Exploring the role of CCNB1, CCNB2 and CDK1 in lung adenocarcinoma based on bioinformatics data_West China Medical Journal

Authors：

LU Sifen ¹ , WEI Xiaozhen ² , YANG Shengying ³ , YANG Hao ⁴ , CHEN Bojiang ^1,5 ,  LI Weimin ^1,5

1. Precision Medicine Key Laboratory of Sichuan Province and Precision Medicine Center, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, P. R. China;
2. Department of Anesthesiology, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, P. R. China;
3. Department of Computer & Software, Jincheng College of Chengdu, Chengdu, Sichuan 611700, P. R. China;
4. Transplantation Immunology Laboratory, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, P. R. China;
5. Department of Pulmonary and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, P. R. China;

Corresponding author：

LI Weimin, Email: weimi003@scu.edu.cn

Keywords：

Lung adenocarcinoma; differentially expressed gene; hub gene; prognostic biomarker; therapeutic target

DOI：

10.7507/1002-0179.202210220

Video：

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Objective To explore the role of cyclin B1 (CCNB1), cyclin B2 (CCNB2) and cyclin dependent kinase 1 (CDK1) in lung adenocarcinoma (LUAD) using bioinformatic data. Methods First, RNA expression data were downloaded from two datasets in Gene Expression Omnibus (GEO), and DESeq2 software was used to identify deferentially expressed genes (DEGs). Subsequent analyses were conducted based on the results of these DEGs: protein-protein interaction (PPI) network was constructed with STRING database; the modules in PPI network were analyzed by Molecular Complex Detection software, and the most significant modules were selected, the genes included in these modules were the hub genes; high-throughput RNA sequencing data from other databases were used to verify the expression of these hub genes to confirm whether they were DEGs; survival curve analyses of the confirmed DEGs were conducted to select genes that had significant influence on the survival of LUAD; the expression of these hub genes in different stages of LUAD were also analyzed. Then, Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis was performed for these selected hub genes using KOBAS database. MuTarget tool was used to analyze the correlations between the expression of these selected hub genes and gene mutation status in LUAD. The potential value of these hub genes in the treatment of LUAD was explored based on the drug information in GDSC database. Finally, immunohistochemical data from Human Protein Atlas (HPA) database were used to verify the expression of these hub genes in LUAD again. Results According to the expression data in GEO, 594 up-regulated genes and 651 down-regulated genes were identified (P<0.05), among which 30 hub genes were selected for subsequent analyses. The RNA high-throughput sequencing data of other databases verified that 18 genes were DEGs, among which 8 hub genes had significant impact on disease-free survival in LUAD (P<0.05). Moreover, the 8 genes were differentially expressed in different stages of LUAD, which were higher in the middle and late stage of LUAD. Among the 8 genes. CCNB1, CCNB2 and CDK1 were significantly enriched in the cell cycle pathway. The expression of CCNB1, CCNB2 and CDK1 in LUAD was closely related to the TP53 mutation status. In addition, CDK1 was associated with four drugs, revealing the potential value of CDK1 in the treatment of LUAD. Finally, immunohistochemical data from HPA database verified that CCNB1, CCNB2 and CDK1 were highly expressed in LUAD in the protein level. Conclusion Overexpression of CCNB1, CCNB2 and CDK1 are associated with poor prognosis of LUAD, indicating that the three genes may be prognostic biomarkers of LUAD and CDK1 is a potential therapeutic target for LUAD.

Citation： LU Sifen, WEI Xiaozhen, YANG Shengying, YANG Hao, CHEN Bojiang, LI Weimin. Exploring the role of CCNB1, CCNB2 and CDK1 in lung adenocarcinoma based on bioinformatics data. West China Medical Journal, 2023, 38(1): 18-27. doi: 10.7507/1002-0179.202210220 Copy

1.	Chansky K, Sculier JP, Crowley JJ, et al. The International Association for the Study of Lung Cancer Staging Project: prognostic factors and pathologic TNM stage in surgically managed non-small cell lung cancer. J Thorac Oncol, 2009, 4(7): 792-801.
2.	Shukla S, Evans JR, Malik R, et al. Development of a RNA-Seq based prognostic signature in lung adenocarcinoma. J Natl Cancer Inst, 2016, 109(1): djw200.
3.	Ettinger DS, Wood DE, Akerley W, et al. Non-small cell lung cancer, version 6. 2015. J Natl Compr Canc Netw, 2015, 13(5): 515-524.
4.	Song E, Song W, Ren M, et al. Identification of potential crucial genes associated with carcinogenesis of clear cell renal cell carcinoma. J Cell Biochem, 2018, 119(7): 5163-5174.
5.	余海浪, 马文丽, 郑文岭. 用于基因数据挖掘的基因表达数据库 GEO. 中国生物工程杂志, 2007, 27(8): 96-103.
6.	Barrett T, Wilhite SE, Ledoux P, et al. NCBI GEO: archive for functional genomics data sets--update. Nucleic Acids Res, 2013, 41(Database issue): D991-D995.
7.	Xu JY, Zhang C, Wang X, et al. Integrative proteomic characterization of human lung adenocarcinoma. Cell, 2020, 182(1): 245-261.
8.	Yu N, Yong S, Kim HK, et al. Identification of tumor suppressor miRNAs by integrative miRNA and mRNA sequencing of matched tumor-normal samples in lung adenocarcinoma. Mol Oncol, 2019, 13(6): 1356-1368.
9.	Shabalin AA, Tjelmeland H, Fan C, et al. Merging two gene-expression studies via cross-platform normalization. Bioinformatics, 2008, 24(9): 1154-1160.
10.	Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol, 2014, 15(12): 550.
11.	Fang XN, Yin M, Li H, et al. Comprehensive analysis of competitive endogenous RNAs network associated with head and neck squamous cell carcinoma. Sci Rep, 2018, 8(1): 10544.
12.	Szklarczyk D, Gable AL, Lyon D, et al. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res, 2019, 47(D1): D607-D613.
13.	Shannon P, Markiel A, Ozier O, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res, 2003, 13(11): 2498-2504.
14.	Bader GD, Hogue CW. An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics, 2003, 4(1): 2.
15.	Tang Z, Li C, Kang B, et al. GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses. Nucleic Acids Res, 2017, 45(W1): W98-W102.
16.	Xie C, Mao X, Huang J, et al. KOBAS 2.0: a web server for annotation and identification of enriched pathways and diseases. Nucleic Acids Res, 2011, 39(Web Server issue): W316-W322.
17.	Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Series B Stat Methodol, 1995, 57(1): 289-300.
18.	Nagy Á, Győrffy B. muTarget: a platform linking gene expression changes and mutation status in solid tumors. Int J Cancer, 2021, 148(2): 502-511.
19.	Yang W, Soares J, Greninger P, et al. Genomics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells. Nucleic Acids Res, 2013, 41(Database issue): D955-D961.
20.	Thul PJ, Lindskog C. The Human Protein Atlas: a spatial map of the human proteome. Protein Sci, 2018, 27(1): 233-244.
21.	Qin K, Hou H, Liang Y, et al. Prognostic value of TP53 concurrent mutations for EGFR-TKIs and ALK-TKIs based targeted therapy in advanced non-small cell lung cancer: a meta-analysis. BMC Cancer, 2020, 20(1): 328.
22.	Zheng C, Li X, Ren Y, et al. Coexisting EGFR and TP53 mutations in lung adenocarcinoma patients are associated with COMP and ITGB8 upregulation and poor prognosis. Front Mol Biosci, 2020, 7: 30.
23.	Ahrendt SA, Hu Y, Buta M, et al. p53 mutations and survival in stage I non-small-cell lung cancer: results of a prospective study. J Natl Cancer Inst, 2003, 95(13): 961-970.
24.	Hofmann HS, Hansen G, Burdach S, et al. Discrimination of human lung neoplasm from normal lung by two target genes. Am J Respir Crit Care Med, 2004, 170(5): 516-519.
25.	Stav D, Bar I, Sandbank J. Usefulness of CDK5RAP3, CCNB2, and RAGE genes for the diagnosis of lung adenocarcinoma. Int J Biol Markers, 2007, 22(2): 108-113.
26.	Park SH, Yu GR, Kim WH, et al. NF-Y-dependent cyclin B2 expression in colorectal adenocarcinoma. Clin Cancer Res, 2007, 13(3): 858-867.
27.	De Martino I, Visone R, Wierinckx A, et al. HMGA proteins up-regulate CCNB2 gene in mouse and human pituitary adenomas. Cancer Res, 2009, 69(5): 1844-1850.
28.	Mo ML, Chen Z, Li J, et al. Use of serum circulating CCNB2 in cancer surveillance. Int J Biol Markers, 2010, 25(4): 236-242.
29.	Liu L, Chen A, Chen S, et al. CCNB2, NUSAP1 and TK1 are associated with the prognosis and progression of hepatocellular carcinoma, as revealed by co-expression analysis. Exp Ther Med, 2020, 19(4): 2679-2689.
30.	Qian X, Song X, He Y, et al. CCNB2 overexpression is a poor prognostic biomarker in Chinese NSCLC patients. Biomed Pharmacother, 2015, 74: 222-227.
31.	Mashal RD, Lester S, Corless C, et al. Expression of cell cycle-regulated proteins in prostate cancer. Cancer Res, 1996, 56(18): 4159-4163.
32.	Jin S, Antinore MJ, Lung FD, et al. The GADD45 inhibition of Cdc2 kinase correlates with GADD45-mediated growth suppression. J Biol Chem, 2000, 275(22): 16602-16608.
33.	Yang Q, Manicone A, Coursen JD, et al. Identification of a functional domain in a GADD45-mediated G₂/M checkpoint. J Biol Chem, 2000, 275(47): 36892-36898.
34.	Chen EX, Hotte S, Hirte H, et al. A Phase I study of cyclin-dependent kinase inhibitor, AT7519, in patients with advanced cancer: NCIC Clinical Trials Group IND 177. Br J Cancer, 2014, 111(12): 2262-2267.
35.	Wildey G, Chen Y, Lent I, et al. Pharmacogenomic approach to identify drug sensitivity in small-cell lung cancer. PLoS One, 2014, 9(9): e106784.
36.	Nemunaitis JJ, Small KA, Kirschmeier P, et al. A first-in-human, phase 1, dose-escalation study of dinaciclib, a novel cyclin-dependent kinase inhibitor, administered weekly in subjects with advanced malignancies. J Transl Med, 2013, 11: 259.
37.	Prevo R, Pirovano G, Puliyadi R, et al. CDK1 inhibition sensitizes normal cells to DNA damage in a cell cycle dependent manner. Cell Cycle, 2018, 17(12): 1513-1523.

1. Chansky K, Sculier JP, Crowley JJ, et al. The International Association for the Study of Lung Cancer Staging Project: prognostic factors and pathologic TNM stage in surgically managed non-small cell lung cancer. J Thorac Oncol, 2009, 4(7): 792-801.
2. Shukla S, Evans JR, Malik R, et al. Development of a RNA-Seq based prognostic signature in lung adenocarcinoma. J Natl Cancer Inst, 2016, 109(1): djw200.
3. Ettinger DS, Wood DE, Akerley W, et al. Non-small cell lung cancer, version 6. 2015. J Natl Compr Canc Netw, 2015, 13(5): 515-524.
4. Song E, Song W, Ren M, et al. Identification of potential crucial genes associated with carcinogenesis of clear cell renal cell carcinoma. J Cell Biochem, 2018, 119(7): 5163-5174.
5. 余海浪, 马文丽, 郑文岭. 用于基因数据挖掘的基因表达数据库 GEO. 中国生物工程杂志, 2007, 27(8): 96-103.
6. Barrett T, Wilhite SE, Ledoux P, et al. NCBI GEO: archive for functional genomics data sets--update. Nucleic Acids Res, 2013, 41(Database issue): D991-D995.
7. Xu JY, Zhang C, Wang X, et al. Integrative proteomic characterization of human lung adenocarcinoma. Cell, 2020, 182(1): 245-261.
8. Yu N, Yong S, Kim HK, et al. Identification of tumor suppressor miRNAs by integrative miRNA and mRNA sequencing of matched tumor-normal samples in lung adenocarcinoma. Mol Oncol, 2019, 13(6): 1356-1368.
9. Shabalin AA, Tjelmeland H, Fan C, et al. Merging two gene-expression studies via cross-platform normalization. Bioinformatics, 2008, 24(9): 1154-1160.
10. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol, 2014, 15(12): 550.
11. Fang XN, Yin M, Li H, et al. Comprehensive analysis of competitive endogenous RNAs network associated with head and neck squamous cell carcinoma. Sci Rep, 2018, 8(1): 10544.
12. Szklarczyk D, Gable AL, Lyon D, et al. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res, 2019, 47(D1): D607-D613.
13. Shannon P, Markiel A, Ozier O, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res, 2003, 13(11): 2498-2504.
14. Bader GD, Hogue CW. An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics, 2003, 4(1): 2.
15. Tang Z, Li C, Kang B, et al. GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses. Nucleic Acids Res, 2017, 45(W1): W98-W102.
16. Xie C, Mao X, Huang J, et al. KOBAS 2.0: a web server for annotation and identification of enriched pathways and diseases. Nucleic Acids Res, 2011, 39(Web Server issue): W316-W322.
17. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Series B Stat Methodol, 1995, 57(1): 289-300.
18. Nagy Á, Győrffy B. muTarget: a platform linking gene expression changes and mutation status in solid tumors. Int J Cancer, 2021, 148(2): 502-511.
19. Yang W, Soares J, Greninger P, et al. Genomics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells. Nucleic Acids Res, 2013, 41(Database issue): D955-D961.
20. Thul PJ, Lindskog C. The Human Protein Atlas: a spatial map of the human proteome. Protein Sci, 2018, 27(1): 233-244.
21. Qin K, Hou H, Liang Y, et al. Prognostic value of TP53 concurrent mutations for EGFR-TKIs and ALK-TKIs based targeted therapy in advanced non-small cell lung cancer: a meta-analysis. BMC Cancer, 2020, 20(1): 328.
22. Zheng C, Li X, Ren Y, et al. Coexisting EGFR and TP53 mutations in lung adenocarcinoma patients are associated with COMP and ITGB8 upregulation and poor prognosis. Front Mol Biosci, 2020, 7: 30.
23. Ahrendt SA, Hu Y, Buta M, et al. p53 mutations and survival in stage I non-small-cell lung cancer: results of a prospective study. J Natl Cancer Inst, 2003, 95(13): 961-970.
24. Hofmann HS, Hansen G, Burdach S, et al. Discrimination of human lung neoplasm from normal lung by two target genes. Am J Respir Crit Care Med, 2004, 170(5): 516-519.
25. Stav D, Bar I, Sandbank J. Usefulness of CDK5RAP3, CCNB2, and RAGE genes for the diagnosis of lung adenocarcinoma. Int J Biol Markers, 2007, 22(2): 108-113.
26. Park SH, Yu GR, Kim WH, et al. NF-Y-dependent cyclin B2 expression in colorectal adenocarcinoma. Clin Cancer Res, 2007, 13(3): 858-867.
27. De Martino I, Visone R, Wierinckx A, et al. HMGA proteins up-regulate CCNB2 gene in mouse and human pituitary adenomas. Cancer Res, 2009, 69(5): 1844-1850.
28. Mo ML, Chen Z, Li J, et al. Use of serum circulating CCNB2 in cancer surveillance. Int J Biol Markers, 2010, 25(4): 236-242.
29. Liu L, Chen A, Chen S, et al. CCNB2, NUSAP1 and TK1 are associated with the prognosis and progression of hepatocellular carcinoma, as revealed by co-expression analysis. Exp Ther Med, 2020, 19(4): 2679-2689.
30. Qian X, Song X, He Y, et al. CCNB2 overexpression is a poor prognostic biomarker in Chinese NSCLC patients. Biomed Pharmacother, 2015, 74: 222-227.
31. Mashal RD, Lester S, Corless C, et al. Expression of cell cycle-regulated proteins in prostate cancer. Cancer Res, 1996, 56(18): 4159-4163.
32. Jin S, Antinore MJ, Lung FD, et al. The GADD45 inhibition of Cdc2 kinase correlates with GADD45-mediated growth suppression. J Biol Chem, 2000, 275(22): 16602-16608.
33. Yang Q, Manicone A, Coursen JD, et al. Identification of a functional domain in a GADD45-mediated G₂/M checkpoint. J Biol Chem, 2000, 275(47): 36892-36898.
34. Chen EX, Hotte S, Hirte H, et al. A Phase I study of cyclin-dependent kinase inhibitor, AT7519, in patients with advanced cancer: NCIC Clinical Trials Group IND 177. Br J Cancer, 2014, 111(12): 2262-2267.
35. Wildey G, Chen Y, Lent I, et al. Pharmacogenomic approach to identify drug sensitivity in small-cell lung cancer. PLoS One, 2014, 9(9): e106784.
36. Nemunaitis JJ, Small KA, Kirschmeier P, et al. A first-in-human, phase 1, dose-escalation study of dinaciclib, a novel cyclin-dependent kinase inhibitor, administered weekly in subjects with advanced malignancies. J Transl Med, 2013, 11: 259.
37. Prevo R, Pirovano G, Puliyadi R, et al. CDK1 inhibition sensitizes normal cells to DNA damage in a cell cycle dependent manner. Cell Cycle, 2018, 17(12): 1513-1523.

West China Medical Journal

Exploring the role of CCNB1, CCNB2 and CDK1 in lung adenocarcinoma based on bioinformatics data

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