IMR Press / FBL / Volume 11 / Issue 2 / DOI: 10.2741/1885

Frontiers in Bioscience-Landmark (FBL) is published by IMR Press from Volume 26 Issue 5 (2021). Previous articles were published by another publisher on a subscription basis, and they are hosted by IMR Press on imrpress.com as a courtesy and upon agreement with Frontiers in Bioscience.

Open Access Article
LS-CAP: an algorithm for identifying cytogenetic aberrations in hepatocellular carcinoma using microarray data
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1 Department of Health Statistics, Second Military Medical University, Shanghai, 200433, P.R China
2 State Key Laboratory of Genetic Engineering, Institute of Genetics, School of Life Sciences, Fudan University, Shanghai,200433, P.R China
Front. Biosci. (Landmark Ed) 2006, 11(2), 1311–1322; https://doi.org/10.2741/1885
Published: 1 May 2006
Abstract

Biological techniques such as Array-Comparative genomic hybridization (CGH), fluorescent in situ hybridization (FISH) and affymetrix single nucleotide pleomorphism (SNP) array have been used to detect cytogenetic aberrations. However, on genomic scale, these techniques are labor intensive and time consuming. Comparative genomic microarray analysis (CGMA) has been used to identify cytogenetic changes in hepatocellular carcinoma (HCC) using gene expression microarray data. However, CGMA algorithm can not give precise localization of aberrations, fails to identify small cytogenetic changes, and exhibits false negatives and positives. Locally un-weighted smoothing cytogenetic aberrations prediction (LS-CAP) based on local smoothing and binomial distribution can be expected to address these problems. LS-CAP algorithm was built and used on HCC microarray profiles. Eighteen cytogenetic abnormalities were identified, among them 5 were reported previously, and 12 were proven by CGH studies. LS-CAP effectively reduced the false negatives and positives, and precisely located small fragments with cytogenetic aberrations.

Keywords
Cytogenetic Aberration
Tumor
Neoplasis
Cancer
Carcinoma
Hepatoma
Hepatocellular Carcinoma
cDNA Microarray
Comparative Genomic Microarray Analysis
Smoothing Theory
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