Featured Research

from universities, journals, and other organizations

Novel analyses improve identification of cancer-associated genes from microarray data

Date:
May 2, 2014
Source:
The Geisel School of Medicine at Dartmouth
Summary:
A new gene expression analysis approach for identifying cancer genes has been identified by scientists. The study results challenge the current paradigm of microarray data analysis and suggest that the new method may improve identification of cancer-associated genes. Typical microarray-based gene expression analyses compare gene expression in adjacent normal and cancerous tissues. The new approach demonstrated that ranking genes based on inter-tumor variation in gene expression outperforms traditional analytical approaches. The results were consistent across 4 major cancer types: breast, colorectal, lung, and prostate cancer.

Dartmouth Institute for Quantitative Biomedical Sciences (iQBS) researchers developed a new gene expression analysis approach for identifying cancer genes. The paper entitled, "How to get the most from microarray data: advice from reverse genomics," was published online March 21, 2014 in BMC Genomics. The study results challenge the current paradigm of microarray data analysis and suggest that the new method may improve identification of cancer-associated genes.

Related Articles


Typical microarray-based gene expression analyses compare gene expression in adjacent normal and cancerous tissues. In these analyses, genes with strong statistical differences in expression are identified. However, many genes are aberrantly expressed in tumors as a byproduct of tumorigenesis. These "passenger" genes are differentially expressed between normal and tumor tissues, but they are not "drivers" of tumorigenesis. Therefore, better analytical approaches that enrich the list of candidate genes with authentic cancer-associated "driver" genes are needed.

Lead authors of the study, Ivan P. Gorlov, Ph.D., Associate Professor of Community and Family Medicine and Christopher Amos, Ph.D., Professor of Community and Family Medicine and Director of the Center for Genomic Medicine described a new method to analyze microarray data. The research team demonstrated that ranking genes based on inter-tumor variation in gene expression outperforms traditional analytical approaches. The results were consistent across 4 major cancer types: breast, colorectal, lung, and prostate cancer.

The team used text-mining to identify genes known to be associated with breast, colorectal, lung, and prostate cancers. Then, they estimated enrichment factors by determining how frequently those known cancer-associated genes occurred among the top gene candidates identified by different analysis methods. The enrichment factor described how frequently cancer associated genes were identified compared to the frequency of identification that one could expect by pure chance. Across all four cancer types, the new method of selecting candidate genes based on inter-tumor variation in gene expression outperformed the other methods, including the standard method of comparing mean expression in adjacent normal and tumor tissues. Dr. Gorlov and colleagues also used this approach to identify novel cancer-associated genes.

The authors cite tumor heterogeneity as the most likely reason for the success of their variance-based approach. The method is based on the knowledge that different tumors can be driven by different subsets of cancer genes. By identifying genes with high variation in expression between tumors, the method preferentially identifies genes specifically associated with cancer. This same feature, tumor heterogeneity, may reduce the ability to identify critical gene expression changes when comparing mean gene expression in adjacent tumor and normal tissues, as tumors of the same type may have different sets of genes differentially expressed.

The results of the study challenge the model that comparing mean gene expression in adjacent normal and cancer tissues is the best approach to identifying cancer-associated genes. Indeed, the team identified high variation in adjacent "normal" tissue samples, which are typically used as control samples for comparison in analyses based on mean gene expression. The study suggests that methods based on variance may help get the most from existing and future global gene expression studies.


Story Source:

The above story is based on materials provided by The Geisel School of Medicine at Dartmouth. Note: Materials may be edited for content and length.


Journal Reference:

  1. Ivan P Gorlov, Ji-Yeon Yang, Jinyoung Byun, Christopher Logothetis, Olga Y Gorlova, Kim-Anh Do, Christopher Amos. How to get the most from microarray data: advice from reverse genomics. BMC Genomics, 2014; 15 (1): 223 DOI: 10.1186/1471-2164-15-223

Cite This Page:

The Geisel School of Medicine at Dartmouth. "Novel analyses improve identification of cancer-associated genes from microarray data." ScienceDaily. ScienceDaily, 2 May 2014. <www.sciencedaily.com/releases/2014/05/140502130235.htm>.
The Geisel School of Medicine at Dartmouth. (2014, May 2). Novel analyses improve identification of cancer-associated genes from microarray data. ScienceDaily. Retrieved November 22, 2014 from www.sciencedaily.com/releases/2014/05/140502130235.htm
The Geisel School of Medicine at Dartmouth. "Novel analyses improve identification of cancer-associated genes from microarray data." ScienceDaily. www.sciencedaily.com/releases/2014/05/140502130235.htm (accessed November 22, 2014).

Share This


More From ScienceDaily



More Health & Medicine News

Saturday, November 22, 2014

Featured Research

from universities, journals, and other organizations


Featured Videos

from AP, Reuters, AFP, and other news services

WFP: Ebola Risks Heightened Among Women Throughout Africa

WFP: Ebola Risks Heightened Among Women Throughout Africa

AFP (Nov. 21, 2014) Having children has always been a frightening prospect in Sierra Leone, the world's most dangerous place to give birth, but Ebola has presented an alarming new threat for expectant mothers. Duration: 00:37 Video provided by AFP
Powered by NewsLook.com
Could Your Genes Be The Reason You're Single?

Could Your Genes Be The Reason You're Single?

Newsy (Nov. 21, 2014) Researchers in Beijing discovered a gene called 5-HTA1, and carriers are reportedly 20 percent more likely to be single. Video provided by Newsy
Powered by NewsLook.com
Milestone Birthdays Can Bring Existential Crisis, Study Says

Milestone Birthdays Can Bring Existential Crisis, Study Says

Newsy (Nov. 21, 2014) Researchers find that as people approach new decades in their lives they make bigger life decisions. Video provided by Newsy
Powered by NewsLook.com
Ebola: Life Without School in Guinea

Ebola: Life Without School in Guinea

AFP (Nov. 21, 2014) Following the closure of schools and universities in Guinea because of the Ebola virus, students look for temporary work or gather in makeshift classrooms to catch up on their syllabus. Duration: 02:14 Video provided by AFP
Powered by NewsLook.com

Search ScienceDaily

Number of stories in archives: 140,361

Find with keyword(s):
Enter a keyword or phrase to search ScienceDaily for related topics and research stories.

Save/Print:
Share:

Breaking News:

Strange & Offbeat Stories


Health & Medicine

Mind & Brain

Living & Well

In Other News

... from NewsDaily.com

Science News

Health News

Environment News

Technology News



Save/Print:
Share:

Free Subscriptions


Get the latest science news with ScienceDaily's free email newsletters, updated daily and weekly. Or view hourly updated newsfeeds in your RSS reader:

Get Social & Mobile


Keep up to date with the latest news from ScienceDaily via social networks and mobile apps:

Have Feedback?


Tell us what you think of ScienceDaily -- we welcome both positive and negative comments. Have any problems using the site? Questions?
Mobile: iPhone Android Web
Follow: Facebook Twitter Google+
Subscribe: RSS Feeds Email Newsletters
Latest Headlines Health & Medicine Mind & Brain Space & Time Matter & Energy Computers & Math Plants & Animals Earth & Climate Fossils & Ruins