Expression of <i>CCNB1</i> in lung adenocarcinoma and its relationship with prognosis: a bioinformatic analysis_West China Medical Journal

Authors：

CHANG Sisi ¹ , GAO Fuyan ¹ , ZHU Yahui ¹ , LU Juanjuan ¹ , YAN Juntao ¹ , MA Chunzheng ² ,  LI Honglin ²

1. Second Clinical Medical College, Henan University of Traditional Chinese Medicine, Zhengzhou, Henan 450000, P. R. China;
2. Henan Provincial Hospital of Traditional Chinese Medicine (The Second Affiliated Hospital of Henan University of Traditional Chinese Medicine), Zhengzhou, Henan 450000, P. R. China;

Corresponding author：

LI Honglin, Email: 46991701@qq.com

Keywords：

Lung adenocarcinoma; CCNB1 gene; prognosis; immune cell infiltration

DOI：

10.7507/1002-0179.202205082

Video：

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Objective To evaluate the correlation between cyclin B1 (CCNB1) gene expression and the prognosis of lung adenocarcinoma. Methods Oncomine, STRING, Human Protein Atlas, The Cancer Genome Atlas and other databases as well as Kaplan-Meier method, Cox regression, receiver operating characteristic (ROC) curve and Spearman correlation analysis were used to verify the effect of CCNB1 on patients with lung adenocarcinoma. Results CCNB1 was highly expressed in lung adenocarcinoma, and the high expression was correlated with T stage (P=0.001), N stage (P<0.001), pathological stage (P<0.001) and gender (P=0.008). Univariate Cox regression analysis showed that the expression of CCNB1, T stage, N stage, M stage and pathological stage were the factors affecting the overall survival rate of patients with lung adenocarcinoma (P<0.05); multivariate Cox regression analysis showed that the expression of CCNB1 and T stage were independent risk factors for overall survival of patients with lung adenocarcinoma (P<0.05). Kaplan-Meier analysis showed that high expression of CCNB1 was associated with shorter overall survival [hazard ratio (HR)=1.60, 95% confidence interval (CI) (1.20, 2.14), P=0.002], disease-specific survival [HR=1.68, 95%CI (1.16, 2.44), P=0.006] and progression-free interval [HR=1.42, 95%CI (1.09, 1.85), P=0.009]. The ROC curve showed that CCNB1 might be a potential diagnostic molecule for lung adenocarcinoma [area under the curve=0.980, 95%CI (0.967, 0.993)]. Spearman correlation analysis showed that CCNB1 expression was positively correlated with the infiltration of T helper cells 2 (r_s=0.805, P<0.001) and T helper cells (r_s=0.103, P=0.017), and negatively correlated with the infiltration of natural killer cells (r_s=−0.195, P<0.001), macrophages (r_s=−0.134, P=0.002), and T cells (r_s=−0.092, P=0.033). Conclusion CCNB1 is highly expressed in lung adenocarcinoma compared with normal tissues, which is related to poor prognosis and may provide a potential therapeutic target for patients with lung adenocarcinoma.

Citation： CHANG Sisi, GAO Fuyan, ZHU Yahui, LU Juanjuan, YAN Juntao, MA Chunzheng, LI Honglin. Expression of CCNB1 in lung adenocarcinoma and its relationship with prognosis: a bioinformatic analysis. West China Medical Journal, 2023, 38(3): 444-453. doi: 10.7507/1002-0179.202205082 Copy

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3.	Jordan EJ, Kim HR, Arcila ME, et al. Prospective comprehensive molecular characterization of lung adenocarcinomas for efficient patient matching to approved and emerging therapies. Cancer Discov, 2017, 7(6): 596-609.
4.	Ruiz-Cordero R, Devine WP. Targeted therapy and checkpoint immunotherapy in lung cancer. Surg Pathol Clin, 2020, 13(1): 17-33.
5.	Wu P, Zheng Y, Wang Y, et al. Development and validation of a robust immune-related prognostic signature in early-stage lung adenocarcinoma. J Transl Med, 2020, 18(1): 380.
6.	Denisenko TV, Budkevich IN, Zhivotovsky B. Cell death-based treatment of lung adenocarcinoma. Cell Death Dis, 2018, 9(2): 117.
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8.	Zou Y, Ruan S, Jin L, et al. CDK1, CCNB1, and CCNB2 are prognostic biomarkers and correlated with immune infiltration in hepatocellular carcinoma. Med Sci Monit, 2020, 26: e925289.
9.	Zhang H, Zhang X, Li X, et al. Effect of CCNB1 silencing on cell cycle, senescence, and apoptosis through the p53 signaling pathway in pancreatic cancer. J Cell Physiol, 2018, 234(1): 619-631.
10.	Hellmann MD, Ciuleanu TE, Pluzanski A, et al. Nivolumab plus ipilimumab in lung cancer with a high tumor mutational burden. N Engl J Med, 2018, 378(22): 2093-2104.
11.	Camidge DR, Doebele RC, Kerr KM. Comparing and contrasting predictive biomarkers for immunotherapy and targeted therapy of NSCLC. Nat Rev Clin Oncol, 2019, 16(6): 341-355.
12.	Lyssiotis CA, Kimmelman AC. Metabolic interactions in the tumor microenvironment. Trends Cell Biol, 2017, 27(11): 863-875.
13.	Sun Y, Zhang Y, Ren S, et al. Low expression of RGL4 is associated with a poor prognosis and immune infiltration in lung adenocarcinoma patients. Int Immunopharmacol, 2020, 83: 106454.
14.	Waniczek D, Lorenc Z, Śnietura M, et al. Tumor-associated macrophages and regulatory T cells infiltration and the clinical outcome in colorectal cancer. Arch Immunol Ther Exp (Warsz), 2017, 65(5): 445-454.
15.	Gajewski TF, Schreiber H, Fu YX. Innate and adaptive immune cells in the tumor microenvironment. Nat Immunol, 2013, 14(10): 1014-1022.
16.	Halama N, Michel S, Kloor M, et al. Localization and density of immune cells in the invasive margin of human colorectal cancer liver metastases are prognostic for response to chemotherapy. Cancer Res, 2011, 71(17): 5670-5677.
17.	Wu Q, Wang L, Wei H, et al. Integration of multiple key molecules in lung adenocarcinoma identifies prognostic and immunotherapeutic relevant gene signatures. Int Immunopharmacol, 2020, 83: 106477.
18.	Feng H, Gu ZY, Li Q, et al. Identification of significant genes with poor prognosis in ovarian cancer via bioinformatical analysis. J Ovarian Res, 2019, 12(1): 35.
19.	Xu S, Liu R, Da Y. Comparison of tumor related signaling pathways with known compounds to determine potential agents for lung adenocarcinoma. Thorac Cancer, 2018, 9(8): 974-988.
20.	Xie B, Wang S, Jiang N, et al. Cyclin B1/CDK1-regulated mitochondrial bioenergetics in cell cycle progression and tumor resistance. Cancer Lett, 2019, 443: 56-66.
21.	Liu R, Fan M, Candas D, et al. CDK1-mediated SIRT3 activation enhances mitochondrial function and tumor radioresistance. Mol Cancer Ther, 2015, 14(9): 2090-2102.
22.	Candas D, Fan M, Nantajit D, et al. CyclinB1/Cdk1 phosphorylates mitochondrial antioxidant MnSOD in cell adaptive response to radiation stress. J Mol Cell Biol, 2013, 5(3): 166-175.

1. Siegel RL, Miller KD, Fuchs HE, et al. Cancer statistics, 2021. CA Cancer J Clin, 2021, 71(1): 7-33.
2. Sher T, Dy GK, Adjei AA. Small cell lung cancer. Mayo Clin Proc, 2008, 83(3): 355-367.
3. Jordan EJ, Kim HR, Arcila ME, et al. Prospective comprehensive molecular characterization of lung adenocarcinomas for efficient patient matching to approved and emerging therapies. Cancer Discov, 2017, 7(6): 596-609.
4. Ruiz-Cordero R, Devine WP. Targeted therapy and checkpoint immunotherapy in lung cancer. Surg Pathol Clin, 2020, 13(1): 17-33.
5. Wu P, Zheng Y, Wang Y, et al. Development and validation of a robust immune-related prognostic signature in early-stage lung adenocarcinoma. J Transl Med, 2020, 18(1): 380.
6. Denisenko TV, Budkevich IN, Zhivotovsky B. Cell death-based treatment of lung adenocarcinoma. Cell Death Dis, 2018, 9(2): 117.
7. Santarpia M, Aguilar A, Chaib I, et al. Non-small-cell lung cancer signaling pathways, metabolism, and PD-1/PD-L1 antibodies. Cancers (Basel), 2020, 12(6): 1475.
8. Zou Y, Ruan S, Jin L, et al. CDK1, CCNB1, and CCNB2 are prognostic biomarkers and correlated with immune infiltration in hepatocellular carcinoma. Med Sci Monit, 2020, 26: e925289.
9. Zhang H, Zhang X, Li X, et al. Effect of CCNB1 silencing on cell cycle, senescence, and apoptosis through the p53 signaling pathway in pancreatic cancer. J Cell Physiol, 2018, 234(1): 619-631.
10. Hellmann MD, Ciuleanu TE, Pluzanski A, et al. Nivolumab plus ipilimumab in lung cancer with a high tumor mutational burden. N Engl J Med, 2018, 378(22): 2093-2104.
11. Camidge DR, Doebele RC, Kerr KM. Comparing and contrasting predictive biomarkers for immunotherapy and targeted therapy of NSCLC. Nat Rev Clin Oncol, 2019, 16(6): 341-355.
12. Lyssiotis CA, Kimmelman AC. Metabolic interactions in the tumor microenvironment. Trends Cell Biol, 2017, 27(11): 863-875.
13. Sun Y, Zhang Y, Ren S, et al. Low expression of RGL4 is associated with a poor prognosis and immune infiltration in lung adenocarcinoma patients. Int Immunopharmacol, 2020, 83: 106454.
14. Waniczek D, Lorenc Z, Śnietura M, et al. Tumor-associated macrophages and regulatory T cells infiltration and the clinical outcome in colorectal cancer. Arch Immunol Ther Exp (Warsz), 2017, 65(5): 445-454.
15. Gajewski TF, Schreiber H, Fu YX. Innate and adaptive immune cells in the tumor microenvironment. Nat Immunol, 2013, 14(10): 1014-1022.
16. Halama N, Michel S, Kloor M, et al. Localization and density of immune cells in the invasive margin of human colorectal cancer liver metastases are prognostic for response to chemotherapy. Cancer Res, 2011, 71(17): 5670-5677.
17. Wu Q, Wang L, Wei H, et al. Integration of multiple key molecules in lung adenocarcinoma identifies prognostic and immunotherapeutic relevant gene signatures. Int Immunopharmacol, 2020, 83: 106477.
18. Feng H, Gu ZY, Li Q, et al. Identification of significant genes with poor prognosis in ovarian cancer via bioinformatical analysis. J Ovarian Res, 2019, 12(1): 35.
19. Xu S, Liu R, Da Y. Comparison of tumor related signaling pathways with known compounds to determine potential agents for lung adenocarcinoma. Thorac Cancer, 2018, 9(8): 974-988.
20. Xie B, Wang S, Jiang N, et al. Cyclin B1/CDK1-regulated mitochondrial bioenergetics in cell cycle progression and tumor resistance. Cancer Lett, 2019, 443: 56-66.
21. Liu R, Fan M, Candas D, et al. CDK1-mediated SIRT3 activation enhances mitochondrial function and tumor radioresistance. Mol Cancer Ther, 2015, 14(9): 2090-2102.
22. Candas D, Fan M, Nantajit D, et al. CyclinB1/Cdk1 phosphorylates mitochondrial antioxidant MnSOD in cell adaptive response to radiation stress. J Mol Cell Biol, 2013, 5(3): 166-175.

West China Medical Journal

Expression of CCNB1 in lung adenocarcinoma and its relationship with prognosis: a bioinformatic analysis

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