The PCA and LDA Analysis on the Differential Expression of Proteins in Breast Cancer
Breast cancer is a leading cause of mortality in women. In Malaysia, it is the most common cancer to affect women. The most common form of breast cancer is infiltrating ductal carcinoma (IDC). A proteomic approach was undertaken to identify protein profile changes between cancerous and normal breast...
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Online Access: | http://dx.doi.org/10.3233/DMA-2010-0753 |
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doaj-5bbafba9eb294d8499e87d9364979fe92020-11-24T23:59:00ZengHindawi LimitedDisease Markers0278-02401875-86302010-01-0129523124210.3233/DMA-2010-0753The PCA and LDA Analysis on the Differential Expression of Proteins in Breast CancerSeng Liang0Manjit Singh1Saravanan Dharmaraj2Lay-Harn Gam3School of Pharmaceutical Sciences, Universiti Sains Malaysia, Penang, MalaysiaDepartment of Surgery, Penang General Hospital, Penang, MalaysiaSchool of Pharmaceutical Sciences, Universiti Sains Malaysia, Penang, MalaysiaSchool of Pharmaceutical Sciences, Universiti Sains Malaysia, Penang, MalaysiaBreast cancer is a leading cause of mortality in women. In Malaysia, it is the most common cancer to affect women. The most common form of breast cancer is infiltrating ductal carcinoma (IDC). A proteomic approach was undertaken to identify protein profile changes between cancerous and normal breast tissues from 18 patients. Two protein extracts; aqueous soluble and membrane associated protein extracts were studied. Thirty four differentially expressed proteins were identified. The intensities of the proteins were used as variables in PCA and reduced data of six principal components (PC) were subjected to LDA in order to evaluate the potential of these proteins as collective biomarkers for breast cancer. The protein intensities of SEC13-like 1 (isoform b) and calreticulin contributed the most to the first PC while the protein intensities of fibrinogen beta chain precursor and ATP synthase D chain contributed the most to the second PC. Transthyretin precursor and apolipoprotein A-1 precursor contributed the most to the third PC. The results of LDA indicated good classification of samples into normal and cancerous types when the first 6 PCs were used as the variables. The percentage of correct classification was 91.7% for the originally grouped tissue samples and 88.9% for cross-validated samples.http://dx.doi.org/10.3233/DMA-2010-0753 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Seng Liang Manjit Singh Saravanan Dharmaraj Lay-Harn Gam |
spellingShingle |
Seng Liang Manjit Singh Saravanan Dharmaraj Lay-Harn Gam The PCA and LDA Analysis on the Differential Expression of Proteins in Breast Cancer Disease Markers |
author_facet |
Seng Liang Manjit Singh Saravanan Dharmaraj Lay-Harn Gam |
author_sort |
Seng Liang |
title |
The PCA and LDA Analysis on the Differential Expression of Proteins in Breast Cancer |
title_short |
The PCA and LDA Analysis on the Differential Expression of Proteins in Breast Cancer |
title_full |
The PCA and LDA Analysis on the Differential Expression of Proteins in Breast Cancer |
title_fullStr |
The PCA and LDA Analysis on the Differential Expression of Proteins in Breast Cancer |
title_full_unstemmed |
The PCA and LDA Analysis on the Differential Expression of Proteins in Breast Cancer |
title_sort |
pca and lda analysis on the differential expression of proteins in breast cancer |
publisher |
Hindawi Limited |
series |
Disease Markers |
issn |
0278-0240 1875-8630 |
publishDate |
2010-01-01 |
description |
Breast cancer is a leading cause of mortality in women. In Malaysia, it is the most common cancer to affect women. The most common form of breast cancer is infiltrating ductal carcinoma (IDC). A proteomic approach was undertaken to identify protein profile changes between cancerous and normal breast tissues from 18 patients. Two protein extracts; aqueous soluble and membrane associated protein extracts were studied. Thirty four differentially expressed proteins were identified. The intensities of the proteins were used as variables in PCA and reduced data of six principal components (PC) were subjected to LDA in order to evaluate the potential of these proteins as collective biomarkers for breast cancer. The protein intensities of SEC13-like 1 (isoform b) and calreticulin contributed the most to the first PC while the protein intensities of fibrinogen beta chain precursor and ATP synthase D chain contributed the most to the second PC. Transthyretin precursor and apolipoprotein A-1 precursor contributed the most to the third PC. The results of LDA indicated good classification of samples into normal and cancerous types when the first 6 PCs were used as the variables. The percentage of correct classification was 91.7% for the originally grouped tissue samples and 88.9% for cross-validated samples. |
url |
http://dx.doi.org/10.3233/DMA-2010-0753 |
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