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CCNB1、KIF23作为骨肉瘤的核心生物标志物的探索研究
Exploratory Study on CCNB1 and KIF23 as Core Biomarkers of Osteosarcoma

DOI: 10.12677/acm.2024.1451411, PP. 162-177

Keywords: 骨肉瘤,生物信息学,CCNB1,KIF23
Osteosarcoma
, Bioinformatics, CCNB1, KIF23

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Abstract:

目的:骨肉瘤是一种高度侵袭性的原发性骨肿瘤,危害性不容小觑,然而目前骨肉瘤的发病机制和分子生物学特征还未确定。方法:我们利用生物信息技术,在GEO数据库中下载骨肉瘤及正常组织样本的基因表达矩阵,利用R软件包进行差异分析,获取差异基因列表,并进行GOKEGG和GSEA富集分析,构建PPI网络获取核心差异基因,并制作核心基因的表达热图。使用CIBERSORT软件包进行免疫浸润分析,获得了全基因表达矩阵的免疫细胞的比例结果和免疫细胞组分间的共表达模式图,用于研究在免疫系统治疗干预下,对于骨肉瘤的疾病进程的影响。还获取了在CTD网站中,我们找到了与核心基因的最相关的疾病。TargetScan用于筛选调节中枢DEG的miRNA。结果:共获得了1342个差异基因,富集结果显示差异表达基因主要富集在细胞激活、分泌颗粒、脂质结合、细胞周期、P53信号通路。构建了PPI网络,获得了核心基因KIF23、CCNB1,绘制了核心基因在基因表达矩阵中的表达差异情况,结果显示KIF23、CCNB1在骨肉瘤和正常组织中具有显著差异。并在CTD网站中查找到了KIF23、CCNB1和骨肉瘤、肿瘤、炎症等疾病有关。结论:CCNB1和KIF23的高表达可能导致细胞周期的异常,促进骨肉瘤细胞的异常增殖和侵袭,在骨肉瘤的发生发展过程中发挥重要的作用,CCNB1和KIF23的表达越高,则骨肉瘤的预后越差。
Purpose: Osteosarcoma is a highly invasive primary bone tumor, and its harm cannot be underestimated. However, the pathogenesis and molecular biology characteristics of osteosarcoma have not yet been determined. Method: We used bioinformatics technology to download the gene expression matrix of osteosarcoma and normal tissue samples from the GEO database. We used R software package for differential analysis, obtained a list of differential genes, and conducted GOKEGG and GSEA enrichment analysis. We constructed a PPI network to obtain core differential genes and created an expression heatmap of the core genes. The CIBERSORT software package was used for immune infiltration analysis, and the proportion of immune cells in the whole gene expression matrix and the co-expression pattern between immune cell components were obtained, which were used to study the impact of immune system therapy intervention on the disease progression of osteosarcoma. We also found the most relevant diseases related to core genes on the CTD website. TargetScan is used to screen miRNAs that regulate central DEG. Result: A total of 1342 differentially expressed genes were obtained, and the enrichment results showed that differentially expressed genes were mainly enriched in cell activation, secretory granules, lipid binding, cell cycle, and P53 signaling pathway. A PPI network was constructed, and core genes KIF23 and CCNB1 were obtained. The expression differences of core genes in the gene expression matrix were plotted, and the results showed significant differences between KIF23 and CCNB1 in osteosarcoma and normal tissues. It was found on the CTD website that KIF23, CCNB1 are related to diseases such as osteosarcoma, tumors, and inflammation. Conclusion: The high expression of CCNB1 and KIF23 may lead to abnormal cell cycle, promote abnormal proliferation and invasion of osteosarcoma cells, and play an important role in the occurrence and development of osteosarcoma. The higher the

References

[1]  Ottaviani, G. and Jaffe, N. (2009) The Epidemiology of Osteosarcoma. Cancer Treatment and Research, 152, 3-13.
https://doi.org/10.1007/978-1-4419-0284-9_1
[2]  Bielack, S.S., Kempf-Bielack, B., Delling, G., et al. (2002) Prognostic Factors in High-Grade Osteosarcoma of the Extremities or Trunk: An Analysis of 1, 702 Patients Treated on Neoadjuvant Cooperative Osteosarcoma Study Group Protocols. Journal of Clinical Oncology, 20, 776-790.
https://doi.org/10.1200/JCO.2002.20.3.776
[3]  Bielack, S., Jürgens, H., Jundt, G., et al. (2009) Osteosarcoma: The COSS Experience. Cancer Treatment and Research, 152, 289-308.
https://doi.org/10.1007/978-1-4419-0284-9_15
[4]  Ozaki, T., Flege, S., Liljenqvist, U., et al. (2002) Osteosarcoma of the Spine: Experience of the Cooperative Osteosarcoma Study Group. Cancer, 94, 1069-1077.
https://doi.org/10.1002/cncr.10258
[5]  Luetke, A., Meyers, P.A., Lewis, I., et al. (2014) Osteosarcoma Treatment—Where Do We Stand? A State of the Art Review. Cancer Treatment Reviews, 40, 523-532.
https://doi.org/10.1016/j.ctrv.2013.11.006
[6]  Wittig, JC., Bickels, J., Priebat, D., et al. (2002) Osteosarcoma: A Multidisciplinary Approach to Diagnosis and Treatment. American Family Physician, 65, 1123-1132.
[7]  Kager, L., Tamamyan, G. and Bielack, S. (2017) Novel Insights and Therapeutic Interventions for Pediatric Osteosarcoma. Future Oncology, 13, 357-368.
https://doi.org/10.2217/fon-2016-0261
[8]  Normand, R. and Yanai, I. (2013) An Introduction to High-Throughput Sequencing Experiments: Design and Bioinformatics Analysis. Methods in Molecular Biology, 1038, 1-26.
https://doi.org/10.1007/978-1-62703-514-9_1
[9]  Canzoneri, R., Lacunza, E. and Abba, M.C. (2019) Genomics and Bioinformatics as Pillars of Precision Medicine in Oncology. Medicina, 79, 587-592.
[10]  Shulaev, V. (2006) Metabolomics Technology and Bioinformatics. Briefings in Bioinformatics, 7, 128-139.
https://doi.org/10.1093/bib/bbl012
[11]  Angarica, V.E. and Del Sol, A. (2017) Bioinformatics Tools for Genome-Wide Epigenetic Research. Advances in Experimental Medicine and Biology, 978, 489-512.
https://doi.org/10.1007/978-3-319-53889-1_25
[12]  Wang, Y., Zhao, Y., Bollas, A., et al. (2021) Nanopore Sequencing Technology, Bioinformatics and Applications. Nature Biotechnology, 39, 1348-1365.
https://doi.org/10.1038/s41587-021-01108-x
[13]  Zeng, Y. and Fan, R. (2022) Identification and Verification of CCNB1 as a Potential Prognostic Biomarker by Comprehensive Analysis. Scientific Reports, 12, Article No. 16153.
https://doi.org/10.1038/s41598-022-20615-8
[14]  Chen, E.B., Qin, X., Peng, K., et al. (2019) HnRNPR-CCNB1/CENPF Axis Contributes to Gastric Cancer Proliferation and Metastasis. Aging, 11, 7473-7491.
https://doi.org/10.18632/aging.102254
[15]  Simpson, E. and Brown, H.L. (2018) Understanding Osteosarcomas. Journal of the American Academy of Physician Assistants, 31, 15-19.
https://doi.org/10.1097/01.JAA.0000541477.24116.8d
[16]  Yang, C., Tian, Y., Zhao, F., et al. (2020) Bone Microenvironment and Osteosarcoma Metastasis. International Journal of Molecular Sciences, 21, Article 6985.
https://doi.org/10.3390/ijms21196985
[17]  Nomura, M., Rainusso, N., Lee, YC., et al. (2019) Tegavivint and the β-Catenin/ALDH Axis in Chemotherapy-Resistant and Metastatic Osteosarcoma. JNCI: Journal of the National Cancer Institute, 111, 1216-1227.
https://doi.org/10.1093/jnci/djz026
[18]  Krishnan, K., Khanna, C. and Helman, L.J. (2005) The Biology of Metastases in Pediatric Sarcomas. The Cancer Journal, 11, 306-313.
https://doi.org/10.1097/00130404-200507000-00006
[19]  Malumbres, M. and Barbacid, M. (2009) Cell Cycle, CDKs and Cancer: A Changing Paradigm. Nature Reviews Cancer, 9, 153-166.
https://doi.org/10.1038/nrc2602
[20]  Vishnoi, N. and Yao, J. (2017) Single-Cell, Single-MRNA Analysis of Ccnb1 Promoter Regulation. Scientific Reports, 7, Article No. 2065.
https://doi.org/10.1038/s41598-017-02240-y
[21]  Liu, D., Xu, W., Ding, X., et al. (2017) Polymorphisms of CCNB1 Associated with the Clinical Outcomes of Platinum-Based Chemotherapy in Chinese NSCLC Patients. Journal of Cancer, 8, 3785-3794.
https://doi.org/10.7150/jca.21151
[22]  Brcic, L., Heidinger, M., Sever, AZ., et al. (2019) Prognostic Value of Cyclin A2 and B1 Expression in Lung Carcinoids. Pathology, 51, 481-486.
https://doi.org/10.1016/j.pathol.2019.03.011
[23]  Zhan, Q., Antinore, M.J., Wang, X.W., et al. (1999) Association With Cdc2 and Inhibition of Cdc2/Cyclin B1 Kinase Activity by the P53-Regulated Protein Gadd45. Oncogene, 18, 2892-2900.
https://doi.org/10.1038/sj.onc.1202667
[24]  Chai, N., Xie, H.H., Yin, J.P., et al. (2018) FOXM1 Promotes Proliferation in Human Hepatocellular Carcinoma Cells by Transcriptional Activation of CCNB1. Biochemical and Biophysical Research Communications, 500, 924-929.
https://doi.org/10.1016/j.bbrc.2018.04.201
[25]  Hoffmann, T.K., Trellakis, S., Okulicz, K., et al. (2011) Cyclin B1 Expression and P53 Status in Squamous Cell Carcinomas of the Head and Neck. Anticancer Research, 31, 3151-3157.
[26]  Zhou, L., Li, J., Zhao, YP., et al. (2014) The Prognostic Value of Cyclin B1 in Pancreatic Cancer. Medical Oncology, 31, Article No. 107.
https://doi.org/10.1007/s12032-014-0107-4
[27]  Wang, X.X., Wu, H.Y., Yang, Y., et al. (2023) CCNB1 Is Involved in Bladder Cancer Pathogenesis and Silencing CCNB1 Decelerates Tumor Growth and Improves Prognosis of Bladder Cancer. Experimental and Therapeutic Medicine, 26, Article No. 382.
https://doi.org/10.3892/etm.2023.12081
[28]  Zhu, C., Bossy-Wetzel, E. and Jiang, W. (2005) Recruitment of MKLP1 to the Spindle Midzone/Midbody by INCENP Is Essential for Midbody Formation and Completion of Cytokinesis in Human Cells. Biochemical Journal, 389, 373-381.
https://doi.org/10.1042/BJ20050097
[29]  Rath, O. and Kozielski, F. (2012) Kinesins and Cancer. Nature Reviews Cancer, 12, 527-539.
https://doi.org/10.1038/nrc3310
[30]  Neef, R., Klein, U.R., Kopajtich, R., et al. (2006) Cooperation Between Mitotic Kinesins Controls the Late Stages of Cytokinesis. Current Biology, 16, 301-307.
https://doi.org/10.1016/j.cub.2005.12.030
[31]  Fischer, M., Grundke, I., Sohr, S., et al. (2013) P53 and Cell Cycle Dependent Transcription of Kinesin Family Member 23 (KIF23) Is Controlled via a CHR Promoter Element Bound by DREAM and MMB Complexes. PLOS ONE, 8, e63187.
https://doi.org/10.1371/journal.pone.0063187
[32]  Iltzsche, F., Simon, K., Stopp, S., et al. (2017) An Important Role for Myb-MuvB and Its Target Gene KIF23 in a Mouse Model of Lung Adenocarcinoma. Oncogene, 36, 110-121.
https://doi.org/10.1038/onc.2016.181
[33]  Zou, J.X., Duan, Z., Wang, J., et al. (2014) Kinesin Family Deregulation Coordinated by Bromodomain Protein ANCCA and Histone Methyltransferase MLL for Breast Cancer Cell Growth, Survival, and Tamoxifen Resistance. Molecular Cancer Research, 12, 539-549.
https://doi.org/10.1158/1541-7786.MCR-13-0459
[34]  Murakami, H., Ito, S., Tanaka, H., et al. (2013) Establishment of New Intraperitoneal Paclitaxel-Resistant Gastric Cancer Cell Lines and Comprehensive Gene Expression Analysis. Anticancer Research, 33, 4299-4307.
[35]  Vikberg, A.L., Vooder, T., Lokk, K., et al. (2017) Mutation Analysis and Copy Number Alterations of KIF23 in Non-Small-Cell Lung Cancer Exhibiting KIF23 Over-Expression. OncoTargets and Therapy, 10, 4969-4979.
https://doi.org/10.2147/OTT.S138420
[36]  Kato, T., Wada, H., Patel, P., et al. (2016) Overexpression of KIF23 Predicts Clinical Outcome in Primary Lung Cancer Patients. Lung Cancer, 92, 53-61.
https://doi.org/10.1016/j.lungcan.2015.11.018
[37]  Hu, Y., Zheng, M., Wang, C., et al. (2020) Identification of KIF23 as a Prognostic Signature for Ovarian Cancer Based on Large-Scale Sampling and Clinical Validation. American Journal of Translational Research, 12, 4955-4976.
https://doi.org/10.21203/rs.2.23036/v1
[38]  Bai, X., Cao, Y., Yan, X., et al. (2021) Systematic Pan-Cancer Analysis of KIF23 and a Prediction Model Based on KIF23 in Clear Cell Renal Cell Carcinoma (CcRCC). Pharmacogenomics and Personalized Medicine, 14, 1717-1729.
https://doi.org/10.2147/PGPM.S337695

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