Human Mutation最新文献

Ovarian cancer (OC) poses a significant threat to women’s health, with current treatment strategies remaining suboptimal, necessitating the exploration of novel therapeutic targets and immune microenvironment dynamics. This study integrates multiomics data from TCGA, GEO, and IEU-Open-GWAS, employing scRNA-seq, scPagwas, BayesPrism, and WGCNA to identify key cell subpopulations and genes, followed by functional validation through EdU, colony formation, Transwell assays, and ferroptosis markers (MDA, ROS, and ferrous ions). Results reveal MALAT1⁺ epithelial cells as a core cell subpopulation in OC, with higher abundance correlating with shorter overall survival, suppressed immune microenvironments, and potential immunotherapy resistance, while their infiltration levels are closely associated with OC immune dynamics and somatic mutations. Further analysis identifies IPO9 as a core gene upregulated in OC, promoting tumor progression by inhibiting HMOX1-dependent ferroptosis. These findings highlight MALAT1⁺ epithelial cells as drivers of immune suppression in OC and propose IPO9 as a promising therapeutic target, offering new avenues for immunotherapy development.

Lung adenocarcinoma is a very aggressive cancer with poor clinical results. New molecular indicators are desperately needed to improve treatment decision-making. This study looks at the relationship between the immunological microenvironment and genes linked to pyrimidine metabolism, particularly those that undergo acetylation, and the prognostic significance of these genes. Using publicly accessible genomic and clinical data, we used Gene Set Variation Analysis (GSVA) to identify acetylated pyrimidine pathway components that are highly correlated with survival outcomes. Three potential genes—TK1, RRM2B, and NME4—were identified for their prognostic relevance by using sophisticated predictive modeling approaches including CoxBoost and a random forest survival analysis. Using CIBERSORT deconvolution and single-sample gene set enrichment, immunological landscape disparities were identified, and it was discovered that varied gene expression-acetylation patterns were linked to varying immune cell infiltration. Gene activity and acetylation status-based low-risk patients showed positive survival patterns and higher levels of antitumor immune populations, indicating possible receptivity to immune-based treatments. Functional validation experiments targeting TK1, including RNA interference followed by proliferation (CCK-8, EdU), migration (Transwell), and wound healing assays, substantiated its role in promoting tumor aggressiveness. Collectively, our findings suggest that integrating metabolic gene signatures with immunological context offers a promising framework for precision oncology in lung adenocarcinoma.

This study investigated the therapeutic effects and mechanisms of Paeonia suffruticosa fruit pod extract (EMP) and its main component albiflorin (AF) on hyperuricemia-associated cognitive impairment (HUA-CI). A HUA-CI mouse model was established, with cognitive function evaluated via Morris water maze. Hippocampal pathology, inflammation, oxidative stress, and apoptosis were assessed using HE staining, ELISA, TUNEL, and Western blotting. Network pharmacology predicted EMP’s targets, and molecular docking analyzed AF-MAP2K1 binding. In vitro experiments used UA-stimulated BV2 and HT22 cells to explore AF’s effect on HIF-1 signaling. EMP significantly improved cognitive function and reduced pathological damage in the hippocampus of HUA-CI mice. It exerted protective effects by inhibiting inflammatory responses, alleviating oxidative stress, and preventing cell apoptosis. Network pharmacology analysis revealed that EMP acts through multiple targets and pathways, particularly via the strong binding affinity between AF and MAP2K1. Both in vivo and in vitro studies demonstrated that AF inhibited the HIF-1 signaling pathway, thereby reducing microglial activation and associated inflammation, mitigating uric acid-induced neuronal apoptosis, enhancing antioxidant defenses, and protecting neuronal function. Our research indicates that EMP exerts multi-target therapeutic effects on HUA-CI; AF plays a key role by targeting MAP2K1 and inhibiting HIF-1 signaling.

Sepsis, a systemic inflammatory response to infection, remains a significant health challenge with high morbidity and mortality rates. The molecular mechanisms underlying sepsis, particularly the role of programmed cell death (PCD), are not fully understood. This study is aimed at elucidating the transcriptomic changes associated with sepsis, emphasizing PCD, and identifying potential diagnostic biomarkers. Transcriptome data from sepsis and control samples were extracted from the GEO website. Differential expression analysis identified genes perturbed in sepsis. WGCNA revealed 14 highly connected modules, with the turquoise module showing the strongest association with sepsis. A set of 262 hub genes was identified, which were mainly associated with apoptotic signaling pathways. Seven prognostic-related overlapping feature genes (PRGs) were identified. More importantly, the diagnostic model, constructed using eight machine learning algorithms, exhibited high efficacy in distinguishing sepsis patients from controls. The validation of feature genes at the scRNA-seq level adds a layer of robustness to our conclusions. The strong association of genes like S100A9 and KLHL3 with neutrophils, pivotal players in sepsis, suggests potential avenues for therapeutic targeting. Our comprehensive analysis has unveiled the significant role of PCD in sepsis. The insights gained from this study provide a foundation for future therapeutic interventions.

Colon cancer is one of the most prevalent malignant tumors. Accurate evaluation of patient prognosis and optimization of treatment strategies continue to be major research focuses in colon cancer. Based on The Cancer Genome Atlas (TCGA) database, this study is the first to comprehensively analyze the expression, biological roles, and prognosis of itaconate and Hallmark pathway–related genes in colon cancer using bulk transcriptomics, single-cell transcriptomics, and spatial transcriptomics data. Through strict screening in 448 colon cancer patients from TCGA database (training set) and 7 colon cancer prognostic models from the Gene Expression Omnibus (GEO) database (including 1473 cases in the validation set), 10 prognosis-related genes (TIMP1, FJX1, CD36, CXCL1, ETS2, CDKN2A, INHBB, PLEC, TUBB2, and P4HA1) were selected. The optimal prognostic prediction model (Enet [alpha = 0.2]) was constructed and validated, which showed good prognostic predictive value in both the training and validation sets (average C-index > 0.7) and was superior to previous conventional clinical features and 22 prognostic models developed by researchers in the past 4 years. ScRNAseq (GSE225857) and spatial transcriptomics analyses clarified the cell-specific expression and spatial distribution characteristics of these genes in the tumor microenvironment (TME), with high functional scores mainly enriched in epithelial and stromal cells. Tissue microarray (TMA) showed that the high-risk group had higher tumor mutation burden (TMB) and higher expression of immune checkpoint genes, suggesting higher sensitivity to immunotherapy. Drug sensitivity analysis identified four potentially effective drugs, such as sepantronium bromide, which had better effects on high-risk patients. This study provides a theoretical basis and new targets for precise prognosis and stratified treatment of colon cancer.