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ArticleCurrent medicinal chemistry2026

Integration of Single-cell Sequencing Analysis Reveals Disulfidptosis Related Molecular Subtype and Novel Prognosis System for Osteosarcoma.

Houxi Li, Tian Deng, Mingyue Yan, Ronghuan Wang, Xiao Ma, Xiangyu Zong, Tianrui Wang, Feng Li, Xiaolin Wu

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Article in Current medicinal chemistry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 citing paper in PubMed.

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5 · Who and what money

Authors and funding

9 authors.

Houxi LiDepartment of Orthopedics, the Affiliated Hospital of Qingdao University, Qingdao, 266000, China.
Tian DengDepartment of Neurology, Weifang People's Hospital, Weifang, 261041, China.
Mingyue YanDepartment of Orthopedics, the Affiliated Hospital of Qingdao University, Qingdao, 266000, China.
Ronghuan WangDepartment of Orthopedics, the Affiliated Hospital of Qingdao University, Qingdao, 266000, China.
Xiao MaDepartment of Orthopedics, the Affiliated Hospital of Qingdao University, Qingdao, 266000, China.
Xiangyu ZongDepartment of Orthopedics, the Affiliated Hospital of Qingdao University, Qingdao, 266000, China.
Tianrui WangDepartment of Orthopedics, the Affiliated Hospital of Qingdao University, Qingdao, 266000, China.
Feng LiDepartment of Orthopedics, Weifang People's Hospital, Weifang, 261041, China.
Xiaolin WuDepartment of Orthopedics, the Affiliated Hospital of Qingdao University, Qingdao, 266000, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundOsteosarcoma (OS) is one of the most common primary malignancies in children and adolescents. Disulfidptosis, a newly identified form of metabolically induced programmed cell death triggered by disulfide stress, has not yet been explored in OS.

methodsWe integrated data from public databases and applied a series of bioinformatics approaches, including clustering analysis to classify OS subtypes, and Cox and LASSO regression analysis to identify prognostic disulfidptosis-related genes (DRGs). Enrichment analysis was performed to explore the biological pathways associated with DRG-related molecular subtypes. The immune infiltration landscape was assessed to understand the tumor microenvironment in different risk subgroups. Additionally, drug sensitivity analysis was conducted to evaluate the potential clinical therapeutic strategies of the identified DRG score subgroups. The distribution of DRG expression across OS cell subtypes was further analyzed using single-cell RNA sequencing. In vitro assays, including Western blotting, qRT-PCR, and cell migration and invasion assays, were conducted to validate POLR1D expression and function in OS cells.

resultsWe established a DRG-based prognostic model that effectively stratifies OS patients into distinct risk groups with different survival outcomes. The model also revealed significant differences in immune cell infiltration between high and low DRG scores group, suggesting a link between disulfidptosis and the OS immune microenvironment. Drug sensitivity analysis indicated that the DRG signature could guide personalized therapeutic strategies. Single-cell RNA sequencing revealed heterogeneous expression of DRG signature across OS cell subtypes. Functional assays confirmed that POLR1D was aberrantly overexpressed in OS cells and promotes their migration and invasion, supporting its role as a potential oncogenic driver in OS.

conclusionOur study is the first to investigate the role of DRGs for risk stratification in OS, providing new insights and targets into OS pathogenesis.

Indexed as

Bone NeoplasmsDisulfidptosisOsteosarcomaSingle-Cell AnalysisCell Line, TumorHumansPrognosisSingle-Cell Gene Expression Analysisbioinformaticsdisulfidptosisimmune infiltration characterizationOsteosarcomaPOLR1Dsingle-cell RNA sequencing

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.