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Identification of Breast Cancer Subtypes by Integrating Genomic Analysis with the Immune Microenvironment.
Objectives: We aim to identify the breast cancer (BC) subtype clusters and the crucial gene classifier prognostic signatures by integrating genomic analysis with the tumor immune microenvironment (TME). Methods: Data sets of BC were derived from the Cancer Genome Atlas (TCGA), METABRIC, and Gene Expression Omnibus (GEO) databases. Unsupervised consensus clustering was carried out to obtain the subtype clusters of BC patients. Weighted gene coexpression network analysis (WGCNA), least absolute shrinkage and selection operator (LASSO), and univariate and multivariate regression analysis were employed to obtain the gene classifier signatures and their biological functions, which were validated by the BC dataset from the METABRIC database. Additionally, to evaluate the overall survival rates of BC patients, Kaplan-Meier survival analysis was carried out. Moreover, to assess how BC subtype clusters are related to the TME, single-cell analysis was performed. Finally, the drug sensitivity and the immune cell infiltration for different phenotypes of BC patients were also calculated by the CIBERSORT and ESTIMATE algorithms. Results : TCGA-BC samples were divided into three subtype clusters, S1, S2, and S3, among which the prognosis of S2 was poor and that of S1 and S3 were better. Three key pathways and 10 crucial prognostic-related gene signatures are screened. Finally, single-cell analysis suggests that S1 samples have the most types of immune cells, S2 with more sensitivity to tumor treatment drugs are enriched with more neutrophils, and more multilymphoid progenitor cells are involved in subtype cluster S3. Conclusions: Our novelty was to identify the BC subtype clusters and the gene classifier signatures employing a large-amount dataset combined with multiple bioinformatics methods. All of the results provide a basis for clinical precision treatment of BC.
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As a potential predictor of pan-cancer, UBE2S is related to tumor-associated macrophage infiltration.
Background: At the pan-cancer level, exploring the expression and prognostic significance of a gene, such as UBE2S, will help to gain insight into the role of the gene and its feasibility for cancer screening, prognosis assessment and even gene therapy. Methods: The Cancer Genome Atlas, Human Protein Atlas, Kaplan-Meier, Tumor Immunology Estimation Resource and other databases were used to analyze the expression of UBE2S at the pan-cancer level, its prognosis and the role of the immune microenvironment. Immunohistochemistry samples of tumor tissue collected in our clinic were taken as verification. Results: UBE2S is significantly overexpressed in pan-cancer and is closely associated with malignant clinical features, poor prognosis and tumor-associated macrophages. Conclusion: UBE2S may be a potential diagnostic and prognostic marker for pan-cancer and is associated with tumor-associated macrophages.
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Single-Cell Sequencing to Identify Six Heat Shock Protein (HSP) Genes-Mediated Progression Subtypes of Clear Cell Renal Cell Carcinoma.
BACKGROUND: Heat shock proteins (HSPs) are widely involved in tumor occurrence and development and are prognostic markers for multiple tumors. However, the role of HSPs in clear cell renal cell carcinoma (ccRCC) remains unclear. METHODS: We used Cytoscape to identify hub genes in the ccRCC single-cell sequencing data set from the Gene Expression Omnibus (GEO) data repository. We identified subtypes, C1 and C2, of The Cancer Genome Atlas (TCGA) patients based on the expression of hub genes using unsupervised consensus clustering. Principal component analysis (PCA) was used to verify the clustering differences, and Kaplan-Meier (K-M) estimate was used to verify the survival differences between C1 and C2 patients. We used TIMER 2.0 and CIBERSORT to evaluate the immune cell infiltration of HSP genes and C1 and C2 patients. The R package "pRRophetic" was used to evaluate the sensitivity in C1 and C2 patients to the four first-line treatment drugs. RESULTS: We identified six hub genes (HSP90AA1, HSPH1, HSPA1B, HSPA8, and HSPA1A) encoding HSP, five of which were significantly downregulated in TCGA group, and four had a protective effect on prognosis (p <0.05). Survival analysis showed that C1 patients had a better overall survival (p <0.001). TIMER 2.0 analysis showed that three HSP genes were significantly correlated with the infiltration of CD4+ T cells and CD4+ Th1 cells (|cor|>0.5, p<0.001). CIBERSORT showed significant differences in multiple infiltrating immune cells between C1 and C2 patients. Meanwhile, the expression of PD1 was significantly lower in C1 patients than in C2 patients, and the expression of PDL1 is the another way around. Drug sensitivity analysis showed that C1 patients were more sensitive to sorafenib, pazopanib, and axitinib (p <0.001). CONCLUSION: Our research revealed two molecular subtypes of ccRCC based on 6 HSP genes, and revealed significant differences between the two subtypes in terms of clinical prognosis, immune infiltration, and drug sensitivity.
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Telomere-based Risk Model for Prognosis Prediction in Clear Cell Renal Cell Carcinoma.
INTRODUCTION: Telomeres have become extensively studied in renal cell carcinoma (RCC), and this study aims to identify relevant diagnostic biomarkers in the predominant RCC subtype, clear cell RCC (ccRCC). MATERIALS AND METHODS: This study retrieved telomere-related genes from the TelNet database and integrated data from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) to calculate telomere enrichment scores using single-sample gene set enrichment analysis (ssGSEA). Weighted gene co-expression network analysis (WGCNA) and differential expression analysis were then applied to identify candidate genes, which were further refined through protein-protein interaction (PPI) network construction and two machine learning methods: least absolute shrinkage and selection operator (LASSO) regression and support vector machine-recursive feature elimination (SVM-RFE). The associations between the identified feature genes and immune cell infiltration were subsequently evaluated using CIBERSORT and ESTIMATE. Furthermore, single-cell analysis was employed to determine the highly expressed genes in different cell clusters. Finally, using a ccRCC cell line, quantitative real-time PCR, wound healing, and Transwell assays were performed to validate the expression and potential biological functions of the selected key genes. RESULTS: A higher telomere score was observed in ccRCC. The common genes from the DEGs and the gene modules were mainly enriched in cell division- and senescence-related pathways. Moreover, six genes (ASPM, CENPF, CEP55, MELK, BUB1, and EXO1) were identified as feature genes with satisfactory diagnostic efficacy and high expression in ccRCC; they were positively correlated with most immune cells and highly expressed in T cells. Notably, CEP55 knockdown suppressed the migration and invasion of ccRCC cells. DISCUSSION: Our present study, based on the data from the public databases, unraveled 6 genes with diagnostic efficacy in ccRCC, which may aid the development of a relevant future diagnostic method in ccRCC. CONCLUSION: This study identified six telomere-related genes with high expression and strong diagnostic value in ccRCC, highlighting their association with immune infiltration and potential as diagnostic and therapeutic targets.
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Significance of CD47 and Its Association With Tumor Immune Microenvironment Heterogeneity in Ovarian Cancer.
BACKGROUND: It was reported that tumor heterogeneity and the surrounding tumor microenvironment (TME) in ovarian cancer affects immunotherapy efficacy and patient outcomes. And the TME of ovarian cancer is intrinsically heterogeneous. CD47 plays vital roles in cell functional behavior and immune homeostasis relating to cancer prognosis. But how it affects TME and its contribution to heterogeneity in ovarian cancer has not been fully illustrated. Therefore, we aimed to identify a prognostic biomarker which may help explain tumor immune microenvironment heterogeneity of ovarian cancer. METHODS: Cancer single-cell state atlas (CancerSEA) was used to evaluate functional role of CD47. Several bioinformatics database including Oncomine, Gene Expression Profiling Interaction Analysis (GEPIA), Tumor Immune Estimation Resource (TIMER), The Human Protein Atlas (HPA), Ualcan and Kaplan-Meier plotter (KM plotter) were applied to illustrate correlation of CD47 with ovarian cancer prognosis and immune infiltration. Tumor Immune Single-cell Hub (TISCH) single cell database was employed to evaluate correlation of CD47 with tumor microenvironment. GeneMANIA was implemented to identify regulation networks of CD47. Differentially expressed genes (DEGs) between CD47 high and low expression groups were analyzed with R package DESeq2. Kyoto encyclopedia of genes and genomes (KEGG) and Gene Set Enrichment Analysis (GSEA) were utilized to explore how CD47 affect the immune related cell signaling pathway. RESULTS: CD47 expression was upregulated and connected to worse OS and PFS in ovarian cancer. Close relation was found between CD47 expression level and immune infiltration in ovarian cancer, especially with Treg cells, Monocytes, Macrophages and T cell exhaustion (P<0.05). The CD47 expression level was relatively low in plasma cells, dendritic cells and Mono/Macro cells of OV_GSE115007, in myofibroblasts, fibroblasts and endothelial cells of OV_GSE118828, compared to malignant cells of OV_GSE118828 dataset. The cell components and distribution in primary and metastatic ovarian cancer are quite distinct, which may lead to TME heterogeneity of ovarian cancer. CONCLUSION: Our results indicated that CD47 is closely correlated to ovarian cancer immune microenvironment and might induce ovarian cancer heterogeneity. Therefore, CD47 may be used as a candidate prognostic biomarker and provide us with new insights into potential immunotherapy in ovarian cancer patients.
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Functional KCC2 expression marks an evolutionarily conserved population of early-maturing interneurons in the perinatal cortex.
The developmental shift from depolarizing to hyperpolarizing GABA responses is a pivotal step in the maturation of GABAergic transmission and cortical circuits; classically documented in principal neurons during the first postnatal week in the mouse cortex. Surprisingly, whether maturation of GABA-mediated responses follows the same temporal pattern in cortical interneurons (INs) remains unresolved. Leveraging an array of methods, a high-resolution cortical development mouse atlas and single-cell RNA sequencing, we identify and comprehensively characterize a population of early-maturing cortical INs in mice, distinguished by KCC2 expression at embryonic stages and concomitant hyperpolarizing GABAA responses at birth. These early KCC2-expressing INs exhibit precocious intrinsic excitability, synaptic integration, and dendritic complexity at birth, contrasting delayed maturation in principal neurons and other INs. Spatial transcriptomics and differential gene expression (DGE) analyses reveal early KCC2-expressing INs localize predominantly to layer 5, express somatostatin, and show upregulation of synaptogenic genes, consistent with the recorded elevated synaptic activity. Crucially, evolutionary conservation of early KCC2-expressing INs in humans was demonstrated with analogous genetic profiles enriched for signaling and synaptic maturation pathways. This work resolves a critical gap in developmental neurobiology, demonstrating heterogenous GABAergic functional maturation within IN subpopulations and establishing KCC2 as a marker of early-maturing INs.
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PRKCB Mediates Ferroptosis-Immune Crosstalk in Acute Lung Injury: Insights from Integrated Single-Cell Transcriptomics.
BACKGROUND: Acute respiratory distress syndrome (ARDS), previously classified under the broader category of acute lung injury (ALI), a life-threatening condition characterized by uncontrolled inflammation, remains a major challenge in critical care medicine. Ferroptosis, an iron-dependent cell death driven by lipid peroxidation, is implicated in ALI pathogenesis, yet its cell-specific landscape and interplay with the immune microenvironment remain poorly defined. METHODS: We integrated single-cell and bulk transcriptomic data from public databases (12 samples, 50,316 high-quality cells). Cell populations were annotated, and ferroptosis propensity was scored using the FerrDb gene set. Cell-cell communication, transcriptional regulation, and pseudotime trajectory analyses were performed. Key predictions were validated in an LPS-induced alveolar epithelial cell (MLE-12) model using the PRKCB inhibitor Enzastaurin and the ferroptosis inhibitor Ferrostatin-1. RESULTS: We constructed a cellular atlas of ALI, noting a significant shift toward immune cell infiltration (macrophages/neutrophils >80%). Ferroptosis driver scores were significantly elevated in ALI, particularly within epithelial and myeloid cells, but not fibroblasts. CellChat analysis revealed a remodeled communication network centered on myeloid cells, with enhanced interactions involving ferroptosis-high-risk cells. Pseudotime analysis of epithelial cells illustrated a continuous transition toward a ferroptotic state, accompanied by stage-specific gene expression. Multi-algorithm integration identified Protein Kinase C Beta (PRKCB) as a core regulatory gene. In vitro validation confirmed that PRKCB inhibition attenuated LPS-induced cell death, lipid peroxidation, and dysregulation of key ferroptosis markers (GPX4, SLC7A11, ACSL4). CONCLUSION: Our study provides the first single-cell resolution map of ferroptosis heterogeneity in ALI, uncovering a PRKCB-mediated positive feedback loop between ferroptosis and immune dysregulation. These findings identify PRKCB as a potential therapeutic target to disrupt this pathogenic crosstalk.
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Fibroblasts positive for meflin have anti-fibrotic properties in pulmonary fibrosis.
The prognosis of elderly individuals with idiopathic pulmonary fibrosis (IPF) remains poor. Fibroblastic foci, in which aggregates of proliferating fibroblasts and myofibroblasts are involved, are the pathological hallmark lesions in IPF to represent focal areas of active fibrogenesis. Fibroblast heterogeneity in fibrotic lesions hampers the discovery of the pathogenesis of pulmonary fibrosis. Therefore, to determine the pathogenesis of IPF, identification of functional fibroblasts is warranted. The aim of this study was to determine the role of fibroblasts positive for meflin, identified as a potential marker for mesenchymal stromal cells, during the development of pulmonary fibrosis.We characterised meflin-positive cells in a single-cell atlas established by single-cell RNA sequencing (scRNA-seq)-based profiling of 243 472 cells from 32 IPF lungs and 29 normal lung samples. We determined the role of fibroblasts positive for meflin using bleomycin (BLM)-induced pulmonary fibrosis.scRNA-seq combined with in situ RNA hybridisation identified proliferating fibroblasts positive for meflin in fibroblastic foci, not dense fibrosis, of fibrotic lungs in IPF patients. A BLM-induced lung fibrosis model for meflin-deficient mice showed that fibroblasts positive for meflin had anti-fibrotic properties to prevent pulmonary fibrosis. Although transforming growth factor-β-induced fibrogenesis and cell senescence with the senescence-associated secretory phenotype were exacerbated in fibroblasts via the repression or lack of meflin, these were inhibited in meflin-deficient fibroblasts with meflin reconstitution.These findings provide evidence to show the biological importance of meflin expression on fibroblasts and myofibroblasts in the active fibrotic region of pulmonary fibrosis.
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Comprehensive Pan-Cancer Analysis of KIF18A as a Marker for Prognosis and Immunity.
KIF18A belongs to the Kinesin family, which participates in the occurrence and progression of tumors. However, few pan-cancer analyses have been performed on KIF18A to date. We used multiple public databases such as TIMER, The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Human Protein Atlas (HPA) to explore KIF18A mRNA expression in 33 tumors. We performed immunohistochemistry on liver cancer and pancreatic cancer tissues and corresponding normal tissues to examine the expression of KIF18A protein. Univariate Cox regression and Kaplan-Meier survival analysis were applied to detect the effect of KIF18A on overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI) of patients with these tumors. Subsequently, we explored KIF18A gene alterations in different tumor tissues using cBioPortal. The relationship between KIF18A and clinical characteristics, tumor microenvironment (TME), immune regulatory genes, immune checkpoints, tumor mutational burden (TMB), microsatellite instability (MSI), mismatch repairs (MMRs), DNA methylation, RNA methylation, and drug sensitivity was applied for further study using the R language. Gene Set Enrichment Analysis (GSEA) was utilized to explore the molecular mechanism of KIF18A. Bioinformatic analysis and immunohistochemical experiments confirmed that KIF18A was up-regulated in 27 tumors and was correlated with the T stage, N stage, pathological stage, histological grade, and Ki-67 index in many cancers. The overexpression of KIF18A had poor OS, DSS, and PFI in adrenocortical carcinoma (ACC), kidney renal clear cell carcinoma (KIRC), kidney renal papillary cell carcinoma (KIRP), brain lower-grade glioma (LGG), liver cancer (LIHC), lung adenocarcinoma (LUAD), and pancreatic cancer (PAAD). Univariate and multivariate regression analysis confirmed KIF18A as an independent prognostic factor for LIHC and PAAD. The mutation frequency of KIF18A is the highest in endometrial cancer. KIF18A expression levels were positively associated with immunocyte infiltration, immune regulatory genes, immune checkpoints, TMB, MSI, MMRs, DNA methylation, RNA methylation, and drug sensitivity in certain cancers. In addition, we discovered that KIF18A participated in the cell cycle at the single-cell level and GSEA analysis for most cancers. These findings suggested that KIF18A could be regarded as a latent prognostic marker and a new target for cancer immunological therapy.
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Disturbed Flow Induces Reprogramming of Endothelial Cells to Immune-like and Foam Cells under Hypercholesterolemia during Atherogenesis.
BACKGROUND: Atherosclerosis occurs preferentially in the arteries exposed to disturbed flow (d-flow), while the stable flow (s-flow) regions are protected even under hypercholesterolemic conditions. We recently showed that d-flow alone initiates flow-induced reprogramming of endothelial cells (FIRE), including the novel concept of partial endothelial-to-immune-cell-like transition (partial EndIT), but was not validated using a genetic lineage-tracing model. Here, we tested and validated the two-hit hypothesis that d-flow is an initial instigator of partial FIRE but requires hypercholesterolemia to induce a full-blown FIRE and atherosclerotic plaque development. METHODS: Mice were treated with adeno-associated virus expressing proprotein convertase subtilisin/kexin type 9 and a Western diet to induce hypercholesterolemia and/or partial carotid ligation (PCL) surgery to expose the left common carotid artery (LCA) to d-flow. Single-cell RNA sequencing (scRNA-seq) analysis was performed using cells obtained from the intima and leftover LCAs and the control right common carotid arteries at 2 and 4 weeks post-PCL. Comprehensive immunohistochemical staining was performed on EC-specific confetti mice treated with PCL and hypercholesterolemic conditions at 4 weeks post-PCL to validate endothelial reprogramming. RESULTS: Atherosclerotic plaques developed by d-flow under hypercholesterolemia at 2 and 4 weeks post-PCL, but not by d-flow or hypercholesterolemia alone, as expected. The scRNA-seq results of 98,553 single cells from 95 mice revealed 25 cell clusters; 5 EC, 3 vascular smooth muscle cell (SMC), 5 macrophage (MΦ), and additional fibroblast, T cell, natural killer cell, dendritic cell, neutrophil, and B cell clusters. Our scRNA-seq analyses showed that d-flow under hypercholesterolemia transitioned healthy ECs to full immune-like (EndIT) and, more surprisingly, foam cells (EndFT), in addition to inflammatory and mesenchymal cells (EndMT). Further, EC-derived foam cells shared remarkably similar transcriptomic profiles with foam cells derived from SMCs and MΦs. Comprehensive lineage-tracing studies using immunohistochemical staining of canonical protein and lipid markers in the EC-specific confetti mice clearly demonstrated direct evidence supporting the novel FIRE hypothesis, including EndIT and EndFT, when d-flow was combined with hypercholesterolemia. Further, reanalysis of the publicly available human carotid plaque scRNA-seq and Perturb-seq datasets supported the FIRE hypothesis and a potential mechanistic link between the genes and FIRE. CONCLUSION: We provide evidence supporting the two-hit hypothesis: ECs in d-flow regions, such as the branching points, are partially reprogrammed, while hypercholesterolemia alone has minimal endothelial reprogramming effects. Under hypercholesterolemia, d-flow fully reprograms arterial ECs, including the novel EndIT and EndFT, in addition to inflammation and EndMT, during atherogenesis. This single-cell atlas provides a crucial roadmap for developing novel mechanistic understanding and therapeutics targeting flow-sensitive genes, proteins, and pathways of atherosclerosis.