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miRSiC: a regulatory-aware machine learning framework for microRNA expression inference across bulk and single-cell transcriptomes.
MicroRNAs (miRNAs) are key post-transcriptional regulators embedded in gene regulatory networks between upstream transcription factors (TFs) and downstream target genes (TGs), yet most computational approaches infer miRNA expression using unstructured transcriptomic features without explicitly modeling their regulatory architecture. In this study, we develop miRSiC, an interpretable machine learning framework that integrates TFs and experimentally validated TGs for regulatory-aware miRNA expression inference. miRSiC formulates prediction as miRNA-specific regression tasks and evaluates three regulatory configurations (TF-only, TG-only, and combined TF-TG) using Light Gradient Boosting Machine. Applied to The Cancer Genome Atlas (TCGA) breast cancer cohort, the integrated model achieves superior performance (mean Spearman correlation = 0.5462 across 326 miRNAs), outperforming single-layer models and demonstrating the effectiveness of incorporating both upstream and downstream regulatory signals. Feature importance analysis and regulatory network reconstruction show that selected features are enriched in biologically coherent TF-miRNA-target circuits. Prediction performance varies across breast cancer subtypes, reflecting differences in regulatory patterns and sample size. Cross-platform evaluation further reveals that models trained on bulk transcriptomes do not generalize to single-cell data due to distributional shifts and sparsity; however, domain-specific retraining partially restores performance. Together, miRSiC provides an interpretable and biologically grounded framework for miRNA expression inference, highlighting the importance of modeling regulatory context and adopting domain-aware strategies across bulk and single-cell transcriptomic data.
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Endothelial circadian rhythm genes as prognostic modulators of tumor progression and immune interactions: insights from pan-cancer single-cell RNA sequencing.
BACKGROUND: Circadian rhythms govern a wide range of physiological processes essential for cellular homeostasis, such as cell cycle progression, DNA repair, metabolism, and apoptosis. Disruptions to these rhythms are linked to cancer initiation, progression, and therapy response. While the role of circadian genes in cancer cells has been well documented, their impact on the tumor microenvironment (TME), particularly in endothelial cells, remains underexplored. This study investigates the dysregulation of circadian rhythm-related genes in endothelial cells across multiple cancers and their clinical significance. METHODS: We conducted a pan-cancer analysis using single-cell RNA sequencing data from the Tumor Immune Single-cell Hub database. A total of 15 independent datasets were analyzed, involving rigorous data processing steps, such as filtering, quality control, and cell type identification. Circadian rhythm activity was quantified using circadian rhythm scores, and genes with elevated expression in endothelial cells were identified. The Cancer Genome Atlas and Genotype-Tissue Expression datasets were employed for prognostic analysis. A risk model based on circadian rhythm-related genes was developed using least absolute shrinkage and selection operator (LASSO) regression, with validation across multiple independent cohorts. RESULTS: Our analysis revealed that endothelial cells exhibited consistently high circadian rhythm scores, highlighting their crucial role in regulating circadian rhythms within the TME. We identified a gene signature, ENDO.CIRCADIAN.RHYTHM.SIG , consisting of 101 genes positively correlated with endothelial cell-specific circadian rhythms. Dysregulation of these genes was prevalent across various cancer types and correlated with poor prognosis, with specific genes like ITGA5, SLC2A3 , and SERPINE1 identified as potential risk factors. Kaplan-Meier survival analysis indicated that higher expression of these genes was associated with decreased overall survival. The correlation of ENDO.CIRCADIAN.RHYTHM.SIG expression with immune infiltration and stromal components suggested that endothelial circadian rhythms modulate key aspects of the TME. Notably, the circadian rhythm gene set was significantly associated with angiogenesis and inflammatory pathways, especially in liver hepatocellular carcinoma. A prognostic risk model for breast cancer based on these genes showed robust predictive performance. CONCLUSION: Endothelial circadian rhythm genes play a significant role in regulating tumor progression, immune interactions, and patient prognosis. Dysregulation of the ENDO.CIRCADIAN.RHYTHM.SIG gene set correlates with adverse clinical outcomes and may serve as a potential prognostic biomarker and therapeutic target. Our findings suggest that targeting circadian rhythm pathways within the TME could enhance personalized cancer treatment strategies.
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Cancer-associated fibroblast-associated gene IGFBP2 promotes glioma progression through induction of M2 macrophage polarization.
We elucidated the molecular mechanism of cancer-associated fibroblast (CAF)-associated gene insulin-like growth factor binding protein-2 (IGFBP2)-induced M2 macrophage polarization in the tumor microenvironment involved in glioma progression. The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) provided bulk RNA-sequencing datasets, ESTIMATE scores for glioma stromal cells, and overall survival-clinicopathological correlation analyses. TIMER provided CAF abundance in the TCGA glioma-related dataset, differential gene analysis was performed for high- and low-CAF groups, and weighted gene coexpression network analysis identified CAF-related genes. Univariate and multifactorial cyclooxygenase (COX) regression analyses created the CAF risk models single sample gene set enrichment analysis, CIBERSORT, and GSE84465. Mice were implanted with gliomas, and Western blot and RT-quantitative PCR showed IGFBP2 in tumor tissues. Adeno-associated virus (AAV) decreased IGFBP2, flow cytometry measured M1 and M2 macrophage ratios, and immunohistochemistry detected markers. TCGA and CGGA transcriptome data showed malignant gliomas had higher stromal cell scores and worse prognoses. Low- and high-CAF TCGA gliomas were detected, and differential expression, WGCNA, and multifactorial COX identified 132 CAF-related genes and seven high-risk genes (CPQ, EFEMP2, IGFBP2, RAB42, TNFRSF12A, and VASN). Neither CAF risk score, grade, nor 1p/19q affected glioma prognosis. CAF only enriched EFEMP2 and IGFBP2. Gene Expression Profiling Interactive Analysis compared EFEMP2 and IGFBP2 expression in normal brain tissue and gliomas. Low-grade glioma and malignant glioblastoma highly expressed IGFBP2 and EFEMP2. GSEA raised IGFBP2. CIBERSORT linked M2 macrophage infiltration to TCGA glioma immune cell subpopulation IGFBP2 expression. IGFBP2 knockdown stopped mouse glioma and M2 macrophage polarization. CAF plays a procarcinogenic role in glioma, and the CAF-related gene IGFBP2 could promote glioma progression by inducing M2 macrophage polarization.NEW & NOTEWORTHY The cancer-associated fibroblast (CAF)-related gene insulin-like growth factor binding protein-2 (IGFBP2) is highly expressed in gliomas and is associated with poor prognosis. CAF-related gene IGFBP2 promotes glioma progression by inducing polarization of M2 macrophages. This study provides a new basis for an in-depth investigation of the functional mechanisms of the glioma tumor microenvironment and the search for key genes involved in immune regulation in CAF.
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Identification and validation of a cancer-associated fibroblasts-related scoring system to predict prognosis and immune landscape in hepatocellular carcinoma through integrated analysis of single-cell and bulk RNA-sequencing.
BACKGROUND: Cancer-associated fibroblasts (CAFs) regulate the malignant biological behaviour of hepatocellular carcinoma (HCC) as a significant component of the tumour immune microenvironment (TIME). This study aimed to develop a CAFs-based scoring system to predict the prognosis and TIME of patients with HCC. METHODS: Data for the TCGA-LIHC and GSE14520 cohorts were downloaded from The Cancer Genome Atlas and the Gene Expression Omnibus databases. Single-cell RNA-sequencing data for HCC samples were retrieved from the GSE166635 cohort. The Least Absolute Shrinkage and Selection Operator algorithm was employed to develop a CAFs-related scoring system (CAFRss). The predictive value of the CAFRss was determined using Kaplan-Meier, Cox regression and Receiver Operating Characteristic curves. Additionally, the TIMER platform, single sample Gene Set Enrichment Analysis and the Estimation of STromal and Immune cells in MAlignant Tumour tissues using Expression data algorithms were performed to determine the TIME landscape. Finally, the pRRophic algorithm was utilised for drug sensitivity analysis. RESULTS: The evaluation of the CAFRss system demonstrated its superior ability to predict the clinical outcome of patients with HCC. Additionally, CAFRss effectively distinguished HCC populations with distinct TIME landscapes. Furthermore, CAFRss-based risk stratification identified individuals with immune 'hot tumours' and predicted the survival of patients treated with ICBs. CONCLUSIONS: The developed CAFRss can serve as a predictive tool for determining the clinical outcome of HCC and differentiating populations with diverse TIME characteristics.
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Deciphering the Tumor Microenvironment Composition Using Bulk Transcriptomics: A Guide to Recent Advances and Open Challenges.
Tumors are complex ecosystems comprising diverse cell types actively participating to carcinogenesis, tumor progression, and treatment response. Understanding the tumor microenvironment (TME) dynamics has become of primary importance, especially with the increasing clinical implementation of immunotherapy. Low and high-throughput single cell and spatial technologies are providing high-resolution strategies for the study of the tumor ecosystem. However, their cost and complexity limit widespread use. Bulk transcriptomics has become a widely used strategy to obtain the expression profile of large cohorts of tumors or preclinical models. Several methods implementing a deconvolution analysis have been developed to estimate from bulk transcriptomics the prevalence of multiple cell types to reconstruct the tumor ecosystem composition.In this chapter, we introduce deconvolution analysis, the main steps, the recent advancements, and open challenges. Our emphasis lies on robust benchmarking methodologies, highlighting the importance of clear parameter definition and appropriate metric selection for reliable results across different software tools.Using CIBERSORTx and BayesPrism, we conduct a practical analysis on triple-negative breast cancer (TNBC) datasets from The Cancer Genome Atlas (TCGA) dataset. We illustrate the impact of various factors such as preprocessing methods, reference datasets, and software choice on deconvolution outcomes.Integrating insights from benchmarking analyses and real-world applications, we provide guidance to optimize and control for the quality of deconvolution analysis, weighting both its potential and limitations. Deconvolution analysis can contribute to unravelling the complexities of the tumor microenvironment, but further research is needed to enhance accuracy and reproducibility.
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Integrative and comprehensive pan-cancer analysis of ubiquitin specific peptidase 11 (USP11) as a prognostic and immunological biomarker.
The significance of USP11 as a critical regulator in cancer has garnered substantial attention, primarily due to its catalytic activity as a deubiquitinating enzyme. Nonetheless, a thorough evaluation of USP11 across various cancer types in pan-cancer studies remains absent. Our analysis integrates data from a variety of sources, including five immunotherapy cohorts, thirty-three cohorts from The Cancer Genome Atlas (TCGA), and sixteen cohorts from the Gene Expression Omnibus (GEO), two of which involve single-cell transcriptomic data. Our findings indicate that aberrant USP11 expression is predictive of survival outcomes across various cancer types. The highest frequency of genomic alterations was observed in uterine corpus endometrial carcinoma (UCEC), with single-cell transcriptome analysis revealing significantly higher USP11 expression in plasmacytoid dendritic cells and mast cells. Notably, USP11 expression was associated with the infiltration levels of CD8+ T cells and natural killer (NK) activated cells. Additionally, in the skin cutaneous melanoma (SKCM) phs000452 cohort, patients with higher USP11 mRNA levels during immunotherapy experienced a significantly shorter median progression-free survival. USP11 emerges as a promising molecular biomarker with significant potential for predicting patient prognosis and immunoreactivity across various cancer types.
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PCSK6 and clinical outcomes in the Pulmonary Fibrosis Foundation Patient Registry.
BACKGROUND: Higher plasma proprotein convertase subtilisin/kexin type 6 (PCSK6) concentration has been associated with increased all-cause mortality in idiopathic pulmonary fibrosis (IPF). Its role across the broader spectrum of fibrotic interstitial lung disease (ILDs) remains unclear. We examined associations between PCSK6 and respiratory outcomes in participants with pulmonary fibrosis in the Pulmonary Fibrosis Foundation Patient Registry and evaluated its expression in fibrotic lungs. METHODS: PCSK6 concentration was measured in plasma from 428 participants with fibrotic ILD using ELISA and tested for association with time to death, lung transplant, first respiratory hospitalization, and 10% decline in forced vital capacity (FVC) using adjusted Cox models. Single-cell RNA sequencing (scRNA-seq) data obtained from the IPF Cell Atlas were used to examine PCSK6 expression across disease states. RESULTS: PCSK6 levels were not significantly associated with time to death, lung transplantation, respiratory hospitalization, or 10% FVC decline in the overall cohort, or in the subgroup of participants with usual interstitial pneumonia pattern of fibrosis. Among participants with IPF, higher PCSK6 was associated with reduced survival, consistent with prior findings. scRNA-seq of fibrotic lungs demonstrated high PCSK6 expression in HAS1 + fibroblasts. CONCLUSION: PCSK6 was not associated with worse respiratory outcomes in mixed fibrotic ILD but was associated with reduced survival in IPF. Its enrichment in HAS1 + fibroblasts suggests this pathway may be specific to IPF pathogenesis. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12931-026-03642-1.
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A multimodal cell census and atlas of the mammalian primary motor cortex.
Here we report the generation of a multimodal cell census and atlas of the mammalian primary motor cortex as the initial product of the BRAIN Initiative Cell Census Network (BICCN). This was achieved by coordinated large-scale analyses of single-cell transcriptomes, chromatin accessibility, DNA methylomes, spatially resolved single-cell transcriptomes, morphological and electrophysiological properties and cellular resolution input-output mapping, integrated through cross-modal computational analysis. Our results advance the collective knowledge and understanding of brain cell-type organization1-5. First, our study reveals a unified molecular genetic landscape of cortical cell types that integrates their transcriptome, open chromatin and DNA methylation maps. Second, cross-species analysis achieves a consensus taxonomy of transcriptomic types and their hierarchical organization that is conserved from mouse to marmoset and human. Third, in situ single-cell transcriptomics provides a spatially resolved cell-type atlas of the motor cortex. Fourth, cross-modal analysis provides compelling evidence for the transcriptomic, epigenomic and gene regulatory basis of neuronal phenotypes such as their physiological and anatomical properties, demonstrating the biological validity and genomic underpinning of neuron types. We further present an extensive genetic toolset for targeting glutamatergic neuron types towards linking their molecular and developmental identity to their circuit function. Together, our results establish a unifying and mechanistic framework of neuronal cell-type organization that integrates multi-layered molecular genetic and spatial information with multi-faceted phenotypic properties.
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Long non-coding RNA lung cancer-associated transcript-1 promotes glioblastoma progression by enhancing Hypoxia-inducible factor 1 alpha activity.
BACKGROUND: Hypoxia is associated with poor prognosis in many cancers including glioblastoma (GBM). Glioma stem-like cells (GSCs) often reside in hypoxic regions and serve as reservoirs for disease progression. Long non-coding RNAs (lncRNAs) have been implicated in GBM. However, the lncRNAs that modulate GSC adaptations to hypoxia are poorly understood. Identification of these lncRNAs may provide new therapeutic strategies to target GSCs under hypoxia. METHODS: lncRNAs induced by hypoxia in GSCs were identified by RNA-seq. Lung cancer-associated transcript-1 (LUCAT1) expression was assessed by qPCR, RNA-seq, Northern blot, single molecule FISH in GSCs, and interrogated in IvyGAP, The Cancer Genome Atlas, and CGGA databases. LUCAT1 was depleted by shRNA, CRISPR/Cas9, and CRISPR/Cas13d. RNA-seq, Western blot, immunohistochemistry, co-IP, ChIP, ChIP-seq, RNA immunoprecipitation, and proximity ligation assay were performed to investigate mechanisms of action of LUCAT1. GSC viability, limiting dilution assay, and tumorigenic potential in orthotopic GBM xenograft models were performed to assess the functional consequences of depleting LUCAT1. RESULTS: A new isoform of Lucat1 is induced by Hypoxia inducible factor 1 alpha (HIF1α) and Nuclear factor erythroid 2-related factor 2 (NRF2) in GSCs under hypoxia. LUCAT1 is highly expressed in hypoxic regions in GBM. Mechanistically, LUCAT1 formed a complex with HIF1α and its co-activator CBP to regulate HIF1α target gene expression and GSC adaptation to hypoxia. Depletion of LUCAT1 impaired GSC self-renewal. Silencing LUCAT1 decreased tumor growth and prolonged mouse survival in GBM xenograft models. CONCLUSIONS: A HIF1α-LUCAT1 axis forms a positive feedback loop to amplify HIF1α signaling in GSCs under hypoxia. LUCAT1 promotes GSC self-renewal and GBM tumor growth. LUCAT1 is a potential therapeutic target in GBM.
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Panoramic analysis of the biological function and clinical value of SLC38A2 in human cancers: a study based on pan-cancer and single-cell analysis.
BACKGROUND: Glutamine metabolic reprogramming is a hallmark of tumor progression and is highly correlated with poor clinical outcomes. The excessive uptake of glutamine by tumor cells is a key factor contributing to widespread invasion, metastasis, and immune suppression. SLC38A2, an amino acid transporter widely expressed on the surface of tumor cells, has not been thoroughly studied regarding its function and prognostic significance in tumor progression. Our objective is to employ bioinformatics methods to conduct a comprehensive and in-depth analysis of SLC38A2 across various cancers, aiming to elucidate its role and prognostic value in tumor biology. METHODS: By comprehensively incorporating gene expression and clinical data from the TCGA tumor database, GTEx database, Human Protein Atlas, and GEO database, we analyzed the expression profile, mutations, and established prognostic models for SLC38A2 across various cancers. Additionally, we investigated the enrichment of SLC38A2 at the single-cell level in 12 types of cancer and analyzed its temporal expression patterns in different cell subgroups in breast and pancreatic cancer. We also studied the correlation between SLC38A2 and glutathione metabolism. RESULTS: Compared to normal tissues, SLC38A2 exhibits significant differential expression in 15 types of cancer and serves as a prognostic risk factor in BRCA (HR = 1.597, p < 0.05), LUAD (HR = 1.650, p < 0.01), MESO (HR = 2.007, p < 0.05), and PAAD (HR = 1.761, p < 0.05), while acting as a protective factor in KIRC (HR = 0.625, p < 0.05). Furthermore, SLC38A2 is positively correlated with tumor and stromal cells, negatively correlated with immune cell infiltration, and associated with immune exhaustion. In BRCA, SLC38A2 is highly expressed during early differentiation of malignant and stromal cells, and enriched in late differentiation of immune cells. Moreover, the expression of SLC38A2 shows a general positive correlation with glutathione metabolism genes in BRCA, LUAD, MESO, and PAAD, demonstrating diagnostic value. CONCLUSION: SLC38A2 shows widespread changes in expression patterns within tumor tissues, making it an effective diagnostic and prognostic biomarker. It is enriched in malignant cells and tumor-infiltrating stromal cells, while negatively correlated with the infiltration of many cells involved in anti-tumor immunity. Targeting SLC38A2 presents a viable therapeutic strategy by inhibiting glutamine competition and relieving immune suppression in the tumor microenvironment.