ArticleScientific reports2024
Importance of CD8 Tex cell-associated gene signatures in the prognosis and immunology of osteosarcoma.
Article in Scientific reports, 2024. 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, 3 citations in OpenAlex.
- New biological insights into osteosarcoma-lessons from single cell sequencing studies.Cancer metastasis reviews · 2026Review
- EXT2 promotes sarcoma progression and immune evasion via the AKT/c-Myc/PD-L1 axis: a multi-omics and validation study.Journal of translational medicine · 2026Article
- Nutritional biomarkers regulating tumor immune microenvironment in osteosarcoma and as predictors of immune checkpoint inhibitor responses.Frontiers in immunology · 2026Review
- Development and validation of a novel prognostic signature for osteosarcoma.Translational cancer research · 2025Article
- The regulatory role of immune microenvironment-related cells and pathways in the pathogenesis of keloids.Frontiers in immunology · 2025Review
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
As a highly aggressive bone malignancy, osteosarcoma poses a significant therapeutic challenge, especially in the setting of metastasis or recurrence. This study aimed to investigate the potential of CD8-Tex cell-associated genes as prognostic biomarkers to reveal the immunogenomic profile of osteosarcoma and guide therapeutic decisions. mRNA expression data and clinical details of osteosarcoma patients were obtained from the TCGA database (TARGET-OS dataset). The GSE21257 dataset (from the GEO database) was used as an external validation set to provide additional information on osteosarcoma specimens. 84 samples from the TARGET-OS dataset were used as the training set, and 53 samples from the GSE21257 dataset served as the external validation cohort. Univariate Cox regression analysis was utilized to identify CD8 Tex cell genes associated with prognosis. The LASSO algorithm was performed for 1000 iterations to select the best subset to form the CD8 Tex cell gene signature (TRS). Final genes were identified using the multivariate Cox regression model of the LASSO algorithm. Risk scores were calculated to categorize patients into high- and low-risk groups, and clinical differences were explored by Kaplan-Meier survival analysis to assess model performance. Prediction maps were constructed to estimate 1-, 3-, and 5 year survival rates for osteosarcoma patients, including risk scores for CD8 Texcell gene markers and clinicopathologic factors. The ssGSEA algorithm was used to assess the differences in immune function between TRS-defined high- and low-risk groups. TME and immune cell infiltration were further assessed using the ESTIMATE and CIBERSORT algorithms. To explore the relationship between immune checkpoint gene expression levels and the two risk-defined groups. A CD8 Tex cell-associated gene signature was extracted from the TISCH database and prognostic markers including two genes were developed. The high-risk group showed lower survival, and model performance was validated by ROC curves and C-index. Predictive plots were constructed to demonstrate survival estimates, combining CD8 Tex cell gene markers and clinical factors. This study provides valuable insights into the molecular and immune characteristics of osteosarcoma and offers potential avenues for advances in therapeutic approaches.
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