Dataset Information

Construction and Validation of a Potent Epigenetic Modification-Related Prognostic Signature for Osteosarcoma Patients.

ABSTRACT:

Background

Increasing evidence has shown that tumorigenesis correlates with aberrant epigenetic factors, such as DNA methylation, histone modification, RNA m6A modification, RNA binding proteins, and transcription factors. However, it is unclear that how epigenetic genes linked with alteration contribute to osteosarcoma's incidence and clinical prognosis. We developed an epigenetic modification-related prognostic model that may improve the diagnosis and prognosis of osteosarcoma.

Methods

We investigated the epigenetic modification-associated genes and their clinical significance in osteosarcoma in this research. Our gene transcriptome data were obtained from the TARGET database and the GEO database. Bioinformatics techniques were used to investigate their functionalities. The diagnostic and prognostic models were constructed using univariate and multivariate Cox regression. In addition, we developed a nomogram indicating the practicability of the prognostic model described above.

Results

A risk score model constructed based on four epigenetic modification-related genes (MYC, TERT, EIF4E3, and RBM34) can effectively predict the prognosis of patients with osteosarcoma. Based on the risk score and clinical features, we constructed a nomogram.

Conclusion

Epigenetic modification-related genes have been identified as important prognostic markers that may assist in osteosarcoma therapy therapeutic decision-making.

SUBMITTER: Liu S

PROVIDER: S-EPMC8629625 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundOsteosarcoma (OS) poses significant challenges in treatment and lacks reliable prognostic markers. Epigenetic alterations play a crucial role in disease progression. This study aimed to develop an accurate prognostic signature for OS using epigenetic modification genes (EMGs).MethodsThe Therapeutically Applicable Research to Generate Effective Treatments (TARGET)-OS cohort was analyzed. Univariate Cox analysis identified survival-associated EMGs. Based on least absolute shrinkage and selection operator (LASSO) regression and multivariate analysis, a 6-gene prognostic signature termed the epigenetic modification-related prognostic signature (EMRPS) was derived in the testing cohort. Kaplan-Meier and receiver operating characteristic (ROC) curve analysis confirmed predictive accuracy through internal and external validation (GEO accession GSE21257). A prognostic nomogram incorporating EMRPS and clinical features was constructed. Transcriptomic analysis including differential gene expression, Gene Ontology (GO), gene set enrichment analysis (GSEA), and immune infiltration analysis was conducted to explore mechanisms linking EMRPS to OS prognosis. Additionally, EMRPS impact on drug sensitivity was predicted.ResultsA 6-gene EMRPS comprising DDX24, DNAJC1, HDAC4, SIRT7, SP140 and UHRF2 was successfully developed. The high-risk group showed significantly shorter survival, consistently observed in both internal and external validation. EMRPS demonstrated high predictive efficacy for 1-, 3-, and 5-year overall survival, with area under curve (AUC) >0.85 in training and ~0.7 in testing. The nomogram integrating age, gender, metastasis status, and EMRPS exhibited high predictive performance based on concordance index analysis. Mechanistic analysis indicated the low-risk group had increased immune infiltration and activity with higher immune checkpoint expression, reflecting an immune-activated tumor microenvironment (TME) suitable for immunotherapy. Drug sensitivity analysis revealed the low-risk group had increased sensitivity to cisplatin, a first-line OS chemotherapy.ConclusionsOur study successfully established an efficient EMRPS and nomogram, highlighting their potential as novel prognostic markers and indicators for selecting appropriate immunotherapy and chemotherapy candidates in OS treatment.