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Abstract

BACKGROUND:

Colorectal cancer (CRC) is one of the leading causes of cancer-related deaths and mining the molecular factors underlying CRC pathogenesis is imperative for alleviating the disease burden.

OBJECTIVE:

To highlight key molecular pathways, prioritize hub genes and their regulators related to CRC.

METHODS:

Data sets of TCGA-COAD and GTEx were used to identify differentially expressed genes (DEGs) and their functional enrichments in pathways and biological processes were analyzed using bioinformatics tools. Protein-protein interaction network was constructed and hub genes were identified using Cytoscape. Ingenuity Pathway Analysis was used to analyze the relations of the hub genes with diseases and canonical pathways. Key regulators targeting the hub genes such as TFs, miRNAs and their interactions were identified using in silico tools.

RESULTS:

AURKA, CDK1, MYC, CDH1, CCNB1, CDC20, UBE2C, PLK1, KIF11, and CCNA2 were prioritized as hub genes based on their topological properties. Enrichment analyses emphasized the roles of DEGs and hub genes in the cell cycle process. Interactions of the hub genes with TFs and miRNAs suggested TP53, EZH2 and KLF4 as being promising candidate biomarkers for CRC.

CONCLUSIONS:

Our results provide in silico evidence for candidate biomolecules that may have strong biomarker potential for CRC-related translational strategies.

Keywords

Colorectal cancer microRNA transcription factors in silico tools bioinformatics Ingenuity Pathway Analysis

1. Introduction

Colorectal cancer (CRC) is one of the most common devastating cancer that leads to high mortality and morbidity in both women and men worldwide [1, 2]. However, the incidence of CRC was found to continue rising in developed countries especially in Europe along with the decline in mortality rates [3]. CRC is a complex heterogeneous disease and genetic, epigenetic and environmental risk factors such as diet, body fat distribution, and microbiome composition are known to contribute to the disease pathogenesis [4, 5, 6]. Continuing efforts to understand the genetic epidemiology of CRC have been successfully revealed several loci associated with the disease risk, yet early molecular diagnosis and accurate assessment of prognosis of CRC are still challenging [7, 8, 9]. Thus, in recent years, researchers have begun to investigate the missing heritability underlying the etiology of CRC by exploring the possible contributions of molecular regulators of the gene expression such as microRNAs (miRNAs) and transcription factors (TFs).

Figure 1.

The figure depicts the workflow of the study and used bioinformatics tools.

Dysregulation of the genes involved in the critical pathways in colorectal carcinogenesis is known to be associated with CRC. Hence, it is highly imperative to unveil the key mediators and their molecular function influencing the expressions of the genes dysregulated in CRC. Recently, miRNAs were proposed to have functional importance in carcinogenesis and also accumulating evidence indicates their utilization as a diagnostic and prognostic biomarker for CRC [10, 11, 12]. Also, dysregulated expression of some TFs was shown to be a major impact on colorectal tumor growth, CRC-associated oncogenic process as well as CRC metastasis [13, 14, 15, 16, 17]. Nowadays, researchers shed light on the interactions of the miRNAs, TFs and their target genes and suggested to consider them as a network to fully uncover their roles in CRC pathogenesis [18, 19].

Our study was designed as an “in silico” approach to generating novel knowledge related the missing genetic determinants and key regulators of the CRC as well as pinpoint candidate critical molecular pathways which may be targeted for therapeutic approaches and utilized for CRC management.

2. Materials and methods

2.1 Data sets and differential gene expression analyses

The data sets analyzed in this study include the RNA-Sequencing (RNA-Seq) expression data of 624 samples retrieved from the Cancer Genome Atlas (TCGA) and The Genotype-Tissue Expression (GTEx) projects. Of 624 samples, 275 were tumor samples belong to the individuals with colon adenocarcinoma (COAD) in TCGA data sets and 349 were normal samples. Genes with a log2FC $|>$ 2 or log2FC $|<-$ 2 and a Benjamini and Hochberg’s False Discovery Rate (FDR) adjusted $P$ -value ( $q$ -value) $<$ 0.01 were identified as significantly differentially expressed genes (SDEGs) by ANOVA using the Gene expression profiling integrative analyses (GEPIA) web server (http:// gepia.cancer-pku.cn/index.html) [21] and they were included further downstream analyses (Fig. 1).

2.2 Protein-protein interaction network construction and topological analysis

All DEGs were submitted to the STRING database to construct protein-protein interaction (PPI) network [20]. The network was imported to Cytoscape software for visualization. Cytohubba plugin was used to analyze the topological properties of the network and DEGs having the top scores in the degree ranking were defined as “hub genes” [22, 23].

Table 1
Topological properties of the hub genes identified by Cytohubba

Official gene ID	Degree	Betweenness	BottleNeck	Closeness	Clustering coefficient	DMNC	EcCentricity	EPC	MCC	MNC	Radiality	Stress
CDK1	160	39547	5	613.27	0.48	1.11	0.142	498.621	9.22	159	11.46	603506
MYC	152	109132	73	637.65	0.15	0.35	0.124	498.621	9.22	148	11.59	1839834
CDH1	151	132015	58	634.80	0.09	0.22	0.124	498.621	2.87	147	11.58	1758784
CCNB1	145	21007	10	600.84	0.58	1.29	0.142	498.621	9.22	144	11.42	400468
CDC20	144	13705	2	575.94	0.58	1.30	0.142	498.621	9.22	143	11.29	256638
UBE2C	141	19593	4	581.60	0.58	1.28	0.142	498.621	9.22	140	11.32	308194
AURKA	136	10902	3	572.30	0.63	1.37	0.142	498.621	9.22	135	11.28	217082
PLK1	135	14446	7	574.94	0.60	1.34	0.142	498.621	9.22	133	11.30	235036
KIF11	135	7664	4	553.39	0.64	1.41	0.142	498.621	9.22	134	11.19	156356
CCNA2	134	9176	1	584.27	0.67	1.45	0.142	498.621	9.22	133	11.35	250198

2.3 Functional and pathway enrichment analyses

Functional gene set enrichment analyses for DEGs were performed using the g:profiler web tool (https:// biit.cs.ut.ee/gprofiler) [24]. Significant Kyoto Encyclopedia of Genes and Genomes (KEGG) terms related to molecular functions and Gene Ontology (GO) terms related to biological processes for all DEGs were identified by the hypergeometric test. The enrichments of the determined hub genes in molecular pathways and their association with human diseases were also assessed by Ingenuity Pathway Knowledge Base (IPKB) in IPA (Ingenuity Pathway Analysis) software (QIAGEN Inc., https://www.qiagenbioinformatics.com/ products/ingenuity-pathway-analysis). $P$ -value threshold was set as 0.05 for all enrichment analyses conducted in IPA and NetworkAanalyst. The HUGO Gene Nomenclature Committee (HGNC) gene identifiers were used in input files used for all functional and pathway enrichment analyses.

2.4 Identification of putative key regulators

TRRUST v.2 database was used to identify the key TFs interacted with hub genes [25] and significant TFs are predicted to be most likely involved in the colon cancer pathogenesis. We also identified the predicted miRNAs targeting the hub genes by using the microRNA Data Integration Portal (mirDIP) v4.1 database which integrates prediction scores from 30 distinct sources [26]. The miRDIP allows us to submit all hub genes as input and groups the miRNAs that have shared targets in the input list.

Assessment of TF-miRNA regulations and functional over-representation (enrichment) analyses of the miRNAs by their regulating TF were performed using the TransmiR v2.0 database [27]. TransmiR database retrieves data from literature-curated sources and extracts TF binding motifs from the UCSC genome browser as well as incorporates the experimental data from Chip-seq studies, transcriptome studies. Over-representation analysis in TransmiR was conducted by the hypergeometric test using the most stringent level (2) and $p$ -value adjustments were calculated by Bonferroni and Benjamini and Hochberg method (FDR).

3. Results

3.1 Identification of significant differentially expressed genes

Analyzing the RNA-seq data including tumor samples with colorectal adenocarcinoma and normal tissues in GEPIA revealed a total of 1556 SDEGs (log2FC $|>$ 2 or log2FC $|<-$ 2, $q$ -value $<$ 0.01) of which 786 were over-expressed and 770 were under-expressed genes.

3.2 Protein-protein interaction and hub gene identification

Protein-protein interactions of all DEGs were analyzed in the STRING database and data was used for network construction in the Cytoscape. Topological properties were analyzed by Cytohubba in Cytoscape and the top 10 genes (AURKA, CDK1, MYC, CDH1, CCNB1, CDC20, UBE2C, PLK1, KIF11, CCNA2) with the highest nodes’ scores in the topological analyses considering degree ranking were defined as significant nodes and hub genes (Table 1).

3.3 Functional and pathway analyses of the SDEGs and hub genes

Gene set enrichment analyses of the DEGs showed dysregulated genes play role in several molecular pathways of which cell cycle was the most significant molecular pathway (KEGG term: hsa04110) and cell division was the top GO term related biological process (Tables 2 and 3). Functional analyses of the hub genes in the IPA filtering the results by disease categories and functions revealed evidence of significant associations of hub genes with 9 disease categories related to different cancer types (8.76E-10 $\leqslant P\leqslant$ 5.5E-5) and cell cycle progression ( $P\leqslant$ 5.39E-10) (Fig. 2).

Table 2
The top 10 significant KEGG pathways identified by enrichment analyses of DEGs

Kegg term	$P$ -value	FDR
Cell cycle	3.47E-07	0.00011
Arrhythmogenic right ventricular	4.05E-06	0.000645
cardiomyopathy (ARVC)
Tight junction	8.26E-06	0.000876
Cell adhesion molecules (CAMs)	2.80E-05	0.00223
ECM-receptor interaction	1.00E-04	0.00603
Vascular smooth muscle contraction	0.000114	0.00603
Protein digestion and absorption	0.000328	0.0149
Maturity onset diabetes of the young	0.000452	0.0169
Proteoglycans in cancer	0.000478	0.0169
Progesterone-mediated oocyte maturation	0.00103	0.0319
Dilated cardiomyopathy	0.0011	0.0319
cGMP-PKG signaling pathway	0.00139	0.0369
Steroid biosynthesis	0.00205	0.0502

Table 3

The top 10 significant GO Biological process terms identified by enrichment analyses of DEGs

Gene ontology term	$P$ -value	FDR
Cell division	1.70E-09	9.68E-07
Regulation of cell proliferation	2.36E-09	9.68E-07
Mitosis	5.65E-09	1.54E-06
M phase of mitotic cell cycle	1.33E-08	2.72E-06
Cell proliferation	2.14E-08	2.99E-06
Muscle organ development	2.19E-08	2.99E-06
Negative regulation of cell proliferation	4.99E-08	5.85E-06
Cytoskeleton organization	6.00E-08	6.15E-06
Cell cycle phase	5.24E-07	4.56E-05
Mitotic cell cycle	5.57E-07	4.56E-05

Figure 2.

The network displaying the 10 top hub genes and their relationships with the top significant disease and biological functions ( $p$ -value $=$ 0.05). The plot was generated by IPA software (IPA, Qiagen Inc., Germany).

We also analyzed the enrichments of hub genes in the canonical pathways by using IPA and found pathways enriched with the hub genes. “Mitotic Roles of Polo-like Kinase” pathway was the top enriched pathway and 5 hub genes (CDC20, KIF11, CCNB1, PLK1, CDK1) showed evidence to be associated with this pathway. Figure 2 also displays the canonical pathways enriched with at least 4 hub genes identified in this study.

Table 4

Predicted shared miRNAs targeting the hub genes ( $n>$ 3)

miRNAs	Target genes	miRNAs	Target genes
hsa-miR-4282	CCNA2, CCNB1, CDH1, CDK1, KIF11, MYC	hsa-miR-561-3p	AURKA, CCNB1, KIF11, MYC
hsa-miR-203a-3p	AURKA, CCNA2, CDH1, CDK1, KIF11, MYC	hsa-miR-101-3p	CCNB1, CDK1, KIF11, PLK1
hsa-miR-140-5p	AURKA, CCNA2, CCNB1, CDK1, KIF11	hsa-miR-20b-5p	CCNB1, CDH1,CDK1, KIF11
hsa-miR-3163	CCNB1, CCNA2, CDK1, KIF11, CDH1	hsa-let-7d-5p, hsa-let-7f-5p	AURKA, CCNA2, CDH1, MYC
hsa-miR-548n	CCNA2, CCNB1, CDH1, CDK1, MYC	hsa-miR-129-5p	AURKA, CCNA2, CDK1, CDH1
hsa-miR-103a-3p	AURKA, CDH1, CCNB1, PLK1, UBE2C	hsa-miR-1297, hsa-miR-199b-5p,	AURKA, CCNA2, KIF11, CDH1
		hsa-miR-218-5p, hsa-miR-26b-5p
hsa-miR-107	AURKA, CCNB1, CDH1, PLK1, UBE2C	hsa-miR-448	AURKA, CCNA2, KIF11, PLK1
hsa-miR-24-3p	CCNB1, CCNA2, KIF11, PLK1, MYC	hsa-miR-4500	AURKA, CCNA2, PLK1, CDH1
hsa-miR-186-5p	AURKA, CCNA2, CDK1, KIF11, MYC	hsa-miR-143-3p	CCNA2, CDK1, KIF11, CDH1
hsa-miR-300,	AURKA, CCNA2, CDK1, KIF11, UBE2C	hsa-miR-16-5p	CCNA2, CDK1, PLK1, CDH1
hsa-miR-381-3p
hsa-miR-98-5p	AURKA, CCNA2, CDH1, PLK1, MYC	hsa-miR-200b-3p, hsa-miR-374b-5p,	CCNA2, CDK1, KIF11, MYC
		hsa-miR-200c-3p, hsa-miR-429
hsa-miR-548c-3p	CDH1, CDK1, KIF11, MYC, UBE2C	hsa-miR-495-3p	CCNA2, CDK1, UBE2C, CDH1
hsa-miR-145-5p	CCNB1, CCNA2, MYC, CDH1	hsa-miR-138-5p	CDC20, CDH1, PLK1, UBE2C
hsa-miR-548a-3p	CCNA2, CCNB1, CDH1, CDK1	hsa-miR-23b-3p	CDK1, CDH1, KIF11, PLK1
hsa-miR-590-3p	CCNA2, CCNB1, CDK1, MYC	hsa-miR-296-3p	PLK1, KIF11, CDH1, MYC

Table 5

Transcription factors identified in the enrichment analyses of the hub genes using TRRUST

Transcription factor	$P$ -value	$Q$ -value	List of overlapped genes	Transcription factor	$P$ -value	$Q$ -value	List of overlapped genes
E2F3	1.62E-14	5.03E-13	CCNB1, PLK1, AURKA, CDK1, CCNA2	MTA1	6.93E-05	0.000126	MYC, CDH1
E2F1	4.10E-09	6.35E-08	CCNB1, CDK1, AURKA, PLK1, MYC	HDAC2	8.81E-05	0.000152	CDH1, MYC
TP53	1.13E-08	1.17E-07	CCNA2, MYC, PLK1, CCNB1, CDK1	ETS2	0.000124	0.000203	CDK1, MYC
PTTG1	4.86E-08	3.77E-07	MYC, CCNB1, CDK1	EZH2	0.000195	0.000288	CDH1, MYC
FOXM1	7.26E-08	4.50E-07	CDH1, CCNB1, MYC	KLF4	0.000195	0.000288	CCNB1, CDH1
E2F4	1.89E-07	9.75E-07	PLK1, CCNB1, MYC	SIRT1	0.000281	0.000397	MYC, CDH1
YBX1	4.32E-07	1.85E-06	CCNB1, CDC20, CDH1	IRF1	0.000318	0.000428	CDK1, CCNB1
RB1	4.78E-07	1.85E-06	MYC, CDK1, CDH1	BRCA1	0.000397	0.000493	MYC, CCNB1
MED1	2.53E-06	7.83E-06	AURKA, UBE2C	WT1	0.000397	0.000493	MYC, CDH1
RBL1	2.53E-06	7.83E-06	CCNA2, MYC	RELA	0.000444	0.000519	MYC, CDH1, CCNB1
ESR1	7.38E-06	2.08E-05	MYC, CCNA2, CDH1	NFKB1	0.000452	0.000519	CDH1, CCNB1, MYC
SMAD7	9.08E-06	2.35E-05	MYC, CDK1	TFAP2A	0.000616	0.000682	CCNB1, CDH1
MYC	1.69E-05	4.02E-05	CCNB1, CCNA2, UBE2C	AR	0.00105	0.00113	CDH1, MYC
TCF3	2.29E-05	5.08E-05	CDH1, MYC	STAT3	0.00243	0.00251	CDH1, MYC
TBP	2.64E-05	5.47E-05	CCNB1, MYC	SP1	0.0246	0.0246	MYC, CCNA2
HDAC3	6.93E-05	0.000126	MYC, CDH1

Table 6

Enrichment analyses of the miRNAs regulated by transcription factors

Transcription factors	Count	$P$ -value	Bonferroni	FDR	Enriched miRNAs
TP53	11	0.00202452	0.4555	0.0332	hsa-mir-200b, hsa-mir-200c, hsa-mir-429, hsa-mir-590, hsa-mir-300, hsa-mir-23b, hsa-mir-381, hsa-mir-145, hsa-mir-4500, hsa-mir-107, hsa-mir-143
TGFB1	7	0.00002354	5.30E-03	2.65E-03	hsa-mir-200b, hsa-mir-200c, hsa-let-7d, hsa-mir-24, hsa-mir-429, hsa-mir-145, hsa-mir-143
ZEB1	7	0.00043702	0.0983	0.0138	hsa-mir-200b, hsa-mir-200c, hsa-mir-145, hsa-mir-429, hsa-mir-16, hsa-mir-140, hsa-mir-107
SNAI2	7	0.00222786	0.5013	0.0332	hsa-mir-200b, hsa-mir-200b, hsa-mir-429, hsa-mir-140, hsa-mir-548c, hsa-mir-101, hsa-mir-145
EZH2	7	0.00345128	0.7765	0.0332	hsa-mir-200b, hsa-mir-200c, hsa-mir-429, hsa-mir-186, hsa-mir-101, hsa-mir-143, hsa-mir-218
ZEB2	6	0.00000335	7.55E-04	7.55E-04	hsa-mir-200b, hsa-mir-200c, hsa-mir-16, hsa-mir-429, hsa-mir-145, hsa-mir-107
DNMT1	5	0.00006579	0.0148	3.70E-03	hsa-mir-200b, hsa-mir-296, hsa-mir-429, hsa-mir-23b, hsa-mir-218
SNAI1	5	0.00006579	0.0148	3.70E-03	hsa-mir-200b, hsa-mir-200b, hsa-mir-429, hsa-mir-101, hsa-mir-129
KLF4	5	0.00317741	0.7149	0.0332	hsa-mir-200b, hsa-mir-200b, hsa-mir-429, hsa-mir-145, hsa-mir-143
TP73	4	0.00062019	0.1395	0.0138	hsa-mir-200b, hsa-mir-429, hsa-mir-145, hsa-mir-143
TWIST1	4	0.0038265	0.861	0.0344	hsa-mir-200b, hsa-mir-200c, hsa-mir-429
ASCL2	3	0.00073799	0.166	0.0138	hsa-mir-200b, hsa-mir-200c, hsa-mir-429, hsa-mir-129
BMP4	3	0.00073799	0.166	0.0138	hsa-mir-200b, hsa-mir-200c, hsa-mir-145
KLF2	3	0.00073799	0.166	0.0138	hsa-mir-143, hsa-mir-145, hsa-mir-23b
LIN28A	3	0.00073799	0.166	0.0138	hsa-mir-107, hsa-mir-200c, hsa-mir-143
PELP1	3	0.00073799	0.166	0.0138	hsa-mir-200b, hsa-mir-200c, hsa-mir-429
SIP1	3	0.00073799	0.166	0.0138	hsa-mir-200b, hsa-mir-200c, hsa-mir-429
EED	3	0.00206481	0.4646	0.0332	hsa-mir-200b, hsa-mir-200c, hsa-mir-429
DDX6	2	0.00353916	0.7963	0.0332	hsa-mir-145, hsa-mir-143
IFNB1	2	0.00353916	0.7963	0.0332	hsa-mir-145, hsa-mir-143
IFNG	2	0.00353916	0.7963	0.0332	hsa-mir-145, hsa-mir-143
JAG1	2	0.00353916	0.7963	0.0332	hsa-mir-145, hsa-mir-143
N1ICD	2	0.00353916	0.7963	0.0332	hsa-mir-145, hsa-mir-143
RREB1	2	0.00353916	0.7963	0.0332	hsa-mir-145, hsa-mir-143
SRC	2	0.00353916	0.7963	0.0332	hsa-mir-145, hsa-mir-143

3.4 Identification of miRNA-hub gene interactions

A total of 266 miRNAs targeting at least two hub genes were identified by setting the score class as “high” in the mirDIP tool (Supplementary Table 1). Of these miRNAs, 38 miRNAs were found to target at least 4 of the hub genes and predicted to be the common key regulators of the hub genes by strong evidence, thus they were retrieved and included in further downstream analyses. The mirDIP analyses reveal miRNAs targeting the gene as a group and we did not find any miRNA having interactions with more than 5 hub genes (Table 4).

3.5 Identification of key transcription factors

Enrichment analyses aiming to reveal the TF-hub genes interactions suggested 31 TFs including the E2F3, E2F1 and TP53 as the top TFs that were likely to involve in the transcriptional regulations of the hub genes and may contribute to the CRC development (Table 5). We also examined the miRNA-TF interactions by TransmiR database and enrichment analyses of the 38 miRNAs showed 25 TFs were possibly affecting the regulation of the miRNAs (Table 6). The KLF4, EZH2, and TP53 were the common TFs identified in both enrichment analyses and proposed to be the key transcription factors involving in CRC pathogenesis (Fig. 3).

Figure 3.

Venn diagram depicts the identified candidate key TFs interacting with hub genes and miRNAs targeting the hub genes.

4. Discussion

The etiology of colorectal cancer is still not fully understood for efficiently predicting disease development and designing tailored treatment options. Genetic risk factors are known to be associated with disease pathogenesis, prognosis and survival however there is a lack of knowledge regarding the CRC genetics and this needs to be comprehensively elucidated.

In this work, we used the transcriptomic data of TCGA-COAD samples and GTEx normal samples in an attempt to comprehensively investigate the key molecular pathways and mediators related CRC.

We initially identified the 1556 DEGs and constructed the PPI network by STRING to compute their topological properties and define the hub genes in the network. Ten overexpressed genes (AURKA, CDK1, MYC, CDH1, CCNB1, CDC20, UBE2C, PLK1, KIF11, CCNA2) were prioritized as hub (essential) genes based on the degree centrality in Cytohubba analysis. The degree centrality measure for a protein suggests its interaction level with other proteins in the network and the largest degree pinpoints the most connected and essential protein in the network [28]. Thus, these hub genes are likely to be facilitated in therapeutic approaches for CRC.

Functional enrichment of the DEGs in KEGG pathways and GO biological process highlight their role in the cell cycle and cell division. The IPA results were in concordance with these results and indicate the significant associations of hub genes with cell cycle progression. Moreover, the 5 of the hub genes including the 3 important genes (CDK1, CCNB1, PLK1) of kinase gene family were found to be involved in “Mitotic roles of Polo-like kinase” pathway (Fig. 2). Mitotic kinases are known to have critical functions for proper cell division. Specifically, PLK1 was shown to be a key regulator of mitotic cell division and have tumor suppressor properties by influencing the tumor formation [29, 30]. In recent years, upregulation of PLK1 was reported to be associated with many cancers including colon cancer, thus therapeutic approaches using anti-PLK1 have been exciting research topics for cancer researchers [31, 32]. Furthermore, CDK1 and CCNB1 were also primary cell cycle regulators and overexpressions of these genes were previously shown to contribute to the CRC pathogenesis [33, 34, 35].

In addition to examining the pathways associated with CRC, we mined the key transcriptional and post-transcriptional regulators of the CRC development using in silico tools. MicroRNAs are key mediators of the gene regulation and miRNAs targeting the genes involving in the fundamental pathways were highly important for unraveling the miRNA-mediated post-transcriptional mechanisms in CRC pathogenesis.

Recent research also sheds light on the miRNA-TF-target gene interactions and TFs tuning both miRNAs and their target genes may be considered as key regulators of the particular network and promising target for translational research applications [10, 18, 19, 36]. Considering the TFs as crucial mediators of the regulation of the miRNA expressions, uncovering the TF-miRNA interactions may have a considerable impact on developing novel drug targets and biomarkers. Our analyses revealed 3 key TFs (TP53, EZH2, KLF4) that were predicted to affect the regulations of hub genes and also the miRNAs targeting these genes (Fig. 3). The abnormal function of the tumor suppressor TP53 has been associated with various types of cancers including colorectal cancer and cause poor prognosis [37]. The role of EZH2 in CRC was also shown in several studies and an increased level of EZH2 was suggested to affect CRC development [38, 39]. Our result highlighting the imperative role of KLF4 is consistent with the literature which accumulating research indicates KLF4 as a tumor suppressor gene for colorectal cancer and overexpression of KLF4 was suggested to be a used in novel therapeutic and prognostic applications to alleviate the burden of CRC [40, 41]. Of note, all of the three TFs were predicted to be the common regulators affect 4 miRNA (miR-200b, miR-200c, miR-145, miR-143) (Table 6) which were previously reported to be dysregulated in CRC [12, 42, 43, 44, 45].

The results of our study depend solely on in silico analyses thus creates some limitations. Identified hub genes and key regulators of the gene regulations need to be further confirmed by biological experiments to evaluate their roles in the utilization of novel translational applications. However, our results provide additional insight for the interplay between TFs-miRNAs that were likely to involve in CRC development. It is also worth mentioning that our findings are eminently in accord with previous experimental research which strengths the accuracy of our analyses and research methodology.

Our study suggests key genes in dysregulated pathways implicated in CRC and shows in silico evidence for candidate key components of the transcriptional and post-transcriptional regulatory network in CRC. The results of this study can further enhance the molecular etiology of the CRC as well as can be employed in future research for biomarker and drug development related CRC.

Footnotes

Acknowledgments

I would like to thank my graduate student Niyazi Kaya for his help provided during the manuscript formatting and submission process.

Conflict of interest

The authors declare that they have no competing interests.

Supplementary

Supplementary Table 1

Targeting miRNAs of the 10 hub genes identidied in mirDIP tool

miRNAs	AURKA	CCNA2	CCNB1	CDC20	CDH1	CDK1	KIF11	MYC	PLK1	UBE2C	Shared genes
hsa-miR-4282	$\surd$	$\surd$	$\surd$		$\surd$	$\surd$	$\surd$	$\surd$			6
hsa-miR-203a-3p	$\surd$	$\surd$			$\surd$	$\surd$	$\surd$	$\surd$			6
hsa-miR-140-5p	$\surd$	$\surd$	$\surd$			$\surd$	$\surd$				5
hsa-miR-3163		$\surd$	$\surd$		$\surd$	$\surd$	$\surd$				5
hsa-miR-548n		$\surd$	$\surd$		$\surd$	$\surd$		$\surd$			5
hsa-miR-103a-3p	$\surd$		$\surd$		$\surd$				$\surd$	$\surd$	5
hsa-miR-107	$\surd$		$\surd$		$\surd$				$\surd$	$\surd$	5
hsa-miR-24-3p			$\surd$		$\surd$		$\surd$	$\surd$	$\surd$		5
hsa-miR-186-5p	$\surd$	$\surd$				$\surd$	$\surd$	$\surd$			5
hsa-miR-300	$\surd$	$\surd$				$\surd$	$\surd$			$\surd$	5
hsa-miR-381-3p	$\surd$	$\surd$				$\surd$	$\surd$			$\surd$	5
hsa-miR-98-5p	$\surd$	$\surd$			$\surd$			$\surd$	$\surd$		5
hsa-miR-548c-3p					$\surd$	$\surd$	$\surd$	$\surd$		$\surd$	5
hsa-miR-145-5p		$\surd$	$\surd$		$\surd$			$\surd$			4
hsa-miR-548a-3p		$\surd$	$\surd$		$\surd$	$\surd$					4
hsa-miR-590-3p		$\surd$	$\surd$			$\surd$		$\surd$			4
hsa-miR-561-3p	$\surd$		$\surd$				$\surd$	$\surd$			4
hsa-miR-101-3p			$\surd$			$\surd$	$\surd$		$\surd$		4
hsa-miR-20b-5p			$\surd$		$\surd$	$\surd$	$\surd$				4
hsa-let-7d-5p	$\surd$	$\surd$			$\surd$			$\surd$			4
hsa-let-7f-5p	$\surd$	$\surd$			$\surd$			$\surd$			4
hsa-miR-129-5p	$\surd$	$\surd$				$\surd$	$\surd$				4
hsa-miR-1297	$\surd$	$\surd$			$\surd$		$\surd$				4
hsa-miR-199b-5p	$\surd$	$\surd$			$\surd$		$\surd$				4
hsa-miR-218-5p	$\surd$	$\surd$			$\surd$			$\surd$			4
hsa-miR-26b-5p	$\surd$	$\surd$			$\surd$		$\surd$				4
hsa-miR-448	$\surd$	$\surd$					$\surd$		$\surd$		4
hsa-miR-4500	$\surd$	$\surd$			$\surd$				$\surd$		4
hsa-miR-143-3p		$\surd$			$\surd$	$\surd$	$\surd$				4
hsa-miR-16-5p		$\surd$			$\surd$	$\surd$			$\surd$		4
hsa-miR-200b-3p		$\surd$				$\surd$	$\surd$	$\surd$			4
hsa-miR-200c-3p		$\surd$				$\surd$	$\surd$	$\surd$			4
hsa-miR-374b-5p		$\surd$				$\surd$	$\surd$	$\surd$			4
hsa-miR-429		$\surd$				$\surd$	$\surd$	$\surd$			4
hsa-miR-495-3p		$\surd$			$\surd$	$\surd$				$\surd$	4
hsa-miR-138-5p				$\surd$	$\surd$				$\surd$	$\surd$	4
hsa-miR-23b-3p					$\surd$	$\surd$	$\surd$		$\surd$		4
hsa-miR-296-3p					$\surd$		$\surd$	$\surd$	$\surd$		4
hsa-miR-34a-5p		$\surd$	$\surd$					$\surd$			3
hsa-miR-410-3p		$\surd$	$\surd$			$\surd$					3
hsa-miR-454-3p		$\surd$	$\surd$				$\surd$				3
hsa-miR-570-3p		$\surd$	$\surd$				$\surd$				3
hsa-miR-183-5p	$\surd$		$\surd$						$\surd$		3
hsa-miR-106a-5p			$\surd$		$\surd$		$\surd$				3
hsa-miR-106b-5p			$\surd$		$\surd$		$\surd$				3
hsa-miR-17-5p			$\surd$		$\surd$		$\surd$				3
hsa-miR-181a-5p			$\surd$		$\surd$	$\surd$					3
hsa-miR-181b-5p			$\surd$		$\surd$	$\surd$					3
hsa-miR-181c-5p			$\surd$		$\surd$	$\surd$					3
hsa-miR-3609			$\surd$		$\surd$		$\surd$				3
hsa-miR-519d-3p			$\surd$		$\surd$		$\surd$				3
hsa-miR-548ah-5p			$\surd$		$\surd$		$\surd$				3
hsa-miR-93-5p			$\surd$		$\surd$		$\surd$				3
hsa-miR-124-3p	$\surd$	$\surd$					$\surd$				3
hsa-let-7a-3p		$\surd$					$\surd$			$\surd$	3

Supplementary Table 1, continued
miRNAs	AURKA	CCNA2	CCNB1	CDC20	CDH1	CDK1	KIF11	MYC	PLK1	UBE2C	Shared genes
hsa-let-7b-3p		$\surd$					$\surd$			$\surd$	3
hsa-let-7f-1-3p		$\surd$					$\surd$			$\surd$	3
hsa-miR-1323		$\surd$			$\surd$	$\surd$					3
hsa-miR-146a-5p		$\surd$			$\surd$	$\surd$					3
hsa-miR-146b-5p		$\surd$			$\surd$	$\surd$					3
hsa-miR-199a-5p		$\surd$			$\surd$		$\surd$				3
hsa-miR-19a-3p		$\surd$					$\surd$		$\surd$		3
hsa-miR-211-5p		$\surd$				$\surd$	$\surd$				3
hsa-miR-219a-5p		$\surd$			$\surd$	$\surd$					3
hsa-miR-320a		$\surd$			$\surd$					$\surd$	3
hsa-miR-374a-5p		$\surd$				$\surd$		$\surd$			3
hsa-miR-4782-3p		$\surd$			$\surd$	$\surd$					3
hsa-miR-5011-5p		$\surd$				$\surd$	$\surd$				3
hsa-miR-508-3p		$\surd$			$\surd$	$\surd$					3
hsa-miR-548o-3p		$\surd$			$\surd$	$\surd$					3
hsa-miR-5688		$\surd$			$\surd$	$\surd$					3
hsa-miR-641		$\surd$				$\surd$				$\surd$	3
hsa-miR-7-5p		$\surd$				$\surd$	$\surd$				3
hsa-miR-9-3p		$\surd$			$\surd$				$\surd$		3
hsa-let-7a-5p	$\surd$				$\surd$			$\surd$			3
hsa-let-7b-5p	$\surd$				$\surd$			$\surd$			3
hsa-let-7c-5p	$\surd$				$\surd$			$\surd$			3
hsa-let-7e-5p	$\surd$				$\surd$			$\surd$			3
hsa-let-7g-5p	$\surd$				$\surd$			$\surd$			3
hsa-let-7i-5p	$\surd$				$\surd$			$\surd$			3
hsa-miR-122-5p	$\surd$				$\surd$		$\surd$				3
hsa-miR-149-3p	$\surd$				$\surd$				$\surd$		3
hsa-miR-25-3p	$\surd$				$\surd$		$\surd$				3
hsa-miR-329-3p	$\surd$				$\surd$	$\surd$					3
hsa-miR-362-3p	$\surd$				$\surd$	$\surd$					3
hsa-miR-363-3p	$\surd$				$\surd$		$\surd$				3
hsa-miR-3941	$\surd$					$\surd$	$\surd$				3
hsa-miR-603	$\surd$				$\surd$		$\surd$				3
hsa-miR-1284						$\surd$	$\surd$			$\surd$	3
hsa-miR-195-5p					$\surd$	$\surd$			$\surd$		3
hsa-miR-22-5p					$\surd$	$\surd$	$\surd$				3
hsa-miR-23a-3p					$\surd$		$\surd$		$\surd$		3
hsa-miR-31-5p						$\surd$		$\surd$	$\surd$		3
hsa-miR-340-5p					$\surd$	$\surd$	$\surd$				3
hsa-miR-371a-5p					$\surd$	$\surd$	$\surd$				3
hsa-miR-424-5p					$\surd$	$\surd$			$\surd$		3
hsa-miR-497-5p					$\surd$	$\surd$			$\surd$		3
hsa-miR-130a-3p		$\surd$	$\surd$								2
hsa-miR-192-3p		$\surd$	$\surd$								2
hsa-miR-301a-3p		$\surd$	$\surd$								2
hsa-miR-548a-5p		$\surd$	$\surd$								2
hsa-miR-548ab		$\surd$	$\surd$								2
hsa-miR-548ak		$\surd$	$\surd$								2
hsa-miR-548am-5p		$\surd$	$\surd$								2
hsa-miR-548ap-5p		$\surd$	$\surd$								2
hsa-miR-548aq-5p		$\surd$	$\surd$								2
hsa-miR-548ar-5p		$\surd$	$\surd$								2
hsa-miR-548as-5p		$\surd$	$\surd$								2
hsa-miR-548au-5p		$\surd$	$\surd$								2
hsa-miR-548b-5p		$\surd$	$\surd$								2
hsa-miR-548c-5p		$\surd$	$\surd$								2
hsa-miR-548d-5p		$\surd$	$\surd$								2
hsa-miR-548h-5p		$\surd$	$\surd$								2
hsa-miR-548i		$\surd$	$\surd$								2
hsa-miR-548j-5p		$\surd$	$\surd$								2

Supplementary Table 1, continued
miRNAs	AURKA	CCNA2	CCNB1	CDC20	CDH1	CDK1	KIF11	MYC	PLK1	UBE2C	Shared genes
hsa-miR-548o-5p		$\surd$	$\surd$								2
hsa-miR-548w		$\surd$	$\surd$								2
hsa-miR-548y		$\surd$	$\surd$								2
hsa-miR-559		$\surd$	$\surd$								2
hsa-miR-607		$\surd$	$\surd$								2
hsa-miR-149-5p	$\surd$		$\surd$								2
hsa-miR-199a-3p	$\surd$		$\surd$								2
hsa-miR-1301-3p			$\surd$						$\surd$		2
hsa-miR-139-5p			$\surd$		$\surd$						2
hsa-miR-142-5p			$\surd$		$\surd$						2
hsa-miR-144-3p			$\surd$				$\surd$				2
hsa-miR-181d-5p			$\surd$			$\surd$					2
hsa-miR-214-3p			$\surd$		$\surd$						2
hsa-miR-3121-3p			$\surd$			$\surd$					2
hsa-miR-4262			$\surd$			$\surd$					2
hsa-miR-496			$\surd$			$\surd$					2
hsa-miR-498			$\surd$				$\surd$				2
hsa-miR-520g-3p			$\surd$				$\surd$				2
hsa-miR-520h			$\surd$				$\surd$				2
hsa-miR-184	$\surd$	$\surd$									2
hsa-miR-4458	$\surd$	$\surd$									2
hsa-miR-4465	$\surd$	$\surd$									2
hsa-miR-1237-3p		$\surd$					$\surd$				2
hsa-miR-1283		$\surd$					$\surd$				2
hsa-miR-148a-3p		$\surd$				$\surd$					2
hsa-miR-148b-3p		$\surd$				$\surd$					2
hsa-miR-150-5p		$\surd$					$\surd$				2
hsa-miR-152-3p		$\surd$				$\surd$					2
hsa-miR-196a-5p		$\surd$						$\surd$			2
hsa-miR-196b-5p		$\surd$						$\surd$			2
hsa-miR-19b-3p		$\surd$					$\surd$				2
hsa-miR-204-5p		$\surd$				$\surd$					2
hsa-miR-208a-3p		$\surd$				$\surd$					2
hsa-miR-208b-3p		$\surd$				$\surd$					2
hsa-miR-2110		$\surd$			$\surd$						2
hsa-miR-22-3p		$\surd$			$\surd$						2
hsa-miR-3182		$\surd$					$\surd$				2
hsa-miR-320b		$\surd$								$\surd$	2
hsa-miR-320c		$\surd$								$\surd$	2
hsa-miR-34c-5p		$\surd$						$\surd$			2
hsa-miR-3660		$\surd$				$\surd$					2
hsa-miR-378a-5p		$\surd$				$\surd$					2
hsa-miR-449b-5p		$\surd$						$\surd$			2
hsa-miR-497-3p		$\surd$					$\surd$				2
hsa-miR-513a-3p		$\surd$				$\surd$					2
hsa-miR-520d-5p		$\surd$					$\surd$				2
hsa-miR-524-5p		$\surd$					$\surd$				2
hsa-miR-548ar-3p		$\surd$				$\surd$					2
hsa-miR-548d-3p		$\surd$						$\surd$			2
hsa-miR-548e-3p		$\surd$				$\surd$					2
hsa-miR-548f-3p		$\surd$				$\surd$					2
hsa-miR-548g-3p		$\surd$				$\surd$					2
hsa-miR-548p		$\surd$						$\surd$			2
hsa-miR-548x-3p		$\surd$						$\surd$			2
hsa-miR-568		$\surd$				$\surd$					2
hsa-miR-589-3p		$\surd$			$\surd$						2
hsa-miR-606		$\surd$					$\surd$				2
hsa-miR-890		$\surd$			$\surd$						2
hsa-miR-98-3p		$\surd$					$\surd$				2
hsa-miR-1226-5p	$\surd$						$\surd$				2

Supplementary Table 1, continued
miRNAs	AURKA	CCNA2	CCNB1	CDC20	CDH1	CDK1	KIF11	MYC	PLK1	UBE2C	Shared genes
hsa-miR-129-2-3p	$\surd$					$\surd$					2
hsa-miR-132-3p	$\surd$						$\surd$				2
hsa-miR-1321	$\surd$						$\surd$				2
hsa-miR-135a-5p	$\surd$							$\surd$			2
hsa-miR-142-3p	$\surd$						$\surd$				2
hsa-miR-153-3p	$\surd$								$\surd$		2
hsa-miR-154-5p	$\surd$					$\surd$					2
hsa-miR-17-3p	$\surd$								$\surd$		2
hsa-miR-206	$\surd$						$\surd$				2
hsa-miR-212-3p	$\surd$						$\surd$				2
hsa-miR-26a-5p	$\surd$						$\surd$				2
hsa-miR-30a-3p	$\surd$							$\surd$			2
hsa-miR-30e-3p	$\surd$							$\surd$			2
hsa-miR-32-5p	$\surd$						$\surd$				2
hsa-miR-326	$\surd$				$\surd$						2
hsa-miR-330-5p	$\surd$				$\surd$						2
hsa-miR-4739	$\surd$						$\surd$				2
hsa-miR-484	$\surd$				$\surd$						2
hsa-miR-490-3p	$\surd$					$\surd$					2
hsa-miR-493-5p	$\surd$						$\surd$				2
hsa-miR-506-3p	$\surd$						$\surd$				2
hsa-miR-510-5p	$\surd$				$\surd$						2
hsa-miR-532-5p	$\surd$					$\surd$					2
hsa-miR-562	$\surd$						$\surd$				2
hsa-miR-574-5p	$\surd$						$\surd$				2
hsa-miR-581	$\surd$				$\surd$						2
hsa-miR-605-5p	$\surd$						$\surd$				2
hsa-miR-92a-3p	$\surd$						$\surd$				2
hsa-miR-92b-3p	$\surd$						$\surd$				2
hsa-miR-10a-5p					$\surd$	$\surd$					2
hsa-miR-10b-5p					$\surd$	$\surd$					2
hsa-miR-1224-3p					$\surd$				$\surd$		2
hsa-miR-1244						$\surd$	$\surd$				2
hsa-miR-1262					$\surd$		$\surd$				2
hsa-miR-1276					$\surd$					$\surd$	2
hsa-miR-1304-5p					$\surd$			$\surd$			2
hsa-miR-155-5p						$\surd$	$\surd$				2
hsa-miR-186-3p							$\surd$	$\surd$			2
hsa-miR-18a-5p					$\surd$		$\surd$				2
hsa-miR-18b-5p					$\surd$		$\surd$				2
hsa-miR-193b-5p							$\surd$			$\surd$	2
hsa-miR-195-3p						$\surd$		$\surd$			2
hsa-miR-205-5p					$\surd$	$\surd$					2
hsa-miR-20a-5p					$\surd$		$\surd$				2
hsa-miR-217					$\surd$				$\surd$		2
hsa-miR-222-3p						$\surd$	$\surd$				2
hsa-miR-224-5p					$\surd$		$\surd$				2
hsa-miR-298					$\surd$		$\surd$				2
hsa-miR-302a-3p						$\surd$	$\surd$				2
hsa-miR-302d-3p						$\surd$	$\surd$				2
hsa-miR-3170							$\surd$			$\surd$	2
hsa-miR-3180-5p					$\surd$		$\surd$				2
hsa-miR-32-3p						$\surd$	$\surd$				2
hsa-miR-330-3p					$\surd$	$\surd$					2
hsa-miR-331-5p							$\surd$	$\surd$			2
hsa-miR-335-5p					$\surd$	$\surd$					2
hsa-miR-337-3p						$\surd$				$\surd$	2
hsa-miR-361-5p					$\surd$		$\surd$				2
hsa-miR-369-3p							$\surd$	$\surd$			2
hsa-miR-372-3p						$\surd$	$\surd$				2

Supplementary Table 1, continued
miRNAs	AURKA	CCNA2	CCNB1	CDC20	CDH1	CDK1	KIF11	MYC	PLK1	UBE2C	Shared genes
hsa-miR-373-3p						$\surd$	$\surd$				2
hsa-miR-374c-5p						$\surd$	$\surd$				2
hsa-miR-382-5p					$\surd$	$\surd$					2
hsa-miR-423-5p							$\surd$		$\surd$		2
hsa-miR-4289							$\surd$	$\surd$			2
hsa-miR-432-5p					$\surd$		$\surd$				2
hsa-miR-449a							$\surd$	$\surd$			2
hsa-miR-4760-3p						$\surd$			$\surd$		2
hsa-miR-4770					$\surd$	$\surd$					2
hsa-miR-485-5p					$\surd$					$\surd$	2
hsa-miR-489-3p						$\surd$	$\surd$				2
hsa-miR-491-5p									$\surd$	$\surd$	2
hsa-miR-494-3p							$\surd$	$\surd$			2
hsa-miR-5003-3p					$\surd$		$\surd$				2
hsa-miR-525-5p							$\surd$			$\surd$	2
hsa-miR-532-3p							$\surd$		$\surd$		2
hsa-miR-539-5p						$\surd$	$\surd$				2
hsa-miR-551b-5p					$\surd$			$\surd$			2
hsa-miR-5700					$\surd$					$\surd$	2
hsa-miR-576-5p					$\surd$	$\surd$					2
hsa-miR-577							$\surd$			$\surd$	2
hsa-miR-582-5p						$\surd$	$\surd$				2
hsa-miR-586								$\surd$		$\surd$	2
hsa-miR-599					$\surd$	$\surd$					2
hsa-miR-612					$\surd$					$\surd$	2
hsa-miR-655-3p						$\surd$	$\surd$				2
hsa-miR-769-5p						$\surd$	$\surd$				2
hsa-miR-9-5p					$\surd$		$\surd$				2
hsa-miR-936						$\surd$		$\surd$			2
hsa-miR-939-5p					$\surd$				$\surd$		2
hsa-miR-940					$\surd$			$\surd$			2

$\surd$ : miRNA TargeT

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Integrative analyses of molecular pathways and key candidate biomarkers associated with colorectal cancer

Abstract

BACKGROUND:

OBJECTIVE:

METHODS:

RESULTS:

CONCLUSIONS:

Keywords

1. Introduction

2.1 Data sets and differential gene expression analyses

2.2 Protein-protein interaction network construction and topological analysis

Table 1 Topological properties of the hub genes identified by Cytohubba

2.4 Identification of putative key regulators

3. Results

3.1 Identification of significant differentially expressed genes

3.2 Protein-protein interaction and hub gene identification

3.3 Functional and pathway analyses of the SDEGs and hub genes

Table 2 The top 10 significant KEGG pathways identified by enrichment analyses of DEGs

3.5 Identification of key transcription factors

Footnotes

Acknowledgments

Conflict of interest

Supplementary

References

Table 1
Topological properties of the hub genes identified by Cytohubba

Table 2
The top 10 significant KEGG pathways identified by enrichment analyses of DEGs