Vol. 6 (01) pp. 1482-1491 DOI: 10.21474/IJAR01/6377

DISCRIMINANT SCORE (D SCORE) A MODEL TO PREDICT LUNG CANCER BASED ON THE EXPRESSION STUDY OF PANEL OF GENES AND MICRORNAS IN THE PLASMA.

  • MB ChB (Baghdad), MSc Path (Glasgow UK) and Arab Board of surgical pathology and PhD molecular pathology.
7 Downloads 135 Views
Crossref

Download Full Paper (PDF)

Abstract

Background: Lung cancer is the leading cause of death from cancer worldwide in adult, and accounting for (13%) of all newly diagnosed cancer cases. Inspite of that there is no effective method for screening of highly susceptible groups such as chronic heavy smokers. The present study depends on expression of panel of genes and microRNAs that are involved or the result of lung cancer formation in blood samples taken from cancer and non-cancer lung cases to design a discrimination score that can exclude or predict lung cancer. Objective: Is to design a discrimination score (D score) to predict lung cancer in blood samples based of expression study of a panel of genes and microRNAs. Patients and Method: A case control study on expression of P53, KRAS, c-MYC, and Her-2/neu genes, microRNAs 21, 34a, 92, and 98 in blood samples taken from patients positive and negative for lung cancer. Results: The discrimination score (D score) was obtained per a mathematical equation for each sample. According to the value of discrimination score: any new case with a discrimination score (D score) more than 0.0 is considered as a lung cancer and the higher the result the more we are confident the case is lung cancer. Any case with discrimination score below 0.0 is considered as a benign lung condition, and the lower the result the more we are confident the case is a benign condition.

Keywords

How to Cite This Article

Hussain Abady Aljebori. (2018); DISCRIMINANT SCORE (D SCORE) A MODEL TO PREDICT LUNG CANCER BASED ON THE EXPRESSION STUDY OF PANEL OF GENES AND MICRORNAS IN THE PLASMA., International Journal of Advanced Research (IJAR), 6 (01), 1482-1491, ISSN 2320-5407. DOI: https://doi.org/10.21474/IJAR01/6377

Corresponding Author

Dr Hussain Abady Aljebori
Arab Board of surgical pathology, PhD molecular pathology.

Article Analytics

References

  1. Rebecca Siegel, Deepa Naishadham, and Ahmedin Jemal. Cancer statistics 2013. CA: A Cancer Journal for Clinicians; January2013, 63(1): 11?30.
  2. Al Hasnawi., S.M., et al. (2009). Cancer in Iraq: Distribution by primary tumor site. The new Iraqi Journal of Medicine, 5 (1), pp. 5 ? 8.
  3. Jiang Y, Jin P, Chang RS, Yao SX, Zhou QH, Fan YG, and Qiao YL. Analysis of sensitivity and specificity of sputum cytology screening for lung cancer with different positive criteria]. Zhonghua Yu Fang Yi Xue Za Zhi. 2011 Jul; 45 (7): 605 - 8.
  4. Mitul B Modi, Mitesh R Rathva, Nupur R Shah, Manasi Trivedi and Harshad Patel. Role of FNAC in Lung Carcinoma and its Histo-Cytological Correlation. J Lung Pulm Respir Res 3(4):00090.
  5. Marco Andolfi, Rossella Potenza, Rosanna Capozzi, Valeria Liparulo, Francesco Puma, and Kazuhiro Yasufuku. The role of bronchoscopy in the diagnosis of early lung cancer: a review. J Thorac Dis. Nov 2016; 8(11): 3329 - 37.
  6. Doina-Ecaterina Tofolean, Ariadna-Peronela Fildan, Elena Dantes, Laura Mazilu, E. Dumitru, and Liliana Tuta. The Importance of Bronchoscopy in Early Lung Cancer (LC) Diagnosis. The Journal of "Ovidius" University of Constanta (2017) 22 (8): 270 -77.
  7. Bert Gold, Milena Cankovic, Larissa V. Furtado,Frederick Meier, and Christopher D. Gocke. Do Circulating Tumor Cells, Exosomes, and Circulating Tumor Nucleic Acids Have Clinical Utility? Journal of Molecular Diagnosis. May 2015; 17(3): 209?24. doi: 1016/j.jmoldx.2015.02.001.
  8. Schwarzenbach H1, Hoon DS, and Pantel K. Cell-free nucleic acids as biomarkers in cancer patients. Nat Rev Cancer (June 2011);11(6):426-37. doi: 10.1038/nrc3066. Epub 2011 May 12.
  9. Xiao Han, Junyun Wang, and Yingli Sun. Circulating Tumor DNA as Biomarkers for Cancer Detection. Genomics, Proteomics & Bioinformatics (April 2017); V 15 (2): 59-72.
  10. Akira Mogi and Hiroyuki Kuwano. TP53 Mutations in Non-Small Cell Lung Cancer. Journal of Biomedicine and Biotechnology, 2011Volume (2011), Article ID 583929, 9 pages.
  11. V?h?kangas, K.H., Bennett, W.P., Castr?n, K., Welsh, J.A., Khan, M.A., Bl?meke, B., Alavanja, M.C., & Harris, C.C. (2001). p53 and K-ras mutations in lung cancers from former and never-smoking women. Cancer Res, 61(11), pp. 4350-6.
  12. Peter M. K. Westcott1,3 and Minh D. To. The genetics and biology of KRAS in lung cancer. Chin J Cancer. 2013 Feb; 32(2): 63?70.
  13. Eun Young Kim, Arum Kim, Se Kyu Kim, and Yoon Soo Chang. MYC expression correlates with PD-L1 expression in non-small cell lung cancer. Lung Cancer; August 2017, vol 110; 63 ? 67.
  14. ?d?m Nagy, Lőrinc S?ndor Pongor, Andr?s Szab?, Mariacarmela Santarpia, and Bal?zs Győrffy. KRAS driven expression signature has prognostic power superior to mutation status in non‐small cell lung cancer. Int J Cancer. (Feb 2017); 140(4): 930?37.
  15. Fazli Wahida, Adeeb Shehzada, Taous Khanb, and You YoungKim. MicroRNAs: Synthesis, mechanism, function, and recent clinical trials. Biochimica et Biophysica Acta (BBA) - Molecular Cell Research; November 2010, Volume 1803(11); 1231-1243.
  16. Yang Y, Meng H, Peng Q, Yang X, Gan R, and Zhao L et al. Downregulation of microRNA-21 expression restrains non-small cell lung cancer cell proliferation and migration through upregulation of programmed cell death.
  17. Xue X, Liu Y, Wang Y, Meng M, Wang K, and Zang X et al. MiR-21 and MiR-155 promote non-small cell lung cancer progression by downregulating SOCS1, SOCS6, and PTEN. Oncotarget; Dec 2016, 7(51): 84508 ? 84519.
  18. Shi Y, Liu C, Liu X, Tang DG, and Wang J. The microRNA miR-34a inhibits non-small cell lung cancer (NSCLC) growth and the CD44hi stem-like NSCLC cells. PLoS One; March 2014, 9(3). doi: 10.1371/journal.
  19. Yu Y, Zuo J, Tan Q, Zar Thin K, Li P, and Zhu M et al. Plasma miR-92a-2 as a biomarker for small cell lung cancer. Cancer Biomark; 2017;18(3):319 - 327. doi: 10.3233/CBM-160254.
  20. Du L1, Schageman JJ, Subauste MC, Saber B, Hammond SM, and Prudkin L et al. miR-93, miR-98, and miR-197 regulate expression of tumor suppressor gene FUS1. Mol Cancer Res. Aug 2009;7(8):1234-43. doi: 10.1158/1541-7786.
  21. mirVana? miRNA Isolation Kit, with phenol. Retrieved online from; https://www.thermofisher.com/order/catalog/product/AM1560 on 2nd of March 2017.
  22. High-Capacity RNA-to-cDNA? Kit protocol from applied Biosystem. Retrieved online March 2017 from: https://www.thermofisher.com/order/catalog/product/4387406.
  23. Applied Biosystems? SYBR? Green PCR Master Mix. Retrieved online at May 2017 from: https://www.fishersci.com/shop/products/applied-biosystems-sybr-green-pcr-master-mix-6/p-4927004.
  24. Barber RD, Harmer DW, Coleman RA, and Clark BJ. GAPDH as a housekeeping gene: analysis of GAPDH mRNA expression in a panel of 72 human tissues. Physiol Genomics, May 2005; 21(3): 389-95.
  25. Applied Biosystems? TaqMan? Universal Master Mix II, No UNG. Retrieved online at may 2017 from:https://www.fishersci.com/shop/products/taqman-universal-mmix-ii/4440040.
  26. Alexandra Rogue, Emilie Floch, Roy Forster and Nick Pearson. miRNA quantification and evaluation of suitable housekeeping genes in the major pre-clinical species. Safety and Health Research Laboratories. Retrieved online at May 2017 from: https://www.citoxlab.com/wp-content/uploads/2014/07/ miRNA-quantification-and-evaluation-of-suitable-housekeeping-genes-in-the-major-pre-clinical-species.pdf.
  27. MxPro QPCR Software Instruction Manual for Mx3000P and Mx3005P QPCR Systems. Retried online at 6th of January 2017 from; https://arboretum.harvard. edu/wp-content/uploads/Mx3000P_and_ Mx3005P_User_Guide1.pdf.
  28. Using SPSS for item analysis retrieved online at March 2017 from: https://www. google.iq/search?source=hp&ei=jeVJWs2bFMjKsAfzgo3AAQ&q=Using+SPSS+for+item+analysis&oq=Using+SPSS+for+item+analysis&gs_l=psy-ab.3... 6111.6111.0.7866.1.1.0.0.0.0.0.0..0.0....0...1c.2.64.psy-ab..1.0.0....0. Tq6bukk4Txg.
  29. IBM SPSS Statistics v24 Free download. Retrieved online at 25th of June 2017 from: https://rahim-soft.org/ibm-spss-statistics-v24-free-download/.
  30. Calculating Difficulty, Discrimination and Reliability Index/Standard Error of Measurement. Retrieved online at 4th of March 2017 from: https://ppukm.org/2015/04/02/calculating-omr-indexes/.
  31. Discrimination | Item Analysis - Sites at Penn State. Retrieved online at 4th of March 2017 from http://sites.psu.edu/itemanalysis/discrimination/.
  32. Al-Alusi F. Lung cancer in Iraq, the trend in the decade (1986-1995)-A clinicopathological study. J Fan Med Baghdad, 2002, 44, pp. 175-178.
  33. Menkhi, A. Molecular study in lung cancer for detection of RB tumor suppressor gene mRNA and c-MYC, Her-2/neu, K-RAS oncogenes. PhD thesis, Baghdad College of Medicine, 2011.
  34. Saeed R M. P53 overexpression in lung cancer - A histopathological study FICMS. Pathol. Thesis 2007, Baghdad College of medicine.
  35. Thabit MA. Changing pattern of lung cancer: a pathological and immunohistochemical- A histopathological study FICMS. Pathol. Thesis, 2001, Baghdad College of Medicine.
  36. Kersting CF, Kraus A & Behn M. Differential Frequencies of p16INK4a Promoter Hypermethylation, p53 Mutation, and K-ras Mutation in Exfoliative Material Mark the Development of Lung Cancer in Symptomatic Chronic Smokers. Journal of Clinical Oncology, 2000, 18(18), pp. 3221-3229.
  37. Abdul-Majeed BA. P53 mRNA in- Situ Hybridization analysis and Immunohisto chemical Expression in Lung Cancer: A Comparative Study. Journal of the Faculty of Medicine, 2010, 52(3), pp. 370-377.
  38. Paone, G. Discriminant analysis on small cell lung cancer and non-small cell lung cancer by means of NSE and CYFRA-21.1. Euro Respiratory Journal, 1995, 8, pp. 1136-1140.
  39. Marchevsky, A.M. Classification of individual lung cancer cell lines based on DNA methylation markers: use of linear discriminant analysis and artificial neyral networks. J Mol Diagn, 2004, 6(1), pp. 28-36.

Similar Articles

Creative Commons Attribution 4.0 This work is licensed under a Creative Commons Attribution 4.0 International License.