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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 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.
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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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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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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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Exploring the Molecular Mechanisms and Shared Genetic Characteristics of Monoclonal Gammopathy of Undetermined Significance and Multiple Myeloma.
BACKGROUND: Monoclonal gammopathy of undetermined significance (MGUS) is a precursor to multiple myeloma (MM), but the mechanisms of progression remain unclear. METHODS AND OBJECTIVES: Transcriptomic datasets procured from the Gene Expression Omnibus (GEO) underwent thorough analysis to ascertain disease-related modules using weighted gene co-expression network analysis. A prognostic model (MGUSscore) was constructed via least absolute shrinkage and selection operator (LASSO) regression within the GSE136337 cohort and validated across independent datasets (The Cancer Genome Atlas - multiple myeloma [TCGA-MM], GSE4581, GSE57317). Crucially, the investigation integrated original single-cell ATAC-seq profiling, immune landscape characterization, and pharmacogenomic sensitivity prediction. Protein-level disparities were validated in clinical specimens using immunohistochemistry and multiplex immunofluorescence. RESULTS: DAP3 and UBE2S were identified as central drivers of progression. The MGUSscore effectively stratified patients into risk categories, with high-risk individuals exhibiting significantly inferior survival outcomes (p < 0.001). Notably, the high-risk group was characterized by distinct immune infiltration patterns and predicted responsiveness to specific chemotherapies. Experimental validation confirmed markedly elevated DAP3 and UBE2S protein expression in MM compared to MGUS tissues. CONCLUSION: Collectively, DAP3 and UBE2S may constitute promising therapeutic targets for MM intervention, meriting additional investigative efforts.
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Population-based high-dimensional analyses identify multiple intrinsic characters for cancer vaccines against lung squamous cell carcinoma.
In lung squamous cell carcinoma (LUSC), current cancer vaccines show promising effects, despite a lack of benefit for a large number of patients. We first identified the tumor antigens into shared and private antigens, and determined the population by clustering analysis in public datasets. For vaccine development, The Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC) were collected. WGCNA method was furthermore applied to construct a consensus gene co-expression network based on TCGA and CPTAC datasets. The main analyses in bulk sequencing included survival, clinical features, tumor microenvironment (TME), and pathways enrichment. In addition, single-cell RNA (scRNA) analysis of cancer epithelium dissected consensus subtype. We identified the ideal population for cancer vaccines, and candidate neoantigens including AOC1, COL5A2, LGI2, and POSTN. According to subtype analysis, Lung squamous 1 (LSQ1) type exhibited a higher tumor mutational load (TMB) and copy number but no immune infiltration, whereas lung squamous 2 (LSQ2) tumors had a higher global methylation level and more fibroblasts but had less stemness. Meanwhile, trajectory analysis further revealed that the evolution of TME influenced prognosis. We emphasized specific pathways or targets with the potential for combination immunotherapy by consensus network and single-cell RNA analyses. Anti-androgen therapy has been validated in vitro experiments of LUSC as proof of concept. In conclusion, LSQ1 was linked to immune exclusion and might be utilized for vaccination, while LSQ2 was linked to immune dysfunction and could be used for programmed cell death protein 1 (PD1) blocking therapy.
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Crotonylation and the Risk of Head and Neck Cancer: Insights from a Two-Sample Mendelian Randomization Study.
BACKGROUND: Head and neck cancer (HNC) remains a major global health burden with limited improvement in survival. Crotonylation has been implicated in tumour biology, but its causal contribution to HNC risk is unclear. METHODS: We conducted a two-sample Mendelian randomization (MR) study using expression quantitative trait loci (eQTL)-instrumented expression of crotonylation-related genes. Differential expression was assessed in The Cancer Genome Atlas (TCGA). Mediation MR evaluated 1,400 metabolites and 731 immune-cell traits. Single-cell RNA sequencing (scRNA-seq) mapped cell type - specific expression, and quantitative PCR (qPCR) validated expression in tumour versus normal tissues. RESULTS: MR analysis identified that genetically predicted expression of Glutaryl-CoA Dehydrogenase (GCDH) and Acyl-CoA synthetase, short-chain family member 2 (ACSS2) were causally associated with an increased risk of HNC, and Double PHD fingers 2 (DPF2) and Histone deacetylase 7 (HDAC7) were found to have protective effects against HNC. Moreover, TCGA analysis confirmed a consistent association between GCDH and HNC risk. Mediation MR showed no significant metabolite mediators, although 16α-hydroxy DHEA 3-sulfate levels exhibited a borderline signal. In contrast, two immune-cell traits: CX3CR1 on CD14⁺CD16⁺ monocytes and CD8 on CD39⁺ CD8^br cells partially mediated the effect of GCDH on HNC. scRNA-seq demonstrated lineage-dependent expression of GCDH, and GCDH was enriched in malignant and stromal compartments, with qPCR confirming elevated GCDH expression in tumour samples compared with adjacent normal tissues. CONCLUSION: This study suggests a potential causal association between GCDH expression and increased HNC risk, partially mediated by immune-cell traits, pending further validation via colocalization and functional assays. Our findings highlight GCDH as a promising target for further mechanistic studies and potential therapeutic intervention in HNC. CLINICAL RELEVANCE: The identification of GCDH as a potential risk factor in HNC opens new avenues for targeted therapies, offering potential improvements in patient outcomes through precision medicine approaches.
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In Silico Multi-omics Analyses of Kinesin Family Member 15 in Pan-Cancer and Validation in Tissue Microarray.
INTRODUCTION: Kinesin family member 15 (KIF15) is a molecular motor protein that participates in bipolar spindle assembly and centrosome separation during metaphase in normal cells. However, accumulating evidence has shown that KIF15 is aberrantly regulated in the state of malignancy and is associated with the progression of cancers. MATERIALS AND METHODS: We analyzed the mRNA expression, protein expression, single-cell gene expression, prognoses, and immune reactivities of KIF15 using the data from various sources, including the Cancer Cell Line Encyclopedia, The Cancer Genome Atlas database, the Clinical Proteomic Tumor Analysis Consortium Confirmatory/Discovery database, and Cancer Single-cell Expression Map. Additionally, immunohistochemistry was performed to validate KIF15 protein expression in human tumor samples. RESULTS: The data from the present pan-cancer analysis elucidated that KIF15 expression was increased in more than 25 human tumors. Furthermore, it was found to be highly corre-lated with biological pathways, immune cell infiltration, and poor prognoses across multiple cancers. Additionally, tissue microarray analysis confirmed abundant cytoplasmic KIF15 expression in ovarian serous carcinoma and colorectal adenocarcinoma, with significantly higher levels compared to adjacent normal tissues. DISCUSSION: Consistent patterns of high KIF15 expression were also evident across various cancer cell lines in the CCLE dataset. Collectively, these observations suggest KIF15 is in-timately involved in the progression of multiple human cancers. By analyzing both the pro-teins that interact with KIF15 and the genes that show correlation with its expression, KEGG pathway enrichment indicated that processes such as cell cycle regulation, gap and tight junction dynamics, involvement of microRNAs in cancer, and the p53 signaling pathway may underlie the role of KIF15 in cancer development. CONCLUSION: KIF15 may serve as a potential biomarker and oncogenic factor in multiple cancer types.