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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References 35

1	GDOWSKI A S， RANJAN A， VISHWANATHA J K. Current concepts in bone metastasis， contemporary therapeutic strategies and ongoing clinical trials［J］. J Exp Clin Cancer Res， 2017， 36： 108.
2	MESSERSCHMITT P J， GARCIA R M， ABDUL-KARIM F W， et al. Osteosarcoma［J］. J Am Acad Orthop Surg， 2009， 17（8）： 515-527.
3	LIN Y H， JEWELL B E， GINGOLD J， et al. Osteosarcoma： molecular pathogenesis and iPSC modeling［J］. Trends Mol Med， 2017， 23（8）： 737-755.
4	LI W， YUAN B， ZHAO Y， et al. Transcriptome profiling reveals target in primary myelofibrosis together with structural biology study on novel natural inhibitors regarding JAK2［J］. Aging （Albany NY），2021，13（6）： 8248-8275.
5	何远，张丽娜，朱兴文. 大数据技术在生物信息学中的应用综述［J］. 软件导刊， 2016， 15（4）： 147-148.
6	GAUTIER L， COPE L， BOLSTAD B M， et al. Affy： analysis of Affymetrix GeneChip data at the probe level［J］. Bioinformatics， 2004， 20（3）： 307-315.
7	COLAPRICO A， SILVA T C， OLSEN C， et al. TCGAbiolinks： an R/Bioconductor package for integrative analysis of TCGA data［J］. Nucleic Acids Res， 2016， 44（8）： e71.
8	DENNIS G， SHERMAN B T， HOSACK D A， et al. DAVID： database for annotation， visualization， and integrated discovery［J］. Genome Biol， 2003， 4（5）： P3.
9	SZKLARCZYK D， GABLE A L， LYON D， et al. STRING v11： protein-protein association networks with increased coverage， supporting functional discovery in genome-wide experimental datasets［J］. Nucleic Acids Res， 2019， 47（d1）： D607-D613.
10	CHIN C H， CHEN S H， WU H H， et al. cytoHubba： identifying hub objects and sub-networks from complex interactome［J］. BMC Syst Biol， 2014， 8（）： S11.
11	ARNDT C A， ROSE P S， FOLPE A L， et al. Common musculoskeletal tumors of childhood and adolescence［J］. Mayo Clin Proc， 2012，87（5）： 475-487.
12	SAMPO M， KOIVIKKO M， TASKINEN M， et al. Incidence， epidemiology and treatment results of osteosarcoma in Finland - a nationwide population-based study［J］. Acta Oncol， 2011， 50（8）： 1206-1214.
13	PICCI P. Osteosarcoma （osteogenic sarcoma）［J］. Orphanet J Rare Dis， 2007， 2： 6.
14	XI L， ZHANG Y F， KONG S N， et al. miR-34 inhibits growth and promotes apoptosis of osteosarcoma in nude mice through targetly regulating TGIF₂ expression［J］. Biosci Rep， 2018， 38（3）：BSR20180078.
15	LUETKE A， MEYERS P A， LEWIS I， et al. Osteosarcoma treatment - Where do we stand？ A state of the art review［J］. Cancer Treat Rev， 2014， 40（4）： 523-532.
16	任娟，徐叶峰，匡唐洪，等. 104例骨肉瘤肺转移患者的预后及其影响因素［J］.中华肿瘤杂志， 2017，39（4）： 263-268.
17	YANG L， LI W， ZHAO Y， et al. Computational study of novel natural inhibitors targeting O⁶-methylguanine-DNA methyltransferase［J］. World Neurosurg， 2019， 130： e294-e306.
18	ZHONG S， LI W H， BAI Y， et al. Computational study on new natural compound agonists of stimulator of interferon genes（STING）［J］.PLoS One，2019，14（5）：e0216678.
19	MOEK K L， DE GROOT D J A， DE VRIES E G E， et al. The antibody-drug conjugate target landscape across a broad range of tumour types［J］. Ann Oncol， 2017， 28（12）： 3083-3091.
20	BUDDINGH E P， RUSLAN S E N， REIJNDERS C M A， et al. Mesenchymal stromal cells of osteosarcoma patients do not show evidence of neoplastic changes during long-term culture［J］. Clin Sarcoma Res， 2015， 5： 16.
21	PAN Y， LU L， CHEN J，et al. Identification of potential crucial genes and construction of microRNA-mRNA negative regulatory networks in osteosarcoma［J］. Hereditas， 2018， 155： 21.
22	HENCKAERTS L， CLEYNEN I， BRINAR M， et al. Genetic variation in the autophagy gene ULK1 and risk of Crohnʼs disease［J］. Inflamm Bowel Dis， 2011， 17（6）： 1392-1397.
23	BILALIC S， VEITINGER M， AHRER K H， et al. Identification of Non-HLA antigens targeted by alloreactive antibodies in patients undergoing chronic hemodialysis［J］. J Proteome Res， 2010， 9（2）： 1041-1049.
24	LIU S S， CUI H， LI Q L， et al. RhoGDI2 is expressed in human trophoblasts and involved in their migration by inhibiting the activation of RAC1［J］. Biol Reprod， 2014， 90（4）： 88.
25	ZHU H， WU H， LIU X， et al. Regulation of autophagy by a beclin 1-targeted microRNA， miR-30a， in cancer cells［J］. Autophagy， 2009， 5（6）： 816-823.
26	SWEET R A， NICKERSON K M， CULLEN J L，et al. B cell–extrinsic Myd88 and Fcer1g negatively regulate autoreactive and normal B cell immune responses［J］. J Immunol， 2017， 199（3）： 885-893.
27	LE CONIAT M， KINET J P， BERGER R. The human genes for the alpha and gamma subunits of the mast cell receptor for immunoglobulin E are located on human chromosome band 1q23［J］. Immunogenetics， 1990， 32（3）： 183-186.
28	RAJARAMAN P， BRENNER A V， NETA G， et al. Risk of meningioma and common variation in genes related to innate immunity［J］. Cancer Epidemiol Biomarkers Prev， 2010， 19（5）： 1356-1361.
29	MAHACHIE JOHN J M， BAURECHT H， RODRÍGUEZ E， et al. Analysis of the high affinity IgE receptor genes reveals epistatic effects of FCER1A variants on eczema risk［J］. Allergy， 2010， 65（7）： 875-882.
30	HAN S， LAN Q， PARK A K， et al. Polymorphisms in innate immunity genes and risk of childhood leukemia［J］. Hum Immunol， 2010， 71（7）： 727-730.
31	CHEN L， YUAN L， WANG Y， et al. Co-expression network analysis identified FCER1G in association with progression and prognosis in human clear cell renal cell carcinoma［J］. Int J Biol Sci， 2017， 13（11）： 1361-1372.
32	PALONEVA J， KESTILÄ M， WU J， et al. Loss-of-function mutations in TYROBP （DAP12） result in a presenile dementia with bone cysts［J］. Nat Genet， 2000， 25（3）： 357-361.
33	HAMERMAN J A， NI M J， KILLEBREW J R， et al. The expanding roles of ITAM adapters FcRgamma and DAP12 in myeloid cells［J］. Immunol Rev， 2009， 232（1）： 42-58.
34	施锦涛，张凯，张芮浩，等.PD-1/PD-L1轴与骨肉瘤治疗研究进展［J］.解放军医学杂志，2021，46（1）：89-94.
35	HAURE-MIRANDE J V， AUDRAIN M， FANUTZA T， et al. Deficiency of TYROBP， an adapter protein for TREM2 and CR3 receptors， is neuroprotective in a mouse model of early Alzheimer’s pathology［J］. Acta Neuropathol， 2017， 134（5）： 769-788.

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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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