title: [Prognostic markers of advanced non-small cell lung carcinoma -  assessing the significance of oncomarkers using data-mining techiques RPA].
abstract: BACKGROUND
  Identification of new prognostic factors can help in designing future clinical studies. In the case of advanced non-small cell lung cancer, there might be good candidates - tumor markers CYFRA 21-1, CEA or NSE [1-8]. It is possible to evaluate the relationship between their expression and prognosis by data mining technique recursive partitioning and amalgamation.
  
  
  METHODS
  We analyzed retrospective data of 162 patients of Oncology clinics in Trnava. All of these patients were admitted between 2008 and 2012 for the administration of first-line chemotherapy according to current recommendations. We evaluated the impact of known pretreatment prognostic markers - performance status, weight loss, smoking, age, sex, stage, histologic subtype, comorbidity and oncomarkers CYFRA 21-1, CEA or NSE, as well as combinations of these factors on survival.
  
  
  RESULTS
  Our analyses showed that there are three subgroups of patients with good, intermediate and unfavorable prognosis. Oncomarkers played an important role in formation of a subgroup of 49 patients with good prognosis - including patients with no pretreatment weight loss and low levels of CEA ( 4.1 ng/ml) or NSE ( 11.1 ng/ml). In this subgroup, the median survival time was at least 16 months (not achieved) and the difference in survival compared to the rest of the group was highly statistically significant (risk ratio 5.21, 95% CI 1.41-19.28; p < 0.0001).
  
  
  CONCLUSIONS
  We showed the prognostic significance of low levels of NSE and CEA oncomarkers in the group of patients with no pretreatment weight loss. Recursive partitioning and amalgamation is a useful data mining method, but the generated hypothesis needs to be confirmed by further clinical study designed for this purpose
authors.forename: S
authors.initials: S
authors.lastname: Cingelová
authors.forename: V
authors.initials: V
authors.lastname: Labudová
authors.forename: D
authors.initials: D
authors.lastname: Berkešová
authors.forename: M
authors.initials: M
authors.lastname: Dienerová
authors.forename: A
authors.initials: A
authors.lastname: Dammak
authors.forename: E
authors.initials: E
authors.lastname: Grmanová
authors.forename: O
authors.initials: O
authors.lastname: Nádaská
authors.forename: I
authors.initials: I
authors.lastname: Vasilenková
authors.forename: E
authors.initials: E
authors.lastname: Najšelová
authors.forename: V
authors.initials: V
authors.lastname: Skarbová
authors.forename: M
authors.initials: M
authors.lastname: Migašová
authors.forename: Z
authors.initials: Z
authors.lastname: Viktorínová
authors.forename: L
authors.initials: L
authors.lastname: Jurga
mesh_terms: D000951:Antigens, Neoplasm / Q000378:metabolism*; D014408:Biomarkers, Tumor / Q000378:metabolism*; D002272:Carcinoembryonic Antigen / Q000378:metabolism*; D002289:Carcinoma, Non-Small-Cell Lung / Q000188:drug therapy / Q000378:metabolism* / Q000401:mortality / Q000473:pathology; D057225:Data Mining / Q000379:methods*; D006801:Humans; D053539:Keratin-19 / Q000378:metabolism*; D008175:Lung Neoplasms / Q000188:drug therapy / Q000378:metabolism* / Q000401:mortality / Q000473:pathology; D010751:Phosphopyruvate Hydratase / Q000378:metabolism*; D011379:Prognosis; D012189:Retrospective Studies; D014891:Weight Lifting
chemical_list: D000951:Antigens, Neoplasm; D014408:Biomarkers, Tumor; D002272:Carcinoembryonic Antigen; D053539:Keratin-19; C102012:antigen CYFRA21.1; D010751:Phosphopyruvate Hydratase