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Improving Detection Accuracy of Lung Cancer Serum Proteomic Profiling via Two-Stage Training Process.

Hsu PS, Wang YS, Huang SC, Lin YH, Chang CC, Tsang YW, Jiang JS, Kao SJ, Uen WC, Chi KH - Proteome Sci (2011)

Bottom Line: The predominantly expressed protein peak at 11480 Da was the primary splitter regardless of one- or two-stage analysis.This peak was suspected to be SAA (Serum Amyloid A) due to the similar m/z countered around this area.This hypothesis was further tested using an SAA ELISA assay.

View Article: PubMed Central - HTML - PubMed

Affiliation: Division of Radiation Therapy and Oncology, Shin Kong Wu Ho-Su Memorial Hospital, Taipei, Taiwan. M002047@ms.skh.org.tw.

ABSTRACT

Background: Surface-Enhanced Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (SELDI-TOF-MS) is a frequently used technique for cancer biomarker research. The specificity of biomarkers detected by SELDI can be influenced by concomitant inflammation. This study aimed to increase detection accuracy using a two-stage analysis process.

Methods: Sera from 118 lung cancer patients, 72 healthy individuals, and 31 patients with inflammatory disease were randomly divided into training and testing groups by 3:2 ratio. In the training group, the traditional method of using SELDI profile analysis to directly distinguish lung cancer patients from sera was used. The two-stage analysis of distinguishing the healthy people and non-healthy patients (1st-stage) and then differentiating cancer patients from inflammatory disease patients (2nd-stage) to minimize the influence of inflammation was validated in the test group.

Results: In the test group, the one-stage method had 87.2% sensitivity, 37.5% specificity, and 64.4% accuracy. The two-stage method had lower sensitivity (> 70.1%) but statistically higher specificity (80%) and accuracy (74.7%). The predominantly expressed protein peak at 11480 Da was the primary splitter regardless of one- or two-stage analysis. This peak was suspected to be SAA (Serum Amyloid A) due to the similar m/z countered around this area. This hypothesis was further tested using an SAA ELISA assay.

Conclusions: Inflammatory disease can severely interfere with the detection accuracy of SELDI profiles for lung cancer. Using a two-stage training process will improve the specificity and accuracy of detecting lung cancer.

No MeSH data available.


Related in: MedlinePlus

The splitting peaks in two-stage training protocol and the density peaks of these two proteins in all sera samples. (A) The first splitting peak from the first-stage training set marker showing a protein peak marker of 11480 Da on the CM10 chip (vertical line). Protein mass spectra were shown in sera of individuals with lung cancer (left), healthy control (middle), and inflammatory disease (right). (B) The first splitting peak of the second-stage training set marker showing a protein peak of 12580 Da (vertical line). (C) Density peak of 11480 Da protein in the sera of all samples. (D) Density peak of 12580 Da protein in the sera of all samples.
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Figure 4: The splitting peaks in two-stage training protocol and the density peaks of these two proteins in all sera samples. (A) The first splitting peak from the first-stage training set marker showing a protein peak marker of 11480 Da on the CM10 chip (vertical line). Protein mass spectra were shown in sera of individuals with lung cancer (left), healthy control (middle), and inflammatory disease (right). (B) The first splitting peak of the second-stage training set marker showing a protein peak of 12580 Da (vertical line). (C) Density peak of 11480 Da protein in the sera of all samples. (D) Density peak of 12580 Da protein in the sera of all samples.

Mentions: Comparing the one-stage method to the two-stage method in the testing group, the two-stage method had higher specificity and accuracy (p < 0.05). The represented peaks from the two-stage training set were shown in Figures 4A and 4B. In first-stage training set, the peak was 11480 Da protein. This peak was increased in both lung cancer and inflammatory disease patients, but was very low in the health controls (Figure 4C). In the second-stage training set, the protein peaks with 12580, 6836, and 3185 Da was used to separate cancer patients from non-cancer inflammatory disease. The average peak intensity of 12580 was higher in lung cancer patients and health controls, but relatively lower in inflammatory disease patients (Figure 4D). The predominately expressed protein peak at 11480 Da was the primary splitter (distinguishing factor) in all classification trees generated to separate healthy individuals, which were most likely to have SAA with average m/z countered around this area [22,25,26,34].


Improving Detection Accuracy of Lung Cancer Serum Proteomic Profiling via Two-Stage Training Process.

Hsu PS, Wang YS, Huang SC, Lin YH, Chang CC, Tsang YW, Jiang JS, Kao SJ, Uen WC, Chi KH - Proteome Sci (2011)

The splitting peaks in two-stage training protocol and the density peaks of these two proteins in all sera samples. (A) The first splitting peak from the first-stage training set marker showing a protein peak marker of 11480 Da on the CM10 chip (vertical line). Protein mass spectra were shown in sera of individuals with lung cancer (left), healthy control (middle), and inflammatory disease (right). (B) The first splitting peak of the second-stage training set marker showing a protein peak of 12580 Da (vertical line). (C) Density peak of 11480 Da protein in the sera of all samples. (D) Density peak of 12580 Da protein in the sera of all samples.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
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getmorefigures.php?uid=PMC3102603&req=5

Figure 4: The splitting peaks in two-stage training protocol and the density peaks of these two proteins in all sera samples. (A) The first splitting peak from the first-stage training set marker showing a protein peak marker of 11480 Da on the CM10 chip (vertical line). Protein mass spectra were shown in sera of individuals with lung cancer (left), healthy control (middle), and inflammatory disease (right). (B) The first splitting peak of the second-stage training set marker showing a protein peak of 12580 Da (vertical line). (C) Density peak of 11480 Da protein in the sera of all samples. (D) Density peak of 12580 Da protein in the sera of all samples.
Mentions: Comparing the one-stage method to the two-stage method in the testing group, the two-stage method had higher specificity and accuracy (p < 0.05). The represented peaks from the two-stage training set were shown in Figures 4A and 4B. In first-stage training set, the peak was 11480 Da protein. This peak was increased in both lung cancer and inflammatory disease patients, but was very low in the health controls (Figure 4C). In the second-stage training set, the protein peaks with 12580, 6836, and 3185 Da was used to separate cancer patients from non-cancer inflammatory disease. The average peak intensity of 12580 was higher in lung cancer patients and health controls, but relatively lower in inflammatory disease patients (Figure 4D). The predominately expressed protein peak at 11480 Da was the primary splitter (distinguishing factor) in all classification trees generated to separate healthy individuals, which were most likely to have SAA with average m/z countered around this area [22,25,26,34].

Bottom Line: The predominantly expressed protein peak at 11480 Da was the primary splitter regardless of one- or two-stage analysis.This peak was suspected to be SAA (Serum Amyloid A) due to the similar m/z countered around this area.This hypothesis was further tested using an SAA ELISA assay.

View Article: PubMed Central - HTML - PubMed

Affiliation: Division of Radiation Therapy and Oncology, Shin Kong Wu Ho-Su Memorial Hospital, Taipei, Taiwan. M002047@ms.skh.org.tw.

ABSTRACT

Background: Surface-Enhanced Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (SELDI-TOF-MS) is a frequently used technique for cancer biomarker research. The specificity of biomarkers detected by SELDI can be influenced by concomitant inflammation. This study aimed to increase detection accuracy using a two-stage analysis process.

Methods: Sera from 118 lung cancer patients, 72 healthy individuals, and 31 patients with inflammatory disease were randomly divided into training and testing groups by 3:2 ratio. In the training group, the traditional method of using SELDI profile analysis to directly distinguish lung cancer patients from sera was used. The two-stage analysis of distinguishing the healthy people and non-healthy patients (1st-stage) and then differentiating cancer patients from inflammatory disease patients (2nd-stage) to minimize the influence of inflammation was validated in the test group.

Results: In the test group, the one-stage method had 87.2% sensitivity, 37.5% specificity, and 64.4% accuracy. The two-stage method had lower sensitivity (> 70.1%) but statistically higher specificity (80%) and accuracy (74.7%). The predominantly expressed protein peak at 11480 Da was the primary splitter regardless of one- or two-stage analysis. This peak was suspected to be SAA (Serum Amyloid A) due to the similar m/z countered around this area. This hypothesis was further tested using an SAA ELISA assay.

Conclusions: Inflammatory disease can severely interfere with the detection accuracy of SELDI profiles for lung cancer. Using a two-stage training process will improve the specificity and accuracy of detecting lung cancer.

No MeSH data available.


Related in: MedlinePlus