Single-cell Transcriptomic Datasets Research Articles

Single-cell delineation of the microbiota-gut-brain axis: Probiotic intervention in Chd8 haploinsufficient mice.

Emerging research underscores the gut microbiome's impact on the nervous system via the microbiota-gut-brain axis, yet comprehensive insights remain limited. Using a CHD8-haploinsufficient model for autism spectrum disorder (ASD), we explored host-gut microbiota interactions by constructing a single-cell transcriptome atlas of brain and intestinal tissues in wild-type and mutant mice across three developmental stages. CHD8 haploinsufficiency caused delayed development of radial glial precursors and excitatory neural progenitors in the E14.5 brain, inflammation in the adult brain, immunodeficiency, and abnormal intestinal development. Selective CHD8 knockdown in intestinal epithelial cells generated Chd8ΔIEC mice, which exhibited normal sociability but impaired social novelty recognition. Probiotic intervention with Lactobacillus murinus selectively rescued social deficits in Chd8ΔIEC mice, with single-cell transcriptome analysis revealing underlying mechanisms. This study provides a detailed single-cell transcriptomic dataset of ASD-related neural and intestinal changes, advancing our understanding of the gut-brain axis and offering potential therapeutic strategies for ASD.

Spatial single-cell maps reveal ST6GAL1 promoting ovarian cancer metastasis.

In this study, spatial and single-cell transcriptome techniques were used to investigate the role of beta-galactoside alpha-2,6-sialyltransferase 1 (ST6GAL1) in promoting peritoneal metastasis in ovarian cancer epithelial cells. We collected single-cell transcriptomic (GSE130000) and spatial transcriptomic datasets (GSE211956) from the Gene Expression Omnibus and RNA-sequencing data from The Cancer Genome Atlas. The Robust Cell Type Decomposition (RCTD) approach was implemented to integrate spatial and single-cell transcriptomic data. In addition, pseudo-time trajectory analysis, cell-cell communication networks, transcription factor activity profiling, spatial interaction mapping, and prognostic significance of gene expression were assessed. A significant enrichment of ST6GAL1 was observed in the epithelial cells of ovarian cancer, particularly in peritoneal metastases, which exhibited elevated metabolic activity compared to primary tumors. The levels of ST6GAL1 were significantly high in peritumoral and adjacent non-tumorous tissues, with increased metabolic activity, while the tumor core demonstrated ST6GAL1-negative epithelial cells. Extensive cell-cell communication and transcription factor networks were unraveled, potentially influencing vascular permeability and intracellular signaling. Clinically, high expression of ST6GAL1 in epithelial cells is associated with diminished progression-free survival, indicating its prognostic potential. In conclusion, ST6GAL1 is likely to significantly impact the progression and metastasis of ovarian cancer.

A quantitative prediction method utilizing whole omics data for biosensing

Omics data provide a plethora of quantifiable information that can potentially be used to identify biomarkers targeting the physiological processes and ecological phenomena of organisms. However, omics data have not been fully utilized because current prediction methods in biomarker construction are susceptible to data multidimensionality and noise. We developed OmicSense, a quantitative prediction method that uses a mixture of Gaussian distributions as the probability distribution, yielding the most likely objective variable predicted for each biomarker. Our benchmark test using a transcriptome dataset revealed that OmicSense achieves accurate and robust prediction against background noise without overfitting. Weighted gene co-expression network analysis revealed that OmicSense preferentially utilized hub nodes of the network, indicating the interpretability of the method. Application of OmicSense to single-cell transcriptome, metabolome, and microbiome datasets confirmed high prediction performance (r > 0.8), suggesting applicability to diverse scientific fields. Given the recent rapidly expanding availability of omics data, the developed prediction tool OmicSense, can accelerate the use of omics data as a “biosensor” based on an assemblage of potential biomarkers.

An integrated investigation of mitochondrial genes in COPD reveals the causal effect of NDUFS2 by regulating pulmonary macrophages

BackgroundDespite the increasing body of evidence that mitochondrial activities implicate in chronic obstructive pulmonary disease (COPD), we are still far from a causal-logical and mechanistic understanding of the mitochondrial malfunctions in COPD pathogenesis.ResultsDifferential expression genes (DEGs) from six publicly available bulk human lung tissue transcriptomic datasets of COPD patients were intersected with the known mitochondria-related genes from MitoCarta3.0 to obtain mitochondria-related DEGs associated with COPD (MitoDEGs). The 32 hub MitoDEGs identified from protein-protein interaction (PPI) networks demonstrated superior overall diagnostic efficacy to non-hub MitoDEGs. Random forest (RF) analysis, least absolute shrinkage and selection operator (LASSO) regression, and Mendelian Randomization (MR) analysis of hub MitoDEGs further nominated NDUFS2, CAT, and MRPL2 as causal MitoDEGs for COPD, whose predominate expressions in pulmonary macrophages were revealed by an independent single-cell transcriptomic dataset of COPD human lungs. Finally, NDUFS2 was evaluated as the top-ranked contributor to COPD in the nomogram model and its downregulation in pulmonary macrophages could result in pro-inflammatory secretion, enhanced intercellular communications, whereas depressed phagocytosis of macrophages as revealed by gene set variation analysis (GSVA) and cell-cell interaction (CCI) analysis of single-cell transcriptomic dataset of COPD human lungs, which was later confirmed in COPD mouse model and macrophage cell lines.ConclusionsOur study established the causal linkage between mitochondrial malfunctions and COPD, providing a potential therapeutic avenue to alleviate pulmonary inflammation accounting for COPD by targeting mitochondria-related genes. NDUFS2, a canonical component of mitochondrial electron respiratory chain, was highlighted instrumental for the susceptibility of risk-exposed individuals to COPD.

Genetic Analysis of Retinal Cell Types in Neuropsychiatric Disorders

As an accessible part of the central nervous system, the retina provides a unique window to study pathophysiological mechanisms of brain disorders in humans. Imaging and electrophysiological studies have revealed retinal alterations across several neuropsychiatric and neurological disorders, but it remains largely unclear which specific cell types and biological mechanisms are involved. To determine whether specific retinal cell types are affected by genomic risk for neuropsychiatric and neurological disorders and to explore the mechanisms through which genomic risk converges in these cell types. This genetic association study combined findings from genome-wide association studies in schizophrenia, bipolar disorder, major depressive disorder, multiple sclerosis, Parkinson disease, Alzheimer disease, and stroke with retinal single-cell transcriptomic datasets from humans, macaques, and mice. To identify susceptible cell types, Multi-Marker Analysis of Genomic Annotation (MAGMA) cell-type enrichment analyses were applied and subsequent pathway analyses performed. The cellular top hits were translated to the structural level using retinal optical coherence tomography (acquired between 2009 and 2010) and genotyping data in the large population-based UK Biobank cohort study. Data analysis was conducted between 2022 and 2024. Cell type-specific enrichment of genetic risk loading for neuropsychiatric and neurological disorder traits in the gene expression profiles of retinal cells. Expression profiles of amacrine cells (interneurons within the retina) were robustly enriched in schizophrenia genetic risk across mammalian species and in different developmental stages. This enrichment was primarily driven by genes involved in synapse biology. Moreover, expression profiles of retinal immune cell populations were enriched in multiple sclerosis genetic risk. No consistent cell-type associations were found for bipolar disorder, major depressive disorder, Parkinson disease, Alzheimer disease, or stroke. On the structural level, higher polygenic risk for schizophrenia was associated with thinning of the ganglion cell inner plexiform layer, which contains dendrites and synaptic connections of amacrine cells (B, -0.09; 95% CI, -0.16 to -0.03; P = .007; n = 36 349; mean [SD] age, 57.50 [8.00] years; 19 859 female [54.63%]). Higher polygenic risk for multiple sclerosis was associated with increased thickness of the retinal nerve fiber layer (B, 0.06; 95% CI, 0.02 to 0.10; P = .007; n = 36 371; mean [SD] age, 57.51 [8.00] years; 19 843 female [54.56%]). This study provides novel insights into the cellular underpinnings of retinal alterations in neuropsychiatric and neurological disorders and highlights the retina as a potential proxy to study synaptic pathology in schizophrenia.

PCA-based spatial domain identification with state-of-the-art performance.

The identification of biologically meaningful domains is a central step in the analysis of spatial transcriptomic data. Following Occam's razor, we show that a simple PCA-based algorithm for unsupervised spatial domain identification rivals the performance of ten competing state-of-the-art methods across six single-cell spatial transcriptomic datasets. Our reductionist approach, NichePCA, provides researchers with intuitive domain interpretation and excels in execution speed, robustness, and scalability. The code is available at https://github.com/imsb-uke/nichepca. Supplementary data are available at Bioinformatics online.

Endothelial Dysfunction in the Tubule Area Accelerates the Progression of Early Diabetic Kidney Disease

Diabetic kidney disease (DKD) is the leading cause of end-stage renal disease. Therefore, understanding the molecular regulatory mechanisms underlying the pathogenesis of DKD is imperative. In this study, we aimed to explore the molecular mechanisms of tubule region endothelial dysfunction in early DKD. Early-stage DKD model was established in 16-week-old female db/db mice for 16 weeks. Body weight, glucose level, and urine albumin-to-creatinine ratio (UACR) were measured. Hematoxylin and eosin (H&E) and periodic acid-Schiff (PAS) staining were performed to evaluate pathological lesions. RNA sequencing data of the kidneys and integrated publicly available single-cell and spatial transcriptome datasets were used to investigate the mechanism of endothelial dysfunction. There was a significant increase in body weight (p = 0.001), glucose levels (p=0.0008), and UACR (p=0.006) in db/db mice compared with db/m mice. H&E and PAS staining showed that vacuolar lesions and protein casts of tubules were the major histopathological changes observed in early-stage DKD mice. The apoptotic pathway in endothelial cells was notably activated in DKD, and Thbs1 was identified as the central gene involved in this apoptotic process. Deconvolution of the cell composition in the RNA sequencing data showed a decrease in the proportion of endothelial cells in the DKD mice. Further analysis of the activity and regulatory network of transcription factors showed that Creb1 was activated in both mouse and human early-stage DKD, suggesting that Creb1 activation may be involved in early kidney injury. The endothelial cell apoptotic pathway is activated in DKD, and the proportion of endothelial cells was reduced in the DKD mice, which is significantly associated with Thbs1.

Mesenchymal Traits as an Intrinsic Feature of Undifferentiated Cells

Since its first conceptualization over a century ago, the mesenchymal phenotype has traditionally been viewed as either a transient phase between successive epithelial stages or as a feature of cell types primarily devoted to structural support. However, recent findings in cancer research challenge this limited view, demonstrating that mesenchymal traits and hybrid mesenchymal/epithelial states can mark cancer cells with stem cell properties. By analyzing publicly available single-cell transcriptome datasets from early embryonic stages and adult tissues, this study aims to extend this concept beyond pathological contexts, suggesting that a partial or fully mesenchymal phenotype may represent the morphological expression of undifferentiated and multipotent states in both the developing embryo and adult organs.

Deciphering the cell type-specific and zonal distribution of drug-metabolizing enzymes, transporters, and transcription factors in livers of mice using single-cell transcriptomics.

The liver contains multiple cell types, including resident cell types and immune cells. The liver is also categorized into 3 zones: periportal (zone 1), midzonal (zone 2), and centrilobular (zone 3). The goal of this study was to characterize the distribution of drug-processing genes (DPGs) in mouse liver using published single-cell and nuclei transcriptomic datasets, which were subjected to zonal deconvolution. Filtering, normalization, clustering, and differential expression analyses were performed using Seurat V5 in R. Hepatocytes were assigned to 3 zones based on known zonal markers and validated with published spatial transcriptomics data. Among the 195 DPGs profiled, most were expressed highest in hepatocytes (61.3%). Interestingly, certain DPGs were expressed most highly in nonparenchymal cells, such as in cholangiocytes (11.2%, eg, carboxylesterase [Ces] 2e, Ces2g), endothelial cells (7.2%, eg, aldo-keto reductase [Akr] 1c19, Akr1e1), Kupffer cells (5.3%, eg, Akr1a1, Akr1b10), stellate cells (5.1%, eg, retinoic acid receptor [Rar] α, Rarβ), myofibroblasts (2.9%, RAR-related orphan receptor [Rar] α), and a few were expressed in immune cell types. In hepatocytes, 72.4% of phase-I enzymes were enriched in zone 3. Phase-II conjugation enzymes such as UDP-glucuronosyltransferases (75%) were enriched in zone 3, whereas sulfotransferases (40%) were enriched in zone 1. Hepatic xenobiotic transporters were enriched in zone 3. The xenobiotic biotransformation-regulating transcription factors were enriched in zone 3 hepatocytes. The enrichment of DPGs in liver cell types, including non-parenchymal cells and zone 1 hepatocytes, may serve as an additional repertoire for xenobiotic biotransformation. SIGNIFICANCE STATEMENT: Our study is among the first to systematically characterize the baseline mRNA enrichment of important drug-processing genes in different cell types and zones in the liver. This finding will aid in further understanding the mechanisms of chemical-induced liver injury with improved resolution and precision.

Current transcriptome database and biomarker discovery for immunotherapy by immune checkpoint blockade.

Immune checkpoint blockade (ICB) has revolutionized the current immuno-oncology and significantly improved clinical outcome for cancer treatment. Despite the advancement in clinics, only a small subset of patients derives immune response to the ICB therapy. Therefore, a robust predictive biomarker that identifies potential candidate becomes increasingly crucial in delivering this technology to the public. In this review, we first discuss the biomarkers that focus on tumor genome, tumor microenvironment and tumor-host interaction. Then, we compare existing databases for biomarker discovery for ICB response. We also present IOhub - an interactive web portal that incorporates 36 bulk and 10 single-cell transcriptome datasets for benchmark analysis of the current biomarkers. Finally, we highlight the trending interest in antibody drug conjugate and combination treatment and their use in precision immuno-oncology.

Tumour heterogeneity and personalized treatment screening based on single-cell transcriptomics.

According to global cancer statistics for the year 2022, based on updated estimates from the International Agency for Research on Cancer, there were approximately 20 million new cases of cancer in 2022 alongside 9.7 million related deaths. Lung, breast, colorectal, gastric, and liver cancers are the most common types of cancer. Despite advancements in anticancer drugs and optimised chemotherapy regimens that have improved cure rates for malignant tumours, the presence of tumour heterogeneity has resulted in substantial variations among patients in terms of disease progression, clinical response, sensitivity to therapy, and prognosis, posing significant challenges in attaining optimal therapeutic outcomes for each patient. Here, we collected five single-cell transcriptome datasets from patients with lung, breast, colorectal, gastric, and liver cancers and constructed multiple cancer blueprints of tumour cell heterogeneity. By integrating multiple bioinformatics analyses, we explored the biological differences underlying tumour cell heterogeneity at the single-cell level and identified tumour cell subcluster-specific biomarkers and potential therapeutic drugs for each subcluster. Interestingly, although tumour cell subpopulations exhibit dramatic differences within the same cancer type and between different cancers at both the genomic and transcriptomic levels, some demonstrate similar oncogenic pathway activities and phenotypes. Tumour cell subpopulations from the five cancers listed above were classified into three major groups corresponding to different treatment strategies. The findings of this study not only focus on the differences but also on the similarities among tumour cell subpopulations across different cancers, providing new insights for individualised therapy.

IDclust: Iterative clustering for unsupervised identification of cell types with single cell transcriptomics and epigenomics.

The increasing diversity of single-cell datasets require systematic cell type characterization. Clustering is a critical step in single-cell analysis, heavily influencing downstream analyses. However, current unsupervised clustering algorithms rely on biologically irrelevant parameters that require manual optimization and fail to capture hierarchical relationships between clusters. We developed IDclust, a framework that identifies clusters with significant biological features at multiple resolutions using biologically meaningful thresholds like fold change, adjusted P-value and fraction of expressing cells. By iteratively processing and clustering subsets of the dataset, IDclust guarantees that all clusters found have significantly different features and stops only when no more interpretable cluster is found. It also creates a hierarchy of clusters, enabling visualization of the hierarchical relationships between different clusters. Analyzing multiple single-cell transcriptomic reference datasets, IDclust achieves superior clustering accuracy compared to state of the art algorithms. We showcase its utility by identifying previously unannotated clusters and identifying branching patterns in scATAC datasets. Using it's unsupervised nature and ability to analyze different -omics, we compare the resolution of different histone marks in multi-omic paired-tag dataset. Overall, IDclust automates single-cell exploration, facilitates cell type annotation and provides a biologically interpretable basis for clustering.

An integrated single-cell atlas of blood immune cells in aging

Recent advances in single-cell technologies have facilitated studies on age-related alterations in the immune system. However, previous studies have often employed different marker genes to annotate immune cell populations, making it challenging to compare results. In this study, we combined seven single-cell transcriptomic datasets, comprising more than a million cells from one hundred and three donors, to create a unified atlas of human peripheral blood mononuclear cells (PBMC) from both young and old individuals. Using a consistent set of marker genes for immune cell annotation, we standardized the classification of immune cells and assessed their prevalence in both age groups. The integrated dataset revealed several consistent trends related to aging, including a decline in CD8+ naive T cells and MAIT cells and an expansion of non-classical monocyte compartments. However, we observed significant variability in other cell types. Our analysis of the long non-coding RNA MALAT1hi T cell population, previously implicated in age-related T cell exhaustion, showed that this population is highly heterogeneous with a mixture of naïve-like and memory-like cells. Despite substantial variation among the datasets when comparing gene expression between age groups, we identified a high-confidence signature of CD8+ naive T cell aging marked by an increased expression of pro-inflammatory genes. In conclusion, our study emphasizes the importance of standardizing existing single-cell datasets to enable the comprehensive examination of age-related cellular changes across multiple datasets.

Supervised analysis of alternative polyadenylation from single-cell and spatial transcriptomics data with spvAPA.

Alternative polyadenylation (APA) is an important driver of transcriptome diversity that generates messenger RNA isoforms with distinct 3' ends. The rapid development of single-cell and spatial transcriptomic technologies opened up new opportunities for exploring APA data to discover hidden cell subpopulations invisible in conventional gene expression analysis. However, conventional gene-level analysis tools are not fully applicable to APA data, and commonly used unsupervised dimensionality reduction methods often disregard experimentally derived annotations such as cell type identities. Here, we proposed a supervised analytical framework termed spvAPA, specifically used for APA analysis from both single-cell and spatial transcriptomics data. First, an iterative imputation method based on weighted nearest neighbor was designed to recover missing APA signatures, by integrating both gene expression and APA modalities. Second, a supervised feature selection method based on sparse partial least squares discriminant analysis was devised to identify APA features distinguishing cell types or spatial morphologies. Additionally, spvAPA improves the visualization of high-dimensional data for discovering novel cell subtypes, which considers APA features and dual modalities of gene expression and APA. Evaluations across nine single-cell and spatial transcriptomics datasets demonstrate the effectiveness and applicability of spvAPA. spvAPA is available at https://github.com/BMILAB/spvAPA.

An integrated transcriptomic cell atlas of human neural organoids

Human neural organoids, generated from pluripotent stem cells in vitro, are useful tools to study human brain development, evolution and disease. However, it is unclear which parts of the human brain are covered by existing protocols, and it has been difficult to quantitatively assess organoid variation and fidelity. Here we integrate 36 single-cell transcriptomic datasets spanning 26 protocols into one integrated human neural organoid cell atlas totalling more than 1.7 million cells1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25–26. Mapping to developing human brain references27, 28, 29–30 shows primary cell types and states that have been generated in vitro, and estimates transcriptomic similarity between primary and organoid counterparts across protocols. We provide a programmatic interface to browse the atlas and query new datasets, and showcase the power of the atlas to annotate organoid cell types and evaluate new organoid protocols. Finally, we show that the atlas can be used as a diverse control cohort to annotate and compare organoid models of neural disease, identifying genes and pathways that may underlie pathological mechanisms with the neural models. The human neural organoid cell atlas will be useful to assess organoid fidelity, characterize perturbed and diseased states and facilitate protocol development.

M5C related-regulator-mediated methylation modification patterns and prognostic significance in breast cancer

5-Methylcytosine (m5C) is closely associated with cancer. However, the role of m5C in breast cancer(BC)remains unclear. This study combined single-cell RNA sequencing (scRNA-Seq) and transcriptomics datasets to screen m5C regulators associated with BC progression and analyze their clinical values. Firstly, This study elucidates the mechanisms of the m5C landscape and the specific roles of m5C regulators in BC patients. we found that the dysregulation of m5C regulators with m5Cscore play the essential role of the carcinogenesis and progression in epithelial cells and myeloid cells of BC at single cell level. External validation was conducted using an independent scRNA-Seq datasets. Then, three distinct m5C modification patterns were identified by transcriptomics datasets. Based on the m5C differentially expressed regulators, the m5Cscore was constructed, and used to divide patients with BC into high and low m5Cscore groups. Patients with a high m5Cscore had more abundant immune cell infiltration, stronger antitumor immunity, and better prognoses. Finally, Quantitative real-time (PCR) and immunohistochemistry were used for the in vitro experimental validation, which had extensive prognostic value. In this study, we aimed to assess the expression of m5C regulators involved in BC and investigate their correlation with the tumor microenvironment, clinicopathological characteristics, and prognosis of BC. The m5C regulators could be used to effectively assess the cell specific regulation prognosis of patients with BC and develop more effective immunotherapy strategies.

Single cell analysis identified a basal cell transition state associated with the development and progression of bladder cancer

BackgroundBladder cancer (BC) is a prevalent malignancy characterized by significant cellular heterogeneity. While single-cell multi-omics studies have provided valuable insights, much of the existing data remains underexplored, limiting our understanding of BC’s molecular mechanisms. Uncovering the pathogenesis of BC and finding new treatment methods are urgent problems to be solved. This study aims to address this gap by re-analyzing available single-cell datasets to uncover novel insights into BC.MethodsIn this study, we retrieved three single-cell transcriptome datasets by searching the Gene Expression Omnibus (GEO) database, focusing on single-cell sequencing of normal mouse bladder within the past 5 years. Through quality control and batch effect elimination, we obtained a total of 24,930 cells including epithelial, stromal, and immune cells. Subgroup analysis, pseudotemporal analysis, cell–cell communication, and transcription factor analysis were conducted specifically on epithelial cells to identify a transitional state during basal cell differentiation. We further compared the expression profiles of key transcription factors in cancer and normal tissues. In addition, we also performed immunohistochemical staining and survival analysis for key transcription factors.ResultsSubgroup analysis revealed multiple subtypes of epithelial cells, including basal, umbrella, and intermediate cells. Through pseudotemporal analysis, we discovered the developmental trajectory from basal cells to umbrella cells and further found that Basal_I is a transitional state for basal cell differentiation. Cell-to-cell communication analyses highlighted the pivotal role of Basal_I in cell–cell interactions, and key ligand-receptor pairs associated with cancer progression were also identified. Furthermore, elevated expression levels of key transcription factors in Basal_I were found to be closely associated with the stage and prognosis of BC. Immunohistochemical staining results further confirmed the upregulated expression of these transcription factors in BC.ConclusionsCollectively, we found a transitional state of basal cells in normal bladder epithelial cells in mice, which may be related to the occurrence and development of BC, providing important clues for further understanding of the pathogenesis of BC. Our study provided possible molecular mechanisms or target for the research and treatment of BC.

Five-gene prognostic model based on autophagy-dependent cell death for predicting prognosis in lung adenocarcinoma

Non-small cell lung adenocarcinoma (LUAD) is the predominant form of lung cancer originating from lung epithelial cells, making it the most prevalent pathological type. Currently, reliable indicators for predicting treatment efficacy and disease prognosis are lacking. Despite extensive validation of autophagy-dependent cell death (ADCD) in solid tumor studies and its correlation with immunotherapy effectiveness and cancer prognosis, systematic research on ADCD-related genes in LUAD is limited. We utilized AddModuleScore, ssGSEA, and WGCNA to identify genes associated with ADCD across single-cell and bulk transcriptome datasets. The TCGA dataset, comprising 598 cases, was randomly divided into training and validation sets to develop an ADCD-related LUAD prediction model. Internal validation was performed using the TCGA validation set. For external validation, datasets GSE13213 (119 LUAD samples), GSE26939 (115 LUAD samples), GSE29016 (39 LUAD samples), and GSE30219 (86 LUAD samples) were employed. We evaluated the model’s accuracy and effectiveness in predicting prognostic risk. Additionally, CIBERSORT, ESTIMATE, and ssGSEA techniques were used to explore immunological characteristics, drug response, and gene expression in LUAD. Real-time RT-PCR was conducted to assess variations in mRNA expression levels of the gene XCR1 between cancerous and normal tissues in 10 lung cancer patients. We identified 249 genes associated with autophagy-dependent cell death (ADCD) at both single-cell and bulk transcriptome levels. Univariate COX regression analysis revealed that 18 genes were significantly associated with overall survival (OS). Using LASSO-Cox analysis, we developed an ADCD signature based on five genes (BIRC3, TAP1, SLAMF1, XCR1, and HLA-DMB) and created the ADCD-related risk scoring system (ADCDRS). Validation of this model demonstrated its ability to predict disease prognosis and its correlation with clinical characteristics, immune cell infiltration, and the tumor microenvironment. To enhance clinical applicability, we integrated an ADCDRS nomogram. Furthermore, we identified potential drugs targeting specific risk subgroups. We successfully identified a model based on five ADCD genes to predict disease prognosis and treatment efficacy in LUAD, as well as to assess the tumor immune microenvironment. An efficient and practical ADCDRS nomogram was designed.

Single-cell and bulk transcriptomic datasets enable the development of prognostic models based on dynamic changes in the tumor immune microenvironment in patients with hepatocellular carcinoma and portal vein tumor thrombus.

Hepatocellular carcinoma (HCC) patients exhibiting portal vein tumor thrombosis (PVTT) face a high risk of rapid malignant progression and poor outcomes, with this issue being compounded by a lack of effective treatment options. The integration of bulk RNA-sequencing (RNA-seq) and single-cell RNA-seq (scRNA-seq) datasets focused on samples from HCC patients with PVTT has the potential to yield unprecedented insight into the dynamic changes in the tumor microenvironment (TME) and associated immunological characteristics in these patients, providing an invaluable tool for the reliable prediction of disease progression and treatment responses. scRNA-seq data from both primary tumor (PT) and PVTT cells were downloaded from the Gene Expression Omnibus (GEO) database, while the International Cancer Genome Consortium (ICGC) and Cancer Genome Atlas (TCGA) databases were used to access bulk RNA-seq datasets. scRNA-seq, clustering, GSVA enrichment, mutational profiling, and predictive immunotherapeutic treatment analyses were conducted using these data with the goal of systematically assessing the heterogeneity of PT and PVTT cells and establishing a model capable of predicting immunotherapeutic and prognostic outcomes in patients with HCC. These analyses revealed that PVTT cells exhibited patterns of tumor proliferation, stromal activation, and low levels of immune cell infiltration, presenting with immune desert and immune rejection-like phenotypes. PT cells, in contrast, were found to exhibit a pattern of immunoinflammatory activity. Core PVTT-associated genes were clustered into three patterns consistent with the tumor immune rejection and immune desert phenotypes. An established clustering model was capable of predicting tumor inflammatory stage, subtype, TME stromal activity, and patient outcomes. PVTT signature genes were further used to establish a risk model, with the risk scores derived from this model providing a tool to evaluate patient clinicopathological features including clinical stage, tumor differentiation, histological subtype, microsatellite instability status, and tumor mutational burden. These risk scores were also able to serve as an independent predictor of patient survival outcomes, responses to adjuvant chemotherapy, and responses to immunotherapy. In vitro experiments were used to partially validate the biological prediction results. These results offer new insight into the biological and immunological landscape of PVTT in HCC patients, By utilizing individual patient risk scores, providing an opportunity to guide more effective immunotherapeutic interventional efforts.

Co-inhibition of PGF and VEGFA enhances the effectiveness of immunotherapy in bladder cancer.

Background: Anti-angiogenic inhibitors and immune checkpoint blockade combination therapy offers a novel approach to circumvent the challenges associated with limited responsiveness to checkpoint inhibitors in bladder cancer. However, the effective strategies for inhibiting angiogenesis in bladder cancer need further elucidation. Objective: This work aims to identify key targets for the effective inhibition of angiogenesis in bladder cancer and to explore the potential benefits of combining anti-angiogenic therapies with immune checkpoint blockade strategies in the treatment of this disease. Methods: Cell-cell interaction analysis was performed using bladder cancer single-cell transcriptome datasets downloaded from the Gene Expression Omnibus (GEO) database to determine the regulatory network driving angiogenesis in bladder cancer. The bladder cancer cell line MBT2 was orthotopically transplanted into mice to investigate the impact of pro-angiogenic molecules on angiogenesis and tumor growth, and to evaluate the synergistic therapeutic potential of a combination therapy targeting angiogenesis and Programmed Cell Death Protein 1 (PD-1). Proliferation and tube formation assays with Human Umbilical Vein Endothelial Cells (HUVECs) were used to explore the regulatory functions of pro-angiogenic molecules in angiogenesis. Results: Placental growth factor (PGF) is a pro-angiogenic factor in bladder cancer, in addition to vascular endothelial growth factor A (VEGFA). Suppression of PGF reduced the tumor size and angiogenesis in bladder cancer. The expression level of vascular endothelial growth factor receptor 1 (VEGFR1) is higher than that of vascular endothelial growth factor receptor2 (VEGFR2) in the endothelial cells of bladder cancer. The pro-angiogenic activity of PGF is dependent on the expression level of VEGFR1 in endothelial cells. The combined inhibition of PGF and VEGFA exerts a synergistic effect on suppressing tumor growth and angiogenesis. The concurrent inhibition of PGF and VEGFA stands out as the only intervention capable of significantly enhancing the infiltration of CD8+ cytotoxic T cells within the bladder cancer microenvironment. In the bladder cancer mouse model, the introduction of an anti- programmed cell death protein 1 (PD-1) therapeutic regimen combined with the targeted inhibition of PGF and VEGFA, led to a significantly elevated survival rate compared to the outcome observed with anti-PD-1 monotherapy. Conclusion: PGF is a pro-angiogenic molecule in bladder cancer that requires significant expression levels of VEGFR1 in endothelial cells. Notably, the concurrent inhibition of PGF and VEGFA amplifies the therapeutic impact of anti-PD-1 treatment in bladder cancer. These findings provide further insights into the role of PGF in angiogenesis regulation and have conceptual implications for combining anti-angiogenic therapy with immune therapy in bladder cancer treatment.