Interfacing Seurat with the R tidy universe

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.

Role of the "inflammation-immunity-metabolism" network in non-small cell lung cancer: a multi-omics analysis

Lung cancer remains one of the leading causes of cancer-related mortality, with non-small cell lung cancer (NSCLC) accounting for 85% of cases worldwide. NSCLC pathogenesis and progression are intricately linked to inflammatory stimuli, immune evasion, and metabolic reprogramming. In this study, the impact of inflammation, immunity, and metabolism on NSCLC was investigated by a Mendelian randomization analysis taking 91 inflammatory factors, 731 immune cells, and 1400 metabolites as exposures, and the FinnGen database NSCLC cohort (ncases = 5315, ncontrol = 314,193) was the outcome. A number of metabolites, inflammatory proteins, and immune cells were identified as potentially associated with NSCLC based on mendelian randomization analysis. Validation in the UK Biobank database lung cancer cohort (ncases = 2671, ncontrols = 372,016) further confirmed the inhibitory role of the metabolite N-acetyl-aspartyl-glutamate (NAAG) on lung cancer. Subsequently, single-cell and protein-protein interaction analyses identified inflammatory protein expression patterns in NSCLC, distribution ratios of immune cells in NSCLC. Subsequent multi-omics network analysis showed key interaction nodes between NAAG and inflammatory proteins. These findings enhance the understanding of the roles of inflammation, immunity, and metabolism in NSCLC occurrence and progression, offering potential targets and strategies for further research on its treatment and management.

Development of a prognostic model for osteosarcoma based on macrophage polarization-related genes using machine learning: implications for personalized therapy

While neoadjuvant chemotherapy combined with surgical resection has improved the prognosis for patients with osteosarcoma, its impact on metastatic and recurrent cases remains limited. Immunotherapy is emerging as a promising alternative. However, the relationship between the phenotype of tumor-associated macrophages and the prognosis of osteosarcoma remains unclear. Differentially expressed gene during macrophage polarization were identified using the Monocle package. Weighted gene co-expression network analysis was conducted to select genes regulating macrophage polarization. The least absolute shrinkage and selection operator algorithm and multivariate Cox regression were used to construct long-term survival predictive strategies. Multiple machine learning algorithms identified target genes for pan-cancer analysis. Lentiviral transfection created stable strains with target gene knockdown, and CCK-8 and transwell migration assays verified the target gene's effects. Western blot and flow cytometry assessed the impact of target genes on macrophage polarization. A total of 141 genes regulating macrophage polarization were identified, from which eight genes were selected to construct prognostic models. Significant differences between high-risk and low-risk groups were observed in immune cell activation, immune-related signaling pathways, and immune function. The prognostic model and target gene were validated to provide more precise immunotherapy options for osteosarcoma and other tumors. BNIP3 knockdown decreased osteosarcoma cell proliferation and migration and promoted macrophage polarization to the M2 phenotype. The constructed prognostic model offers precise immunotherapy regimens and valuable insights into mechanisms underlying current studies. Furthermore, BNIP3 may serve as a potential immunotherapeutic target for osteosarcoma and other tumors.

The CD38+HLA-DR+ T cells with activation and exhaustion characteristics as predictors of severity and mortality in COVID-19 patients

Background

The COVID-19 pandemic remains a global health challenge. Severe cases often respond poorly to standard treatments, highlighting the necessity for novel therapeutic targets and early predictive biomarkers.

Methods

We utilized flow cytometry to analyze peripheral immune cells from healthy, bacterial pneumonia patients, and COVID-19 patients. The expansion of activated T cells (CD38+HLA-DR+), monocytes, and myeloid-derived suppressor cells (MDSCs) were detected and correlated with clinical outcomes to evaluate prognostic potential. The single-cell RNA sequencing (scRNA-seq) was applied to characterize the critical cell subset associated with prognosis and elucidate its phenotype in COVID-19.

Results

We revealed a significant increase in CD38+HLA-DR+ T cells in non-survivor COVID-19 patients, establishing them as an independent risk factor for 28-day mortality. The scRNA-seq analysis identified the CD38+HLA-DR+ T cell as a terminally differentiated, Treg-like subset exhibiting both activation and exhaustion characteristics. This subset presented the highest IL-6 and IL-10 mRNA levels among all T-cell subsets. Further functional analysis demonstrated its enhanced major histocompatibility complex class II (MHC-II) cross-signaling and correspondingly enriched cytoskeletal rearrangement processes. In addition, there was dysregulated NAD+ metabolism in CD38+HLA-DR+ T cells via scRNA-seq, accompanied by elevated adenosine and decreased NAD+ levels in serums from COVID-19 patients.

Conclusions

We identified the selective expansion of CD38+HLA-DR+ T cells as a novel prognostic indicator for COVID-19 outcomes. These cells' unique activated-exhausted phenotype, along with their impact on NAD+ metabolism, provides new insights into COVID-19 immunopathogenesis.

Identification of Recurrence-associated Gene Signatures and Machine Learning-based Prediction in IDH-Wildtype Histological Glioblastoma

Glioblastoma (GBM) is a highly aggressive brain tumor with frequent recurrence, yet the molecular mechanisms driving recurrence remain poorly understood. Identifying recurrence-associated genes may improve prognosis and treatment strategies. We applied weighted gene co-expression network analysis (WGCNA) to transcriptomic data from IDH-wildtype histological GBM in the CGGA-693 (n = 190) and CGGA-325 (n = 111) cohorts to identify recurrence-associated genes. These genes were validated using RT-qPCR and single-cell RNA sequencing (scRNA-seq) datasets (GSE174554, GSE131928). Their associations with immune cell composition were analyzed. Finally, we evaluated 113 machine learning algorithms to develop a multi-gene predictive model for GBM recurrence, with model performance assessed using receiver operating characteristic (ROC) curves and confusion matrix analysis. We identified eight recurrence-associated genes (CERS2, EML2, FNBP1, ICOSLG, MFAP3L, NPC1, ROGDI, SLAIN1) that were significantly differentially expressed between primary and recurrent GBM. The scRNA-seq analysis revealed cell-type-specific expression patterns, with eight genes predominantly enriched in oligodendrocytes, malignant GBM subtypes, and immune cells. Immune cell deconvolution showed significant alterations in macrophage polarization and NK cell activation in recurrent GBM. Machine learning analysis demonstrated that random forest (RF) was the most effective model, achieving AUC values of 0.998, 0.968, and 0.998 in the training, CGGA-693 validation, and CGGA-325 validation cohorts, respectively, suggesting high predictive accuracy. This study identifies novel recurrence-associated molecular signatures and establishes a machine learning-based predictive model in IDH-wildtype histological GBM.

Identification of a Novel Mitochondrial-Related Gene Signature for BMSCs in Osteoporosis Combining Single-Cell and Bulk Transcriptome Data

Osteoporosis (OS) is a prevalent skeletal disorder characterized by reduced bone mass and increased fracture risk, often linked to compromised functions of bone mesenchymal stem cells (BMSCs). Mitochondrial dysfunction and aberrant mitophagy are implicated in OS pathogenesis. This study aimed to identify a novel mitochondrial-related gene signature in BMSCs from OS patients by integrating single-cell and bulk transcriptome data. We analyzed single-cell RNA sequencing data from GSE147287 and bulk transcriptome data from GSE35956 to identify differentially expressed mitochondrial-related genes (MRGs) in BMSCs between healthy individuals and OS patients. Key genes were identified using LASSO logistic regression and random forest algorithms, and their differential expression was validated by RT-qPCR, Western blot, and immunofluorescence. Functional assays, including osteogenic differentiation and β-galactosidase staining, were conducted following siRNA-mediated knockdown of DUT. We identified 28 differentially expressed MRGs, with four key genes (DUT, UQCR10, DNAJC4, and MRPL33) further confirmed. Electron microscopy scanning showed damage to BMSCs mitochondria and decreased osteogenic differentiation ability in OS. Silencing DUT significantly impairs the mitochondrial function and osteogenic differentiation ability of BMSCs, indicating its potential role in OS development. This study identifies a mitochondrial gene signature in BMSCs linked to osteoporosis, with DUT emerging as a key regulator. DUT silencing impairs mitochondrial function and osteogenic differentiation, suggesting it as a potential therapeutic target for OS.

Unveiling the power of Treg.Sig: a novel machine-learning derived signature for predicting ICI response in melanoma

Background

Although immune checkpoint inhibitor (ICI) represents a significant breakthrough in cancer immunotherapy, only a few patients benefit from it. Given the critical role of Treg cells in ICI treatment resistance, we explored a Treg-associated signature in melanoma, which had never been elucidated yet.

Methods

A new Treg signature, Treg.Sig, was created using a computational framework guided by machine learning, utilizing transcriptome data from both single-cell RNA-sequencing (scRNA-seq) and bulk RNA-sequencing (bulk-seq). Among the 10 Treg.Sig genes, hub gene STAT1's function was further validated in ICI resistance in melanoma mice receiving anti-PD-1 treatment.

Results

Treg.Sig, based on machine learning, was able to forecast survival outcomes for melanoma across training dataset and external test dataset, and more importantly, showed superior predictive power than 51 previously established signatures. Analysis of the immune profile revealed that groups with high Treg.Sig levels exhibited immune-suppressive conditions, with inverse correlations observed between Treg.Sig and anti-cancer immune responses. Notably, among the 10 Treg.Sig genes, hub gene STAT1 mutation harbored lower response rate in ICIs-treated cohort. Mechanistically, STAT1 impinged on ICI resistances by modulating the phenotypic switch in N2 neutrophil polarization in melanoma mice receiving anti-PD-1 therapy, which affects overall survival.

Conclusion

The study developed a promising Treg.Sig signature that predicts ICI response of melanomas and could be used for selecting patients for immunotherapy. Meanwhile, our study potentially paves the way for overcoming immune resistance by targeting Treg-associated genes.

PTEN: A Novel Diabetes Nephropathy Protective Gene Related to Cellular Senescence

Diabetic nephropathy (DN) is the leading cause of end-stage renal disease (ESRD). The current diagnostic and therapeutic approaches need to be improved. Cellular senescence has been implicated in the pathogenesis of DN, but its precise role remains unclear. This study aimed to identify key pathogenic genes related to cellular senescence in DN and explore their potential as diagnostic biomarkers. Using transcriptomic data from GEO datasets (GSE96804, GSE30122, GSE142025, and GSE104948) and cellular senescence-related genes sourced from the GenAge database, we integrated multiple bioinformatics approaches, including differential expression analysis, weighted gene co-expression network analysis (WGCNA), machine learning and protein-protein interaction (PPI), to identify diagnostic genes. PTEN was identified as a key diagnostic gene. Immune infiltration analysis revealed that PTEN expression is positively correlated with macrophage M2 and dendritic cell resting infiltration and negatively correlated with monocytes and neutrophils. snRNA analysis revealed that PTEN is mainly expressed in mesangial cells. Finally, RT-PCR results revealed that the mRNA expression of PTEN was upregulated in kidneys from db/db mice. Additionally, high-glucose treatment significantly upregulated PTEN expression in cultured human mesangial cells. This study identifies PTEN as a potential diagnostic biomarker for DN which may contribute to early detection and personalized therapeutic strategies.

β-Blockers Influence Oncological Outcomes in Gastric Cancer Patients Treated with Neoadjuvant Chemotherapy Based on the Pathological Subtype: A Retrospective Cohort Study

INTRODUCTION

Preclinical studies suggest that β-blockers (BBs), traditionally used for cardiovascular diseases, may improve cancer outcomes. This study assessed the effect of BB intake on oncological outcomes and response to chemotherapy in gastric cancer (GC) patients and the influence of ß2-adrenergic receptor (ADRB2) expression on local tumor innervation.

METHODS

We retrospectively analyzed the BB intake of 361 patients who underwent surgery with curative intent for GC after neoadjuvant chemotherapy at the University Hospital of Heidelberg. Resection specimens were analyzed and immunohistochemical stainings were performed to evaluate ADRB2 expression and neuronal markers (protein gene product 9 [PGP.9]). Survival rates were estimated using Kaplan-Meier curves, and multivariable Cox regression analysis was performed to control for confounding variables.

RESULTS

In patients with diffuse GC (DGC), BB users demonstrated improved overall survival (OS) and significantly improved recurrence-free survival (RFS) compared with non-users (median OS: not reached vs. 34 months [p = 0.072]; median RFS: not reached vs. 16 months [p = 0.031]). BB intake emerged as an independent prognostic factor in multivariable analysis for this subgroup (OS: hazard ratio [HR] 0.36, 95% confidence interval [CI] 0.17-0.76; RFS: HR 0.41, 95% CI 0.20-0.87). In contrast, BB use was associated with worse OS in intestinal subtype GC (median OS: 30 months vs. not reached; p = 0.044), an effect that diminished after adjusting for cardiovascular risk profiles. Higher ADRB2 expression was associated with less lymph node involvement in the DGC subtype (p = 0.030).

CONCLUSION

This study suggests a differential impact of BB use on GC subtypes and underscores the importance of considering cancer subtypes and patient comorbidities when evaluating the potential benefits of BBs in cancer therapy.

Comprehensive multiomics analysis identifies PYCARD as a key pyroptosis-related gene in osteoarthritis synovial macrophages

Background

Osteoarthritis (OA) is a chronic joint disease that significantly impairs quality of life. Synovitis plays a pivotal role in OA progression, and pyroptosis, a form of programmed cell death associated with innate immune inflammation, may contribute to the pathogenesis of OA synovitis. Nevertheless, the precise role of pyroptosis in OA pathogenesis remains poorly understood.

Methods

We performed an analysis of bulk RNA sequencing data to examine the expression profiles of pyroptosis-related genes in the OA synovium. A LASSO-Cox regression model was employed to identify pivotal genes. Single-cell RNA sequencing data were used to validate the expression of these genes in specific synovial cell clusters. Differentially expressed genes (DEGs) in macrophages with high or low expression levels of core genes were subjected to enrichment analysis. A protein-protein interaction (PPI) network was constructed to identify hub genes, and potential therapeutic compounds were predicted. Consensus clustering analysis was performed to examine the correlations between hub genes and disease status. After identifying PYCARD as the core pyroptosis gene in OA macrophages, we assessed the expression levels of PYCARD in the OA synovium and validated the expression of PYCARD and its related core genes in M1 macrophages.

Results

A total of twenty pyroptosis-related DEGs were identified, and six core genes were selected through LASSO regression. PYCARD was identified as the key pyroptosis gene in macrophages. Furthermore, 57 therapeutic compounds targeting these genes were predicted. Validation confirmed the upregulation of PYCARD in the OA synovium and M1 macrophages.

Conclusion

PYCARD was identified as the core pyroptosis gene in OA macrophages, and 57 potential therapeutic compounds were identified. This study offers valuable insights into potential treatment targets for OA.

Genetic dissection of internode length confers improvement for ideal plant architecture in maize

The optimal plant architecture, characterized by short stature, helps mitigate lodging, enables high-density planting, and facilitates mechanized harvesting. Internode length (IL), a crucial component of plant height in maize, plays a significant role in these processes. However, the genetic mechanisms underlying internode elongation remain poorly understood. In this study, we conducted a genome-wide association study to dissect the genetic architecture of IL in maize. The lengths of five internodes above and below the ear (referred as IL-related traits) were collected across multiple environments, revealing substantial variation. A total of 108 quantitative trait loci (QTL) were associated with 11 IL-related traits, with 17 QTL co-detected by different traits. Notably, three QTL have been selected in maize breeding progress. Three hundred and three genes associated with IL were found to operate through plant hormone signal transduction, receptor activity, and carbon metabolism pathways, influencing internode elongation. ZmIL1, which encodes alcohol dehydrogenase, exhibited a high expression level in internodes during the vegetative stage and has been selected in Chinese modern maize breeding. Additionally, ZmIL2 and ZmIL3 emerged as other crucial regulators of IL. Importantly, ZmIL1 has potential applications in maize varieties in the Huang-Huai-Hai region. This study represents the first comprehensive report on the genetic architecture of nearly all ILs in maize, providing profound insights into internode elongation mechanisms and genetic resources. These findings hold significant implications for dwarf breeding programs aimed at optimizing plant architecture for enhancing agronomic performance.

Comprehensive Pan-Cancer Analysis of RAC1 Decoding the Impact on Cancer Prognosis and B cell immune Regulation

RAC1, a member of the Rho family GTPases, has been implicated in various cancers, yet its pan-cancer landscape and role in the tumor immune microenvironment remain underexplored. This study presents a comprehensive analysis of RAC1 across 33 cancer types, revealing its high expression in a broad range of cancers and its association with poor prognosis. RAC1 expression correlates with genomic alterations, including CNVs, TMB, and MSI. RAC1 knockdown reduces cell proliferation and metastasis in breast and lung cancer cells, suggesting its oncogenic potential. Notably, RAC1 is negatively correlated with B cell infiltration, indicating its role in regulating the immune microenvironment. Functional enrichment analysis showed that high RAC1 expression is linked to lower enrichment in B cell activation and immune response pathways. Single-cell transcriptome analysis identified RAC1 expression primarily in epithelial cells, associated with tumor progression, and spatial transcriptome analysis showed a mutually exclusive co-localization between B cell infiltration regions and RAC1-expressing epithelial cells. Based on RAC1 expression and B cell interaction, a prognostic signature was established to predict prognosis at the pan-cancer level.

Dissecting the cell microenvironment of ovarian endometrioma through single-cell RNA sequencing

Ovarian endometrioma (OE), also known as "chocolate cysts," is a cystic mass that develops in the ovaries due to endometriosis and is a common gynecological condition characterized by the growth of endometrial tissue outside the uterus, leading to symptoms such as dysmenorrhea, pelvic pain, and infertility. However, the precise molecular and cellular mechanisms driving this pathophysiology remain largely unknown, posing challenges for diagnosis and treatment. Here, we employed integrated single-cell transcriptomic profiling of over 52,000 individual cells from endometrial tissues of OE patients and healthy donors and identified twelve major cell populations. We identified notable alterations in cell type-specific proportions and molecular signatures associated with OE. Notably, the activation of IGFBP5+ macrophages with pro-inflammatory properties, NK cell exhaustion, and aberrant proliferation of IQCG+ and KLF2+ epithelium are key features and may be the potential mechanisms underlying the pathogenesis of OE. Collectively, our data contribute to a better understanding of OE at the single cell level and may pave the way for the development of novel therapeutic strategies.

Single-cell RNA sequencing identifies the expression of hemoglobin in chondrocyte cell subpopulations in osteoarthritis

In recent years, chondrocytes have been found to contain hemoglobin, which might be an alternative strategy for adapting to the hypoxic environment, while the potential mechanisms of that is still unclear. Here, we report the expression characteristics and potential associated pathways of hemoglobin in chondrocytes using single-cell RNA sequencing (scRNA-seq). We downloaded data of normal people and patients with osteoarthritis (OA) from the Gene Expression Omnibus (GEO) database and cells are unbiased clustered based on gene expression pattern. We determined the expression levels of hemoglobin in various chondrocyte subpopulations. Meanwhile, we further explored the difference in the enriched signaling pathways and the cell-cell interaction in chondrocytes of the hemoglobin high-expression and low-expression groups. Specifically, we found that SPP1 was closely associated with the expression of hemoglobin in OA progression. Our findings provide new insights into the distribution characteristics of hemoglobin in chondrocytes and provide potential clues to the underlying role of hemoglobin in OA and the mechanisms related to that, providing potential new ideas for the treatment of OA.

Single-Cell RNA Sequencing, Cell Communication, and Network Pharmacology Reveal the Potential Mechanism of Senecio scandens Buch.-Ham in Hepatocellular Carcinoma Inhibition

BACKGROUND

Hepatocellular carcinoma (HCC), a prevalent form of primary liver malignancy, arises from liver-specific hepatocytes. Senecio scandens Buch.-Ham(Climbing senecio) is a bitter-tasting plant of the Compositae family with anti-tumor properties. This study aims to identify the molecular targets and pathways through which Climbing senecio regulates HCC.

METHODS

Active ingredients of Climbing senecio were collected from four online databases and mapped to relevant target databases to obtain predicted targets. After recognizing the key pathways through which Climbing senecio acts in HCC. Gene expression data from GSE54238 Underwent differential expression and weighted gene correlation network analyses to identify HCC-related genes. The "Climbing senecio-Hepatocellular Carcinoma Targets" network was constructed using Cytoscape 3.10.1 software, followed by topology analysis to identify core genes. The expression and distribution of key targets were evaluated, and the differential expression of each key target between normal and diseased samples was calculated. Moreover, single-cell data from the Gene Expression Omnibus (GSE202642) were used to assess the distribution of Climbing senecio's bioactive targets within major HCC clusters. An intersection analysis of these clusters with pharmacological targets and HCC-related genes identified Climbing senecio's primary targets for this disease. Cell communication, receiver operating characteristic (ROC)analysis, survival analysis, immune filtration analysis, and molecular docking studies were conducted for detailed characterization.

RESULTS

Eleven components of Climbing senecio were identified, along with 520 relevant targets, 300 differentially expressed genes, and 3765 co-expression module genes associated with HCC. AKR1B1, CA2, FOS, CXCL2, SRC, ABCC1, and PLIN1 were identified within the intersection of HCC-related genes and Climbing senecio targets. TGFβ, IL-1, VEGF, and CXCL were identified as significant factors in the onset and progression of HCC. These findings underscore the anti-HCC potential and mode of action of Climbing senecio, providing insights into multi-targeted treatment approaches for HCC.

CONCLUSIONS

This study revealed that Climbing senecio may target multiple pathways and genes in the process of regulating HCC and exert potential drug effects through a multi-target mechanism, which provides a new idea for the treatment of HCC. However, the research is predicated on network database analysis and bioinformatics, offering insights into HCC therapeutic potential while emphasizing the need for further validation.

Integrated analysis of single-cell RNA-seq and bulk RNA-seq revealed key genes for bone metastasis and chemoresistance in prostate cancer

BACKGROUND

Prostate cancer (PCa) is a serious malignancy. The main causes of PCa aggravation and death are unexplained resistance to chemotherapy and bone metastases.

OBJECTIVE

This study aimed to investigate the molecular mechanisms associated with the dynamic processes of progression, bone metastasis, and chemoresistance in PCa.

METHODS

Through comprehensive analysis of single-cell RNA sequencing (scRNA-seq) data, Gene Expression Omnibus (GEO) tumor progression and metastasis-related genes were identified. These genes were subjected to lasso regression modeling using the Cancer Genome Atlas (TCGA) database. Tartrate-resistant acid phosphatase (TRAP) staining and real-time quantitative PCR (RT-qPCR) were used to evaluate osteoclast differentiation. CellMiner was used to confirm the effect of LDHA on chemoresistance. Finally, the relationship between LDHA and chemoresistance was verified using doxorubicin-resistant PCa cell lines.

RESULTS

7928 genes were identified as genes related to tumor progression and metastasis. Of these, 7 genes were found to be associated with PCa prognosis. The scRNA-seq and TCGA data showed that the expression of LDHA was higher in tumors and associated with poor prognosis of PCa. In addition, upregulation of LDHA in PCa cells induces osteoclast differentiation. Additionally, high LDHA expression was associated with resistance to Epirubicin, Elliptinium acetate, and doxorubicin. Cellular experiments demonstrated that LDHA knockdown inhibited doxorubicin resistance in PCa cells.

CONCLUSIONS

LDHA may play a potential contributory role in PCa initiation and development, bone metastasis, and chemoresistance. LDHA is a key target for the treatment of PCa.

Inferring the genetic relationships between unsupervised deep learning-derived imaging phenotypes and glioblastoma through multi-omics approaches

This study aimed to investigate the genetic association between glioblastoma (GBM) and unsupervised deep learning-derived imaging phenotypes (UDIPs). We employed a combination of genome-wide association study (GWAS) data, single-nucleus RNA sequencing (snRNA-seq), and scPagwas (pathway-based polygenic regression framework) methods to explore the genetic links between UDIPs and GBM. Two-sample Mendelian randomization analyses were conducted to identify causal relationships between UDIPs and GBM. Colocalization analysis was performed to validate genetic associations, while scPagwas analysis was used to evaluate the relevance of key UDIPs to GBM at the cellular level. Among 512 UDIPs tested, 23 were found to have significant causal associations with GBM. Notably, UDIPs such as T1-33 (OR = 1.007, 95% CI = 1.001 to 1.012, P = .022), T1-34 (OR = 1.012, 95% CI = 1.001-1.023, P = .028), and T1-96 (OR = 1.009, 95% CI = 1.001-1.019, P = .046) were found to have a genetic association with GBM. Furthermore, T1-34 and T1-96 were significantly associated with GBM recurrence, with P-values < .0001 and P < .001, respectively. In addition, scPagwas analysis revealed that T1-33, T1-34, and T1-96 are distinctively linked to different GBM subtypes, with T1-33 showing strong associations with the neural progenitor-like subtype (NPC2), T1-34 with mesenchymal (MES2) and neural progenitor (NPC1) cells, and T1-96 with the NPC2 subtype. T1-33, T1-34, and T1-96 hold significant potential for predicting tumor recurrence and aiding in the development of personalized GBM treatment strategies.

Identification and analysis of a cell communication prognostic signature for oral squamous cell carcinoma at bulk and single-cell levels

Head and neck squamous cancer (HNSC) is a heterogenous malignant tumour disease with poor prognosis and has become the current major public health concern worldwide. Oral squamous cell carcinoma (OSCC) is the majority of HNSC. It is still in lack of comprehensive tumour immune microenvironment analysis and prognostic model development for OSCC's clinic practice. Single-cell sequencing data analysis was conducted to identify immune cell subtypes and illustrate cell-cell interaction status in OSCC via R package 'Seurat', 'Harmony', 'elldex' and 'CellChat'. Base on the bulk sequencing data, WGCNA analysis was employed to identify the CD8+ T cell related gene module. XGBoost was used to construct the gene prognostic model for OSCC. Validation sets and immunotherapy data sets were analysed to further evaluate the model's effectiveness and immunotherapy responsiveness predicting potential. siRNA was used to down regulate FCRL4 expression. Real-time PCR and Western blot were used to validate target gene expression. The effects of FCRL4 on OSCC cells were detected by wound healing, Trans well and clone formation assays. Communication between epithelial cells and tissue stem cells may be the potential key regulators for OSCC progression. By integrating single-cell sequencing data analysis and bulk sequencing data analysis, we constructed a novel immune-related gene prognostic model. The model can effectively predict the prognosis and immunotherapy responsiveness of OSCC patients. In addition, the effects of FCRL4 on OSCC cells were validated. We comprehensively interpreted the immune microenvironment pattern of OSCC based on the single-cell sequencing data and bulk sequencing data analysis. A robust immune feature-based prognostic model was developed for the precise treatment and prognosis evaluation of OSCC.

Unveiling osmoregulation and immunological adaptations in Eleutheronema tetradactylum gills through high-throughput single-cell transcriptome sequencing

The fourfinger threadfin fish (Eleutheronema tetradactylum) is an economically significant species renowned for its ability to adapt to varying salinity environments, with gills serving as their primary organs for osmoregulation and immune defense. Previous studies focused on tissue and morphological levels, whereas ignored the cellular heterogeneity and the crucial gene information related to core cell subsets within E. tetradactylum gills. In this study, we utilized high-throughput single-cell RNA sequencing (scRNA-seq) to analyze the gills of E. tetradactylum, characterizing 16 distinct cell types and identifying unique gene markers and enriched functions associated within each cell type. Additionally, we subdivided ionocyte cells into four distinct subpopulations for the first time in E. tetradactylum gills. By employing weighted gene co-expression network analysis (WGCNA), we further investigated the cellular heterogeneity and specific response mechanisms to salinity fluctuant. Our findings revealed the intricate osmoregulation and immune functions of gill cells, highlighting their crucial roles in maintaining homeostasis and adapting to fluctuating salinity levels. This comprehensive cell-type atlas provides valuable insights into the species adaptive strategies, contributing to the conservation and management of this commercially significant fish as well as other euryhaline species.

Prognostic and therapeutic potential of gene profiles related to tertiary lymphoid structures in colorectal cancer

The role of tertiary lymphoid structures (TLS) in oncology is gaining interest, particularly in colorectal carcinoma, yet a thorough analysis remains elusive. This study pioneered a novel TLS quantification system for prognostic and therapeutic response prediction in colorectal carcinoma, alongside a comprehensive depiction of the TLS landscape. Utilizing single-cell sequencing, we established a TLS score within the Tumor Immune Microenvironment (TIME). Analysis of tertiary lymphoid structure-related genes (TLSRGs) in 1,184 patients with colon adenocarcinoma/rectum adenocarcinoma (COADREAD) from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases led to the identification of two distinct molecular subtypes. Differentially expressed genes (DEGs) further segregated these patients into gene subtypes. A TLS score was formulated using gene set variation analysis (GSVA) and its efficacy in predicting immunotherapy outcomes was validated in two independent cohorts. High-scoring patients exhibited a 'hot' immune phenotype, correlating with enhanced immunotherapy efficacy. Key genes in our model, including C5AR1, APOE, CYR1P1, and SPP1, were implicated in COADREAD cell proliferation, invasion, and PD-L1 expression. These insights offer a novel approach to colorectal carcinoma treatment, emphasizing TLS targeting as a potential anti-tumor strategy.

Exploring macrophage heterogeneity in IgA nephropathy: Mechanisms of renal impairment and current therapeutic targets

The lack of understanding of the mechanism of renal injury in IgA nephropathy (IgAN) hinders the development of personalized treatment plans and targeted therapies. Improved insight into the cause of renal dysfunction in IgAN is necessary to enhance the effectiveness of strategies for slowing the progression of the disease. This study examined single cell RNA sequencing (scRNA seq) and bulk-RNA seq data and found that the gene expression of renal intrinsic cells (RIC) was significantly changed in patients with renal impairment, with a primary focus on energy metabolism. We discovered a clear metabolic reprogramming of RIC during renal function impairment (RF) using the 'scMetabolism' package, which manifested as a weakening of oxidative phosphorylation, alterations in fatty acid metabolism, and changes in glycolysis. Cellular communication analysis revealed that communication between macrophages (Ma) and RIC became more active and impacted cell function through the ligand-receptor-transcription factor (L-R-TF) axis in patients with RF. Our studies showed a notable upsurge in the expression of gene CLU and the infiltration of CLU+ Ma in patients with RF. CLU is a multifunctional protein, extensively involved in processes such as cell apoptosis and immune responses. Data obtained from the Nephroseq V5 database and multiplex immunohistochemistry (mIHC) were used to validate the findings, which were found to be robustly correlated with estimated glomerular filtration rate (eGFR) of the IgAN patients, as demonstrated by linear regression (LR). This study provides new insights into the cellular and molecular changes that occur in IgAN during renal impairment, revealing that elevated expression of CLU and CLU+ Ma percolation are common features in patients with RF. These findings offer potential targets and strategies for personalized management and targeted therapy of IgAN.

Multi-modal transcriptomic analysis reveals metabolic dysregulation and immune responses in chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD), a progressive inflammatory condition of the airways, emerges from the complex interplay between genetic predisposition and environmental factors. Notably, its incidence is on the rise, particularly among the elderly demographic. Current research increasingly highlights cellular senescence as a key driver in chronic lung pathologies. Despite this, the detailed mechanisms linking COPD with senescent genomic alterations remain elusive. To address this gap, there is a pressing need for comprehensive bioinformatics methodologies that can elucidate the molecular intricacies of this link. This approach is crucial for advancing our understanding of COPD and its association with cellular aging processes. Utilizing a spectrum of advanced bioinformatics techniques, this research delved into the potential mechanisms linking COPD with aging-related genes, identifying four key genes (EP300, MTOR, NFE2L1, TXN) through machine learning and weighted gene co-expression network analysis (WGCNA) analyses. Subsequently, a precise diagnostic model leveraging an artificial neural network was developed. The study further employed single-cell analysis and molecular docking to investigate senescence-related cell types in COPD tissues, particularly focusing on the interactions between COPD and NFE2L1, thereby enhancing the understanding of COPD's molecular underpinnings. Leveraging artificial neural networks, we developed a robust classification model centered on four genes-EP300, MTOR, NFE2L1, TXN-exhibiting significant predictive capability for COPD and offering novel avenues for its early diagnosis. Furthermore, employing various single-cell analysis techniques, the study intricately unraveled the characteristics of senescence-related cell types in COPD tissues, enriching our understanding of the disease's cellular landscape. This research anticipates offering novel biomarkers and therapeutic targets for early COPD intervention, potentially alleviating the disease's impact on individuals and healthcare systems, and contributing to a reduction in global COPD-related mortality. These findings carry significant clinical and public health ramifications, bolstering the foundation for future research and clinical strategies in managing and understanding COPD.

The complex association between the immune system and the skeletal system in osteoporosis: A study of single-cell RNA sequencing

OBJECTIVE

Osteoporosis (OP) is a disease characterized by decreased bone mass, deteriorated microstructure, and increased fragility and fracture risk. The diagnosis and prevention of OP and its complications have become major public health challenges. Therefore, exploring the complex ecological connections between the immune and skeletal systems may provide new insights for clinical prevention and treatment strategies.

METHODS

First, we performed single-cell RNA sequencing on human lumbar lamina tissue and conducted clustering and subgroup analysis of quality-controlled single-cell transcriptome data to identify target subgroups. Subsequently, enrichment analysis and pseudotime analysis were performed. In addition, we conducted in-depth studies on the gene regulatory network between different cell subgroups and the communication between bone immune cells.

RESULTS

In this study, we identified several cell subgroups that may be involved in the progression of OP. For example, the CCL4+ NKT and CXCL8+ neutrophils subgroups promote OP progression by mediating an inflammatory environment that disrupts bone homeostasis, and the MNDA+ Mac subgroup promotes osteoclast differentiation to promote OP. Moreover, the TNFAIP6+ Obl, NR4A2+ B and HMGN2+ erythrocyte subgroups promoted the balance of bone metabolism and suppressed OP. In the cell communication network, Obl closely interacts with immune cell subgroups through the CXCR4-CXCL12, CTGF-ITGB2, and TNFSF14-TNFRSF14 axes.

CONCLUSION

Our research revealed specific subgroups and intercellular interactions that play crucial roles in the pathogenesis of OP, providing potential new insights for more precise therapeutic interventions for OP.

Decoding the past and future of distant metastasis from papillary thyroid carcinoma: a bibliometric analysis from 2004 to 2023

Background

Papillary thyroid carcinoma (PTC) is the most common thyroid malignancy, and its distant metastasis (PTCDM), although uncommon, seriously affects the survival rate and quality of life of patients. With the rapid development of science and technology, research in the field of PTCDM has accumulated rapidly, presenting a complex knowledge structure and development trend.

Methods

In this study, bibliometric analysis was used to collect 479 PTCDM-related papers published between 2004 and 2023 through the Web of Science (WoS) Core Collection (WoSCC) database. Keyword clustering analysis was performed using VOSviewer and citespace, as well as dual-map overlay analysis, to explore knowledge flows and interconnections between different disciplines.

Results

The analysis indicated that China, the United States, and South Korea were the most active countries in conducting research activities. Italy's research was notable due to its higher average citation count. Keyword analysis revealed that "cancer," "papillary thyroid carcinoma," and "metastasis" were the most frequently used terms in these studies. The journal co-citation analysis underscored the dominant roles of molecular biology, immunology, and clinical medicine, as well as the growing importance of computer science in research.

Conclusion

This study identified the main trends and scientific structure of PTCDM research, highlighting the importance of interdisciplinary approaches and the crucial role of top academic journals in promoting high-quality research. The findings not only provide valuable information for basic and clinical research on thyroid cancer but also offer guidance for future research directions.

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.

Unraveling the relationship between anoikis-related genes and cancer-associated fibroblasts in liver hepatocellular carcinoma

This study intended to determine the molecular subtypes of liver hepatocellular carcinoma (LIHC) on the strength of anoikis-related genes (ARGs) and to assess their prognostic value and prospective relationship with immune cell infiltration and cancer-associated fibroblasts (CAFs). Univariate Cox regression analysis yielded 66 prognosis-related ARGs and classified LIHC into two distinct subtypes, with subtype A demonstrating overexpression of most prognosis-related ARGs and a significant survival disadvantage. Furthermore, a reliable prediction model was developed using ARGs to evaluate the risk of LIHC patients. This model served as an independent prognostic indicator and a quantitative tool for clinical prognostic prediction. Additionally, subtype-specific differences in immune cell infiltration were observed, and the risk score was potentially linked to immune-related characteristics. Moreover, the study identified a significant association between CAF score and LIHC prognosis, with a low CAF score indicating a favorable patient prognosis. In conclusion, this study reveals the molecular mechanisms underlying the development and progression of LIHC and identifies potential therapeutic targets for the disease.

Novel Insights into Post-Myocardial Infarction Cardiac Remodeling through Algorithmic Detection of Cell-Type Composition Shifts

Background

Recent advances in single cell sequencing have led to an increased focus on the role of cell-type composition in phenotypic presentation and disease progression. Cell-type composition research in the heart is challenging due to large, frequently multinucleated cardiomyocytes that preclude most single cell approaches from obtaining accurate measurements of cell composition. Our in silico studies reveal that ignoring cell type composition when calculating differentially expressed genes (DEGs) can have significant consequences. For example, a relatively small change in cell abundance of only 10% can result in over 25% of DEGs being false positives.

Methods

We have implemented an algorithmic approach that uses snRNAseq datasets as a reference to accurately calculate cell type compositions from bulk RNAseq datasets through robust data cleaning, gene selection, and multi-sample cross-subject and cross-cell-type deconvolution. We applied our approach to cardiomyocyte-specific α1A adrenergic receptor (CM-α1A-AR) knockout mice. 8-12 week-old mice (either WT or CM-α1A-KO) were subjected to permanent left coronary artery (LCA) ligation or sham surgery (n=4 per group). Transcriptomes from the infarct border zones were collected 3 days later and analyzed using our algorithm to determine cell-type abundances, corrected differential expression calculations using DESeq2, and validated these findings using RNAscope.

Results

Uncorrected DEGs for the CM-α1A-KO X LCA interaction term featured many cell-type specific genes such as Timp4 (fibroblasts) and Aplnr (cardiomyocytes) and overall GO enrichment for terms pertaining to cardiomyocyte differentiation (P=3.1E-4). Using our algorithm, we observe a striking loss of cardiomyocytes and gain in fibroblasts in the α1A-KO + LCA mice that was not recapitulated in WT + LCA animals, although we did observe a similar increase in macrophage abundance in both conditions. This recapitulates prior results that showed a much more severe heart failure phenotype in CM-α1A-KO + LCA mice. Following correction for cell-type, our DEGs now highlight a novel set of genes enriched for GO terms such as cardiac contraction (P=3.7E-5) and actin filament organization (P=6.3E-5).

Conclusions

Our algorithm identifies and corrects for cell-type abundance in bulk RNAseq datasets opening new avenues for research on novel genes and pathways as well as an improved understanding of the role of cardiac cell types in cardiovascular disease.

Multi-omics evaluation of the prognostic value and immune signature of FCN1 in pan-cancer and its relationship with proliferation and apoptosis in acute myeloid leukemia

Background

The FCN1 gene encodes the ficolin-1 protein, implicated in the pathogenesis of various diseases, though its precise role in tumorigenesis remains elusive. This study aims to elucidate the prognostic significance, immune signature, and treatment response associated with FCN1 across diverse cancer types.

Methods

Employing multi-omics data, we conducted a comprehensive assessment, encompassing tissue-specific and single-cell-specific expression disparities, pan-cancer expression patterns, epigenetic modifications affecting FCN1 expression, and the immune microenvironment. Our investigation primarily focused on the clinical prognostic attributes, immune profiles, potential molecular mechanisms, and candidate therapeutic agents concerning FCN1 and acute myeloid leukemia (AML). Additionally, in vitro experiments were performed to scrutinize the impact of FCN1 knockdown on cell proliferation, apoptosis, and cell cycle dynamics within the AML cell line U937 and NB4.

Results

FCN1 expression exhibits widespread dysregulation across various cancers. Through both univariate and multivariate Cox regression analyses, FCN1 has been identified as an independent prognostic indicator for AML. Immunological investigations elucidate FCN1's involvement in modulating inflammatory responses within the tumor microenvironment and its correlation with treatment efficacy. Remarkably, the deletion of FCN1 influences the proliferation, apoptosis, and cell cycle dynamics of U937 cells and NB4 cells.

Conclusion

These findings underscore FCN1 as a promising pan-cancer biomarker indicative of macrophage infiltration, intimately linked with the tumor microenvironment and treatment responsiveness, and pivotal for cellular mechanisms within AML cell lines.

Cuproptosis-related gene DLAT is a biomarker of the prognosis and immune microenvironment of gastric cancer and affects the invasion and migration of cells

OBJECTIVE

Cuproptosis is a novel cell death dependent on mitochondrial respiration and regulated by copper. This study aimed to investigate the cuproptosis-related gene DLAT potential value in gastric cancer (GC).

METHODS

Bioinformatics was used to analyze DLAT expression. DLAT expression in GC cell lines was detected using qRT-PCR. Cell proliferation ability was assessed using CCK8 and cell cycle assay. Cell migration and invasion were assessed using wound healing and transwell assay. A prognostic assessment was performed through survival and Cox regression analysis. DLAT protein expression was analyzed through HPA immunohistochemistry. Biological functions and processes were analyzed through GO and KEGG enrichment analysis and PPI. Correlation with immune cell infiltration and immune checkpoint genes was analyzed for DLAT.

RESULTS

DLAT expression was upregulated in GC tissues and cells and correlated with shorter survival for patients. Age, gender, histological typing, lymph node metastasis, and distant metastasis were identified as independent prognostic factors affecting OS in GC. DLAT protein was upregulated in GC. The biological functions and pathways enriched in DLAT were mainly linked to mitochondrial respiration and the TCA cycle. The expression of DLAT was found to be positively correlated with the infiltration of Th and Th2 immune cells and only positively correlated with the expression of the BTN2A1 immune checkpoint gene.

CONCLUSION

DLAT has the potential to serve as a prognostic assessment factor in GC. The expression of DLAT was correlated with immune infiltration and tumor immune escape, providing a new target for immunotherapy of GC.

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.

Prognostic and therapeutic insights into colorectal carcinoma through immunogenic cell death gene profiling

While the significance of immunogenic cell death (ICD) in oncology is acknowledged, its specific impact on colorectal carcinoma remains underexplored. In this study, we delved into the role of ICD in colorectal carcinoma, a topic not yet comprehensively explored. A novel ICD quantification system was developed to forecast patient outcomes and the effectiveness of immunotherapy. Utilizing single-cell sequencing, we constructed an ICD score within the tumor immune microenvironment (TIME) and examined immunogenic cell death related genes (ICDRGs). Using data from TCGA and GEO, we discovered two separate molecular subcategories within 1,184 patients diagnosed with colon adenocarcinoma/rectum adenocarcinoma (COADREAD). The ICD score was established by principal component analysis (PCA), which classified patients into groups with low and high ICD scores. Further validation in three independent cohorts confirmed the model's accuracy in predicting immunotherapy success. Patients with higher ICD scores exhibited a "hot" immune phenotype and showed increased responsiveness to immunotherapy. Key genes in the model, such as AKAP12, CALB2, CYR61, and MEIS2, were found to enhance COADREAD cell proliferation, invasion, and PD-L1 expression. These insights offered a new avenue for anti-tumor strategies by targeting ICD, marking advances in colorectal carcinoma treatment.

Prognositic value of anoikis and tumor immune microenvironment-related gene in the treatment of osteosarcoma

OBJECTIVES

Osteosarcoma is a highly aggressive primary malignant bone tumor commonly seen in children and adolescents, with a poor prognosis. Anchorage-dependent cell death (anoikis) has been proven to be indispensable in tumor metastasis, regulating the migration and adhesion of tumor cells at the primary site. However, as a type of programmed cell death, anoikis is rarely studied in osteosarcoma, especially in the tumor immune microenvironment. This study aims to clarify prognostic value of anoikis and tumor immune microenvironment-related gene in the treatment of osteosarcoma.

METHODS

Anoikis-related genes (ANRGs) were obtained from GeneCards. Clinical information and ANRGs expression profiles of osteosarcoma patients were sourced from the therapeutically applicable research to generate effective therapies and Gene Expression Omnibus (GEO) databases. ANRGs highly associated with tumor immune microenvironment were identified by the estimate package and the weighted gene coexpression network analysis (WGCNA) algorithm. Machine learning algorithms were performed to construct long-term survival predictive strategy, each sample was divided into high-risk and low-risk subgroups, which was further verified in the GEO cohort. Finally, based on single-cell RNA-seq from the GEO database, analysis was done on the function of signature genes in the osteosarcoma tumor microenvironment.

RESULTS

A total of 51 hub ANRGs closely associated with the tumor microenvironment were identified, from which 3 genes (MERTK, BNIP3, S100A8) were selected to construct the prognostic model. Significant differences in immune cell activation and immune-related signaling pathways were observed between the high-risk and low-risk groups based on tumor microenvironment analysis (all P<0.05). Additionally, characteristic genes within the osteosarcoma microenvironment were identified in regulation of intercellular crosstalk through the GAS6-MERTK signaling pathway.

CONCLUSIONS

The prognostic model based on ANRGs and tumor microenvironment demonstrate good predictive power and provide more personalized treatment options for patients with osteosarcoma.

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.

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.

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.