Skip to main content
Log in

Circulating Copper and Liver Cancer

  • Published:
Biological Trace Element Research Aims and scope Submit manuscript

Abstract

The association between circulating copper and the risk of liver cancer has been investigated by previous studies, while the findings were inconsistent. Thus, we aimed to evaluate the association between circulating copper and liver cancer by using meta-analysis and Mendelian randomization (MR). For meta-analysis, PubMed and Web of Science were searched to identify eligible studies published before April 4, 2022. Standardized mean difference (SMD) with 95% confidence interval (CI) in circulating copper level between liver cancer patients and controls were pooled. Furthermore, we selected genetic instruments for circulating copper from a genome-wide association study (GWAS) to conduct MR analysis. The summary statistics related to liver cancer were obtained from two large independent cohorts, UKBB and FinnGen, respectively. MR analysis was performed mainly by inverse-variance weighted (IVW) approach, followed by maximum-likelihood method as sensitivity analysis. In meta-analysis of eight studies, circulating copper was found to be higher in liver cancer patients (SMD: 1.65; 95% CI: 0.65 to 2.65) with high heterogeneity (I2 = 96.40%, P = 0.001). However, inconsistent findings were observed among subgroups with high evidence. In MR analysis, genetically predicted circulating copper was not significantly associated with the risk of liver cancer by IVW in UKBB (OR: 1.38; 95% CI: 0.72 to 2.65) and FinnGen (OR: 1.10; 95% CI: 0.69 to 1.73) separately, and the pooled results produced similar results (OR: 1.18, 95% CI: 0.81 to 1.72). Moreover, non-significant finding was confirmed by using maximum-likelihood method. There is no sufficient evidence to demonstrate that high levels of circulating copper increase the risks of liver cancer.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Data Availability

All data described in the manuscript and codes would be available upon request pending.

References

  1. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F (2021) Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 71:209–249. https://doi.org/10.3322/caac.21660

    Article  PubMed  Google Scholar 

  2. Aleksandrova K, Boeing H, Nothlings U, Jenab M, Fedirko V, Kaaks R, Lukanova A, Trichopoulou A, Trichopoulos D, Boffetta P, Trepo E, Westhpal S, Duarte-Salles T, Stepien M, Overvad K, Tjonneland A, Halkjaer J, Boutron-Ruault MC, Dossus L, Racine A, Lagiou P, Bamia C, Benetou V, Agnoli C, Palli D, Panico S, Tumino R, Vineis P, Bueno-de-Mesquita B, Peeters PH, Gram IT, Lund E, Weiderpass E, Quiros JR, Agudo A, Sanchez MJ, Gavrila D, Barricarte A, Dorronsoro M, Ohlsson B, Lindkvist B, Johansson A, Sund M, Khaw KT, Wareham N, Travis RC, Riboli E, Pischon T (2014) Inflammatory and metabolic biomarkers and risk of liver and biliary tract cancer. Hepatol 60:858–871. https://doi.org/10.1002/hep.27016

    Article  CAS  Google Scholar 

  3. Gurusamy K (2007) Trace element concentration in primary liver cancers–a systematic review. Biol Trace Elem Res 118:191–206. https://doi.org/10.1007/s12011-007-0008-x

    Article  CAS  PubMed  Google Scholar 

  4. Thomas MB, Zhu AX (2005) Hepatocellular carcinoma: the need for progress. J Clin Oncol 23:2892–2899. https://doi.org/10.1200/JCO.2005.03.196

    Article  PubMed  Google Scholar 

  5. Tashiro H, Kawamoto T, Okubo T, Koide O (2003) Variation in the distribution of trace elements in hepatoma. Biol Trace Elem Res 95:49–63. https://doi.org/10.1385/BTER:95:1:49

    Article  CAS  PubMed  Google Scholar 

  6. Mandishona E, MacPhail AP, Gordeuk VR, Kedda MA, Paterson AC, Rouault TA, Kew MC (1998) Dietary iron overload as a risk factor for hepatocellular carcinoma in Black Africans. Hepatol 27:1563–1566. https://doi.org/10.1002/hep.510270614

    Article  CAS  Google Scholar 

  7. Chen XB, Wei YH, Chen XK, Zhong J, Zou YB, Nie JY (2019) Manganese levels and hepatocellular carcinoma: a systematic review and meta-analysis based on Asian cohort. Med (Baltimore) 98:e16748. https://doi.org/10.1097/MD.0000000000016748

    Article  CAS  Google Scholar 

  8. Shenkin A (2006) The key role of micronutrients. Clin Nutr 25:1–13. https://doi.org/10.1016/j.clnu.2005.11.006

    Article  CAS  PubMed  Google Scholar 

  9. Maeda T, Shimada M, Harimoto N, Tsujita E, Maehara S, Rikimaru T, Tanaka S, Shirabe K, Maehara Y (2005) Role of tissue trace elements in liver cancers and non-cancerous liver parenchyma. Hepatogastroenterol 52:187–190

    CAS  Google Scholar 

  10. Liaw KY, Lee PH, Wu FC, Tsai JS, Lin-Shiau SY (1997) Zinc, copper, and superoxide dismutase in hepatocellular carcinoma. Am J Gastroenterol 92:2260–2263

    CAS  PubMed  Google Scholar 

  11. Fang AP, Chen PY, Wang XY, Liu ZY, Zhang DM, Luo Y, Liao GC, Long JA, Zhong RH, Zhou ZG, Xu YJ, Xu XJ, Ling WH, Chen MS, Zhang YJ, Zhu HL (2019) Serum copper and zinc levels at diagnosis and hepatocellular carcinoma survival in the Guangdong Liver Cancer Cohort. Int J Cancer 144:2823–2832. https://doi.org/10.1002/ijc.31991

    Article  CAS  PubMed  Google Scholar 

  12. Poo JL, Rosas-Romero R, Montemayor AC, Isoard F, Uribe M (2003) Diagnostic value of the copper/zinc ratio in hepatocellular carcinoma: a case control study. J Gastroenterol 38:45–51. https://doi.org/10.1007/s005350300005

    Article  PubMed  Google Scholar 

  13. Stepien M, Hughes DJ, Hybsier S, Bamia C, Tjonneland A, Overvad K, Affret A, His M, Boutron-Ruault MC, Katzke V, Kuhn T, Aleksandrova K, Trichopoulou A, Lagiou P, Orfanos P, Palli D, Sieri S, Tumino R, Ricceri F, Panico S, Bueno-de-Mesquita HB, Peeters PH, Weiderpass E, Lasheras C, BonetBonet C, Molina-Portillo E, Dorronsoro M, Huerta JM, Barricarte A, Ohlsson B, Sjoberg K, Werner M, Shungin D, Wareham N, Khaw KT, Travis RC, Freisling H, Cross AJ, Schomburg L, Jenab M (2017) Circulating copper and zinc levels and risk of hepatobiliary cancers in Europeans. Br J Cancer 116:688–696. https://doi.org/10.1038/bjc.2017.1

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Lawlor DA, Harbord RM, Sterne JA, Timpson N, Davey Smith G (2008) Mendelian randomization: using genes as instruments for making causal inferences in epidemiology. Stat Med 27:1133–1163. https://doi.org/10.1002/sim.3034

    Article  PubMed  Google Scholar 

  15. Zeng Z, Zhang W, Qian Y, Huang H, Wu DJH, He Z, Ye D, Mao Y, Wen C (2020) Association of telomere length with risk of rheumatoid arthritis: a meta-analysis and Mendelian randomization. Rheumatol (Oxford) 59:940–947. https://doi.org/10.1093/rheumatology/kez524

    Article  CAS  Google Scholar 

  16. Sekula P, Del Greco MF, Pattaro C, Kottgen A (2016) Mendelian randomization as an approach to assess causality using observational data. J Am Soc Nephrol 27:3253–3265. https://doi.org/10.1681/ASN.2016010098

    Article  PubMed  PubMed Central  Google Scholar 

  17. Hartwig FP, Borges MC, Horta BL, Bowden J, Davey Smith G (2017) Inflammatory biomarkers and risk of schizophrenia: a 2-sample Mendelian randomization study. JAMA Psychiat 74:1226–1233. https://doi.org/10.1001/jamapsychiatry.2017.3191

    Article  Google Scholar 

  18. Stang A (2010) Critical evaluation of the Newcastle-Ottawa scale for the assessment of the quality of nonrandomized studies in meta-analyses. Eur J Epidemiol 25:603–605. https://doi.org/10.1007/s10654-010-9491-z

    Article  PubMed  Google Scholar 

  19. Islam MA, Khandker SS, Alam SS, Kotyla P, Hassan R (2019) Vitamin D status in patients with systemic lupus erythematosus (SLE): a systematic review and meta-analysis. Autoimmun Rev 18:102392. https://doi.org/10.1016/j.autrev.2019.102392

    Article  CAS  PubMed  Google Scholar 

  20. Chinn S (2000) A simple method for converting an odds ratio to effect size for use in meta-analysis. Stat Med 19:3127–3131. https://doi.org/10.1002/1097-0258(20001130)19:22%3c3127::aid-sim784%3e3.0.co;2-m

    Article  CAS  PubMed  Google Scholar 

  21. Yue H, Shan L, Bin L (2018) The significance of OLGA and OLGIM staging systems in the risk assessment of gastric cancer: a systematic review and meta-analysis. Gastric Cancer 21:579–587. https://doi.org/10.1007/s10120-018-0812-3

    Article  PubMed  Google Scholar 

  22. Egger M, Davey Smith G, Schneider M, Minder C (1997) Bias in meta-analysis detected by a simple, graphical test. BMJ 315:629–634. https://doi.org/10.1136/bmj.315.7109.629

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Begg CB, Mazumdar M (1994) Operating characteristics of a rank correlation test for publication bias. Biometrics 50:1088–1101

    Article  CAS  PubMed  Google Scholar 

  24. Si S, Li J, Tewara MA, Xue F (2021) Genetically determined chronic low-grade inflammation and hundreds of health outcomes in the UK Biobank and the FinnGen population: a phenome-wide Mendelian randomization study. Front Immunol 12:720876. https://doi.org/10.3389/fimmu.2021.720876

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Yuan S, Carter P, Vithayathil M, Kar S, Giovannucci E, Mason AM, Burgess S, Larsson SC (2020) Iron status and cancer risk in UK Biobank: a two-sample Mendelian randomization study. Nutrients 12. https://doi.org/10.3390/nu12020526

  26. Evans DM, Zhu G, Dy V, Heath AC, Madden PA, Kemp JP, McMahon G, St Pourcain B, Timpson NJ, Golding J, Lawlor DA, Steer C, Montgomery GW, Martin NG, Smith GD, Whitfield JB (2013) Genome-wide association study identifies loci affecting blood copper, selenium and zinc. Hum Mol Genet 22:3998–4006. https://doi.org/10.1093/hmg/ddt239

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Park JH, Wacholder S, Gail MH, Peters U, Jacobs KB, Chanock SJ, Chatterjee N (2010) Estimation of effect size distribution from genome-wide association studies and implications for future discoveries. Nat Genet 42:570–575. https://doi.org/10.1038/ng.610

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Palmer TM, Lawlor DA, Harbord RM, Sheehan NA, Tobias JH, Timpson NJ, Davey Smith G, Sterne JA (2012) Using multiple genetic variants as instrumental variables for modifiable risk factors. Stat Methods Med Res 21:223–242. https://doi.org/10.1177/0962280210394459

    Article  PubMed  PubMed Central  Google Scholar 

  29. Wu F, Huang Y, Hu J, Shao Z (2020) Mendelian randomization study of inflammatory bowel disease and bone mineral density. BMC Med 18:312. https://doi.org/10.1186/s12916-020-01778-5

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Zhou J, Liu C, Sun Y, Francis M, Ryu MS, Grider A, Ye K (2021) Genetically predicted circulating levels of copper and zinc are associated with osteoarthritis but not with rheumatoid arthritis. Osteoarthritis Cartilage 29:1029–1035. https://doi.org/10.1016/j.joca.2021.02.564

    Article  CAS  PubMed  Google Scholar 

  31. Bowden J, Del Greco MF, Minelli C, Davey Smith G, Sheehan N, Thompson J (2017) A framework for the investigation of pleiotropy in two-sample summary data Mendelian randomization. Stat Med 36:1783–1802. https://doi.org/10.1002/sim.7221

    Article  PubMed  PubMed Central  Google Scholar 

  32. Bowden J, Davey Smith G, Haycock PC, Burgess S (2016) Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator. Genet Epidemiol 40:304–314. https://doi.org/10.1002/gepi.21965

    Article  PubMed  PubMed Central  Google Scholar 

  33. Elzeiny MA, Elzefzafy WM, Shahin RS, Atef N, Ahmed MG (2010) Serum levels of selenium, zinc, copper and iron in patients with post viral hepatitis liver cirrhosis& hepatocellular carcinoma. ASIAN ACAD MANAG J 8:1

  34. Nayak SB, Yashwanth S, Pinto SM, Bhat VR, Mayya SS (2005) Serum copper, ceruloplasmin, protein thiols and thiobarbituric acid reactive substance status in liver cancer associated with elevated levels of alpha-fetoprotein. Indian J Physiol Pharmacol 49:341–344

    CAS  PubMed  Google Scholar 

  35. Lin CC, Huang JF, Tsai LY, Huang YL (2006) Selenium, iron, copper, and zinc levels and copper-to-zinc ratios in serum of patients at different stages of viral hepatic diseases. Biol Trace Elem Res 109:15–24. https://doi.org/10.1385/BTER:109:1:015

    Article  CAS  PubMed  Google Scholar 

  36. Nagasue N, Kolno H, Chang YC, Nakamura T (1989) Iron, copper and zinc levels in serum and cirrhotic liver of patients with and without hepatocellular carcinoma. Oncol 46:293–296. https://doi.org/10.1159/000226735

    Article  CAS  Google Scholar 

  37. Porcu C, Antonucci L, Barbaro B, Illi B, Nasi S, Martini M, Licata A, Miele L, Grieco A, Balsano C (2018) Copper/MYC/CTR1 interplay: a dangerous relationship in hepatocellular carcinoma. Oncotarget 9:9325–9343. https://doi.org/10.18632/oncotarget.24282

    Article  PubMed  PubMed Central  Google Scholar 

  38. Fu L, Xie H, Huang J, Chen L (2020) Rapid determination of trace elements in serum of hepatocellular carcinoma patients by inductively coupled plasma tandem mass spectrometry. Anal Chim Acta 1112:1–7. https://doi.org/10.1016/j.aca.2020.03.054

    Article  CAS  PubMed  Google Scholar 

  39. Shanbhag VC, Gudekar N, Jasmer K, Papageorgiou C, Singh K, Petris MJ (2021) Copper metabolism as a unique vulnerability in cancer. Biochim Biophys Acta Mol Cell Res 1868:118893. https://doi.org/10.1016/j.bbamcr.2020.118893

    Article  CAS  PubMed  Google Scholar 

  40. Osredkar J, Sustar N (2011) Copper and zinc, biological role and significance of copper/zinc imbalance. J Clinic Toxicol S3:001. https://doi.org/10.4172/2161-0495.S3-001

  41. Gurusamy K, Davidson BR (2007) Trace element concentration in metastatic liver disease: a systematic review. J Trace Elem Med Biol 21:169–177. https://doi.org/10.1016/j.jtemb.2007.03.003

    Article  CAS  PubMed  Google Scholar 

  42. Lin L, Yan L, Liu Y, Qu C, Ni J, Li H (2020) The burden and trends of primary liver cancer caused by specific etiologies from 1990 to 2017 at the global, regional, national, age, and sex level results from the Global Burden of Disease Study 2017. Liver Cancer 9:563–582. https://doi.org/10.1159/000508568

    Article  PubMed  PubMed Central  Google Scholar 

  43. Verduijn M, Siegerink B, Jager KJ, Zoccali C, Dekker FW (2010) Mendelian randomization: use of genetics to enable causal inference in observational studies. Nephrol Dial Transplant 25:1394–1398. https://doi.org/10.1093/ndt/gfq098

    Article  PubMed  Google Scholar 

  44. Song J, Liu K, Chen W, Liu B, Yang H, Lv L, Sun X, Mao Y, Ye D (2021) Circulating vitamin D levels and risk of vitiligo: evidence from meta-analysis and two-sample Mendelian randomization. Front Nutr 8:782270. https://doi.org/10.3389/fnut.2021.782270

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. He B, Shi J, Wang X, Jiang H, Zhu HJ (2020) Genome-wide pQTL analysis of protein expression regulatory networks in the human liver. BMC Biol 18:97. https://doi.org/10.1186/s12915-020-00830-3

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Chen MH, Raffield LM, Mousas A, Sakaue S, Huffman JE, Moscati A, Trivedi B, Jiang T, Akbari P, Vuckovic D, Bao EL, Zhong X, Manansala R, Laplante V, Chen M, Lo KS, Qian H, Lareau CA, Beaudoin M, Hunt KA, Akiyama M, Bartz TM, Ben-Shlomo Y, Beswick A, Bork-Jensen J, Bottinger EP, Brody JA, van Rooij FJA, Chitrala K, Cho K, Choquet H, Correa A, Danesh J, Di Angelantonio E, Dimou N, Ding J, Elliott P, Esko T, Evans MK, Floyd JS, Broer L, Grarup N, Guo MH, Greinacher A, Haessler J, Hansen T, Howson JMM, Huang QQ, Huang W, Jorgenson E, Kacprowski T, Kahonen M, Kamatani Y, Kanai M, Karthikeyan S, Koskeridis F, Lange LA, Lehtimaki T, Lerch MM, Linneberg A, Liu Y, Lyytikainen LP, Manichaikul A, Martin HC, Matsuda K, Mohlke KL, Mononen N, Murakami Y, Nadkarni GN, Nauck M, Nikus K, Ouwehand WH, Pankratz N, Pedersen O, Preuss M, Psaty BM, Raitakari OT, Roberts DJ, Rich SS, Rodriguez BAT, Rosen JD, Rotter JI, Schubert P, Spracklen CN, Surendran P, Tang H, Tardif JC, Trembath RC, Ghanbari M, Volker U, Volzke H, Watkins NA, Zonderman AB, Program VAMV, Wilson PWF, Li Y, Butterworth AS, Gauchat JF, Chiang CWK, Li B, Loos RJF, Astle WJ, Evangelou E, van Heel DA, Sankaran VG, Okada Y, Soranzo N, Johnson AD, Reiner AP, Auer PL, Lettre G (2020) Trans-ethnic and ancestry-specific blood-cell genetics in 746,667 individuals from 5 global populations. Cell 182:1198-1213e1114. https://doi.org/10.1016/j.cell.2020.06.045

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors sincerely thank the researchers and participants of the observational articles included in meta-analysis and the researchers of GWAS study for their collection and sharing of large-scale data.

Funding

This work was supported by grants from the Natural Science Foundation of Zhejiang Province (Nos. LQ20H260008 and LQ21H260001) and the Zhejiang Chinese Medical University Foundation (2020ZG01, 2020ZG16).

Author information

Authors and Affiliations

Authors

Contributions

DY and ST L conceived and designed the study. WW C, DJ, and KL conducted data analysis and interpreted the results. WW C and ZW Z drafted the manuscript, XH S, YY M, ST L, and DY revised the manuscript. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Songtao Li or Ding Ye.

Ethics declarations

Ethics Approval

Because the current study was based on public data and published articles, no additional ethical approval was required.

Consent to Participate

The written informed consent of all participants was obtained by the respective Institutional Review Boards.

Competing Interests

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 299 KB)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, W., Zhang, Z., Liu, K. et al. Circulating Copper and Liver Cancer. Biol Trace Elem Res 201, 4649–4656 (2023). https://doi.org/10.1007/s12011-023-03554-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12011-023-03554-x

Keywords

Navigation