Pitfalls in Experimental Designs for Characterizing the Transcriptional, Methylational and Copy Number Changes of Oncogenes and Tumor Suppressor Genes

<div><p>Background</p><p>It is a common practice that researchers collect a set of samples without discriminating the mutants and their wild-type counterparts to characterize the transcriptional, methylational and/or copy number changes of pre-defined candidate oncogenes or tumor suppressor genes (TSGs), although some examples are known that carcinogenic mutants may express and function completely differently from their wild-type counterparts.</p> <p>Principal Findings</p><p>Based on various high-throughput data without mutation information for typical cancer types, we surprisingly found that about half of known oncogenes (or TSGs) pre-defined by mutations were down-regulated (or up-regulated) and hypermethylated (or hypomethylated) in their corresponding cancer types. Therefore, the overall expression and/or methylation changes of genes detected in a set of samples without discriminating the mutants and their wild-type counterparts cannot indicate the carcinogenic roles of the mutants. We also found that about half of known oncogenes were located in deletion regions, whereas all known TSGs were located in deletion regions. Thus, both oncogenes and TSGs may be located in deletion regions and thus deletions can indicate TSGs only if the gene is found to be deleted as a whole. In contrast, amplifications are restricted to oncogenes and thus can be used to support either the dysregulated wild-type gene or its mutant as an oncogene.</p> <p>Conclusions</p><p>We demonstrated that using the transcriptional, methylational and/or copy number changes without mutation information to characterize oncogenes and TSGs, which is a currently still widely adopted strategy, will most often produce misleading results. Our analysis highlights the importance of evaluating expression, methylation and copy number changes together with gene mutation data in the same set of samples in order to determine the distinct roles of the mutants and their wild-type counterparts.</p> </div

The expression, methylation and copy number changes of C-oncogenes and the related mechanisms.

<p><b>A</b>, Numbers of C-oncogenes with expression, methylation and copy number changes in the corresponding cancer types. A full list of these C-oncogenes is given in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0058163#pone.0058163.s004" target="_blank">Table S4</a>. <b>B</b>, The mechanisms underlying the overall down-regulation of C-oncogenes in cancer patient population.</p

<i>TP53</i> in different mutation states can be deregulated in different directions in a cancer type.

<p><b>A</b>, In the ovarian cancer, <i>TP53</i> with activating mutations was up-regulated, whereas both inactivated <i>TP53</i> and wild-type <i>TP53</i> were down-regulated; <b>B</b>, In the brain cancer, <i>TP53</i> with the activating mutations and the wild-type <i>TP53</i> were up-regulated, whereas <i>TP53</i> with inactivating mutations were not found to be significantly changed possibly due to the small sample size.</p

Directional agreement of DE, DM and CNA genes.

<p>Note: For the two lists of DE, DM or CNA genes detected from two datasets for each cancer, the directional agreement rates were calculated as the number of DE, DM or CNA genes with consistent directions across the two datasets divided by the number of all DE, DM or CNA genes commonly detected in both datasets (in parenthesis); NA, not available.</p

Functional Comparison between Genes Dysregulated in Ulcerative Colitis and Colorectal Carcinoma

<div><p>Background</p><p>Patients with ulcerative colitis (UC) are predisposed to colitis-associated colorectal cancer (CAC). However, the transcriptional mechanism of the transformation from UC to CAC is not fully understood.</p><p>Methodology</p><p>Firstly, we showed that CAC and non-UC-associated CRC were very similar in gene expression. Secondly, based on multiple datasets for UC and CRC, we extracted differentially expressed (DE) genes in UC and CRC versus normal controls, respectively. Thirdly, we compared the dysregulation directions (upregulation or downregulation) between DE genes of UC and CRC in CRC-related functions overrepresented with the DE genes of CRC, and proposed a regulatory model to explain the CRC-like dysregulation of genes in UC. A case study for “positive regulation of immune system process” was done to reveal the functional implication of DE genes with reversal dysregulations in these two diseases.</p><p>Principal Findings</p><p>In all the 44 detected CRC-related functions except for “viral transcription”, the dysregulation directions of DE genes in UC were significantly similar with their counterparts in CRC, and such CRC-like dysregulation in UC could be regulated by transcription factors affected by pro-inflammatory stimuli for colitis. A small portion of genes in each CRC-related function were dysregulated in opposite directions in the two diseases. The case study showed that genes related to humoral immunity specifically expressed in B cells tended to be upregulated in UC but downregulated in CRC.</p><p>Conclusions</p><p>The CRC-like dysregulation of genes in CRC-related functions in UC patients provides hints for understanding the transcriptional basis for UC to CRC transition. A small portion of genes with distinct dysregulation directions in each of the CRC-related functions in the two diseases implicate that their reversal dysregulations might be critical for UC to CRC transition. The cases study indicates that the humoral immune response might be inhibited during the transformation from UC to CRC.</p></div

The CRC-related functions in the directed acyclic graph of Biological Process.

<p><b>A.</b> All the CRC-related functions. <b>B.</b> A case for both the ancestor and offspring terms retained simultaneously. <b>C.</b> A case for just one term retained in a biological process branch.</p

The consistency of every two datasets for UC.

<p>The consistency of every two datasets for UC.</p

The microarray datasets analyzed in this study.

<p><b>Notes:</b> Patients with CRC from GSE9348 were at an early stage (Stage I/II), patients with CRC from GSE18105 were at stage II and stage III, and patients with CRC from GSE23878 and GSE20916 were metastasis-negative. In the datasets GSE18105, we just used the 17 paired CRC and adjacent normal samples to assure the clinical characteristics matching.</p

The significant regulatory links between UC-stimulus-functions and CRC-related functions in UC.

<p><b>A.</b> The significant regulatory relationships between functions. The gray nodes represent the stimulus-related functions in UC, whereas the other nodes in each color represent functions that are located in the same branch of the Gene Ontology Biological Process (GO BP) tree. Edges represent the significant links between transcription factors (TFs) dysregulated in stimulus-related functions and their differentially expressed (DE) target genes in CRC-related functions in UC (see details in Materials and Methods), and its thickness is proportional to the significance level (-log₁₀[P value]). <b>B.</b> A case for the significant regulatory links from “response to oxidative stress” to “cell proliferation”. <b>C.</b> Another case for the significant regulatory links from “inflammatory response” to “apoptosis”. These pink and green diamond nodes, in “response to oxidative stress” and “inflammatory response”, represent upregulated and downregulated DE genes in UC, respectively. In “cell proliferation” and “apoptosis”, the pink and green diamond nodes, respectively, represent genes consistently upregulated or downregulated in UC and CRC. An arrow represents the regulation relationship between a TF and one of its targets.</p

The consistency of every two datasets for CRC.

<p><b>Notes:</b> *(number1/number2) followed the percentage of the DE genes with consistent dysregulation direction in all commonly detected DE genes between two datasets represent the number of the DE genes with consistent dysregulation direction and the number of all commonly detected DE genes, respectively.</p