Construction and evaluation of a prognostic model based on the expression of the metabolism-related signatures in patients with osteosarcoma.

IF 2.4 3区医学 Q2 ORTHOPEDICS BMC Musculoskeletal Disorders Pub Date : 2025-03-27 DOI:10.1186/s12891-025-08439-9

Tieli Wu, Xingyi Wu

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Abstract

Background: The aim of this study was to screen three major substance metabolism-related genes and establish a prognostic model for osteosarcoma.

Methods: RNA-seq expression data for osteosarcoma were downloaded from The Cancer Genome Atlas (TCGA) and GEO databases. Differentially expressed (DE) RNAs were selected, followed by the selection of metabolic-related DE mRNAs. Using Cox regression analysis, prognostic DE RNAs were identified to construct a prognostic model. Subsequently, independent prognostic clinical factors were screened, and the functions of the long non-coding RNAs (lncRNAs) were analyzed. Finally, the expression of signature genes was further tested in osteosarcoma cells using quantitative reverse transcription quantitative real-time polymerase chain reaction (qRT-PCR) and western blotting.

Results: A total of 432 DE RNAs, comprising 79 DE lncRNAs and 353 DE mRNAs were obtained, and then 107 metabolic-related DE mRNAs. Afterwards signature genes (LINC00545, LINC01537, FOXC2-AS1, CYP27B1, PFKFB4, PHKG1, PHYKPL, PXMP2, and XYLB) served as optimal combinations, and a prognostic score model was successfully proposed. Three verification datasets (GSE16091, GSE21257, and GSE39055) showed that the model had high specificity and sensitivity. In addition, two independent prognostic clinical factors (age and tumor metastasis) were identified. Finally, the concordance rate between the in silico analysis, qRT-PCR, and western blotting analysis was 88.89% (8/9), suggesting the robustness of our analysis.

Conclusions: The prognostic model based on the nine signature genes accurately predicted the prognosis of patients with osteosarcoma; CYP27B1, PFKFB4, PHKG1, PHYKPL, PXMP2, and XYLB may serve as metabolism-related biomarkers in osteosarcoma.

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基于骨肉瘤患者代谢相关特征表达的预后模型的构建和评估。

背景：本研究的目的是筛选三个主要的物质代谢相关基因，建立骨肉瘤的预后模型。方法：从癌症基因组图谱（TCGA）和GEO数据库下载骨肉瘤的RNA-seq表达数据。选择差异表达（DE） rna，然后选择代谢相关的DE mrna。采用Cox回归分析，鉴定预后DE rna，构建预后模型。随后，筛选独立的预后临床因素，并分析长链非编码rna （lncRNAs）的功能。最后，利用定量逆转录定量实时聚合酶链反应（qRT-PCR）和western blotting进一步检测骨肉瘤细胞中特征基因的表达。结果：共获得DE rna 432个，其中DE lncrna 79个，DE mrna 353个，代谢相关DE mrna 107个。以特征基因LINC00545、LINC01537、FOXC2-AS1、CYP27B1、PFKFB4、PHKG1、PHYKPL、PXMP2、XYLB作为最优组合，成功建立预后评分模型。三个验证数据集（GSE16091、GSE21257和GSE39055）表明该模型具有较高的特异性和敏感性。此外，确定了两个独立的预后临床因素（年龄和肿瘤转移）。最后，芯片分析、qRT-PCR和western blotting分析的一致性率为88.89%(8/9)，表明我们的分析具有稳健性。结论：基于9个特征基因的骨肉瘤预后预测模型能够准确预测骨肉瘤患者的预后；CYP27B1、PFKFB4、PHKG1、PHYKPL、PXMP2和XYLB可能是骨肉瘤代谢相关的生物标志物。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

BMC Musculoskeletal Disorders 医学-风湿病学

CiteScore

3.80

自引率

8.70%

发文量

1017

审稿时长

3-6 weeks

期刊介绍： BMC Musculoskeletal Disorders is an open access, peer-reviewed journal that considers articles on all aspects of the prevention, diagnosis and management of musculoskeletal disorders, as well as related molecular genetics, pathophysiology, and epidemiology. The scope of the Journal covers research into rheumatic diseases where the primary focus relates specifically to a component(s) of the musculoskeletal system.