Dataset Information

Abnormal signal pathways and tumor heterogeneity in osteosarcoma.

ABSTRACT:

Background

Osteosarcoma (OS) is the most frequent and aggressive primary malignant sarcoma among adolescents and chemotherapy has not substantially progressed for decades. New insights into OS development and therapeutic strategies are urgently needed.

Methods

We analyzed integrated single-cell transcriptomes, bulk RNA-seq, and microarray data from Gene Expression Omnibus (GEO) datasets. We also used Weighted Gene Co-expression Network Analysis (WGCNA), Gene set enrichment analysis (GSEA), and Gene set variation analysis (GSVA), along with Simple ClinVar and Enrichr web servers.

Results

The findings of integrated single-cell analysis showed that OS arises from imperfect osteogenesis during development. Novel abnormalities comprised deficient TGFβ and P53 signal pathways, and cell cycle pathway activation, and a potentially new driver mutation in the interferon induced transmembrane protein 5 (IFITM5) that might function as a pathogenic factor in OS. Osteosarcoma is characterized by oncocyte heterogeneity, especially in immunogenic and adipocyte-like subtypes that respectively promote and hamper OS treatment. Etoposide is a promising chemotherapeutic that provides palliation by affecting the subtype of OS and correcting the abnormal pathways.

Conclusion

Various abnormal signal pathways play indispensable roles in OS development. We explored the heterogeneity and underlying mechanisms of OS and generated findings that will assist with OS assessment and selecting optimal therapies.

SUBMITTER: Sun Y

PROVIDER: S-EPMC9912612 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature

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Publications

Abnormal signal pathways and tumor heterogeneity in osteosarcoma.

Sun Yifeng Y Zhang Chunming C Fang Qiongxuan Q Zhang Wenqiang W Liu Wei W

Journal of translational medicine 20230209 1

<h4>Background</h4>Osteosarcoma (OS) is the most frequent and aggressive primary malignant sarcoma among adolescents and chemotherapy has not substantially progressed for decades. New insights into OS development and therapeutic strategies are urgently needed.<h4>Methods</h4>We analyzed integrated single-cell transcriptomes, bulk RNA-seq, and microarray data from Gene Expression Omnibus (GEO) datasets. We also used Weighted Gene Co-expression Network Analysis (WGCNA), Gene set enrichment analysi ...[more]

PMID: 36759884

Similar Datasets

Project description:Osteosarcoma (OS) is a heterogeneous and rare disease with a disproportionate impact because it mainly affects children and adolescents. Lamentably, more than half of patients with OS succumb to metastatic disease. Clarification of the etiology of the disease, development of better strategies to manage progression, and methods to guide personalized treatments are among the unmet health needs for OS patients. Progress in managing the disease has been hindered by the extreme heterogeneity of OS; thus, better models that accurately recapitulate the natural heterogeneity of the disease are needed. For this study, we used cell lines derived from two spontaneous canine OS tumors with distinctly different biological behavior (OS-1 and OS-2) for heterotypic in vivo modeling that recapitulates the heterogeneous biology and behavior of this disease. Both cell lines demonstrated stability of the transcriptome when grown as orthotopic xenografts in athymic nude mice. Consistent with the behavior of the original tumors, OS-2 xenografts grew more rapidly at the primary site and had greater propensity to disseminate to lung and establish microscopic metastasis. Moreover, OS-2 promoted formation of a different tumor-associated stromal environment than OS-1 xenografts. OS-2-derived tumors comprised a larger percentage of the xenograft tumors than OS-1-derived tumors. In addition, a robust pro-inflammatory population dominated the stromal cell infiltrates in OS-2 xenografts, whereas a mesenchymal population with a gene signature reflecting myogenic signaling dominated those in the OS-1 xenografts. Our studies show that canine OS cell lines maintain intrinsic features of the tumors from which they were derived and recapitulate the heterogeneous biology and behavior of bone cancer in mouse models. This system provides a resource to understand essential interactions between tumor cells and the stromal environment that drive the progression and metastatic propensity of OS.

Project description:The identification of optimal methylation biomarkers to achieve maximum diagnostic ability remains a challenge. The present study aimed to elucidate the potential molecular mechanisms underlying osteosarcoma (OS) using DNA methylation analysis. Based on the GSE36002 dataset obtained from the Gene Expression Omnibus database, differentially methylated genes were extracted between patients with OS and controls using t‑tests. Subsequently, hierarchical clustering was performed to segregate the samples into two distinct clusters, OS and normal. Gene Ontology (GO) and pathway enrichment analyses for differentially methylated genes were performed using the Database for Annotation, Visualization and Integrated Discovery tool. A protein‑protein interaction (PPI) network was established, followed by hub gene identification. Using the cut‑off threshold of ≥0.2 average β‑value difference, 3,725 unique CpGs (2,862 genes) were identified to be differentially methylated between the OS and normal groups. Among these 2,862 genes, 510 genes were differentially hypermethylated and 2,352 were differentially hypomethylated. The differentially hypermethylated genes were primarily involved in 20 GO terms, and the top 3 terms were associated with potassium ion transport. For differentially hypomethylated genes, GO functions principally included passive transmembrane transporter activity, channel activity and metal ion transmembrane transporter activity. In addition, a total of 10 significant pathways were enriched by differentially hypomethylated genes; notably, neuroactive ligand‑receptor interaction was the most significant pathway. Based on a connectivity degree >90, 7 hub genes were selected from the PPI network, including neuromedin U (NMU; degree=103) and NMU receptor 1 (NMUR1; degree=103). Functional terms (potassium ion transport, transmembrane transporter activity, and neuroactive ligand‑receptor interaction) and hub genes (NMU and NMUR1) may serve as potential targets for the treatment and diagnosis of OS.

Dataset Information

Abnormal signal pathways and tumor heterogeneity in osteosarcoma.

Background

Methods

Results

Conclusion

Publications

Abnormal signal pathways and tumor heterogeneity in osteosarcoma.

Similar Datasets

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