Bulk RNA-seq Data Research Articles

GBMPurity: A Machine Learning Tool for Estimating Glioblastoma Tumour Purity from Bulk RNA-seq Data.

Glioblastoma (GBM) presents a significant clinical challenge due to its aggressive nature and extensive heterogeneity. Tumour purity, the proportion of malignant cells within a tumour, is an important covariate for understanding the disease, having direct clinical relevance or obscuring signal of the malignant portion in molecular analyses of bulk samples. However, current methods for estimating tumour purity are non-specific and technically demanding. Therefore, we aimed to build a reliable and accessible purity estimator for GBM. We developed GBMPurity, a deep-learning model specifically designed to estimate the purity of IDH-wildtype primary GBM from bulk RNA-seq data. The model was trained using simulated pseudobulk tumours of known purity from labelled single-cell data acquired from the GBmap resource. The performance of GBMPurity was evaluated and compared to several existing tools using independent datasets. GBMPurity outperformed existing tools, achieving a mean absolute error of 0.15 and a concordance correlation coefficient of 0.88 on validation datasets. We demonstrate the utility of GBMPurity through inference on bulk RNA-seq samples and observe reduced purity of the Proneural molecular subtype relative to the Classical, attributed to the increased presence of healthy brain cells. GBMPurity provides a reliable and accessible tool for estimating tumour purity from bulk RNA-seq data, enhancing the interpretation of bulk RNA-seq data and offering valuable insights into GBM biology. To facilitate the use of this model by the wider research community, GBMPurity is available as a web-based tool at: https://gbmdeconvoluter.leeds.ac.uk/.

Pre-immunotherapy alters stereotactic ablative radiotherapy-induced systemic T cell responses in early-stage NSCLC

BackgroundStereotactic ablative radiotherapy (SABR) is thought to activate T cell responses in patients with cancer, leading to its combination with immunotherapy and chemotherapy for treatment of non-small-cell lung cancer (NSCLC). Here, we aimed to provide a high-resolution transcriptomic profiling of the systemic T cell response following SABR, with or without preceding immunotherapy/chemotherapy.MethodsWe conducted single-cell RNA and T cell receptor (TCR) sequencing of T cells from peripheral blood of seven patients with early-stage NSCLC taken pre- and post-SABR without or with prior immunotherapy and chemotherapy (icSABR). Other flow cytometry, single-cell RNA-seq data and bulk RNA-seq data were used to validate the results.ResultsWe uncovered distinct T cell response patterns induced by these treatments: while terminal effector CD8+ T cells showed increased cytotoxic and inhibitory scores, and upregulated immune-activated pathways post-SABR, the reverse responses occurred post-icSABR. Furthermore, the proportion of large T cell clones increased and single clone decreased post-SABR, while the opposite was seen post-icSABR. Of note, both SABR and icSABR largely changed TCR clonotypes, which were mainly large clones post-SABR but single clone post-icSABR, and predominantly from terminal effector CD8+ T cells and T helper cells, respectively.ConclusionsThese findings reveal a complex interplay between SABR and immunotherapy, with potentially valuable implications for treatment strategies involving SABR and immunotherapy to induce systemic T cell responses for tumor eradication in patients with NSCLC.

Network analyses of brain tumor multiomic data reveal pharmacological opportunities to alter cell state transitions

Glioblastoma Multiforme (GBM) remains a particularly difficult cancer to treat, and survival outcomes remain poor. In addition to the lack of dedicated drug discovery programs for GBM, extensive intratumor heterogeneity and epigenetic plasticity related to cell-state transitions are major roadblocks to successful drug therapy in GBM. To study these phenomenon, publicly available snRNAseq and bulk RNAseq data from patient samples were used to categorize cells from patients into four cell states (i.e., phenotypes), namely: (i) neural progenitor-like (NPC-like), (ii) oligodendrocyte progenitor-like (OPC-like), (iii) astrocyte-like (AC-like), and (iv) mesenchymal-like (MES-like). Patients were subsequently grouped into subpopulations based on which cell-state was the most dominant in their respective tumor. By incorporating phosphoproteomic measurements from the same patients, a protein–protein interaction network (PPIN) was constructed for each cell state. These four-cell state PPINs were pooled to form a single Boolean network that was used for in silico protein knockout simulations to investigate mechanisms that either promote or prevent cell state transitions. Simulation results were input into a boosted tree machine learning model which predicted the cell states or phenotypes of GBM patients from an independent public data source, the Glioma Longitudinal Analysis (GLASS) Consortium. Combining the simulation results and the machine learning predictions, we generated hypotheses for clinically relevant causal mechanisms of cell state transitions. For example, the transcription factor TFAP2A can be seen to promote a transition from the NPC-like to the MES-like state. Such protein nodes and the associated signaling pathways provide potential drug targets that can be further tested in vitro and support cell state-directed (CSD) therapy.

COMPREHENSIVE CHARACTERIZATION OF CYTOKINES IN PATIENTS UNDER EXTRACORPOREAL MEMBRANE OXYGENATION: EVIDENCE FROM INTEGRATED BULK AND SINGLE-CELL RNA SEQUENCING DATA USING MULTIPLE MACHINE LEARNING APPROACHES.

Background : Extracorporeal membrane oxygenation (ECMO) is an effective technique for providing short-term mechanical support to the heart, lungs, or both. During ECMO treatment, the inflammatory response, particularly involving cytokines, plays a crucial role in pathophysiology. However, the potential effects of cytokines on patients receiving ECMO are not comprehensively understood. Methods : We acquired three ECMO support datasets, namely two bulk and one single-cell RNA sequencing (RNA-seq), from the Gene Expression Omnibus (GEO) combined with hospital cohorts to investigate the expression pattern and potential biological processes of cytokine-related genes (CRGs) in patients under ECMO. Subsequently, machine learning approaches, including support vector machine (SVM), random forest (RF), modified Lasso penalized regression, extreme gradient boosting (XGBoost), and artificial neural network (ANN), were applied to identify hub CRGs, thus developing a prediction model called CRG classifier. The predictive and prognostic performance of the model was comprehensively evaluated in GEO and hospital cohorts. Finally, we mechanistically analyzed the relationship between hub cytokines, immune cells, and pivotal molecular pathways. Results : Analyzing bulk and single-cell RNA-seq data revealed that most CRGs were significantly differentially expressed; the enrichment scores of cytokine and cytokine-cytokine receptor (CCR) interaction were significantly higher during ECMO. Based on multiple machine learning algorithms, nine key CRGs (CCL2, CCL4, IFNG, IL1R2, IL20RB, IL31RA, IL4, IL7, and IL7R) were used to develop the CRG classifier. The CRG classifier exhibited excellent prognostic values (AUC > 0.85), serving as an independent risk factor. It performed better in predicting mortality and yielded a larger net benefit than other clinical features in GEO and hospital cohorts. Additionally, IL1R2, CCL4, and IL7R were predominantly expressed in monocytes, NK cells, and T cells, respectively. Their expression was significantly positively correlated with the relative abundance of corresponding immune cells. Gene set variation analysis (GSVA) revealed that para-inflammation, complement and coagulation cascades, and IL6/JAK/STAT3 signaling were significantly enriched in the subgroup that died after receiving ECMO. Spearman correlation analyses and Mantel tests revealed that the expression of hub cytokines (IL1R2, CCL4, and IL7R) and pivotal molecular pathways scores (complement and coagulation cascades, IL6/JAK/STAT3 signaling, and para-inflammation) were closely related. Conclusion : A predictive model (CRG classifier) comprising nine CRGs based on multiple machine learning algorithms was constructed, potentially assisting clinicians in guiding individualized ECMO treatment. Additionally, elucidating the underlying mechanistic pathways of cytokines during ECMO will provide new insights into its treatment.

Epithelial cell diversity and immune remodeling in bladder cancer progression: insights from single-cell transcriptomics.

The progression of bladder cancer (BC) from non-muscle-invasive bladder cancer (NMIBC) to muscle-invasive bladder cancer (MIBC) significantly increases disease severity. Although the tumor microenvironment (TME) plays a pivotal role in this process, the heterogeneity of tumor cells and TME components remains underexplored. We characterized the transcriptomes of single cells from 11 BC samples, including 4 NMIBC, 4 MIBC, and 3 adjacent normal tissues. Bulk RNA-seq data were used to validate the clinical features of characteristic cells, and protein levels of these cells were further confirmed through immunohistochemistry (IHC) and multiplex immunofluorescence. Bladder cancer progression was associated with distinct transcriptomic features in the TME. Tumor cells in MIBC displayed enhanced glycolytic activity and downregulation of chemokines and MHC-II molecules, reducing immune cell recruitment and facilitating immune evasion. This highlights glycolysis as a potential therapeutic target for disrupting tumor progression. We identified a T cell exhaustion pathway from naive CD8 + T cells (CD8 + TCF7) to terminally exhausted CD8 + STMN1 cells, with progressively declining immune surveillance. Targeting intermediate exhaustion states may restore T cell function and improve anti-tumor immunity. Macrophages polarized toward a pro-tumorigenic phenotype, while VEGFA + mast cells promoted angiogenesis in early-stage BC, suggesting their role as potential targets for therapeutic intervention in NMIBC. Furthermore, conventional dendritic cells (DCs) transformed into LAMP3 + DCs, contributing to an immunosuppressive microenvironment and enabling immune evasion. This study reveals dynamic changes in the TME during BC progression, including enhanced glycolysis, T cell exhaustion, and immune cell remodeling, which contribute to immune evasion and tumor progression. These findings identify critical pathways and cell populations as potential therapeutic targets, offering new strategies to improve treatment outcomes in BC patients.

Heterogeneity analysis and prognostic model construction of HPV negative oral squamous cell carcinoma T cells using ScRNA-seq and bulk-RNA analysis

BackgroundT cells are involved in every stage of tumor development and significantly influence the tumor microenvironment (TME). Our objective was to assess T-cell marker gene expression profiles, develop a predictive risk model for human papilloma virus (HPV)-negative oral squamous cell carcinoma (OSCC) utilizing these genes, and examine the correlation between the risk score and the immunotherapy response.MethodsWe acquired scRNA-seq data for HPV-negative OSCC from the GEO datasets. We performed cell‒cell communication, trajectory, and pathway enrichment analyses of T-cell-associated genes. In addition, we constructed and validated a T-cell-associated gene prognostic model for HPV-negative OSCC patients using TCGA and GEO data and assessed the immune infiltration status of HPV-negative OSCC patients .qRT-PCR was used to detect the expression level of prognosis-related genes in different risk groups.ResultsScRNA-seq was conducted on 28,000 cells derived from 14 HPV-negative OSCC samples and 6 normal samples. We identified 4,635 T cells from these cells and identified 774 differentially expressed genes(DEGs) associated with T cells across five distinct T-cell subtypes. Through the integration of bulk-RNAseq data, we established a prognostic model based on DEGs related to T cells. By separating patients into high-risk and low-risk groups according to these prognostic related genes, we can accurately predict their survival rates and the immune infiltration status of the TME.qRT-PCR results showed that compared with the patients of low risk group, the expression of PMEPA1, SH2D2A, SMS and PRDX4 were significantly up-regulated in high risk group.ConclusionThis study provides a resource for understanding the heterogeneity of T cells in HPV-negative OSCC patients and associated prognostic risk models. It provides new insights for predicting survival and level of immune infiltration in patients with HPV-negative OSCC.

Exploring the molecular characterization of PANoptosis-related genes with features of immune dysregulation in Alzheimer's disease based on bulk and single-nuclei RNA sequencing.

The immune system has emerged as a major factor in the pathogenesis of Alzheimer's disease (AD). PANoptosis is a newly defined programmed cell death mechanism related to many inflammatory diseases. This study aimed to identify the differentially expressed (DE) PANoptosis-related genes with characteristics of immune dysregulation (PRGIDs) in AD using bioinformatics analysis of bulk RNA-seq and single-nuclei RNA sequencing (snRNA-seq) data. To improve the robustness of gene selection, we integrated 3 microarray and 6 snRNA-seq datasets from the Gene Expression Omnibus (GEO), which allowed us to not only examine overall gene expression patterns but also assess the cellular specificity of gene expression at the single-cell level. This approach helped to identify cell-type-specific gene alterations that may be masked in bulk RNA-seq analyses. Relevant PANoptosis, immune dysregulation, and AD-related genes were obtained from the Genecards database. The AlzData database was also used in this study. Expression validation, the least absolute shrinkage and selection operator (LASSO) regression model, and CytoHubba algorithms were applied for key DE-PRGIDs selection. LASSO, Logistic, and Cox regressions were used to construct prognostic models. The receiver operating characteristic (ROC) curve and correlation analyses were conducted on key DE-PRGIDs. The Seurat package in R software was employed for performing snRNA-seq data processing. Uniform manifold approximation and projection (UMAP) was utilized for cell type annotation and PRGID cell visualization. The violin plot was applied for displaying expression levels of PRGIDs. High-dimensional consensus weighted gene co-expression network analysis (hdWGCNA) was conducted on microglia to identify gene modules and hub genes. Venn diagram analysis identified 250 PRGIDs and 39 DE-PRGIDs. NFKBIA was identified as the key gene. Prognostic models based on the expression level of NFKBIA were obtained. ROC curve analysis revealed its area under the curve (AUC) value: 0.661 in training set and 0.836 in validation set. The heatmap displayed the result of correlation analysis. SnRNA-seq data analysis identified 7 cell types. The UMAP and violin plots revealed highly expressed PRGIDs in microglia with remarkable differences between healthy controls and AD. hdWGCNA identified PVT1 and APOE as hub genes associated with microglia. In conclusion, our findings provide evidence that PANoptosis may play a role in the immune dysregulation observed in AD. PVT1 has been implicated in AD pathogenesis, potentially exerting its effects through the miR-488-3p/FOXD3/SCN2A axis.

Integration of Chromogenic RNAscope In Situ Hybridization for Target Validation in Drug Discovery.

Characterizing the expression of novel targets in normal and diseased tissues is a fundamental component of a target validation data package. Often these targets are presented to the pathology team for assessment with bulk or single-cell RNAseq data and limited to no spatial tissue expression data. In situ hybridization to detect mRNA (RNAscope) is a valuable tool to (1) identify cells that may express the target protein and to corroborate protein expression during immunohistochemical (IHC) assay development or (2) to use as surrogate for single-cell expression IHC when antibodies are not available. Chromogenic RNAscope in situ hybridization (CISH) can be performed on frozen or formalin-fixed, paraffin-embedded (FFPE) tissues. This CISH workflow starts with RNA qualification of the tissue (to assess RNA integrity) by measuring the expression of housekeeping genes. RNA-qualified tissues then undergo CISH for the target in question, and positive CISH signals are quantified in VisioPharm by a combination of color deconvolution, size gating, and dot density thresholding. This RNA workflow can complement IHC or standalone in target validation for spatial characterization of novel targets.

Integrative Single-Cell and Bulk RNA Sequencing Identifies a Macrophage-Related Prognostic Signature for Predicting Prognosis and Therapy Responses in Colorectal Cancer.

Colorectal cancer (CRC) is one of the most common malignant tumors, characterized by a high incidence and mortality rate. Macrophages, as a key immune cell type within the tumor microenvironment (TME), play a key role in tumor immune evasion and the progression of CRC. Therefore, identifying macrophage biomarkers is of great significance for predicting the prognosis of CRC patients. This study integrates scRNA-seq and bulk RNA-seq data to identify macrophage-related genes in CRC. By applying a comprehensive machine learning framework, the macrophage-related prognostic signature (MRPS) was constructed by 15 macrophage-related genes with prognostic values. The MRPS demonstrated strong predictive performance across multiple datasets, effectively stratifying high-risk and low-risk patients in terms of overall survival (OS) and disease-specific survival (DSS). Furthermore, immune analysis revealed significant differences between the high-risk and low-risk groups in immune cell infiltration levels and immune checkpoint gene expression patterns. Drug screening identified several small molecules, including Bortezomib and Mitoxantrone, as potential therapeutic options for high-risk patients. Pseudotime trajectory analysis further highlighted the potential role of genes comprising the MRPS in macrophage differentiation. This study provides a powerful tool for personalized prognosis prediction in CRC patients, offering new insights into macrophage-driven mechanisms in tumor progression and potential therapeutic strategies.

Integrating single-cell RNA-Seq and bulk RNA-Seq data to explore the key role of fatty acid metabolism in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a predominant cause of cancer-related mortality globally, noted for its propensity towards late-stage diagnosis and scarcity of effective treatment modalities. The process of metabolic reprogramming, with a specific emphasis on lipid metabolism, is instrumental in the progression of HCC. Nevertheless, the precise mechanisms through which lipid metabolism impacts HCC and its viability as a therapeutic target have yet to be fully elucidated. In the current investigation, single-cell RNA sequencing in conjunction with weighted gene co-expression network analysis (WGCNA) was utilized to delineate lipid metabolism-related genes correlated with the prognostic outcomes of hepatocellular carcinoma (HCC). Data procurement encompassed transcriptomic and clinical datasets from HCC patients, sourced from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) repositories. Subsequent to this, consensus clustering analysis was implemented to stratify patients into distinct subgroups, contingent upon the expression patterns of lipid metabolism genes. Further analytical procedures involved functional enrichment analysis, evaluation of immune infiltration, and examination of the mutation landscape.PTGES3 was identified as a pivotal gene associated with lipid metabolism. Subsequent to its identification, cellular communication analysis was employed to assess the immunological attributes of PTGES3 within the tumor microenvironment. The functional role of PTGES3 was further corroborated through molecular docking simulations and in vitro experimental assays. We identified 27 genes associated with lipid metabolism, 18 of which exhibited significant correlation with overall survival in HCC patients. PTGES3 emerged as a central gene, demonstrating a robust association with immune cell infiltration and unfavorable prognosis. Cellular communication analysis revealed that PTGES3 exhibits the highest communication intensity with T cells, modulating the tumor microenvironment by potentiating the FN1/CD44 + MDK/NCL signaling pathway. Elevated expression of PTGES3 was linked to immunosuppressive cascades, diminished responsiveness to immunotherapy, and inferior overall survival outcomes. Molecular docking analysis indicated that etoposide, methotrexate, and doxorubicin could effectively bind to PTGES3. In vitro experiments confirmed that PTGES3 knockdown significantly impaired the proliferation, invasion, and migration of HCC cells. This study highlights the pivotal role of lipid metabolism in HCC progression and identifies PTGES3 as a potential prognostic biomarker and therapeutic target. These findings offer new insights into the development of targeted therapies for HCC, particularly in patients with high PTGES3 expression.

Systematical identification of regulatory GPCRs by single-cell trajectory inference reveals the role of ADGRD1 and GPR39 in adipogenesis.

Adipogenesis is the healthy expansion of white adipose tissue (WAT), serving as a compensatory response to maintain metabolic homeostasis in the presence of excess energy in the body. Therefore, the identification of novel regulatory molecules in adipogenesis, specifically membrane receptors such as G protein-coupled receptors (GPCRs), holds significant clinical promise. These receptors can serve as viable targets for pharmaceuticals, offering potential for restoring metabolic homeostasis in individuals with obesity. We utilized trajectory inference methods to analyze three distinct single-nucleus sequencing (sNuc-seq) datasets of adipose tissue and systematically identified GPCRs with the potential to regulate adipogenesis. Through verification in primary adipose progenitor cells (APCs) of mice, we discovered that ADGRD1 promoted the differentiation of APCs, while GPR39 inhibits this process. In the obese mouse model induced by a high-fat diet (HFD), both gain-of-function and loss-of-function studies validated that ADGRD1 promoted adipogenesis, thereby improving metabolic homeostasis, while GPR39 inhibited adipogenesis, leading to metabolic dysfunction. Additionally, through the analysis of 2,400 ChIP-seq data and 1,204 bulk RNA-seq data, we found that the transcription factors (TFs) MEF2D and TCF12 regulated the expression of ADGRD1 and GPR39, respectively. Our study revealed the regulatory role of GPCRs in adipogenesis, providing novel targets for clinical intervention of metabolic dysfunction in obese patients.

The microenvironment cell index is a novel indicator for the prognosis and therapeutic regimen selection of cancers

BackgroundIt is worthwhile to establish a prognostic prediction model based on microenvironment cells (MCs) infiltration and explore new treatment strategies for triple-negative breast cancer (TNBC).MethodsThe xCell algorithm was used to quantify the cellular components of the TNBC microenvironment based on bulk RNA sequencing (bulk RNA-seq) data. The MCs index (MCI) was constructed using the least absolute shrinkage and selection operator Cox (LASSO-Cox) regression analysis. Single-cell RNA sequencing (scRNA-seq), spatially resolved transcriptomics (SRT), and multiplex immunofluorescence (mIF) staining analyses verified MCI. The mechanism of action of the MCI was investigated in tumor-bearing mice.ResultsMCI consists of the six types of MCs, which can precisely predict the prognosis of the TNBC patients. scRNA-seq, SRT, and mIF analyses verified the existence and proportions of these cells. Furthermore, combined with the spatial distribution characteristics of the six types of MCs, an MCI-enhanced (MCI-e) model was constructed, which could predict the prognosis of the TNBC patients more accurately. More importantly, inhibition of the insulin signaling pathway activated in the cancer cells of the MCIhigh the TNBC patients significantly prolonged the survival time of tumor-bearing mice.ConclusionsOverall, our results demonstrate that MCs infiltration can be exploited as a novel indicator for the prognosis and therapeutic regimen selection of the TNBC patients.

Exploring RNA-Seq Data Analysis Through Visualization Techniques and Tools: A Systematic Review of Opportunities and Limitations for Clinical Applications.

RNA sequencing (RNA-seq) has emerged as a prominent resource for transcriptomic analysis due to its ability to measure gene expression in a highly sensitive and accurate manner. With the increasing availability of RNA-seq data analysis from clinical studies and patient samples, the development of effective visualization tools for RNA-seq analysis has become increasingly important to help clinicians and biomedical researchers better understand the complex patterns of gene expression associated with health and disease. This review aims to outline the current state-of-the-art data visualization techniques and tools commonly used to frame clinical inferences from RNA-seq data and point out their benefits, applications, and limitations. A systematic review of English articles using PubMed, Scopus, Web of Science, and IEEE Xplore databases was performed. Search terms included "RNA-seq", "visualization", "plots", and "clinical". Only full-text studies reported between 2017 and 2024 were included for analysis. Following PRISMA guidelines, a total of 126 studies were identified, of which 33 studies met the inclusion criteria. We found that 18% of studies have visualization techniques and tools for circular RNA-seq data, 56% for single-cell RNA-seq data, 23% for bulk RNA-seq data, and 3% for long non-coding RNA-seq data. Overall, this review provides a comprehensive overview of the common visualization tools and their potential applications, which is a useful resource for researchers and clinicians interested in using RNA-seq data for various clinical purposes (e.g., diagnosis or prognosis).

A unified framework for cell-type-specific eQTL prioritization by integrating bulk and scRNA-seq data.

Genome-wide association studies (GWASs) have identified numerous genetic variants associated with complex traits, yet the biological interpretation remains challenging, especially for variants in non-coding regions. Expression quantitative trait locus (eQTL) studies have linked these variations to gene expression, aiding in identifying genes involved in disease mechanisms. Traditional eQTL analyses using bulk RNA sequencing (bulk RNA-seq) provide tissue-level insights but suffer from signal loss and distortion due to unaddressed cellular heterogeneity. Recently, single-cell RNA-seq (scRNA-seq) has provided higher resolution, enabling cell-type-specific eQTL (ct-eQTL) analyses. However, these studies are limited by their smaller sample sizes and technical constraints. In this paper, we present a statistical framework, IBSEP, which integrates bulk RNA-seq and scRNA-seq data for enhanced ct-eQTL prioritization. Our method employs a hierarchical linear model to combine summary statistics from both data types, overcoming the limitations while leveraging the advantages associated with each technique. Through extensive simulations and real data analyses, including peripheral blood mononuclear cells and brain cortex datasets, IBSEP demonstrated superior performance in identifying ct-eQTLs compared to existing methods. Our approach unveils transcriptional regulatory mechanisms specific to cell types, offering deeper insights into the genetic basis of complex diseases at a cellular resolution.

Single-cell RNA sequencing and immune microenvironment analysis reveal PLOD2-driven malignant transformation in cervical cancer.

Cervical cancer is the fourth most common cancer in women globally, and the main cause of the disease has been found to be ongoing HPV infection. Cervical cancer remains the primary cause of cancer-related death despite major improvements in screening and treatment approaches, especially in low- and middle-income nations. Therefore, it is crucial to investigate the tumor microenvironment in advanced cervical cancer in order to identify possible treatment targets. In order to better understand malignant cervical cancer epithelial cells (EPCs), this study used bulk RNA-seq data from UCSC in conjunction with single-cell RNA sequencing data from the ArrayExpress database. After putting quality control procedures into place, cell type identification and clustering analysis using the Seurat software were carried out. To clarify functional pathways, enrichment analysis and differential gene expression were carried out. The CIBERSORT and ESTIMATE R packages were used to evaluate the immune microenvironment characteristics, and univariate and multivariate Cox regression analyses were used to extract prognostic features. Furthermore, assessments of drug sensitivity and functional enrichment were carried out. Eight cell types were identified, with EPCs showing high proliferative and stemness features. Five EPC subpopulations were defined, with C1 NNMT+ CAEPCs driving tumor differentiation. A NNMT CAEPCs Risk Score (NCRS) model was developed, revealing a correlation between elevated NCRS scores and adverse patient outcomes characterized by immune evasion. In vitro experiments validated that the prognostic gene PLOD2 significantly enhances proliferation, migration, and invasion of cervical cancer cells. This investigation delineated eight cell types and five subpopulations of malignant EPCs in cervical cancer, establishing the C1 NNMT+ CAEPCs as a crucial therapeutic target. The NCRS model demonstrated its prognostic capability, indicating that higher scores are associated with poorer clinical outcomes. The validation of PLOD2 as a prognostic gene highlights its therapeutic potential, underscoring the critical need for integrating immunotherapy and targeted treatment strategies to enhance diagnostic and therapeutic approaches in cervical cancer.

Single-cell sequencing analysis reveals cancer-associated pericyte subgroup in esophageal squamous cell carcinoma to predict prognosis.

The role of cancer-associated pericytes (CAPs) in tumor microenvironment (TME) suggests that they are potential targets for cancer treatment. The mechanism of CAP heterogeneity in esophageal squamous cell carcinoma (ESCC) remains unclear, which has limited the development of treatments for tumors through CAPs. Therefore, a comprehensive understanding of the classification, function, cellular communication and spatial distribution of CAP subpopulations in ESCC is urgently needed. This study used large-sample single-cell transcriptome sequencing (scRNA-seq) data to investigate pericytes' subpopulation characteristics, functions, upstream and downstream regulation and interactions with other components of the TME in the ESCC, and analyzed prognostically in conjunction with Bulk RNA-seq data. In addition, pericyte subpopulations were validated and their spatial distribution in the ESCC TME was observed by multiplex immunofluorescence. Drug prediction and molecular docking was further used to validate the medicinal value of drug targets. CAPs in the ESCC TME were found to be highly heterogeneous, and we identified six pericyte subtypes: c1_ARHGDIB, c2_BCAM, c3_LUM, c4_SOD2, c5_TYMS, and c6_KRT17, which have commonality in a part of their functions, and each of them has a major function to play, by having different strengths of interaction with different components in the TME. In addition, we found that c4_SOD2 was negatively correlated with prognosis, conversely, c5_TYMS was positively correlated with prognosis. The drug with a better effect on c5_TYMS was docetaxel (binding energy = -8.1, -8.7 kcal/mol); raloxifene may be more effective against c4_SOD2, although raloxifene has a slightly lower binding energy to SOD2 (-6.4 kcal/mol), it has a higher binding energy to PDGFRβ (-8.1 kcal/mol). The present study identified and discovered pericyte subpopulations that were significantly associated with prognosis, which provides new biomarkers for predicting patient prognosis and adds usable targets for immunotherapy, and it is also important for gaining insights into the composition of the TME in ESCC.

Single-Cell RNA Sequencing Reveals the Cellular Origin and Evolution of Small-Cell Neuroendocrine Carcinoma of the Cervix.

Small-cell neuroendocrine cancer (SCNEC) of the uterine cervix is an exceedingly rare, highly aggressive tumor with an extremely poor prognosis. The cellular heterogeneity, origin, and tumorigenesis trajectories of SCNEC of the cervix remain largely unclear. We performed single-cell RNA sequencing and whole-exome sequencing on tumor tissues and adjacent normal cervical tissues from two patients diagnosed with SCNEC of the cervix. Here, we provide the first comprehensive insights into the cellular composition, HPV infection-related features, and gene expression profiles of SCNEC of the cervix at single-cell resolution. Correlation analyses suggested that SCNEC of the cervix may originate from squamous epithelial cells, and this observation was validated with bulk RNA-seq data from external cervical neuroendocrine cancer. Furthermore, sex-determining region Y-box 2 (SOX2), a key transcription factor that functions in direct neural differentiation, was located in the copy number gain region and highly expressed in neuroendocrine tumor cells from both patients. Notable, the distributions of the HPV-infected epithelium and SOX2 highly expressed epithelium were consistent with each other. Therefore, we supposed that high-risk HPV infection and amplification of SOX2 in the squamous epithelium may contribute to the progression of small-cell neuroendocrine tumorigenesis in the cervix.

Multi-omics analysis unveils the role of inflammatory cancer-associated fibroblasts in chordoma progression.

Cancer-associated fibroblasts (CAFs) constitute the primary cellular component of the stroma in chordomas, characterized by an abundance of mucinous stromal elements, potentially facilitating their initiation and progression; however, this inference has yet to be fully confirmed. In this study, single-cell RNA sequencing (scRNA-seq), spatial transcriptomics (ST), bulk RNA-seq, multiplexed quantitative immunofluorescence (QIF), and in vivo and in vitro experiments were performed to determine the heterogeneity, spatial distribution, and clinical significance of CAFs in chordoma. ScRNA-seq was performed on 87,693 single cells derived from seven tumor samples and four control nucleus pulposus samples. A distinct CAF cluster distinguished by the upregulated expression of inflammatory genes and enriched functionality in activating inflammation-associated cells was identified. Pseudotime trajectory and cell communication analyses suggested that this inflammatory CAF (iCAF) subset originated from normal fibroblasts and interacted extensively with tumors and various other cell types. By integrating the scRNA-seq results with ST, the presence of iCAF in chordoma tissue was further confirmed, indicating their positioning at a distance from the tumor cells. Bulk RNA-seq data analysis from 126 patients revealed a correlation between iCAF signature scores, chordoma invasiveness, and poor prognosis. QIF validation involving an additional 116 patients found that although iCAFs were not in close proximity to tumor cells compared with other CAF subsets, their density correlated with malignant tumor phenotypes and adverse outcomes. In vivo and in vitro experiments further confirmed that iCAFs accelerate the malignant progression of chordomas. These findings could provide insights into the development of novel therapeutic strategies. © 2024 The Pathological Society of Great Britain and Ireland.

Integrative Analyses of Single-Cell RNA-Sequencing and Bulk RNA-Sequencing Reveal Prognostic Markers of Peripheral Blood Mononuclear Cells in Patients with Cardiogenic Shock Receiving Venous-Arterial Extracorporeal Membrane Oxygenation Support Collected before Venous-Arterial Extracorporeal Membrane Oxygenation Establishment.

The impact of changes in peripheral blood mononuclear cells (PBMCs) on the prognosis of patients with cardiogenic shock (CS) receiving venous-arterial extracorporeal membrane oxygenation (VA-ECMO) remains unclear. A single-cell RNA-sequencing (scRNA-seq) and a bulk RNA-sequencing (bulk RNA-seq) data sets of pre-ECMO PBMCs of CS patients were obtained from the gene expression omnibus database, which were analyzed using the "Seurat" and "limma" packages, respectively. The counts of different PMBC cell types, differential expression genes (DEGs), pathway enrichment analysis, cell-cell communication analysis, pseudotime analysis, and immune cell infiltration analysis were compared between VA-ECMO groups with different prognoses. The intersectional DEGs of the two data sets were screened. PBMCs were collected from VA-ECMO patients for experimental verification. For scRNA-seq analysis, ten kinds of PBMCs were identified, and B and NK/NKT cells which had significant differences in cell counts across groups were further divided into four subsets. The counts of B and NK/NKT cells with high expression levels of HSPA1B were higher in the poor-prognosis group, which was consistent with the bulk RNA-seq analysis. Pseudotime analysis also indicated that B-HSPA1B cells gradually increased in the poor-prognosis group. HSPA1B was found to be the intersectional upregulated DEG in both data sets, which was consistent with the experimental verification using clinical samples. The increased counts of B and NK/NKT cells as well as high expression levels of HSPA1B in these cell types or in the total pre-ECMO PBMCs were predictors of poor prognosis.

Integrating single-cell RNA-seq and bulk RNA-seq to construct a neutrophil prognostic model for predicting prognosis and immune response in oral squamous cell carcinoma

BackgroundOral squamous cell carcinoma (OSCC) is an aggressive malignancy with poor prognosis. Neutrophil infiltration has been associated with unfavorable outcomes in OSCC, but the underlying molecular mechanisms remain unclear.MethodsThis study integrated single-cell transcriptomics (scRNA-seq) with bulk RNA-seq data to analyze neutrophil infiltration patterns in OSCC and identify key gene modules using weighted gene co-expression network analysis (hdWGCNA). A prognostic model was developed based on univariate and Lasso-Cox regression analyses, stratifying patients into high- and low-risk groups. Immune landscape and drug sensitivity analyses were conducted to explore group-specific differences. Additionally, Mendelian randomization analysis was employed to identify genes causally related to OSCC progression.ResultsSeveral key pathways associated with neutrophil interactions in OSCC progression were identified, leading to the construction of a prognostic model based on significant module genes. The model demonstrated strong predictive performance in distinguishing survival rates between high- and low-risk groups. Immune landscape analysis revealed significant differences in cell infiltration patterns and TIDE scores between the groups. Drug sensitivity analysis highlighted differences in drug responsiveness between high- and low-risk groups.ConclusionThis study elucidates the critical role of neutrophils and their associated gene modules in OSCC progression. The prognostic model provides a novel reference for patient stratification and targeted therapy. These findings offer potential new targets for OSCC diagnosis, prognosis, and immunotherapy.