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基于凋亡相关基因的骨肉瘤预后特征。

Apoptosis-related genes-based prognostic signature for osteosarcoma.

机构信息

Department of Orthopaedics, Zibo Central Hospital, Zibo 255036, Shandong, China.

出版信息

Aging (Albany NY). 2022 May 3;14(9):3813-3825. doi: 10.18632/aging.204042.

DOI:10.18632/aging.204042
PMID:35504036
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9134960/
Abstract

Osteosarcoma (OS) is a common malignant primary tumor of skeleton, especially in children and adolescents, characterized by high lung metastasis rate. Apoptosis has been studied in various tumors, while the prognostic role of apoptosis-related genes in OS has been seldom studied. Three OS related datasets were downloaded from Gene Expression Omnibus (GEO) database. Univariate Cox and LASSO Cox regression analysis identified optimal genes, which were used for building prognostic Risk score. Subsequent multivariate Cox regression analysis and Kaplan-Meier survival analysis determined the independent prognostic factors for OS. The immune cell infiltration was analyzed in CIBERSORT. Basing on 680 apoptosis-related genes, the OS patients could be divided into 2 clusters with significantly different overall survival. Among which, 6 optimal genes were identified to construct Risk score. In both training set (GSE21257) and validation set (meta-GEO dataset), high risk OS patients had significantly worse overall survival compared with the low risk patients. Besides, high Risk score was an independent poor prognostic factor for OS with various ages or genders. Three immune cells were differentially infiltrated between high and low risk OS patients. In conclusion, a six-gene (TERT, TRAP1, DNM1L, BAG5, PLEKHF1 and PPP3CB) based prognostic Risk score signature is probably conducive to distinguish different prognosis of OS patients.

摘要

骨肉瘤（OS）是一种常见的骨骼原发性恶性肿瘤，尤其多见于儿童和青少年，其特征是肺转移率高。细胞凋亡在各种肿瘤中都有研究，而凋亡相关基因在 OS 中的预后作用则研究较少。从基因表达综合数据库（GEO）下载了三个与 OS 相关的数据集。单变量 Cox 和 LASSO Cox 回归分析确定了最佳基因，这些基因用于构建预后风险评分。随后的多变量 Cox 回归分析和 Kaplan-Meier 生存分析确定了 OS 的独立预后因素。使用 CIBERSORT 分析免疫细胞浸润。基于 680 个凋亡相关基因，OS 患者可分为两组，其总体生存率有显著差异。其中，确定了 6 个最佳基因来构建风险评分。在训练集（GSE21257）和验证集（meta-GEO 数据集）中，高风险 OS 患者的总体生存率明显低于低风险患者。此外，高风险评分是 OS 不同年龄或性别的独立不良预后因素。高低风险 OS 患者之间有三种免疫细胞的差异浸润。总之，基于 TERT、TRAP1、DNM1L、BAG5、PLEKHF1 和 PPP3CB 这六个基因的预后风险评分特征可能有助于区分不同 OS 患者的预后。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/97a6/9134960/d0154883e185/aging-14-204042-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/97a6/9134960/f1da38248beb/aging-14-204042-g001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/97a6/9134960/c8b0078b0c75/aging-14-204042-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/97a6/9134960/d0154883e185/aging-14-204042-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/97a6/9134960/f1da38248beb/aging-14-204042-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/97a6/9134960/48c5dfcb11fb/aging-14-204042-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/97a6/9134960/79d22b459e13/aging-14-204042-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/97a6/9134960/b94020baf4b4/aging-14-204042-g004.jpg
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TERT-Regulation and Roles in Cancer Formation.TERT 调控与癌症形成中的作用。
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