ArticleFrontiers in immunology2023
A novel stratification framework based on anoikis-related genes for predicting the prognosis in patients with osteosarcoma.
Article in Frontiers in immunology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 7 citations in OpenAlex.
- Harnessing multi‑omics to revolutionize understanding and management of osteosarcoma: A pathway to precision medicine (Review).International journal of molecular medicine · 2025Review
- Multi-cohort validation based on a novel prognostic signature of anoikis for predicting prognosis and immunotherapy response of esophageal squamous cell carcinoma.Frontiers in oncology · 2025Article
- Clinical potential and experimental validation of prognostic genes in hepatocellular carcinoma revealed by risk modeling utilizing single cell and transcriptome constructs.Frontiers in immunology · 2025Article
- PCK2, a SASP-Associated Gene, Serves as an Independent Prognostic Indicator in Diffuse Large B-Cell Lymphoma.Journal of inflammation research · 2025Article
- Development of an anoikis-related gene signature and prognostic model for predicting the tumor microenvironment and response to immunotherapy in colorectal cancer.Frontiers in immunology · 2024Article
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
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Abstract
Background: Anoikis resistance is a prerequisite for the successful development of osteosarcoma (OS) metastases, whether the expression of anoikis-related genes (ARGs) correlates with OS prognosis remains unclear. This study aimed to investigate the feasibility of using ARGs as prognostic tools for the risk stratification of OS. Methods: The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases provided transcriptome information relevant to OS. The GeneCards database was used to identify ARGs. Differentially expressed ARGs (DEARGs) were identified by overlapping ARGs with common differentially expressed genes (DEGs) between OS and normal samples from the GSE16088, GSE19276, and GSE99671 datasets. Anoikis-related clusters of patients were obtained by consistent clustering, and gene set variation analysis (GSVA) of the different clusters was completed. Next, a risk model was created using Cox regression analyses. Risk scores and clinical features were assessed for independent prognostic values, and a nomogram model was constructed. Subsequently, a functional enrichment analysis of the high- and low-risk groups was performed. In addition, the immunological characteristics of OS samples were compared between the high- and low-risk groups, and their sensitivity to therapeutic agents was explored. Results: Seven DEARGs between OS and normal samples were obtained by intersecting 501 ARGs with 68 common DEGs. Conclusion: The prognostic stratification framework of patients with OS based on ARGs, such as
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