ArticleFrontiers in endocrinology2022
Identification of risk model based on glycolysis-related genes in the metastasis of osteosarcoma.
Article in Frontiers in endocrinology, 2022. 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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16 citing papers in PubMed, 22 citations in OpenAlex.
- Identification of glycolysis -correlative features for predicting prognosis and investigating immune landscape in prostate cancer.BMC cancer · 2026Article
- Machine learning-based integration of tumor deposit molecular signatures improves prognostic stratification in colon adenocarcinoma.International journal of colorectal disease · 2026Article
- Identification of endothelial INSR as an osteosarcoma-related biomarker and therapeutic target based on weighted gene co-expression network analysis.Discover oncology · 2025Article
- Identification of glycolysis-related molecular subtypes and prognostic model in intrahepatic cholangiocarcinoma.Discover oncology · 2025Article
- Feasibility of machine learning-based modeling and prediction to assess osteosarcoma outcomes.Scientific reports · 2025Article
- Identification and verification of a polyamine metabolism-related gene signature for predicting prognosis and immune infiltration in osteosarcoma.Journal of orthopaedic surgery and research · 2025Article
- Construction and evaluation of a prognostic model based on the expression of the metabolism-related signatures in patients with osteosarcoma.BMC musculoskeletal disorders · 2025Article
- Decoding Osteosarcoma's Lactylation Gene Expression: Insights Into Prognosis, Immune Dynamics, and Treatment.Analytical cellular pathology (Amsterdam) · 2025Article
- Employing splice-switching oligonucleotides and AAVrh74.U7 snRNA to target insulin receptor splicing and cancer hallmarks in osteosarcoma.Molecular therapy. Oncology · 2024Article
- Prognostic significance of HS2ST1 expression in patients with hepatocellular carcinoma.Genes & genomics · 2024Article
- Evaluating the prognostic value of tumor deposits in non-metastatic lymph node-positive colon adenocarcinoma using Cox regression and machine learning.International journal of colorectal disease · 2024Article
- Advances in prognostic models for osteosarcoma risk.Heliyon · 2024Review
- Exploring the relationship between metabolism and immune microenvironment in osteosarcoma based on metabolic pathways.Journal of biomedical science · 2024Article
- Integration of transcriptome and machine learning to identify the potential key genes and regulatory networks affecting drip loss in pork.Journal of animal science · 2024Article
- Machine learning for predicting the survival in osteosarcoma patients: Analysis based on American and Hebei Province cohort.Biomolecules & biomedicine · 2023Article
- Identification and validation of novel biomarkers associated with immune infiltration for the diagnosis of osteosarcoma based on machine learning.Frontiers in genetics · 2023Article
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5 authors.
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Abstract
Background: Glycolytic metabolic pathway has been confirmed to play a vital role in the proliferation, survival, and migration of malignant tumors, but the relationship between glycolytic pathway-related genes and osteosarcoma (OS) metastasis and prognosis remain unclear. Methods: We performed Gene set enrichment analysis (GSEA) on the osteosarcoma dataset in the TARGET database to explore differences in glycolysis-related pathway gene sets between primary osteosarcoma (without other organ metastases) and metastatic osteosarcoma patient samples, as well as glycolytic pathway gene set gene difference analysis. Then, we extracted OS data from the TCGA database and used Cox proportional risk regression to identify prognosis-associated glycolytic genes to establish a risk model. Further, the validity of the risk model was confirmed using the GEO database dataset. Finally, we further screened OS metastasis-related genes based on machine learning. We selected the genes with the highest clinical metastasis-related importance as representative genes for Results: Using the TARGET osteosarcoma dataset, we identified 5 glycolysis-related pathway gene sets that were significantly different in metastatic and non-metastatic osteosarcoma patient samples and identified 29 prognostically relevant genes. Next, we used multivariate Cox regression to determine the inclusion of 13 genes (ADH5, DCN, G6PD, etc.) to construct a prognostic risk score model to predict 1- (AUC=0.959), 3- (AUC=0.899), and 5-year (AUC=0.895) survival under the curve. Ultimately, the KM curves pooled into the datasets GSE21257 and GSE39055 also confirmed the validity of the prognostic risk model, with a statistically significant difference in overall survival between the low- and high-risk groups (P<0.05). In addition, machine learning identified INSR as the gene with the highest importance for OS metastasis, and the transwell assay verified that INSR significantly promoted OS cell metastasis. Conclusions: A risk model based on seven glycolytic genes (INSR, FAM162A, GLCE, ADH5, G6PD, SDC3, HS2ST1) can effectively evaluate the prognosis of osteosarcoma, and
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