Epithelial to Mesenchymal Transition Relevant Subtypes with Distinct Prognosis and Responses to Chemo- or Immunotherapies in Osteosarcoma.

Citation metadata

Date: July 4, 2022
Publisher: Hindawi Limited
Document Type: Article
Length: 6,799 words
Lexile Measure: 1440L

Document controls

Main content

Abstract :

Objective. Currently, clinical classification of osteosarcoma cannot accurately predict the survival outcomes and responses to chemo- or immunotherapies. Our goal was to classify osteosarcoma patients into clinical/biological subtypes based on EMT molecules. Methods. This study retrospectively curated the RNA expression profiling of osteosarcoma patients from the TARGET and GSE21257 cohorts. Consensus clustering analyses were conducted in accordance with the expression profiling of prognostic EMT genes derived from univariate analyses. Immunological features were evaluated through immune cell infiltration, immune checkpoint expression, and activity of cancer immunity cycle. Drug sensitivity was estimated with the GDSC database. WGCNA approach was adopted to determine the EMT-derived genes. Following univariate analyses, a multivariate cox regression model was developed and externally verified. Predictive independency was evaluated with uni- and multivariate analyses. GSEA was presented to uncover relevant molecular mechanisms. Results. Prognostic EMT genes across osteosarcoma patients were stratified into distinct subtypes, namely, subtypes A and B. Patients in subtype B presented desirable prognosis, high immune activation, and enhanced sensitivity to cisplatin. Meanwhile, patients in subtype A were more sensitive to gemcitabine. In total, 86 EMT-derived hub genes were determined, and an EMT score was conducted for osteosarcoma prognosis. Following external verification, this EMT score was reliably and independently predictive of patients' survival outcomes. Additionally, it was positively linked to steroid biosynthesis. Conclusion. Overall, our findings proposed EMT-relevant molecular subtypes and signatures for predicting prognosis and therapeutic responses, contributing to personalized treatment and clinical implication for osteosarcoma.

Source Citation

Source Citation   

Gale Document Number: GALE|A710569613