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Establishing and Validating an Aging-Related Prognostic Signature in Osteosarcoma

Stem cells international, 2023-02, Vol.2023, p.1-19 [Peer Reviewed Journal]

Copyright © 2023 Yibo Ma et al. ;COPYRIGHT 2023 Hindawi Limited ;Copyright © 2023 Yibo Ma et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0 ;Copyright © 2023 Yibo Ma et al. 2023 ;ISSN: 1687-966X ;ISSN: 1687-9678 ;EISSN: 1687-9678 ;DOI: 10.1155/2023/6245160

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  • Title:
    Establishing and Validating an Aging-Related Prognostic Signature in Osteosarcoma
  • Author: Ma, Yibo ; Zheng, Shuo ; Xu, Mingjun ; Chen, Changjian ; He, Hongtao
  • Guan, Fanglin ; Fanglin Guan
  • Subjects: Aging ; Algorithms ; Bioaccumulation ; Biological activity ; Bone cancer ; Cancer ; Chemotherapy ; Gene expression ; Genetic aspects ; Genomes ; Genomics ; Immune checkpoint ; Immunotherapy ; Infiltration ; Medical prognosis ; Metastases ; Metastasis ; Nomograms ; Osteosarcoma ; Patients ; Prognosis ; Regression analysis ; Risk groups ; Sarcoma ; Senescence ; Stem cells ; Transcriptomes ; Tumors
  • Is Part Of: Stem cells international, 2023-02, Vol.2023, p.1-19
  • Description: Aging is an inevitable process that biological changes accumulate with time and results in increased susceptibility to different tumors. But currently, aging-related genes (ARGs) in osteosarcoma were not clear. We investigated the potential prognostic role of ARGs and established an ARG-based prognostic signature for osteosarcoma. The transcriptome data and corresponding clinicopathological information of patients with osteosarcoma were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Molecular subtypes were generated based on prognosis-related ARGs obtained from univariate Cox analysis. With ARGs, a risk signature was built by univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses. Differences in clinicopathological features, immune infiltration, immune checkpoints, responsiveness to immunotherapy and chemotherapy, and biological pathways were assessed according to molecular subtypes and the risk signature. Based on risk signature and clinicopathological variables, a nomogram was established and validated. Three molecular subtypes with distinct clinical outcomes were classified based on 36 prognostic ARGs for osteosarcoma. A nine-ARG-based signature in the TCGA cohort, including BMP8A, CORT, SLC17A9, VEGFA, GAL, SSX1, RASGRP2, SDC3, and EVI2B, has been created and developed and could well perform patient stratification into the high- and low-risk groups. There were significant differences in clinicopathological features, immune checkpoints and infiltration, responsiveness to immunotherapy and chemotherapy, cancer stem cell, and biological pathways among the molecular subtypes. The risk signature and metastatic status were identified as independent prognostic factors for osteosarcoma. A nomogram combining ARG-based risk signature and metastatic status was established, showing great prediction accuracy and clinical benefit for osteosarcoma OS. We characterized three ARG-based molecular subtypes with distinct characteristics and built an ARG-based risk signature for osteosarcoma prognosis, which could facilitate prognosis prediction and making personalized treatment in osteosarcoma.
  • Publisher: New York: Hindawi
  • Language: English
  • Identifier: ISSN: 1687-966X
    ISSN: 1687-9678
    EISSN: 1687-9678
    DOI: 10.1155/2023/6245160
  • Source: PubMed Central
    ROAD: Directory of Open Access Scholarly Resources
    ProQuest Central
    DOAJ Directory of Open Access Journals
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