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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

Decision tree algorithms from three different training protocols. (A) Serum samples of individuals with lung cancer and inflammatory disease, and healthy controls. (B) Serum samples of individuals with lung cancer and healthy controls. (C) Serum samples of individuals with lung cancer and inflammatory disease.
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Figure 2: Decision tree algorithms from three different training protocols. (A) Serum samples of individuals with lung cancer and inflammatory disease, and healthy controls. (B) Serum samples of individuals with lung cancer and healthy controls. (C) Serum samples of individuals with lung cancer and inflammatory disease.

Mentions: Using the spectrum, a decision tree classification algorithm was built and three protein peaks at 11480, 8802 and 3185 Da were automatically selected as splitters (p < 0.05, for each). The 11480 Da peak was used as the root node in the classification tree to divide the 134 samples into two groups (Figure 2A). The left node (node 2) included cases with peak intensity ≦ 0.53. The right node (node 3) contained the remaining with peak intensity ≦-0.061. Finally, all 134 cases in the training set were classified in the 4 terminal nodes, and a classification tree was obtained (Figure 2A). The sensitivity and specificity of the training set were 81.7% (58 of 71) and 85.7% (54 of 63), respectively. When the validity of this classification tree algorithm was challenged by the test set (87 cases), there was still high sensitivity (87.2%, 41 of 47) but low specificity (37.5%, 15 of 40) and accuracy (64.4%, 56 of 87) (Table 2).


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)

Decision tree algorithms from three different training protocols. (A) Serum samples of individuals with lung cancer and inflammatory disease, and healthy controls. (B) Serum samples of individuals with lung cancer and healthy controls. (C) Serum samples of individuals with lung cancer and inflammatory disease.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3102603&req=5

Figure 2: Decision tree algorithms from three different training protocols. (A) Serum samples of individuals with lung cancer and inflammatory disease, and healthy controls. (B) Serum samples of individuals with lung cancer and healthy controls. (C) Serum samples of individuals with lung cancer and inflammatory disease.
Mentions: Using the spectrum, a decision tree classification algorithm was built and three protein peaks at 11480, 8802 and 3185 Da were automatically selected as splitters (p < 0.05, for each). The 11480 Da peak was used as the root node in the classification tree to divide the 134 samples into two groups (Figure 2A). The left node (node 2) included cases with peak intensity ≦ 0.53. The right node (node 3) contained the remaining with peak intensity ≦-0.061. Finally, all 134 cases in the training set were classified in the 4 terminal nodes, and a classification tree was obtained (Figure 2A). The sensitivity and specificity of the training set were 81.7% (58 of 71) and 85.7% (54 of 63), respectively. When the validity of this classification tree algorithm was challenged by the test set (87 cases), there was still high sensitivity (87.2%, 41 of 47) but low specificity (37.5%, 15 of 40) and accuracy (64.4%, 56 of 87) (Table 2).

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