Detection of genetic alterations in gastric cancer patients from Saudi Arabia using comparative genomic hybridization (CGH).

BACKGROUND: The present study was conducted to discover genetic imbalances such as DNA copy number variations (CNVs) associated with gastric cancer (GC) and to examine their association with different genes involved in the process of gastric carcinogenesis in Saudi population. METHODS: Formalin-fixed paraffin-embedded (FFPE) tissues samples from 33 gastric cancer patients and 15 normal gastric samples were collected. Early and late stages GC samples were genotyped and CNVs were assessed by using Illumina HumanOmni1-Quad v.1.0 BeadChip. RESULTS: Copy number gains were more frequent than losses throughout all GC samples compared to normal tissue samples. The mean number of the altered chromosome per case was 64 for gains and 40 for losses, and the median aberration length was 679115bp for gains and 375889bp for losses. We identified 7 high copy gain, 52 gains, 14 losses, 32 homozygous losses, and 10 copy neutral LOHs (loss of heterozygosities). Copy number gains were frequently detected at 1p36.32, 1q12, 1q22, 2p11.1, 4q23-q25, 5p12-p11, 6p21.33, 9q12-q21.11, 12q11-q12, 14q32.33, 16p13.3, 17p13.1, 17q25.3, 19q13.32, and losses at 1p36.23, 1p36.32, 1p32.1, 1q44, 3q25.2, 6p22.1, 6p21.33, 8p11.22, 10q22.1, 12p11.22, 14q32.12 and 16q24.2. We also identified 2 monosomy at chromosome 14 and 22, 52 partially trisomy and 22 whole chromosome 4 neutral loss of heterozygosities at 13q14.2-q21.33, 5p15.2-p15.1, 5q11.2-q13.2, 5q33.1-q34 and 3p14.2-q13.12. Furthermore, 11 gains and 2 losses at 1p36.32 were detected for 11 different GC samples and this region has not been reported before in other populations. Statistical analysis confirms significant association of H. pylori infection with T4 stage of GC as compare to control and other stages. CONCLUSIONS: We found that high frequency of copy number gains and losses at 1p36.23, 1p32.1, 1p36.32, 3q25.2, 6p21.33 and 16q24.2 may be common events in gastric cancer. While novel CNVs at 1p36.32 harbouring PRDM16, TP73 and TP73-AS1 genes showed 11 gains and 2 losses for 11 different GC cases and this region is not reported yet in Database of Genomic Variants may be specific to Saudi population.

An identification and prediction methods for feature-subsets of CpG islands methylation based on human peripheral blood leukocytes of chromosome 21q.

The pace of technology has allowed classification of feature-subset of methylated and unmethylated of CpG islands of DNA sequence properties. As methylation of CpG islands is involved in various biological phenomena and function of the DNA methylation is correlated to various human diseases such as cancer, analysis of the CpG islands has become important and useful in characterizing and modelling biological phenomena and understanding mechanism of such diseases. However, analysis of the data associated with the CpG islands is a quite new and challenging subject in bioinformatics, systems biology and epigenetics. In this paper, the problems associated with prediction of methylated and unmethylated CpG islands on human chromosome 21q are addressed. In order to carry out the prediction, a data set of 132 samples of the CpG islands from human peripheral blood leukocytes of chromosomes 21q and 4 different feature sub-sets totalling 44 attributes that characterise the methylated and unmethylated groups is extracted for each sample. Due to the nature of this unbalanced data set, in order to avoid disadvantages of traditional leave-one-out (LOO) and m-fold cross validation methods, the LOO method is modified by incorporating the m-fold cross validation approach. In addition, K-nearest neighbour classifier is then adapted for the prediction. The results gained through 440 different comprehensive analyses shows that the methylated CpG islands can be distinguished from the unmethylated CpG islands by a predictive accuracy of between 75% and 80%. More importantly, the modified LOO identifies more clearly and reliably when the feature sub-sets are combined. Another interesting observation is that the modified-LOO-based analysis reveals that the CpGI-specific feature-set achieve the highest predictive accuracy when combined with the other feature sets, which is not the case in the traditional LOO. This also further supports the robustness of the modified-LOO cross validation approach as CpGI-specific feature-set is one of the most important and effective attributes shown in other studies.

A comparative study for characterisation and prediction of tissue-specific DNA methylation of CpG islands in chromosomes 6, 20 and 22.

Advanced technology has enabled identification of tissue-specific methylated CpG islands of different human tissues. As methylation of CpG islands is involved in various biological phenomena and function of the DNA methylation is linked to various human diseases such as cancer, analysis of the CpG islands has become important and useful in characterising and modelling biological phenomena and understanding mechanism of such diseases. However, analysis of the data associated with the CpG islands is a quite new and challenging subject in bioinformatics, systems biology and epigenetics.