Evaluating the Impact of Oral Contraceptives on Pancreatic Cancer Risk: A Two-Sample Mendelian Randomization Analysis
Abstract
:1. Background
2. Methods
2.1. Overall Design
2.2. Study Population and Datasets
2.3. Identification of Drug Target Proteins
2.4. Selection of Instrumental Variables
2.5. Mendelian Randomization
2.6. Sensitivity Analysis
2.7. Colocalization Analysis
2.8. Differentially Expressed Genes
2.9. Single Cell-Type Expression Analysis
2.10. Protein Interaction Networks and Protein Function Queries
3. Results
3.1. MR Results Identified PC-Related OC Target Proteins
3.2. Sensitivity Analysis and Colocalization Analysis
3.3. Identified Protein-Coding Genes Differentially Expressed Between Tumor and Normal Tissue
3.4. Single-Cell Type Expression in PC Tissues and Pathway Enrichment
3.5. Protein PPI Network and Gene Function
4. Discussion
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
PC | Pancreatic cancer |
pQTL | Protein quantitative trait loci |
Cis-pQTL | Cis-acting protein quantitative trait loci |
MHC | Major histocompatibility complex |
MAF | Minor allele frequency |
GWAS | Genome-wide association study |
UKB-PPP | UK Biobank Pharma Proteomics Project |
MR | Mendelian randomization |
LD | Linkage disequilibrium |
IVs | Instrumental variables |
SNP | Single nucleotide polymorphism |
PPI | Protein–protein interaction |
KEGG | Kyoto Encyclopedia of Genes and Genomes |
STRING | Search Tool for the Retrieval of Interacting Genes |
OR | Odds ratio |
CI | Confidence interval |
GEO | Gene Expression Omnibus |
scRNA-seq | Single-cell RNA sequencing |
References
- Luo, W.; Tao, J.; Zheng, L.; Zhang, T. Current epidemiology of pancreatic cancer: Challenges and opportunities. Chin. J. Cancer Res. Chung-Kuo Yen Cheng Yen Chiu 2020, 32, 705–719. [Google Scholar] [CrossRef] [PubMed]
- Mizrahi, J.D.; Surana, R.; Valle, J.W.; Shroff, R.T. Pancreatic cancer. Lancet Lond. Engl. 2020, 395, 2008–2020. [Google Scholar] [CrossRef] [PubMed]
- Park, W.; Chawla, A.; O’Reilly, E.M. Pancreatic Cancer: A Review. JAMA 2021, 326, 851–862. [Google Scholar] [CrossRef]
- Siegel, R.L.; Miller, K.D.; Fuchs, H.E.; Jemal, A. Cancer Statistics, 2021. CA Cancer J. Clin. 2021, 71, 7–33. [Google Scholar] [CrossRef]
- Rahib, L.; Smith, B.D.; Aizenberg, R.; Rosenzweig, A.B.; Fleshman, J.M.; Matrisian, L.M. Projecting cancer incidence and deaths to 2030: The unexpected burden of thyroid, liver, and pancreas cancers in the United States. Cancer Res. 2014, 74, 2913–2921. [Google Scholar] [CrossRef]
- Collaborative Group on Hormonal Factors in Breast Cancer Breast cancer and hormonal contraceptives: Collaborative reanalysis of individual data on 53 297 women with breast cancer and 100 239 women without breast cancer from 54 epidemiological studies. Lancet Lond. Engl. 1996, 347, 1713–1727. [CrossRef] [PubMed]
- Havrilesky, L.J.; Gierisch, J.M.; Moorman, P.G.; Coeytaux, R.R.; Urrutia, R.P.; Lowery, W.J.; Dinan, M.; McBroom, A.J.; Wing, L.; Musty, M.D.; et al. Oral contraceptive use for the primary prevention of ovarian cancer. Evid. Rep. Assess. 2013, 212, 1–514. [Google Scholar]
- Bouman, A.; Heineman, M.J.; Faas, M.M. Sex hormones and the immune response in humans. Hum. Reprod. Update 2005, 11, 411–423. [Google Scholar] [CrossRef]
- Michels, K.A.; Brinton, L.A.; Pfeiffer, R.M.; Trabert, B. Oral Contraceptive Use and Risks of Cancer in the NIH-AARP Diet and Health Study. Am. J. Epidemiol. 2018, 187, 1630–1641. [Google Scholar] [CrossRef]
- Zhang, Y.; Coogan, P.F.; Palmer, J.R.; Strom, B.L.; Rosenberg, L. A case-control study of reproductive factors, female hormone use, and risk of pancreatic cancer. Cancer Causes Control CCC 2010, 21, 473–478. [Google Scholar] [CrossRef]
- Lee, E.; Horn-Ross, P.L.; Rull, R.P.; Neuhausen, S.L.; Anton-Culver, H.; Ursin, G.; Henderson, K.D.; Bernstein, L. Reproductive factors, exogenous hormones, and pancreatic cancer risk in the CTS. Am. J. Epidemiol. 2013, 178, 1403–1413. [Google Scholar] [CrossRef] [PubMed]
- Bae, J.-M. Use of oral contraceptives and risk of pancreatic cancer in women: A recalculated meta-analysis of prospective cohort studies. World J. Gastroenterol. 2021, 27, 8374–8377. [Google Scholar] [CrossRef]
- Butt, S.A.; Lidegaardi, Ø.; Skovlund, C.; Hannaford, P.C.; Iversen, L.; Fielding, S.; Mørch, L.S. Hormonal contraceptive use and risk of pancreatic cancer-A cohort study among premenopausal women. PLoS ONE 2018, 13, e0206358. [Google Scholar] [CrossRef]
- Smith, G.D.; Ebrahim, S. Mendelian randomization: Prospects, potentials, and limitations. Int. J. Epidemiol. 2004, 33, 30–42. [Google Scholar] [CrossRef] [PubMed]
- Larsson, S.C.; Butterworth, A.S.; Burgess, S. Mendelian randomization for cardiovascular diseases: Principles and applications. Eur. Heart J. 2023, 44, 4913–4924. [Google Scholar] [CrossRef] [PubMed]
- Schmidt, A.F.; Finan, C.; Gordillo-Marañón, M.; Asselbergs, F.W.; Freitag, D.F.; Patel, R.S.; Tyl, B.; Chopade, S.; Faraway, R.; Zwierzyna, M.; et al. Genetic drug target validation using Mendelian randomisation. Nat. Commun. 2020, 11, 3255. [Google Scholar] [CrossRef]
- Burgess, S.; Butterworth, A.; Malarstig, A.; Thompson, S.G. Use of Mendelian randomisation to assess potential benefit of clinical intervention. BMJ 2012, 345, e7325. [Google Scholar] [CrossRef]
- Gill, D.; Georgakis, M.K.; Walker, V.M.; Schmidt, A.F.; Gkatzionis, A.; Freitag, D.F.; Finan, C.; Hingorani, A.D.; Howson, J.M.M.; Burgess, S.; et al. Mendelian randomization for studying the effects of perturbing drug targets. Wellcome Open Res. 2021, 6, 16. [Google Scholar] [CrossRef]
- Walker, V.M.; Davey Smith, G.; Davies, N.M.; Martin, R.M. Mendelian randomization: A novel approach for the prediction of adverse drug events and drug repurposing opportunities. Int. J. Epidemiol. 2017, 46, 2078–2089. [Google Scholar] [CrossRef]
- Patel, A.; Gill, D.; Shungin, D.; Mantzoros, C.S.; Knudsen, L.B.; Bowden, J.; Burgess, S. Robust use of phenotypic heterogeneity at drug target genes for mechanistic insights: Application of cis-multivariable Mendelian randomization to GLP1R gene region. Genet. Epidemiol. 2024, 48, 151–163. [Google Scholar] [CrossRef]
- Zheng, G.; Chattopadhyay, S.; Sundquist, J.; Sundquist, K.; Ji, J. Antihypertensive drug targets and breast cancer risk: A two-sample Mendelian randomization study. Eur. J. Epidemiol. 2024, 39, 535–548. [Google Scholar] [CrossRef] [PubMed]
- Ferkingstad, E.; Sulem, P.; Atlason, B.A.; Sveinbjornsson, G.; Magnusson, M.I.; Styrmisdottir, E.L.; Gunnarsdottir, K.; Helgason, A.; Oddsson, A.; Halldorsson, B.V.; et al. Large-scale integration of the plasma proteome with genetics and disease. Nat. Genet. 2021, 53, 1712–1721. [Google Scholar] [CrossRef]
- Sun, B.B.; Chiou, J.; Traylor, M.; Benner, C.; Hsu, Y.-H.; Richardson, T.G.; Surendran, P.; Mahajan, A.; Robins, C.; Vasquez-Grinnell, S.G.; et al. Plasma proteomic associations with genetics and health in the UK Biobank. Nature 2023, 622, 329–338. [Google Scholar] [CrossRef] [PubMed]
- Kurki, M.I.; Karjalainen, J.; Palta, P.; Sipilä, T.P.; Kristiansson, K.; Donner, K.M.; Reeve, M.P.; Laivuori, H.; Aavikko, M.; Kaunisto, M.A.; et al. FinnGen provides genetic insights from a well-phenotyped isolated population. Nature 2023, 613, 508–518. [Google Scholar] [CrossRef]
- Wishart, D.S.; Feunang, Y.D.; Guo, A.C.; Lo, E.J.; Marcu, A.; Grant, J.R.; Sajed, T.; Johnson, D.; Li, C.; Sayeeda, Z.; et al. DrugBank 5.0: A major update to the DrugBank database for 2018. Nucleic Acids Res. 2018, 46, D1074–D1082. [Google Scholar] [CrossRef]
- Davis, A.P.; Wiegers, T.C.; Johnson, R.J.; Sciaky, D.; Wiegers, J.; Mattingly, C.J. Comparative Toxicogenomics Database (CTD): Update 2023. Nucleic Acids Res. 2023, 51, D1257–D1262. [Google Scholar] [CrossRef] [PubMed]
- Su, W.; Gu, X.; Dou, M.; Duan, Q.; Jiang, Z.; Yin, K.; Cai, W.; Cao, B.; Wang, Y.; Chen, Y. Systematic druggable genome-wide Mendelian randomisation identifies therapeutic targets for Alzheimer’s disease. J. Neurol. Neurosurg. Psychiatry 2023, 94, 954–961. [Google Scholar] [CrossRef]
- Maina, J.G.; Balkhiyarova, Z.; Nouwen, A.; Pupko, I.; Ulrich, A.; Boissel, M.; Bonnefond, A.; Froguel, P.; Khamis, A.; Prokopenko, I.; et al. Bidirectional Mendelian Randomization and Multiphenotype GWAS Show Causality and Shared Pathophysiology Between Depression and Type 2 Diabetes. Diabetes Care 2023, 46, 1707–1714. [Google Scholar] [CrossRef]
- Papadimitriou, N.; Dimou, N.; Tsilidis, K.K.; Banbury, B.; Martin, R.M.; Lewis, S.J.; Kazmi, N.; Robinson, T.M.; Albanes, D.; Aleksandrova, K.; et al. Physical activity and risks of breast and colorectal cancer: A Mendelian randomisation analysis. Nat. Commun. 2020, 11, 597. [Google Scholar] [CrossRef]
- Burgess, S.; Dudbridge, F.; Thompson, S.G. Combining information on multiple instrumental variables in Mendelian randomization: Comparison of allele score and summarized data methods. Stat. Med. 2016, 35, 1880–1906. [Google Scholar] [CrossRef]
- Hemani, G.; Zheng, J.; Elsworth, B.; Wade, K.H.; Haberland, V.; Baird, D.; Laurin, C.; Burgess, S.; Bowden, J.; Langdon, R.; et al. The MR-Base platform supports systematic causal inference across the human phenome. eLife 2018, 7, e34408. [Google Scholar] [CrossRef] [PubMed]
- Burgess, S.; Zuber, V.; Valdes-Marquez, E.; Sun, B.B.; Hopewell, J.C. Mendelian randomization with fine-mapped genetic data: Choosing from large numbers of correlated instrumental variables. Genet. Epidemiol. 2017, 41, 714–725. [Google Scholar] [CrossRef] [PubMed]
- Giambartolomei, C.; Vukcevic, D.; Schadt, E.E.; Franke, L.; Hingorani, A.D.; Wallace, C.; Plagnol, V. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 2014, 10, e1004383. [Google Scholar] [CrossRef]
- Kia, D.A.; Zhang, D.; Guelfi, S.; Manzoni, C.; Hubbard, L.; Reynolds, R.H.; Botía, J.; Ryten, M.; Ferrari, R.; Lewis, P.A.; et al. Identification of Candidate Parkinson Disease Genes by Integrating Genome-Wide Association Study, Expression, and Epigenetic Data Sets. JAMA Neurol. 2021, 78, 464–472. [Google Scholar] [CrossRef]
- Goldman, M.J.; Craft, B.; Hastie, M.; Repečka, K.; McDade, F.; Kamath, A.; Banerjee, A.; Luo, Y.; Rogers, D.; Brooks, A.N.; et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat. Biotechnol. 2020, 38, 675–678. [Google Scholar] [CrossRef]
- Ritchie, M.E.; Phipson, B.; Wu, D.; Hu, Y.; Law, C.W.; Shi, W.; Smyth, G.K. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015, 43, e47. [Google Scholar] [CrossRef]
- Barrett, T.; Wilhite, S.E.; Ledoux, P.; Evangelista, C.; Kim, I.F.; Tomashevsky, M.; Marshall, K.A.; Phillippy, K.H.; Sherman, P.M.; Holko, M.; et al. NCBI GEO: Archive for functional genomics data sets--update. Nucleic Acids Res. 2013, 41, D991–D995. [Google Scholar] [CrossRef]
- Steele, N.G.; Carpenter, E.S.; Kemp, S.B.; Sirihorachai, V.R.; The, S.; Delrosario, L.; Lazarus, J.; Amir, E.-A.D.; Gunchick, V.; Espinoza, C.; et al. Multimodal Mapping of the Tumor and Peripheral Blood Immune Landscape in Human Pancreatic Cancer. Nat. Cancer 2020, 1, 1097–1112. [Google Scholar] [CrossRef] [PubMed]
- Halbrook, C.J.; Thurston, G.; Boyer, S.; Anaraki, C.; Jiménez, J.A.; McCarthy, A.; Steele, N.G.; Kerk, S.A.; Hong, H.S.; Lin, L.; et al. Differential integrated stress response and asparagine production drive symbiosis and therapy resistance of pancreatic adenocarcinoma cells. Nat. Cancer 2022, 3, 1386–1403. [Google Scholar] [CrossRef]
- Dennis, G.; Sherman, B.T.; Hosack, D.A.; Yang, J.; Gao, W.; Lane, H.C.; Lempicki, R.A. DAVID: Database for Annotation, Visualization, and Integrated Discovery. Genome Biol. 2003, 4, P3. [Google Scholar] [CrossRef]
- Sitruk-Ware, R.; Plu-Bureau, G.; Menard, J.; Conard, J.; Kumar, S.; Thalabard, J.-C.; Tokay, B.; Bouchard, P. Effects of oral and transvaginal ethinyl estradiol on hemostatic factors and hepatic proteins in a randomized, crossover study. J. Clin. Endocrinol. Metab. 2007, 92, 2074–2079. [Google Scholar] [CrossRef] [PubMed]
- van Rooijen, M.; Silveira, A.; Hamsten, A.; Bremme, K. Sex hormone--binding globulin--a surrogate marker for the prothrombotic effects of combined oral contraceptives. Am. J. Obstet. Gynecol. 2004, 190, 332–337. [Google Scholar] [CrossRef] [PubMed]
- United Nations Development Programme; United Nations Population Fund; World Health Organization; World Bank Special Programme of Research, Development and Research Training in Human Reproduction. Task Force on Long-acting Systemic Agents for Fertility Regulaion Comparative study of the effects of two once-a-month injectable contraceptives (Cyclofem and Mesigyna) and one oral contraceptive (Ortho-Novum 1/35) on coagulation and fibrinolysis. Contraception 2003, 68, 159–176. [Google Scholar] [CrossRef]
- Lippi, G.; Manzato, F.; Brocco, G.; Franchini, M.; Guidi, G. Prothrombotic effects and clinical implications of third-generation oral contraceptives use. Blood Coagul. Fibrinolysis Int. J. Haemost. Thromb. 2002, 13, 69–72. [Google Scholar] [CrossRef] [PubMed]
- Oral Contraceptive and Hemostasis Study Group The effects of seven monophasic oral contraceptive regimens on hemostatic variables: Conclusions from a large randomized multicenter study. Contraception 2003, 67, 173–185. [CrossRef]
- Sitruk-Ware, R.L.; Menard, J.; Rad, M.; Burggraaf, J.; de Kam, M.L.; Tokay, B.A.; Sivin, I.; Kluft, C. Comparison of the impact of vaginal and oral administration of combined hormonal contraceptives on hepatic proteins sensitive to estrogen. Contraception 2007, 75, 430–437. [Google Scholar] [CrossRef]
- Machado, R.B.; Fabrini, P.; Cruz, A.M.; Maia, E.; da Cunha Bastos, A. Clinical and metabolic aspects of the continuous use of a contraceptive association of ethinyl estradiol (30 microg) and gestodene (75 microg). Contraception 2004, 70, 365–370. [Google Scholar] [CrossRef]
- Wiegratz, I.; Lee, J.H.; Kutschera, E.; Winkler, U.H.; Kuhl, H. Effect of four oral contraceptives on hemostatic parameters. Contraception 2004, 70, 97–106. [Google Scholar] [CrossRef]
- Zuber, V.; Grinberg, N.F.; Gill, D.; Manipur, I.; Slob, E.A.W.; Patel, A.; Wallace, C.; Burgess, S. Combining evidence from Mendelian randomization and colocalization: Review and comparison of approaches. Am. J. Hum. Genet. 2022, 109, 767–782. [Google Scholar] [CrossRef]
- Wang, D.; Li, Y.; Ge, H.; Ghadban, T.; Reeh, M.; Güngör, C. The Extracellular Matrix: A Key Accomplice of Cancer Stem Cell Migration, Metastasis Formation, and Drug Resistance in PDAC. Cancers 2022, 14, 3998. [Google Scholar] [CrossRef]
- Mancini, A.; Gentile, M.T.; Pentimalli, F.; Cortellino, S.; Grieco, M.; Giordano, A. Multiple aspects of matrix stiffness in cancer progression. Front. Oncol. 2024, 14, 1406644. [Google Scholar] [CrossRef] [PubMed]
- Zhu, B.T. Catechol-O-Methyltransferase (COMT)-mediated methylation metabolism of endogenous bioactive catechols and modulation by endobiotics and xenobiotics: Importance in pathophysiology and pathogenesis. Curr. Drug Metab. 2002, 3, 321–349. [Google Scholar] [CrossRef] [PubMed]
- Lu, H.; Cassis, L.A.; Kooi, C.W.V.; Daugherty, A. Structure and functions of angiotensinogen. Hypertens. Res. Off. J. Jpn. Soc. Hypertens. 2016, 39, 492–500. [Google Scholar] [CrossRef]
- Rowland, A.; Miners, J.O.; Mackenzie, P.I. The UDP-glucuronosyltransferases: Their role in drug metabolism and detoxification. Int. J. Biochem. Cell Biol. 2013, 45, 1121–1132. [Google Scholar] [CrossRef] [PubMed]
- Mennillo, E.; Yang, X.; Weber, A.A.; Maruo, Y.; Verreault, M.; Barbier, O.; Chen, S.; Tukey, R.H. Intestinal UDP-Glucuronosyltransferase 1A1 and Protection against Irinotecan-Induced Toxicity in a Novel UDP-Glucuronosyltransferase 1A1 Tissue-Specific Humanized Mouse Model. Drug Metab. Dispos. Biol. Fate Chem. 2022, 50, 33–42. [Google Scholar] [CrossRef]
- Magnusson, M.K.; Mosher, D.F. Fibronectin: Structure, assembly, and cardiovascular implications. Arterioscler. Thromb. Vasc. Biol. 1998, 18, 1363–1370. [Google Scholar] [CrossRef]
- Soikkeli, J.; Podlasz, P.; Yin, M.; Nummela, P.; Jahkola, T.; Virolainen, S.; Krogerus, L.; Heikkilä, P.; von Smitten, K.; Saksela, O.; et al. Metastatic outgrowth encompasses COL-I, FN1, and POSTN up-regulation and assembly to fibrillar networks regulating cell adhesion, migration, and growth. Am. J. Pathol. 2010, 177, 387–403. [Google Scholar] [CrossRef] [PubMed]
- Efthymiou, G.; Saint, A.; Ruff, M.; Rekad, Z.; Ciais, D.; Van Obberghen-Schilling, E. Shaping Up the Tumor Microenvironment With Cellular Fibronectin. Front. Oncol. 2020, 10, 641. [Google Scholar] [CrossRef]
- Dalton, C.J.; Lemmon, C.A. Fibronectin: Molecular Structure, Fibrillar Structure and Mechanochemical Signaling. Cells 2021, 10, 2443. [Google Scholar] [CrossRef]
- Khalaf, N.; El-Serag, H.B.; Abrams, H.R.; Thrift, A.P. Burden of Pancreatic Cancer: From Epidemiology to Practice. Clin. Gastroenterol. Hepatol. Off. Clin. Pract. J. Am. Gastroenterol. Assoc. 2021, 19, 876–884. [Google Scholar] [CrossRef]
- Alvarez, A.; Benjaminsen Borch, K.; Rylander, C. Reproductive Factors, Use of Exogenous Hormones, and Pancreatic Cancer Incidence: The Norwegian Women and Cancer Study. Clin. Epidemiol. 2021, 13, 67–80. [Google Scholar] [CrossRef] [PubMed]
- Kabat, G.C.; Kamensky, V.; Rohan, T.E. Reproductive factors, exogenous hormone use, and risk of pancreatic cancer in postmenopausal women. Cancer Epidemiol. 2017, 49, 1–7. [Google Scholar] [CrossRef] [PubMed]
- Richmond, R.C.; Davey Smith, G. Mendelian Randomization: Concepts and Scope. Cold Spring Harb. Perspect. Med. 2022, 12, a040501. [Google Scholar] [CrossRef]
- Sekula, P.; Del Greco, M.F.; Pattaro, C.; Köttgen, A. Mendelian Randomization as an Approach to Assess Causality Using Observational Data. J. Am. Soc. Nephrol. JASN 2016, 27, 3253–3265. [Google Scholar] [CrossRef]
- Zhang, X.; Luo, Y.; Cen, Y.; Qiu, X.; Li, J.; Jie, M.; Yang, S.; Qin, S. MACC1 promotes pancreatic cancer metastasis by interacting with the EMT regulator SNAI1. Cell Death Dis. 2022, 13, 923. [Google Scholar] [CrossRef]
- Jiang, P.; Li, Z.; Tian, F.; Li, X.; Yang, J. Fyn/heterogeneous nuclear ribonucleoprotein E1 signaling regulates pancreatic cancer metastasis by affecting the alternative splicing of integrin β1. Int. J. Oncol. 2017, 51, 169–183. [Google Scholar] [CrossRef]
- Lei, X.; Chen, G.; Li, J.; Wen, W.; Gong, J.; Fu, J. Comprehensive analysis of abnormal expression, prognostic value and oncogenic role of the hub gene FN1 in pancreatic ductal adenocarcinoma via bioinformatic analysis and in vitro experiments. PeerJ 2021, 9, e12141. [Google Scholar] [CrossRef] [PubMed]
- Yang, H.; Messina-Pacheco, J.; Corredor, A.; Gregorieff, A.; Liu, J.; Nehme, A.; Najafabadi, H.; Riazalhosseini, Y.; Gao, B.; Gao, Z. An integrated model of acinar to ductal metaplasia-related N7-methyladenosine regulators predicts prognosis and immunotherapy in pancreatic carcinoma based on digital spatial profiling. Front. Immunol. 2022, 13, 961457. [Google Scholar] [CrossRef]
- Hiroshima, Y.; Kasajima, R.; Kimura, Y.; Komura, D.; Ishikawa, S.; Ichikawa, Y.; Bouvet, M.; Yamamoto, N.; Oshima, T.; Morinaga, S.; et al. Novel targets identified by integrated cancer-stromal interactome analysis of pancreatic adenocarcinoma. Cancer Lett. 2020, 469, 217–227. [Google Scholar] [CrossRef]
- Wang, H.; Zhang, J.; Li, H.; Yu, H.; Chen, S.; Liu, S.; Zhang, C.; He, Y. FN1 is a prognostic biomarker and correlated with immune infiltrates in gastric cancers. Front. Oncol. 2022, 12, 918719. [Google Scholar] [CrossRef]
- Zhang, X.-X.; Luo, J.-H.; Wu, L.-Q. FN1 overexpression is correlated with unfavorable prognosis and immune infiltrates in breast cancer. Front. Genet. 2022, 13, 913659. [Google Scholar] [CrossRef] [PubMed]
- Li, J.; Chen, C.; Chen, B.; Guo, T. High FN1 expression correlates with gastric cancer progression. Pathol. Res. Pract. 2022, 239, 154179. [Google Scholar] [CrossRef]
- Pan, H.; Luo, Z.; Lin, F.; Zhang, J.; Xiong, T.; Hong, Y.; Sun, B.; Yang, Y. FN1, a reliable prognostic biomarker for thyroid cancer, is associated with tumor immunity and an unfavorable prognosis. Oncol. Lett. 2024, 28, 510. [Google Scholar] [CrossRef]
- Eble, J.A.; Niland, S. The extracellular matrix in tumor progression and metastasis. Clin. Exp. Metastasis 2019, 36, 171–198. [Google Scholar] [CrossRef]
- Chakkera, M.; Foote, J.B.; Farran, B.; Nagaraju, G.P. Breaking the stromal barrier in pancreatic cancer: Advances and challenges. Biochim. Biophys. Acta Rev. Cancer 2024, 1879, 189065. [Google Scholar] [CrossRef] [PubMed]
- Li, W.; Li, T.; Sun, C.; Du, Y.; Chen, L.; Du, C.; Shi, J.; Wang, W. Identification and prognostic analysis of biomarkers to predict the progression of pancreatic cancer patients. Mol. Med. 2022, 28, 43. [Google Scholar] [CrossRef] [PubMed]
- Xavier, C.P.R.; Castro, I.; Caires, H.R.; Ferreira, D.; Cavadas, B.; Pereira, L.; Santos, L.L.; Oliveira, M.J.; Vasconcelos, M.H. Chitinase 3-like-1 and fibronectin in the cargo of extracellular vesicles shed by human macrophages influence pancreatic cancer cellular response to gemcitabine. Cancer Lett. 2021, 501, 210–223. [Google Scholar] [CrossRef]
- Zhu, L.; Ma, M.; Zhang, L.; Wang, S.; Guo, Y.; Ling, X.; Lin, H.; Lai, N.; Lin, S.; Du, L.; et al. System Analysis Based on Lipid-Metabolism-Related Genes Identifies AGT as a Novel Therapy Target for Gastric Cancer with Neoadjuvant Chemotherapy. Pharmaceutics 2023, 15, 810. [Google Scholar] [CrossRef]
- Wu, F.; Zhang, L.; Wang, L.; Zhang, D. AGT May Serve as a Prognostic Biomarker and Correlated with Immune Infiltration in Gastric Cancer. Int. J. Gen. Med. 2022, 15, 1865–1878. [Google Scholar] [CrossRef]
- Chen, W.; Chen, Y.; Zhang, K.; Yang, W.; Li, X.; Zhao, J.; Liu, K.; Dong, Z.; Lu, J. AGT serves as a potential biomarker and drives tumor progression in colorectal carcinoma. Int. Immunopharmacol. 2021, 101, 108225. [Google Scholar] [CrossRef]
- Wu, W.; Zhang, J.; Zhou, L.; You, L.; Zhao, Y.; Li, J. Increased COMT expression in pancreatic cancer and correlation with clinicopathologic parameters. Sci. China Life Sci. 2012, 55, 747–752. [Google Scholar] [CrossRef] [PubMed]
- Dai, H.; Wang, X.; Hong, X.; Wu, H.; Di, W.; Wang, W.; Xu, P.; Jia, C.; Wang, J.; Chen, H.; et al. Overexpression of catechol-O-methyltransferase occurs early in the progression of pancreatic cancer. J. Pancreatol. 2018, 1, 39. [Google Scholar] [CrossRef]
- Creveling, C.R. The role of catechol-O-methyltransferase in the inactivation of catecholestrogen. Cell. Mol. Neurobiol. 2003, 23, 289–291. [Google Scholar] [CrossRef] [PubMed]
- Boverhof, D.R.; Burgoon, L.D.; Williams, K.J.; Zacharewski, T.R. Inhibition of estrogen-mediated uterine gene expression responses by dioxin. Mol. Pharmacol. 2008, 73, 82–93. [Google Scholar] [CrossRef]
- Yadetie, F.; Brun, N.R.; Vieweg, I.; Nahrgang, J.; Karlsen, O.A.; Goksøyr, A. Transcriptome responses in polar cod (Boreogadus saida) liver slice culture exposed to benzo[a]pyrene and ethynylestradiol: Insights into anti-estrogenic effects. Toxicol. Vitr. Int. J. Publ. Assoc. BIBRA 2021, 75, 105193. [Google Scholar] [CrossRef]
Protein | Drug | Drug Target Interactions | Effect of the Protein on PC |
---|---|---|---|
COMT | ethinyl estradiol | substrate | Risk factors |
AGT | ethinyl estradiol | increased expression | Risk factors |
ethinyl estradiol co-treated with levonorgestrel | increased expression | Risk factors | |
FN1 | ethinyl estradiol co-treated with gestodene | increased expression | Risk factors |
UGT1A1 | ethinyl estradiol | substrate|inducer | Risk factors |
desogestrel | inducer | Risk factors | |
estradiol valerate | inducer | Risk factors | |
SERPINC1 | ethinyl estradiol co-treated with levonorgestrel | decreased expression | Protective factors |
ethinyl estradiol co-treated with norgestimate | decreased expression | Protective factors | |
ethinyl estradiol co-treated with dienogest | decreased activity | Protective factors | |
ethinyl estradiol co-treated with gestodene | decreased expression | Protective factors |
Primary Step MR | Sensitivity Step MR | Colocalization | ||||||
---|---|---|---|---|---|---|---|---|
Protein | OR | p-Value | OR | p-Value | Adjust p-Value | Revers Effect | Sensitivity Analyses | PPH4 |
FN1 | 1.20 | 0.0086 | 1.71 | 0.003 | 0.015 | No | Passed | 51% |
COMT | 2.31 | 0.032 | 1.15 | 0.001 | 0.005 | No | Passed | 21% |
AGT | 2.52 | 0.035 | 2.16 | 0.002 | 0.01 | No | Passed | 93.20% |
UGT1A1 | 1.31 | 0.045 | 1.26 | 0.02 | 0.1 | No | Failed | 59.90% |
SERPINC1 | 0.12 | 0.0007 | 0.12 | 0.0007 | 0.0035 | No | Passed | 100% |
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Tang, Y.; Zhang, Y.; Wang, S.; Shi, X.; Ruan, X.; Cheng, Y.; Yan, F.; Liu, T. Evaluating the Impact of Oral Contraceptives on Pancreatic Cancer Risk: A Two-Sample Mendelian Randomization Analysis. Biomedicines 2025, 13, 1351. https://doi.org/10.3390/biomedicines13061351
Tang Y, Zhang Y, Wang S, Shi X, Ruan X, Cheng Y, Yan F, Liu T. Evaluating the Impact of Oral Contraceptives on Pancreatic Cancer Risk: A Two-Sample Mendelian Randomization Analysis. Biomedicines. 2025; 13(6):1351. https://doi.org/10.3390/biomedicines13061351
Chicago/Turabian StyleTang, Yuxin, Yu Zhang, Shuaiyi Wang, Xinyi Shi, Xinjia Ruan, Yu Cheng, Fangrong Yan, and Tiantian Liu. 2025. "Evaluating the Impact of Oral Contraceptives on Pancreatic Cancer Risk: A Two-Sample Mendelian Randomization Analysis" Biomedicines 13, no. 6: 1351. https://doi.org/10.3390/biomedicines13061351
APA StyleTang, Y., Zhang, Y., Wang, S., Shi, X., Ruan, X., Cheng, Y., Yan, F., & Liu, T. (2025). Evaluating the Impact of Oral Contraceptives on Pancreatic Cancer Risk: A Two-Sample Mendelian Randomization Analysis. Biomedicines, 13(6), 1351. https://doi.org/10.3390/biomedicines13061351