Identifying squalene epoxidase as a metabolic vulnerability in high‐risk osteosarcoma using an artificial intelligence‐derived prognostic index

Clinical and Translational Medicine, Journal Year: 2024, Volume and Issue: 14(2)

Published: Feb. 1, 2024

Osteosarcoma (OSA) presents a clinical challenge and has low 5-year survival rate. Currently, the lack of advanced stratification models makes personalized therapy difficult. This study aims to identify novel biomarkers stratify high-risk OSA patients guide treatment.

Language: Английский

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Advances in understanding the role of squalene epoxidase in cancer prognosis and resistance

Molecular Biology Reports, Journal Year: 2025, Volume and Issue: 52(1)

Published: Jan. 27, 2025

Language: Английский

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The Role of Cholesterol Metabolism and Its Regulation in Tumor Development

Cancer Medicine, Journal Year: 2025, Volume and Issue: 14(7)

Published: March 27, 2025

ABSTRACT Background Within the tumor microenvironment, cells undergo metabolic reprogramming of cholesterol due to intrinsic cellular alterations and changes in extracellular milieu. Furthermore, within this microenvironment influences immune landscape tumors, facilitating evasion consequently promoting tumorigenesis. These biological involve modifications numerous enzymes associated with uptake synthesis, including NPC1L1, SREBP, HMGCR, SQLE, PCSK9. Review This review systematically summarizes role metabolism its cancer progression, examines mechanisms through which dysregulation affects discusses recent advancements therapies that target metabolism. Conclusion Targeting metabolism‐related can inhibit growth, reshape landscapes, rejuvenate antitumor immunity, offering potential therapeutic avenues treatment.

Language: Английский

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Identifying PSIP1 as a critical R-loop regulator in osteosarcoma via machine-learning and multi-omics analysis

Cancer Cell International, Journal Year: 2025, Volume and Issue: 25(1)

Published: April 22, 2025

Dysregulation of R-loops has been implicated in tumor development, progression, and the regulation immune microenvironment (TME). However, their roles osteosarcoma (OS) remain underexplored. In this study, we firstly constructed a novel R-loop Gene Prognostic Score Model (RGPSM) based on RNA-sequencing (RNA-seq) datasets evaluated relationships between RGPSM scores TME. Additionally, identified key R-loop-related genes involved OS progression using single-cell RNA sequencing (scRNA-seq) dataset, validated these findings through experiments. We found that patients with high-RGPSM exhibited poorer prognosis, lower Huvos grades more suppressive Moreover, proportion malignant cells was significantly higher group. And integrated analysis RNA-seq scRNA-seq revealed PC4 SRSF1 Interacting Protein 1 (PSIP1) highly expressed osteoblastic proliferative cells. Notably, high expression PSIP1 associated poor prognosis patients. Subsequent experiments demonstrated knockdown inhibited both vivo vitro, leading increased accumulation DNA damage. Conversely, overexpression facilitated resolution reduced damage induced by cisplatin. conclusion, developed effectively predicted outcomes across diverse cohorts as critical modulator regulating

Language: Английский

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The molecular characteristics of DNA damage and repair related to P53 mutation for predicting the recurrence and immunotherapy response in hepatocellular carcinoma

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: April 29, 2025

Language: Английский

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Enhancing Ovarian Cancer Prognosis with an Artificial Intelligence-Derived Model: Multi-Omics Integration and Therapeutic Implications

Published: Jan. 1, 2024

Language: Английский

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Development of a prognostic model related to homologous recombination deficiency in glioma based on multiple machine learning

Frontiers in Immunology, Journal Year: 2024, Volume and Issue: 15

Published: Oct. 7, 2024

Background Despite advances in neuro-oncology, treatments of glioma and tools for predicting the outcome patients remain limited. The objective this research is to construct a prognostic model using Homologous Recombination Deficiency (HRD) score validate its predictive capability glioma. Methods We consolidated datasets from TCGA, various cancer types pan-cancer HRD analysis, two additional RNAseq GEO CGGA databases. scores, mutation data, other genomic indices were calculated. Using machine learning algorithms, we identified signature genes constructed an HRD-related risk model. model’s performance was validated across multiple cohorts. also assessed immune infiltration conducted molecular docking identify potential therapeutic agents. Results Our analysis established correlation between higher scores instability gliomas. model, based on seven key genes, significantly patient prognosis. Moreover, surpassed models terms prediction efficacy different cancers. Differential cell patterns observed groups, with implications immunotherapy. Molecular highlighted several compounds, notably Panobinostat, as promising high-risk patients. Conclusions threshold associated offers new insights into immunological landscapes, potentially guiding strategies. differential profiles HRD-risk groups could inform immunotherapeutic interventions, our findings paving way personalized medicine treatment.

Language: Английский

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Deciphering key roles of B cells in prognostication and tailored therapeutic strategies for lung adenocarcinoma: a multi-omics and machine learning approach towards predictive, preventive, and personalized treatment strategies

The EPMA Journal, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 17, 2024

Language: Английский

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