MOTIVATION: Studying the evolutionary conservation of cancer genes can improve our understanding of the genetic basis of human cancers. Functionally related proteins encoded by genes tend to interact with each other in a modular fashion, which may affect both the mode and tempo of their evolution. RESULTS: In the human PPI network, we searched for subnetworks within each of which all proteins have evolved at similar rates since the human and mouse split. Identified at a given co-evolving level, the subnetworks with non-randomly large sizes were defined as co-evolving modules. We showed that proteins within modules tend to be conserved, evolutionarily old and enriched with housekeeping genes, while proteins outside modules tend to be less-conserved, evolutionarily younger and enriched with genes expressed in specific tissues. Viewing cancer genes from co-evolving modules showed that the overall conservation of cancer genes should be mainly attributed to the cancer proteins enriched in the conserved modules. Functional analysis further suggested that cancer proteins within and outside modules might play different roles in carcinogenesis, providing a new hint for studying the mechanism of cancer.

Bioinformatics 23(16), 2121–2128. We would like to correct the author list in this manuscript. We apologize to the two authors Lei Wang and Shaoqi Rao who were missed from the published version of the manuscript. The corrected author list is: Zheng Guo, Lei Wang, Yongjin Li, Xue Gong, Chen Yao, Wencai Ma, Dong Wang, Yanhhui Li, Jing Zhu, Min Zhang, Da Yang, Shaoqi Rao and Jing Wang

MOTIVATION: In microarray studies, numerous tools are available for functional enrichment analysis based on GO categories. Most of these tools, due to their requirement of a prior threshold for designating genes as differentially expressed genes (DEGs), are categorized as threshold-dependent methods that often suffer from a major criticism on their changing results with different thresholds. RESULTS: In the present article, by considering the inherent correlation structure of the GO categories, a continuous measure based on semantic similarity of GO categories is proposed to investigate the functional consistence (or stability) of threshold-dependent methods. The results from several datasets show when simply counting overlapping categories between two groups, the significant category groups selected under different DEG thresholds are seemingly very different. However, based on the semantic similarity measure proposed in this article, the results are rather functionally consistent for a wide range of DEG thresholds. Moreover, we find that the functional consistence of gene lists ranked by SAM metric behaves relatively robust against changing DEG thresholds. AVAILABILITY: Source code in R is available on request from the authors.

MOTIVATION: Current high-throughput protein-protein interaction (PPI) data do not provide information about the condition(s) under which the interactions occur. Thus, the identification of condition-responsive PPI sub-networks is of great importance for investigating how a living cell adapts to changing environments. RESULTS: In this article, we propose a novel edge-based scoring and searching approach to extract a PPI sub-network responsive to conditions related to some investigated gene expression profiles. Using this approach, what we constructed is a sub-network connected by the selected edges (interactions), instead of only a set of vertices (proteins) as in previous works. Furthermore, we suggest a systematic approach to evaluate the biological relevance of the identified responsive sub-network by its ability of capturing condition-relevant functional modules. We apply the proposed method to analyze a human prostate cancer dataset and a yeast cell cycle dataset. The results demonstrate that the edge-based method is able to efficiently capture relevant protein interaction behaviors under the investigated conditions. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.