Integrative analyses of bulk and single-cell RNA-seq reveals the correlation between SPP1+ macrophages and resistance to neoadjuvant chemoimmunotherapy in esophageal squamous cell carcinoma.

Neoadjuvant chemoimmunotherapy (NACI) has significant implications for the treatment of esophageal cancer. However, its clinical efficacy varies considerably among patients, necessitating further investigation into the underlying mechanisms. The rapid advancement of single-cell RNA sequencing (scRNA-seq) technology facilitates the analysis of patient heterogeneity at the cellular level, particularly regarding treatment outcomes. In this study, we first analyzed scRNA-seq data of esophageal squamous cell carcinoma (ESCC) following NACI, obtained from the Gene Expression Omnibus (GEO) database. After performing dimensionality reduction, clustering, and annotation on the scRNA-seq data, we employed CellChat to investigate differences in cell-cell communication among samples from distinct efficacy groups. The results indicated that macrophages in the non-responder exhibited stronger cell communication intensity compared to those in responders, with SPP1 and GALECTIN signals showing the most significant differences between the two groups. This finding underscores the crucial role of macrophages in the efficacy of NACI. Subsequently, reclustering of macrophages revealed that Mac-SPP1 may be primarily responsible for treatment resistance, while Mac-C1QC appears to promote T cell activation. Finally, we conducted transcriptome sequencing on ESCC tissues obtained from 32 patients who underwent surgery following NACI. Utilizing CIBERSORT, CIBERSORTx, and WGCNA, we analyzed the heterogeneity of tumor microenvironment among different efficacy groups and validated the correlation between SPP1+ macrophages and resistance to NACI in ESCC using publicly available transcriptome sequencing datasets. These findings suggest that SPP1+ macrophages may represent a key factor contributing to resistance against NACI in ESCC.

Mechanisms of Berberine in anti-pancreatic ductal adenocarcinoma revealed by integrated multi-omics profiling.

This study integrates pharmacology databases with bulk RNA-seq and scRNA-seq to reveal the latent anti-PDAC capacities of BBR. Target genes of BBR were sifted through TargetNet, CTD, SwissTargetPrediction, and Binding Database. Based on the GSE183795 dataset, DEG analysis, GSEA, and WGCNA were sequentially run to build a disease network. Through sub-network filtration acquired PDAC-related hub genes. A PPI network was established using the shared genes. Degree algorithm from cytoHubba screened the key cluster in the network. Analysis of differential mRNA expression and ROC curves gauged the diagnostic performance of clustered genes. CYBERSORT uncovered the potential role of the key cluster on PDAC immunomodulation. ScRNA-seq analysis evaluated the distribution and expression profile of the key cluster at the single-cell level, assessing enrichment within annotated cell subpopulations to delineate the target distribution of BBR in PDAC. We identified 425 drug target genes and 771 disease target genes, using 57 intersecting genes to construct the PPI network. CytoHubba anchored the top 10 highest contributing genes to be the key cluster. mRNA expression levels and ROC curves confirmed that these genes showed good robustness for PDAC. CYBERSORT revealed that the key cluster influenced immune pathways predominantly associated with Macrophages M0, CD8 T cells, and naïve B cells. ScRNA-seq analysis clarified that BBR mainly acted on epithelial cells and macrophages in PDAC tissues. BBR potentially targets CDK1, CCNB1, CTNNB1, CDK2, TOP2A, MCM2, RUNX2, MYC, PLK1, and AURKA to exert therapeutic effects on PDAC. The mechanisms of action appear to significantly involve macrophage polarization-related immunological responses.

Integrative transcriptomic analysis reveals Cd72 as a novel pro-inflammatory factor in microglia following experimental ischemic stroke.

Ischemic stroke remains a leading cause of global mortality and disability, with neuroinflammation playing a critical role in determining patient outcomes. Microglia, the brain's resident immune cells, can both exacerbate neuroinflammation and neuronal damage by releasing neurotoxic mediators and engaging in excessive phagocytosis, while also aiding recovery through the production of anti-inflammatory cytokines and debris clearance. However, the molecular mechanisms governing microglial activation and polarization after ischemic stroke are not well elucidated. In this study, we combined integrative transcriptomic analyses with experimental validation in a murine model of middle cerebral artery occlusion/reperfusion (MCAO/R) to explore microglial heterogeneity and identify key regulatory factors in ischemic stroke. Bioinformatics analysis identified Cd72 as a novel pro-inflammatory modulator within ischemia-associated microglial phenotypes. We observed significant upregulation of Cd72 in microglia following MCAO/R, and selective knockdown of Cd72 using CX3CR1Cre/ERT2 mice and Cre recombinase-dependent adeno-associated virus reduced MCAO/R-induced infarct volume, neuronal apoptosis, and neurological deficits. Furthermore, Cd72 expression in microglia was positively correlated with pro-inflammatory pathways and cytokines, including TNF-α, IL-1ß, and IL-6. Knockdown of Cd72 significantly reduced these pro-inflammatory factors, highlighting its potential as a therapeutic target for mitigating inflammation in ischemic stroke. In conclusion, this study identifies Cd72 as a critical pro-inflammatory regulator in microglia following ischemic stroke, with its knockdown effectively reducing neuroinflammation and associated brain injury, highlighting Cd72 as a promising therapeutic target.

Epithelial cell-related prognostic risk model in breast cancer based on single-cell and bulk RNA sequencing.

Objective: This study aims to construct an epithelial cell-related prognostic risk model for breast cancer (BRCA) and explore its significance. Methods: GSE42568, GSE10780, GSE245601, and TCGA-BRCA datasets were sourced from public databases. Epithelial cell-related differentially expressed genes were identified using single-cell data analysis. Venn diagrams determined the intersecting genes between epithelial cell-related and BRCA-related genes. Batch Kaplan-Meier (K-M) survival analysis identified core intersecting genes for BRCA overall survival. Consensus clustering, enrichment, LASSO, and COX regression analyses were performed on the core intersecting genes, and then a prognostic risk model was constructed. The diagnostic and prognostic effectiveness of the risk model was subsequently evaluated and immune infiltration analysis was conducted. Finally, qRT-PCR was used to verify the expression of genes in the risk model. Results: There were 374 intersecting genes between epithelial cell-related and BRCA-related genes, among which 51 core intersecting genes were associated with BRCA prognosis. Consensus clustering categorized TCGA-BRCA into C1 and C2, with shared regulation of the estrogen signaling pathway. Three genes (DIRC3, SLC6A2, TUBA3D) were independent predictors of BRCA prognosis, forming the basis for a risk model. Except for exhibiting satisfactory diagnostic efficacy, the risk score elevation correlated with poor prognosis, elevated matrix, immune, and ESTIMATE scores, and negative correlation with microsatellite instability. The in vitro results confirmed the differential expression levels of DIRC3, SLC6A2, and TUBA3D. Conclusion: The prognostic risk model associated with epithelial cells demonstrates effective diagnostic performance in BRCA, serving as an independent prognostic factor for BRCA patients. Additionally, it exhibits a correlation with immune scores.

Comprehensive analysis of scRNA-Seq and bulk RNA-Seq reveals ubiquitin promotes pulmonary fibrosis in chronic pulmonary diseases.

It is estimated that there are 544.9 million people suffering from chronic respiratory diseases in the world, which is the third largest chronic disease. Although there are various clinical treatment methods, there is no specific drug for chronic pulmonary diseases, including chronic obstructive pulmonary disease (COPD), interstitial lung disease (ILD) and idiopathic pulmonary fibrosis (IPF). Therefore, it is urgent to clarify the pathological mechanism and medication development. Single-cell transcriptome data of human and mouse from GEO database were integrated by "Harmony" algorithm. The data was standardized and normalized by using "Seurat" package, and "SingleR" algorithm was used for cell grouping annotation. The "Findmarker" function is used to find differentially expressed genes (DEGs), which were enriched and analyzed by using "clusterProfiler", and a protein interaction network was constructed for DEGs, and four algorithms are used to find the hub genes. The expression of hub genes were analyzed in independent human and mouse single-cell transcriptome data. Bulk RNA data were used to integrate by the "SVA" function, verify the expression levels of hub genes and build a diagnostic model. The L1000FWD platform was used to screen potential drugs. Through exploring the similarities and differences by integrated single-cell atlas, we found that the lung parenchymal cells showed abnormal oxidative stress, cell matrix adhesion and ubiquitination in COPD, corona virus disease 2019 (COVID-19), ILD and IPF. Meanwhile, the lung resident immune cells showed abnormal Toll-like receptor signals, interferon signals and ubiquitination. However, unlike acute pneumonia (COVID-19), chronic pulmonary disease shows enhanced ubiquitination. This phenomenon was confirmed in independent external human single-cell atlas, but unfortunately, it was not confirmed in mouse single-cell atlas of bleomycin-induced pulmonary fibrosis model and influenza virus-infected mouse model, which means that the model needs to be optimized. In addition, the bulk RNA-Seq data of COVID-19, ILD and IPF was integrated, and we found that the immune infiltration of lung tissue was enhanced, consistent with the single-cell level, UBA52, UBB and UBC were low expressed in COVID-19 and high expressed in ILD, and had a strong correlation with the expression of cell matrix adhesion genes. UBA52 and UBB have good diagnostic efficacy, and salermide and SSR-69071 can be used as their candidate drugs. Our study found that the disorder of protein ubiquitination in chronic pulmonary diseases is an important cause of pathological phenotype of pulmonary fibrosis by integrating scRNA-Seq and bulk RNA-Seq, which provides a new horizons for clinicopathology, diagnosis and treatment.

Identifying epithelial-mesenchymal transition-related genes as prognostic biomarkers and therapeutic targets of hepatocellular carcinoma by integrated analysis of single-cell and bulk-RNA sequencing data.

Background: Hepatocellular carcinoma (HCC) remains one of the most lethal cancers globally. Patients with advanced HCC tend to have poor prognoses and shortened survival. Recently, data from bulk RNA sequencing have been employed to discover prognostic markers for various cancers. However, they fall short in precisely identifying core molecular and cellular activities within tumor cells. In our present study, we combined bulk-RNA sequencing (bulk RNA-seq) data with single-cell RNA sequencing (scRNA-seq) to develop a prognostic model for HCC. The goal of our research is to uncover new biomarkers and enhance the accuracy of HCC prognosis prediction. Methods: Integrating single-cell sequencing data with transcriptomics were used to identify epithelial-mesenchymal transition (EMT)-related genes (ERGs) implicated in HCC progression and their clinical significance was elucidated. Utilizing marker genes derived from core cells and ERGs, we constructed a prognostic model using univariate Cox analysis, exploring a multitude of algorithmic combinations, and further refining it through multivariate Cox analysis. Additionally, we conducted an in-depth investigation into the disparities in clinicopathological features, immune microenvironment composition, immune checkpoint expression, and chemotherapeutic drug sensitivity profiles between high- and low-risk patient cohorts. Results: We developed a prognostic model predicated on the expression profiles of eight signature genes, namely HSP90AA1, CIRBP, CCR7, S100A9, ADAM17, ENG, PGF, and INPP4B, aiming at predicting overall survival (OS) outcomes. Notably, patients classified with high-risk scores exhibited a propensity towards diminished OS rates, heightened frequencies of stage III-IV disease, increased tumor mutational burden (TMB), augmented immune cell infiltration, and diminished responsiveness to immunotherapeutic interventions. Conclusions: This study presented a novel prognostic model for predicting the survival of HCC patients by integrating scRNA-seq and bulk RNA-seq data. The risk score emerges as a promising independent prognostic factor, showing a correlation with the immune microenvironment and clinicopathological features. It provided new clinical tools for predicting prognosis and aided future research into the pathogenesis of HCC.

Unraveling the role of ADAMs in clinical heterogeneity and the immune microenvironment of hepatocellular carcinoma: insights from single-cell, spatial transcriptomics, and bulk RNA sequencing.

Background: Hepatocellular carcinoma (HCC) is a prevalent and heterogeneous tumor with limited treatment options and unfavorable prognosis. The crucial role of a disintegrin and metalloprotease (ADAM) gene family in the tumor microenvironment of HCC remains unclear. Methods: This study employed a novel multi-omics integration strategy to investigate the potential roles of ADAM family signals in HCC. A series of single-cell and spatial omics algorithms were utilized to uncover the molecular characteristics of ADAM family genes within HCC. The GSVA package was utilized to compute the scores for ADAM family signals, subsequently stratified into three categories: high, medium, and low ADAM signal levels through unsupervised clustering. Furthermore, we developed and rigorously validated an innovative and robust clinical prognosis assessment model by employing 99 mainstream machine learning algorithms in conjunction with co-expression feature spectra of ADAM family genes. To validate our findings, we conducted PCR and IHC experiments to confirm differential expression patterns within the ADAM family genes. Results: Gene signals from the ADAM family were notably abundant in endothelial cells, liver cells, and monocyte macrophages. Single-cell sequencing and spatial transcriptomics analyses have both revealed the molecular heterogeneity of the ADAM gene family, further emphasizing its significant impact on the development and progression of HCC. In HCC tissues, the expression levels of ADAM9, ADAM10, ADAM15, and ADAM17 were markedly elevated. Elevated ADAM family signal scores were linked to adverse clinical outcomes and disruptions in the immune microenvironment and metabolic reprogramming. An ADAM prognosis signal, developed through the utilization of 99 machine learning algorithms, could accurately forecast the survival duration of HCC, achieving an AUC value of approximately 0.9. Conclusions: This study represented the inaugural report on the deleterious impact and prognostic significance of ADAM family signals within the tumor microenvironment of HCC.

A Study of Disease Prognosis in Lung Adenocarcinoma Using Single-Cell Decomposition and Immune Signature Analysis.

Background: The development of tumors is a highly complex process that entails numerous interactions and intricate relationships between the host immune system and cancer cells. It has been demonstrated in studies that the treatment response of patients can be correlated with the tumor microenvironment (TME). Consequently, the examination of diverse immune profiles within the TME can facilitate the elucidation of tumor development and the development of advantageous models for diagnoses and prognoses. Methods: In this study, we utilized a single-cell decomposition method to analyze the relationships between cell proportions and immune signatures in lung adenocarcinoma (LUAD) patients. Results: Our findings indicate that specific immune cell populations and immune signatures are significantly associated with patient prognosis. By identifying poor prognosis signatures (PPS), we reveal the critical role of immune profiles and cellular composition in disease outcomes, emphasizing their diagnostic potential for predicting patient prognosis. Conclusions: This study highlights the importance of immune signatures and cellular composition, which may serve as valuable biomarkers for disease prognosis in LUAD patients.

Scanning sample-specific miRNA regulation from bulk and single-cell RNA-sequencing data.

BACKGROUND: RNA-sequencing technology provides an effective tool for understanding miRNA regulation in complex human diseases, including cancers. A large number of computational methods have been developed to make use of bulk and single-cell RNA-sequencing data to identify miRNA regulations at the resolution of multiple samples (i.e. group of cells or tissues). However, due to the heterogeneity of individual samples, there is a strong need to infer miRNA regulation specific to individual samples to uncover miRNA regulation at the single-sample resolution level. RESULTS: Here, we develop a framework, Scan, for scanning sample-specific miRNA regulation. Since a single network inference method or strategy cannot perform well for all types of new data, Scan incorporates 27 network inference methods and two strategies to infer tissue-specific or cell-specific miRNA regulation from bulk or single-cell RNA-sequencing data. Results on bulk and single-cell RNA-sequencing data demonstrate the effectiveness of Scan in inferring sample-specific miRNA regulation. Moreover, we have found that incorporating the prior information of miRNA targets can generally improve the accuracy of miRNA target prediction. In addition, Scan can contribute to construct cell/tissue correlation networks and recover aggregate miRNA regulatory networks. Finally, the comparison results have shown that the performance of network inference methods is likely to be data-specific, and selecting optimal network inference methods is required for more accurate prediction of miRNA targets. CONCLUSIONS: Scan provides a useful method to help infer sample-specific miRNA regulation for new data, benchmark new network inference methods and deepen the understanding of miRNA regulation at the resolution of individual samples.

Identifying potential therapeutic targets in lung adenocarcinoma: a multi-omics approach integrating bulk and single-cell RNA sequencing with Mendelian randomization.

Our research aimed to identify new therapeutic targets for Lung adenocarcinoma (LUAD), a major subtype of non-small cell lung cancer known for its low 5-year survival rate of 22%. By employing a comprehensive methodological approach, we analyzed bulk RNA sequencing data from 513 LUAD and 59 non-tumorous tissues, identifying 2,688 differentially expressed genes. Using Mendelian randomization (MR), we identified 74 genes with strong evidence for a causal effect on risk of LUAD. Survival analysis on these genes revealed significant differences in survival rates for 13 of them. Our pathway enrichment analysis highlighted their roles in immune response and cell communication, deepening our understanding. We also utilized single-cell RNA sequencing (scRNA-seq) to uncover cell type-specific gene expression patterns within LUAD, emphasizing the tumor microenvironment's heterogeneity. Pseudotime analysis further assisted in assessing the heterogeneity of tumor cell populations. Additionally, protein-protein interaction (PPI) network analysis was conducted to evaluate the potential druggability of these identified genes. The culmination of our efforts led to the identification of five genes (tier 1) with the most compelling evidence, including SECISBP2L, PRCD, SMAD9, C2orf91, and HSD17B13, and eight genes (tier 2) with convincing evidence for their potential as therapeutic targets.

APOE ε4-associated downregulation of the IL-7/IL-7R pathway in effector memory T cells: Implications for Alzheimer's disease.

INTRODUCTION: The apolipoprotein E (APOE) ε4 allele exerts a significant influence on peripheral inflammation and neuroinflammation, yet the underlying mechanisms remain elusive. METHODS: The present study enrolled 54 patients diagnosed with late-onset Alzheimer's disease (AD; including 28 APOE ε4 carriers and 26 non-carriers). Plasma inflammatory cytokine concentration was assessed, alongside bulk RNA sequencing (RNA-seq) and single-cell RNA sequencing (scRNA-seq) analysis of peripheral blood mononuclear cells (PBMCs). RESULTS: Plasma tumor necrosis factor α, interferon γ, and interleukin (IL)-33 levels increased in the APOE ε4 carriers but IL-7 expression notably decreased. A negative correlation was observed between plasma IL-7 level and the hippocampal atrophy degree. Additionally, the expression of IL-7R and CD28 also decreased in PBMCs of APOE ε4 carriers. ScRNA-seq data results indicated that the changes were mainly related to the CD4+ Tem (effector memory) and CD8+ Tem T cells. DISCUSSION: These findings shed light on the role of the downregulated IL-7/IL-7R pathway associated with the APOE ε4 allele in modulating neuroinflammation and hippocampal atrophy. HIGHLIGHTS: The apolipoprotein E (APOE) ε4 allele decreases plasma interleukin (IL)-7 and aggravates hippocampal atrophy in Alzheimer's disease. Plasma IL-7 level is negatively associated with the degree of hippocampal atrophy. The expression of IL-7R signaling decreased in peripheral blood mononuclear cells of APOE ε4 carriers Dysregulation of the IL-7/IL-7R signal pathways enriches T cells.

Prognostic model of fibroblasts in idiopathic pulmonary fibrosis by combined bulk and single-cell RNA-sequencing.

Background: Fibroblasts play an important role in the development of idiopathic pulmonary fibrosis (IPF). Methods: We employed single-cell RNA-sequencing data obtained from the Gene Expression Omnibus database to perform cell clustering and annotation analyses. We then performed secondary clustering of fibroblasts and conducted functional enrichment and cell trajectory analyses of the two newly defined fibroblast subtypes. Bulk RNA-sequencing data were used to perform consensus clustering and weighted gene co-expression network analysis. We constructed a fibroblast-related prognostic model using least absolute shrinkage, selection operator regression, and Cox regression analysis. The prognostic model was validated using a validation dataset. Immune infiltration and functional enrichment analyses were conducted for patients in the high- and low-risk IPF groups. Results: We characterized two fibroblast subtypes that are active in IPF (F3+ and ROBO2+). Using fibroblast-related genes, we identified five genes (CXCL14, TM4SF1, CYTL1, SOD3, and MMP10) for the prognostic model. The area under the curve values of our prognostic model were 0.852, 0.859, and 0.844 at one, two, and three years in the training set, and 0.837, 0.758, and 0.821 at one, two, and three years in the validation set, respectively. Conclusion: This study annotates and characterizes different subtypes of fibroblasts in IPF.

Disease heterogeneity and molecular classification of inflammatory palmoplantar diseases.

BACKGROUND: Palmoplantar pustulosis (PPP) is an inflammatory disease characterized by relapsing eruptions of neutrophil-filled, sterile pustules on the palms and soles that can be clinically difficult to differentiate from non-pustular palmoplantar psoriasis (palmPP) and dyshidrotic palmoplantar eczema (DPE). OBJECTIVE: We sought to identify overlapping and unique PPP, palmPP, and DPE drivers to provide molecular insight into their pathogenesis. METHODS: We performed bulk RNA sequencing of lesional PPP (n = 33), palmPP (n = 5), and DPE (n = 28) samples, as well as 5 healthy nonacral and 10 healthy acral skin samples. RESULTS: Acral skin showed a unique immune environment, likely contributing to a unique niche for palmoplantar inflammatory diseases. Compared to healthy acral skin, PPP, palmPP, and DPE displayed a broad overlapping transcriptomic signature characterized by shared upregulation of proinflammatory cytokines (TNF, IL-36), chemokines, and T-cell-associated genes, along with unique disease features of each disease state, including enriched neutrophil processes in PPP and to a lesser extent in palmPP, and lipid antigen processing in DPE. Strikingly, unsupervised clustering and trajectory analyses demonstrated divergent inflammatory profiles within the 3 disease states. These identified putative key upstream immunologic switches, including eicosanoids, interferon responses, and neutrophil degranulation, contributing to disease heterogeneity. CONCLUSION: A molecular overlap exists between different inflammatory palmoplantar diseases that supersedes clinical and histologic assessment. This highlights the heterogeneity within each condition, suggesting limitations of current disease classification and the need to move toward a molecular classification of inflammatory acral diseases.

DVsc: An Automated Framework for Efficiently Detecting Viral Infection from Single-cell Transcriptomics Data.

Single-cell RNA sequencing (scRNA-seq) has emerged as a valuable tool for studying cellular heterogeneity in various fields, particularly in virological research. By studying the viral and cellular transcriptomes, the dynamics of viral infection can be investigated at a single-cell resolution. However, limited studies have been conducted to investigate whether RNA transcripts from clinical samples contain substantial amounts of viral RNAs, and a specific computational framework for efficiently detecting viral reads based on scRNA-seq data has not been developed. Hence, we introduce DVsc, an open-source framework for precise quantitative analysis of viral infection from single-cell transcriptomics data. When applied to approximately 200 diverse clinical samples that were infected by more than 10 different viruses, DVsc demonstrated high accuracy in systematically detecting viral infection across a wide array of cell types. This innovative bioinformatics pipeline could be crucial for addressing the potential effects of surreptitiously invading viruses on certain illnesses, as well as for designing novel medicines to target viruses in specific host cell subsets and evaluating the efficacy of treatment. DVsc supports the FASTQ format as an input and is compatible with multiple single-cell sequencing platforms. Moreover, it could also be applied to sequences from bulk RNA sequencing data. DVsc is available at http://62.234.32.33:5000/DVsc.

Improving human cardiac organoid design using transcriptomics.

Cardiovascular disease (CVD) is the leading cause of death worldwide. To this end, human cardiac organoids (hCOs) have been developed for improved organotypic CVD modeling over conventional in vivo animal models. Utilizing human cells, hCOs hold great promise to bridge key gaps in CVD research pertaining to human-specific conditions. hCOs are multicellular 3D models which resemble heart structure and function. Varying hCOs fabrication techniques leads to functional and phenotypic differences. To investigate heterogeneity across hCO platforms, we performed a transcriptomic analysis utilizing bulk RNA-sequencing from four previously published unique hCO studies. We further compared selected hCOs to 2D and 3D hiPSC-derived cardiomyocytes (hiPSC-CMs), as well as fetal and adult human myocardium bulk RNA-sequencing samples. Upon investigation utilizing Principal Component Analysis, K-means clustering analysis of key genes, and further downstream analyses such as Gene Set Enrichment (GSEA), Gene Set Variation (GSVA), and GO term enrichment, we found that hCO fabrication method influences maturity and cellular heterogeneity across models. Thus, we propose that adjustment of fabrication method will result in an hCO with a defined maturity and transcriptomic profile to facilitate its specified applications, in turn maximizing its modeling potential.

Characterizing mitochondrial features in osteoarthritis through integrative multi-omics and machine learning analysis.

Purpose: Osteoarthritis (OA) stands as the most prevalent joint disorder. Mitochondrial dysfunction has been linked to the pathogenesis of OA. The main goal of this study is to uncover the pivotal role of mitochondria in the mechanisms driving OA development. Materials and methods: We acquired seven bulk RNA-seq datasets from the Gene Expression Omnibus (GEO) database and examined the expression levels of differentially expressed genes related to mitochondria in OA. We utilized single-sample gene set enrichment analysis (ssGSEA), gene set enrichment analysis (GSEA), and weighted gene co-expression network analysis (WGCNA) analyses to explore the functional mechanisms associated with these genes. Seven machine learning algorithms were utilized to identify hub mitochondria-related genes and develop a predictive model. Further analyses included pathway enrichment, immune infiltration, gene-disease relationships, and mRNA-miRNA network construction based on these hub mitochondria-related genes. genome-wide association studies (GWAS) analysis was performed using the Gene Atlas database. GSEA, gene set variation analysis (GSVA), protein pathway analysis, and WGCNA were employed to investigate relevant pathways in subtypes. The Harmonizome database was employed to analyze the expression of hub mitochondria-related genes across various human tissues. Single-cell data analysis was conducted to examine patterns of gene expression distribution and pseudo-temporal changes. Additionally, The real-time polymerase chain reaction (RT-PCR) was used to validate the expression of these hub mitochondria-related genes. Results: In OA, the mitochondria-related pathway was significantly activated. Nine hub mitochondria-related genes (SIRT4, DNAJC15, NFS1, FKBP8, SLC25A37, CARS2, MTHFD2, ETFDH, and PDK4) were identified. They constructed predictive models with good ability to predict OA. These genes are primarily associated with macrophages. Unsupervised consensus clustering identified two mitochondria-associated isoforms that are primarily associated with metabolism. Single-cell analysis showed that they were all expressed in single cells and varied with cell differentiation. RT-PCR showed that they were all significantly expressed in OA. Conclusion: SIRT4, DNAJC15, NFS1, FKBP8, SLC25A37, CARS2, MTHFD2, ETFDH, and PDK4 are potential mitochondrial target genes for studying OA. The classification of mitochondria-associated isoforms could help to personalize treatment for OA patients.

Integrated analysis of single-cell RNA-seq, bulk RNA-seq, Mendelian randomization, and eQTL reveals T cell-related nomogram model and subtype classification in rheumatoid arthritis.

Objective: Rheumatoid arthritis (RA) is a systemic disease that attacks the joints and causes a heavy economic burden on humans worldwide. T cells regulate RA progression and are considered crucial targets for therapy. Therefore, we aimed to integrate multiple datasets to explore the mechanisms of RA. Moreover, we established a T cell-related diagnostic model to provide a new method for RA immunotherapy. Methods: scRNA-seq and bulk-seq datasets for RA were obtained from the Gene Expression Omnibus (GEO) database. Various methods were used to analyze and characterize the T cell heterogeneity of RA. Using Mendelian randomization (MR) and expression quantitative trait loci (eQTL), we screened for potential pathogenic T cell marker genes in RA. Subsequently, we selected an optimal machine learning approach by comparing the nine types of machine learning in predicting RA to identify T cell-related diagnostic features to construct a nomogram model. Patients with RA were divided into different T cell-related clusters using the consensus clustering method. Finally, we performed immune cell infiltration and clinical correlation analyses of T cell-related diagnostic features. Results: By analyzing the scRNA-seq dataset, we obtained 10,211 cells that were annotated into 7 different subtypes based on specific marker genes. By integrating the eQTL from blood and RA GWAS, combined with XGB machine learning, we identified a total of 8 T cell-related diagnostic features (MIER1, PPP1CB, ICOS, GADD45A, CD3D, SLFN5, PIP4K2A, and IL6ST). Consensus clustering analysis showed that RA could be classified into two different T-cell patterns (Cluster 1 and Cluster 2), with Cluster 2 having a higher T-cell score than Cluster 1. The two clusters involved different pathways and had different immune cell infiltration states. There was no difference in age or sex between the two different T cell patterns. In addition, ICOS and IL6ST were negatively correlated with age in RA patients. Conclusion: Our findings elucidate the heterogeneity of T cells in RA and the communication role of these cells in an RA immune microenvironment. The construction of T cell-related diagnostic models provides a resource for guiding RA immunotherapeutic strategies.

Integrative analysis of bulk and single-cell RNA sequencing reveals the gene expression profile and the critical signaling pathways of type II CPAM.

BACKGROUD: Type II congenital pulmonary airway malformation (CPAM) is a rare pulmonary microcystic developmental malformation. Surgical excision is the primary treatment for CPAM, although maternal steroids and betamethasone have proven effective in reducing microcystic CPAM. Disturbed intercellular communication may contribute to the development of CPAM. This study aims to investigate the expression profile and analyze intercellular communication networks to identify genes potentially associated with type II CPAM pathogenesis and therapeutic targets. METHODS: RNA sequencing (RNA-seq) was performed on samples extracted from both the cystic area and the adjacent normal tissue post-surgery in CPAM patients. Iterative weighted gene correlation network analysis (iWGCNA) was used to identify genes specifically expressed in type II CPAM. Single-cell RNA-seq (scRNA-seq) was integrated to unveil the heterogeneity in cell populations and analyze the communication and interaction within epithelial cell sub-populations. RESULTS: A total of 2,618 differentially expressed genes were identified, primarily enriched in cilium-related biological process and inflammatory response process. Key genes such as EDN1, GPR17, FPR2, and CHRM1, involved in the G protein-coupled receptor (GPCR) signaling pathway and playing roles in cell differentiation, apoptosis, calcium homeostasis, and the immune response, were highlighted based on the protein-protein interaction network. Type II CPAM-associated modules, including ciliary function-related genes, were identified using iWGCNA. By integrating scRNA-seq data, AGR3 (related to calcium homeostasis) and SLC11A1 (immune related) were identified as the only two differently expressed genes in epithelial cells of CPAM. Cell communication analysis revealed that alveolar type 1 (AT1) and alveolar type 2 (AT2) cells were the predominant communication cells for outgoing and incoming signals in epithelial cells. The ligands and receptors between epithelial cell subtypes included COLLAGEN genes enriched in PI3K-AKT singaling and involved in epithelial to mesenchymal transition. CONCLUSIONS: In summary, by integrating bulk RNA-seq data of type II CPAM with scRNA-seq data, the gene expression profile and critical signaling pathways such as GPCR signaling and PI3K-AKT signaling pathways were revealed. Abnormally expressed genes in these pathways may disrupt epithelial-mesenchymal transition and contribute to the development of CPAM. Given the effectiveness of prenatal treatments of microcystic CPAM using maternal steroids and maternal betamethasone administration, targeting the genes and signaling pathways involved in the development of CPAM presents a promising therapeutic strategy.

Comprehensive Analysis of Immune Cell Infiltration and M2-Like Macrophage Biomarker Expression Patterns in Atrial Fibrillation.

Background: Macrophages play a crucial role in the progression of AF, closely linked to atrial inflammation and myocardial fibrosis. However, the functions and molecular mechanisms of different phenotypic macrophages in AF are not well understood. This study aims to analyze the infiltration characteristics of atrial immune cells in AF patients and further explore the role and molecular expression patterns of M2 macrophage-related genes in AF. Methods: This study integrates single-cell and large-scale sequencing data to analyze immune cell infiltration and molecular characterization of the LAA in patients with AF, using SR as a control group. CIBERSORT assesses immune cell types in LAA tissues; WGCNA identifies signature genes; cell clustering analyzes cell types and subpopulations; cell communication explores macrophage interactions; hdWGCNA identifies M2 macrophage gene modules in AF. AF biomarkers are identified using LASSO and Random Forest, validated with ROC curves and RT-qPCR. Potential molecular mechanisms are inferred through TF-miRNA-mRNA networks and single-gene enrichment analyses. Results: Myeloid cell subsets varied considerably between the AF and SR groups, with a significant increase in M2 macrophages in the AF group. Signals of inflammation and matrix remodeling were observed in AF. M2 macrophage-related genes IGF1, PDK4, RAB13, and TMEM176B were identified as AF biomarkers, with RAB13 and TMEM176B being novel markers. A TF-miRNA-mRNA network was constructed using target genes, which are enriched in the PPAR signaling pathway and fatty acid metabolism. Conclusion: Over infiltration of M2 macrophages may be an important factor in the progression of AF. The M2 macrophage-related genes IGF1, RAB13, TMEM176B and PDK4 may regulate the progression of AF through the PPAR signaling pathway and fatty acid metabolism.

Genomic and transcriptomic profiling of inflammatory breast cancer reveals distinct molecular characteristics to non-inflammatory breast cancers.

PURPOSE: Inflammatory breast cancer (IBC), a rare and highly aggressive form of breast cancer, accounts for 10% of breast cancer-related deaths. Previous omics studies of IBC have focused solely on one of genomics or transcriptomics and did not discover common differences that could distinguish IBC from non-IBC. METHODS: Seventeen IBC patients and five non-IBC patients as well as additional thirty-three Asian breast cancer samples from TCGA-BRCA were included for the study. We performed whole-exon sequencing (WES) to investigate different somatic genomic alterations, copy number variants, and large structural variants between IBC and non-IBC. Bulk RNA sequencing (RNA-seq) was performed to examine the differentially expressed genes, pathway enrichment, and gene fusions. WES and RNA-seq data were further investigated in combination to discover genes that were dysregulated in both genomics and transcriptomics. RESULTS: Copy number variation analysis identified 10 cytobands that showed higher frequency in IBC. Structural variation analysis showed more frequent deletions in IBC. Pathway enrichment and immune infiltration analysis indicated increased immune activation in IBC samples. Gene fusions including CTSC-RAB38 were found to be more common in IBC. We demonstrated more commonly dysregulated RAS pathway in IBC according to both WES and RNA-seq. Inhibitors targeting RAS signaling and its downstream pathways were predicted to possess promising effects in IBC treatment. CONCLUSION: We discovered differences unique in Asian women that could potentially explain IBC etiology and presented RAS signaling pathway as a potential therapeutic target in IBC treatment.