Mobile classification in microarray experiments

Differential effects of selective frankincense (Ru Xiang) essential oil versus non-selective sandalwood (Tan Xiang) essential oil on cultured bladder cancer cells: a microarray and bioinformatics study

BACKGROUND

Frankincense (Boswellia carterii, known as Ru Xiang in Chinese) and sandalwood (Santalum album, known as Tan Xiang in Chinese) are cancer preventive and therapeutic agents in Chinese medicine. Their biologically active ingredients are usually extracted from frankincense by hydrodistillation and sandalwood by distillation. This study aims to investigate the anti-proliferative and pro-apoptotic activities of frankincense and sandalwood essential oils in cultured human bladder cancer cells.

METHODS

The effects of frankincense (1,400-600 dilutions) (v/v) and sandalwood (16,000-7,000 dilutions) (v/v) essential oils on cell viability were studied in established human bladder cancer J82 cells and immortalized normal human bladder urothelial UROtsa cells using a colorimetric XTT cell viability assay. Genes that responded to essential oil treatments in human bladder cancer J82 cells were identified using the Illumina Expression BeadChip platform and analyzed for enriched functions and pathways. The chemical compositions of the essential oils were determined by gas chromatography-mass spectrometry.

RESULTS

Human bladder cancer J82 cells were more sensitive to the pro-apoptotic effects of frankincense essential oil than the immortalized normal bladder UROtsa cells. In contrast, sandalwood essential oil exhibited a similar potency in suppressing the viability of both J82 and UROtsa cells. Although frankincense and sandalwood essential oils activated common pathways such as inflammatory interleukins (IL-6 signaling), each essential oil had a unique molecular action on the bladder cancer cells. Heat shock proteins and histone core proteins were activated by frankincense essential oil, whereas negative regulation of protein kinase activity and G protein-coupled receptors were activated by sandalwood essential oil treatment.

CONCLUSION

The effects of frankincense and sandalwood essential oils on J82 cells and UROtsa cells involved different mechanisms leading to cancer cell death. While frankincense essential oil elicited selective cancer cell death via NRF-2-mediated oxidative stress, sandalwood essential oil induced non-selective cell death via DNA damage and cell cycle arrest.

Internal standard-based analysis of microarray data2--analysis of functional associations between HVE-genes

In this work we apply the Internal Standard-based analytical approach that we described in an earlier communication and here we demonstrate experimental results on functional associations among the hypervariably-expressed genes (HVE-genes). Our working assumption was that those genetic components, which initiate the disease, involve HVE-genes for which the level of expression is undistinguishable among healthy individuals and individuals with pathology. We show that analysis of the functional associations of the HVE-genes is indeed suitable to revealing disease-specific differences. We show also that another possible exploit of HVE-genes for characterization of pathological alterations is by using multivariate classification methods. This in turn offers important clues on naturally occurring dynamic processes in the organism and is further used for dynamic discrimination of groups of compared samples. We conclude that our approach can uncover principally new collective differences that cannot be discerned by individual gene analysis.

Gene expression analysis of biological systems driving an organotypic model of endometrial carcinogenesis and chemoprevention

An organotypic model of endometrial carcinogenesis and chemoprevention was developed in which normal endometrial organotypic cultures exposed to the carcinogen, DMBA (7,12-dimethylbenz[a]anthracene), developed a cancerous phenotype in the absence, but not presence of subsequent treatment with a flexible heteroarotinoid (Flex-Het), called SHetA2. A discriminant function based on karyometric features of cellular nuclei and an agar clonogenic assay confirmed these histologic changes. Interpretation of microarray data using an internal standard approach identified major pathways associated with carcinogenesis and chemoprevention governed by c-myc, p53, TNFα and Jun genes. Cluster analysis of functional associations of hypervariable genes demonstrated that carcinogenesis is accompanied by a stimulating association between a module of genes that includes tumor necrosis factor α (TNFα), c-myc, and epidermal growth factor-receptor (EGF-R) and a module that includes insulin-like growth factor I-receptor (IGF-IR), p53, and Jun genes. Two secreted proteins involved in these systems, tenascin C and inhibin A, were validated at the protein level. Tenascin C is an EGF-R ligand, and therefore may contribute to the increased EGF-R involvement in carcinogenesis. The known roles of the identified molecular systems in DMBA and endometrial carcinogenesis and chemoprevention supports the validity of this model and the potential clinical utility of SHetA2 in chemoprevention.

Internal standard-based analysis of microarray data. Part 1: analysis of differential gene expressions

Genome-scale microarray experiments for comparative analysis of gene expressions produce massive amounts of information. Traditional statistical approaches fail to achieve the required accuracy in sensitivity and specificity of the analysis. Since the problem can be resolved neither by increasing the number of replicates nor by manipulating thresholds, one needs a novel approach to the analysis. This article describes methods to improve the power of microarray analyses by defining internal standards to characterize features of the biological system being studied and the technological processes underlying the microarray experiments. Applying these methods, internal standards are identified and then the obtained parameters are used to define (i) genes that are distinct in their expression from background; (ii) genes that are differentially expressed; and finally (iii) genes that have similar dynamical behavior.