Esophageal cancer is the eighth most commonly diagnosed cancer and the sixth most common cause of cancer related mortality globally. There are two different types of esophageal cancer, esophageal squamous cell carcinoma (ESCC) and esophageal adenocarcinoma (EAC), each accounting for half of the cases. EAC develops in the lower one third of the esophagus as a consequence of chronic gastroesophageal reflux disease (GERD) from precancerous metaplastic condition Barrett's esophagus (BE). Apart from GERD and BE which are major risk factors for EAC, other known risk factors include age, male gender, obesity, Caucasian race, low intake of fruits and vegetables in diet, and Helicobacter pylori negative status. Due to changing life style and prevalence of risk factors, the incidences of EAC have been rising for past few decades and now it has become one of the fastest growing malignancies. The survival rate is very poor with only 1 in 5 patients survive more than 5 years after EAC diagnosis, likely due to diagnosis at late stages. To diagnose early treatable dysplastic changes in progression from BE to EAC, BE patients undergo routine endoscopy-biopsies, with the biopsy evaluated by a histopathologist to confirm the dysplastic changes. This current method is invasive and prone to sampling error as well as interobserver variability. Endoscopy requires patient hospitalization and specialist appointment, leading to high expense. Moreover, BE is an asymptomatic condition which means a pool of BE patients are undiagnosed hence not enrolled into the surveillance program. Collectively, it has been shown that current endoscopy-biopsies based diagnostic is impractical and expensive for population wide BE screening or surveillance programs. In contrast to endoscopy-biopsy, biomarkers from the blood are amenable to population-screening strategies, due to the ease of access and low cost of testing. Moreover, EAC pathogenesis has been associated with changes in the serum glycan profile. However, specific glycoproteins that undergo differential glycosylation are unknown. Therefore, the aims of this thesis were to (i) identify serum diagnostic glycoprotein biomarker candidates for BE and EAC using biomarker discovery pipeline, (ii) develop a targeted proteomics approach to measure biomarker candidates for timely verification, (iii) verify serum glycoprotein candidates in an independent patient cohort, and (iv) test feasibility of using electrochemical detection methodology for the glycoprotein detection. This translational research project utilizes lectins, naturally occurring proteins with specificity to bind with glycan structures, as affinity agents to isolate glycoproteins with different glycan structures. Our laboratory has previously established lectin magnetic bead array (LeMBA) methodology to identify serum glycoprotein biomarker candidates showing differential lectin binding (Loo et al., J Proteome Res 2010 and Choi et al., Electrophoresis 2011). With the help of a bioinformatician and biostatisticians, GlycoSelector database incorporating statistical analysis pipeline was developed for biomarker discovery using LeMBA platform (http://glycoselector.di.uq.edu.au). Serum samples from 29 patients (healthy - 9, BE - 10 and EAC - 10) were screened using LeMBA-GlycoSelector pipeline. A ranked list of candidate glycoprotein biomarkers that distinguish (i) EAC from BE (ii) BE from healthy and (iii) EAC from healthy group was identified. GlycoSelector analysis resulted in identification of total 183 unique lectin-protein biomarker candidates for targeted verification. Out of the 20 lectins employed for the biomarker discovery, 6 lectins showing differential binding with glycoprotein candidates were selected for verification. Multiple reaction monitoring-mass spectrometry (MRM-MS) assay was set up for 41 promising glycoprotein candidates. After testing linearity and reproducibility of MRM-MS assay, serum samples from an independent patient cohort were screened using customized LeMBA coupled with MRM-MS. Online web-portal Shiny mixOmics (http://mixomics-projects.di.uq.edu.au/Shiny) was used for statistical analysis. Of the 246 glycoforms measured in the verification stage, 40 glycoforms (as measured by lectin affinity) verified as candidate serum markers. The top candidate for distinguishing healthy from BE was Narcissus pseudonarcissus lectin (NPL)-reactive Apolipoprotein B-100; BE vs EAC, Aleuria aurantia lectin (AAL)-reactive complement component C9; healthy vs EAC, Erythroagglutinin Phaseolus vulgaris (EPHA)-reactive gelsolin. A panel of 8 glycoforms showed an area under receiver operating characteristic curve (AUROC) of 0.94 to discriminate EAC from BE. Two biomarker candidates were independently verified by LeMBA-immunoblotting, confirming the validity of the relative quantitation approach employed. Mass spectrometry methods employed for biomarker discovery and verification are best suited for research laboratories but not for routine clinical practice whereas electrochemical detection methods have been successfully applied for development of point-of-care diagnostics e.g. glucose biosensor. In this thesis, the feasibility of using electrochemical method for glycoprotein detection has been tested with success using a model glycoprotein ovalbumin with Sambucus nigra agglutinin (SNA lectin). A detection limit of 10 pg/mL was demonstrated, in the background of diluted human serum. Taken together, this study firstly identified and then verified serum diagnostic glycoprotein biomarker candidates using two independent patient cohorts for BE/EAC. The biomarker candidates described here require further clinical evaluation in a large patient cohort including early dysplastic patient samples. Electrochemical detection method described in the last part of this thesis can be developed further into in vitro diagnostic for clinical use employing glycoprotein biomarker candidates.