REVIEW PAPER
Can Nidogen-1 and Nidogen-2 improve our preoperative cancer detection rate?
1
1st Chair and Department of Gynaecologial Oncology and Gynaecology, Medical University of Lublin, Poland
2
Medical University of Lublin, Poland
Corresponding author
Agnieszka Kwiatkowska
1 Ist Chair and Department of Gynaecologial Oncology and Gynaecology, Medical University of Lublin, Staszica 16 str, 20-081 Lublin, Poland, Staszica 16, 20-081, Lublin, Poland
1 Ist Chair and Department of Gynaecologial Oncology and Gynaecology, Medical University of Lublin, Staszica 16 str, 20-081 Lublin, Poland, Staszica 16, 20-081, Lublin, Poland
J Pre Clin Clin Res. 2021;15(2):80-86
KEYWORDS
TOPICS
ABSTRACT
Introduction and objective:
Ovarian cancer (OC) is the third most commonly diagnosed gynecological cancer among women worldwide and the second most common in Poland. Early-stage ovarian cancer is still very difficult to diagnose and concerns only about 20–30% of all ovarian cancers. Most cases (approximately 70%) of ovarian cancer are diagnosed at more advanced stages (III and IV). The aim of the review is to bring closer new potential biological markers – Nidogen-1 and Nidogen-2 in the diagnosis of ovarian cancer.
Brief description of the state of knowledge:
To date, the best serum marker for ovarian cancer is Ca-125, but its use as a screening marker is limited due to high false positive rates. Ca-125 could be elevated in other benign and malignant conditions. Serum concentrations of Nidogen-1 and Nidogen-2 are higher in the advanced stagegroup (Stage III and IV), in comparison to the early stage group (Stage I and II) in serous ovarian cancer, and can reflect the tumour burden. Analysis showed that Nidogens discriminate against patients with serous ovarian carcinomas from healthy controls. The concentrations of both of them correlate with concentration Ca-125, especially Nidogen-2. The above biomarkers were compared with the results of the preoperative detection of ovarian cancer that are often used in clinical practice – IOTA Simple Rules, Risk of Malignancy Index and Risk of Ovarian Malignancy Algorithm.
Conclusions:
Nidogen-1 and Nidogen-2 are new promising biomarkers for ovarian cancer, especially for the serous type, although there is still a need for prospective studies proving their good diagnostic value.
Ovarian cancer (OC) is the third most commonly diagnosed gynecological cancer among women worldwide and the second most common in Poland. Early-stage ovarian cancer is still very difficult to diagnose and concerns only about 20–30% of all ovarian cancers. Most cases (approximately 70%) of ovarian cancer are diagnosed at more advanced stages (III and IV). The aim of the review is to bring closer new potential biological markers – Nidogen-1 and Nidogen-2 in the diagnosis of ovarian cancer.
Brief description of the state of knowledge:
To date, the best serum marker for ovarian cancer is Ca-125, but its use as a screening marker is limited due to high false positive rates. Ca-125 could be elevated in other benign and malignant conditions. Serum concentrations of Nidogen-1 and Nidogen-2 are higher in the advanced stagegroup (Stage III and IV), in comparison to the early stage group (Stage I and II) in serous ovarian cancer, and can reflect the tumour burden. Analysis showed that Nidogens discriminate against patients with serous ovarian carcinomas from healthy controls. The concentrations of both of them correlate with concentration Ca-125, especially Nidogen-2. The above biomarkers were compared with the results of the preoperative detection of ovarian cancer that are often used in clinical practice – IOTA Simple Rules, Risk of Malignancy Index and Risk of Ovarian Malignancy Algorithm.
Conclusions:
Nidogen-1 and Nidogen-2 are new promising biomarkers for ovarian cancer, especially for the serous type, although there is still a need for prospective studies proving their good diagnostic value.
ACKNOWLEDGEMENTS
The authors gratefully acknowledge the multiple databases from which the data was obtained.
CONFLICT OF INTEREST
No potential conflict of interest was reported by the author(s). The article is an original work, has not been published previously, and is not under consideration for publication elsewhere in its final form – printed or electronic. All authors
participated in the conception and design of the study; all authors approved the final manuscript as submitted, and agree to be accountable for all aspects of the study.The study did not receive any grant from funding agencies in the public, commercial, or not-for-profit sectors.
Kwiatkowska A, Krawczyk D, Kułak K. Can Nidogen-1 and Nidogen-2 improve our preoperative cancer detection rate? J Pre-Clin Clin Res. 2021; 15(2): 80–86. doi: 10.26444/jpccr/138308
REFERENCES (56)
1.
Tan TZ, Miow QH, Miki Y, et al. Epithelial-mesenchymal transition spectrum quantification and its efficacy in deciphering survival and drug responses of cancer patients. EMBO Mol Med. 2014; 6(10): 1279–1293. doi: 10.15252/emmm.201404208.
2.
Siegel RL, Miller KD, Jemal A. Cancer statistics. CA: A Cancer Journal for Clinicians. 2020; 70(1): 7–30. doi: 10.3322/caac.21590.
3.
Globocan. Summary statistic 2018. Males. Females. Both sexes. 2018. Available from: https://gco.iarc.fr/today/data....
4.
Dochez V, Caillon H, Vaucel E, et al. Biomarkers and algorithms for diagnosis of ovarian cancer: CA125, HE4, RMI and ROMA, a review. J Ovarian Res. 2019; 12(1): 28. doi: 10.1186/s13048-019-0503-7.
5.
Huy NVQ, Van Khoa V, Tam LM, et al. Standard and optimal cut-off values of serum ca-125, HE4 and ROMA in preoperative prediction of ovarian cancer in Vietnam. Gynecol Oncol Rep. 2018; 25: 110–114. doi: 10.1016/j.gore.2018.07.002.
6.
Lee YJ, Kim YM, Kang JS, et al. Comparison of Risk of Ovarian Malignancy Algorithm and cancer antigen 125 to discriminate between benign ovarian tumour and early-stage ovarian cancer according to imaging tumour subtypes. Oncol Lett. 2020; 20(1): 931–938. doi: 10.3892/ol.2020.11629.
7.
Yanaranop M, Anakrat V, Siricharoenthai S, et al. Is the Risk of Ovarian Malignancy Algorithm Better Than Other Tests for Predicting Ovarian Malignancy in Women with Pelvic Masses? Gynecol Obstet Invest. 2017; 82(1): 47–53. doi: 10.1159/000446238.
8.
Goff BA, Agnew K, Neradilek MB, et al. Combining a symptom index, CA125 and HE4 (triple screen) to detect ovarian cancer in women with a pelvic mass. Gynecol Oncol. 2017; 147(2): 291–295. doi: 10.1016/j.ygyno.2017.08.020.
9.
Shin KH, Kim HH, Kwon BS, et al. Clinical Usefulness of Cancer Antigen (CA) 125, Human Epididymis 4, and CA72-4 Levels and Risk of Ovarian Malignancy Algorithm Values for Diagnosing Ovarian Tumours in Korean Patients With and Without Endometriosis. Ann Lab Med. 2020; 40(1): 40–47. doi: 10.3343/alm.2020.40.1.40.
10.
Lycke M, Kristjansdottir B, Sundfeldt K. A multicenter clinical trial validating the performance of HE4, CA125, risk of ovarian malignancy algorithm and risk of malignancy index. Gynecol Oncol. 2018; 151(1): 159–165. doi: 10.1016/j.ygyno.2018.08.025.
11.
Kim B, Park Y, Kim B, et al. Diagnostic performance of CA 125, HE4, and risk of Ovarian Malignancy Algorithm for ovarian cancer. J Clin Lab Anal. 2019; 33(1): e22624. doi: 10.1002/jcla.22624.
12.
Choi HJ, Lee YY, Sohn I, et al. Comparison of CA 125 alone and risk of ovarian malignancy algorithm (ROMA) in patients with adnexal mass: A multicenter study. Curr Probl Cancer. 2020; 44(2): 100508. doi: 10.1016/j.currproblcancer.2019.100508.
13.
Torky HA, Sherif A, Abo-Louz A, et al. Evaluation of Serum Nidogen-2 as a Screening and Diagnostic Tool for Ovarian Cancer. Gynecol Obstet Invest. 2018; 83: 461–465. doi: 10.1159/000481798.
14.
Timmerman D, Van Calster B, Testa A, et al. Predicting the risk of malignancy in adnexal masses based on the Simple Rules from the International Ovarian Tumour Analysis group. Am J Obstet Gynecol. 2016 Apr; 214(4): 424–437. doi: 10.1016/j.ajog.2016.01.007.
15.
Auekitrungrueng R, Tinnangwattana D, Tantipalakorn C, et al. Comparison of the diagnostic accuracy of International Ovarian Tumour Analysis simple rules and the risk of malignancy index to discriminate between benign and malignant adnexal masses. Int J Gynaecol Obstet. 2019; 146(3): 364–369. doi: 10.1002/ijgo.12891.
16.
Garg S, Kaur A, Mohi JK, et al. Evaluation of IOTA Simple Ultrasound Rules to Distinguish Benign and Malignant Ovarian Tumours. J Clin Diagn Res. 2017; 11(8): TC06-TC09. doi: 10.7860/JCDR/2017/26790.10353.
17.
Antovska V, Trajanova M. An original risk of ovarian malignancy index and its predictive value in evaluating the nature of ovarian tumour. Southern African Journal of Gynaecological Oncology. 2015; 7(2): 52–59. doi: 10.1080/20742835.2015.1081486.
18.
Tongsong T, Tinnangwattana D, Vichak-Ururote L, et al. Comparison of Effectiveness in Differentiating Benign from Malignant Ovarian Masses between IOTA Simple Rules and Subjective Sonographic Assessment. Asian Pac J Cancer Prev. 2016; 17(9): 4377–4380.
19.
Ning CP, Ji X, Wang HQ, et al. Association between the sonographer’s experience and diagnostic performance of IOTA simple rules. World J Surg Oncol. 2018; 16(1): 179. doi: 10.1186/s12957-018-1479-2.
20.
Knafel A, Banas T, Nocun A, et al. The Prospective External Validation of International Ovarian Tumour Analysis (IOTA) Simple Rules in the Hands of Level I and II Examiners. Ultraschall Med. 2016; 37(5): 516–523. English. doi: 10.1055/s-0034-1398773.
21.
Shetty J, Saradha A, Pandey D, et al. IOTA Simple Ultrasound Rules for Triage of Adnexal Mass: Experience from South India. J Obstet Gynaecol India. 2019; 69(4): 356–362. doi: 10.1007/s13224-019-01229-z.
22.
Hidalgo JJ, Ros F, Aubá M, et al. Prospective external validation of IOTA three-step strategy for characterizing and classifying adnexal masses and retrospective assessment of alternative two-step strategy using simple-rules risk. Ultrasound Obstet Gynecol. 2019; 53(5): 693–700. doi: 10.1002/uog.20163.
23.
Al-Musalhi K, Al-Kindi M, Ramadhan F, et al. Validity of Cancer Antigen-125 (CA-125) and Risk of Malignancy Index (RMI) in the Diagnosis of Ovarian Cancer. Oman Med J. 2015; 30(6): 428–434. doi: 10.5001/omj.2015.85.
24.
Dora SK, Dandapat AB, Pande B, Hota JP. A prospective study to evaluate the risk malignancy index and its diagnostic implication in patients with suspected ovarian mass. J Ovarian Res. 2017; 10(1): 55. doi: 10.1186/s13048-017-0351-2.
25.
Chopra S, Vaishya R, Kaur J. An Evaluation of the Applicability of the Risk of Malignancy Index for Adnexal Masses to Patients Seen at a Tertiary Hospital in Chandigarh, India. J Obstet Gynaecol India. 2015; 65(6): 405–410. doi: 10.1007/s13224-014-0583-7.
26.
Campos C, Sarian LO, Jales RM, et al. Performance of the Risk of Malignancy Index for Discriminating Malignant Tumours in Women With Adnexal Masses. J Ultrasound Med. 2016; 35(1): 143–152. doi: 10.7863/ultra.15.01068.
27.
Anton C, Carvalho FM, Oliveira EI, et al. A comparison of CA125, HE4, risk ovarian malignancy algorithm (ROMA), and risk malignancy index (RMI) for the classification of ovarian masses. Clinics (Sao Paulo). 2012; 67(5): 437–441. doi: 10.6061/clinics/2012(05)06.
28.
Manegold-Brauer G, Buechel J, Knipprath-Mészaros A, et al. Improved Detection Rate of Ovarian Cancer Using a 2-Step Triage Model of the Risk of Malignancy Index and Expert Sonography in an Outpatient Screening Setting. Int J Gynecol Cancer. 2016; 26(6): 1062–1069. doi: 10.1097/IGC.0000000000000718.
29.
Meys EM, Kaijser J, Kruitwagen RF, et al. Subjective assessment versus ultrasound models to diagnose ovarian cancer: A systematic review and meta-analysis. Eur J Cancer. 2016; 58: 17–29. doi: 10.1016/j.ejca.2016.01.007.
30.
Chacón E, Dasí J, Caballero C, et al. Risk of Ovarian Malignancy Algorithm versus Risk Malignancy Index-I for Preoperative Assessment of Adnexal Masses: A Systematic Review and Meta-Analysis. Gynecol Obstet Invest. 2019; 84(6): 591–598. doi: 10.1159/000501681.
31.
Wei SU, Li H, Zhang B. The diagnostic value of serum HE4 and CA-125 and ROMA index in ovarian cancer. Biomed Rep. 2016 Jul; 5(1): 41–44. doi: 10.3892/br.2016.682.
32.
Cui R, Wang Y, Li Y, et al. Clinical value of ROMA index in diagnosis of ovarian cancer: meta-analysis. Cancer Manag Res. 2019; 11: 2545–2551. doi: 10.2147/CMAR.S199400.
33.
Abdalla N, Piorkowski R, Bachanek M, et al. Does the Risk of Ovarian Malignancy Algorithm Provide Better Diagnostic Performance Than HE4 and CA125 in the Presurgical Differentiation of Adnexal Tumours in Polish Women? Dis Markers. 2018: 5289804. doi: 10.1155/2018/5289804.
34.
Kruegel J, Miosge N. Basement membrane components are key players in specialized extracellular matrices. Cell Mol Life Sci. 2010; 67(17): 2879–2895. doi: 10.1007/s00018-010-0367-x.
35.
Dai J, Estrada B, Jacobs S, et al. Dissection of Nidogen function in Drosophila reveals tissue-specific mechanisms of basement membrane assembly. PLoS Genet. 2018; 14(9): e1007483. doi: 10.1371/journal.pgen.1007483.
36.
Miosge N, Sasaki T, Timpl R. Evidence of nidogen-2 compensation for nidogen-1 deficiency in transgenic mice. Matrix Biol. 2002; 21(7): 611–621. doi: 10.1016/s0945-053x(02)00070-7.
37.
Chai AW, Cheung AK, Dai W, et al. Metastasis-suppressing NID2, an epigenetically-silenced gene, in the pathogenesis of nasopharyngeal carcinoma and esophageal squamous cell carcinoma. Oncotarget. 2016 Nov 29; 7(48): 78859–78871. doi: 10.18632/oncotarget.12889.
38.
Patel TR, Bernards C, Meier M, et al. Structural elucidation of full-length nidogen and the laminin-nidogen complex in solution. Matrix Biol. 2014; 33: 60–7. doi: 10.1016/j.matbio.2013.07.009.
39.
Lakshmanan HHS, Melrose AR, Sepp AI, et al. The basement membrane protein nidogen-1 supports platelet adhesion and activation. Platelets. 2020; 1: 1–5. doi: 10.1080/09537104.2020.1745170.
40.
Breitkreutz D, Koxholt I, Thiemann K, et al. Skin Basement Membrane: The Foundation of Epidermal Integrity—BM Functions and Diverse Roles of Bridging Molecules Nidogen and Perlecan. BioMed Research International. 2013; 1–16. doi: 10.1155/2013/179784.
41.
Saikia P, Thangavadivel S, Medeiros CS, et al. IL-1 and TGF-ß Modulation of Epithelial Basement Membrane Components Perlecan and Nidogen Production by Corneal Stromal Cells. Invest Ophthalmol Vis Sci. 2018 Nov 1; 59(13): 5589–5598. doi: 10.1167/iovs.18-25202.
42.
Meyer IFG. Investigating the role of nidogens, a family of basement membrane proteins, at the neuromuscular junction in health and disease. https://discovery.ucl.ac.uk/id... (access 2021.03.12).
43.
Baranowsky A, Mokkapati S, Bechtel M, et al. Impaired wound healing in mice lacking the basement membrane protein nidogen 1. Matrix Biol. 2010; 29(1): 15–21. doi: 10.1016/j.matbio.2009.09.004.
44.
Strelnikov VV, Kuznetsova EB, Tanas AS, et al. Abnormal promoter DNA hypermethylation of the integrin, nidogen, and dystroglycan genes in breast cancer. Sci Rep. 2021; 11: 2264. doi: 10.1038/s41598-021-81851-y.
45.
Lössl P, Kölbel K, Tänzler D, et al. Analysis of nidogen-1/laminin ?1 interaction by cross-linking, mass spectrometry, and computational modeling reveals multiple binding modes. PLoS One. 2014; 9(11): e112886. doi: 10.1371/journal.pone.0112886.
46.
Yu ZH, Wang YM, Jiang YZ, et al. NID2 can serve as a potential prognosis prediction biomarker and promotes the invasion and migration of gastric cancer. Pathol Res Pract. 2019; 215(10): 152553. doi: 10.1016/j.prp.2019.152553.
47.
Feng X, Xie X, Zheng B, et al. The more potential performance of nidogen 2 methylation by tissue or plasma DNA over brichoalveolar lavage DNA in diagnosis of nonsmall cell lung cancer. J Cancer Res Ther. 2018; 14(Supplement): S341-S346. doi: 10.4103/0973-1482.235352.
48.
Chai AWY, Cheung AKL, Dai W, et al. Elevated levels of serum nidogen-2 in esophageal squamous cell carcinoma. Cancer Biomark. 2018; 21(3): 583–590.
49.
Li L, Zhang Y, Li N, et al. Nidogen-1: a candidate biomarker for ovarian serous cancer. Jpn J Clin Oncol. 2015; 45(2): 176–82. doi: 10.1093/jjco/hyu187.
50.
Zhou Y, Zhu Y, Fan X, et al. NID1, a new regulator of EMT required for metastasis and chemoresistance of ovarian cancer cells. Oncotarget. 2017; 8(20): 33110–33121. doi: 10.18632/oncotarget.16145.
51.
Yuan D, Qian H, Guo T, et al. LncRNA-ATB Promotes the Tumourigenesis of Ovarian Cancer via Targeting miR-204–3p. Onco Targets Ther. 2020; 13: 573–583. Published 2020 Jan 20. doi: 10.2147/OTT.S230552.
52.
Kuk C, Gunawardana CG, Soosaipillai A, et al. Nidogen-2: a new serum biomarker for ovarian cancer. Clin Biochem. 2010; 43(4–5): 355–361. doi: 10.1016/j.clinbiochem.2009.10.012.
53.
Gupta KK, Gupta VK, Naumann RW. Ovarian cancer: screening and future directions. Int J Gynecol Cancer. 2019; 1: 195–200. doi: 10.1136/ijgc-2018-000016.
54.
Bonifácio VDB. Ovarian Cancer Biomarkers: Moving Forward in Early Detection. Adv Exp Med Biol. 2020; 1219: 355–363. doi: 10.1007/978-3-030-34025-4_18.
55.
Muinao T, Deka Boruah HP, Pal M. Diagnostic and Prognostic Biomarkers in ovarian cancer and the potential roles of cancer stem cells – An updated review. Exp Cell Res. 2018; 362(1): 1–10. doi: 10.1016/j.yexcr.2017.10.018.
56.
Chen Y, Jin P, Zhang HX, et al. Clinical significance of serum nidogen-2 and carbohydrate antigen 125 in patients with ovarian serous cystadenocarcinoma. Tumour. 2011; 31(4): 339–342. doi: 10.3781/j.issn.1000-7431.2011.04.011.
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