{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Nie J"],"funding":["Jiangxi Provincial Natural Science Foundation","National Natural Science Foundation of China","Jiangxi Province Postgraduate Innovation Special Fund Project"],"pagination":["159"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12016182"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["25(1)"],"pubmed_abstract":["Dysregulation of R-loops has been implicated in tumor development, progression, and the regulation of tumor immune microenvironment (TME). However, their roles in osteosarcoma (OS) remain underexplored. In this study, we firstly constructed a novel R-loop Gene Prognostic Score Model (RGPSM) based on the RNA-sequencing (RNA-seq) datasets and evaluated the relationships between the RGPSM scores and the TME. Additionally, we identified key R-loop-related genes involved in OS progression using single-cell RNA sequencing (scRNA-seq) dataset, and validated these findings through experiments. We found that patients with high-RGPSM scores exhibited poorer prognosis, lower Huvos grades and a more suppressive TME. Moreover, the proportion of malignant cells was significantly higher in the high-RGPSM"],"journal":["Cancer cell international"],"pubmed_title":["Identifying PSIP1 as a critical R-loop regulator in osteosarcoma via machine-learning and multi-omics analysis."],"pmcid":["PMC12016182"],"funding_grant_id":["82360543","20232ACB206043","YC2023-B067"],"pubmed_authors":["Liu J","Liu Z","Wang S","Nie J","Zhong Y","Yang F"],"additional_accession":[]},"is_claimable":false,"name":"Identifying PSIP1 as a critical R-loop regulator in osteosarcoma via machine-learning and multi-omics analysis.","description":"Dysregulation of R-loops has been implicated in tumor development, progression, and the regulation of tumor immune microenvironment (TME). However, their roles in osteosarcoma (OS) remain underexplored. In this study, we firstly constructed a novel R-loop Gene Prognostic Score Model (RGPSM) based on the RNA-sequencing (RNA-seq) datasets and evaluated the relationships between the RGPSM scores and the TME. Additionally, we identified key R-loop-related genes involved in OS progression using single-cell RNA sequencing (scRNA-seq) dataset, and validated these findings through experiments. We found that patients with high-RGPSM scores exhibited poorer prognosis, lower Huvos grades and a more suppressive TME. Moreover, the proportion of malignant cells was significantly higher in the high-RGPSM","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Apr","modification":"2026-06-02T04:12:53.844Z","creation":"2025-07-06T03:04:31.003Z"},"accession":"S-EPMC12016182","cross_references":{"pubmed":["40264164"],"doi":["10.1186/s12935-025-03775-1"]}}