1.中山大学附属第一医院骨肿瘤科,广东 广州510080
2.中山大学附属第一医院影像科,广东 广州510080
许明贤,硕士,研究方向:骨肿瘤,E-mail:hsumingxian@foxmail.com
纸质出版日期:2022-11-20,
收稿日期:2022-05-06,
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许明贤,李瑞希,郭伟堂等.基于高通量测序分析骨肉瘤发生和转移的分子特征[J].中山大学学报(医学科学版),2022,43(06):985-994.
XU Ming-xian,LI Rui-xi,GUO Wei-tang,et al.Molecular Characteristics of Osteosarcoma Occurrence and Metastasis Based on High Throughput Sequencing Technology[J].Journal of Sun Yat-sen University(Medical Sciences),2022,43(06):985-994.
许明贤,李瑞希,郭伟堂等.基于高通量测序分析骨肉瘤发生和转移的分子特征[J].中山大学学报(医学科学版),2022,43(06):985-994. DOI: 10.13471/j.cnki.j.sun.yat-sen.univ(med.sci).2022.0614.
XU Ming-xian,LI Rui-xi,GUO Wei-tang,et al.Molecular Characteristics of Osteosarcoma Occurrence and Metastasis Based on High Throughput Sequencing Technology[J].Journal of Sun Yat-sen University(Medical Sciences),2022,43(06):985-994. DOI: 10.13471/j.cnki.j.sun.yat-sen.univ(med.sci).2022.0614.
目的
2
本研究通过第二代高通量测序(NGS)检测配对骨肉瘤转移灶和原发灶标本,分析骨肉瘤原发灶与转移灶的基因图谱差异,以期发现促进骨肉瘤发生和转移的相关分子及可能机制。
方法
2
12例转移患者的原发灶与转移病灶肿瘤组织样本,使用第二代高通量测序(NGS)进行检测,其中9对panel检测(678个基因)以及3对全外显子检测(WES),分析比较骨肉瘤原发灶与转移灶的基因图谱差异:基因拷贝数变异通过EXCAVATOR检测;DNA层面的融合突变通过Lumpy检测;RNA层面的融合突变通过Defuse + STAR-Fusion,并通过Circos图显示突变的分布;Metascape 用于分析差异基因的 GO 和 KEGG 信号通路富集;使用Pyclone+Citup / LICHEE进行克隆进化分析。
结果
2
骨肉瘤基因总体突变模式主要以基因扩增为主,主要包括FLCN(37.5%),GID4(37.5%),TP53(33.3%),ATRX(25%),CALR(25%),CCND3(25%),CCNE1(25%),NOCR1(25%),TFEB(25%),VEGFA(25%)等基因。GO聚类结果提示细胞周期通路突变频率最高。KT1、PLCG2、EGFR这3个基因在转移灶显著富集的信号通路中多次出现,可能与骨肉瘤的转移密切相关。转移灶肿瘤负荷突变(TMB)频率显著高于原发灶(
P
=0.013)。3例进行WES检测的转移患者均呈现线性克隆进化,提示骨肉瘤转移基因的突变可能呈次序性累积。同时我们确定了可能在骨肉瘤进展中发挥作用的新候选基因,包括PLCG2、MYO15A与PEX6。
结论
2
骨肉瘤发生与转移的基因突变模式主要以基因扩增为主,骨肉瘤患者肿瘤负荷突变频率在转移灶中显著高于原发灶,转移患者存在相互关联的线性基因克隆进化。本研究发现了可能在骨肉瘤进展中发挥作用的3个新基因PLCG2、MYO15A和PEX6。
Objective
2
The purpose of this study was to explore the difference in expression of gene between the osteosarcoma metastasis and primary specimens through NGS detection, in order to find the molecular mechanism of inducing osteosarcoma occurrence and metastasis.
Methods
2
Next generation sequencing (NGS) was used to detect the tumor tissue samples of paired primary and metastatic lesions in 12 patients with metastasis, including 9 pairs of Panel detection (678 genes) and 3 pairs of whole exon detection (WES). Copy number variation was detected by Excavator. DNA mutations were detected by Lumpy fusion, and RNA mutations were detected with Defuse + Star fusion. The distribution of mutations was shown by Circos. Metascape was used to analyze the enrichment of Go and KEGG signal pathways of differential genes. Pyclone+Citup/LICHEE was used for clonal evolution analysis.
Results
2
The most frequent gene mutation mode of osteosarcoma was gene amplification, high-frequency mutation genes were FLCN(37.5%),GID4(37.5%),TP53(33.3%),ATRX(25%),CALR(25%),CCND3(25%),CCNE1(25%),NOCR1(25%),TFEB(25%),VEGFA(25%), and the mutation frequency of cell cycle pathway was the highest. KT1, PLCG2 and EGFR were closely related to the metastasis of osteosarcoma. The frequency of tumor mutation burden (TMB) in metastatic lesions was significantly higher than primary lesions (P = 0.013). Three patients detected by WES showed linear clonal evolution, suggesting that the mutations of osteosarcoma metastasis genes may be accumulated sequentially. We identified new candidate genes that may play an important role in the progression of osteosarcoma, including PLCG2, MYO15A and PEX6.
Conclusions
2
The most frequent gene mutation pattern of osteosarcoma occurrence and metastasis is gene amplification, and the frequency of tumor mutation burden (TMB) in metastatic lesions is significantly higher than that in primary lesions. There is an interrelated linear gene clonal evolution in patients with metastasis. This study suggests that PLCG2, MYO15A and PEX6 may play an important role in the progression of osteosarcoma.
骨肉瘤肺转移第二代高通量测序基因图谱基因突变
osteosarcomalung metastasisNGSgene mapgene mutation
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