Bioinformatics analysis of CA3 expression in breast cancer tissues and its impact on prognosis_CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY

Authors：

ZHANG Jiangtao , RUAN Huichao ,  WU Xianghua

Department of Gastrointestinal Glandular Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning 530021, P. R. China;

Corresponding author：

WU Xianghua, Email: wuzhuone@163.com

Keywords：

breast cancer; carbonic anhydrase 3; prognosis; immune infiltration; bioinformatics

DOI：

10.7507/1007-9424.202104102

Video：

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Objective To analyze the relationship between the expression of carbonic anhydrase 3 (CA3) in breast cancer tissues, its prognostic potential and the number of immune cells by a variety of online databases. Methods GEPIA2.0 and TIMER databases were used to analyze the difference of CA3 mRNA expression in breast cancer tissues. Bc-GenExMinerv4.7 database was used to analyze the difference of CA3 mRNA expression in breast cancer subcategories. Kaplan-Meier plotter, Bc-GenExMinerv4.7 and PrognoScan databases were used to analyze the effect of CA3 mRNA expression levels on prognosis of patient. LinkedOmics database was used to analyze of the biological behavior involved in CA3 co-expressed genes. TIMER database was used to analyze the relationship between CA3 mRNA expression and immune cells infiltration in breast cancer tissues. Results The expression of CA3 mRNA in breast cancer tissues was lower than that in normal breast tissues (P<0.05), and the expression levels of CA3 mRNA were higher in ER negative (P<0.05), PR negative (P<0.05), HER2 negative (P<0.05) and no lymphatic metastasis (P<0.05). In addition, the expression level of CA3 in breast cancer patients with high Ki67 expression was lower (P<0.05) and closely related to SBR and NPI grade (P<0.05). Breast cancer patients with low expression of CA3 mRNA had lower overall survivall, recurrence free survival, and disease free survival ( P<0.05). Ten of the top 50 positively correlated co-expressed genes screened out had low risk ratio (P<0.05), and 11 of the top 50 negatively correlated co-expressed genes screened out had high risk ratio (P<0.05). The expression of CA3 mRNA was positively correlated with CD4⁺ T cells and CD8⁺ T cells in breast cancer tissues (r_s=0.175, P<0.001; r_s=0.137, P<0.001), and negatively correlated with T cell failure markers LAG3, TIM-3 and PVRL2 (r_s=–0.100, P<0.01; r_s=–0.143, P<0.001; r_s=–0.082, P<0.05). Conclusions The low expression of CA3 mRNA in breast cancer tissues is correlated with the occurrence, development and prognosis of breast cancer. CA3 can be used as a potential independent prognostic marker for breast cancer and may be related to immune infiltration.

Citation： ZHANG Jiangtao, RUAN Huichao, WU Xianghua. Bioinformatics analysis of CA3 expression in breast cancer tissues and its impact on prognosis. CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY, 2022, 29(1): 51-58. doi: 10.7507/1007-9424.202104102 Copy

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12.	Györffy B, Lanczky A, Eklund AC, et al. An online survival analysis tool to rapidly assess the effect of 22 277 genes on breast cancer prognosis using microarray data of 1 809 patients. Breast Cancer Res Treat, 2010, 123(3): 725-731.
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14.	Mizuno H, Kitada K, Nakai K, et al. PrognoScan: a new database for meta-analysis of the prognostic value of genes. BMC Med Genomics, 2009, 2: 18.
15.	Vasaikar SV, Straub P, Wang J, et al. LinkedOmics: analyzing multi-omics data within and across 32 cancer types. Nucleic Acids Res, 2018, 46(D1): D956-D963.
16.	Oulas A, Minadakis G, Zachariou M, et al. Systems bioinformatics: increasing precision of computational diagnostics and therapeutics through network-based approaches. Brief Bioinform, 2019, 20(3): 806-824.
17.	Penault-Llorca F, Radosevic-Robin N. Ki67 assessment in breast cancer: an update. Pathology, 2017, 49(2): 166-171.
18.	Yen MC, Huang YC, Kan JY, et al. S100B expression in breast cancer as a predictive marker for cancer metastasis. Int J Oncol, 2018, 52(2): 433-440.
19.	Yogosawa S, Nakayama J, Nishi M, et al. Carbonic anhydrase 13 suppresses bone metastasis in breast cancer. Cancer Treat Res Commun, 2021, 27: 100332.
20.	Beniaminov AD, Puzanov GA, Krasnov GS, et al. Deep sequencing revealed a CpG methylation pattern associated with ALDH1L1 suppression in breast cancer. Front Genet, 2018, 9: 169.
21.	Nunziata C, Polo A, Sorice A, et al. Structural analysis of human SEPHS2 protein, a selenocysteine machinery component, over-expressed in triple negative breast cancer. Sci Rep, 2019, 9(1): 16131.
22.	Synnott NC, Madden SF, Bykov VJN, et al. The mutant p53-targeting compound APR-246 induces ROS-modulating genes in breast cancer cells. Transl Oncol, 2018, 11(6): 1343-1349.
23.	Liu H, Ma Y, He HW, et al. SLC9A3R1 stimulates autophagy via BECN1 stabilization in breast cancer cells. Autophagy, 2015, 11(12): 2323-2334.
24.	Lei X, Lei Y, Li JK, et al. Immune cells within the tumor microenvironment: Biological functions and roles in cancer immunotherapy. Cancer Lett, 2020, 470: 126-133.
25.	Basu A, Ramamoorthi G, Jia Y, et al. Immunotherapy in breast cancer: Current status and future directions. Adv Cancer Res, 2019, 143: 295-349.
26.	Porcu M, De Silva P, Solinas C, et al. Immunotherapy associated pulmonary toxicity: biology behind clinical and radiological features. Cancers (Basel), 2019, 11(3): 305.
27.	Birsoy K, Possemato R, Lorbeer FK, et al. Metabolic determinants of cancer cell sensitivity to glucose limitation and biguanides. Nature, 2014, 508(7494): 108-112.
28.	Sica V, Bravo-San Pedro JM, Stoll G, et al. Oxidative phosphorylation as a potential therapeutic target for cancer therapy. Int J Cancer, 2020, 146(1): 10-17.
29.	Jiang S. Mitochondrial oxidative phosphorylation is linked to T-cell exhaustion. Aging (Albany NY), 2020, 12(17): 16665-16666.
30.	Song S, Xie M, Scott AW, et al. A Novel YAP1 inhibitor targets CSC-enriched radiation-resistant cells and exerts strong antitumor activity in esophageal adenocarcinoma. Mol Cancer Ther, 2018, 17(2): 443-454.
31.	邓明星. YAP1 抑制剂 CA3 对非小细胞型肺癌和淋巴细胞的影响. 锦州: 锦州医科大学, 2020.
32.	马芳. JSI-124 与 YAP1 抑制剂 CA3 联合用药对结肠癌细胞系和淋巴细胞的影响. 锦州: 锦州医科大学, 2020.

1. Sung H, Ferlay J, Siegel RL, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin, 2021, 71(3): 209-249.
2. Borniger JC. Central regulation of breast cancer growth and metastasis. J Cancer Metastasis Treat, 2019, 5: 23.
3. Redig AJ, McAllister SS. Breast cancer as a systemic disease: a view of metastasis. J Intern Med, 2013, 274(2): 113-126.
4. Mboge MY, Mahon BP, McKenna R, et al. Carbonic anhydrases: role in pH control and cancer. Metabolites, 2018, 8(1): 19.
5. Alterio V, Monti SM, De Simone G. Thermal-stable carbonic anhydrases: a structural overview. Subcell Biochem, 2014, 75: 387-404.
6. 梁嘉恩, 汪艳. Yes 相关蛋白参与肝脏疾病作用机制的研究进展. 中华肝脏病杂志, 2019, 27(7): 572-576.
7. Wang C, Davis JS. At the center of cervical carcinogenesis: synergism between high-risk HPV and the hyperactivated YAP1. Mol Cell Oncol, 2019, 6(5): e1612677.
8. Kandasamy S, Adhikary G, Rorke EA, et al. The YAP1 signaling inhibitors, verteporfin and CA3, suppress the mesothelioma cancer stem cell phenotype. Mol Cancer Res, 2020, 18(3): 343-351.
9. Pereira CM, de Carvalho AC, da Silva FR, et al. In vitro and in silico validation of CA3 and FHL1 downregulation in oral cancer. BMC Cancer, 2018, 18(1): 193.
10. 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.
11. Li T, Fan J, Wang B, et al. TIMER: a web server for comprehensive analysis of tumor-infiltrating immune cells. Cancer Res, 2017, 77(21): e108-e110.
12. Györffy B, Lanczky A, Eklund AC, et al. An online survival analysis tool to rapidly assess the effect of 22 277 genes on breast cancer prognosis using microarray data of 1 809 patients. Breast Cancer Res Treat, 2010, 123(3): 725-731.
13. Jézéquel P, Campone M, Gouraud W, et al. bc-GenExMiner: an easy-to-use online platform for gene prognostic analyses in breast cancer. Breast Cancer Res Treat, 2012, 131(3): 765-775.
14. Mizuno H, Kitada K, Nakai K, et al. PrognoScan: a new database for meta-analysis of the prognostic value of genes. BMC Med Genomics, 2009, 2: 18.
15. Vasaikar SV, Straub P, Wang J, et al. LinkedOmics: analyzing multi-omics data within and across 32 cancer types. Nucleic Acids Res, 2018, 46(D1): D956-D963.
16. Oulas A, Minadakis G, Zachariou M, et al. Systems bioinformatics: increasing precision of computational diagnostics and therapeutics through network-based approaches. Brief Bioinform, 2019, 20(3): 806-824.
17. Penault-Llorca F, Radosevic-Robin N. Ki67 assessment in breast cancer: an update. Pathology, 2017, 49(2): 166-171.
18. Yen MC, Huang YC, Kan JY, et al. S100B expression in breast cancer as a predictive marker for cancer metastasis. Int J Oncol, 2018, 52(2): 433-440.
19. Yogosawa S, Nakayama J, Nishi M, et al. Carbonic anhydrase 13 suppresses bone metastasis in breast cancer. Cancer Treat Res Commun, 2021, 27: 100332.
20. Beniaminov AD, Puzanov GA, Krasnov GS, et al. Deep sequencing revealed a CpG methylation pattern associated with ALDH1L1 suppression in breast cancer. Front Genet, 2018, 9: 169.
21. Nunziata C, Polo A, Sorice A, et al. Structural analysis of human SEPHS2 protein, a selenocysteine machinery component, over-expressed in triple negative breast cancer. Sci Rep, 2019, 9(1): 16131.
22. Synnott NC, Madden SF, Bykov VJN, et al. The mutant p53-targeting compound APR-246 induces ROS-modulating genes in breast cancer cells. Transl Oncol, 2018, 11(6): 1343-1349.
23. Liu H, Ma Y, He HW, et al. SLC9A3R1 stimulates autophagy via BECN1 stabilization in breast cancer cells. Autophagy, 2015, 11(12): 2323-2334.
24. Lei X, Lei Y, Li JK, et al. Immune cells within the tumor microenvironment: Biological functions and roles in cancer immunotherapy. Cancer Lett, 2020, 470: 126-133.
25. Basu A, Ramamoorthi G, Jia Y, et al. Immunotherapy in breast cancer: Current status and future directions. Adv Cancer Res, 2019, 143: 295-349.
26. Porcu M, De Silva P, Solinas C, et al. Immunotherapy associated pulmonary toxicity: biology behind clinical and radiological features. Cancers (Basel), 2019, 11(3): 305.
27. Birsoy K, Possemato R, Lorbeer FK, et al. Metabolic determinants of cancer cell sensitivity to glucose limitation and biguanides. Nature, 2014, 508(7494): 108-112.
28. Sica V, Bravo-San Pedro JM, Stoll G, et al. Oxidative phosphorylation as a potential therapeutic target for cancer therapy. Int J Cancer, 2020, 146(1): 10-17.
29. Jiang S. Mitochondrial oxidative phosphorylation is linked to T-cell exhaustion. Aging (Albany NY), 2020, 12(17): 16665-16666.
30. Song S, Xie M, Scott AW, et al. A Novel YAP1 inhibitor targets CSC-enriched radiation-resistant cells and exerts strong antitumor activity in esophageal adenocarcinoma. Mol Cancer Ther, 2018, 17(2): 443-454.
31. 邓明星. YAP1 抑制剂 CA3 对非小细胞型肺癌和淋巴细胞的影响. 锦州: 锦州医科大学, 2020.
32. 马芳. JSI-124 与 YAP1 抑制剂 CA3 联合用药对结肠癌细胞系和淋巴细胞的影响. 锦州: 锦州医科大学, 2020.

CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY

Bioinformatics analysis of CA3 expression in breast cancer tissues and its impact on prognosis

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