Identification of a novel glycolysis-related gene signature for predicting the prognosis of osteosarcoma patients

Yang, Mengkai; Ma, Xiao‐Jun; Wang, Zhuoying; Zhang, Tao; Hua, Yingqi; Cai, Zhengdong

doi:10.18632/aging.202958

Cited by 19 publications

(21 citation statements)

References 46 publications

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“…However, after we validated them in the training and validation cohorts, we found that the nine-gene signature we generated was superior to others in predicting OS prognosis. Among the nine studies, the one by Yang et al (2021) revealed a gene signature that closely resembled ours. Yang et al first filtered genes through GSEAand then established a three-gene signature prognostic model, which they claimed can accurately predict the prognosis of OS.…”

Section: Discussionsupporting

confidence: 65%

“…Yang et al first filtered genes through GSEAand then established a three-gene signature prognostic model, which they claimed can accurately predict the prognosis of OS. However, in our validation, although the three-gene signature’s ability was similar to ours in Kaplan–Meier analysis, it could not show stable prognostic capacity via Cox regression analysis ( Yang et al, 2021 ).…”

Section: Discussionmentioning

confidence: 52%

“…For example, in the nine studies (Cao et al, 2020;Chen Z. et al, 2020;Song et al, 2020;Wen et al, 2020;Xiao et al, 2020;Yu et al, 2020;Zhu et al, 2020;Fu et al, 2021;Yang et al, 2021) we included in the study based on the inclusion criteria, they all looked for potential survival-related OS gene signatures from public datasets.However, after we validated them in the training and validation cohorts, we found that the nine-gene signature we generated was superior to others in predicting OS prognosis. Among the nine studies, the one by Yang et al (2021)revealed a gene signature that closely resembled ours. Yang et al first filtered genes through GSEAand then established a three-gene signature prognostic model, which they claimed can accurately predict the prognosis of OS.…”

Section: Discussionmentioning

confidence: 99%

“…In addition, more and more studies are involved. For example, in the nine studies ( Cao et al, 2020 ; Chen Z. et al, 2020 ; Song et al, 2020 ; Wen et al, 2020 ; Xiao et al, 2020 ; Yu et al, 2020 ; Zhu et al, 2020 ; Fu et al, 2021 ; Yang et al, 2021 ) we included in the study based on the inclusion criteria, they all looked for potential survival-related OS gene signatures from public datasets. However, after we validated them in the training and validation cohorts, we found that the nine-gene signature we generated was superior to others in predicting OS prognosis.…”

Section: Discussionmentioning

confidence: 99%

“…Osteosarcoma (OS) is a rare malignant tumor that mainly affects children and adolescents ( Yang et al, 2021 ). Since the introduction of chemotherapy in 1970, the 5-year survival rate of patients with non-metastatic OS has reached 70% ( Li et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

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Identification of a Novel Prognostic Gene Signature From the Immune Cell Infiltration Landscape of Osteosarcoma

Fan

Liu

et al. 2021

Front. Cell Dev. Biol.

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Background: The tumor microenvironment (TME) mainly comprises tumor cells and tumor-infiltrating immune cells mixed with stromal components. Latestresearch hasdisplayed that tumor immune cell infiltration (ICI) is associated with the clinical outcome of patients with osteosarcoma (OS). This work aimed to build a gene signature according to ICI in OS for predicting patient outcomes.Methods: The TARGET-OS dataset was used for model training, while the GSE21257 dataset was taken forvalidation. Unsupervised clustering was performed on the training cohort based on the ICI profiles. The Kaplan–Meier estimator and univariate Cox proportional hazards models were used to identify the differentially expressed genes between clusters to preliminarily screen for potential prognostic genes. We incorporated these potential prognostic genes into a LASSO regression analysis and produced a gene signature, which was next assessed with the Kaplan–Meier estimator, Cox proportional hazards models, ROC curves, IAUC, and IBS in the training and validation cohorts. In addition, we compared our signature to previous models. GSEAswere deployed to further study the functional mechanism of the signature. We conducted an analysis of 22 TICsfor identifying the role of TICs in the gene signature’s prognosis ability.Results: Data from the training cohort were used to generate a nine-gene signature. The Kaplan–Meier estimator, Cox proportional hazards models, ROC curves, IAUC, and IBS validated the signature’s capacity and independence in predicting the outcomes of OS patients in the validation cohort. A comparison with previous studies confirmed the superiority of our signature regarding its prognostic ability. Annotation analysis revealed the mechanism related to the gene signature specifically. The immune-infiltration analysis uncoveredkey roles for activated mast cells in the prognosis of OS.Conclusion: We identified a robust nine-gene signature (ZFP90, UHRF2, SELPLG, PLD3, PLCB4, IFNGR1, DLEU2, ATP6V1E1, and ANXA5) that can predict OS outcome precisely and is strongly linked to activated mast cells.

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Section: Discussionsupporting

confidence: 65%

Section: Discussionmentioning

confidence: 52%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Identification of a Novel Prognostic Gene Signature From the Immune Cell Infiltration Landscape of Osteosarcoma

Fan

Liu

et al. 2021

Front. Cell Dev. Biol.

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Development and validation of apoptosis‐related signature and molecular subtype to improve prognosis prediction in osteosarcoma patients

Hong

et al. 2022

Clinical Laboratory Analysis

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Background: Previous evidence has shown that apoptosis performs integral functions in the tumorigenesis and development of various tumors. Therefore, this study aimed to establish a molecular subtype and prognostic signature based on apoptosis-related genes (ARGs) to understand the molecular mechanisms and predict prognosis in patients with osteosarcoma. Methods:The GEO and TARGET databases were utilized to obtain the expression levels of ARGs and clinical information of osteosarcoma patients. Consensus clustering analysis was used to explore the different molecular subtypes based on ARGs. GO, KEGG, GSEA, ESTIMATE, and ssGSEA analyses were performed to examine the differences in biological functions and immune characteristics between the distinct molecular subtypes. Then, we constructed an ARG signature by LASSO analysis. The prognostic significance of the ARG signature in osteosarcoma was determined by Kaplan-Meier plotter, Cox regression, and nomogram analyses.Results: Two apoptosis-related subtypes were identified. Cluster 1 had a better prognosis, higher immunogenicity, and immune cell infiltration, as well as a better response to immunotherapy than Cluster 2. We discovered that patients in the high-risk cohort had a lower survival rate than those in the low-risk cohort according to the ARG signature. Furthermore, Cox regression analysis confirmed that a high risk score independently acted as an unfavorable prognostic marker. Additionally, the nomogram combining risk scores with clinical characteristics can improve prediction efficiency. Conclusion:We demonstrated that patients suffering from osteosarcoma may be classified into two apoptosis-related subtypes. Moreover, we developed an ARG prognostic signature to predict the prognosis status of osteosarcoma patients.

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Signature constructed by glycolysis-immune-related genes can predict the prognosis of osteosarcoma patients

et al. 2022

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Identification of a novel glycolysis-related gene signature for predicting the prognosis of osteosarcoma patients

Cited by 19 publications

References 46 publications

Identification of a Novel Prognostic Gene Signature From the Immune Cell Infiltration Landscape of Osteosarcoma

Identification of a Novel Prognostic Gene Signature From the Immune Cell Infiltration Landscape of Osteosarcoma

Development and validation of apoptosis‐related signature and molecular subtype to improve prognosis prediction in osteosarcoma patients

Signature constructed by glycolysis-immune-related genes can predict the prognosis of osteosarcoma patients

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