Gene network reconstruction reveals cell cycle and antiviral genes as major drivers of cervical cancer

Abstract

Although human papillomavirus (HPV) was identified as an etiological factor in cervical cancer, the key human gene drivers of this disease remain unknown. Here we apply an unbiased approach integrating gene expression and chromosomal aberration data. In an independent group of patients, we reconstruct and validate a gene regulatory meta-network, and identify cell cycle and antiviral genes that constitute two major sub-networks up-regulated in tumour samples. These genes are located within the same regions as chromosomal amplifications, most frequently on 3q. We propose a model in which selected chromosomal gains drive activation of antiviral genes contributing to episomal virus elimination, which synergizes with cell cycle dysregulation. These findings may help to explain the paradox of episomal HPV decline in women with invasive cancer who were previously unable to clear the virus.

Unexpected links reflect the noise in networks

“…At first glance we found surprising such a strong association and sought to further evaluate this phenomenon. Thus we focused on a part of this big network, which is a bi-partite network consisting of 626 correlations between generegulators and gene-targets [16]. In this smaller network, in which correlation links could more obviously correspond to causal links (because gene-regulators have changed their expression as a result of chromosomal aberrations (Additional file 1: Figure S1)), we found similar association between direction of correlation and gene regulation (Fig.…”

“…In order to verify whether there is a relationship between the direction of gene regulation and the sign of correlation we used a gene co-expression network from our recently published paper on network analysis in cervical cancer [16]. We felt that this network should provide excellent real data for this analysis, as it was constructed from a robust metaanalysis of five cancer gene expression datasets (GSE26342, GSE7410, GSE9750, GSE6791, GSE7803) and thus validated by large, independent sources.…”

“…Indeed, medical fields such as cardiology [8], endocrinology [10,11], immunology [4,12,13], hostmicrobiome interactions [10,12] and others have benefited from gene network analysis. Cancer is a very good example of applications of gene expression networks because insights from network analyses were essential for identification of key drivers of carcinogenesis for brain [14,15], breast [15], skin [9] and cervical [16] tumors.…”

Investigating a holobiont: Microbiota perturbations and transkingdom networks

“…1 Indeed, we have faced the common problem that there is evidence of a microbiota effect on a host phenotype, but it was unclear which microbe was responsible. Building on our previous experience with gene networks, 25,48,50 we developed a new approach that models host-microbiota interaction that we called transkingdom network. 1,49 To build this model, microbial gene abundances and mouse transcriptome data were integrated into one network.…”

“…Indeed, reconstruction and analysis of gene regulatory networks has proven to be an excellent tool for causal inference, allowing researchers to uncover gene-drivers of carcinogenesis of different tumors. [46][47][48] Moreover, these networks can be built from different types of variables using a variety of available tools 49 as long as all parameters are all measured in the same sample.…”