, DNAI1, FBXL5, UHRF2, and WDR53), and the gene signature had a good performance in predicting prognosis for OS patients (AUC values at 1/3/5 years were 0.957, 0.890, and 0.919). Multivariate Cox regression indicated that the risk score model and prognosis stage were also independent prognostic prediction factors. Moreover, analyses of immune cells and immune-related functions showed a significant difference in different risk score groups and the three clusters. The drug sensitivity suggested that IC50 of proteasome inhibitor (MG-132) showed a notable significance between the risk score groups (p < 0.05). Through the NMF algorithm, we obtained the three clusters, and cluster 3 showed better survival outcomes. The expression of ubiquitin-related genes (CORO6, UBE2L3, FBXL5, DNAI1, and DCAF8) showed an obvious significance in normal and osteosarcoma tissues.

Conclusion: We developed a novel ubiquitin-related gene signature which showed better predictive prognostic ability for OS and provided additional information on chemotherapy and immunotherapy. The OS molecular subtypes would also give a useful guide for individualized therapy.

The Role of NR4A1 in the Pathophysiology of Osteosarcoma: A Comprehensive Bioinformatics Analysis of the Single-Cell RNA Sequencing Dataset

Osteosarcoma is a kind of aggressive human malignancy, and the prognosis of the patients with osteosarcoma remains low. Studies have demonstrated that the tumor microenvironment plays a key role in regulating osteosarcoma progression. Recent studies have also shown that scRNA-seq plays an essential role in understanding the tumor heterogeneity and distinct subpopulations of tumors. In order to further understand the scRNA-seq data of osteosarcoma tissues, the present study further analyzed the scRNA-seq dataset (GSE152048) and explored the potential role of nuclear receptor-related genes in the pathophysiology of osteosarcoma. In our analysis, we identified 11 cell types in all the osteosarcoma tissues and nuclear receptors (NRs) were distributed in all types of cells. Further stratification analysis showed that NRs were mainly detected in “TIL” and “Osteoblastic” of the metastasis osteosarcoma, in “TIL”, “Myoblast”, “Endothelial”, and “Myeloid” of the primary osteosarcoma, and in “Chondroblastic”, “Osteoblast”, and “Pericyte” of the recurrent osteosarcoma. The NRs were also differentially expressed in different cell types among the metastasis, primary, and recurrent osteosarcoma. Furthermore, several NRs such as NR4A2, NR4A1, and NR3C1 have been found to be differentially expressed in most types of DEGs among metastasis, primary, and recurrent osteosarcoma. A high expression of NR4A1 in the osteosarcoma tissues was significantly correlated with a shorter 5-year overall survival of patients with osteosarcoma. On the other hand, there was no significant association between NR4A2 expression and the 5-year overall survival of patients with osteosarcoma. The expression of NR4A1 was significantly higher in the metastasis osteosarcoma tissues than in the primary osteosarcoma tissues as validated from GSE32981 and GSE154540. The expression of NR4A1 was significantly higher in osteosarcoma tissues from patients with poor chemosensitivity than that from patients with good chemosensitivity as validated from GSE154540. Further analysis of the scRNA-seq data revealed that the percentage of osteoblasts with a high NR4A1 expression was higher in the recurrent osteosarcoma tissues than that with a low NR4A1 expression. In conclusion, the present study may suggest that NR4A1 may be an important prognostic biomarker for osteosarcoma progression. However, further validation studies should be performed to confirm our findings.

Integrated Single-Cell RNA-Sequencing Analysis of Gastric Cancer Identifies FABP1 as a Novel Prognostic Biomarker

Gastric cancer (GC) is usually diagnosed in an advanced stage at the first visit due to the atypical clinical symptoms. The low surgical resection rate and chemotherapy sensitivity result in dismal survival. Therefore, it is urgent to develop novel biomarkers with high sensitivity and specificity to accurately assess the prognosis of GC patients. In the present study, 3385 differentially expressed genes (DEGs) were obtained from the single-cell RNA sequencing data of GC specimens. Using the unsupervised dimensionality reduction, we further found 3 subsets of cells including gastric cells, plasmacytoid dendritic cells, and memory T cells. Based on the cell clustering, we explored the key regulatory genes for GC progression by pseudo-time analysis and functional enrichment analysis. According to the results, the significant differentially expressed fatty acid-binding protein 1 (FABP1) verified by pseudo-time analysis was identified as the hub gene of GC progression. FABP1 was shown to be closely related to the long-term survival and the age at diagnosis of patients with GC in analysis based on the TCGA (The Cancer Genome Atlas) database. To further verify the role of FABP1 in GC, we performed immunohistochemical (IHC) analysis using the GC tissue microarray and found that the expression level of FABP1 was higher in GC tissues than in the adjacent tissues. Moreover, GC patients with higher expression of FABP1 had a worse clinical outcome. In summary, our study revealed that FABP1 is a potential effective biomarker for the prognosis of GC, and high expression of FABP1 predicts unsatisfactory survival.

A Machine-Learning Approach to Developing a Predictive Signature Based on Transcriptome Profiling of Ground-Glass Opacities for Accurate Classification and Exploring the Immune Microenvironment of Early-Stage LUAD

Screening for early-stage lung cancer with low-dose computed tomography is recommended for high-risk populations; consequently, the incidence of pure ground-glass opacity (pGGO) is increasing. Ground-glass opacity (GGO) is considered the appearance of early lung cancer, and there remains an unmet clinical need to understand the pathology of small GGO (&lt;1 cm in diameter). The objective of this study was to use the transcriptome profiling of pGGO specimens &lt;1 cm in diameter to construct a pGGO-related gene risk signature to predict the prognosis of early-stage lung adenocarcinoma (LUAD) and explore the immune microenvironment of GGO. pGGO-related differentially expressed genes (DEGs) were screened to identify prognostic marker genes with two machine learning algorithms. A 15-gene risk signature was constructed from the DEGs that were shared between the algorithms. Risk scores were calculated using the regression coefficients for the pGGO-related DEGs. Patients with Stage I/II LUAD or Stage IA LUAD and high-risk scores had a worse prognosis than patients with low-risk scores. The prognosis of high-risk patients with Stage IA LUAD was almost identical to that of patients with Stage II LUAD, suggesting that treatment strategies for patients with Stage II LUAD may be beneficial in high-risk patients with Stage IA LUAD. pGGO-related DEGs were mainly enriched in immune-related pathways. Patients with high-risk scores and high tumor mutation burden had a worse prognosis and may benefit from immunotherapy. A nomogram was constructed to facilitate the clinical application of the 15-gene risk signature. Receiver operating characteristic curves and decision curve analysis validated the predictive ability of the nomogram in patients with Stage I LUAD in the TCGA-LUAD cohort and GEO datasets.

NADH dehydrogenase subunit 1/4/5 promotes survival of acute myeloid leukemia by mediating specific oxidative phosphorylation

Acute myeloid leukemia (AML) is a type of hematological malignancy caused by uncontrolled clonal proliferation of hematopoietic stem cells. The special energy metabolism mode of AML relying on oxidative phosphorylation is different from the traditional ‘Warburg effect’. However, its mechanism is not clear. In the present study, it was demonstrated that the mRNA expression levels of NADH dehydrogenase subunit 1, 4 and 5 (ND1, ND4 and ND5) were upregulated in AML samples from The Cancer Genome Atlas database using the limma package in the R programming language. Reverse transcription-quantitative PCR and ELISA were used to verify the upregulation of ND1, ND4 and ND5 in clinical samples. Pan-cancer analysis revealed that the expression of ND1 was upregulated only in AML, ND2 was upregulated only in AML and thymoma, and ND4 was upregulated only in AML and kidney chromophobe. In the present study, it was demonstrated that silencing of ND1/4/5 could inhibit the proliferation of AML cells in transplanted tumor of nude mice. Additionally, it was found that oxidative phosphorylation and energy metabolism of AML cells were decreased after silencing of ND1/4/5. In conclusion, the present study suggested that ND1/4/5 may be involved in the regulation of oxidative phosphorylation metabolism in AML as a potential cancer-promoting factor.

Exercise Modifies the Transcriptional Regulatory Features of Monocytes in Alzheimer's Patients: A Multi-Omics Integration Analysis Based on Single Cell Technology

Monocytes have been reported to be important mediators of the protective effect of exercise against the development of Alzheimer's disease (AD). This study aims explored the mechanism by which monocytes achieve this. Using single cell transcriptome analysis, results showed that CD14 + and CD16 + monocytes interacted with other cells in the circulating blood. TNF, CCR1, APP, and AREG, the key ligand-receptor-related genes, were found to be differentially expressed between exercise-treated and AD patients. The SCENIC analysis was performed to identify individual clusters of the key transcription factors (TFs). Nine clusters (M1-M9) were obtained from the co-expression network. Among the identified TFs, MAFB, HES4, and FOSL1 were found to be differentially expressed in AD. Moreover, the M4 cluster to which MAFB, HES4, and FOSL1 belonged was defined as the signature cluster for AD phenotype. Differential analysis by bulkRNA-seq revealed that the expression of TNF, CCR1, and APP were all upregulated after exercise (p < 0.05). And ATF3, MAFB, HES4, and KLF4 that were identified in M4 clusters may be the TFs that regulate TNF, CCR1, and APP in exercise prescription. After that, APP, CCR1, TNF, ATF3, KLF4, HES4, and MAFB formed a regulatory network in the ERADMT gene set, and all of them were mechanistically linked. The ERADMT gene set has been found to be a potential risk marker for the development of AD and can be used as an indicator of compliance to exercise therapy in AD patients. Using single-cell integration analysis, a network of exercise-regulating TFs in monocytes was constructed for AD disease. The constructed network reveals the mechanism by which exercise regulated monocytes to confer therapeutic benefits against AD and its complications. However, this study, as a bioinformatic research, requires further experimental validation.

Construction and external validation of a 5-gene random forest model to diagnose non-obstructive azoospermia based on the single-cell RNA sequencing of testicular tissue

Non-obstructive azoospermia (NOA) is among the most severe factors for male infertility, but our understandings of the latent biological mechanisms remain insufficient. The single-cell RNA sequencing (scRNA-seq) data of 432 testicular cells isolated from the patient with NOA was analyzed, and the cell samples were grouped into 5 cell clusters. A sum of 455 cell markers was identified and then included in the protein-protein interaction network. The Top 5 most critical genes in the network, including CCT8, CDC6, PSMD1, RPS4X, RPL36A, were selected for the diagnosis model construction through the random forest (RF). The RF model was a strong classifier for NOA and obstructive azoospermia (OA), which was validated in the training cohort (n = 58, AUC = 1) and external validation cohort (n = 20, AUC = 0.9). We collected the seminal plasma samples and testicular biopsy samples from 20 OA and 20 NOA cases from the local hospital, and the gene expression was detected via Real-Time quantitative Polymerase Chain Reaction (RT-qPCR) and Immunohistochemistry. The RF model also exhibited high accuracy (AUC = 0.725) in the local cohort. In summary, a novel gene signature was developed and externally validated based on scRNA-seq analysis, providing some new biomarkers to uncover the underlying mechanisms and a promising clinical tool for diagnosis in NOA.