Prediction of Lung Cancer Based on Serum Biomarkers by Gene Expression Programming Methods

Abstract

In diagnosis of lung cancer, rapid distinction between small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC) tumors is very important. Serum markers, including lactate dehydrogenase (LDH), C-reactive protein (CRP), carcino-embryonic antigen (CEA), neurone specific enolase (NSE) and Cyfra21-1, are reported to reflect lung cancer characteristics. In this study classification of lung tumors was made based on biomarkers (measured in 120 NSCLC and 60 SCLC patients) by setting up optimal biomarker joint models with a powerful computerized tool -gene expression programming (GEP). GEP is a learning algorithm that combines the advantages of genetic programming (GP) and genetic algorithms (GA). It specifically focuses on relationships between variables in sets of data and then builds models to explain these relationships, and has been successfully used in formula finding and function mining. As a basis for defining a GEP environment for SCLC and NSCLC prediction, three explicit predictive models were constructed. CEA and NSE are requentlyused lung cancer markers in clinical trials, CRP, LDH and Cyfra21-1 have significant meaning in lung cancer, basis on CEA and NSE we set up three GEP models-GEP 1(CEA, NSE, Cyfra21-1), GEP2 (CEA, NSE, LDH), GEP3 (CEA, NSE, CRP). The best classification result of GEP gained when CEA, NSE and Cyfra21-1 were combined: 128 of 135 subjects in the training set and 40 of 45 subjects in the test set were classified correctly, the accuracy rate is 94.8% in training set; on collection of samples for testing, the accuracy rate is 88.9%. With GEP2, the accuracy was significantly decreased by 1.5% and 6.6% in training set and test set, in GEP3 was 0.82% and 4.45% respectively. Serum Cyfra21-1 is a useful and sensitive serum biomarker in discriminating between NSCLC and SCLC. GEP modeling is a promising and excellent tool in diagnosis of lung cancer.

Combination of Circulating Tumor Cells with Serum Carcinoembryonic Antigen Enhances Clinical Prediction of Non-Small Cell Lung Cancer

“…The finding of our study may suggest a cost-effective method for NSCLC patients to decide who should run for this expensive assay. Tumor markers are always used as biomarkers for earlier diagnosis, treatment efficacy and prognosis in NSCLC [ 4 – 5 , 12 , 37 – 38 ]. As serum CEA is associated with CTCs, it may be possible to recommend the CellSearch assay for those NSCLC patients who are CEA-positive.…”

Tumour-associated antigens and their anti-cancer applications

“…In addition, CEA is also a frequently used lung cancer marker in clinical trials and carries prognostic information in NSCLC (Grunnet & Sorensen ; Yu et al . ). Meanwhile, CEA may serve as a target for cancer immunotherapy.…”

“…The combined evaluation of multiple markers, including CA19-9, TPS and TAG-72, which have potential value for the diagnosis, monitoring and prognosis of CRC, as well as CEA, is considered to possess a more precise index for cancer treatment (Mckeown et al 2014). In addition, CEA is also a frequently used lung cancer marker in clinical trials and carries prognostic information in NSCLC (Grunnet & Sorensen 2012;Yu et al 2014). Meanwhile, CEA may serve as a target for cancer immunotherapy.…”

Recurrent Syncope Associated with Lung Cancer

“…Serum tumor markers are considered as biological indicators detected from the serum or plasma of suspected tumor patients with insufficient sensitivity or specificity [ 6 ], which possess the advantages of easy detection, noninvasive operation, and cost-effectiveness [ 6 , 7 ]. For years, the oncologic literature suggests that serum CYFRA21-1 and NSE can be a potential serologic biomarker in evaluation of lung cancer, especially small cell lung cancer (SCLC) [ 8 ]. Although the chest X-ray was normal, we put the further imaginative examination with enhanced chest CT and PET-CT, which confirmed the suspicious diagnosis of lung cancer.…”