title: Profiling of differentially expressed proteins in stage IV colorectal cancers with good and poor outcomes.
abstract: Screening patients at high risk of recurrence of cancer would allow for more accurate and personalized treatment. In this study, we tried to identify the prognosis-related protein profile by applying two different quantitative proteomic techniques, difference in-gel electrophoresis and cleavable isotope-coded affinity tag method. Six tumor tissues were obtained from stage IV colorectal cancer (CRC) patients, of which three have survived more than five years (good prognostic group, GPG) and the other three died within 25 months (poor prognostic group, PPG) after palliative surgery and subsequent chemotherapy treatment. From the two independent quantitative analyses, we identified 175 proteins with abundance ratios greater than 2-fold. Gene ontology analysis revealed that proteins related to cellular assembly/organization and movement were generally increased in the PPG. Twenty-two proteins, including caveolin-1, were chosen for confirmatory studies by Western blot and immunohistochemistry. The Western blot data for each individual protein were analyzed with Mann-Whitney tests, and a multi-marker panel was generated by logistic regression analysis. Five proteins, fatty acid binding protein 1, intelectin 1, transitional endoplasmic reticulum ATPase, transgelin and tropomyosin 2, were significantly different between the two prognostic groups within 95% confidence. No single protein could completely distinguish the two groups from each other. However, a combination of the five proteins effectively distinguished PPG from GPG patients (AUC=1). Our study suggests a multi-marker panel composed of proteome signatures that provide accurate predictive information and potentially assist personalized therapy. This article is part of a Special Issue entitled: Proteomics: The clinical link.
authors.affiliation: Life Sciences Division, Korea Institute of Science and Technology, Seoul, Republic of Korea.
authors.forename: Hye-Jung
authors.initials: HJ
authors.lastname: Kim
authors.forename: Un-Beom
authors.initials: UB
authors.lastname: Kang
authors.forename: Hanna
authors.initials: H
authors.lastname: Lee
authors.forename: Ji-Han
authors.initials: JH
authors.lastname: Jung
authors.forename: Seung-Taek
authors.initials: ST
authors.lastname: Lee
authors.forename: Myeong-Hee
authors.initials: MH
authors.lastname: Yu
authors.forename: Hoguen
authors.initials: H
authors.lastname: Kim
authors.forename: Cheolju
authors.initials: C
authors.lastname: Lee
mesh_terms: D000328:Adult; D000368:Aged; D014408:Biomarkers, Tumor / Q000032:analysis / Q000378:metabolism; D002277:Carcinoma / Q000175:diagnosis* / Q000378:metabolism / Q000401:mortality / Q000473:pathology; D015179:Colorectal Neoplasms / Q000175:diagnosis* / Q000378:metabolism / Q000401:mortality / Q000473:pathology; D005260:Female; D006801:Humans; D008297:Male; D055442:Metabolome; D008875:Middle Aged; D008954:Models, Biological; D009363:Neoplasm Proteins / Q000032:analysis / Q000378:metabolism*; D009367:Neoplasm Staging; D011379:Prognosis; D020543:Proteome / Q000032:analysis / Q000378:metabolism; D040901:Proteomics / Q000379:methods
chemical_list: D014408:Biomarkers, Tumor; D009363:Neoplasm Proteins; D020543:Proteome