CGO: utilizing and integrating gene expression microarray data in clinical research and data management

The Changing Treatment Paradigm in Patients With Newly Diagnosed Multiple Myeloma: Implications for Nursing

PURPOSE/OBJECTIVES

To review the changing treatment paradigm for newly diagnosed multiple myeloma and its implications for nursing.

DATA SOURCES

Journal articles, textbooks, published research data.

DATA SYNTHESIS

The treatment approaches to newly diagnosed multiple myeloma are varied, and no consensus exists about the best choice of induction therapy prior to high-dose chemotherapy with autologous stem cell transplantation. Novel therapies that have shown strong clinical activity in patients with relapsed or refractory myeloma currently are being explored as first-line therapy with associated higher incidence of serious complications.

CONCLUSIONS

Novel approaches in the treatment of newly diagnosed multiple myeloma may lead to better overall patient survival. Research is ongoing to find ways to improve progression-free and overall survival in patients with multiple myeloma.

IMPLICATIONS FOR NURSING

Oncology nurses play vital roles in the assessment and monitoring of serious complications associated with various therapies for patients with newly diagnosed multiple myeloma. Key responsibilities include safe and effective administration of complex chemotherapeutic regimens, management of side effects, patient and family education, and coordination of a multidisciplinary approach.

CGI: Java Software for Mapping and Visualizing Data from Array-based Comparative Genomic Hybridization and Expression Profiling

With the increasing application of various genomic technologies in biomedical research, there is a need to integrate these data to correlate candidate genes/regions that are identified by different genomic platforms. Although there are tools that can analyze data from individual platforms, essential software for integration of genomic data is still lacking. Here, we present a novel Java-based program called CGI (Cytogenetics-Genomics Integrator) that matches the BAC clones from array-based comparative genomic hybridization (aCGH) to genes from RNA expression profiling datasets. The matching is computed via a fast, backend MySQL database containing UCSC Genome Browser annotations. This program also provides an easy-to-use graphical user interface for visualizing and summarizing the correlation of DNA copy number changes and RNA expression patterns from a set of experiments. In addition, CGI uses a Java applet to display the copy number values of a specific BAC clone in aCGH experiments side by side with the expression levels of genes that are mapped back to that BAC clone from the microarray experiments. The CGI program is built on top of extensible, reusable graphic components specifically designed for biologists. It is cross-platform compatible and the source code is freely available under the General Public License.

A database for tracking toxicogenomic samples and procedures

Reproductive toxicogenomic studies generate large amounts of toxicological and genomic data. On the toxicology side, a substantial quantity of data accumulates from conventional endpoints such as histology, reproductive physiology and biochemistry. The largest source of genomics data is DNA microarrays, which generate enormous amounts of information in the course of profiling gene expression. Thus, data storage and management become essential and require a more sophisticated system than lab notebooks and electronic spreadsheets. We developed a database for tracking toxicogenomic samples and procedures (TSP 1.0) for our reproductive studies based on the MIAME-Tox guidelines and relational database theory. This database stores the various types of data from both toxicological and genomic assays in a hierarchical fashion. The user-friendly interface provides easy procedures for researchers to add, edit, save, delete, and navigate different records. Finally, TSP facilitates exporting microarray data into public databases.

A microarray-based gastric carcinoma prewarning system

AIM: To develop a microarray-based prewarning system consisting of gastric cancer chip, prewarning data and analysis software for early detection of gastric cancer and pre-cancerous lesions.

METHODS: Two high-density chips with 8 464 human cDNA sites were used to primarily identify potential genes specific for normal gastric mucosa, pre-cancerous lesion and gastric cancer. The low-density chips, composed of selected genes associated with normal gastric mucosa, precancerous lesion and gastric cancer, were fabricated and used to screen 150 specimens including 60 specimens of gastric cancer, 60 of pre-cancerous tissues and 30 of normal gastric mucosa. CAD software was used to screen out the relevant genes and their critical threshold values of expression levels distinguishing normal mucosa from pre-cancerous lesion and cancer. All data were stored in a computer database to establish a prewarning data library for gastric cancer. Two potential markers brcaa1 and ndr1 were identified by Western blot and immunohistochemistry.

RESULTS: A total of 412 genes associated with three stages of gastric cancer development were identified. There were 216 genes displaying higher expression in gastric cancer, 85 genes displaying higher expression in pre-cancerous lesion and 88 genes displaying higher expression in normal gastric mucosa. Also 15 genes associated with metastasis of gastric cancer and 8 genes associated with risk factors were screened out for target genes of diagnosis chip of early gastric cancer. The threshold values of 412 selected genes to distinguish gastric cancer, pre-cancerous lesion from normal gastric mucosa were defined as 6.01±2.40, 4.86±1.94 and 5.42±2.17, respectively. These selected 412 genes and critical threshold values were compiled into an analysis software, which can automatically provide reports by analyzing the results of 412 genes obtained by examining gastric tissues. All data were compiled into a prewarning database for gastric cancer by CGO software. Northern blot and immunohistochemistry analysis confirmed that gene and protein of brcaa1 displayed lower expression in normal gastric mucosa and higher expression in gastric cancer tissues, conversely, ndr1 displayed lower expression in gastric cancer and higher expression in normal gastric mucosa.

CONCLUSION: The microarray-based prewarning system for gastric cancer was developed. This system consisted of gastric cancer-associated gene chip, prewarning data and analysis software, which has a high potential for applications in the early detection of gastric cancer. The two potential markers brcaa1 and ndr1 identified may be used to distinguish cancer status fand non-cancer status.