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Deciphering intratumoral heterogeneity of hepatocellular carcinoma with microvascular invasion with radiogenomic analysis.
BACKGROUND AND AIMS: The recurrence and metastasis of hepatocellular carcinoma (HCC) are mainly caused by microvascular invasion (MVI). Our study aimed to uncover the cellular atlas of MVI+ HCC and investigate the underlying immune infiltration patterns with radiomics features. METHODS: Three MVI positive HCC and three MVI negative HCC samples were collected for single-cell RNA-seq analysis. 26 MVI positive HCC and 30 MVI negative HCC tissues were underwent bulk RNA-seq analysis. For radiomics analysis, radiomics features score (Radscore) were built using preoperative contrast MRI for MVI prediction and overall survival prediction. We deciphered the metabolism profiles of MVI+ HCC using scMetabolism and scFEA. The correlation of Radscore with the level of APOE+ macrophages and iCAFs was identified. Whole Exome Sequencing (WES) was applied to distinguish intrahepatic metastasis (IM) and multicentric occurrence (MO). Transcriptome profiles were compared between IM and MO. RESULTS: Elevated levels of APOE+ macrophages and iCAFs were detected in MVI+ HCC. There was a strong correlation between the infiltration of APOE+ macrophages and iCAFs, as confirmed by immunofluorescent staining. MVI positive tumors exhibited increased lipid metabolism, which was attributed to the increased presence of APOE+ macrophages. APOE+ macrophages and iCAFs were also found in high levels in IM, as opposed to MO. The difference of infiltration level and Radscore between two nodules in IM was relatively small. Furthermore, we developed Radscore for predicting MVI and HCC prognostication that were also able to predict the level of infiltration of APOE+ macrophages and iCAFs. CONCLUSION: This study demonstrated the interactions of cell subpopulations and distinct metabolism profiles in MVI+ HCC. Besides, MVI prediction Radscore and MVI prognostic Radscore were highly correlated with the infiltration of APOE+ macrophages and iCAFs, which helped to understand the biological significance of radiomics and optimize treatment strategy for MVI+ HCC.
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Identification of metabolic signatures related to metastasis and immunotherapy resistance in oral squamous cell carcinoma.
OBJECTIVES: In this study, we aimed to identify the metabolic genes associated with the metastasis and immunotherapy resistance of oral squamous cell carcinoma (OSCC) and to construct a metabolic gene-related predictive model for the prognosis of OSCC. METHODS: RNA-seq data were download from The Cancer Genome Atlas (TCGA). Weighted gene co-expression network analysis (WGCNA) was applied to identify the modules related to EMT, stemness, and checkpoint signatures in OSCC. Univariate Cox and the least absolute shrinkage and selection operator (LASSO) methods were used to construct the metabolic gene signature. Furthermore, the scRNA-seq data were obtained from Gene Expression Omnibus (GEO) database and analyzed using "Seurat" and "CopyKAT" packages. RESULTS: The risk prediction model was constructed using the 12 metabolic-related gene signature. Based on this model, risk score of each sample was calculated and used to divide the samples into low- and high-risk groups. Our model was effective as the risk score was significantly associated with clinical features and genetic mutations. Meanwhile, we found that lipid metabolism, glycolysis, amino acid metabolism, and drug metabolism differed between high- and low-risk groups. Pathways associated with malignant tumor and immunosuppression were enriched in high-risk group. Furthermore, low-risk group showed a more activated immune status and was predicted to have better response to immunotherapy. Finally, through single-cell transcriptome analysis, we assessed the expression of these 12 genes in tumor and non-tumor cells and verified the existence of two clusters of tumor cells with different degrees of malignancy at the cellular level. CONCLUSIONS: Our study demonstrates the clinical significance of metabolic related gene signature for the treatment of OSCC and suggests potential therapeutic targets and pathways for OSCC.
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A radiotherapy resistance-related prognostic signature predicts survival and the immune landscape in rectal cancer.
BACKGROUND: Radiotherapy resistance remains a significant challenge in the management of locally advanced rectal cancer (LARC). To date, no universally applicable and reliable prognostic marker has been established for clinical practice. Thus, reliable biomarkers need to be identified and the molecular mechanisms underlying radiotherapy resistance need to be investigated to improve patient prognosis. The study aimed to identify a radiotherapy-related signature for evaluating radiotherapy response and predicting the overall survival (OS) of patients with rectal cancer. METHODS: Multiple independent sources of transcriptomic datasets from The Cancer Genome Atlas (TCGA) (training cohort, n=154) and the Gene Expression Omnibus (GEO), including GSE35452 (containing radiotherapy response and non-response cohorts) and GSE87211 (validation cohort), were systematically integrated. A prognostic signature was developed by identifying radiotherapy-related genes through differential expression analysis and weighted gene correlation network analysis (WGCNA), followed by least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses. Functional enrichment, immune microenvironment characterization, and single-cell RNA sequencing (scRNA-seq) analyses were subsequently performed to explore the mechanisms underlying radiotherapy resistance. RESULTS: A four-gene prognostic signature comprising CUTA, IZUMO2, PALB2, and PSCA was established. The signature effectively stratified patients into high- and low-risk groups with distinct OS outcomes (TCGA: P<0.001; GSE87211: P=0.002) and served as an independent prognostic factor [multivariate Cox hazard ratio (HR) =2.532, P<0.001]. The high-risk group exhibited distinct immune environment characteristics and enrichment of epithelial-mesenchymal transition (EMT) pathways compared to the low-risk group. The scRNA-seq analysis revealed that PSCA expression was restricted to an epithelial subpopulation characterized by enhanced cell-cell communication and a more advanced pseudotime trajectory. CONCLUSIONS: This study developed and validated a four-gene signature for survival prediction in rectal cancer (RC). Functional enrichment and immune microenvironment characterization analyses indicated that the signature was associated with tumor heterogeneity and differential treatment responses in RC. The single-cell analysis indicated that PSCA may be a key gene in this process.
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Practical bioinformatics pipelines for single-cell RNA-seq data analysis.
Single-cell RNA sequencing (scRNA-seq) is a revolutionary tool to explore cells. With an increasing number of scRNA-seq data analysis tools that have been developed, it is challenging for users to choose and compare their performance. Here, we present an overview of the workflow for computational analysis of scRNA-seq data. We detail the steps of a typical scRNA-seq analysis, including experimental design, pre-processing and quality control, feature selection, dimensionality reduction, cell clustering and annotation, and downstream analysis including batch correction, trajectory inference and cell-cell communication. We provide guidelines according to our best practice. This review will be helpful for the experimentalists interested in analyzing their data, and will aid the users seeking to update their analysis pipelines.
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Single-nucleus transcriptomic analysis reveals subfield-specific cell-to-cell synaptic reorganisation of the mouse hippocampus in focal temporal lobe epilepsy.
BACKGROUND: Preclinical animal models are critical tools to study the pathophysiology of epilepsy. The intra-amygdala microinjection of kainic acid in mice recapitulates key features of drug-resistant temporal lobe epilepsy, including unilateral hippocampal pathology and broad resistance to anti-seizure medicines, and is increasingly used to discover novel targets for disease-modification. Defining the gene expression landscape at single-cell resolution may reveal how different cell types adapt to chronic seizures and help identify additional biomarkers and targets in this model. METHODS: We generated a single-nucleus RNA-seq dataset using ipsilateral hippocampi collected two weeks after status epilepticus in the mouse intra-amygdala kainic acid model. Samples from epileptic and control mice were processed using the 10X Genomics platform, with a total of 34,737 nuclei sequenced representing all major cellular subtypes in the hippocampus. RESULTS: Differential gene expression analysis revealed cell subtype- and subfield-specific dysregulation across the hippocampus in epileptic mice. Activity-regulated genes were increased in neurons in the dentate gyrus, CA1 and CA3 subfields. Dentate gyrus neurons displayed the greatest differential gene expression, enriched for synaptogenesis, plasticity and NMDA receptor signalling. Transcription profiles for CA3 glutamatergic neurons indicated mechanical and injury responses whereas CA1 signatures were enriched for neuroinflammation and wound healing. Astrocytes displayed the most differential gene expression among glial populations, characterised by mixed reactive pro- and anti-inflammatory responses and altered metabolic states. Cell-cell communication analysis predicted extensive remodelling of hippocampal signalling across cell subtypes, including for glutamate and latrophilin-mediated ligand-receptor pairing among dentate gyrus and CA2 neuron connections. CONCLUSIONS: Our study provides an atlas of transcriptomic changes at the single cell level for the mouse intra-amygdala kainic acid model of temporal lobe epilepsy, highlighting network adaptations to hyperexcitability. The findings support a cell subtype basis for gene expression changes underlying pathophysiology and can guide novel therapeutic strategies to reduce or reverse chronic neuronal hyperexcitability in drug-resistant epilepsy.
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Conduction and validation of a novel prognostic signature in cervical cancer based on the necroptosis characteristic genes via integrating of multiomics data.
The significance of necroptosis in recurrent or metastatic cervical cancer remains unclear. In this study, we utilized various bioinformatics methods to analyze the cancer genome atlas (TCGA) data, gene chip and the single-cell RNA-sequencing (scRNA seq) data. And a necroptosis-related genes signature for prognostic assessment of patients with cervical cancer was constructed successfully. Survival analysis, receiver operating characteristic (ROC) curve, the support vector machine recursive feature elimination (SVM-RFE) algorithm and random forest analysis were performed to validate this signature. Patients in TCGA-CESC cohort were grouped into "high-necroptosis score (H-NCPS)" vs "low-necroptosis score (L-NCPS)" subgroups based on the median of necroptosis score of each patient. Analyses of the tumor microenvironment manifested "H-NCPS" patients associated with lower degree of immune infiltration. Through the utilization of survival analysis, cell communication, and Gene Set Enrichment Analysis (GSEA), PGK1 was determined to be the pivotal gene within the 9-gene signature associated with necroptosis. The high expression of PGK1 in cervical cancer cells was confirmed through the utilization of quantitative real-time polymerase chain reaction (RT-qPCR) and the human protein atlas (HPA). In the interim, PGK1 prompted the transition of M1 macrophages to M2 macrophages and influenced the occurrence and development of necroptosis. In conclusion, the 9-gene signature developed from necroptosis-related genes has shown significant predictive capabilities for the prognosis of cervical cancer, offered valuable guidance for individualized and targeted treatment approaches for patients.
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Integrated Single-Cell and Machine Learning Analysis Identifies Fibroblast-Associated Hub Genes and Potential Therapeutics in Ischemic Cardiomyopathy.
Ischemic cardiomyopathy (ICM), a leading cause of heart failure, is characterised by complex cellular heterogeneity and a dysregulated microenvironment. A systematic computational dissection of its molecular mechanisms and a coherent pipeline from discovery to potential therapeutics is currently lacking. We integrated single-cell RNA sequencing (scRNA-seq) data from ICM patients with four independent bulk transcriptomic cohorts. A cardiac cellular atlas was constructed, and candidate genes were filtered through differential expression analysis. Subsequently, a benchmark of 127 machine learning algorithm-feature selection combinations was performed to identify robust diagnostic hub genes. Their functions were validated at single-cell resolution via UCell scoring, pseudotime trajectory analysis, and virtual knockout perturbations using scTenifoldKnk. The immune infiltration landscape was assessed using CIBERSORT and MCP-counter. Finally, computational drug repositioning and molecular docking were employed to screen for potential compounds targeting the hub genes. Machine learning identified a core 5-gene signature (NPPA, HTRA1, LUM, ASPN, and OGN) demonstrating excellent diagnostic performance across independent datasets (AUC > 0.83). Single-cell analysis revealed that these genes were most abundantly expressed in fibroblasts and were consistently upregulated in ICM. Pseudotemporal trajectory analysis illustrated their dynamic expression patterns. Virtual knockout and functional enrichment indicated that four of these genes (ASPN, HTRA1, LUM, OGN) significantly perturbed pathways related to the regulation of inflammatory response. Immune profiling revealed increased infiltration of fibroblasts and plasma cells in ICM. Molecular docking identified the compound LDN-193189 as a potential lead molecule with high predicted binding affinity (binding energy < -9 kcal·mol-1) for ASPN, LUM, and OGN. Through multi-omics integration and computational biology, this study systematically delineates a fibroblast-centric molecular network involving key hub genes and an altered immune microenvironment in ICM and computationally proposes a potential therapeutic candidate. These findings provide a crucial computational foundation and experimental direction for understanding ICM pathology and developing novel therapeutic strategies.
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Pan-cancer macrophage atlas uncovers that MHC-IIhigh TAMs induce Treg activation through physical interactions.
Dendritic cells (DCs) are recognized as the primary antigen-presenting cells (APCs) within the tumor microenvironment (TME), orchestrating T cell responses via the major histocompatibility complex class II (MHC-II). However, the contribution of tumor-associated macrophages (TAMs) to antigen presentation within the TME remains largely unexplored. By integrating single-cell RNA-seq data from 10 cancer types, we discover that tumor-enriched TAMs universally exhibit elevated phagocytosis and MHC-II-mediated antigen presentation, distinct from canonical tumor-promoting M2 macrophages. Notably, MHC-IIhigh TAMs preferentially interact with regulatory T cells (Tregs) across cancers. Using a mouse model of lung adenocarcinoma, we demonstrate that MHC-IIhigh TAMs promote Treg activation and expansion through antigen presentation and direct contact, while their depletion restrains Treg activation and suppresses tumor growth. Moreover, clinical datasets from patients receiving immunotherapy reveal that the presence of MHC-IIhigh TAMs correlates with immunotherapy resistance. Together, these findings redefine macrophage antigen presentation in the TME and reveal new therapeutic opportunities.
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Varied cellular abnormalities in thin vs. normal endometrium in recurrent implantation failure by single-cell transcriptomics.
BACKGROUND: Reduced endometrium thickness and receptivity are two important reasons for recurrent implantation failure (RIF). In order to elucidate differences between these two types of endometrial defects in terms of molecular signatures, cellular interactions, and structural changes, we systematically investigated the single-cell transcriptomic atlas across three distinct groups: RIF patients with thin endometrium (≤ 6 mm, TE-RIF), RIF patients with normal endometrium thickness (≥ 8 mm, NE-RIF), and fertile individuals (Control). METHODS: The late proliferative and mid-secretory phases of the endometrium were collected from three individuals in the TE-RIF group, two in the NE-RIF group, and three in the control group. The study employed a combination of advanced techniques. Single-cell RNA sequencing (scRNA-seq) was utilized to capture comprehensive transcriptomic profiles at the single-cell level, providing insights into gene expression patterns within specific cell types. Scanning and transmission electron microscopy were employed to visualize ultrastructural details of the endometrial tissue, while hematoxylin and eosin staining facilitated the examination of tissue morphology and cellular composition. Immunohistochemistry techniques were also applied to detect and localize specific protein markers relevant to endometrial receptivity and function. RESULTS: Through comparative analysis of differentially expressed genes among these groups and KEGG pathway analysis, the TE-RIF group exhibited notable dysregulations in the TNF and MAPK signaling pathways, which are pivotal in stromal cell growth and endometrial receptivity. Conversely, in the NE-RIF group, disturbances in energy metabolism emerged as a primary contributor to reduced endometrial receptivity. Additionally, using CellPhoneDB for intercellular communication analysis revealed aberrant interactions between epithelial and stromal cells, impacting endometrial receptivity specifically in the TE-RIF group. CONCLUSION: Overall, our findings provide valuable insights into the heterogeneous molecular pathways and cellular interactions associated with RIF in different endometrial conditions. These insights may pave the way for targeted therapeutic interventions aimed at improving endometrial receptivity and enhancing reproductive outcomes in patients undergoing ART. Further research is warranted to validate these findings and translate them into clinical applications for personalized fertility treatments. TRIAL REGISTRATION: Not applicable.
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Single-Cell Analyses Reveal Mechanisms of Cancer Stem Cell Maintenance and Epithelial-Mesenchymal Transition in Recurrent Bladder Cancer.
PURPOSE: Bladder cancer treatment remains a major clinical challenge due to therapy resistance and a high recurrence rate. Profiling intratumor heterogeneity can reveal the molecular mechanism of bladder cancer recurrence. EXPERIMENTAL DESIGN: Here, we performed single-cell RNA sequencing and Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) on tumors from 13 patients with low recurrence risk, high recurrence risk, and recurrent bladder cancer. RESULTS: Our study generated a comprehensive cancer-cell atlas consisting of 54,971 single cells and identified distinct cell subpopulations. We found that the cancer stem-cell subpopulation is enriched during bladder cancer recurrence with elevated expression of EZH2. We further defined a subpopulation-specific molecular mechanism whereby EZH2 maintains H3K27me3-mediated repression of the NCAM1 gene, thereby inactivating the cell invasive and stemness transcriptional program. Furthermore, taking advantage of this large single-cell dataset, we elucidated the spectrum of epithelial-mesenchymal transition (EMT) in clinical samples and revealed distinct EMT features associated with bladder cancer subtypes. We identified that TCF7 promotes EMT in corroboration with single-cell ATAC with high-throughput sequencing (scATAC-seq) analysis. Additionally, we constructed regulatory networks specific to recurrent bladder cancer. CONCLUSIONS: Our study and analytic approaches herein provide a rich resource for the further study of cancer stem cells and EMT in the bladder cancer research field.