ArticleTechnology in cancer research & treatment
Identification of 9-Gene Epithelial-Mesenchymal Transition Related Signature of Osteosarcoma by Integrating Multi Cohorts.
Article in Technology in cancer research & treatment. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 20 citations in OpenAlex.
- A Pan-Cancer Single-Cell Atlas to Evaluate Tumor Identity, Cell Line Concordance, and Dependency Mapping.bioRxiv : the preprint server for biology · 2026Article
- Review
- Regulatory influence of α-Pinene on MATN3 expression in hepatocellular carcinoma: Extending to pan-cancer analysis.PloS one · 2025Article
- Integrating transcriptomic data and digital pathology for NRG-based prediction of prognosis and therapy response in gastric cancer.Annals of medicine · 2024Article
- Decoding the Impact of Tumor Microenvironment in Osteosarcoma Progression and Metastasis.Cancers · 2023Review
- Impairment of rigidity sensing caused by mutant TP53 gain of function in osteosarcoma.Bone research · 2023Article
- Signal Pathways and microRNAs in Osteosarcoma Growth and the Dual Role of Mesenchymal Stem Cells in Oncogenesis.International journal of molecular sciences · 2023Review
- Regulation of the Epithelial to Mesenchymal Transition in Osteosarcoma.Biomolecules · 2023Review
- A novel epithelial-mesenchymal transition gene signature for the immune status and prognosis of hepatocellular carcinoma.Hepatology international · 2022Article
- Epithelial to Mesenchymal Transition Relevant Subtypes with Distinct Prognosis and Responses to Chemo- or Immunotherapies in Osteosarcoma.Journal of immunology research · 2022Article
- Risk Factors, Prognostic Factors, and Nomograms for Distant Metastasis in Patients With Newly Diagnosed Osteosarcoma: A Population-Based Study.Frontiers in endocrinology · 2021Article
- Development and Verification of a Hypoxic Gene Signature for Predicting Prognosis, Immune Microenvironment, and Chemosensitivity for Osteosarcoma.Frontiers in molecular biosciences · 2021Article
- Global Characterization of Metabolic Genes Regulating Survival and Immune Infiltration in Osteosarcoma.Frontiers in genetics · 2021Article
- Analysis of Immune-Stromal Score-Based Gene Signature and Molecular Subtypes in Osteosarcoma: Implications for Prognosis and Tumor Immune Microenvironment.Frontiers in genetics · 2021Article
- Identification of a Novel Prognostic Gene Signature From the Immune Cell Infiltration Landscape of Osteosarcoma.Frontiers in cell and developmental biology · 2021Article
- Identification of MATN3 as a Novel Prognostic Biomarker for Gastric Cancer through Comprehensive TCGA and GEO Data Mining.Disease markers · 2021Article
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe prognosis of patients with osteosarcoma is still poor due to the lack of effective prognostic markers. The EMT (epithelial-mesenchymal transition) serves as a promoter in the progression of osteosarcoma. This study systematically analyzed EMT-related genes to explore new markers for predicting the prognosis of osteosarcoma.
methodsRNA-Seq data and clinical information were obtained from the GEO database; GSVA and GSEA analysis were used to enrich pathways related to osteosarcoma progression; LASSO method analysis was used to construct the prognosis risk signature. The "Nomogram" package generated the risk prediction nomogram, and its clinical applicability was evaluated by decision curve analysis (DCA).
resultsGSVA and GSEA analysis showed that the EMT signaling pathway was closely related to osteosarcoma progression. A 9-genes signature (LAMA3, LGALS1, SGCG, VEGFA, WNT5A, MATN3, ANPEP, FUCA1, and FLNA) was constructed. The overall survival (OS) of the high-risk scores group was significantly lower than the low-risk scores group. The 9-gene signature demonstrated good predictive accuracy. Cox regression analysis showed that the 9-gene signature provided independent prognostic factors for osteosarcoma patients. In addition, the predictive nomogram model could effectively predict the prognosis of osteosarcoma patients.
conclusionThis study constructed a 9-gene signature as a new prognostic marker to predict osteosarcoma patients' survival.
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