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Prognostic model based on M2 macrophage-related signatures for predicting outcomes, enhancing risk stratification, and providing therapeutic insights in diffuse large B-cell lymphoma.
PURPOSE: The tumor microenvironment (TME) in lymphoma is influenced by M2 macrophages. This research proposes an novel predictive model that leverages M2 macrophage-associated genes to categorize risk, forecast outcomes, and evaluate the immune profile in patients with newly diagnosed diffuse large B-cell lymphoma (DLBCL) undergoing R-CHOP therapy. METHODS: Gene expression data and clinical information from DLBCL patients were retrieved from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Co-expressed genes linked to M2 macrophage in DLBCL were analyzed using CIBERSORT. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were conducted to explore associated signaling pathways. The M2 macrophage-related gene prognostic model was developed and validated using Cox and LASSO regression. Prognostic signature genes were verified by single-cell RNA-seq analysis. RESULTS: 92 M2 macrophage-related genes were identified based on bulk-seq data. MS4A4A, CCL13, LTB, CCL23, CCL18, XKR4, IL22RA2, and FOLR2 were used to construct the risk model. AUC values for 1-, 3-, and 5-year survival were 0.74, 0.72, and 0.72, respectively. High-risk patients demonstrated elevated immune scores and poorer overall survival. The high-risk subgroup also exhibited greater sensitivity to both chemotherapeutic agents and immune checkpoint inhibitors. CONCLUSION: This study presents an accurate and reliable M2 macrophage-related risk model, enhancing understanding of distinct prognostic subsets in DLBCL. It offers potential novel drug options for future treatments.
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Identification of a novel immunogenic death-associated model for predicting the immune microenvironment in lung adenocarcinoma from single-cell and Bulk transcriptomes.
Background: Studies on immunogenic death (ICD) in lung adenocarcinoma are limited, and this study aimed to determine the function of ICD in LUAD and to construct a novel ICD-based prognostic model to improve immune efficacy in lung adenocarcinoma patients. Methods: The data for lung adenocarcinoma were obtained from the Cancer Genome Atlas (TCGA) database and the National Center for Biotechnology Information (GEO). The single-cell data were obtained from Bischoff P et al. To identify subpopulations, we performed descending clustering using TSNE. We collected sets of genes related to immunogenic death from the literature and identified ICD-related genes through gene set analysis of variance (GSVA) and weighted gene correlation network analysis (WGCNA). Lung adenocarcinoma patients were classified into two types using consistency clustering. The difference between the two types was analyzed to obtain differential genes. An immunogenic death model (ICDRS) was established using LASSO-Cox analysis and compared with lung adenocarcinoma models of other individuals. External validation was performed in the GSE31210 and GSE50081 cohorts. The efficacy of immunotherapy was assessed using the TIDE algorithm and the IMvigor210, GSE78220, and TCIA cohorts. Furthermore, differences in mutational profiles and immune microenvironment between different risk groups were investigated. Subsequently, ROC diagnostic curves and KM survival curves were used to screen ICDRS key regulatory genes. Finally, RT-qPCR was used to verify the differential expression of these genes. Results: Eight ICD genes were found to be highly predictive of LUAD prognosis and significantly correlated with it. Multivariate analysis showed that patients in the low-risk group had a higher overall survival rate than those in the high-risk group, indicating that the model was an independent predictor of LUAD. Additionally, ICDRS demonstrated better predictive ability compared to 11 previously published models. Furthermore, significant differences in biological function and immune cell infiltration were observed in the tumor microenvironment between the high-risk and low-risk groups. It is noteworthy that immunotherapy was also significant in both groups. These findings suggest that the model has good predictive efficacy. Conclusions: The ICD model demonstrated good predictive performance, revealing the tumor microenvironment and providing a new method for evaluating the efficacy of pre-immunization. This offers a new strategy for future treatment of lung adenocarcinoma.
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Integrative single-cell and spatial transcriptomics analysis reveals a baicalein-responsive 10-gene signature for non-small cell lung cancer.
BACKGROUND: Non‑small cell lung cancer (NSCLC) remains a leading cause of cancer‑related mortality worldwide. Baicalein, a natural flavonoid, has shown anti‑cancer activity but its molecular targets and cell‑type‑specific effects in the tumor microenvironment (TME) are incompletely understood. METHODS: We integrated network pharmacology, single‑cell RNA‑seq, bulk transcriptomics, spatial transcriptomics, machine learning, and in vitro experiments to identify baicalein‑responsive genes in NSCLC. RESULTS: Single‑cell analysis resolved 21 cell populations, revealing that baicalein targets were enriched in a 32‑gene core set. Consensus clustering defined three molecular subtypes (cluster 1-cluster 3) with distinct immune infiltration; cluster 1 showed an immune‑cold phenotype with upregulation of cell cycle and metabolic pathways, while cluster 3 was immune‑hot. Machine learning selected a 10‑gene signature (TOP2A, CDH1, CCNB1, SATB2, CA9, HMGB2, MB, NQO1, AURKB, CCNB2) with high diagnostic accuracy. SHAP analysis identified TOP2A as the most influential contributor. Spatial transcriptomics confirmed significantly elevated expression of all ten genes in tumor versus adjacent/normal tissues. Cell‑cell communication analysis highlighted enhanced MIF pathway signaling in the TME, with epithelial cells and SPP1+ TAMs acting as key senders. Molecular docking showed strong binding affinities (ΔG ≤ -8.5 kcal/mol) between baicalein and AURKB, MB, TOP2A, and CCNB2, and molecular dynamics simulations further confirmed the stability of these complexes. In vitro, baicalein (30 μM, 24 h) differentially modulated signature gene expression in PC‑9 and A549 cells and suppressed viability in a dose‑ and time‑dependent manner. CONCLUSIONS: This integrative study provides a comprehensive single‑cell and spatial atlas of baicalein‑responsive genes in NSCLC, identifies a robust diagnostic signature, and offers mechanistic insights for developing baicalein as a potential therapeutic agent.
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Challenges and opportunities to computationally deconvolve heterogeneous tissue with varying cell sizes using single-cell RNA-sequencing datasets.
Deconvolution of cell mixtures in "bulk" transcriptomic samples from homogenate human tissue is important for understanding disease pathologies. However, several experimental and computational challenges impede transcriptomics-based deconvolution approaches using single-cell/nucleus RNA-seq reference atlases. Cells from the brain and blood have substantially different sizes, total mRNA, and transcriptional activities, and existing approaches may quantify total mRNA instead of cell type proportions. Further, standards are lacking for the use of cell reference atlases and integrative analyses of single-cell and spatial transcriptomics data. We discuss how to approach these key challenges with orthogonal "gold standard" datasets for evaluating deconvolution methods.
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A cell abundance analysis based on efficient PAM clustering for a better understanding of the dynamics of endometrial remodelling.
BACKGROUND: Single-cell RNA sequencing (scRNA-seq) is a powerful tool for investigating cell abundance changes during tissue regeneration and remodeling processes. Differential cell abundance supports the initial clustering of all cells; then, the number of cells per cluster and sample are evaluated, and the dependence of these counts concerning the phenotypic covariates of the samples is studied. Analysis heavily depends on the clustering method. Partitioning Around Medoids (PAM or k-medoids) represents a well-established clustering procedure that leverages the downstream interpretation of clusters by pinpointing real individuals in the dataset as cluster centers (medoids) without reducing dimensions. Of note, PAM suffers from high computational costs and memory requirements. RESULTS: This paper proposes a method for differential abundance analysis using PAM as a clustering method and negative binomial regression as a statistical model to relate covariates to cluster/cell counts. We used this approach to study the differential cell abundance of human endometrial cell types throughout the natural secretory phase of the menstrual cycle. We developed a new R package -scellpam-, that incorporates an efficient parallel C++ implementation of PAM, and applied this package in this study. We compared the PAM-BS clustering method with other methods and evaluated both the computational aspects of its implementation and the quality of the classifications obtained using distinct published datasets with known subpopulations that demonstrate promising results. CONCLUSIONS: The implementation of PAM-BS, included in the scellpam package, exhibits robust performance in terms of speed and memory usage compared to other related methods. PAM allowed quick and robust clustering of sets of cells with a size ranging from 70,000 to 300,000 cells. https://cran.r-project.org/web/packages/scellpam/index.html . Finally, our approach provides important new insights into the transient subpopulations associated with the fertile time frame when applied to the study of changes in the human endometrium during the secretory phase of the menstrual cycle.
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scAI-SNP: a method for inferring ancestry from single-cell data.
Collaborative efforts, such as the Human Cell Atlas, are rapidly accumulating large amounts of single-cell data. To ensure that single-cell atlases are representative of human genetic diversity, we need to determine the ancestry of the donors from whom single-cell data are generated. Self-reporting of race and ethnicity, although important, can be biased and is not always available for the datasets already collected. Here, we introduce scAI-SNP, a tool to infer ancestry directly from single-cell genomics data. To train scAI-SNP, we identified 4.5 million ancestry-informative single-nucleotide polymorphisms (SNPs) in the 1000 Genomes Project dataset across 3201 individuals from 26 population groups. For a query single-cell data set, scAI-SNP uses these ancestry-informative SNPs to compute the contribution of each of the 26 population groups to the ancestry of the donor from whom the cells were obtained. Using diverse single-cell data sets with matched whole-genome sequencing data, we show that scAI-SNP is robust to the sparsity of single-cell data, can accurately and consistently infer ancestry from samples derived from diverse types of tissues and cancer cells, and can be applied to different modalities of single-cell profiling assays, such as single-cell RNA-seq and single-cell ATAC-seq. Finally, we argue that ensuring that single-cell atlases represent diverse ancestry, ideally alongside race and ethnicity, is ultimately important for improved and equitable health outcomes by accounting for human diversity.
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Novel single-cell preservation and RNA sequencing technology unlocks field studies for Plasmodium natural infections.
Single-cell RNA sequencing (scRNA-seq) is a powerful technology used to investigate cellular heterogeneity. When applied to unicellular eukaryotes such as Plasmodium, scRNA-seq provides a single-cell resolution valuable to study complex infections comprised of mixed lifecycle stages and clones. Until now, the application of scRNA-seq has been mainly limited to in vitro and animal malaria models due to the challenges of working with Plasmodium natural infections in endemic settings. We validated sample preparation methods and a single-cell RNA preservation and sequencing technology in P. knowlesi for the future implementation in low-resource settings. We recovered 22,345 P. knowlesi single-cell transcriptomes containing all asexual blood stages from 6 samples to generate the most extensive P. knowlesi single-cell dataset to date. All 6 samples produced reproducible circular UMAP projections with consistent cluster localization and high gene expression correlation, regardless of the sample preparation method. Biomarker expression and life stage annotation using the Malaria Cell Atlas P. knowlesi reference dataset further confirmed these results. In conclusion, adaptable sample preparation methods combined with novel scRNA-seq preservation technology has the potential to unlock scRNA-seq for field studies which will lead to additional insights into Plasmodium biology and fundamentally transform how we study malaria natural infections.
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Dissecting the single-cell transcriptome network underlying esophagus non-malignant tissues and esophageal squamous cell carcinoma.
BACKGROUND: Esophageal squamous cell carcinoma (ESCC) is among the most prevalent causes of cancer-related death in adults. Tumor microenvironment (TME) has been associated with therapeutic failure and lethal outcomes for patients. However, published reports on the heterogeneity and TME in ESCC are scanty. METHODS: Five tumor samples and five corresponding non-malignant samples were subjected to scRNA-seq analysis. Bulk RNA sequencing data were retrieved in publicly available databases. FINDINGS: From the scRNA-seq data, a total of 128,688 cells were enrolled for subsequent analyses. Gene expression and CNV status exhibited high heterogeneity of tumor cells. We further identified a list of tumor-specific genes and four malignant signatures, which are potential new markers for ESCC. Metabolic analysis revealed that energy supply-related pathways are pivotal in cancer metabolic reprogramming. Moreover, significant differences were found in stromal and immune cells between the esophagus normal and tumor tissues, which promoted carcinogenesis at both cellular and molecular levels in ESCC. Immune checkpoints, regarded as potential targets for immunotherapy in ESCC were significantly highly expressed in ESCC, including LAG3 and HAVCR2. Eventually, we constructed a cell-to-cell communication atlas based on cancer cells and immune cells and performed the flow cytometry, qRT-PCR, immunofluorescence, and immunohistochemistry analyses to validate the results. INTERPRETATION: This study demonstrates a widespread reprogramming across multiple cellular elements within the TME in ESCC, particularly in transcriptional states, cellular functions, and cell-to-cell interactions. The findings offer an insight into the exploration of TME and heterogeneity in the ESCC and provide new therapeutic targets for its clinical management in the future. FUNDING: The work was supported by the Shanghai Pujiang Program (2020PJD009) and Research Development Fund of Zhongshan Hospital, Fudan University (2019ZSFZ002 and 2019ZSFZ19).
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Mitochondrial dysregulation as a central mechanism in autism spectrum disorder pathogenesis.
Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder influenced by both genetic predispositions and environmental insults. However, the precise molecular mechanisms linking prenatal environmental perturbations to neurodevelopmental impairments remain poorly defined. This study investigates the role of mitochondrial dysfunction and metabolic disturbances in ASD pathogenesis using various preclinical models, including the maternal immune activation (MIA) and ASD high-risk gene knockout models. We performed transcriptomic profiling on mouse brain tissues to identify differentially expressed genes (DEGs) associated with mitochondrial and metabolic pathways. Functional enrichment analyses, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG), revealed significant disruptions in pathways such as oxidative phosphorylation, tricarboxylic acid, and energy metabolism. These findings point to mitochondrial dysfunction as a central mechanism contributing to metabolic imbalances in ASD. Comparative analysis with publicly available RNA-seq datasets from PTEN knockout model revealed both shared and unique metabolic signatures. Single-cell RNA-seq data from the MIA model further identified cell-type-specific metabolic alterations in distinct neuronal and glial populations. Additionally, analysis of the Human Fetal Single-Cell Atlas highlighted the relevance of these metabolic pathways in human brain development. Collectively, these results emphasize mitochondrial metabolism as a potential therapeutic target for ASD, offering insights into the molecular basis of this disorder.
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Gene signatures for cancer research: A 25-year retrospective and future avenues.
Over the past two decades, extensive studies, particularly in cancer analysis through large datasets like The Cancer Genome Atlas (TCGA), have aimed at improving patient therapies and precision medicine. However, limited overlap and inconsistencies among gene signatures across different cohorts pose challenges. The dynamic nature of the transcriptome, encompassing diverse RNA species and functional complexities at gene and isoform levels, introduces intricacies, and current gene signatures face reproducibility issues due to the unique transcriptomic landscape of each patient. In this context, discrepancies arising from diverse sequencing technologies, data analysis algorithms, and software tools further hinder consistency. While careful experimental design, analytical strategies, and standardized protocols could enhance reproducibility, future prospects lie in multiomics data integration, machine learning techniques, open science practices, and collaborative efforts. Standardized metrics, quality control measures, and advancements in single-cell RNA-seq will contribute to unbiased gene signature identification. In this perspective article, we outline some thoughts and insights addressing challenges, standardized practices, and advanced methodologies enhancing the reliability of gene signatures in disease transcriptomic research.