[1]苏国威,秦刚,刘金富,等.基于生物信息学方法筛选骨肉瘤核心基因及实验验证[J].西部中医药,2026,39(09):89-96.[doi:10.12174/j.issn.2096-9600.2026.09.18]
　SU Guowei,QIN Gang,LIU Jinfu,et al.Screening of Core Genes in Osteosarcoma and Experimental Validation Based on Bioinformatics Approaches[J].Western Journal of Traditional Chinese Medicine,2026,39(09):89-96.[doi:10.12174/j.issn.2096-9600.2026.09.18]
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基于生物信息学方法筛选骨肉瘤核心基因及实验验证()

《西部中医药》[ISSN:2096-9600/CN:62-1204/R]

卷:
39
期数:
2026年09期
页码:
89-96
栏目:
二次研究
出版日期:
2026-09-07

文章信息/Info

Title:
Screening of Core Genes in Osteosarcoma and Experimental Validation Based on Bioinformatics Approaches
作者:
苏国威1, 秦刚2, 刘金富2, 肖世富1, 李威材1, 刘俊良1, 吴广涛1, 范以东1
1.广西中医药大学研究生院，广西 南宁 530299
2.广西中医药大学第一附属医院，广西 南宁 530023
Author(s):
SU Guowei1, QIN Gang2, LIU Jinfu2, XIAO Shifu1, LI Weicai1, LIU Junliang1, WU Guangtao1, FAN Yidong1
1.Graduate School of Guangxi University of Chinese Medicine, Nanning 530299, China
2.The First Affiliated Hospital of Guangxi University of Chinese Medicine, Nanning 530023, China
关键词:
骨肉瘤; 基因表达; 网络分析; T检验; 聚合酶链式反应
Keywords:
osteosarcoma; gene expression; network analysis; Student’s t-test; PCR
分类号:
R273
DOI:
10.12174/j.issn.2096-9600.2026.09.18
文献标志码:
A
摘要:
目的筛选骨肉瘤新的关键生物标志物，以期为该病的预后评估及靶向治疗提供潜在靶点。 方法从基因表达综合数据库（gene expression omnibus，GEO）下载3个与骨肉瘤相关的数据集（GSE14359、GSE12865和GSE19276），共纳入9个正常组织样本和74个骨肉瘤组织样本，通过R语言对合并校正后的数据集进行差异表达基因分析和加权共表达基因网络分析，并对其结果取交集，然后对交集基因进行功能富集分析、蛋白质相互作用网络分析，并运用Cytoscape进行网络可视化，以Cytohubba程序包计算出degree排名前10的10个基因作为核心基因。另外用GEO数据集GSE16088以及GSE39262对degree前10的基因进行T检验，最后收集骨肉瘤患者组织和非骨肉瘤患者组织各4例，对关键基因进行实时荧光定量聚合酶链式反应（real-time quantitative polymerase chain reaction，qRT-PCR）验证，评估关键基因的表达水平和诊断价值。 结果在骨肉瘤与正常人组织中共筛选出124个差异表达基因（differentially expressed genes，DEGs），其中上调基因36个，下调基因88个。加权基因共表达网络分析（weighted co-expression network construction analysis，WGCNA）显示最相关模块基因171个。DEGs与WGCNA关键模块基因中共有46个交集基因，基因本体论结果显示，其基因功能主要富集在与免疫相关的功能；京都基因与基因组百科全书富集结果表明，交集基因主要富集在中性粒细胞胞外吸收形成信号通路、甘氨酸、丝氨酸和苏氨酸代谢信号通路，以及癌症中的转录失调、病毒蛋白与细胞因子和细胞因子受体的相互作用等信号通路。degree排名前10的10个基因分别为ELANE、MPO、HP、DEFA4、CAMP、LTF、ARG1、AZU1、CTSG和PGLYRP1；ELANE、MPO、CAMP、LTF和ARG1在骨肉瘤患者组织中均呈低表达，差异具有统计学意义（P＜0.05）；只有ELANE和MPO在骨肉瘤患者组织中呈低表达，且差异有统计学意义（P＜0.05）。 结论综合生物信息学分析方法可为进一步了解骨肉瘤发展和转移提供可靠的新见解，并且ELANE和MPO可作为骨肉瘤预后的潜在生物标志物。
Abstract:
ObjectiveTo screen novel key biomarkers for osteosarcoma, aiming to provide potential targets for prognostic assessment and targeted therapy of this malignancy. MethodsThree osteosarcoma-related datasets (GSE14359, GSE12865, and GSE19276) were downloaded from the Gene Expression Omnibus (GEO) database, comprising a total of 9 normal tissue samples and 74 osteosarcoma tissue samples. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were performed on the merged and batch-corrected datasets using R language, and the results were intersected. Subsequently, functional enrichment analysis and protein-protein interaction (PPI) network analysis were conducted on the overlapping genes. Network visualization was carried out using Cytoscape, and the top 10 genes ranked by degree value were identified as hub genes via the Cytohubba plugin. Additionally, two independent GEO datasets (GSE16088 and GSE39262) were used to perform Student’s t-test on the top 10 genes ranked by degree value. Finally, tissue samples were collected from 4 osteosarcoma patients and 4 non-osteosarcoma controls. Real-time quantitative polymerase chain reaction (qRT-PCR) was performed to validate the expression levels of the key genes and to evaluate their diagnostic value. ResultsA total of 124 DEGs (36 upregulated, 88 downregulated) and 171 WGCNA module genes were identified, yielding 46 overlapping genes. GO analysis showed enrichment in immune-related functions, while KEGG analysis revealed pathways including neutrophil extracellular trap formation, glycine/serine/threonine metabolism, transcriptional misregulation in cancer, and viral protein–cytokine/cytokine receptor interactions. The top 10 genes ranked by degree value were ELANE, MPO, HP, DEFA4, CAMP, LTF, ARG1, AZU1, CTSG, and PGLYRP1. The expression levels of ELANE, MPO, CAMP, LTF, and ARG1 were significantly downregulated in osteosarcoma tissues compared with normal controls (P<0.05). However, only ELANE and MPO exhibited consistently low expression in osteosarcoma tissues with statistical significance (P<0.05) across the validation datasets. ConclusionIntegrated bioinformatics analysis can provide reliable new insights into the understanding of osteosarcoma development and metastasis. Moreover, ELANE and MPO may serve as potential biomarkers for the prognosis of osteosarcoma.

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备注/Memo

备注/Memo:
国家自然科学基金（81860793）；广西自然科学基金（2020JJA140375）。苏国威（1994—），男，硕士学位。研究方向：骨与软组织肿瘤的中医药防治及研究。
更新日期/Last Update: 2026-09-15