<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Nie J</submitter><funding>Jiangxi Provincial Natural Science Foundation</funding><funding>National Natural Science Foundation of China</funding><funding>Jiangxi Province Postgraduate Innovation Special Fund Project</funding><pagination>159</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC12016182</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>25(1)</volume><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</pubmed_abstract><journal>Cancer cell international</journal><pubmed_title>Identifying PSIP1 as a critical R-loop regulator in osteosarcoma via machine-learning and multi-omics analysis.</pubmed_title><pmcid>PMC12016182</pmcid><funding_grant_id>82360543</funding_grant_id><funding_grant_id>20232ACB206043</funding_grant_id><funding_grant_id>YC2023-B067</funding_grant_id><pubmed_authors>Liu J</pubmed_authors><pubmed_authors>Liu Z</pubmed_authors><pubmed_authors>Wang S</pubmed_authors><pubmed_authors>Nie J</pubmed_authors><pubmed_authors>Zhong Y</pubmed_authors><pubmed_authors>Yang F</pubmed_authors></additional><is_claimable>false</is_claimable><name>Identifying PSIP1 as a critical R-loop regulator in osteosarcoma via machine-learning and multi-omics analysis.</name><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</description><dates><release>2025-01-01T00:00:00Z</release><publication>2025 Apr</publication><modification>2026-06-02T04:12:53.844Z</modification><creation>2025-07-06T03:04:31.003Z</creation></dates><accession>S-EPMC12016182</accession><cross_references><pubmed>40264164</pubmed><doi>10.1186/s12935-025-03775-1</doi></cross_references></HashMap>