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Found: 4 939
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Integration of transcriptomics and machine learning for insights into breast cancer: exploring lipid metabolism and immune interactions.
BACKGROUND: Breast cancer (BRCA) represents a substantial global health challenge marked by inadequate early detection rates. The complex interplay between the tumor immune microenvironment and fatty acid metabolism in BRCA requires further investigation to elucidate the specific role of lipid metabolism in this disease. METHODS: We systematically integrated nine machine learning algorithms into 184 unique combinations to develop a consensus model for lipid metabolism-related prognostic genes (LMPGS). Additionally, transcriptomics analysis provided a comprehensive understanding of this prognostic signature. Using the ESTIMATE method, we evaluated immune infiltration among different risk subgroups and assessed their responsiveness to immunotherapy. Tailored treatments were screened for specific risk subgroups. Finally, we verified the expression of key genes through in vitro experiments. RESULTS: We identified 259 differentially expressed genes (DEGs) related to lipid metabolism through analysis of the cancer genome atlas program (TCGA) database. Subsequently, via univariate Cox regression analysis and C-index analysis, we developed an optimal machine learning algorithm to construct a 21-gene LMPGS model. We used optimal cutoff values to divide the lipid metabolism prognostic gene scores into two groups according to high and low scores. Our study revealed distinct biological functions and mutation landscapes between high-scoring and low-scoring patients. The low-scoring group presented a greater immune score, whereas the high-scoring group presented enhanced responses to both immunotherapy and chemotherapy drugs. Single-cell analysis highlighted significant upregulation of CPNE3 in epithelial cells. Moreover, by employing molecular docking, we identified niclosamide as a potential targeted therapeutic drug. Finally, our experiments demonstrated high expression of MTMR9 and CPNE3 in BRCA and their significant correlation with prognosis. CONCLUSION: By employing bioinformatics and diverse machine learning algorithms, we successfully identified genes associated with lipid metabolism in BRCA and uncovered potential therapeutic agents, thereby offering novel insights into the mechanisms and treatment strategies for BRCA.
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An atlas of alternative splicing profiles and functional associations reveals new regulatory programs and genes that simultaneously express multiple major isoforms.
Alternative splicing (AS) generates remarkable regulatory and proteomic complexity in metazoans. However, the functions of most AS events are not known, and programs of regulated splicing remain to be identified. To address these challenges, we describe the Vertebrate Alternative Splicing and Transcription Database (VastDB), the largest resource of genome-wide, quantitative profiles of AS events assembled to date. VastDB provides readily accessible quantitative information on the inclusion levels and functional associations of AS events detected in RNA-seq data from diverse vertebrate cell and tissue types, as well as developmental stages. The VastDB profiles reveal extensive new intergenic and intragenic regulatory relationships among different classes of AS and previously unknown and conserved landscapes of tissue-regulated exons. Contrary to recent reports concluding that nearly all human genes express a single major isoform, VastDB provides evidence that at least 48% of multiexonic protein-coding genes express multiple splice variants that are highly regulated in a cell/tissue-specific manner, and that >18% of genes simultaneously express multiple major isoforms across diverse cell and tissue types. Isoforms encoded by the latter set of genes are generally coexpressed in the same cells and are often engaged by translating ribosomes. Moreover, they are encoded by genes that are significantly enriched in functions associated with transcriptional control, implying they may have an important and wide-ranging role in controlling cellular activities. VastDB thus provides an unprecedented resource for investigations of AS function and regulation.
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Construction of an immune-related gene signature for the prognosis and diagnosis of glioblastoma multiforme.
BACKGROUND: Increasing evidence has suggested that inflammation is related to tumorigenesis and tumor progression. However, the roles of immune-related genes in the occurrence, development, and prognosis of glioblastoma multiforme (GBM) remain to be studied. METHODS: The GBM-related RNA sequencing (RNA-seq), survival, and clinical data were acquired from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), Chinese Glioma Genome Atlas (CGGA), and Gene Expression Omnibus (GEO) databases. Immune-related genes were obtained from the Molecular Signatures Database (MSigDB). Differently expressed immune-related genes (DE-IRGs) between GBM and normal samples were identified. Prognostic genes associated with GBM were selected by Kaplan-Meier survival analysis, Least Absolute Shrinkage and Selection Operator (LASSO)-penalized Cox regression analysis, and multivariate Cox analysis. An immune-related gene signature was developed and validated in TCGA and CGGA databases separately. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to explore biological functions of the signature. The correlation between immune cell infiltration and the signature was analyzed by single-sample gene set enrichment analysis (ssGSEA), and the diagnostic value was investigated. The gene set enrichment analysis (GSEA) was performed to explore the potential function of the signature genes in GBM, and the protein-protein interaction (PPI) network was constructed. RESULTS: Three DE-IRGs [Pentraxin 3 (PTX3), TNFSF9, and bone morphogenetic protein 2 (BMP2)] were used to construct an immune-related gene signature. Receiver operating characteristic (ROC) curves and Cox analyses confirmed that the 3-gene-based prognostic signature was a good independent prognostic factor for GBM patients. We found that the signature was mainly involved in immune-related biological processes and pathways, and multiple immune cells were disordered between the high- and low-risk groups. GSEA suggested that PTX3 and TNFSF9 were mainly correlated with interleukin (IL)-17 signaling pathway, nuclear factor kappa B (NF-κB) signaling pathway, tumor necrosis factor (TNF) signaling pathway, and Toll-like receptor signaling pathway, and the PPI network indicated that they could interact directly or indirectly with inflammatory pathway proteins. Quantitative real-time PCR (qRT-PCR) indicated that the three genes were significantly different between target tissues. CONCLUSION: The signature with three immune-related genes might be an independent prognostic factor for GBM patients and could be associated with the immune cell infiltration of GBM patients.
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Single-cell spatial transcriptome atlas and whole-brain connectivity of the macaque claustrum.
Claustrum orchestrates brain functions via its connections with numerous brain regions, but its molecular and cellular organization remains unresolved. Single-nucleus RNA sequencing of 227,750 macaque claustral cells identified 48 transcriptome-defined cell types, with most glutamatergic neurons similar to deep-layer insular neurons. Comparison of macaque, marmoset, and mouse transcriptomes revealed macaque-specific cell types. Retrograde tracer injections at 67 cortical and 7 subcortical regions defined four distinct distribution zones of retrogradely labeled claustral neurons. Joint analysis of whole-brain connectivity and single-cell spatial transcriptome showed that these four zones containing distinct compositions of glutamatergic (but not GABAergic) cell types preferentially connected to specific brain regions with a strong ipsilateral bias. Several macaque-specific glutamatergic cell types in ventral vs. dorsal claustral zones selectively co-projected to two functionally related areas-entorhinal cortex and hippocampus vs. motor cortex and putamen, respectively. These data provide the basis for elucidating the neuronal organization underlying diverse claustral functions.
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A novel programmed-cell-death-related prognostic risk model for cervical cancer based on mitochondrial genes.
BACKGROUND: Despite established links between mitochondrial (mt) function, programmed cell death (PCD) dysregulation, and cervical cancer progression, the prognostic potential of integrating PCD- with mitochondria-related genes remains elusive. To address this gap, this study aims to develop a reliable RiskScore model leveraging PCD and mt characteristics for cervical cancer prognostic prediction. METHODS: mRNA expression profiles with clinical data of 309 cervical cancer patients from The Cancer Genome Atlas (TCGA), complemented by datasets from the Gene Expression Omnibus (GEO) and the European Molecular Biology Laboratory's European Bioinformatics Institute (EMBL-EBI) databases were utilized. We identified differentially expressed genes (DEGs) using the R "limma" package. Least absolute shrinkage and selection operator (LASSO) Cox regression analysis refined gene selection for RiskScore construction. The "rms" package was employed to develop a nomogram model. Additionally, we leveraged generalized structured component analysis (GSCA) for mutation analysis and Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) for immune infiltration assessment. Single-cell sequencing data were processed with the "Seurat" package. RESULTS: Our study identified 119 PCD-related mt DEGs. A prognostic model was developed based on IL1B, NAMPT, PRKAB2, SLC2A1, ACAA2, E2F1, GZMB, and BNIP3, yielding a RiskScore that stratified patients into low- and high-risk groups with significant survival differences. The model's predictive accuracy was validated with high area under the curve (AUC) values and confirmed as an independent prognostic factor. A nomogram model was constructed for predicting patient survival rates, showing good agreement with actual outcomes. Mutation analysis indicated a low mutation rate in our model genes, with a significant negative correlation between RiskScore and tumor mutation burden. Single-cell analysis uncovered GZMB's high expression in T/natural killer (NK) cells, with dynamic changes along pseudotime trajectories. CONCLUSIONS: This study introduces a RiskScore model by using PCD-related mt genes for cervical cancer prognosis, offering a personalized therapeutic strategy and insights into immunotherapy potential, potentially advancing future clinical applications.
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Progressive natural killer cell dysfunction in advanced-stage clear-cell renal cell carcinoma and association with clinical outcomes.
BACKGROUND: Natural killer (NK) cells are important contributors to antitumor immunity in clear-cell renal cell carcinoma (ccRCC). However, their phenotype, function, and association with clinical outcomes in ccRCC remain poorly understood. MATERIALS AND METHODS: We analyzed single-cell RNA sequencing data from 13 primary tumors, 1 localized tumor extension, and 1 metastasis from ccRCC patients at different clinical stages. For each primary tumor specimen, paired normal kidneys were also analyzed. Differential gene expression analysis was carried out to investigate NK cell phenotypes and to derive a gene expression signature. Gene signatures from NK cell subclusters of interest were used to interrogate bulk transcriptomic datasets and expression with clinical outcomes. Finally, tumor-infiltrating NK cell function (cytokine production and cytotoxicity) was assessed by isolation of live NK cells from ccRCC tissue, co-culture with K562 target cells, and measurement of cytokine production (interferon-γ) and cytotoxicity (CD107a) markers by flow cytometry. RESULTS: Single-cell transcriptomic data were analyzed from 13 patients with ccRCC (tumor/normal kidney), resulting in 21 139 NK cells. Clustering analysis revealed six NK cell subsets. Bright-like NK cells were significantly enriched in advanced ccRCC compared with localized ccRCC and normal kidney, expressed markers of tissue residency (ZNF683/Hobit, ITGA1/CD49a, CD9, ITGAE/CD103), and had decreased expression of cytotoxicity genes (GZMB/Granzyme-B, PRF1/perforin). In independent cohorts (The Cancer Genome Atlas ccRCC cohort, CheckMate 025), a gene expression score representing this dysfunctional NK cell phenotype was enriched in advanced ccRCC and was associated with worse overall survival. Functional interrogation of tumor-infiltrating NK cells from ccRCC confirmed that tumor-resident CD49a+CD9+ NK cells had impaired cytotoxicity compared with CD49a-CD9- NK cells. CONCLUSIONS: A dysfunctional, tumor-resident NK cell phenotype was enriched among patients with metastatic disease and associated with worse survival in patients with advanced ccRCC across multiple patient cohorts. Restoration of NK cell function (via cytokine stimulation or NK cell engineering) could provide a novel avenue for therapeutic intervention against ccRCC.
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Single-cell atlas of reproductive endocrine organs reveals transcriptomic responses to type 1 diabetes mellitus in nonhuman primates.
Type 1 diabetes mellitus characterized by insulin deficiency and hyperglycemia is associated with female subfertility. However, how hyperglycemia affects the hypothalamic-pituitary-ovarian-uterine axis remains poorly understood. In this study, we performed single-cell transcriptomic profiling of the hypothalamus, pituitary, ovary and uterus during the proliferative phase of the menstrual cycle in type 1 diabetic macaques to systematically characterize changes in tissue-specific cellular heterogeneity, gene expression, and intercellular communication networks under diabetic conditions. Our analysis revealed significant upregulation of the TNF signaling pathway across multiple tissues, concomitant with marked activation of inflammation-related pathways. Notably, the macrophage migration inhibitory factor signaling pathway exhibited a tissue-specific regulatory pattern, being significantly upregulated in the hypothalamus and pituitary but downregulated in the ovary and uterus, suggesting divergent inflammatory modulation along the reproductive endocrine axis in response to diabetes. Moreover, we observed that diabetes leads to reduced FSHR expression during granulosa cell differentiation, and this process is further exacerbated by the upregulated expression of SFRP4, a known antagonist of follicle-stimulating hormone signaling, resulting in diminished granulosa cell responsiveness to follicle-stimulating hormone. Consequently, this dysregulation is correlated with increased FSHB expression in pituitary gonadotropes, likely due to disrupted ovarian feedback signaling. Collectively, our findings provide a comprehensive landscape of cellular and molecular alterations in immune and endocrine compartments in the female reproductive system in diabetic states, advancing our understanding of immune‒endocrine cell crosstalk in the context of metabolic disease.
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The intersectional genetics landscape for humans.
BACKGROUND: The human body is made up of hundreds-perhaps thousands-of cell types and states, most of which are currently inaccessible genetically. Intersectional genetic approaches can increase the number of genetically accessible cells, but the scope and safety of these approaches have not been systematically assessed. A typical intersectional method acts like an "AND" logic gate by converting the input of 2 or more active, yet unspecific, regulatory elements (REs) into a single cell type specific synthetic output. RESULTS: Here, we systematically assessed the intersectional genetics landscape of the human genome using a subset of cells from a large RE usage atlas (Functional ANnoTation Of the Mammalian genome 5 consortium, FANTOM5) obtained by cap analysis of gene expression sequencing (CAGE-seq). We developed the heuristics and algorithms to retrieve and quality-rank "AND" gate intersections. Of the 154 primary cell types surveyed, >90% can be distinguished from each other with as few as 3 to 4 active REs, with quantifiable safety and robustness. We call these minimal intersections of active REs with cell-type diagnostic potential "versatile entry codes" (VEnCodes). Each of the 158 cancer cell types surveyed could also be distinguished from the healthy primary cell types with small VEnCodes, most of which were robust to intra- and interindividual variation. Methods for the cross-validation of CAGE-seq-derived VEnCodes and for the extraction of VEnCodes from pooled single-cell sequencing data are also presented. CONCLUSIONS: Our work provides a systematic view of the intersectional genetics landscape in humans and demonstrates the potential of these approaches for future gene delivery technologies.
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Astrocyte Subtype-Specific Expression of the Sodium-Coupled Citrate Transporter SLC13A5 and Citrate Metabolism Genes Across Alzheimer's Disease Pseudoprogression: A Single-Nucleus RNA Sequencing Analysis of the Human Middle Temporal Gyrus.
The sodium-coupled citrate transporter NaCT (SLC13A5) imports extracellular citrate into cells. In the CNS, SLC13A5 is described to be expressed predominantly in neurons. Cytosolic citrate levels rely on citrate generated in mitochondria and imported from other CNS cells, regulating intermediary metabolism and supplying acetyl-CoA for lipid synthesis and histone acetylation. Despite evidence for NaCT's role in neurometabolic homeostasis, its transcriptional behavior across Alzheimer's disease (AD) progression and across astrocyte subtypes remains uncharacterized at single-cell resolution. We analyzed single-nucleus RNA sequencing data from 1,378,211 nuclei across 84 donors in the Seattle Alzheimer's Disease Brain Cell Atlas (SEA-AD) Middle Temporal Gyrus dataset to profile SLC13A5 and seven citrate metabolism genes across a continuous AD pseudoprogression score. SLC13A5 expression was restricted to astrocytes (~20% prevalence) and concentrated in the Astro 2 supertype (24.0%), a homeostatic subtype characterized by low C3 (1.6%) and CD44 (5.5%), which expanded with pseudoprogression (Spearman rho = +0.345, FDR < 0.001). The A1-reactive Astro 3 supertype, where SLC13A5 prevalence was 0.87%, declined concordantly (rho = -0.393). Opposing compositional and transcriptional forces produced apparent stability in overall SLC13A5 prevalence. SLC13A3 and ACO1 showed progressive donor-level declines correlating with Braak stage and Thal phase (rho range: -0.307 to -0.349, FDR < 0.01). APOE4 carriers exhibited lower SLC13A5 prevalence specifically within Astro 2 nuclei (median 17.6% vs. 25.9%; Wilcoxon p = 0.025), though this association did not survive multivariate regression. No difference in Astro 2 SLC13A5 expression was detected between cognitively resilient and expected-AD donors with equivalent high Braak burden (p = 0.888). Contrary to the prevailing description of NaCT as a neuronal transporter, SLC13A5 transcript in the SEA-AD MTG dataset was detected almost exclusively in astrocyte nuclei, concentrated in the homeostatic Astro 2 subtype, and maintained as this subtype expanded with advancing AD pathology. Because these are nuclear transcript measurements, they delimit where SLC13A5 mRNA is detectable rather than establishing the cellular site of NaCT protein or activity, which requires in situ validation.
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An etiology-stratified single-cell atlas identifies FABP4 as a prognostic marker for MASLD-related HCC.
BACKGROUND & AIMS: The impact of HBV infection and metabolic dysfunction-associated steatotic liver disease (MASLD) on the tumor microenvironment in hepatocellular carcinoma (HCC) remains unclear. The aim of this study was to compare the tumor microenvironment among HBV-MASLD-, HBV-MASLD+, HBV+MASLD-, HBV+MASLD+ HCC, and to identify etiology-specific prognostic biomarkers. METHODS: Single-cell RNA-sequencing was performed on paired tumor and adjacent tissues from 12 patients with HCC. Bulk RNA-sequencing data from the TCGA-LIHC and two immunotherapy-treated HCC cohorts were analyzed to assess FABP4's role in vascular normalization and prognosis. Validation included immunohistochemistry on 224 samples, multiplex immunohistochemistry, in vitro and in vivo experiments. RESULTS: We compared the prognosis of patients with different etiologies of HCC and found that those with HBV+MASLD+ HCC exhibited the most favorable outcome following immunotherapy. Compared to MASLD- HCC, MASLD+ HCC showed increased infiltration of CD8+ T cells and reduced macrophage abundance. In HBV+ HCC, tumor-reactive and tumor-specific T-cell scores, and M1-like macrophage signatures were significantly higher than in HBV- cases. HBV+MASLD+ HCC also featured a higher proportion of precursor-exhausted CD8+ T cells. Additionally, FABP4 was predominantly expressed in tumor endothelial cells in MASLD-related HCC, regulated by PPARγ. FABP4 facilitated vascular normalization and enhanced CD8+ T-cell infiltration. Mechanistically, FABP4 overexpression upregulated multiple vascular-stabilizing genes and downregulated destabilizing genes, associated with Notch1 pathway activation. Inhibition of FABP4 using BMS309403 impaired anti-PD-1 efficacy and attenuated vascular normalization and cytotoxic CD8+ T-cell infiltration in MASLD-related HCC mouse models. Clinically, high FABP4 expression correlated with better prognosis. CONCLUSIONS: These findings reveal etiology-heterogeneous immune landscapes in HCC and identify FABP4 as a potential prognostic biomarker to guide immunotherapy in MASLD-related HCC. IMPACT AND IMPLICATIONS: This study systematically analyzed the impact of HBV, metabolic dysfunction-associated steatotic liver disease (MASLD), and their coexistence on responses to immunotherapy and outcomes in hepatocellular carcinoma (HCC). By integrating single-cell sequencing of patient samples, it highlights the importance of etiological heterogeneity, particularly from the perspective of CD8+ T cells. The study identifies FABP4 as being highly expressed in MASLD-related HCC and implicates it in promoting vascular normalization and CD8+ T-cell infiltration. Most importantly, FABP4 emerges as a potential prognostic biomarker for HCC, especially in MASLD-related cases, offering a practical tool to identify patients who are more likely to benefit from immunotherapy.