ArticleBMC cancer2021
The tumor immune microenvironment and immune-related signature predict the chemotherapy response in patients with osteosarcoma.
Article in BMC cancer, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed, 30 citations in OpenAlex.
- Proteomics in bone malignancies: from bulk profiling to single-cell and ultra-low-input technologies.Journal of translational medicine · 2026Review
- Multifunctional pectin derivatives as anticancer agents in colorectal cancer via synthesis, computational insights, and modulation of NRF2/HO-1, HIF-1α, and VEGF/PDGF-D signaling pathways.Scientific reports · 2026Article
- Integrative bioinformatics and machine learning approaches identify novel diagnostic signatures for oxaliplatin-resistant colorectal cancer.International journal of colorectal disease · 2026Article
- Paired single cell analysis reveals chemotherapy resistance in osteosarcoma.Cancer drug resistance (Alhambra, Calif.) · 2026Article
- Feasibility of machine learning-based modeling and prediction to assess osteosarcoma outcomes.Scientific reports · 2025Article
- Review
- Large-Scale Comparative Analysis of Canine and Human Osteosarcomas Uncovers Conserved Clinically Relevant Tumor Microenvironment Subtypes.Clinical cancer research : an official journal of the American Association for Cancer Research · 2024Article
- Exploring the impact of PDGFD in osteosarcoma metastasis through single-cell sequencing analysis.Cellular oncology (Dordrecht, Netherlands) · 2024Article
- Spatial multiplexed immunofluorescence analysis reveals coordinated cellular networks associated with overall survival in metastatic osteosarcoma.Bone research · 2024Article
- Influence of Gut Microbiota-Mediated Immune Regulation on Response to Chemotherapy.Pharmaceuticals (Basel, Switzerland) · 2024Review
- Unlocking the tumor-immune microenvironment in osteosarcoma: insights into the immune landscape and mechanisms.Frontiers in immunology · 2024Review
- Evaluation of the neoadjuvant chemotherapy response in osteosarcoma using the MRI DWI-based machine learning radiomics nomogram.Frontiers in oncology · 2024Article
- Comprehensive bioinformatics analysis reveals the oncogenic role of FoxM1 and its impact on prognosis, immune microenvironment, and drug sensitivity in osteosarcoma.Journal of applied genetics · 2023Article
- Strategies to Overcome Resistance to Immune-Based Therapies in Osteosarcoma.International journal of molecular sciences · 2023Review
- PDGF-D-induced immunoproteasome activation and cell-cell interactions.Computational and structural biotechnology journal · 2023Article
- Integrative profiling analysis reveals prognostic significance, molecular characteristics, and tumor immunity of angiogenesis-related genes in soft tissue sarcoma.Frontiers in immunology · 2023Article
- Identification of Immune-Related Breast Cancer Chemotherapy Resistance GenesFrontiers in oncology · 2022Article
- An Immunity-Related Gene Model Predicts Prognosis in Cholangiocarcinoma.Frontiers in oncology · 2022Article
- Integrated Analysis of Multi-Omics Data to Establish a Hypoxia-Related Prognostic Model in Osteosarcoma.Evolutionary bioinformatics online · 2022Article
- Development of a Chemoresistant Risk Scoring Model for Prechemotherapy Osteosarcoma Using Single-Cell Sequencing.Frontiers in oncology · 2022Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundGenome-wide expression profiles have been shown to predict the response to chemotherapy. The purpose of this study was to develop a novel predictive signature for chemotherapy in patients with osteosarcoma.
methodsWe analysed the relevance of immune cell infiltration and gene expression profiles of the tumor samples of good responders with those of poor responders from the TARGET and GEO databases. Immune cell infiltration was evaluated using a single-sample gene set enrichment analysis (ssGSEA) and the CIBERSORT algorithm between good and poor chemotherapy responders. Differentially expressed genes were identified based on the chemotherapy response. LASSO regression and binary logistic regression analyses were applied to select the differentially expressed immune-related genes (IRGs) and developed a predictive signature in the training cohort. A receiver operating characteristic (ROC) curve analysis was employed to assess and validate the predictive accuracy of the predictive signature in the validation cohort.
resultsThe analysis of immune infiltration showed a positive relationship between high-level immune infiltration and good responders, and T follicular helper cells and CD8 T cells were significantly more abundant in good responders with osteosarcoma. Two hundred eighteen differentially expressed genes were detected between good and poor responders, and a five IRGs panel comprising TNFRSF9, CD70, EGFR, PDGFD and S100A6 was determined to show predictive power for the chemotherapy response. A chemotherapy-associated predictive signature was developed based on these five IRGs. The accuracy of the predictive signature was 0.832 for the training cohort and 0.720 for the validation cohort according to ROC analysis.
conclusionsThe novel predictive signature constructed with five IRGs can be effectively utilized to predict chemotherapy responsiveness and help improve the efficacy of chemotherapy in patients with osteosarcoma.
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