基于骨肉瘤核心驱动基因筛选的生物信息学分析和患者生存期预测基因模型的构建

doi:10.13481/j.1671-587X.20210631

Abstract

Abstract: Objective

To screen the core driving genes of the occurrence and development of osteosarcoma （OS）， and to explore the pathogenic mechanism of OS at the molecular level as well as to construct the gene model to predict the survival time of the OS patients.

Methods

The matrix data of gene chips in OS patients were downloaded from the Gene Expression Omnibus （GEO） database： GSE12865，GSE14359 and GSE36001.The differentially expressed genes （DEGs） between the normal tissue and OS tissue were screened through the bioinformatic method. The molecular functions and pathways of DEGs were comprehensively understood through Gene Ontology （GO） and Kyoto Encyclopedia of Genes and Genomes （KEGG） analysis. The protein-protein interaction （PPI） network was constructed by STRING data， and Cytoscape software was conducted to analyze the correlation between DEGs to identify the most related gene set in the progression of OS as well as to figure out the core pathogenic genes of OS. The clinical record information and transcriptome data of 379 samples of OS were obtained from The Cancer Genome Atlas （TCGA） database， and Kaplan-Meier （K-M） survival analysis was further performed to clarify the relationship between hub genes and survival time of the OS patients， then other factors related to prognosis such as gender and race were searched and discussed. The expression amounts of 6 gene sets were modeled to predict the survival time of the patients.

Results

The top ten DEGs analyzed by MCC algorithm were TYROBP， LAPTM5， FCER1G， CD74， HCLS1， ARHGDIB， HLA-DPA1， CD93， GIMAP4， and LYZ，and the expression difference in these 10 DEGs between OS and normal patients showed statistical significance （P<0.05）.The GO and KEGG results revealed that the DEGs were chiefly enriched in PI3K-AKT and Notch signaling pathways.The K-M survival analysis results demonstrated that the OS patients with lower expressions of 6 genes （ARHGDIB， CD74， FCER1G， HCLS1， HLA-DPA1， and TYROBP） had longer overall survival time than those with higher expressions （P<0.05）. The C-index of the gene set composed of these 6 genes in the construction of prediction model was 0.71.

Conclusion

The high expressions of screened core driving genes are correlated with the occurrence and development of OS.The abnormal signaling pathways of occurrence and development of OS are PI3K-AKT and Notch signal pathways. The prediction model constituted by 6 characteristic gene sets of OS possesses a good predictive ability.

Key words: osteosarcoma, The Cancer Genome Atlas, molecular mechanism, tumor biomarker, tumor prognostic model

CLC Number:

R738.1

Weihang LI,Ziyi DING,Dong WANG,Yikai PAN,Yuhui LIU,Shilei ZHANG,Jing LI,Ming YAN. Bioinformatics analysis based on screening of core driving genes in osteosarcoma and construction of gene model for prediction of survival time of patients[J].Journal of Jilin University(Medicine Edition), 2021, 47(6): 1570-1580.

Figures/Tables 7

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Bioinformatics analysis based on screening of core driving genes in osteosarcoma and construction of gene model for prediction of survival time of patients

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