基于骨肉瘤患者代谢相关标志物表达的预后模型构建与评估
Construction and evaluation of a prognostic model based on the expression of the metabolism-related signatures in patients with osteosarcoma.
作者信息
Wu Tieli, Wu Xingyi
机构信息
Hainan Vocational University of Science and Technology, Hainan Province, Haikou, 570000, China.
Department of Internal Medicine, Qiqihar First Factory Hospital, 27 Xinming Street, Qiqihar, 161000, Heilongjiang Province, China.
出版信息
BMC Musculoskelet Disord. 2025 Mar 27;26(1):303. doi: 10.1186/s12891-025-08439-9.
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.
背景
本研究旨在筛选三个主要的物质代谢相关基因，并建立骨肉瘤的预后模型。
方法
从癌症基因组图谱（TCGA）和基因表达综合数据库（GEO）下载骨肉瘤的RNA测序表达数据。选择差异表达（DE）RNA，随后选择与代谢相关的DE mRNA。使用Cox回归分析确定预后DE RNA以构建预后模型。随后筛选独立的预后临床因素，并分析长链非编码RNA（lncRNA）的功能。最后，使用定量逆转录定量实时聚合酶链反应（qRT-PCR）和蛋白质免疫印迹法在骨肉瘤细胞中进一步检测特征基因的表达。
结果
共获得432个DE RNA，包括79个DE lncRNA和353个DE mRNA，然后是107个与代谢相关的DE mRNA。之后，特征基因（LINC00545、LINC01537、FOXC2-AS1、CYP27B1、PFKFB4、PHKG1、PHYKPL、PXMP2和XYLB）作为最佳组合，成功提出了预后评分模型。三个验证数据集（GSE16091、GSE21257和GSE39055）表明该模型具有高特异性和敏感性。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，计算机分析、qRT-PCR和蛋白质免疫印迹分析之间的一致性率为88.89%（8/9），表明我们分析的稳健性。
结论
基于九个特征基因的预后模型准确预测了骨肉瘤患者的预后；CYP27B1、PFKFB4、PHKG1、PHYKPL、PXMP2和XYLB可能作为骨肉瘤中与代谢相关的生物标志物。
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