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Utilization of machine learning algorithms for the identification of the RLN associated prognostic model and feature biomarkers of RLN-related subtypes in breast cancer.
BACKGROUND: Breast cancer (BC) is the most common malignancy afflicting women worldwide, yet the role of relaxin-related genes (RLN) in BC progression remains unclear. This study aims to elucidate the relationship between RLN and BC outcomes through immune microenvironment and metabolic pathway analysis. METHODS: Gene expression and clinical data were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Relaxin-related genes were identified using KEGG and Genecard databases. A prognostic model, the RLN Associated Prognostic Model (TRAPM), was established using 101 combinations of 10 machine learning algorithms and validated at the single-cell level. Multi-omics analysis, including the IMvigor210 cohort, was performed to assess TRAPM's applicability in immunotherapy and drug selection. RESULTS: TRAPM, comprising nine prognostic genes (MMP1, RXFP1, PRKCZ, JUN, NFKBIA, GNAI2, NOS2, MMP9, and MMP13), showed significant associations with immune and metabolic profiles. Using TRAPM, a novel BC subtype RC3 and its key marker genes (MTHFD1L, CAVIN4, MMP1, ADGRG6, B3GNT5, SMYD2, and TFRC) were identified. Experimental validation through RT-qPCR and Western Blot confirmed the role of these markers in six BRCA cell lines. CONCLUSIONS: The identification of TRAPM and the RC3 subtype enhances our understanding of BC heterogeneity and highlights potential therapeutic targets. This study provides a foundation for personalized treatment strategies by clarifying the biological significance and clinical relevance of the RC3 subtype.
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An immune and epithelial-mesenchymal transition-related risk model and immunotherapy strategy for grade II and III gliomas.
Grade II and III gliomas are heterogeneous and aggressive diseases. More efficient prognosis models and treatment methods are needed. This study aims to construct a new risk model and propose a new strategy for grade II and III gliomas. The data were downloaded from The Cancer Genome Atlas (TCGA), the Gene Expression Omnibus (GEO), gene set enrichment analysis (GSEA), and the EMTome website for analysis. The Human Cell Landscape website and the Genomics of Drug Sensitivity in Cancer website were used for single-cell analysis and drug susceptibility analysis. Gene set enrichment analysis, gene function enrichment analysis, univariate and multivariate Cox regression analyses, Pearson's correlation analysis, log-rank test, Kaplan-Meier survival analysis, and ROC analysis were performed. We constructed an immune-related prognostic model associated with the isocitrate dehydrogenase 1 (IDH1) mutation status. By analyzing the immune microenvironment of patients with different risk scores, we found that high-risk patients were more likely to have an inflammatory immune microenvironment and a higher programmed death ligand-1 (PD-L1) expression level. Epithelial-mesenchymal transition (EMT)-related gene sets were significantly enriched in the high-risk group, and the epithelial-mesenchymal transition phenotype was associated with a decrease in CD8+ T cells and an increase in M2 macrophages. Transforming growth factor-β (TGF-β) signaling was the most important signaling in inducing epithelial-mesenchymal transition, and TGFB1/TGFBR1 was correlated with an increase in CD8+ T cytopenia and M2 macrophages. Survival analysis showed that simultaneous low expression of TGFBR1 and PD-L1 had better survival results. Through single-cell analysis, we found that TGFB1 is closely related to microglia and macrophages, especially M2 macrophages. Finally, we discussed the sensitivity of TGFB1 inhibitors in gliomas using cell line susceptibility data. These results demonstrated a potential immunotherapy strategy in combination with the TGFB1/TGFBR1 inhibitor and PD-1/PD-L1 inhibitor for grade II and III gliomas.
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Single-Cell Profiling Identifies the SPDEF/GAS5 Axis as a Potential Driver of Tamoxifen Resistance in Estrogen Receptor-Positive Breast Cancer.
Background Estrogen receptor-positive (ER+) breast cancer is the most prevalent breast cancer subtype, and tamoxifen remains the cornerstone of adjuvant endocrine therapy. However, a significant proportion of patients eventually develop acquired tamoxifen resistance, leading to disease recurrence and progression. Methodology To characterize the cellular and molecular mechanisms underlying this resistance at single-cell resolution, we performed an integrative analysis of publicly available single-cell RNA sequencing data from 16 ER+ breast cancer specimens, comprising 13 treatment-naive primary tumors and three tamoxifen-resistant recurrent tumors. Following rigorous quality control, batch correction, and dimensionality reduction, we identified 11 major cell populations in the tumor microenvironment. Copy number variation inference using InferCNV was utilized to confirm the malignant identity of the epithelial cells. Results Gene Ontology Biological Process enrichment analysis revealed that recurrent malignant cells were significantly enriched for pathways associated with translation, chromatin remodeling, and cell division compared with primary tumor cells. Sub-clustering of malignant epithelial cells resolved nine distinct subpopulations, among which a GAS5+ (growth arrest-specific 5-positive) subpopulation was markedly expanded in tamoxifen-resistant recurrent tumors. This subpopulation exhibited distinctive metabolic reprogramming characterized by enhanced aerobic respiration and energy metabolism. Monocle2-based pseudotemporal trajectory analysis positioned GAS5+ cells at a terminally differentiated state, likely derived from CLDN3+ and CEACAM6+ tumor progenitor cells. Single-cell regulatory network inference using SCENIC identified SPDEF (SAM pointed domain-containing ETS transcription factor) as a putative key transcription factor specifically active in GAS5+ tumor cells, and SPDEF expression was highly enriched in this subcluster. Validation in The Cancer Genome Atlas breast cancer cohort demonstrated that high SPDEF expression was significantly associated with worse overall survival (log-rank p = 0.011). Conclusions These findings implicate the SPDEF-GAS5+ regulatory axis as a potential driver of tamoxifen resistance and a compelling candidate for future therapeutic targeting in ER+ breast cancer.
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USP31 serves as a potential biomarker for predicting prognosis and immune responses for clear cell renal cell carcinoma via single-cell and bulk RNA-sequencing.
BACKGROUND: Currently, there is no research available on the prognosis, potential effect and therapeutic value of USP31 in clear cell renal cell carcinoma (ccRCC). To address this gap, the present study aimed to shed light on its potential roles and possible mechanisms in ccRCC. METHODS: R software was utilized to conduct bioinformatics analyses with the data derived from The Cancer Genome Atlas (i.e. KIRC) and Gene Expression Omnibus datasets. The expression of USP31 in ccRCC was validated by a PCR. The independent prognostic ability of USP31 was evaluated by Cox regression analysis. We conducted gene set enrichment analysis (GSEA) to explore the potential USP31-related pathways. We also discussed the relationships between USP31 and immunity, by predicting its possible upstream transcription factors (TFs) by ChEA3. RESULTS: In ccRCC, USP31 demonstrated a high level of expression and this increased expression was correlated with a poor prognosis (p < 0.05). Through univariate and multivariate Cox regression analysis, USP31 was identified as an independent prognostic factor for ccRCC (p < 0.05). Furthermore, eight USP31-related pathways were identified by GSEA (p < 0.05). Moreover, USP31 was found to be associated with microsatellite instability, tumor microenvironment, a variety of immune cells and immune checkpoints and immune infiltration (p < 0.05). Additionally, Patients with high USP31 expression in ccRCC were shown to have better curative effects after immunotherapy (p < 0.05). Finally, we found that AR, USF1, MXI1 and CLOCK could be the potential upstream TFs of USP31. CONCLUSIONS: USP31 could serve as a potential biomarker for predicting both prognosis and immune responses, revealing its potential mechanisms of TF-USP31 mRNA networks in ccRCC.
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A Tumor-Promoting Inflammatory SPP1+ Macrophage-IL6-CRP Axis Drives Immune Dysfunction in Bladder Cancer.
UNLABELLED: Immune checkpoint blockade (ICB) has revolutionized treatment for urothelial bladder cancer, yet response rates remain limited. Inflammation promotes disease progression and treatment resistance, with macrophages shaping the tumor microenvironment (TME). Although elevated blood C-reactive protein (CRP) is associated with poor clinical outcomes in urothelial bladder cancer, its relationship to the TME remains unclear. In this study, we show that elevated plasma IL6 and CRP associate with increased tumor macrophage infiltration across multiple ICB-treated cohorts. Single-cell RNA sequencing (RNA-seq) of the largest urothelial bladder cancer atlas to date, integrated with bulk RNA-seq, identifies enrichment of immunosuppressive SPP1+ macrophages in TMEs from patients with high plasma IL6. Spatial and functional analyses demonstrate that SPP1+ macrophages suppress T-cell activity partly via IL6 signaling, whereas CXCL9+ macrophages promote T-cell activation. These findings link systemic inflammation to local immune dysfunction and define a macrophage-driven axis associated with ICB resistance and therapeutic targets to improve immunotherapy outcomes in urothelial bladder cancer. SIGNIFICANCE: Single-cell and bulk RNA-seq, spatial analyses, and functional experiments identify opposing SPP1+ and CXCL9+ macrophage programs that regulate T-cell function and ICB therapy response in bladder cancer. Elevated plasma CRP and IL6 mark SPP1+ macrophage-driven immune suppression, defining a targetable IL1β/IL6 axis that contributes to immunotherapy resistance.
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Leveraging a disulfidptosis‑related lncRNAs signature for predicting the prognosis and immunotherapy of glioma.
BACKGROUND: Gliomas, a prevalent form of primary brain tumors, are linked with a high mortality rate and unfavorable prognoses. Disulfidptosis, an innovative form of programmed cell death, has received scant attention concerning disulfidptosis-related lncRNAs (DRLs). The objective of this investigation was to ascertain a prognostic signature utilizing DRLs to forecast the prognosis and treatment targets of glioma patients. METHODS: RNA-seq data were procured from The Cancer Genome Atlas database. Disulfidptosis-related genes were compiled from prior research. An analysis of multivariate Cox regression and the least absolute selection operator was used to construct a risk model using six DRLs. The risk signature's performance was evaluated via Kaplan-Meier survival curves and receiver operating characteristic curves. Additionally, functional analysis was carried out using GO, KEGG, and single-sample GSEA to investigate the biological functions and immune infiltration. The research also evaluated tumor mutational burden, therapeutic drug sensitivity, and consensus cluster analysis. Reverse transcription quantitative PCR was conducted to validate the expression level of DRLs. RESULTS: A prognostic signature comprising six DRLs was developed to predict the prognosis of glioma patients. High-risk patients had significantly shorter overall survival than low-risk patients. The robustness of the risk model was validated by receiver operating characteristic curves and subgroup survival analysis. Risk model was used independently as a prognostic indicator for the glioma patients. Notably, the low-risk patients displayed a substantial decrease in the immune checkpoints, the proportion of immune cells, ESTIMATE and immune score. IC50 values from the different risk groups allowed us to discern three drugs for the treatment of glioma patients. Lastly, the potential clinical significance of six DRLs was determined. CONCLUSIONS: A novel six DRLs signature was developed to predict prognosis and may provide valuable insights for patients with glioma seeking novel immunotherapy and targeted therapy.
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Iterative single-cell multi-omic integration using online learning.
Integrating large single-cell gene expression, chromatin accessibility and DNA methylation datasets requires general and scalable computational approaches. Here we describe online integrative non-negative matrix factorization (iNMF), an algorithm for integrating large, diverse and continually arriving single-cell datasets. Our approach scales to arbitrarily large numbers of cells using fixed memory, iteratively incorporates new datasets as they are generated and allows many users to simultaneously analyze a single copy of a large dataset by streaming it over the internet. Iterative data addition can also be used to map new data to a reference dataset. Comparisons with previous methods indicate that the improvements in efficiency do not sacrifice dataset alignment and cluster preservation performance. We demonstrate the effectiveness of online iNMF by integrating more than 1 million cells on a standard laptop, integrating large single-cell RNA sequencing and spatial transcriptomic datasets, and iteratively constructing a single-cell multi-omic atlas of the mouse motor cortex.
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A Molecular, Spatial, and Regulatory Atlas of the Hydra vulgaris Nervous System.
Hydra vulgaris, a cnidarian with a simple nerve net, is an emerging model for developmental, regenerative, and functional neuroscience. Its genetic tractability and capacity for whole-system imaging make it well suited for studying neuron replacement, regeneration, and neural circuit function. Here, we present the most comprehensive molecular and spatial characterization of the H. vulgaris nervous system to date. Using single-cell RNA sequencing, we identified eight neuron types, each defined by distinct neuropeptide expression, and further resolved these into fifteen transcriptionally distinct subtypes with unique spatial distributions and morphologies. To investigate the gene regulatory networks underlying neuronal differentiation, we applied trajectory inference, identified key transcription factors, and performed ATAC-seq on sorted neurons to map chromatin accessibility. All datasets are available through an interactive, user-friendly web portal to support broad use by the research community. Together, these resources provide a foundation for uncovering molecular mechanisms that govern nervous system development, homeostasis, and regeneration in H. vulgaris.
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Development of a novel prognostic signature based on single-cell combined bulk RNA analysis in breast cancer.
BACKGROUND: Breast cancer (BC), a malignant tumor, is a significant cause of death and disability among women globally. Recent research indicates that copy number variation plays a crucial role in tumor development. In this study, we employed the Single-Cell Variational Aneuploidy Analysis (SCEVAN) algorithm to differentiate between malignant and non-malignant cells, aiming to identify genetic signatures with prognostic relevance for predicting patient survival. METHODS: We analyzed gene expression profiles and associated clinical data from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Using the SCEVAN algorithm, we distinguished malignant from non-malignant cells and investigated cellular interactions within the tumor microenvironment (TME). We categorized TCGA samples based on differentially expressed genes (DEGs) between these cell types. Subsequent Kyoto Encyclopedia of Genes and Genomes pathway analysis was conducted. Additionally, we developed polygenic models for the DEGs using least absolute shrinkage and selection operator-penalized Cox regression analysis. To assess the prognostic accuracy of these characteristics, we generated Kaplan-Meier and receiver operating characteristic curves from training and validation datasets. We also monitored the expression variations of prognostic genes across the pseudotime of malignant cells. Patients were divided into high-risk and low-risk groups based on median risk scores to compare their TME and identify potential therapeutic agents. Lastly, polymerase chain reaction was used to validate seven pivotal genes. RESULTS: The SCEVAN algorithm identified distinct malignant and non-malignant cells in GSE180286. Cellchat analysis revealed significantly increased cellular communication, particularly between fibroblasts, endothelial cells and malignant cells. The DEGs were predominantly involved in immune-related pathways. TCGA samples were classified into clusters A and B based on these genes. Cluster A, enriched in immune pathways, was associated with poorer prognosis, whereas cluster B, predominantly involved in circadian rhythm pathways, showed better outcomes. We constructed a 14-gene prognostic signature, validated in a 1:1 internal TCGA cohort and external GEO datasets (GSE42568 and GSE146558). Kaplan-Meier analysis confirmed the prognostic signature's accuracy (p < 0.001). Receiver operating characteristic curve analysis demonstrated the predictive reliability of these prognostic features. Single-cell pseudotime analysis with monocle2 highlighted the distinct expression trends of these genes in malignant cells, underscoring the intratumoral heterogeneity. Furthermore, we explored the differences in TME between high- and low-risk groups and identified 16 significantly correlated drugs. CONCLUSION: Our findings suggest that the 14-gene prognostic signature could serve as a novel biomarker for forecasting the prognosis of BC patients. Additionally, the immune cells and pathways in different risk groups indicate that immunotherapy may be a crucial component of treatment strategies for BC patients.
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Immunogenomic Profiling Demonstrate AC003092.1 as an Immune-Related eRNA in Glioblastoma Multiforme.
Enhancer RNAs, a type of long non-coding RNAs (lncRNAs), play a critical role in the occurrence and development of glioma. RNA-seq data from 161 glioblastoma multiforme (GBM) samples were acquired from The Cancer Genome Atlas database. Then, 70 eRNAs were identified as prognosis-related genes, which had significant relations with overall survival (log-rank test, p < 0.05). AC003092.1 was demonstrated as an immune-related eRNA by functional enrichment analysis. We divided samples into two groups based on AC003092.1 expression: AC003092.1 High (AC003092.1_H) and AC003092.1 Low (AC003092.1_L) and systematically analyzed the influence of AC003092.1 on the immune microenvironment by single-sample gene-set enrichment analysis and CIBERSORTx. We quantified AC003092.1 and TFPI2 levels in 11 high-grade gliomas, 5 low-grade gliomas, and 7 GBM cell lines. Our study indicates that AC003092.1 is related to glioma-immunosuppressive microenvironment, and these results offer innovative sights into GBM immune therapy.