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. 2019 Jun 4;20(2):1259–1269. doi: 10.3892/mmr.2019.10336

Screening key genes and signaling pathways in colorectal cancer by integrated bioinformatics analysis

Chang Yu 1,*, Fuqiang Chen 1,*, Jianjun Jiang 2, Hong Zhang 3,4,✉, Meijuan Zhou 1,✉
PMCID: PMC6625394  PMID: 31173250

Abstract

The aim of the present study was to identify potential key genes associated with the progression and prognosis of colorectal cancer (CRC). Differentially expressed genes (DEGs) between CRC and normal samples were screened by integrated analysis of gene expression profile datasets, including the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was conducted to identify the biological role of DEGs. In addition, a protein-protein interaction network and survival analysis were used to identify the key genes. The profiles of GSE9348, GSE22598 and GSE113513 were downloaded from the GEO database. A total of 405 common DEGs were identified, including 236 down- and 169 upregulated. GO analysis revealed that the downregulated DEGs were mainly enriched in ‘detoxification of copper ion’ [biological process, (BP)], ‘oxidoreductase activity, acting on CH-OH group of donors, NAD or NADP as acceptor’ [molecular function, (MF)] and ‘brush border’ [cellular component, (CC)]. Upregulated DEGs were mainly involved in ‘nuclear division’ (BP), ‘snoRNA binding’ (MF) and ‘nucleolar part’ (CC). KEGG pathway analysis revealed that DEGs were mainly involved in ‘mineral absorption’, ‘nitrogen metabolism’, ‘cell cycle’ and ‘caffeine metabolism’. A PPI network was constructed with 268 nodes and 1,027 edges. The top one module was selected, and it was revealed that module-related genes were mainly enriched in the GO terms ‘sister chromatid segregation’ (BP), ‘chemokine activity’ (MF), and ‘condensed chromosome (CC)’. The KEGG pathway was mainly enriched in ‘cell cycle’, ‘progesterone-mediated oocyte maturation’, ‘chemokine signaling pathway’, ‘IL-17 signaling pathway’, ‘legionellosis’, and ‘rheumatoid arthritis’. DNA topoisomerase II-α (TOP2A), mitotic arrest deficient 2 like 1 (MAD2L1), cyclin B1 (CCNB1), checkpoint kinase 1 (CHEK1), cell division cycle 6 (CDC6) and ubiquitin conjugating enzyme E2 C (UBE2C) were indicated as hub genes. Furthermore, survival analysis revealed that TOP2A, MAD2L1, CDC6 and CHEK1 may serve as prognostic biomarkers in CRC. The present study provided insights into the molecular mechanism of CRC that may be useful in further investigations.

Keywords: colorectal cancer, bioinformatics analysis, differentially expressed genes, biomarkers, prognostic

Introduction

Colorectal cancer (CRC) is a global burden ranking third in terms of incidence and second in terms of mortality, with >1.8 million new CRC cases and 881,000 estimated deaths in 2018 (1). The main reason for the poor 5-year overall survival in CRC is late detection, when the opportunity for treatment has passed. Despite progress in novel therapies, early detection remains a challenge (2). A variety of tests are available for screening and detecting CRC; all, however, have their disadvantages (3–5). It is therefore urgent to identify novel diagnostic and prognostic biomarkers for CRC.

In recent years, high-quality microarray and high- throughput sequencing have been effective in detecting the development and progression of CRC, and even in screening biomarkers for CRC diagnosis, therapy and prognosis.

A number of gene profiles can be obtained from public databases such as Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA), both of which can expand our understanding of cancer. Limitations and inconsistent results may exist, due to different microarray platforms and small sample sizes, but integrated bioinformatics methods may overcome these limitations.

Several recent studies have identified certain key genes and pathways in CRC using bioinformatics analysis (6,7). Based on these articles, updated datasets were selected and a gene classification method (clusterProfiler package in R) was used for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis; the prognostic role of hub genes was then ascertained using the TCGA dataset. In the present study, integrated analysis was first performed to identify the common DEGs from multiple microarrays (GSE113513, GSE9348 and GSE22598) and TCGA CRC RNA-seq data. Next, GO and KEGG enrichment analysis was conducted to identify the potential biological role of DEGs. A protein-protein interaction (PPI) network created based on the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING; version 10.5; http://string-db.org/) database, and the significant modules and hub genes were then selected from the network. The prognostic roles of hub genes were analyzed using TCGA.

Materials and methods

Gene expression profile data

The raw microarray data of CRC [GSE9348 (8), GSE22598 (9), GSE113513 (unpublished, 2018)] were downloaded from the GEO database (10) (Table I). All datasets fulfilled the following criteria: i) Human CRC tissue samples were used for the profiles; ii) normal samples were matched to the tumor tissues, when data could not be matched, matching was based on patient information, such as age and sex; and iii) The sample size per dataset was >10. The level 3 RNA sequencing data of CRC and normal samples as well as the CRC clinical information were downloaded from the Genomic Data Commons (GDC) which were retrieved from TCGA (https://tcga-data.nci.nih.gov/tcga/) database.

Table I.

Information on the datasets included in the current study.

Dataset Reference Platform No. of samples (normal/tumor)
GSE9348 Hong et al, 2010 (8) [HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array 12/12
GSE22598 Okazaki et al, 2011 (9) [HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array 17/17
GSE113513 Peng et al, 2018 (unpublished) [PrimeView] Affymetrix Human Gene Expression Array 14/14
TCGA_CRC The Cancer Genome Atlas (TCGA) data portal IlluminaHiseq (Illumina, San Diego, CA) 51/647
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Data pre-processing and DEG identification

CEL files from three Affymetrix microarrays were downloaded from GEO, and pre-processed using the Affy package (version 1.60.0; http://bioconductor.org/packages/release/bioc/html/affy.html) in R software (version 3.4.3; http://www.r-project.org/). The Robust Multi-array Average (RMA) method (11) was used for the pre-processing, which included background correcting, normalizing and calculating expression. The latest annotation files were downloaded for re-annotation. The Limma package (version 3.34.8) (12) in R software was subsequently used to screen DEGs between CRC and matched normal tissues in the microarray. The RNA sequencing data were obtained from TCGA and Ensembl Release 93 (http://jul2018.archive.ensembl.org/index.html) files were used for annotation and all the data processing and normalization were finished using the Perl (version 5.28.2; http://www.perl.org/) and R scripts. The edgeR package (version 3.24.3) (13) was used for DEG screening and the trimmed mean of M-values normalization method in edgeR was used to normalize the raw data. Notably, |log2FC|>1, P<0.05 and adjusted P<0.05 were considered the cut-off criteria. Intersect function was used to identify the common DEGs, and a Venn diagram was created using Venny (version 2.1) (14). All common DEGs in these datasets were selected for further study.

GO and KEGG pathway analyses

To elucidate the potential gene functional annotation and pathway enrichment associated with the common DEGs. GO (15,16) and KEGG (17–19) analyses were performed using the clusterProfiler (version 3.10.1) package (20). The enrichplot and DOSE (21) packages were used to supply enrichment result visualization to help interpretation. P<0.05 and adjusted P<0.05 were set as the threshold values.

PPI network construction and module selection

The online STRING database was used to identify potential interaction among the common DEGs, and a confidence score of ≥0.4 was set as the threshold. Cytoscape (version 3.6.1) (22) was used to visualize the PPI network of common DEGs. The MCODE plug-in (23) was used to search sub-networks of the PPI network and the default parameters (Degree cutoff ≥10, node score cutoff ≥0.4, K-core ≥4, and max depth=100.) were set in the functional interface of Cytoscape software. GO and KEGG enrichment analyses of cluster modules were performed using ClueGO plug-in (24) with default parameters. The Cytohubba plug-in (25) was used to explore hub genes, and the top ten were generated using Maximal Clique Centrality (MCC), closeness and degree methods. The intersect function was used to identify the common hub genes.

Survival analysis of hub genes

In order to identify the potential prognostic role of hub genes, Kaplan-Meier analysis was performed based on the expression and clinical data of TCGA, and Perl was used to merge data. Hub genes were divided into two strata based on expression level and median value. The survival (version 2.44–1.1; http://cran.r-project.org/web/packages/survival/index.html) and survminer package (version 0.4.3; http://cran.r-project.org/web/packages/survminer/index.html) in R were used.

Results

Identification of DEGs in GEO and TCGA

Following pre-processing of the raw data, DEGs were identified by R package. In total, 931 DEGs (543 down- and 388 upregulated genes), 1,420 DEGs (759 down- and 661 upregulated genes), 1,324 DEGs (659 down- and 665 upregulated genes) and 12,237 DEGs (3,776 down- and 8,461 upregulated genes) were screened from the GSE113513, GSE9348, GSE22598 and TCGA datasets, respectively. Heatmaps of DEGs are presented in Fig. S1, and volcano plots in Fig. S2. The intersect function revealed 405 common DEGs, including 236 down- and 169 upregulated DEGs from four independent datasets (Fig. 1). The top 20 down- and upregulated DEGs from the four datasets are listed in Table II.

Figure 1.

Figure 1.

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Intersected analysis was used for the identification of 405 simultaneously changed DEGs from four datasets (GSE113513, GSE9348, GSE22598 and TCGA). (A) A total of 236 downregulated and (B) 169 upregulated overlapping DEGs was identified. Different colored regions represent different datasets, and the intersective area denotes the simultaneously changed DEGs. DEGs, differentially expressed genes; TCGA, The Cancer Genome Atlas.

Table II.

Top 20 down- and upregulated overlapping DEGs in GSE113513, GSE9348, GSE22598 and TCGA were screened by intersected analysis.

Genes GSE113513 GSE9348 GSE22598 TCGA Regulation
AQP8 −4.00 −6.74 −2.75 −6.87 Down
CLCA4 −2.30 −7.61 −4.91 −5.35 Down
GUCA2B −4.82 −5.08 −3.37 −6.06 Down
MS4A12 −3.68 −6.04 −3.54 −5.36 Down
GUCA2A −4.10 −5.00 −3.72 −5.11 Down
CA2 −3.68 −5.32 −3.76 −4.81 Down
ABCG2 −2.39 −6.03 −3.93 −4.68 Down
CLDN8 −3.69 −4.40 −3.65 −5.07 Down
GCG −4.57 −3.92 −4.15 −4.13 Down
ZG16 −3.18 −4.70 −4.45 −4.44 Down
PKIB −3.21 −4.99 −3.85 −3.95 Down
CA4 −3.39 −4.54 −3.07 −4.76 Down
BEST4 −3.50 −2.72 −3.66 −5.85 Down
CA1 −3.33 −3.20 −1.94 −6.51 Down
MT1M −3.29 −3.90 −3.35 −4.30 Down
CD177 −2.84 −4.29 −2.35 −5.22 Down
HSD17B2 −2.63 −5.14 −3.04 −3.39 Down
INSL5 −3.31 −2.03 −3.00 −5.82 Down
ADH1C −3.26 −3.50 −3.10 −4.01 Down
CLCA1 −3.45 −3.77 −3.62 −2.54 Down
FOXQ1 4.47 5.16 5.55 6.47 Up
KRT23 3.69 4.25 4.28 7.37 Up
LY6G6D 3.50 4.09 3.63 5.42 Up
MMP7 3.15 3.11 2.38 7.03 Up
CDH3 2.77 2.31 2.76 5.77 Up
MMP3 2.91 2.94 2.77 4.83 Up
CST1 1.25 2.42 1.00 8.33 Up
CRNDE 3.17 2.36 2.73 4.60 Up
DPEP1 2.97 3.06 4.14 2.61 Up
MMP1 2.46 2.59 3.14 4.56 Up
EPHX4 3.01 2.32 2.95 4.43 Up
CTHRC1 1.21 4.47 3.15 3.78 Up
CLDN1 2.18 2.81 2.74 4.84 Up
CEL 1.56 2.53 2.37 6.10 Up
CLDN2 1.54 2.57 2.75 5.57 Up
SLC35D3 1.79 2.97 3.24 4.35 Up
COL11A1 1.59 2.49 1.93 6.33 Up
CXCL3 3.22 3.60 2.34 2.92 Up
SLCO1B3 1.43 1.64 2.55 6.44 Up
CKMT2 3.33 2.55 2.68 3.46 Up
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Each column represents a dataset and each row a gene. The values in the table represent the log2FC in the datasets. DEGs, differentially expressed genes; TCGA, The Cancer Genome Atlas; FC, fold change.

GO and KEGG enrichment analysis

To explore the potential biological function of common DEGs, GO and KEGG pathways enrichment analyses were conducted using the ClusterProfiler package. In the present study, downregulated DEGs were mainly enriched in ‘detoxification of copper ion’ (BP), ‘oxidoreductase activity, acting on CH-OH group of donors, NAD or NADP as acceptor’ (MF) and ‘brush border’ (CC) respectively. Upregulated DEGs were mainly involved in ‘nuclear division’ (BP), ‘snoRNA binding’ (MF) and ‘nucleolar part’ (CC).'The GO distribution of down- and upregulated DEGs is presented in Fig. 2A and B and details of the top 15 GO terms in Table III.

Figure 2.

Figure 2.

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GO and KEGG enrichment analysis of the overlapping DEGs. (A and B) Top significantly enriched GO terms of down- and upregulated DEGs, including BP, CC and MF. The x-axis represents the number of DEGs involved in GO terms, and the y-axis the significantly enriched GO terms. (C) KEGG pathway enrichment analysis of overlapping DEGs. The x-axis indicates the number of DEGs involved in the significant KEGG pathway, and the y-axis the terms of the significant KEGG pathway. (D) DEGs associated with the significant KEGG pathway. GO, gene ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; DEGs, differentially expressed genes. (a) Oxidoreductase activity, acting on the CH-OH group of donors, NAD or NADP as acceptor; (b) oxidoreductase activity, acting on CH-OH group of donors; (c) steroid dehydrogenase activity, acting on the CH-OH group of donors, NAD or NADP as acceptor; (d) oxidoreductase activity, acting on the CH-NH2 group of donors, oxygen as acceptor; (e) regulation of mitotic cell cycle phase transition; (f) condensed chromosome, centromeric region; (g) condensed nuclear chromosome, centromeric region; (h) chromosome, centromeric region; (i) cyclin-dependent protein serine/threonine kinase regulator activity; (j) transcription factor activity, RNA polymerase II proximal promoter sequence-specific DNA binding.

Table III.

GO analysis of down- and upregulated overlapping DEGs associated with CRC.

Expression Terms ID Description q-value Genes
Upregulated BP GO:0000280 Nuclear division 4.33×10−7 RRS1/CKS2/TRIP13/CCND1/FIGNL1/TPX2/ANLN/UBE2C/CHEK1/NUF2/RAD54B/DLGAP5/CDC6/MAD2L1/TOP2A/TTK/CCNB1/ASPM/BUB1B/AURKA
BP GO:0048285 Organelle fission 1.16×10−6 RRS1/CKS2/TRIP13/CCND1/FIGNL1/TPX2/ANLN/UBE2C/CHEK1/NUF2/RAD54B/DLGAP5/CDC6/MAD2L1/TOP2A/TTK/CCNB1/ASPM/BUB1B/AURKA
BP GO:0140014 Mitotic nuclear division 6.51×10−6 RRS1/TRIP13/CCND1/TPX2/ANLN/UBE2C/CHEK1/NUF2/DLGAP5/CDC6/MAD2L1/TTK/CCNB1/BUB1B/AURKA
BP GO:0007088 Regulation of mitotic nuclear division 1.18×10−5 TRIP13/CCND1/ANLN/UBE2C/CHEK1/DLGAP5/CDC6/MAD2L1/TTK/CCNB1/BUB1B/AURKA
BP GO:0000075 Cell cycle checkpoint 1.18×10−5 TRIP13/CCND1/ARID3A/CHEK1/CDC6/MAD2L1/TOP2A/TTK/CCNB1/SOX4/BUB1B/AURKA/PROX1
CC GO:0044452 Nucleolar part 4.66×10−5 TAF1D/DKC1/UTP4/WDR43/NOP58/RRS1/RPP40/NUFIP1/ORC6/POLR1D/E2F5
CC GO:0000793 Condensed chromosome 4.66×10−5 RRS1/SKA3/CHEK1/NUF2/CENPA/MAD2L1/TOP2A/CCNB1/SPC25/ERCC6L/BUB1B/AURKA
CC GO:0000779 Condensed chromosome, centromeric region 5.94×10−5 SKA3/NUF2/CENPA/MAD2L1/CCNB1/SPC25/ERCC6L/BUB1B/AURKA
CC GO:0000780 Condensed nuclear Chromosome, centromeric region 1.13×10−4 NUF2/CENPA/CCNB1/BUB1B/AURKA
CC GO:0000776 Kinetochore 1.13×10−4 SKA3/NUF2/CENPA/MAD2L1/TTK/CCNB1/SPC25/ERCC6L/BUB1B
MF GO:0030515 snoRNA binding 2.99×10−3 DKC1/NOP58/DDX21/NUFIP1
MF GO:0009982 Pseudouridine synthase activity 3.70×10−3 PUS1/PUS7/DKC1
MF GO:0035173 Histone kinase activity 7.05×10−3 CHEK1/CCNB1/AURKA
MF GO:0001664 G protein-coupled receptor binding 8.09×10−3 CXCL3/RNF43/HOMER1/NMU/CXCL8/ZNRF3/CXCL1/CTHRC1/CXCL5
MF GO:0140098 Catalytic activity, acting on RNA 1.15×10−2 NOB1/PUS1/EXOSC5/NOP2/RPP40/DDX21/RNASEH2A/POLR1D/RAD54B/AZGP1
Downregulated BP GO:0010273 Detoxification of copper ion 1.22×10−5 MT1M/MT1X/MT1E/MT1H/MT1F/MT1G
BP GO:1990169 Stress response to copper ion 1.22×10−5 MT1M/MT1X/MT1E/MT1H/MT1F/MT1G
BP GO:0061687 Detoxification of inorganic compound 1.22×10−5 MT1M/MT1X/MT1E/MT1H/MT1F/MT1G
BP GO:0071280 Cellular response to copper ion 1.22×10−5 MT1M/MT1X/MT1E/MT1H/MT1F/MT1G/AOC1
BP GO:0097501 Stress response to metal ion 1.35×10−5 MT1M/MT1X/MT1E/MT1H/MT1F/MT1G
CC GO:0005903 Brush border 6.96×10−5 CDHR5/TRPM6/SCIN/CDHR2/CA4/LIMA1/CYBRD1/MYO1A/SLC26A3/SI
CC GO:0045177 Apical part of cell 3.15×10−4 ABCG2/CA2/RAB27A/CDHR5/PTPRH/TRPM6/AQP8/CDHR2/CA4/SCNN1B/CLCA4/CEACAM1/FABP1/CEACAM7/MYO1A/SLC26A3/SI
CC GO:0031526 Brush border membrane 3.15×10−4 CDHR5/TRPM6/CDHR2/CA4/LIMA1/CYBRD1/SLC26A3
CC GO:0005902 Microvillus 6.78×10−4 CA2/CDHR5/PTPRH/CDHR2/CEACAM1/CLCA1/AOC3/MYO1A
CC GO:0098862 Cluster of actin-based cell projections 8.31×10−4 CDHR5/TRPM6/SCIN/CDHR2/CA4/LIMA1/CYBRD1/MYO1A/SLC26A3/SI
MF GO:0016616 Oxidoreductase activity, acting on the CH-OH group of donors, NAD or NADP as acceptor 6.48×10−5 UGDH/ADH1B/HPGD/LDHD/ADH1C/DHRS11/DHRS9/HSD17B2/AKR1B10/HSD11B2/BMP2
MF GO:0016614 Oxidoreductase activity, acting on CH-OH group of donors 1.09×10−4 UGDH/ADH1B/HPGD/LDHD/ADH1C/DHRS11/DHRS9/HSD17B2/AKR1B10/HSD11B2/BMP2
MF GO:0004089 Carbonate dehydratase activity 1.26×10−4 CA7/CA2/CA12/CA1/CA4
MF GO:0048038 Quinone binding 4.90×10−3 ETFDH/TP53I3/AOC3/AOC1
MF GO:0033764 Steroid dehydrogenase activity, acting on the CH-OH group of donors, NAD or NADP as acceptor 1.52×10−2 DHRS11/DHRS9/HSD17B2/HSD11B2
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Adjusted P<0.05 was considered to be significant. DEGs, differentially expressed genes; CRC, colorectal cancer; q-value, adjusted P-value; GO, Gene Ontology; BP, biological process; CC, cellular component; MF, molecular function.

According to KEGG enrichment analysis, four significant pathways of common DEGs were identified, including: ‘Mineral absorption’, ‘nitrogen metabolism’, ‘cell cycle’ and ‘caffeine metabolism’ (Fig. 2C). The significant genes in these pathways are presented in Fig. 2D.

PPI network and module selection

The PPI network of DEGs was constructed with 268 nodes and 1,027 edges (Fig. 3A). The Degree, MCC and Closeness methods were performed to calculate the top 10 ranking hub genes. The results revealed 6 genes identified as hub genes, including DNA topoisomerase II-α (TOP2A), mitotic arrest deficient 2 like 1 (MAD2L1), cyclin B1 (CCNB1), checkpoint kinase 1 (CHEK1), cell division cycle 6 (CDC6), ubiquitin conjugating enzyme E2 C (UBE2C) (Fig. 3B). MCODE was used to identify the significant cluster modules in the PPI network and the top module was selected (Fig. 3C). Following GO annotation screening, the module (44 nodes and 462 edges) was revealed to be associated with ‘sister chromatid segregation’ (BP), ‘chemokine activity’ (MF), ‘condensed chromosome (CC)’ (Table IV) and KEGG pathway enrichment analysis revealed that the top module was mainly enriched in ‘cell cycle’, ‘progesterone-mediated oocyte maturation’, ‘chemokine signaling pathway’, ‘IL-17 signaling pathway’, ‘legionellosis’ and ‘rheumatoid arthritis’; related information were represent in Table V.

Figure 3.

Figure 3.

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PPI network and hub clustering modules. (A) PPI networks of the overlapping DEGs. (B) Hub genes were screened from the PPI network using the Closeness, Degree and MCC methods. (C) The top module was selected from the PPI network. Red circles indicate upregulated genes and green circles downregulated genes. PPI, protein-protein interaction; DEGs, differentially expressed genes.

Table IV.

GO analysis of selected module-associated DEGs.

ID GO Terms Ontology q-value Genes
GO:0000819 Sister chromatid segregation BP 5.54×10−13 BUB1B/CCNB1/CDC6/CENPA/DLGAP5/ERCC6L/MAD2L1/NUF2/SPC25/TOP2A/TRIP13/TTK/UBE2C
GO:0007088 Regulation of mitotic nuclear division BP 8.77×10−12 ANLN/AURKA/BUB1B/CCNB1/CDC6/CHEK1/DLGAP5/MAD2L1/TRIP13/TTK/UBE2C
GO:0098813 Nuclear chromosome BP 1.24×10−11 segregation BUB1B/CCNB1/CDC6/CENPA/DLGAP5/ERCC6L/MAD2L1/NUF2/SPC25/TOP2A/TRIP13/TTK/UBE2C
GO:0051304 Chromosome separation BP 1.37×10−11 BUB1B/CCNB1/CDC6/DLGAP5/MAD2L1/TOP2A/TRIP13/TTK/UBE2C
GO:0030071 Regulation of mitotic metaphase/anaphase transition BP 1.46×10−11 BUB1B/CCNB1/CDC6/DLGAP5/MAD2L1/TRIP13/TTK/UBE2C
GO:0000793 Condensed chromosome CC 2.53×10−10 AURKA/BUB1B/CCNB1/CENPA/CHEK1/ERCC6L/MAD2L1/NUF2/SKA3/SPC25/TOP2A
GO:0000779 Condensed chromosome/centromeric region CC 5.40×10−10 AURKA/BUB1B/CCNB1/CENPA/ERCC6L/MAD2L1/NUF2/SKA3/SPC25
GO:0000775 Chromosome/centromeric region CC 1.59×10−9 AURKA/BUB1B/CCNB1/CENPA/ERCC6L/MAD2L1/NUF2/SKA3/SPC25/TTK
GO:0000776 Kinetochore CC 1.80×10−9 BUB1B/CCNB1/CENPA/ERCC6L/MAD2L1/NUF2/SKA3/SPC25/TTK
GO:0000777 Condensed chromosome kinetochore CC 8.17×10−9 BUB1B/CCNB1/CENPA/ERCC6L/MAD2L1/NUF2/SKA3/SPC25
GO:0008009 Chemokine activity MF 1.41×10−9 CCL19/CCL28/CXCL1/CXCL12/CXCL3/CXCL5/CXCL8
GO:0042379 Chemokine receptor binding MF 8.09×10−9 CCL19/CCL28/CXCL1/CXCL12/CXCL3/CXCL5/CXCL8
GO:0045236 CXCR chemokine receptor binding MF 1.46×10−8 CXCL1/CXCL12/CXCL3/CXCL5/CXCL8
GO:0035173 Histone kinase activity MF 1.99×10−4 AURKA/CCNB1/CHEK1
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Adjusted P<0.05 was considered as significanct. DEGs, differentially expressed genes; CRC, colorectal cancer; q-value, adjusted P-value; GO, Gene Ontology; BP, biological process; CC, cellular component; MF, molecular function.

Table V.

KEGG pathway analysis of selected module-associated DEGs.

KEGG Count q-value Genes
Cell cycle 6 1.88×10−5 BUB1B/CCNB1/CDC6/CHEK1/MAD2L1/TTK
Progesterone-mediated oocyte maturation 4 7.64×10−4 AURKA/CCNB1/GNAI1/MAD2L1
Chemokine signaling pathway 9 4.69×10−8 CCL19/CCL28/CXCL1/CXCL12/CXCL3/CXCL5/CXCL8/GNAI1/GNG4
IL-17 signaling pathway 4 9.02×10−4 CXCL1/CXCL3/CXCL5/CXCL8
Legionellosis 3 9.37×10−4 CXCL1/CXCL3/CXCL8
Rheumatoid arthritis 4 1.06×10−3 CXCL1/CXCL12/CXCL5/CXCL8
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DEGs, differentially expressed genes; KEGG, Kyoto Encyclopedia of Genes and Genomes.

Survival analysis

The prognostic role of hub genes was analyzed using Kaplan-Meier method based on the TCGA data. Among these hub genes, a low expression of CDC6, CHEK1, MAD2L1 and TOP2A was associated with poor prognosis in CRC patients (Fig. 4).

Figure 4.

Figure 4.

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Survival analysis showing the correlation between the expression of hub genes and overall survival in CRC patients. (A) CDC6, (B) CHEK1, (C) MAD2L1 and (D) TOP2A. Red lines represent high and green lines low expression genes. Dashed lines represent CI. The x-axis shows the survival time of CRC patients and the y-axis the survival probability. CRC, colorectal cancer; CDC6, Cell Division Cycle 6; CHEK1, Checkpoint Kinase 1; MAD2L1, Mitotic Arrest Deficient 2 Like 1; TOP2A, Topoisomerase II Alpha; CI, confidence interval.

Discussion

In the present study, three GEOs and TCGA data were integrated, and non-paired GEO data were manually matched to increase accuracy and stabilization. In total, 236 down- and 169 upregulated DEGs were identified. The common downregulated DEGs were mostly enriched in ‘detoxification of copper ion’ (BP), ‘oxidoreductase activity, acting on CH-OH group of donors, NAD or NADP as acceptor’ (MF) and ‘brush border’ (CC), and the upregulated DEGs were mainly associated with ‘nuclear division’ (BP), ‘snoRNA binding’ (MF) and ‘nucleolar part’ (CC). Moreover, KEGG enrichment analysis identified that the common DEGs were largely involved in ‘mineral absorption’, ‘nitrogen metabolism’, ‘cell cycle’ and ‘caffeine metabolism’. Investigating these significant pathways may promote the understanding of CRC development.

Minerals are one of the five fundamental groups of nutrients; regular mineral absorption plays a vital role in sustaining life. Deficiency and insufficiency of minerals may be associated with and increase the risk of cancer; for example, efficient absorption of vitamin D may prevent CRC (26). The cell cycle is controlled by various mechanisms, which ensure correct cell division; loss of normal cell cycle control is a hallmark of cancer (27). An increasing number of studies has revealed that targeting the deregulation of the cell cycle in cancer is a potential therapeutic strategy (28). Therefore, investigating the cell cycle pathway may promote the understanding of carcinogenic mechanisms and insights into CRC treatment options. Nitrogen is an essential component of life that is involved in processes of both proteins and nucleic acids. Aberrant expression of nitrogen species could affect the risk of cancer (29). Certain studies have reported that caffeine could decrease the risk of CRC (30), inhibit colon cancer cell proliferation (31,32). Metabolic profiling revealed that caffeine metabolism differed significantly between colorectal adenoma polyps and CRC patients which may influence CRC development and outcome (33). Investigating these significant pathways may elucidate the mechanism of CRC progression.

Recently, certain important biomarkers, including CCL19, CXCL1, CXCL5, CXCL11, CXCL12, GNG4, INSL5, NMU, PYY and SST, were identified using integrated bioinformatics analysis. Furthermore, a prognostic gene signature consisting of 9 genes was also identified (34). In the present study, data from three GEO datasets (GSE22598, GSE113513 and GSE9348) and TCGA were combined for screening stable DEGs, then different calculation methods and intersect function analysis were used, and potential biomarkers were revealed that had not been previously screened out. A PPI network was constructed with DEGs, and then 6 hub genes (TOP2A, MAD2L1, CCNB1, CHEK1, CDC6 and UBE2C) were identified. Following survival analysis based on the TCGA data, the low expression of CDC6, CHEK1, MAD2L1 and TOP2A was revealed to be associated with poor prognosis. TOP2A was an important nuclear enzyme involved in DNA transcription and replication. Aberrant TOP2A expression has been identified in several types of cancer, such as CRC, breast carcinomas, gastric cancer and bladder carcinomas (35–38). In addition, the high expression of TOP2A was revealed to be correlated with a worse survival in breast cancer, small-cell lung cancer and malignant melanoma, indicating that TOP2A can serve as a prognostic biomarker (39–41). TOP2A was also revealed to be associated with advanced CRC and chemotherapeutic resistance via the inhibition of apoptosis (35). Zhou et al (42) revealed that TOP2A involvement in T-cell factor transcription may transmit a mechanism of multidrug resistance to TOP2A inhibitors, which can be an effective treatment option for CRC. Therefore, further investigation is required to elucidate the mechanism of TOP2A in the development, progression and treatment of CRC. CDC6 is essential for the initiation of DNA replication and contains ATP-binding and hydrolytic activities, which are required for the formation of the pre-replicative complex (43,44). It is also involved in the cell cycle by localizing to the centrosomes during the S and G2 phases (45). It has been reported that CDC6 affects proliferation during the early differentiation stages (46), and that the overexpression of CDC6 in tumors could signify that it may be an oncogenic target. CHEK1 is a serine/threonine kinase involved in delaying cell cycle progression; it is also required for the activation of DNA repair in response to the presence of DNA damage or unreplicated DNA (47). MAD2L1 (also termed MAD2) plays an important role in maintaining the spindle checkpoint function. MAD2L1 was revealed to be correlated with disease outcome in patients with breast cancer (48), and an increased MAD2L1 expression in neuroblastoma cells to be associated with poor prognosis (49).

The PPI network module analysis revealed that the development of CRC was mainly associated with the cell cycle pathway. It has been reported that cell cycle signaling pathways play a vital role in controlling normal progression, and regulating cell proliferation and apoptosis; an uncontrolled cell cycle may result in cancer (27). Therefore, cell cycle regulation is a useful way of interfering with the development of CRC.

Recent studies have indicated that mRNAs, miRNAs and lncRNAs play a crucial role in a variety of biological processes associated with human diseases (50,51). Chen et al (52–55), have constructed powerful computational models to predict potential associations between miRNAs/lncRNAs and human diseases, providing a reliable and powerful tool for disease-association prediction. With the application of computational models, more stable and effective biomarkers (miRNAs/lncRNAs) would be revealed, and the pathogenesis of CRC would be explained at different molecular levels.

The present results provided useful information for early diagnosis and prevention, and supplied an effective therapeutic target for CRC. However, the study has certain limitations: i) By mixing all cancer types together, the focused insights in genetic characteristics of different subtypes of tumors may not be revealed, therefore, a stratification analysis of major clinical information should be performed; ii) more databases should be used for validation of the DEGs in future research; iii) in the present study, all tumor samples were included for the evaluation of the prognositic role of hub genes, however, it would be better to exclude CRC without matching normal controls, which would reduce the difference arising from different clinical information, and iv) intersect function analysis of GEO and TCGA was performed to screen DEGs, however, among these data sets, GSE22598 included colon cancer treated with chemotherapy which may affect the results, therefore, sample information should be more focused in future research. In addition, biological experiments are required to confirm these results. In conclusion, in the present study DEGs were identified by integrated bioinformatics analysis, in order to explore the role of DEGs in the progression and prognosis of CRC. Consequently, 405 DEGs were screened out, and hub genes CDC6, CHEK1, MAD2L1 and TOP2A were revealed to be promising prognostic biomarkers among CRC patients.

Supplementary Material

Supporting Data
Supplementary_Data.pdf (1.7MB, pdf)

Acknowledgements

Not applicable.

Funding

The present study was supported by grants from the National Natural Science Foundation of China (grant nos. 81472922 and 81673105).

Availability of data and materials

All data generated or analyzed during the present study are included in this article, and the R codes are available at https://github.com/Yuchang66/CRC-project.

Authors' contributions

All authors conceived and designed the experiments. CY contributed to the bioinformatics analysis and drafted the manuscript. CY, FC and JJ contributed to the data mining. HZ and MZ contributed to the statistical analysis and modified the manuscript. All authors read and approved the final version of the manuscript and agree to be accountable for all aspects of the research in ensuring that the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Ethics approval and consent to participate

Not applicable.

Patient consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supporting Data
Supplementary_Data.pdf (1.7MB, pdf)

Data Availability Statement

All data generated or analyzed during the present study are included in this article, and the R codes are available at https://github.com/Yuchang66/CRC-project.

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