Bioinformatics mini research project

Task 1: Topic selection

Pathway Analysis

Task 2: Literature review

Comprehensive expression profiling of tumor cell lines identifies molecular signatures of melanoma progression (PLos ONE July 2007 issue 1-13)

The incidence of melanoma is increasing at one of the highest rates for any form of cancer in the world. The key to improved survival in all affected individual remains early diagnosis and treatment. Thus, the identification of molecular signatures of melanoma progression which can be used to develop accurate prognostic markers and effective targeted therapies. In order to gain an improved understanding of the molecular basis of melanoma progression, we have compared gene expression profiling data from melanoma cell lines representing discrete stages of malignant. These clustering identified two distinct molecular subclasses of melanoma segregating aggressive metastatic tumor cell line from less aggressive primary tumor cell lines. Further analysis of expression signature associated with melanoma progression using functional annotation categorized these transcripts into three classes of genes: 1. Up-regulation of activator of cell cycle progression, DNA replication and repair. 2. Loss of genes associated with cellular adhesion and melanocyte differentiation, 3. Up-regulation of genes associated with resistance to apoptosis.

Inclusion, the gene expression profiling studies of melanoma cell lines from varying stages of malignant progression and primary human melanocytes have identified several important melanoma signatures. It is expected that the novel melanoma progression associated genes identified in this study will provide new insights into the molecular defects associated with this malignancy and ultimately pave the way for the development of new melanoma biomarkers and novel targeted therapies.

Task 3: Research Question and Objectives

Any other subclasses of gene associated with the melanoma progression from primary to metastasis?

Objectives

To find the potential genes from Entrez and other literature and then test them by microarray and pathway analysis, in order to demonstrate the relationship between the group of genes and metastatic melanoma.

Task 4: Methods

A number of studies have identified multiple biomarkers for metastatic progression. Theses studies consider only direct comparison between metastatic and non-metastatic classes of samples. To apply this concept in cancer gene expression studies, my analysis utilize a combination of microarray and pathway analysis to test the relation between different groups of primary and metastatic cancer.

Many studies indicate that regardless of the tissue of origin, all metastatic tumors share a number of common feature related to change in basic energy metabolism, cell adhesion/cytoskeleton remodeling, antigen presentation and cell cycle regulation. There are several biological pathways differentially expressed between primary solid and metastatic tumors including oxidative phosphorylation, ubiquinone metabolism, cytoskeleton remodeling_Keratin filaments and cell adhesion_ECM remodeling. By Entrez search, we will find the information about the genes involve into the particular biological pathways including GEO number. By the GEO search, we can download the microarray data represented the particular genes.

All the microarray data of the various genes can be input into the Carmaweb to perform hierarchical clustering and renders a heat map of the expression profiles.

We can use another bioinformatics tool to analyze the interaction prediction of different biological pathway which is Search Tool for the Retrieval of Interacting Genes/Proteins (STRING)

Subclass of genes	Gene Symbol
Group 1 Up-regulation of activators of cell cycle progression	CDCA2, NCAPH, NCAPG,
Group 2 Loss of genes associated with cellular adhesion and melanocyte differentiation	CDH3, CDH1, PAX3, CITED1
Group 3 Up-regulation of genes associated with resistance to apoptosis	BIRC5/Survivin
New potential gene	Plasmin, TGF-beta1, PLAT, C1 Inhibitor

Task 5: Result and Discussion

.Global gene expression pattern were obtained using Affymetrix gene chips and comparison of gene expression profiles was performed using hierarchical clustering analysis. This clustering analysis identified two distinct groups of melanoma cell lines based on the similarity of their expression patterns. The first group was characterized as less aggressive primary melanoma and the second group was characterized as more aggressive.

 

 Molecular profiling studies of melanoma to date have been variably successful and often inconsistent. Much of this inconsistency has been attributed to the heterogeneous nature of this malignancy and the lack of significant sources of meaningful archived tissue specimen for analysis. In addition, variable sample preparation techniques are also likely to lead to disparate results between investigators. The global gene expression profiles of melanoma cell lines allow for the classification of tumor cells into 2 groups. While all radial growth phase melanoma clustering in Group 1 and all metastatic melanoma clustered in Group 2. As a whole, this gene signature suggests a series of molecular alteration occurs in aggressive melanomas that promote melanoma cell growth, survival and apoptotic resistance.

Literature review

Task 1 Topic: VIPR: A probabilistic algorithm for analysis of microbial detection microarrays (BMC Bioinformatics 2010, 11:384)

Task 2 Summary

Today, the clinical diagnostic assays in use focus on detecting the presence of a single, well defined target agent or a set of agents. In recent years, microarray-based diagnostics have been developed that greatly apply the highly parallel detection of multiple micro-organisms that may be present in a clinical specimen.

A novel interpretive algorithm, VIPR (Viral Identification using a Probabilistic algorithm), which uses Bayesian inference to optimize detection sensitivity by capitalizing on empirical training data. To illustrate this approach, the detection of viruses that cause hemorrhagic fever using a custom HF-virus microarray had been used to demonstrate. HF viruses belong to four virus families: Arenaviridae, Bunyaviridae, Flaviviridae and Filoviridae. A custom microarray was designed to detect all known HF viruses and closely related viruses. Furthermore, VIPR’s performance was compared with that of the existing interpretive algorithms that are not capable of utilizing training data in this fashion.

The probes were selected 35, 45 or 60 nucleotides in length which were designed to bind to viral genomes from the four families that contain all viruses known to cause HF. A total of 51 strains of 33 distinct virus species were obtained. In total, 110 hybridizations were performed including 102 positive controls, 4 Vero negative controls and 4 c6/36 negative controls. All raw microarray data are available in NCBI GEO.

Normalization was preformed to account for variation in reagent concentration or fluorescence across the microarray. Log transformation of the data was desirable for the estimation of normal distributions. Candidate genomes to be scored in the VIPR algorithm were limited to all complete genomes in the NCBI virus RefSeq database.  The entire set of oligonucleotide probe on the microarray was aligned using BLASTN against each of the RefSeq viral genomes.

The comparison of VIPR with the existing algorithm for analyzing diagnostic microarrays, the results of accuracy are 94%, 83%, 61% and 49% to VIPR, DetectiV, E-Predict and PhyloDetect respectively. The high accuracy represent a proof of VIPR will be useful in clinical laboratory settings to analyze microarrays.

Task 3

VIPR focus on detecting the presence of known virus, is it possible to apply to detect a set of genes in the bacteria?

Objectives: I would like to use the microarray technology to detect the multiple antibiotic resistant genes in the pathogens. It can provide a guide to the clinician for selecting an appropriated antimicrobial therapy.