Details

Autor(en) / Beteiligte

• Zhang, Jie
• Lu, Kewei
• Xiang, Yang
• Islam, Muhtadi
• Kotian, Shweta
• Kais, Zeina
• Lee, Cindy
• Arora, Mansi
• Liu, Hui-Wen
• Parvin, Jeffrey D
• Huang, Kun
• Rzhetsky, Andrey

Titel

Weighted frequent gene co-expression network mining to identify genes involved in genome stability

Ist Teil von

• PLoS computational biology, 2012-08, Vol.8 (8), p.e1002656-e1002656

Ort / Verlag

United States: Public Library of Science

Erscheinungsjahr

2012

Quelle

MEDLINE

Beschreibungen/Notizen

• Gene co-expression network analysis is an effective method for predicting gene functions and disease biomarkers. However, few studies have systematically identified co-expressed genes involved in the molecular origin and development of various types of tumors. In this study, we used a network mining algorithm to identify tightly connected gene co-expression networks that are frequently present in microarray datasets from 33 types of cancer which were derived from 16 organs/tissues. We compared the results with networks found in multiple normal tissue types and discovered 18 tightly connected frequent networks in cancers, with highly enriched functions on cancer-related activities. Most networks identified also formed physically interacting networks. In contrast, only 6 networks were found in normal tissues, which were highly enriched for housekeeping functions. The largest cancer network contained many genes with genome stability maintenance functions. We tested 13 selected genes from this network for their involvement in genome maintenance using two cell-based assays. Among them, 10 were shown to be involved in either homology-directed DNA repair or centrosome duplication control including the well-known cancer marker MKI67. Our results suggest that the commonly recognized characteristics of cancers are supported by highly coordinated transcriptomic activities. This study also demonstrated that the co-expression network directed approach provides a powerful tool for understanding cancer physiology, predicting new gene functions, as well as providing new target candidates for cancer therapeutics.