Using genetic variants to evaluate the causal effect of cholesterol lowering on head and neck cancer risk: A Mendelian randomization study
Autoři:
Mark Gormley aff001; James Yarmolinsky aff001; Tom Dudding aff001; Kimberley Burrows aff001; Richard M. Martin aff001; Steven Thomas aff002; Jessica Tyrrell aff005; Paul Brennan aff006; Miranda Pring aff002; Stefania Boccia aff007; Andrew F. Olshan aff009; Brenda Diergaarde aff010; Rayjean J. Hung aff011; Geoffrey Liu aff012; Danny Legge aff014; Eloiza H. Tajara aff015; Patricia Severino aff016; Martin Lacko aff017; Andrew R. Ness aff004; George Davey Smith aff001; Emma E. Vincent aff001; Rebecca C. Richmond aff001
Působiště autorů:
MRC Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
aff001; Bristol Dental Hospital and School, University of Bristol, Bristol, United Kingdom
aff002; Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
aff003; National Institute for Health Research Bristol Biomedical Research Centre at the University Hospitals Bristol and Weston NHS Foundation Trust, University of Bristol, Bristol, United Kingdom
aff004; University of Exeter Medical School, RILD Building, RD&E Hospital, Exeter, United Kingdom
aff005; Genetic Epidemiology Group, World Health Organization, International Agency for Research on Cancer, Lyon, France
aff006; Section of Hygiene, University Department of Life Sciences and Public Health, Università Cattolica del Sacro Cuore, Roma, Italia
aff007; Department of Woman and Child Health and Public Health, Public Health Area, Fondazione Policlinico Universitario A. Gemelli IRCCS, Roma, Italy
aff008; Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, North Carolina, United States of America
aff009; Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, and UPMC Hillman Cancer Center, Pittsburgh, Pennsylvania, United States of America
aff010; Prosserman Centre for Population Health Research, Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Canada
aff011; Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
aff012; Princess Margaret Cancer Centre, Toronto, Canada
aff013; School of Cellular and Molecular Medicine, University of Bristol, Bristol, United Kingdom
aff014; School of Medicine of São José do Rio Preto, São Paulo, Brazil
aff015; Albert Einstein Research and Education Institute, Hospital Israelita Albert Einstein, São Paulo, Brazil
aff016; Department of Otorhinolaryngology and Head and Neck Surgery, Research Institute GROW, Maastricht University Medical Center, Maastricht, The Netherlands
aff017
Vyšlo v časopise:
Using genetic variants to evaluate the causal effect of cholesterol lowering on head and neck cancer risk: A Mendelian randomization study. PLoS Genet 17(4): e1009525. doi:10.1371/journal.pgen.1009525
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pgen.1009525
Souhrn
Head and neck squamous cell carcinoma (HNSCC), which includes cancers of the oral cavity and oropharynx, is a cause of substantial global morbidity and mortality. Strategies to reduce disease burden include discovery of novel therapies and repurposing of existing drugs. Statins are commonly prescribed for lowering circulating cholesterol by inhibiting HMG-CoA reductase (HMGCR). Results from some observational studies suggest that statin use may reduce HNSCC risk. We appraised the relationship of genetically-proxied cholesterol-lowering drug targets and other circulating lipid traits with oral (OC) and oropharyngeal (OPC) cancer risk using two-sample Mendelian randomization (MR). For the primary analysis, germline genetic variants in HMGCR, NPC1L1, CETP, PCSK9 and LDLR were used to proxy the effect of low-density lipoprotein cholesterol (LDL-C) lowering therapies. In secondary analyses, variants were used to proxy circulating levels of other lipid traits in a genome-wide association study (GWAS) meta-analysis of 188,578 individuals. Both primary and secondary analyses aimed to estimate the downstream causal effect of cholesterol lowering therapies on OC and OPC risk. The second sample for MR was taken from a GWAS of 6,034 OC and OPC cases and 6,585 controls (GAME-ON). Analyses were replicated in UK Biobank, using 839 OC and OPC cases and 372,016 controls and the results of the GAME-ON and UK Biobank analyses combined in a fixed-effects meta-analysis. We found limited evidence of a causal effect of genetically-proxied LDL-C lowering using HMGCR, NPC1L1, CETP or other circulating lipid traits on either OC or OPC risk. Genetically-proxied PCSK9 inhibition equivalent to a 1 mmol/L (38.7 mg/dL) reduction in LDL-C was associated with an increased risk of OC and OPC combined (OR 1.8 95%CI 1.2, 2.8, p = 9.31 x10-05), with good concordance between GAME-ON and UK Biobank (I2 = 22%). Effects for PCSK9 appeared stronger in relation to OPC (OR 2.6 95%CI 1.4, 4.9) than OC (OR 1.4 95%CI 0.8, 2.4). LDLR variants, resulting in genetically-proxied reduction in LDL-C equivalent to a 1 mmol/L (38.7 mg/dL), reduced the risk of OC and OPC combined (OR 0.7, 95%CI 0.5, 1.0, p = 0.006). A series of pleiotropy-robust and outlier detection methods showed that pleiotropy did not bias our findings. We found limited evidence for a role of cholesterol-lowering in OC and OPC risk, suggesting previous observational results may have been confounded. There was some evidence that genetically-proxied inhibition of PCSK9 increased risk, while lipid-lowering variants in LDLR, reduced risk of combined OC and OPC. This result suggests that the mechanisms of action of PCSK9 on OC and OPC risk may be independent of its cholesterol lowering effects; however, this was not supported uniformly across all sensitivity analyses and further replication of this finding is required.
Klíčová slova:
Statins – Cancer risk factors – Cancers and neoplasms – Genetics – Head and neck cancers – Cholesterol – Lipids – Single nucleotide polymorphisms
Zdroje
1. Warnakulasuriya S. Global epidemiology of oral and oropharyngeal cancer. Oral Oncol. 2009;45(4–5):309–16. doi: 10.1016/j.oraloncology.2008.06.002 18804401
2. Saba NF, Goodman M, Ward K, Flowers C, Ramalingam S, Owonikoko T, et al. Gender and ethnic disparities in incidence and survival of squamous cell carcinoma of the oral tongue, base of tongue, and tonsils: a surveillance, epidemiology and end results program-based analysis. Oncology. 2011;81(1):12–20. doi: 10.1159/000330807 21912193
3. Cancer Research UK (CRUK). Head and neck cancer statistics 2019 [cited 2019 11/04/2019]. Available from: https://www.cancerresearchuk.org/health-professional/cancer-statistics/statistics-by-cancer-type/head-and-neck-cancers#heading-Two.
4. Mehanna H, Kong A, Ahmed SK. Recurrent head and neck cancer: United Kingdom National Multidisciplinary Guidelines. J Laryngol Otol. 2016;130(S2):S181–S90. doi: 10.1017/S002221511600061X 27841130
5. De Boer MF, McCormick LK, Pruyn JF, Ryckman RM, van den Borne BW. Physical and psychosocial correlates of head and neck cancer: a review of the literature. Otolaryngol Head Neck Surg. 1999;120(3):427–36. doi: 10.1016/S0194-5998(99)70287-1 10064650
6. Thomas SJ, Penfold CM, Waylen A, Ness AR. The changing aetiology of head and neck squamous cell cancer: A tale of three cancers? Clin Otolaryngol. 2018;43(4):999–1003. doi: 10.1111/coa.13144 29770611
7. Ang KK, Harris J, Wheeler R, Weber R, Rosenthal DI, Nguyen-Tan PF, et al. Human papillomavirus and survival of patients with oropharyngeal cancer. N Engl J Med. 2010;363(1):24–35. doi: 10.1056/NEJMoa0912217 20530316
8. Elrefaey S, Massaro MA, Chiocca S, Chiesa F, Ansarin M. HPV in oropharyngeal cancer: the basics to know in clinical practice. Acta Otorhinolaryngol Ital. 2014;34(5):299–309. 25709145
9. Sleire L, Forde HE, Netland IA, Leiss L, Skeie BS, Enger PO. Drug repurposing in cancer. Pharmacol Res. 2017;124:74–91. doi: 10.1016/j.phrs.2017.07.013 28712971
10. Hu M, Cheung BM, Tomlinson B. Safety of statins: an update. Ther Adv Drug Saf. 2012;3(3):133–44. doi: 10.1177/2042098612439884 25083232
11. Audi S, Burrage DR, Lonsdale DO, Pontefract S, Coleman JJ, Hitchings AW, et al. The ‘top 100’ drugs and classes in England: an updated ‘starter formulary’ for trainee prescribers. British Journal of Clinical Pharmacology. 2018;84(11):2562–71. doi: 10.1111/bcp.13709 29975799
12. Istvan ES, Deisenhofer J. Structural mechanism for statin inhibition of HMG-CoA reductase. Science. 2001;292(5519):1160–4. doi: 10.1126/science.1059344 11349148
13. Ference BA, Kastelein JJP, Ginsberg HN, Chapman MJ, Nicholls SJ, Ray KK, et al. Association of Genetic Variants Related to CETP Inhibitors and Statins With Lipoprotein Levels and Cardiovascular Risk. JAMA. 2017;318(10):947–56. doi: 10.1001/jama.2017.11467 28846118
14. Nielsen SF, Nordestgaard BG, Bojesen SE. Statin Use and Reduced Cancer-Related Mortality. New Engl J Med. 2012;367(19):1792–802. doi: 10.1056/NEJMoa1201735 23134381
15. Mehibel M, Ortiz-Martinez F, Voelxen N, Boyers A, Chadwick A, Telfer BA, et al. Statin-induced metabolic reprogramming in head and neck cancer: a biomarker for targeting monocarboxylate transporters. Sci Rep. 2018;8(1):16804. doi: 10.1038/s41598-018-35103-1 30429503
16. Chawda JG, Jain SS, Patel HR, Chaduvula N, Patel K. The relationship between serum lipid levels and the risk of oral cancer. Indian J Med Paediatr Oncol. 2011;32(1):34–7. doi: 10.4103/0971-5851.81888 21731214
17. Mehta R, Gurudath S, Dayansoor S, Pai A, Ganapathy KS. Serum lipid profile in patients with oral cancer and oral precancerous conditions. Dent Res J (Isfahan). 2014;11(3):345–50. doi: 10.4103/1735-3327.135889 25097644
18. Kao LT, Hung SH, Kao PF, Liu JC, Lin HC. Inverse association between statin use and head and neck cancer: Population-based case-control study in Han population. Head Neck-J Sci Spec. 2019;41(5):1193–8. doi: 10.1002/hed.25501 30809863
19. Dickerman BA, Garcia-Albeniz X, Logan RW, Denaxas S, Hernan MA. Avoidable flaws in observational analyses: an application to statins and cancer. Nat Med. 2019;25(10):1601–6. doi: 10.1038/s41591-019-0597-x 31591592
20. Smith GD, Ebrahim S. ’Mendelian randomization’: can genetic epidemiology contribute to understanding environmental determinants of disease? Int J Epidemiol. 2003;32(1):1–22. doi: 10.1093/ije/dyg070 12689998
21. Davey Smith G, Hemani G. Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Hum Mol Genet. 2014;23(R1):R89–98. doi: 10.1093/hmg/ddu328 25064373
22. Davies NM, Holmes MV, Davey Smith G. Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians. BMJ. 2018;362:k601. doi: 10.1136/bmj.k601 30002074
23. Walker VM, Smith GD, Davies NM, Martin RM. Mendelian randomization: a novel approach for the prediction of adverse drug events and drug repurposing opportunities. Int J Epidemiol. 2017;46(6):2078–89. doi: 10.1093/ije/dyx207 29040597
24. Teslovich TM, Musunuru K, Smith AV, Edmondson AC, Stylianou IM, Koseki M, et al. Biological, clinical and population relevance of 95 loci for blood lipids. Nature. 2010;466(7307):707–13. doi: 10.1038/nature09270 20686565
25. Willer CJ, Schmidt EM, Sengupta S, Peloso GM, Gustafsson S, Kanoni S, et al. Discovery and refinement of loci associated with lipid levels. Nat Genet. 2013;45(11):1274-+. doi: 10.1038/ng.2797 24097068
26. Ference BA, Ginsberg HN, Graham I, Ray KK, Packard CJ, Bruckert E, et al. Low-density lipoproteins cause atherosclerotic cardiovascular disease. 1. Evidence from genetic, epidemiologic, and clinical studies. A consensus statement from the European Atherosclerosis Society Consensus Panel. Eur Heart J. 2017;38(32):2459–72. doi: 10.1093/eurheartj/ehx144 28444290
27. Ference BA, Majeed F, Penumetcha R, Flack JM, Brook RD. Effect of naturally random allocation to lower low-density lipoprotein cholesterol on the risk of coronary heart disease mediated by polymorphisms in NPC1L1, HMGCR, or both: a 2 x 2 factorial Mendelian randomization study. J Am Coll Cardiol. 2015;65(15):1552–61. doi: 10.1016/j.jacc.2015.02.020 25770315
28. Ference BA, Robinson JG, Brook RD, Catapano AL, Chapman MJ, Neff DR, et al. Variation in PCSK9 and HMGCR and Risk of Cardiovascular Disease and Diabetes. N Engl J Med. 2016;375(22):2144–53. doi: 10.1056/NEJMoa1604304 27959767
29. Carter P, Vithayathil M, Kar S, Potluri R, Mason AM, Larsson SC, et al. Predicting the effect of statins on cancer risk using genetic variants: a Mendelian randomization study in UK Biobank. medRxiv. 2020:2020.02.28.20028902. doi: 10.7554/eLife.57191 33046214
30. Yarmolinsky J, Bull CJ, Vincent EE, Robinson J, Walther A, Smith GD, et al. Association Between Genetically Proxied Inhibition of HMG-CoA Reductase and Epithelial Ovarian Cancer. JAMA. 2020;323(7):646–55. doi: 10.1001/jama.2020.0150 32068819
31. Ference BA, Ray KK, Catapano AL, Ference TB, Burgess S, Neff DR, et al. Mendelian Randomization Study of ACLY and Cardiovascular Disease. N Engl J Med. 2019;380(11):1033–42. doi: 10.1056/NEJMoa1806747 30865797
32. Kettunen J, Demirkan A, Wurtz P, Draisma HH, Haller T, Rawal R, et al. Genome-wide study for circulating metabolites identifies 62 loci and reveals novel systemic effects of LPA. Nat Commun. 2016;7:11122. doi: 10.1038/ncomms11122 27005778
33. Lesseur C, Diergaarde B, Olshan AF, Wunsch V, Ness AR, Liu G, et al. Genome-wide association analyses identify new susceptibility loci for oral cavity and pharyngeal cancer. Nat Genet. 2016;48(12):1544–50. doi: 10.1038/ng.3685 27749845
34. Consortium O. Consortium launches genotyping effort. Cancer Discov. 2013;3(12):1321–2. doi: 10.1158/2159-8290.CD-NB2013-159 24327678
35. Dudding T, Johansson M, Thomas SJ, Brennan P, Martin RM, Timpson NJ. Assessing the causal association between 25-hydroxyvitamin D and the risk of oral and oropharyngeal cancer using Mendelian randomization. Int J Cancer. 2018;143(5):1029–36. doi: 10.1002/ijc.31377 29536507
36. Brion MJ, Shakhbazov K, Visscher PM. Calculating statistical power in Mendelian randomization studies. Int J Epidemiol. 2013;42(5):1497–501. doi: 10.1093/ije/dyt179 24159078
37. Machiela MJ, Chanock SJ. LDlink: a web-based application for exploring population-specific haplotype structure and linking correlated alleles of possible functional variants. Bioinformatics. 2015;31(21):3555–7. doi: 10.1093/bioinformatics/btv402 26139635
38. Yavorska OO, Burgess S. MendelianRandomization: an R package for performing Mendelian randomization analyses using summarized data. Int J Epidemiol. 2017;46(6):1734–9. doi: 10.1093/ije/dyx034 28398548
39. Burgess S, Scott RA, Timpson NJ, Davey Smith G, Thompson SG. Using published data in Mendelian randomization: a blueprint for efficient identification of causal risk factors. Eur J Epidemiol. 2015;30(7):543–52. doi: 10.1007/s10654-015-0011-z 25773750
40. Burgess S, Dudbridge F, Thompson SG. Combining information on multiple instrumental variables in Mendelian randomization: comparison of allele score and summarized data methods. Stat Med. 2016;35(11):1880–906. doi: 10.1002/sim.6835 26661904
41. Nikpay M, Goel A, Won HH, Hall LM, Willenborg C, Kanoni S, et al. A comprehensive 1,000 Genomes-based genome-wide association meta-analysis of coronary artery disease. Nat Genet. 2015;47(10):1121–30. doi: 10.1038/ng.3396 26343387
42. Hemani G, Bowden J, Smith GD. Evaluating the potential role of pleiotropy in Mendelian randomization studies. Hum Mol Genet. 2018;27(R2):R195–R208. doi: 10.1093/hmg/ddy163 29771313
43. Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol. 2015;44(2):512–25. doi: 10.1093/ije/dyv080 26050253
44. Bowden J, Smith GD, Haycock PC, Burgess S. Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator. Genet Epidemiol. 2016;40(4):304–14. doi: 10.1002/gepi.21965 27061298
45. Hartwig FP, Smith GD, Bowden J. Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption. Int J Epidemiol. 2017;46(6):1985–98. doi: 10.1093/ije/dyx102 29040600
46. Bowden J, Del Greco MF, Minelli C, Smith GD, Sheehan NA, Thompson JR. Assessing the suitability of summary data for two-sample Mendelian randomization analyses using MR-Egger regression: the role of the I-2 statistic. Int J Epidemiol. 2016;45(6):1961–74. doi: 10.1093/ije/dyw220 27616674
47. Bowden J, Spiller W, Del Greco F, Sheehan N, Thompson J, Minelli C, et al. Improving the visualization, interpretation and analysis of two-sample summary data Mendelian randomization via the Radial plot and Radial regression. Int J Epidemiol. 2018;47(4):1264–78. doi: 10.1093/ije/dyy101 29961852
48. Verbanck M, Chen CY, Neale B, Do R. Publisher Correction: Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet. 2018;50(8):1196. doi: 10.1038/s41588-018-0164-2 29967445
49. Wallace C. Eliciting priors and relaxing the single causal variant assumption in colocalisation analyses. PLOS Genetics. 2020;16(4):e1008720. doi: 10.1371/journal.pgen.1008720 32310995
50. Bycroft C, Freeman C, Petkova D, Band G, Elliott LT, Sharp K, et al. Genome-wide genetic data on ~500,000 UK Biobank participants. bioRxiv. 2017:166298.
51. Mitchell R, Hemani G, Dudding T, Corbin L, Harrison S, Paternoster L. UK Biobank Genetic Data: MRC-IEU Quality Control, version 2. https://doi.org/10.5523/bris.1ovaau5sxunp2cv8rcy88688v 2019
52. Mitchell R, Elsworth BL, Mitchell R, Raistrick CA, Paternoster L, Hemani G, Gaunt, TR MRC IEU UK Biobank GWAS pipeline version 2. https://doi.org/10.5523/bris.pnoat8cxo0u52p6ynfaekeigi. 2019.
53. Schwarzer G. meta: an R package for metaanalysis. R News. 2007;7:40–5.
54. Higgins SG. Measuring inconsistency in metaanalyses. BMJ Br Med J. 2003;327:557–60.
55. Ni G, Moser G, Ripke S, Neale BM, Corvin A, Walters JTR, et al. Estimation of Genetic Correlation via Linkage Disequilibrium Score Regression and Genomic Restricted Maximum Likelihood. The American Journal of Human Genetics. 2018;102(6):1185–94. doi: 10.1016/j.ajhg.2018.03.021 29754766
56. Safarova MS, Satterfield BA, Fan X, Austin EE, Ye Z, Bastarache L, et al. A phenome-wide association study to discover pleiotropic effects of PCSK9, APOB, and LDLR. NPJ Genom Med. 2019;4:3. doi: 10.1038/s41525-019-0078-7 30774981
57. Stoekenbroek RM, Lambert G, Cariou B, Hovingh GK. Inhibiting PCSK9—biology beyond LDL control. Nat Rev Endocrinol. 2018;15(1):52–62. doi: 10.1038/s41574-018-0110-5 30367179
58. Lee S, Zhang C, Liu Z, Klevstig M, Mukhopadhyay B, Bergentall M, et al. Network analyses identify liver-specific targets for treating liver diseases. Mol Syst Biol. 2017;13(8):938. doi: 10.15252/msb.20177703 28827398
59. Marimuthu A, Subbannayya Y, Sahasrabuddhe NA, Balakrishnan L, Syed N, Sekhar NR, et al. SILAC-based quantitative proteomic analysis of gastric cancer secretome. Proteomics Clin Appl. 2013;7(5–6):355–66. doi: 10.1002/prca.201200069 23161554
60. Dong B, Singh AB, Fung C, Kan K, Liu J. CETP inhibitors downregulate hepatic LDL receptor and PCSK9 expression in vitro and in vivo through a SREBP2 dependent mechanism. Atherosclerosis. 2014;235(2):449–62. doi: 10.1016/j.atherosclerosis.2014.05.931 24950000
61. Barter PJ, Cochran BJ, Rye KA. CETP inhibition, statins and diabetes. Atherosclerosis. 2018;278:143–6. doi: 10.1016/j.atherosclerosis.2018.09.033 30278356
62. Atlas THP. PCKS9 2020 [Available from: https://www.proteinatlas.org/ENSG00000169174-PCSK9/pathology.
63. Atlas TH. LDLR Tissue 2020 [Available from: https://www.proteinatlas.org/ENSG00000130164-LDLR/tissue.
64. Lee JS, Mukhopadhyay P, Matyas C, Trojnar E, Paloczi J, Yang YR, et al. PCSK9 inhibition as a novel therapeutic target for alcoholic liver disease. Scientific Reports. 2019;9(1):17167. doi: 10.1038/s41598-019-53603-6 31748600
65. Lee JS, Rosoff D, Luo A, Longley M, Phillips M, Charlet K, et al. PCSK9 is Increased in Cerebrospinal Fluid of Individuals With Alcohol Use Disorder. Alcoholism: Clinical and Experimental Research. 2019;43(6):1163–9. doi: 10.1111/acer.14039 30933362
66. Wang K, Gerke TA, Chen X, Prosperi M. Association of statin use with risk of Gleason score-specific prostate cancer: A hospital-based cohort study. Cancer Medicine. 2019;8(17):7399–407. doi: 10.1002/cam4.2500 31595713
67. Poynter JN, Gruber SB, Higgins PD, Almog R, Bonner JD, Rennert HS, et al. Statins and the risk of colorectal cancer. N Engl J Med. 2005;352(21):2184–92. doi: 10.1056/NEJMoa043792 15917383
68. Khurana V, Bejjanki HR, Caldito G, Owens MW. Statins reduce the risk of lung cancer in humans: a large case-control study of US veterans. Chest. 2007;131(5):1282–8. doi: 10.1378/chest.06-0931 17494779
69. Shannon J, Tewoderos S, Garzotto M, Beer TM, Derenick R, Palma A, et al. Statins and prostate cancer risk: a case-control study. Am J Epidemiol. 2005;162(4):318–25. doi: 10.1093/aje/kwi203 16014776
70. Cholesterol Treatment Trialists C, Emberson JR, Kearney PM, Blackwell L, Newman C, Reith C, et al. Lack of effect of lowering LDL cholesterol on cancer: meta-analysis of individual data from 175,000 people in 27 randomised trials of statin therapy. PLoS One. 2012;7(1):e29849. doi: 10.1371/journal.pone.0029849 22276132
71. Dale KM, Coleman CI, Henyan NN, Kluger J, White CM. Statins and cancer risk: a meta-analysis. JAMA. 2006;295(1):74–80. doi: 10.1001/jama.295.1.74 16391219
72. Hernan MA, Sauer BC, Hernandez-Diaz S, Platt R, Shrier I. Specifying a target trial prevents immortal time bias and other self-inflicted injuries in observational analyses. J Clin Epidemiol. 2016;79:70–5. doi: 10.1016/j.jclinepi.2016.04.014 27237061
73. Saka Herran C, Jane-Salas E, Estrugo Devesa A, Lopez-Lopez J. Protective effects of metformin, statins and anti-inflammatory drugs on head and neck cancer: A systematic review. Oral Oncol. 2018;85:68–81. doi: 10.1016/j.oraloncology.2018.08.015 30220322
74. Dimitroulakos J, Marhin WH, Tokunaga J, Irish J, Gullane P, Penn LZ, et al. Microarray and biochemical analysis of lovastatin-induced apoptosis of squamous cell carcinomas. Neoplasia. 2002;4(4):337–46. doi: 10.1038/sj.neo.7900247 12082550
75. Osmak M. Statins and cancer: current and future prospects. Cancer Lett. 2012;324(1):1–12. doi: 10.1016/j.canlet.2012.04.011 22542807
76. Gallagher EJ, LeRoith D. Obesity and Diabetes: The Increased Risk of Cancer and Cancer-Related Mortality. Physiol Rev. 2015;95(3):727–48. doi: 10.1152/physrev.00030.2014 26084689
77. Joost HG. Diabetes and cancer: Epidemiology and potential mechanisms. Diabetes Vasc Dis Re. 2014;11(6):390–4. doi: 10.1177/1479164114550813 25268021
78. Hursting SD, DiGiovanni J, Dannenberg AJ, Azrad M, LeRoith D, Demark-Wahnefried W, et al. Obesity, Energy Balance, and Cancer: New Opportunities for Prevention. Cancer Prev Res. 2012;5(11):1260–72.
79. Bhat M, Skill N, Marcus V, Deschenes M, Tan X, Bouteaud J, et al. Decreased PCSK9 expression in human hepatocellular carcinoma. BMC Gastroenterol. 2015;15:176. doi: 10.1186/s12876-015-0371-6 26674961
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