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Prognostic models of immune-related cell death and stress unveil mechanisms driving macrophage phenotypic evolution in colorectal cancer.
BACKGROUND: Tumor microenvironment (TME), particularly immune cell infiltration, programmed cell death (PCD) and stress, has increasingly become a focal point in colorectal cancer (CRC) treatment. Uncovering the intricate crosstalk between these factors can enhance our understanding of CRC, guide therapeutic strategies, and improve patient prognosis. METHODS: We constructed an immune-related cell death and stress (ICDS) prognostic model utilizing machine learning methodologies. Furthermore, we performed enrichment analyses and deconvolution algorithms to elucidate the complex interactions between immune cell infiltration and the processes of PCD and stress within a substantial array of transcriptomic data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus data base (GEO) related to CRC. Single-cell sequencing and biochemical experiments were used to validate the interaction between the model genes and programmed cell death in tumor cells. RESULTS: The ICDS prognostic model exhibited robust predictive performance in seven independent cohorts, revealing an inverse correlation between model scores and patient prognosis. Meanwhile, the ICDS index was positively correlated with clinical stage. Model analysis indicated that patient subgroups with low ICDS index exhibited heightened immune activation features and elevated activity in PCD and stress pathways. Single-cell analysis further revealed that macrophages were the central drivers of immune characteristics underlying prognostic differences within the ICDS prognostic model. Pseudotime analysis and cellular experiments indicated that the model gene GAL3ST4 promotes the transition of macrophages toward an M2 pro-tumor phenotype. Furthermore, cell communication analysis and experimental validation revealed that the cuproptosis in tumor cells suppress GAL3ST4 expression, thereby inhibiting M2-like macrophage polarization. CONCLUSION: In summary, we constructed the ICDS prognostic model and uncovered the mechanism by which tumor cells downregulate GAL3ST4 expression via cuproptosis to inhibit M2-like macrophage polarization, providing new targets and biomarkers for CRC treatment and prognosis evaluation.
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Integration of single-cell and bulk transcriptomics to explore prognostic and immunotherapeutic characteristics of sodium overload-induced cell death in lung adenocarcinoma.
BACKGROUND: Recent studies have identified a novel cell death mechanism, termed sodium death, triggered by sustained activation of TRPM4 ion channels. Prolonged TRPM4 channel activity results in excessive sodium ion influx and membrane depolarization, ultimately inducing necrotic cell death. However, the role of sodium death-associated genes (NECSOs) in lung adenocarcinoma (LUAD) remains poorly understood. We used NECSOs to construct a prognostic model of LUAD for prognostic prediction and to explore the features of sodium overload-induced cell death in single cells. METHODS: This study conducted a comprehensive analysis of The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases to investigate TRPM4, a NECSO identified in the literature. Through co-expression analysis, 40 NECSOs were identified. Using the AddModuleScore enrichment algorithm, sodium death scores were calculated for distinct cell clusters and categorized into high- and low-score groups. Differentially expressed genes (DEGs) between these groups were subjected to weighted gene co-expression network analysis (WGCNA) to identify gene modules associated with LUAD. Subsequent differential expression and univariate Cox regression analyses were performed on genes within these modules. Consensus clustering analysis was employed to characterize molecular subtypes, while least absolute shrinkage and selection operator (LASSO) regression was used to develop a prognostic model based on NECSOs. Multiple immune infiltration scoring algorithms and cellular communication analyses were applied to evaluate the relationship between sodium overload (NECSO) and the tumor microenvironment (TME), with the aim of identifying potential therapeutic targets for LUAD. RESULTS: Single-cell and bulk transcriptome analyses identified 126 NECSOs, of which 28 were differentially expressed between tumor and normal tissues. Among these, 14 genes were significantly associated with overall survival (OS). Pseudotime analysis of epithelial cells revealed that risk-associated genes were predominantly upregulated in later stages, whereas protective genes exhibited reduced expression over time. Eight genes were selected to construct a prognostic signature for NECSOs. Patients in the low-NECSOs group demonstrated significantly better prognosis compared to those in the high-NECSOs group. Cellular communication analysis indicated that epithelial cells with elevated NECSOs expression exhibited enhanced activity in the epidermal growth factor (EGF), Midkine (MK), and COMPLEMENT signaling pathways. CONCLUSIONS: The prognostic model developed from NECSOs enables robust survival prediction for LUAD patients and provides insights into the immune landscape of LUAD. Notably, epithelial cells with higher NECSOs scores displayed a greater propensity for transformation into tumor cells, highlighting their potential as therapeutic targets.
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Integrative multi-omics analysis identifies SLCO1B3 as a prognostic marker and functional oncogenic driver in head and neck squamous cell carcinoma.
BACKGROUND: Head and neck squamous cell carcinoma (HNSCC) is a leading cause of cancer mortality with limited prognostic biomarkers. Given the role of metabolic reprogramming in tumor progression, this study investigated the clinical relevance and biological function of the lipid metabolism-associated transporter SLCO1B3 in HNSCC. METHODS: We integrated bulk transcriptomic data from The Cancer Genome Atlas (TCGA) and multiple independent cohorts with single-cell RNA sequencing, high-dimensional weighted gene co-expression network analysis (hdWGCNA), spatial transcriptomics, and in vitro loss-of-function assays in SCC9 cells. RESULTS: SLCO1B3 was significantly upregulated in HNSCC and independently predicted poor survival across multiple cohorts. Single-cell analysis localized SLCO1B3 to an epithelial subpopulation with enhanced stemness, evidenced by elevated CytoTRACE scores and pseudotime trajectory mapping. hdWGCNA identified co-expression modules enriched in cell cycle regulation and stress responses within SLCO1B3-positive epithelial cells. Spatial transcriptomics revealed preferential colocalization of SLCO1B3-positive cells with fibroblasts and smooth muscle cells. Cell-cell communication analysis further showed distinct ligand-receptor interaction patterns mediated by SLCO1B3-positive epithelial cells. Functionally, SLCO1B3 knockdown induced G0/G1 arrest, suppressed migration and invasion, and promoted apoptosis, accompanied by altered expression of Cyclin D1, PCNA, and p21. CONCLUSION: These findings establish SLCO1B3 as an independent prognostic biomarker and functional driver that sustains stemness and malignant phenotypes in HNSCC, providing a rationale for its therapeutic targeting.
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Expressional and functional characteristics of checkpoint kinase 1 as a prognostic biomarker in hepatocellular carcinoma.
BACKGROUND: Hepatocellular carcinoma (HCC) is the most common pathological subtype of liver cancer and is the third leading cause of cancer death worldwide. Checkpoint kinase 1 (CHEK1), an essential serine/threonine kinase that regulates the cell cycle, is reported to be associated with carcinogenesis. However, the biological role and clinical significance of CHEK1 in HCC are still incompletely known. METHODS: In this research, CHEK1 messenger RNA (mRNA) levels in various liver hepatocellular carcinoma (LIHC) cohorts from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases were evaluated. The Kaplan-Meier database was applied to identify the correlation between survival time and CHEK1 expression in patients with HCC. Gene set enrichment analysis (GSEA) was performed to explore the potential mechanism of CHEK1 in HCC, and NetworkAnalyst v. 3.0 (https://www.networkanalyst.ca/) was used to construct the regulatory networks of CHEK1 in HCC. Discriminant Regulon Expression Analysis (DoRothEA) was used to detect the activity of transcriptional factors (TFs) in gene-enriched cells (EC) with CHEK1 coexpression. In vitro experiments were conducted to investigate the effects of CHEK1 on the biological function of HCC cells. RESULTS: The CHEK1 mRNA level was overexpressed in HCC, and increased CHEK1 expression correlated with poor survival outcomes. The homo sapiens-microRNA-195 (hsa-miR-195) may have contributed to the upregulation of CHEK1 in HCC. GSEA and NetworkAnalyst v. 3.0 showed that CHEK1 played a crucial part in tumor proliferation of HCC and may be regulated by TF E2F1. DoRothEA showed increased transcriptional activity of E2F1 in gene-EC with CHEK1 coexpression. Moreover, experiments of cell function showed that the knockdown of CHEK1 weakened the aggressive behavior and proliferation of HCC cells. Overexpression of E2F1 increased the proliferation and invasion of HCC cells in vitro, while the silencing of CHEK1 dampened cell invasion induced by E2F1 overexpression. CONCLUSIONS: These results identified the prognostic significance and expression characteristics of CHEK1 in HCC through bioinformatics analysis and experimental verification. This lays the foundation for further research on the diagnosis and treatment of HCC.
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Graph domain adaptation-based framework for gene expression enhancement and cell type identification in large-scale spatially resolved transcriptomics.
Spatially resolved transcriptomics (SRT) technologies facilitate gene expression profiling with spatial resolution in a naïve state. Nevertheless, current SRT technologies exhibit limitations, manifesting as either low transcript detection sensitivity or restricted gene throughput. These constraints result in diminished precision and coverage in gene measurement. In response, we introduce SpaGDA, a sophisticated deep learning-based graph domain adaptation framework for both scenarios of gene expression imputation and cell type identification in spatially resolved transcriptomics data by impartially transferring knowledge from reference scRNA-seq data. Systematic benchmarking analyses across several SRT datasets generated from different technologies have demonstrated SpaGDA's superior effectiveness compared to state-of-the-art methods in both scenarios. Further applied to three SRT datasets of different biological contexts, SpaGDA not only better recovers the well-established knowledge sourced from public atlases and existing scientific literature but also yields a more informative spatial expression pattern of genes. Together, these results demonstrate that SpaGDA can be used to overcome the challenges of current SRT data and provide more accurate insights into biological processes or disease development. The SpaGDA is available in https://github.com/shenrb/SpaGDA.
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Systematic transcriptional analysis of human cell lines for gene expression landscape and tumor representation.
Cell lines are valuable resources as model for human biology and translational medicine. It is thus important to explore the concordance between the expression in various cell lines vis-à-vis human native and disease tissues. In this study, we investigate the expression of all human protein-coding genes in more than 1,000 human cell lines representing 27 cancer types by a genome-wide transcriptomics analysis. The cell line gene expression is compared with the corresponding profiles in various tissues, organs, single-cell types and cancers. Here, we present the expression for each cell line and give guidance for the most appropriate cell line for a given experimental study. In addition, we explore the cancer-related pathway and cytokine activity of the cell lines to aid human biology studies and drug development projects. All data are presented in an open access cell line section of the Human Protein Atlas to facilitate the exploration of all human protein-coding genes across these cell lines.
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Integrated multi-omics profiling characterizes the crucial role of human dental epithelium during tooth development.
The development of early human tooth primordia is not well understood. Here, we linked single-cell RNA sequencing, spatial transcriptomics, and secretome analysis to characterize human fetal tooth development over time. A spatiotemporal atlas of human tooth development at multiple levels was mapped, identifying previously uncharacterized epithelial subpopulations with distinct gene expression profiles and spatial localization. Dynamic changes in epithelial-mesenchymal interactions across developmental stages were characterized. Secretome analysis confirmed the extensive paracrine signaling from the epithelial to mesenchymal compartments and uncovered signaling factors produced by dental epithelium (DE) that regulate mesenchymal cell fate and differentiation. Integration of these datasets highlighted the crucial role of the DE in orchestrating tooth morphogenesis. Our multi-omics approach not only provides unprecedented insights into the cellular and molecular mechanisms of ectoderm-derived tissue development but also serves as a valuable resource, which is publicly available online, for future studies on human tooth regeneration and related diseases.
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HiCat: a semi-supervised approach for cell type annotation.
Existing cell type annotation methods face significant hurdles: supervised approaches often fail to differentiate between novel cell types not present in reference data, while unsupervised techniques can suffer from cluster impurity and difficulties in robustly distinguishing multiple distinct unknown cell populations. This critical gap motivated the development of HiCat, a semi-supervised pipeline specifically designed to overcome these limitations. HiCat is a semi-supervised pipeline that integrates both approaches, leveraging reference (labeled) and query (unlabeled) genomic data to simultaneously enhance annotation accuracy for known cell types and improve the discovery and differentiation of novel ones. HiCat follows a structured pipeline: (1) removing batch effects and generate a low-dimensional embedding; (2) nonlinear dimensionality reduction for capturing key patterns; (3) unsupervised clustering for proposing novel cell type candidates; (4) merging multi-resolution features from previous steps into a condensed feature space; (5) training a classifier on reference data for supervised annotation; and (6) resolving inconsistencies between supervised predictions and unsupervised clusters to finalize annotations, particularly for unseen types. Performance was evaluated across 10 public genomic datasets and perform a case study on a molecular cell atlas of the human lung. HiCat demonstrated superior performance in both known cell type classification and novel cell type identification. In benchmark evaluations, HiCat consistently outperformed existing methods, critically excelling in identifying and distinguishing multiple novel cell types. HiCat presents a robust framework for scRNA-seq cell annotation, improving classification accuracy and novel type identification. In addition, it provides a scalable and transferable solution for biomedical research, directly addressing key challenges in automated cell annotation.
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Maternal obesity remodels nutrient transport transcriptional programs in early mouse embryonic and extraembryonic cell lineages.
BACKGROUND: Maternal obesity increases the risk of congenital anomalies and later-life metabolic disease in offspring. Still, underlying mechanisms remain unclear, particularly in extraembryonic lineages at the maternal-fetal interface, which remain poorly studied. METHODS: We jointly profiled gene expression and chromatin accessibility in single nuclei from mouse embryos and extraembryonic tissues in a diet-induced obesity model at embryonic day 8.5, when multiple organogenesis programs are underway. RESULTS: This analysis generated an atlas of 36 cell lineages, including derivatives of all three germ layers and trophoblast populations. Lineage allocation was preserved in embryos from obese dams. However, transcription was widely dysregulated. Oxidative phosphorylation genes were broadly suppressed, and genes involved in hypoxia, cytoskeleton remodeling, and cell migration were enriched among upregulated pathways. Chromatin accessibility changed in a few lineages, most notably in extraembryonic visceral endoderm and parietal trophoblast giant cells. Differently accessible chromatin was enriched in binding motifs for retinoic acid receptors. Indeed, genes involved in retinol and lipoprotein transport were suppressed, and RNA in situ hybridization confirmed reduced expression of retinol transporters Ttr, Rbp4, and Stra6, and lipoprotein transporter Apoa1 in visceral yolk sac. CONCLUSION: Obesity during pregnancy causes early transcriptional dysregulation that impairs retinoic acid and lipoprotein transport at the maternal-fetal interface, suggesting a mechanism through which maternal obesity could influence long-term developmental outcomes.
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Colorectal Cancer Cell Differentiation Trajectory Predicts Patient Immunotherapy Response and Prognosis.
OBJECTIVES: This study aimed to investigate the differentiation state and clinical significance of colorectal cancer cells, as well as to predict the immune response and prognosis of patients based on differentiation-related genes of colorectal cancer. INTRODUCTION: Colorectal cancer cells exhibit different differentiation states under the influence of the tumor microenvironment, which determines the cell fates. METHODS: We combined single-cell sequencing (scRNA-seq) data from The Cancer Genome Atlas source with extensive transcriptome data from the Gene Expression Omnibus database. We obtained colorectal cancer differentiation-related genes using cell trajectory analysis and developed a colorectal cancer differentiation-related gene based molecular typing and prognostic model to predict the immune response and prognosis of patients with colorectal cancer. RESULTS: We identified 5 distinct cell differentiation subsets and 620 colorectal cancer differentiation-related genes. Colorectal cancer differentiation-related genes were significantly associated with metabolism, angiogenesis, and immunity. We separated patients into 3 subtypes based on colorectal cancer differentiation-related gene expression in the tumor and found differences among the different subtypes in immune infiltration status, immune checkpoint gene expression, clinicopathological features, and overall survival. Immunotherapeutic interventions involving a highly expressed immune checkpoint blockade may be selectively effective in the corresponding cancer subtypes. We built a risk score prediction model (5-year AUC: .729) consisting of the 4 most important predictors of survival (TIMP1, MMP1, LGALS4, and ITLN1). Finally, we generated and validated a nomogram consisting of the risk score and clinicopathological variables. CONCLUSION: This study highlights the significance of genes involved in cell differentiation for clinical prognosis and immunotherapy in patients and provides prospective therapeutic targets for colorectal cancer.