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. 2015 Jan 6;13(Suppl 7):45-53.
doi: 10.4137/CIN.S16341. eCollection 2014.

Detection of pancreatic cancer biomarkers using mass spectrometry

Kiyoun Kim  1 , Soohyun Ahn  1 , Johan Lim  1 , Byong Chul Yoo  2 , Jin-Hyeok Hwang  3 , Woncheol Jang  1
Affiliations

Detection of pancreatic cancer biomarkers using mass spectrometry

Kiyoun Kim et al. Cancer Inform. .

Abstract

Background: Pancreatic cancer is the fourth leading cause of cancer-related deaths. Therefore, in order to improve survival rates, the development of biomarkers for early diagnosis is crucial. Recently, diabetes has been associated with an increased risk of pancreatic cancer. The aims of this study were to search for novel serum biomarkers that could be used for early diagnosis of pancreatic cancer and to identify whether diabetes was a risk factor for this disease.

Methods: Blood samples were collected from 25 patients with diabetes (control) and 93 patients with pancreatic cancer (including 53 patients with diabetes), and analyzed using matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF/MS). We performed preprocessing, and various classification methods with imputation were used to replace the missing values. To validate the selection of biomarkers identified in pancreatic cancer patients, we measured biomarker intensity in pancreatic cancer patients with diabetes following surgical resection and compared our results with those from control (diabetes-only) patients.

Results: By using various classification methods, we identified the commonly splitting protein peaks as m/z 1,465, 1,206, and 1,020. In the follow-up study, in which we assessed biomarkers in pancreatic cancer patients with diabetes after surgical resection, we found that the intensities of m/z at 1,465, 1,206, and 1,020 became comparable with those of diabetes-only patients.

Keywords: biomarker; classification; mass spectrometry; pancreatic cancer.

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Figures

Figure 1
Figure 1
Preprocessing.
Figure 2
Figure 2
σ estimation using LOESS.
Figure 3
Figure 3
Boxplots of misclassification rates for each imputation methods with four classifiers.
Figure 4
Figure 4
ROC curves for each imputation methods with four classifiers.
Figure 5
Figure 5
Boxplots for AUC.
Figure 6
Figure 6
Intensities of biomarkers between control and case.
Figure 7
Figure 7
Distances between pancreatic cancer with diabetes and without diabetes groups with 3 different linkage methods.
See this image and copyright information in PMC

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