|Year : 2015 | Volume
| Issue : 4 | Page : 846-851
Protein-protein interaction networks and modules analysis for colorectal cancer and serrated adenocarcinoma
Hualong Yu1, Lan Ye2, Jianxin Wang1, Lei Jin1, Yanfeng Lv1, Miao Yu1
1 Department of Anorectal Surgery, The Second Hospital of Shandong University, Jinan, Shandong Province 250033, China
2 Department of Oncology, The Second Hospital of Shandong University, Jinan, Shandong Province 250033, China
|Date of Web Publication||15-Feb-2016|
Department of Anorectal Surgery, The Second Hospital of Shandong University, Jinan, Shandong Province 250033
Source of Support: None, Conflict of Interest: None
Purpose: To screen key modules and explore the potential mechanism of conventional colorectal cancer (CRC) and colorectal serrated adenocarcinoma (SAC).
Materials and Methods: The microarray data of GSE36758 and GSE8671 were downloaded from Gene Expression Omnibus database. The differentially expressed genes (DEGs) in SAC versus colon carcinoma (CC) and CC versus normal control (NC) group were analyzed and the protein-protein interaction (PPI) networks for DEGs were constructed. The modules of PPI networks were further analyzed and the function enrichment analysis of all enrolled DEGs was carried out based on ToppGene database.
Results: Total eight DEGs (SAC vs. CC) and 445 DEGs (CC vs. NC) were extracted based on the gene expression profile of GSE36758 and GSE8671, respectively. Total three PPI networks were constructed with DEGs in CC versus NC, SAC versus CC group, and DEGs in both two groups. Three modules were extracted from the PPI network of CC versus NC. Meanwhile, three modules were extracted from the network of DEGs in both two groups. Function enrichment analysis showed that DEGs involved in these modules were mainly associated with cellular activities.
Conclusion: DEGs in modules of SAC and CRC were mainly involved in cellular activities pathways. The PPI networks and modules might contribute to the further study of pathogenesis for CRC and SAC based on the molecular level.
Keywords: Colorectal cancer, modules, pathways, protein-protein network analysis, serrated adenocarcinoma
|How to cite this article:|
Yu H, Ye L, Wang J, Jin L, Lv Y, Yu M. Protein-protein interaction networks and modules analysis for colorectal cancer and serrated adenocarcinoma. J Can Res Ther 2015;11:846-51
|How to cite this URL:|
Yu H, Ye L, Wang J, Jin L, Lv Y, Yu M. Protein-protein interaction networks and modules analysis for colorectal cancer and serrated adenocarcinoma. J Can Res Ther [serial online] 2015 [cited 2021 Jan 23];11:846-51. Available from: https://www.cancerjournal.net/text.asp?2015/11/4/846/140805
| > Introduction|| |
Colorectal cancer (CRC) ranks the third disease for the high incidence and mortality in most western countries.  Serrated adenocarcinoma (SAC) has been recognized as a new subtype of CRC according to the WHO classification of tumors in the digestive system.  SAC accounts for about 7.5% of all CRCs and 17.5% of the most proximal CRC.  Different from CRC, there are different histological diagnosis criteria for SAC. , Furthermore, patients with SAC tend to have a worse prognosis than patients with conventional colon carcinoma (CC).  Compared with CC, the higher frequency of adverse histological features  and the different histochemical expression pattern may contribute to the poorer prognosis of SAC.  Although there is a rapid decline in the incidence of CRC for medical technology improvement, the potential mechanism of CRC has not been clarified clearly, especially in the molecular level. 
Recent studies show that genetic alternation is an event during the colorectal tumor development. , It is reported that the frequent mutation of KRAS (Kirsten rat sarcoma viral oncogene homolog) in addition to BRAF (v-Raf murine sarcoma viral oncogene homolog B1) contributes to the process of SAC.  The chromosome 5q21 genes in familial adenomatous polyposis is also proved to be important for the colorectal-tumor development.  Although gene expression profiles contribute to the molecular diagnosis of colorectal tumors, , a single profile analysis of SAC or CC can hardly explain the whole potential interactions of mechanism underlying SAC and CC development. Thus, a systematical analysis of potential interactions for SAC and CC using bioinformatics study based on the related gene expression profile is needed.
In this study, the differentially expressed genes (DEGs) were selected based on the gene expression profiles of SAC and CC via the construction and analysis of interaction networks, we hoped to dig out key modules and the potential diseases-related genes for the further researches.
| > Materials and methods|| |
Differentially expressed genes and microarray data
The microarray data of GSE36758  and GSE8671  were downloaded from Gene Expression Omnibus) database (http://www.ncbi.nlm.nih.gov/geo/). The gene expression profiles data of GSE36758 was developed by 11 SAC tumor tissues and 15 CC tumor tissues and the GSE8671 data was derived from 32 CC tumor tissues and 32 corresponding normal colon tissues normal control (NC). The raw data and the probe annotation files were downloaded based on the platform of GPL4133 (Agilent-014850 Whole Human Genome Microarray 4 x 44K G4112F) and GPL570 (Affymetrix Human Genome U133 Plus 2.0 Array).
The probe files were translated into gene level data and the datasets were normalized by affy package in R.  The DEGs in SAC versus CC and CC versus NC were analyzed by limma package in R  and were tested by the Beniamini-Hochberg method.  P < 0.05 and |log FC | >1 were considered to be the cut-off value.
Protein-protein interaction networks construction
All the protein-protein interaction (PPI) pairs of the human whole genome were downloaded from Human Protein Reference Database (HPRD) (http://hprd.org/). The protein interaction related data were processed by getting rid of the repeated interactions and the interactions between single genes. The DEGs related protein pairs were selected and the PPI networks for DEGs in SAC versus CC, CC versus NC group and both SAC versus CC and CC versus NC group were constructed by MATLAB software (MathWorks, Natick, MA). 
Extraction and evaluation of modules in protein-protein interaction networks
In order to obtain the modules that have close relationships, we performed modules analysis for the three PPI networks. The modules were clustered by Molecular Complex Detection plugin  in Cytoscape software (Institute for Systems Biology, Seattle, Washington, USA).  The modules were evaluated by comparing the function similarity of the gene set in one module with the application of Gene ontology (GO).  The detailed calculation method was performed according to Ruths et al.  The accuracy of modules was proportional to the modules' ranking. We selected the module score larger than two as the threshold value.
Function enrichment analysis
ToppGene  (http://toppgene.cchmc.org/) can be used to carry out the gene function enrichment analysis or optimized related genes. In this study, we performed function enrichment analysis for DEGs identified in this paper based on the ToppGene database (Cincinnati Children's Hospital Medical Center (BMI CCHMC), Cincinnati, OH,USA). The enriched biological processes (BPs) were mainly analyzed. P <0.05 was considered to be significant.
| > Results|| |
Identification of differentially expressed genes
Total eight DEGs in SAC versus CC group were obtained based on gene expression profile of GSE36758 and 445 DEGs in CC versus NC group were obtained based on the gene expression profile of GSE8671. All these DEGs were enrolled in the present study.
Protein-protein interaction networks analysis
There were total 9240 protein interaction pairs for human whole genome based on HPRD database. After preprocessed, 37,080 protein pairs were obtained, among which we selected the DEGs related protein pairs to construct the PPI networks. The PPI network for CC versus NC group was constructed with 362 nodes and 512 edges [Figure 1]. There are 12 nodes and 12 edges in the PPI network of SAC versus CC group [Figure 2] and 371 nodes and 551 edges in the PPI network of all the DEGs in both two groups [Figure 3]. Compared with other two PPI networks, the PPI network of SAC versus CC had the fewest genes involved (such as CTNNB1 and SRC).
|Figure 1: Protein-protein interaction network of colorectal cancer versus control: Squares represent the differentially expressed genes; lines represent the interactions among genes|
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|Figure 2: Protein-protein interaction network of serrated adenocarcinoma versus colorectal cancer: Squares represent the differentially expressed genes; lines represent the interactions among genes|
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|Figure 3: The co-protein-protein interaction network of differentially expressed genes (DEGs) in two gene expression profiles: Squares represent the DEGs; lines represent the interactions among genes|
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Modules analysis of protein-protein interaction networks
Molecular Complex Detection in Cytoscape software was used for the modules analysis. When the threshold of module score was larger than two, three modules was extracted from the PPI network of CC versus NC group [Figure 4] and the detailed information for the three modules were listed in [Table 1]. Meanwhile, three modules were extracted from the PPI network of all DEGs identified in this paper [Figure 5] and the detail information for the three modules were listed in [Table 2]. No module was extracted from the network of SAC versus CC group due to the unsuitable module score. The similarity (Funsim value) showed that compared to the modules containing fewer number of genes, the similarity of the modules that contain a large number of genes was low [Table 1] and [Table 2].
|Figure 4: Three modules extracted from the protein-protein interaction network of colorectal cancer versus control: Squares represent the differentially expressed genes; lines represent the interactions among genes|
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|Figure 5: Three modules extracted from the co-protein-protein interaction network of differentially expressed genes (DEGs) in two gene expression profiles: Squares represent the DEGs; lines represent the interactions among genes|
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|Table 1: The results of modules extracted from PPI network of colorectal cancer versus normal control |
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|Table 2: The result of modules extracted from co-PPI network of two gene profiles |
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Function enrichment analysis of differentially expressed genes
ToppGene database was used for the function enrichment analysis to obtain the main function of all enrolled DEGs. Since BP in GO can better reflect the function of genes, we select BP as the main target in the function enrichment analysis of DEGs in this study. The results of GO enrichment analysis showed that the significantly enriched GO terms of DEGs were mainly related with cellular activities. Top 10 of GO terms enriched by DEGs were listed in [Table 3] according to the P value.
| > Discussion|| |
In the present study, we performed a PPI network and key modules analysis based on the gene expression profiles of SAC and CC. The results showed that total three PPI networks were constructed for DEGs in CC versus NC, SAC versus CC group, and DEGs in both two groups. Three modules were extracted from the PPI network of CC versus NC group. Meanwhile, three modules were extracted from the co-PPI network of two gene profiles. No module was extracted from the PPI network of SAC versus CC group. Function enrichment analysis indicated that DEGs involved in these modules were mainly associated with the BP of cellular activities.
Although CRC are proved to undergo the pathological,  extensive clinical , and molecular analysis, , little is currently known about gene expression changes accompanying their formation. Previous study indicates that the genetic inactivation of AKT1, AKT2, and PDPK1 are vital to the process of human CRC.  Gene expression is also proved to improve prognosis prediction of Stage II and III CRC.  Sabates-Bellver et al.  indicates that KIAA1199 is a novel target of the Wnt signaling pathway and a putative marker of colorectal adenomatous transformation. As a distinct variant of CRC, SAC accounts for about 7.5% of all CRCs.  Previous studies prove that the mutation of some genes and pathways are associated with the process of SAC. ,, Some outstanding results of CC and SAC based on the gene level are reported, but a systematical bioinformatics analysis of two diseases based on the gene expression was lack. In this study, the PPI networks analysis revealed three important networks among CC, SAC and healthy controls. Although there were few genes involved in the PPI network of CC versus SAC, some potential key gene including CTNNB1 might play an important role in the relationship between CC and SAC. The CTNNB1 (β-catenin gene) and CTNNB1 related pathway are proved to be important in many tumor or cancer in vivo, such as endometrioid ovarian carcinomas,  prostate cancers  and desmoid tumors.  Morikawa et al. indicate that the alteration of CTNNB1 is associated with CRC.  Our result was accordance with the previous studies that CTNNB1 was associated with the process of CC. However, the result in this study indicated that there was still a relation between CTNNB1 and SAC. Thus, we speculated that the genes including CTNNB1 in the PPI network of CC versus SAC might have a potential relationship with the disease-related gene expression via the process of CC and SAC. However, a further investigation with a large sample size based on gene expression profile analysis was needed to prove this speculation.
The high gene functional similarity (Funsim value) in the modules extracted from PPI networks showed the high accurateness of these modules in the present study. Compared with the modules that involved with a few genes, the modules that involved with large number of genes tended to have a low gene functional similarity; this phenomenon might be explained by the close relations among genes within module. Function enrichment analysis showed the DEGs in modules were mainly enriched in the cell activities pathways, such as cell migration. Cell migration is a highly integrated multistep process that contributes to tissue repair and regeneration, and drives disease progression in cancer.  Previous study indicates that some influential factors such as CXCR4 and CXCL12 are inversely expressed in CRC cells and modulate cancer cell migration.  Cell migration contributes to the morphogenesis and cancer development.  Thus, the results of enrichment analysis indicated that the modules obtained in the present study were closely related to the process of SAC and CC. However, due to the limitations (such as sample size) in the present study, a further investigation is needed.
| > Conclusion|| |
Genes in the modules clustered in our paper showed close association with CC and SAC development. CTNNB1 may play a key role in both CC and SAC. The module analysis in our paper provided a new perspective to explore the mechanism of CC and SAC.
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[Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5]
[Table 1], [Table 2], [Table 3]