Epithelial to Mesenchymal Transition Relevant Subtypes with Distinct Prognosis and Responses to Chemo- or Immunotherapies in Osteosarcoma. (4th July 2022)
- Record Type:
- Journal Article
- Title:
- Epithelial to Mesenchymal Transition Relevant Subtypes with Distinct Prognosis and Responses to Chemo- or Immunotherapies in Osteosarcoma. (4th July 2022)
- Main Title:
- Epithelial to Mesenchymal Transition Relevant Subtypes with Distinct Prognosis and Responses to Chemo- or Immunotherapies in Osteosarcoma
- Authors:
- Zhou, Yang
Li, Gai
Li, Hu
Lai, Fuchong
Duan, Pingguo
Cheng, Ming - Other Names:
- Wang Fu Academic Editor.
- Abstract:
- 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 externalAbstract : 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. … (more)
- Is Part Of:
- Journal of immunology research. Volume 2022(2022)
- Journal:
- Journal of immunology research
- Issue:
- Volume 2022(2022)
- Issue Display:
- Volume 2022, Issue 2022 (2022)
- Year:
- 2022
- Volume:
- 2022
- Issue:
- 2022
- Issue Sort Value:
- 2022-2022-2022-0000
- Page Start:
- Page End:
- Publication Date:
- 2022-07-04
- Subjects:
- Immunology -- Periodicals
Immunology -- Research -- Periodicals
616.07905 - Journal URLs:
- https://www.hindawi.com/journals/jir/ ↗
- DOI:
- 10.1155/2022/1377565 ↗
- Languages:
- English
- ISSNs:
- 2314-8861
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library HMNTS - ELD Digital store
- Ingest File:
- 22654.xml