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Comprehensive analysis of roles of atrial-fibrillation-related genes in lung adenocarcinoma using bioinformatic methods

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Abstract

Atrial fibrillation (AF) is the most common tachyarrhythmia in the world. Lung cancer is the leading cause of cancer deaths in 93 countries. Previous studies demonstrated that the prevalence of AF was higher in patients with lung cancer. However, research on the associations between AF and lung cancer is still rare. In the present study, we first identified AF-related genes using weighted gene correlation network analysis. We then analyzed the expression profiles, prognosis, immune infiltration, and methylation characteristics of these genes in LUAD patients using bioinformatics analysis. We found several AF-related genes, including CBX3, BUB1, DSC2, P4HA1, and CYP4Z1, which differently expressed between tumor and normal tissues. Survival analysis demonstrated that CYP4Z1 was positively correlated with overall survival in LUAD patients, while CBX3, BUB1, DSC2, and P4HA1 were negatively correlated. Moreover, we found that the methylation level of DSC2 in normal lung tissues was significantly higher than that in tumor tissues, and six methylation sites in the DNA sequences of DSC2 were identified negatively correlated with its expression levels. Immune infiltration analysis suggested that levels of immune cell infiltration were related to gene expression levels in varying degrees. We identified AF-related genes and found these genes were correlated with prognosis, immune infiltration, and methylation levels in lung cancer patients. We also constructed a risk signature based on these genes in LUAD patients. We hoped that the current study could provide a novel insight into roles of AF-related genes in lung cancer patients.

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Data availability

The datasets analyzed during the current study are available in the TCGA (https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga) and GEO (https://www.ncbi.nlm.nih.gov/geo).

References

  1. Andrade J, Khairy P, Dobrev D, Nattel S. The clinical profile and pathophysiology of atrial fibrillation: relationships among clinical features, epidemiology, and mechanisms. Circ Res. 2014;114(9):1453–68.

    Article  CAS  Google Scholar 

  2. January CT, Wann LS, Calkins H, Chen LY, Cigarroa JE, Cleveland JJ, Ellinor PT, Ezekowitz MD, Field ME, Furie KL, et al. 2019 AHA/ACC/HRS focused update of the 2014 AHA/ACC/HRS guideline for the management of patients with atrial fibrillation: a report of the American college of cardiology/american heart association task force on clinical practice guidelines and the heart rhythm society in collaboration with the society of thoracic surgeons. Circulation. 2019;140(2):e125-51.

    Article  Google Scholar 

  3. Hindricks G, Potpara T, Dagres N, Arbelo E, Bax JJ, Blomstrom-Lundqvist C, Boriani G, Castella M, Dan GA, Dilaveris PE, et al. 2020 ESC Guidelines for the diagnosis and management of atrial fibrillation developed in collaboration with the European association for cardio-thoracic surgery (EACTS). Eur Heart J. 2021;42(5):373–498.

    Article  Google Scholar 

  4. Camm AJ, Kirchhof P, Lip GY, Schotten U, Savelieva I, Ernst S, Van Gelder IC, Al-Attar N, Hindricks G, Prendergast B, et al. Guidelines for the management of atrial fibrillation: the task force for the management of atrial fibrillation of the european society of cardiology (ESC). Eur Heart J. 2010;31(19):2369–429.

    Article  Google Scholar 

  5. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71(3):209–49.

    Article  Google Scholar 

  6. Imperatori A, Mariscalco G, Riganti G, Rotolo N, Conti V, Dominioni L. Atrial fibrillation after pulmonary lobectomy for lung cancer affects long-term survival in a prospective single-center study. J Cardiothorac Surg. 2012;7:4.

    Article  Google Scholar 

  7. Onaitis M, D’Amico T, Zhao Y, O’Brien S, Harpole D. Risk factors for atrial fibrillation after lung cancer surgery: analysis of the society of thoracic surgeons general thoracic surgery database. Ann Thorac Surg. 2010;90(2):368–74.

    Article  Google Scholar 

  8. Cardinale D, Martinoni A, Cipolla CM, Civelli M, Lamantia G, Fiorentini C, Mezzetti M. Atrial fibrillation after operation for lung cancer: clinical and prognostic significance. Ann Thorac Surg. 1999;68(5):1827–31.

    Article  CAS  Google Scholar 

  9. O’Neal WT, Lakoski SG, Qureshi W, Judd SE, Howard G, Howard VJ, Cushman M, Soliman EZ. Relation between cancer and atrial fibrillation (from the REasons for Geographic And Racial Differences in Stroke Study). Am J Cardiol. 2015;115(8):1090–4.

    Article  Google Scholar 

  10. Leek JT, Scharpf RB, Bravo HC, Simcha D, Langmead B, Johnson WE, Geman D, Baggerly K, Irizarry RA. Tackling the widespread and critical impact of batch effects in high-throughput data. Nat Rev Genet. 2010;11(10):733–9.

    Article  CAS  Google Scholar 

  11. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43(7):e47.

    Article  Google Scholar 

  12. Zhang B, Horvath S. A general framework for weighted gene co-expression network analysis. Stat Appl Genet Mol Biol. 2005;4:e17.

    Article  Google Scholar 

  13. Tang Z, Li C, Kang B, Gao G, Li C, Zhang Z. GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses. Nucleic Acids Res. 2017;45(W1):W98-102.

    Article  CAS  Google Scholar 

  14. Uhlen M, Fagerberg L, Hallstrom BM, Lindskog C, Oksvold P, Mardinoglu A, Sivertsson A, Kampf C, Sjostedt E, Asplund A, et al. Proteomics. tissue-based map of the human proteome. Science. 2015;347(6220):1260419.

    Article  Google Scholar 

  15. Gao J, Aksoy BA, Dogrusoz U, Dresdner G, Gross B, Sumer SO, Sun Y, Jacobsen A, Sinha R, Larsson E, et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci Signal. 2013;6(269): l1.

    Article  Google Scholar 

  16. Warde-Farley D, Donaldson SL, Comes O, Zuberi K, Badrawi R, Chao P, Franz M, Grouios C, Kazi F, Lopes CT, Maitland A, Mostafavi S, Montojo J, Shao Q, Wright G, Bader GD, Morris Q. The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function. Nucleic Acids Res. 2010;38(Web Server issue):W214-20.

    Article  CAS  Google Scholar 

  17. Kertesz M, Iovino N, Unnerstall U, Gaul U, Segal E. The role of site accessibility in microRNA target recognition. Nat Genet. 2007;39(10):1278–84.

    Article  CAS  Google Scholar 

  18. Miranda KC, Huynh T, Tay Y, Ang YS, Tam WL, Thomson AM, Lim B, Rigoutsos I. A pattern-based method for the identification of MicroRNA binding sites and their corresponding heteroduplexes. Cell. 2006;126(6):1203–17.

    Article  CAS  Google Scholar 

  19. Vejnar CE, Zdobnov EM. MiRmap: comprehensive prediction of microRNA target repression strength. Nucleic Acids Res. 2012;40(22):11673–83.

    Article  CAS  Google Scholar 

  20. Paraskevopoulou MD, Georgakilas G, Kostoulas N, Vlachos IS, Vergoulis T, Reczko M, Filippidis C, Dalamagas T, Hatzigeorgiou AG. DIANA-microT web server v5.0: service integration into miRNA functional analysis workflows. Nucleic Acids Res. 2013;41(Web Server issue):W169-73.

    Article  Google Scholar 

  21. Chen K, Rajewsky N. Natural selection on human microRNA binding sites inferred from SNP data. Nat Genet. 2006;38(12):1452–6.

    Article  CAS  Google Scholar 

  22. Lewis BP, Burge CB, Bartel DP. Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell. 2005;120(1):15–20.

    Article  CAS  Google Scholar 

  23. Li JH, Liu S, Zhou H, Qu LH, Yang JH. starBase v2.0: decoding miRNA-ceRNA, miRNA-ncRNA and protein-RNA interaction networks from large-scale CLIP-Seq data. Nucleic Acids Res. 2014;42(Database issue):D92-7.

    Article  CAS  Google Scholar 

  24. Xiong Y, Wei Y, Gu Y, Zhang S, Lyu J, Zhang B, Chen C, Zhu J, Wang Y, Liu H, Zhang Y. DiseaseMeth version 2.0: a major expansion and update of the human disease methylation database. Nucleic Acids Res. 2017;45(D1):D888-95.

    Article  CAS  Google Scholar 

  25. Chandrashekar DS, Bashel B, Balasubramanya S, Creighton CJ, Ponce-Rodriguez I, Chakravarthi B, Varambally S. UALCAN: a portal for facilitating tumor subgroup gene expression and survival analyses. Neoplasia. 2017;19(8):649–58.

    Article  CAS  Google Scholar 

  26. Modhukur V, Iljasenko T, Metsalu T, Lokk K, Laisk-Podar T, Vilo J. MethSurv: a web tool to perform multivariable survival analysis using DNA methylation data. Epigenomics. 2018;10(3):277–88.

    Article  CAS  Google Scholar 

  27. Koch A, De Meyer T, Jeschke J, Van Criekinge W. MEXPRESS: visualizing expression, DNA methylation and clinical TCGA data. BMC Genomics. 2015;16:636.

    Article  Google Scholar 

  28. Li T, Fan J, Wang B, Traugh N, Chen Q, Liu JS, Li B, Liu XS. TIMER: a web server for comprehensive analysis of tumor-infiltrating immune cells. Cancer Res. 2017;77(21):e108-10.

    Article  CAS  Google Scholar 

  29. Gordon DJ, Resio B, Pellman D. Causes and consequences of aneuploidy in cancer. Nat Rev Genet. 2012;13(3):189–203.

    Article  CAS  Google Scholar 

  30. Wang K, Zhang M, Wang J, Sun P, Luo J, Jin H, Li R, Pan C, Lu L. A systematic analysis identifies key regulators involved in cell proliferation and potential drugs for the treatment of human lung adenocarcinoma. Front Oncol. 2021;11:737152.

    Article  Google Scholar 

  31. Andriani F, Roz E, Caserini R, Conte D, Pastorino U, Sozzi G, Roz L. Inactivation of both FHIT and p53 cooperate in deregulating proliferation-related pathways in lung cancer. J Thorac Oncol. 2012;7(4):631–42.

    Article  CAS  Google Scholar 

  32. Nyati S, Schinske-Sebolt K, Pitchiaya S, Chekhovskiy K, Chator A, Chaudhry N, Dosch J, Van Dort ME, Varambally S, Kumar-Sinha C, Nyati MK, Ray D, Walter NG, Yu H, Ross BD, Rehemtulla A. The kinase activity of the Ser/Thr kinase BUB1 promotes TGF-β signaling. Sci Signal. 2015;8(358):ra1.

    Article  Google Scholar 

  33. Sami E, Bogan D, Molinolo A, Koziol J, ElShamy WM. The molecular underpinning of geminin-overexpressing triple-negative breast cancer cells homing specifically to lungs. Cancer Gene Ther. 2021;29(3):304–25.

    Google Scholar 

  34. Conway K, Edmiston SN, Tse CK, Bryant C, Kuan PF, Hair BY, Parrish EA, May R, Swift-Scanlan T. Racial variation in breast tumor promoter methylation in the carolina breast cancer study. Cancer Epidemiol Biomarkers Prev. 2015;24(6):921–30.

    Article  CAS  Google Scholar 

  35. Lin H, Zhao X, Xia L, Lian J, You J. Clinicopathological and prognostic significance of CBX3 expression in human cancer: a systematic review and meta-analysis. Dis Markers. 2020;12(2020):2412741.

    Google Scholar 

  36. Alam H, Li N, Dhar SS, Wu SJ, Lv J, Chen K, Flores ER, Baseler L, Lee MG. HP1γ promotes lung adenocarcinoma by downregulating the transcription-repressive regulators NCOR2 and ZBTB7A. Cancer Res. 2018;78(14):3834–48.

    Article  CAS  Google Scholar 

  37. Zhang C, Chang L, Yao Y, Chao C, Ge Z, Fan C, Yu H, Wang B, Yang J. Role of the CBX molecular family in lung adenocarcinoma tumorigenesis and immune infiltration. Front Genet. 2021;12:771062.

    Article  CAS  Google Scholar 

  38. Kocher F, Tymoszuk P, Amann A, Sprung S, Salcher S, Daum S, Haybaeck J, Rinnerthaler G, Huemer F, Kauffmann-Guerrero D, Tufman A, Seeber A, Wolf D, Pircher A. Deregulated glutamate to pro-collagen conversion is associated with adverse outcome in lung cancer and may be targeted by renin-angiotensin-aldosterone system (RAS) inhibition. Lung Cancer. 2021;159:84–95.

    Article  CAS  Google Scholar 

  39. Zhao Q, Liu J. P4HA1, a prognostic biomarker that correlates with immune infiltrates in lung adenocarcinoma and pan-cancer. Front Cell Dev Biol. 2021;9:754580.

    Article  Google Scholar 

  40. Zhou H, He Y, Li L, Wu C, Hu G. Overexpression of P4HA1 is correlated with poor survival and immune infiltrates in lung adenocarcinoma. Biomed Res Int. 2020;2020:8024138.

    Article  Google Scholar 

  41. Ning Y, Zheng H, Zhan Y, Liu S, Yang Y, Zang H, Wen Q, Zhang Y, Fan S. Overexpression of P4HA1 associates with poor prognosis and promotes cell proliferation and metastasis of lung adenocarcinoma. J Cancer. 2021;12(22):6685–94.

    Article  Google Scholar 

  42. Yang X, Hutter M, Goh WWB, Bureik M. CYP4Z1—A Human cytochrome P450 enzyme that might hold the key to curing breast cancer. Curr Pharm Des. 2017;23(14):2060–4.

    Article  CAS  Google Scholar 

  43. Zhu L, Zhou D, Guo T, Chen W, Ding Y, Li W, Huang Y, Huang J, Pan X. LncRNA GAS5 inhibits invasion and migration of lung cancer through influencing EMT process. J Cancer. 2021;12(11):3291–8.

    Article  CAS  Google Scholar 

  44. Wang C, Meng X, Zhou Y, Yu J, Li Q, Liao Z, Gu Y, Han J, Linghu S, Jiao Z, et al. Long noncoding RNA CTD-2245E15.3 promotes anabolic enzymes ACC1 and PC to support non-small cell lung cancer growth. Cancer Res. 2021;81(13):3509–24.

    Article  CAS  Google Scholar 

  45. Li W, Huang K, Wen F, Cui G, Guo H, He Z, Zhao S. Intermittent hypoxia-induced downregulation of microRNA-320b promotes lung cancer tumorigenesis by increasing CDT1 via USP37. Mol Ther Nucleic Acids. 2021;24:528–41.

    Article  CAS  Google Scholar 

  46. Salmena L, Poliseno L, Tay Y, Kats L, Pandolfi PP. A ceRNA hypothesis: the rosetta stone of a hidden RNA language? Cell. 2011;146(3):353–8.

    Article  CAS  Google Scholar 

  47. Morishima M, Iwata E, Nakada C, Tsukamoto Y, Takanari H, Miyamoto S, Moriyama M, Ono K. Atrial fibrillation-mediated upregulation of mir-30d regulates myocardial electrical remodeling of the G-protein-gated K(+) channel IK.ACh. Circ J. 2016;80(6):1346–55.

    Article  Google Scholar 

  48. Chen Y, Ren B, Yang J, Wang H, Yang G, Xu R, You L, Zhao Y. The role of histone methylation in the development of digestive cancers: a potential direction for cancer management. Signal Transduct Target Ther. 2020;5(1):143.

    Article  CAS  Google Scholar 

  49. Johnson AM, Boland JM, Wrobel J, Klezcko EK, Weiser-Evans M, Hopp K, Heasley L, Clambey ET, Jordan K, Nemenoff RA, et al. Cancer cell-specific MHCII expression as a determinant of the immune infiltrate organization and function in the non-small cell lung cancer tumor microenvironment. J Thorac Oncol. 2021;31(4):490.

    Google Scholar 

  50. Wang SS, Liu W, Ly D, Xu H, Qu L, Zhang L. Tumor-infiltrating B cells: their role and application in anti-tumor immunity in lung cancer. Cell Mol Immunol. 2019;16(1):6–18.

    Article  CAS  Google Scholar 

  51. Hu Z, Li M, Chen Z, Zhan C, Lin Z, Wang Q. Advances in clinical trials of targeted therapy and immunotherapy of lung cancer in 2018. Transl Lung Cancer Res. 2019;8(6):1091–106.

    Article  CAS  Google Scholar 

  52. Xu F, Huang X, Li Y, Chen Y, Lin L. m(6)A-related lncRNAs are potential biomarkers for predicting prognoses and immune responses in patients with LUAD. Mol Ther Nucleic Acids. 2021;24:780–91.

    Article  CAS  Google Scholar 

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Funding

This work was supported by the General Program of the National Natural Science Foundation of China (No. 81770408).

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  1. Tao Yan, Miao Zhu and Fan Weng have contributed equally to this work.

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  1. Department of Cardiovascular Surgery, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China

    Tao Yan, Miao Zhu, Fan Weng, Shijie Zhu, Chunsheng Wang & Changfa Guo

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Contributions

CW and CG designed this work. TY, MZ, and FW performed bioinformatic analysis and wrote the manuscript. SZ collected data from the databases. All authors have read the final version of this manuscript.

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Correspondence to Chunsheng Wang or Changfa Guo.

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Yan, T., Zhu, M., Weng, F. et al. Comprehensive analysis of roles of atrial-fibrillation-related genes in lung adenocarcinoma using bioinformatic methods. Med Oncol 40, 55 (2023). https://doi.org/10.1007/s12032-022-01912-8

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