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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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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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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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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.
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CD44-Mediated Poor Prognosis in Glioma Is Associated With M2-Polarization of Tumor-Associated Macrophages and Immunosuppression.
BACKGROUND: Glioma is the most common primary brain tumor with a poor prognosis. Key genes that are negatively related to prognosis may provide the therapy targets to cure glioma. To clarify the role of CD44 in glioma, we explored its function at bulk-transcriptome, spatial and single-cell transcriptome levels. METHODS: In total, expression profiles with survival data of whole-grade glioma from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA), RNA-seq data with anatomic information of glioblastoma (GBM) from the Ivy Glioblastoma Atlas Project, RNA-sequencing (RNA-seq) data from recurrent GBM receiving adjuvant anti-PD-1 immunotherapy accessed through GSE121810, and single-cell RNA-seq data of GBM under accession GSE103224 were enrolled in this study. CD44-specific findings were further analyzed by R language. RESULTS: CD44 is positively correlated with WHO grade of malignancy and is negatively related to prognosis in glioma. Meanwhile, CD44 predominantly expresses in GBM mesenchymal subtype, and gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses reveal that CD44 positively coexpressed genes are closely related to glioma immunity. Moreover, CD44+ cells mainly distribute in perinecrotic region with high expression of immune factors. At single-cell resolution, only malignant tumor cells, tumor-associated macrophages (TAMs), and T cells express CD44 in GBM. CD44+ malignant tumor cells are in mesenchymal-1-like (MES1-like) cellular state, and CD44+ TAMs are in M2 phenotype. CD44+ T cells have high expression of both PD-1 and PD-L1. CD44 and its directly interacted inhibitory immunomodulators are upregulated in patients with nonresponder recurrent GBM treated with PD-1 blockade therapy. CONCLUSION: Our work demonstrates that CD44, a new M2 TAM biomarker, is involved in immune suppressor and promote glioma progression in glioma microenvironment. These results expand our understanding of CD44-specific clinical and immune features in glioma.
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SLC12A7 serves as a prognostic and immunotherapeutic biomarker identified by multi-omics analysis.
BACKGROUND: Solute carrier family 12 member 7 (SLC12A7) is a membrane transporter implicated in ion homeostasis. Recent studies have suggested its aberrant expression in various malignancies; however, its functional roles and clinical relevance across cancers remain poorly defined. Given the limited understanding of SLC12A7 in oncogenesis and the urgent need for novel biomarkers, particularly in hepatocellular carcinoma (HCC), we conducted a comprehensive pan-cancer analysis to elucidate its potential oncogenic roles. METHODS: Transcriptomic and clinical data from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), cBioPortal, TIMER 2.0, and Gene Expression Omnibus (GEO) were integrated to investigate the expression patterns, prognostic significance, genetic alterations, and immune correlations of SLC12A7. Single-cell RNA sequencing data were analyzed to examine its heterogeneity within the tumor microenvironment. Functional enrichment analyses were performed to identify associated signaling pathways. In vitro experiments were conducted to validate the effects of SLC12A7 on proliferation, migration, and invasion in HCC cell lines. RESULTS: SLC12A7 was significantly upregulated in multiple cancer types and correlated with unfavorable clinical outcomes. Single-cell analysis revealed its enrichment in malignant cell populations and its involvement in the regulation of the tumor microenvironment. SLC12A7 expression was associated with immune cell infiltration and immune checkpoint gene expression. Functional assays confirmed that SLC12A7 enhances HCC cell proliferation, migration, and invasion. CONCLUSIONS: These findings identify SLC12A7 as a potential prognostic biomarker and immunotherapeutic target across cancers. Its oncogenic role in HCC suggests clinical utility in guiding prognosis and treatment strategies.