Identification of 9-Gene Epithelial–Mesenchymal Transition Related Signature of Osteosarcoma by Integrating Multi Cohorts

Zhang, Yiqi; Liu, Ziyun; Fu, Qin; Wang, Xingli; Yang, Liyu

doi:10.1177/1533033820980769

Cited by 14 publications

(17 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An immune-stromal score-based gene signature containing FPR1, GBP1, FUCA1, PDK1, BNIP3, EVI2B, APBB1IP, FOLR2, and COCH was established for predicting clinical outcomes of osteosarcoma subjects. Consistent with previous research, FUCA1 was distinctly correlated to osteosarcoma patients ( Yiqi et al, 2020 ). PDK1 upregulation was detected in osteosarcoma, which elevated a proliferative capacity of osteosarcoma cells ( Li et al, 2017 ).…”

Section: Discussionsupporting

confidence: 92%

Analysis of Immune–Stromal Score-Based Gene Signature and Molecular Subtypes in Osteosarcoma: Implications for Prognosis and Tumor Immune Microenvironment

et al. 2021

View full text Add to dashboard Cite

Objective: Infiltrating immune and stromal cells are essential for osteosarcoma progression. This study set out to analyze immune–stromal score-based gene signature and molecular subtypes in osteosarcoma.Methods: The immune and stromal scores of osteosarcoma specimens from the TARGET cohort were determined by the ESTIMATE algorithm. Then, immune-stromal score-based differentially expressed genes (DEGs) were screened, followed by univariate Cox regression analysis. A LASSO regression analysis was applied for establishing a prognostic model. The predictive efficacy was verified in the GSE21257 dataset. Associations between the risk scores and chemotherapy drug sensitivity, immune/stromal scores, PD-1/PD-L1 expression, immune cell infiltrations were assessed in the TARGET cohort. NMF clustering analysis was employed for characterizing distinct molecular subtypes based on immune-stromal score-based DEGs.Results: High immune/stromal scores exhibited the prolonged survival duration of osteosarcoma patients. Based on 85 prognosis-related stromal–immune score-based DEGs, a nine-gene signature was established. High-risk scores indicated undesirable prognosis of osteosarcoma patients. The AUCs of overall survival were 0.881 and 0.849 in the TARGET cohort and GSE21257 dataset, confirming the well predictive performance of this signature. High-risk patients were more sensitive to doxorubicin and low-risk patients exhibited higher immune/stromal scores, PD-L1 expression, and immune cell infiltrations. Three molecular subtypes were characterized, with distinct clinical outcomes and tumor immune microenvironment.Conclusion: This study developed a robust prognostic gene signature as a risk stratification tool and characterized three distinct molecular subtypes for osteosarcoma patients based on immune–stromal score-based DEGs, which may assist decision-making concerning individualized therapy and follow-up project.

show abstract

Section: Discussionsupporting

confidence: 92%

Analysis of Immune–Stromal Score-Based Gene Signature and Molecular Subtypes in Osteosarcoma: Implications for Prognosis and Tumor Immune Microenvironment

et al. 2021

View full text Add to dashboard Cite

show abstract

“…The area under the curve (AUC) was determined for assessing the prognostic accuracy. Furthermore, the AUC of this signature was compared with other gene signatures developed by Wen C. et al (2020), Wu Z.-l. et al (2020, Xiao et al (2020) and Zhang et al (Yiqi et al, 2020). Univariate and multivariate cox regression analyses were carried out based on risk score, age, and gender as variables.…”

Section: Establishment Of a Prognostic Gene Signaturementioning

confidence: 99%

Development and Verification of a Hypoxic Gene Signature for Predicting Prognosis, Immune Microenvironment, and Chemosensitivity for Osteosarcoma

Jin

et al. 2022

Front. Mol. Biosci.

View full text Add to dashboard Cite

Objective: Hypoxic tumors contribute to local failure and distant metastases. Nevertheless, the molecular hallmarks of hypoxia remain ill-defined in osteosarcoma. Here, we developed a hypoxic gene signature in osteosarcoma prognoses.Methods: With the random survival forest algorithm, a prognostic hypoxia-related gene signature was constructed for osteosarcoma in the TARGET cohort. Overall survival (OS) analysis, receiver operating characteristic (ROC) curve, multivariate cox regression analysis, and subgroup analysis were utilized for assessing the predictive efficacy of this signature. Also, external validation was presented in the GSE21257 cohort. GSEA was applied for signaling pathways involved in the high- and low-risk samples. Correlation analyses between risk score and immune cells, stromal/immune score, immune checkpoints, and sensitivity of chemotherapy drugs were performed in osteosarcoma. Then, a nomogram was built by integrating risk score, age, and gender.Results: A five-hypoxic gene signature was developed for predicting survival outcomes of osteosarcoma patients. ROC curves confirmed that this signature possessed the well predictive performance on osteosarcoma prognosis. Furthermore, it could be independently predictive of prognosis. Metabolism of xenobiotics by cytochrome P450 and nitrogen metabolism were activated in the high-risk samples while cell adhesion molecules cams and intestinal immune network for IgA production were enriched in the low-risk samples. The low-risk samples were characterized by elevated immune cell infiltrations, stromal/immune scores, TNFRSF4 expression, and sensitivity to cisplatin. The nomogram accurately predicted 1-, 3-, and 5-years survival duration.Conclusion: These findings might offer an insight into the optimization of prognosis risk stratification and individualized therapy for osteosarcoma patients.

show abstract

“…Recently, immunotherapy, such as adoptive cellular therapy, vaccination, and checkpoint inhibitors, has been considered as the effective therapies for osteosarcoma ( Lettieri et al, 2016 ; Heymann et al, 2019 ). Therefore, it is worth studying the underlying molecular mechanisms of occurrence and development of osteosarcoma with a deep focus on the identification of novel diagnostic, therapeutic, and prognostic markers ( Yiqi et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Global Characterization of Metabolic Genes Regulating Survival and Immune Infiltration in Osteosarcoma

et al. 2022

View full text Add to dashboard Cite

Background: The alterations in metabolic profile of tumors have been identified as one of the prognostic hallmarks of cancers, including osteosarcoma. These alterations are majorly controlled by groups of metabolically active genes. However, the regulation of metabolic gene signatures in tumor microenvironment of osteosarcoma has not been well explained.Objectives: Thus, we investigated the sets of previously published metabolic genes in osteosarcoma patients and normal samples.Methods: We applied computational techniques to identify metabolic genes involved in the immune function of tumor microenvironment (TME) and survival and prognosis of the osteosarcoma patients. Potential candidate gene PAICS (phosphoribosyl aminoimidazole carboxylase, phosphoribosyl aminoimidazole succino carboxamide synthetase) was chosen for further studies in osteosarcoma cell lines for its role in cell proliferation, migration and apoptosis.Results: Our analyses identified a list of metabolic genes differentially expressed in osteosarcoma tissues. Next, we scrutinized the list of genes correlated with survival and immune cells, followed by clustering osteosarcoma patients into three categories: C1, C2, and C3. These analyses led us to choose PAICS as potential candidate gene as its expression showed association with poor survival and negative correlation with the immune cells. Furthermore, we established that loss of PAICS induced apoptosis and inhibited proliferation, migration, and wound healing in HOS and MG-63 cell lines. Finally, the results were supported by constructing and validating a prediction model for prognosis of the osteosarcoma patients.Conclusion: Here, we conclude that metabolic genes specifically PAICS play an integral role in the immune cell infiltration in osteosarcoma TME, as well as cancer development and metastasis.

show abstract

Identification of 9-Gene Epithelial–Mesenchymal Transition Related Signature of Osteosarcoma by Integrating Multi Cohorts

Cited by 14 publications

References 41 publications

Analysis of Immune–Stromal Score-Based Gene Signature and Molecular Subtypes in Osteosarcoma: Implications for Prognosis and Tumor Immune Microenvironment

Analysis of Immune–Stromal Score-Based Gene Signature and Molecular Subtypes in Osteosarcoma: Implications for Prognosis and Tumor Immune Microenvironment

Development and Verification of a Hypoxic Gene Signature for Predicting Prognosis, Immune Microenvironment, and Chemosensitivity for Osteosarcoma

Global Characterization of Metabolic Genes Regulating Survival and Immune Infiltration in Osteosarcoma

Contact Info

Product

Resources

About