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Association of MPO Expression with the Immune Microenvironment in Breast Cancer: Insights from Bioinformatics and Single-Cell Analyses.
Breast cancer remains a major cause of cancer-related mortality, and exploratory computational workflows can help prioritize immune-associated markers for further investigation. Here, we used the cancer genome atlas breast invasive carcinoma (TCGA-BRCA) bulk transcriptomic data and the public single-cell dataset GSE161529 to examine associations between myeloperoxidase (MPO) expression, clinical outcomes, immune infiltration, methylation, upstream-regulator annotations, single-cell expression patterns, virtual knockdown sensitivity outputs, drug-gene interaction retrieval, and absorption, distribution, metabolism, excretion, and toxicity (ADMET) annotation. MPO expression was lower in breast cancer tissues than in adjacent non-tumor tissues. Higher MPO expression was associated with a longer progression-free interval, whereas its associations with overall survival and disease-specific survival were not statistically significant. Receiver operating characteristic (ROC) analysis suggested tumor-normal separation within the analyzed public dataset, but this should not be interpreted as clinical diagnostic validation. Immune deconvolution and enrichment analyses indicated that MPO expression mainly tracked with immune- and myeloid-related transcriptional features, rather than establishing tumor-intrinsic regulation of the immune microenvironment. At single-cell resolution, the MPO signal was sparse, with only 85 MPO-positive cells detected before k-nearest neighbor (KNN)-based neighborhood expansion. Detectable MPO signal and MPO-associated scores were interpreted cautiously because they may be influenced by sparse expression, cell-type annotation uncertainty, dropout, doublets, or ambient RNA. In silico virtual knockdown suggested candidate immune- and inflammatory-related transcriptional changes, but these results were considered exploratory and require validation. Drug-gene interaction database (DGIdb)-based drug-gene retrieval and ADMET annotation were used only as preliminary chemical annotations and were not interpreted as therapeutic evidence. Overall, this study provides a reproducible in silico workflow for generating hypotheses about MPO-associated immune/myeloid features in breast cancer, which require external cohort validation and experimental confirmation.
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Integrated analysis of single-cell and bulk RNA sequencing data reveals a pan-cancer stemness signature predicting immunotherapy response.
BACKGROUND: Although immune checkpoint inhibitor (ICI) is regarded as a breakthrough in cancer therapy, only a limited fraction of patients benefit from it. Cancer stemness can be the potential culprit in ICI resistance, but direct clinical evidence is lacking. METHODS: Publicly available scRNA-Seq datasets derived from ICI-treated patients were collected and analyzed to elucidate the association between cancer stemness and ICI response. A novel stemness signature (Stem.Sig) was developed and validated using large-scale pan-cancer data, including 34 scRNA-Seq datasets, The Cancer Genome Atlas (TCGA) pan-cancer cohort, and 10 ICI transcriptomic cohorts. The therapeutic value of Stem.Sig genes was further explored using 17 CRISPR datasets that screened potential immunotherapy targets. RESULTS: Cancer stemness, as evaluated by CytoTRACE, was found to be significantly associated with ICI resistance in melanoma and basal cell carcinoma (both P < 0.001). Significantly negative association was found between Stem.Sig and anti-tumor immunity, while positive correlations were detected between Stem.Sig and intra-tumoral heterogenicity (ITH) / total mutational burden (TMB). Based on this signature, machine learning model predicted ICI response with an AUC of 0.71 in both validation and testing set. Remarkably, compared with previous well-established signatures, Stem.Sig achieved better predictive performance across multiple cancers. Moreover, we generated a gene list ranked by the average effect of each gene to enhance tumor immune response after genetic knockout across different CRISPR datasets. Then we matched Stem.Sig to this gene list and found Stem.Sig significantly enriched 3% top-ranked genes from the list (P = 0.03), including EMC3, BECN1, VPS35, PCBP2, VPS29, PSMF1, GCLC, KXD1, SPRR1B, PTMA, YBX1, CYP27B1, NACA, PPP1CA, TCEB2, PIGC, NR0B2, PEX13, SERF2, and ZBTB43, which were potential therapeutic targets. CONCLUSIONS: We revealed a robust link between cancer stemness and immunotherapy resistance and developed a promising signature, Stem.Sig, which showed increased performance in comparison to other signatures regarding ICI response prediction. This signature could serve as a competitive tool for patient selection of immunotherapy. Meanwhile, our study potentially paves the way for overcoming immune resistance by targeting stemness-associated genes.
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Identification of a novel immune-related gene signature for prognosis and the tumor microenvironment in patients with uveal melanoma combining single-cell and bulk sequencing data.
INTRODUCTION: Uveal melanoma (UVM) is the most invasive intraocular malignancy in adults with a poor prognosis. Growing evidence revealed that immune-related gene is related to tumorigenesis and prognosis. This study aimed to construct an immune-related prognostic signature for UVM and clarify the molecular and immune classification. METHODS: Based on The Cancer Genome Atlas (TCGA) database, single-sample gene set enrichment (ssGSEA) and hierarchical clustering analysis were performed to identify the immune infiltration pattern of UVM and classify patients into two immunity clusters. Then, we proposed univariate and multivariate Cox regression analysis to identify immune-related genes that related to overall survival (OS) and validated in the Gene Expression Omnibus (GEO) external validation cohort. The molecular and immune classification in the immune-related gene prognostic signature defined subgroups were analyzed. RESULTS: The immune-related gene prognostic signature was constructed based on S100A13, MMP9, and SEMA3B genes. The prognostic value of this risk model was validated in three bulk RNA sequencing datasets and one single-cell sequencing dataset. Patients in the low-risk group had better OS than those in the high-risk group. The receiver-operating characteristic (ROC) analysis revealed its strong predictive ability for UVM patients. Lower expression of immune checkpoint genes was presented in the low-risk group. Functional studies showed that S100A13 knockdown via siRNA inhibited UVM cell proliferation, migration, and invasion in vitro, with the increased expression of reactive oxygen species (ROS) related markers in UVM cell lines. DISCUSSION: The immune-related gene prognostic signature is an independent predictive factor for the survival of patients with UVM and provides new information about cancer immunotherapy in UVM.
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A Novel Prognostic Signature Revealed the Interaction of Immune Cells in Tumor Microenvironment Based on Single-Cell RNA Sequencing for Lung Adenocarcinoma.
BACKGROUND: The tumor immune microenvironment (TIME) played an important role in immunotherapy prognosis and treatment response. Immune cells constitute a large part of the tumor microenvironment and regulate tumor progression. Our research is dedicated to studying the infiltrating immune cell in lung adenocarcinoma (LUAD) and seeking potential targets. METHODS: The scRNA-seq data were collected from our FDZSH and two public datasets. The code for cell-type mapping algorithms was downloaded from the CIBERSORTx portal. The bioinformatics data of LUAD patients could be approached from The Cancer Genome Atlas (TCGA) portal. Weighted gene coexpression network analysis (WGCNA) and least absolute shrinkage and selection operator (LASSO) analyses were performed to construct a risk model. TIMER2 and TIDE helped with the immune infiltration estimation, while PROGENy helped the cancer-related pathways' enrichment analysis. GSE31210 dataset and IMVigor ICB therapy cohort validated our findings as the external validation datasets. RESULTS: We clustered the scRNA-seq dataset (integrating our FDZSH datasets and other public datasets) into 23 subpopulations. After curated cell annotation, we implemented Cibersort and WGCNA analysis to anchor the brown module and natural killer cell cluster1 due to the most relationship with tumor trait. The overlap of the brown module gene, natural killer cell signature, and DEGs of tumor and adjacent normal samples was screened by LASSO Cox regression. The obtained 5-gene risk model showed an excellent prognostic performance in the validation dataset. Furthermore, there was a correlation between risk score and tumor-infiltrating immune cells and tumor genomics abnormity. Patients with higher risk scores had a significantly lower immunotherapy response rate. CONCLUSION: Our observations implied that immune cells played a pivotal role in TIME and established a 5-gene signature (including IDH2, ADRB2, SFTPC, CCDC69, and CCND2) on the basement of nature killer markers targeted by WGCNA analysis. The significance of clinical outcome and immunotherapy response prediction was validated robustly.
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Multi-Omics Reveals Dysregulated Neurotransmitter Systems in Aging and CNS Disorders.
The neurotransmitter system (NTS) composed of neurotransmitter receptors and transporters is associated with a variety of diseases and disorders. The identification of disease-associated NTS features is important for understanding disease mechanisms and for prioritizing potential therapeutic targets. The differences in the composition of NTS in aging and different diseases are not well understood. Here, we show how to integrate multi-omics data at scale, including single-cell and spatial transcriptomic, to identify disease-associated NTS. By integrating, annotating, and analyzing 368 publicly available single-nucleus RNA sequencing (snRNA-seq) datasets from 17 cohort studies, we created a snRNA-seq dataset for the prefrontal cortex (PFC) of the human brain. The resulting dataset included > 1 million cells covering healthy individuals throughout the life course after birth and eight common neuropsychiatric disorders. We comprehensively describe the cellular heterogeneity of NTS in aging and disease, and resolve the relationship between age or sex and disease-associated NTS. A module composed of NTS and their regulatory genes was constructed and showed disease-associated discriminatory signal, with supportive validation from protein-level and external datasets. In order to screen NTS networks that can simulate disease brain tissue, we conducted a comparative analysis of five disease-related cerebral organoids and found partial similarity between patient-derived organoids and parental brain tissues. Our work provides a multi-disease cell atlas based on large-scale snRNA-seq data and analyses the disease and sex heterogeneity of NTS, which provides new insights into the mechanisms of multiple CNS diseases and sheds light on disease precision therapy against NTS.
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A novel lactylation-related gene signature to predict prognosis and treatment response in lung adenocarcinoma.
BACKGROUND: Lactylation, a novel post-translational modification, has emerged as a critical regulatory mechanism in various biological processes, including tumor progression. However, its role and associated gene signatures in lung adenocarcinoma (LUAD) remain unclear. METHODS: RNA sequencing data of LUAD patients were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Unsupervised clustering was used to identify lactylation-related genes. A risk prognostic model was constructed using least absolute shrinkage and selection operator regression analysis and subsequently validated. A nomogram was then employed to optimize the clinical applicability of the risk score. Additionally, various algorithms were used to explore the relationship between the risk score and immune infiltration levels, with model genes analyzed based on single-cell sequencing. The effects of RCCD1 knockdown on LUAD cell proliferation and migration were evaluated through CCK8 and transwell assays. RESULTS: Higher risk scores were associated with poorer overall survival prognosis. Immune analysis revealed that the risk score may play a role in regulating the tumor microenvironment. Additionally, these risk scores were found to be associated with chemotherapy drug sensitivity. A series of experiments further demonstrated that RCCD1 promotes LUAD cell proliferation and migration in vitro. CONCLUSION: This study highlights the critical role of lactylation-related gene signatures in LUAD and their association with immune cell infiltration, providing insights into potential therapeutic targets and biomarkers for clinical application.
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Altered AP-1, RUNX and EGR chromatin dynamics drive fibrotic lung disease.
Pulmonary fibrosis, including systemic sclerosis-associated interstitial lung disease (SSc-ILD), involves myofibroblasts and SPP1hi macrophages as drivers of fibrosis. Single-cell RNA sequencing has delineated fibroblast and macrophages transcriptomes, but limited insight into transcriptional control of profibrotic gene programs. To address this challenge, we analyzed multiomic snATAC/snRNA-seq on explanted SSc-ILD and donor control lungs. The neural network tool ChromBPNet inferred increased TF binding at single base pair resolution to profibrotic genes, including CTHRC1 and ADAM12, in fibroblasts and SPP1 and CCL18 in macrophages. The novel algorithm HALO confirmed AP-1, RUNX, and EGR TF activity controlling profibrotic gene programs and established TF-regulatory element-gene networks. This TF action atlas provides comprehensive insights into the transcriptional regulation of fibroblasts and macrophages in healthy and fibrotic human lungs.
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3D tissue model to patient atlas: dynamic network topology reveals PDAC chemoresistance bottlenecks.
Pancreatic ductal adenocarcinoma (PDAC) develops chemoresistance through dynamic cell-state transitions, yet the regulatory network architecture governing drug-tolerant states remains poorly characterised, because static expression snapshots cannot capture how interaction topology evolves along treatment-induced trajectories. We established a 3D PANC-1 tissue model on decellularised porcine intestinal matrix, which reaches patient-like proliferation indices and tissue-niche-specific drug responses, and performed single-cell RNA sequencing across four conditions: control, gemcitabine (GEM), TGF-β1, and their combination. Coupling pseudotime trajectory inference to dynamic protein-protein interaction network analysis, we tracked betweenness centrality continuously across cell-state transitions rather than comparing discrete states. GEM survivors engaged a proliferative programme (E2F1, CDK1, AURKA, TPX2, TOP2A, BIRC5), whereas TGF-β1 induced an independent EMT attractor (SPOCK1, MBOAT2, COL5A1, THBS1, FN1). Where the two converged, an emergent bottleneck appeared: CDK1 betweenness centrality spiked selectively, restrained by CDKN1A (p21) and WEE1, defining an S-phase-persistence state enriched for GEM survivors. Cross-examination in a PDAC atlas of 726,107 cells from 231 patients showed 8.7-fold metastatic enrichment of triple-positive cells. This framework, linking controlled 3D tissue experimentation, single-cell systems analysis and clinical-scale patient data, is applicable to drug resistance across cancer types.
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Glomerular cell atlas of multi-disease model revealed the characteristic changes of glomerular cell subtypes in diseases.
Although a range of glomerular diseases profoundly affect glomerulus-associated cells, a comprehensive understanding of their molecular alterations is still lacking. Here, we performed in-depth analysis of glomerular data from mouse models of primary and secondary glomerulopathies and constructed a multi-disease cellular landscape of glomerular cells. We identified a putative subset of proliferative glomerular endothelial cells(gECs) that highly expresses genetic susceptibility genes associated with multiple glomerular diseases. Podocytes exhibited shared injury-associated cell types across different disease models. A podocyte subset highly expressing Endou, Cd200, Lgmn, Il18, Dmpk, and Spon2 was predominantly derived from ob/ob mice, whereas another podocyte subset with high expression of Selenbp1, Lpar1, S100a8, S100a9, and Sult1a1 was mainly observed in adriamycin-induced mice. Mesangial cells shared common injury-related alterations across diseases (high expression of Cxcl1, egr1, hspa1b, socs3 and dnajb1), while ob/ob mice exhibited a distinct mesangial cell subset (high expression of aldh1a2, thbs1 and fbln5). In contrast, the gECs displayed similar molecular changes across different diseases without giving rise to disease-specific subtypes. Intercellular ligand-receptor analysis underpins the recruitment of immune cells by injured mesangial cells and podocytes via specific engagement of pairs such as CXCL and MIF, respectively. Our study systematically elucidates the molecular alterations of glomerulus-associated cells across various diseases, providing a foundation and strategic insights for future targeted therapies tailored to specific glomerular disease contexts.
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Molecular and spatial profiling of the paraventricular nucleus of the thalamus.
The paraventricular nucleus of the thalamus (PVT) is known to regulate various cognitive and behavioral processes. However, while functional diversity among PVT circuits has often been linked to cellular differences, the molecular identity and spatial distribution of PVT cell types remain unclear. To address this gap, here we used single nucleus RNA sequencing (snRNA-seq) and identified five molecularly distinct PVT neuronal subtypes in the mouse brain. Additionally, multiplex fluorescent in situ hybridization of top marker genes revealed that PVT subtypes are organized by a combination of previously unidentified molecular gradients. Lastly, comparing our dataset with a recently published single-cell sequencing atlas of the thalamus yielded novel insight into the PVT's connectivity with the cortex, including unexpected innervation of auditory and visual areas. This comparison also revealed that our data contains a largely non-overlapping transcriptomic map of multiple midline thalamic nuclei. Collectively, our findings uncover previously unknown features of the molecular diversity and anatomical organization of the PVT and provide a valuable resource for future investigations.