A DNA methylation-associated nomogram predicts the overall survival of osteosarcoma

A comprehensive analysis of minimally differentially methylated regions common to pediatric and adult solid tumors

Cancer is the second most common cause of death in children aged 1-14 years in the United States, with 11,000 new cases and 1200 deaths annually. Pediatric cancers typically have lower mutational burden compared to adult-onset cancers, however, the epigenomes in pediatric cancer are highly altered, with widespread DNA methylation changes. The rarity of pediatric cancers poses a significant challenge to developing cancer-type specific biomarkers for diagnosis, prognosis, or treatment monitoring. In the current study, we explored the potential of a DNA methylation profile common across various pediatric cancers. To do this, we conducted whole genome bisulfite sequencing (WGBS) on 31 recurrent pediatric tumor tissues, 13 normal tissues, and 20 plasma cell-free (cf)DNA samples, representing 11 different pediatric cancer types. We defined minimal focal regions that were differentially methylated across samples in the multiple cancer types which we termed minimally differentially methylated regions (mDMRs). These methylation changes were also observed in 506 pediatric and 5691 adult cancer samples accessed from publicly available databases, and in 44 pediatric cancer samples we analyzed using a targeted hybridization probe capture assay. Finally, we found that these methylation changes were detectable in cfDNA and could serve as potential cfDNA methylation biomarkers for early detection or minimal residual disease.

A novel risk score model based on glycolysis-related genes and a prognostic model for predicting overall survival of osteosarcoma patients

This study aims to construct a novel risk score model based on glycolysis-related genes in osteosarcoma and to build and validate a prognostic model for predicting overall survival of patients with osteosarcoma. The transcriptome data and corresponding clinical data of patients with osteosarcoma were obtained from The Cancer Genome Atlas (TCGA) as the training set, and from Gene Expression Omnibus (GEO) database as the validation set. Univariate Cox regression analysis was used to screen the prognostic glycolysis-related genes. The risk coefficient of each glycolysis-related gene was calculated using LASSO regression analysis. Using the median risk score as the cut-off point, patients were divided into high-risk and low-risk groups. Kaplan-Meier survival analysis was used to determine whether there was a significant difference in the overall survival between the two groups. The nomogram was constructed according to the results of multivariate Cox regression. The C-index was calculated, the calibration chart, clinical decision curve and receiver operating characteristic curve were drawn to evaluate the predictive performance of the nomogram. We performed Gene Ontology and Kyoto encyclopedia of genes and genomics enrichment analysis to explore the potential mechanism of prognostic-related glycolysis genes in osteosarcoma. A total of 88 and 53 cases were obtained from the TCGA and GEO database, respectively. A total of 10 key glycolytic genes related to prognosis were screened out. The Kaplan-Meier survival curve revealed that the overall survival of the high-risk group was significantly shorter than that of the low-risk group. The C indices of the training set and the verification set were 0.882 and 0.828, respectively. Our findings will provide further understanding of clinical prognostic outcomes of osteosarcoma patients.

Integrative analysis of immune-related multi-omics profiles identifies distinct prognosis and tumor microenvironment patterns in osteosarcoma

Osteosarcoma (OS) is the most common primary malignancy of bone. Epigenetic regulation plays a pivotal role in cancer development in various aspects, including immune response. In this study, we studied the potential association of alterations in the DNA methylation and transcription of immune-related genes with changes in the tumor microenvironment (TME) and tumor prognosis of OS. We obtained multi-omics data for OS patients from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) and Gene Expression Omnibus (GEO) databases. By referring to curated immune signatures and using a consensus clustering method, we categorized patients based on immune-related DNA methylation patterns (IMPs), and evaluated prognosis and TME characteristics of the resulting patient subgroups. Subsequently, we used a machine-learning approach to construct an IMP-associated prognostic risk model incorporating the expression of a six-gene signature (MYC, COL13A1, UHRF2, MT1A, ACTB, and GBP1), which was then validated in an independent patient cohort. Furthermore, we evaluated TME patterns, transcriptional variation in biological pathways, somatic copy number alteration, anticancer drug sensitivity, and potential responsiveness to immune checkpoint inhibitor therapy with regard to our IMP-associated signature scoring model. By integrative IMP and transcriptomic analysis, we uncovered distinct prognosis and TME patterns in OS. Finally, we constructed a classifying model, which may aid in prognosis prediction and provide a potential rationale for targeted- and immune checkpoint inhibitor therapy in OS.