Establishing and Validating an Aging-Related Prognostic Signature in Osteosarcoma

Yibo Ma; Shuo Zheng; Mingjun Xu; Changjian Chen; Hongtao He

doi:10.1155/2023/6245160

Establishing and Validating an Aging-Related Prognostic Signature in Osteosarcoma

Stem Cells Int. 2023 Feb 23:2023:6245160. doi: 10.1155/2023/6245160. eCollection 2023.

Authors

Yibo Ma¹, Shuo Zheng², Mingjun Xu³, Changjian Chen⁴, Hongtao He⁵

Affiliations

¹ Graduate School of Dalian Medical University, Dalian Medical University, Dalian, China 116044.
² The Second Ward of Department of Orthopedics, The Second Hospital of Dalian Medical University, Dalian, China 116000.
³ The Second Hospital of Dalian Medical University, Dalian Medical University, Dalian, China 116000.
⁴ The First Ward of Department of Orthopedics, The Second Hospital of Dalian Medical University, Dalian, China 116000.
⁵ The Third Ward of Department of Orthopedics, The Second Hospital of Dalian Medical University, Dalian, China 116000.

Abstract

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.