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A Network View of Microrna and Gene Interactions in Different Pathological Stages of Colon Cancer Jia Wen, Benika Hall and Xinghua Shi*

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A Network View of Microrna and Gene Interactions in Different Pathological Stages of Colon Cancer Jia Wen, Benika Hall and Xinghua Shi* Wen et al. BMC Medical Genomics 2019, 12(Suppl 7):158 https://doi.org/10.1186/s12920-019-0597-1 Research Open Access A network view of microRNA and gene interactions in different pathological stages of colon cancer Jia Wen, Benika Hall and Xinghua Shi* From 14th International Symposium on Bioinformatics Research and Applications (ISBRA’18) Beijing, China. 8–11 June 2018 Abstract Background: Colon cancer is one of the common cancers in human. Although the number of annual cases has decreased drastically, prognostic screening and translational methods can be improved. Hence, it is critical to understand the molecular mechanisms of disease progression and prognosis. Results: In this study, we develop a new strategy for integrating microRNA and gene expression profiles together with clinical information toward understanding the regulation of colon cancer. Particularly, we use this approach to identify microRNA and gene expression networks that are specific to certain pathological stages. To demonstrate the application of our method, we apply this approach to identify microRNA and gene interactions that are specific to pathological stages of colon cancer in The Cancer Genome Atlas (TCGA) datasets. Conclusions: Our results show that there are significant differences in network connections between miRNAs and genes in different pathological stages of colon cancer. These findings point to a hypothesis that these networks signify different roles of microRNA and gene regulation in the pathogenesis and tumorigenesis of colon cancer. Background survival rate is significantly decreased to approximately Colon cancer, which is reported to be one of the few cur- 12%. The drastic decrease in survival rate in colon can- able cancers, is one of the most common cancers around cer speaks to the need for better early diagnostic and the world. The complex progress of colon cancer stage prognostic procedures. induces the poor prediction prognosis of colon cancer. The role of pathologic prognostic markers is impor- According to previous studies, there is a 92% 5-year rela- tant in the advancement of personalized medicine and tive survival rate [1, 2] in stage I colon cancer. For patients can help reduce the risk of recurrence, especially in high- with stage II colon cancer, there are two stage subtypes: risk patients with stage II colon cancer [3–5]. Due to the stage IIA and stage IIB colon cancers [1, 2]. There is an benefits of personalized medicine, these patients have an 87% 5-year relative survival rate for stage IIA and 63% increased overall survival with therapies such as adjuvant for stage IIB. Similarly, for stage III colon cancer there chemotherapy. Gene expression signatures have shown are three subtypes: stage IIIA, IIIB and IIIC colon cancers much promise as prognostic markers [6]. For example, [1, 2]. In patients with stage IIIA, the 5-year relative sur- the progression of colon cancer is directly linked to the vival rate is 89%, for stage IIIB it is 69% and 53% for stage functional epithelial-mesenchymal transition (EMT) gene IIIC [1, 2]. When the cancer has reached stage IV and expression signatures [7]. Genes ZEB1andZEB2are metastasized to other parts of the body, the 5-year relative known repressors that regulate targets in the EMT path- way by changing the phenotype of normal cells to cancer- *Correspondence: [email protected] ous cells [8]. These genes are also known to be present in Department of Bioinformatics and Genomics, College of Computing and Informatics, University of North Carolina at Charlotte, 9201 University City Blvd, the beginning of metastasis. 28223 Charlotte, NC, USA © The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Wen et al. BMC Medical Genomics 2019, 12(Suppl 7):158 Page 2 of 9 Cell invasion and migration are also critical components patterns specific to different pathological stages of colon in colon cancer progression. For instance, genes PRKCQ cancer. We hypothesized that as colon cancer progresses, and PRKCZ are members of the protein kinase family and there are unique functional patterns present in early stages PRKCZ is often involved in cell survival and cell migra- that are not present in later stages and vice versa. We tion in different cancers such as ovarian cancer [9]. It believe that identifying these functional signatures in dif- has also been reported that ARID4B is a key player in ferent pathological stages can lead to improved prognosis pathogenesis and is classified as a metastasis modifier and better understanding in the stage progression of colon gene. Over-expression of this gene is thought to enhance cancer. Ultimately, the identification and analysis of the the cell migration process as well as cell invasion. In con- evolution and dynamics of these miRNA-gene networks trast, the knockdown of ARID4B,causesmetastasisof will improve our understanding of the etiology and treat- cancer cells to other regions of the body [10, 11]. ment of colon cancer. Using The Cancer Genome Atlas More recently, microRNA (miRNA) expression profiles (TCGA) project [21] colon cancer data [22], we developed have been utilized as predictive markers for survival of a novel strategy to integrate miRNA and gene expression colon cancer [12]. Previous studies have reported several data, incorporate known miRNA and gene targets, and miRNAs were relevant with poor survival and therapeu- protein protein interactions to generate a more compre- tic outcome in colon cancer [13–15]. For example, miR − hensive view of the miRNA-gene interactions. 148, miR − 26a − 2andmiR − 130a were identified to be significantly associated with a poor clinical prog- Methods nosis [16]. Exploiting the downstream neighborhoods of Our integrative strategy for this work can be summarized genes with such a critical role in the pathogenesis of as a workflow containing the following five steps (Fig. 1) colon cancer provide long-term benefits in personalized after preprocessing the data. medicine and adjuvant therapies. Studies have also found Step 1. For each pathological stage, we conducted an that the genetic changes varied among different stages association analysis between miRNA expression and gene of colon cancer, specially between stage II and stage III expression quantifications in the same patients respec- [3, 17, 18]. However, the genetic mechanism underly- tively using MatrixeQTL [23]. Those miRNA and gene ing these genetic changes that drive pathological stage associations that are statistically significant are kept for progression are remain poorly studied. further analysis. There are some recent studies on delineating miRNAs Step 2. For the miRNAs that are significantly associ- and genes differentially expressed in different stages of ated with gene expression variations identified in Step 1, colon cancer [19, 20]. These work started with conduct- we obtained their target genes by retrieving from miRNA ing differentially expressed miRNAs and/or differentially target gene databases like miRTarBase[24]. expressed gene analysis to find the pathways that target Step 3. We generated a gene set directly associated genes and/or miRNAs are involved in different patholog- with or targeted by miRNAs for each pathological stage ical stages of colon cancer. In our study, we intend to by merging all genes significantly associated with miR- integrate miRNA and gene expression interactions from NAs output from Step 1 and all of the targeted genes of mining miRNA and gene expression profiles of cancer those miRNAs significantly associated with gene expres- patients to investigate different network communities and sion obtained from Step 2. We then conducted the Fig. 1 An illustration of our analysis workflow Wen et al. BMC Medical Genomics 2019, 12(Suppl 7):158 Page 3 of 9 Gene Ontology (GO) Enrichment analysis for the gene Matrix eQTL [31], which is an R package that uses set of each pathological stage to compare the functional matrix operations to identify pairwise associations. We differences among four stages. conducted miRNA-gene association analysis by running Step 4. We overlapped the gene set directly associ- a linear regression model using Matrix eQTL for each ated with or targeted by miRNAs to InWeb, a compre- pair of miRNA and gene, and selected significant miRNA- hensive interaction database, to generate a preliminary gene associations using a 5% cutoff of false discovery rate miRNA-gene network in each pathological stage. We (FDR). For each pathological stage, we extracted all of the then expanded these preliminary miRNA-gene networks experimentally validated target genes of those miRNAs by adding interacting genes using two different meth- asscociated with gene expressions from the miRTarBase ods and generated an integrated miRNA-gene network target database [24]. At this point, we generated a target for each stage. The integrated miRNA-gene network for and associated gene set of miRNAs for each pathological each pathological stage is now composed of miRNAs sig- stage by merging the genes
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