Open Access

Identification of key genes and pathways downstream of the β‑catenin‑TCF7L1 complex in pancreatic cancer cells using bioinformatics analysis

  • Authors:
    • Yi‑Hang Yuan
    • Jian Zhou
    • Yan Zhang
    • Meng‑Dan Xu
    • Jing Wu
    • Wei Li
    • Meng‑Yao Wu
    • Dao‑Ming Li
  • View Affiliations

  • Published online on: June 6, 2019     https://doi.org/10.3892/ol.2019.10444
  • Pages: 1117-1132
  • Copyright: © Yuan et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

As a key component of the Wnt signaling pathway, the β‑catenin‑transcription factor 7 like 1 (TCF7L1) complex activates transcription and regulates downstream target genes that serve important roles in the pathology of pancreatic cancer. To identify associated key genes and pathways downstream of the β‑catenin‑TCF7L1 complex in pancreatic cancer cells, the current study used the gene expression profiles GSE57728 and GSE90926 downloaded from the Gene Expression Omnibus. GSE57728 is an array containing information regarding β‑catenin knockdown and GSE90926 was developed by high throughput sequencing to provide information regarding TCF7L1 knockdown. Subsequently, differentially expressed genes (DEGs) were sorted separately and the shared 88 DEGs, including 37 upregulated and 51 downregulated genes, were screened. Clustering analysis of these DEGs was performed by heatmap analysis. Functional and pathway enrichment analyses were then performed using FunRich software and Database for Annotation, Visualization and Integrated Discovery, which revealed that the DEGs were predominantly enriched in terms associated with transport, transcription factor activity, and cytokine and chemokine mediated signaling pathway process. A DEG‑associated protein‑protein interaction (PPI) network, consisting of 58 nodes and 171 edges, was then constructed using Cytoscape software and the 15 genes with top node degrees were selected as the hub genes. Overall survival (OS) analysis of the 88 DEGs was performed and the relevant gene expression datasets were downloaded from The Cancer Genome Atlas. Consequently, three upregulated and seven downregulated genes were identified to be associated with prognosis. Furthermore, high expression levels of five downregulated genes, including CXCL5, CYP27C1, FUBP1, CDK14 and TRIM24, were associated with worse OS. In addition, CDK14 and TRIM24 were revealed as hub genes in the PPI network and both were confirmed to be involved in the Wnt/β‑catenin pathway and phosphoinositide 3‑kinase/Akt signaling pathway. Promoter analysis was also applied to the five downregulated DEGs associated with prognosis, which revealed that TCF7L1 may serve as a transcription factor of the DEGs. In conclusion, the genes and pathways identified in the current study may provide potential targets for the diagnosis and treatment of pancreatic cancer.

Introduction

Pancreatic cancer is a highly malignant tumor type of the digestive tract that is ranked as the fourth leading cause of cancer-associated mortality (1), with an estimated 55,440 new cases and an estimated 44,330 mortalities in the USA in 2018 according to statistics from Surveillance, Epidemiology, and End Results (2). Its aggressive biological properties, lack of early symptoms and rapid spread to surrounding organs lead are responsible for the high mortality rate (3). Furthermore, the treatment of pancreatic cancer is limited due to difficulties associated with surgical removal, and poor sensitivity to radiotherapy and chemotherapy (46). Therefore, identification of therapeutic targets is urgently required to improve patient outcome (7).

It has been reported that β-catenin, a versatile protein that mediates intercellular adhesion and gene expression, is abnormally expressed in pancreatic cancer (8). As the transcriptional cofactor of β-catenin, transcription factor 7 like 1 (TCF7L1), also termed transcription factor 3, is a member of the mammalian TCF/LEF family. Nuclear DNA-binding TCF/LEF proteins and β-catenin represent key components of the canonical branch of the Wnt signaling pathway, which serves a key role in pancreatic cancer carcinogenesis (9,10).

Once the Wnt pathway is activated, β-catenin accumulates in the cytoplasm and enters the nucleus, where it engages DNA-bound TCF transcription factors and subsequently regulates the transcription of downstream target genes (11). It is understood that β-catenin and TCF7L1 are pivotal proteins in the Wnt/β-catenin pathway; therefore, the genes they regulate may be drug targets for pancreatic cancer (12).

In recent years, microarray and high throughput sequencing technologies have widely been used to explore the genetic characteristics of tumorigenesis, which may promote the development of diagnostic and treatment strategies (13). Bioinformatics research methods are required to handle large sample data; therefore, different databases have been established to provide convenience for research (14,15). In the present study, the gene expression profiles GSE57728, an array focused on β-catenin, and GSE90926, an array developed by high throughput sequencing regarding TCF7L1, were downloaded from the Gene Expression Omnibus (GEO) and analyzed to obtain the differentially expressed genes (DEGs) between pancreatic control groups and experimental groups. Clustering analysis, and functional and pathways enrichment analysis were performed to identify the associations and functions of the DEGs. In addition, a protein-protein interaction (PPI) network was constructed, and overall survival (OS) and promoter analyses was performed, to identify the associated key genes and pathways downstream of the β-catenin-TCF7L1 complex in pancreatic cancer cells.

Materials and methods

Collection and inclusion criteria of the studies

The GEO database (www.ncbi.nlm.nih.gov/geo/) was searched for the following keywords: ‘pancreatic cancer’ (study keyword), ‘β-catenin’ (study keyword), ‘Homo sapiens’ (organism) and ‘Expression profiling by array or sequencing’ (study type). This search revealed seven studies. The inclusion criteria for the studies were as follows: i) Samples were required to be in two groups, including the control group and the experimental group, ii) the sample count needed to be >10, iii) β-catenin or TCF7L1 in the experimental group should be overexpressed or inhibited, and iv) sufficient information had to be present to perform the analysis. Consequently, GSE57728 (16) was downloaded for analysis regarding β-catenin and GSE90926, which was contributed by Dr David Dawson (Dawson Laboratory, Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, USA), was downloaded for analysis regarding TCF7L1.

Microarray data and validation

Two gene expression profiles (GSE57728 and GSE90926) were downloaded from the GEO database. The array data regarding β-catenin knockdown in GSE57728 included 16 samples, from this the present study selected two control samples with control small interfering RNA (siRNA) transfection and two experimental samples with β-catenin siRNA transfection for analysis. Similarly, the sequencing data regarding TCF7L1 knockdown in GSE90926 included 12 samples and the current study selected three control samples with control siRNA transfection and three experimental samples with TCF7L1 siRNA transfection for further analysis. Subsequently, a microarray assay regarding β-catenin knockdown was conducted to confirm the results from the microarray data downloaded from the GEO database. This was performed based on previous studies in which relevant results regarding the Wnt pathway in pancreatic cancer were revealed, including the identification of FH535 as a small-molecule inhibitor of the Wnt/β-catenin signaling pathway (10,17). FH535, as a classic inhibitor of the β-catenin pathway which could repress pancreatic cancer cell growth and metastasis, played the same role as siRNA in the inhibition of the β-catenin pathway. Sample preparation and processing were performed as described in the GeneChip Expression Analysis Manual (Agilent Technologies, Inc., Santa Clara, CA, USA). Differentially expressed genes were screened using Agilent 44K human whole-genome oligonucleotide microarrays (Agilent Technologies, Inc.). After obtaining the two completed microarrays with different gene expressions, 10 shared genes were selected randomly and the gene expression levels of the control and experimental groups were compared to confirm that the data downloaded from the GEO database was reliable.

Data processing

R (version 3.3.3 for Windows; https://www.r-project.org/) is a software system used for data processing, computing and mapping based on the different R packages. The limma package was used to identify the DEGs by linear modeling of the genes. P<0.05 and a fold change >1.5 or <0.667 were set as the cut-off criteria. Subsequently, a heat map of DEGs was generated using R and P<0.05 was set as the cut-off criterion.

Functional and pathway enrichment analysis, and PPI network construction

Database for Annotation, Visualization and Integrated Discovery (DAVID) provides a comprehensive set of functional annotation tools for investigators to understand the biological meaning behind a large list of genes. FunRich is a stand-alone software tool used predominantly for functional enrichment and interaction network analysis of genes and proteins. The results of the analysis can be depicted graphically in the form of Venn, bar, column, pie and doughnut charts. In the present study, gene ontology (GO) enrichment analysis was performed for the identified DEGs using the FunRich (version 3.1.3 for Windows; http://www.funrich.org/) and DAVID databases (version 6.8; http://david.ncifcrf.gov/). P<0.05 was set as the cut-off criterion, however, for the sake of symmetry and sharp contrast, the P-value of several terms was >0.05. In every figure, eight columns were sorted using Funrich. Pathway enrichment analysis was performed for the identified DEGs using KOBAS (http://kobas.cbi.pku.edu.cn/), which is a web server for gene/protein functional annotation and functional gene set enrichment. In addition, the Kyoto Encyclopedia of Genes and Genomes (KEGG; http://www.kegg.jp/) database was used, which is an integrated database resource for biological interpretation of genome sequences and other high-throughput data (18). P<0.05 was set as the cut-off criterion. In addition, a PPI network of the DEGs was constructed using the STRING database (http://string-db.org/) and Cytoscape (version 3.7.1 for Windows; http://cytoscape.org/), which is a commonly used software to generate integrated models of biomolecular interaction networks. A combined score >0.15 was set as the cut-off criterion. To screen the hub genes, a node degree ≥8 was set as the cut-off criterion.

Survival analysis of DEGs

Gene expression datasets were downloaded from The Cancer Genome Atlas (TCGA; http://tcga-data.nci.nih.gov/tcga) to analyze the prognosis of target DEGs. Data from a total of 178 patients with complete clinicopathological and RNASeq data were collected from the TCGA pancreatic cancer cohort. Clinical characteristics of the 178 patients are presented in Table I, including case ID, sex, year of birth, year of mortality, tumor stage, age at diagnosis measured in days, vital status and time from diagnosis to the last follow-up date or mortality. The patients were divided into two groups according to the expression of a particular gene, including a high expression group and a low expression group. The OS of patients with pancreatic cancer was analyzed using R software and the results were compared using Kaplan-Meier curves on which the P-value was presented. A log-rank test was conducted as the post hoc test.

Table I.

Clinical characteristics of 178 patients used for overall survival analysis.

Table I.

Clinical characteristics of 178 patients used for overall survival analysis.

Case IDSexYear of birthYear of mortalityTumor stageAge at diagnosis, daysAlive at last follow-upDays from diagnosis to mortalityDays from diagnosis to last follow-up
  1Male19292011iib30,092No292
  2Female1942iIb26,179No3751
  3Male1970iib15,807Yes286
  4Male1938ib27,362No498449
  5Female1953iia22,131Yes438
  6Male19472012iib23,962No66
  7Male19382013iib27,082No652
  8Female19382014iib27,662No532
  9Male1972ia14,729Yes1,037
10Male1932iib29,792Yes483
11Male1932ib29,631Yes7
12Female1938iib27,645Yes525
13Female1962iib18,202No913648
14Male1962iib18,357Yes920
15Male1949iib23,152Yes666
16Male19262010iia29,633No1,101
17Female19572012iib20,051No511
18Male19362009iib26,085No1,059
19Female1946ib23,406Yes1,542
20Male19572013iib20,133No607
21Male1941iib24,760Yes2,285
22Female1940iib26,635No732385
23Male1943ib24,621Yes998
24Male1933iib28,174No661240
25Female1936iib24,025No2,0361,953
26Male1937iib27,453Yes743
27Male19652012iib17,294No308
28Female1955iib20,741Yes392
29Female19302011iib29,585No153
30Male1964iib17,794Yes729
31Female1947iv24,291Yes420
32Male19252009iia30,571No480
33Female1932iii29,213Yes462
34Female1948iib23,672Yes635
35Male1964iib18,059Yes404
36Male1938iia27,684No267110
37Female1936iib27,929No5170
38Female1952ib21,732Yes1,103
39Male1941iib26,028Yes80
40Female1939iia27,152Yes467
41Female1946iib22,981Yes228
42Female19422013iib25,920No627
43Male19462012iib23,998No458
44Female19292011iib29,904No568
45Female1959iib19,064No59320
46Female1928ia30,821No15191
47Male1958iib19,904Yes767
48Female1946iib23,868No59621
49Male1952iib21,676Yes522
50Female19472009iib22,990No110
51Female1958iia19,839No29928
52Male1936iib27,637Yes194
53Female19452010iib23,953No31
54Male19392013iib26,936No691
55Female1948iib22,376Yes2,016
56Male1939ia26,947Yes454
57Male19432011iib24,078No1,130
58Female1951iia22,090Yes840
59Female1965iib17,821No278164
60Female1936iib28,434No16011
61Female19452010iib23,580No603
62Male19262011ia31,319No244
63Female1968i14,599Yes2,741
64Male1954iib19,847Yes716
65Female1953ib22,126Yes9
66Male1978iib13,127Yes245
67Male1947iia24,007Yes586
68Male19442012iia24,731No634
69Male1959iia19,677Yes671
70Male1943iv25,849Yes603
71Male1937iib27,850Yes0
72Female19392013ib27,128No144
73Male19382010iib26,239No485
74Female19342008iib26,773No467
75Male19342010iib28,074No143
76Male19632013iib18,315No183
77Male19352009ib26,747No598
78Male19562012iib20,641No277
79Male1940iib26,503Yes657
80Male1937iia28,047Yes517
81Female1968iib16,255No470247
82Female1933iib29,150No233153
83Male1957iib20,071No592360
84Male1945iib24,150No614361
85Female1954iib21,491Yes660
86Male19472011iib23,713No216
87Female1944iib24,891Yes491
88Male19622011iib18,172No123
89Female1946iv24,043No394347
90Female19472012iib23,431No460
91Male1936iib28,403Yes330
92Female1963iib18,607No366202
93Female1956iia20,316Yes969
94Female1929iib30,684Yes225
95Female1940iib26,379Yes319
96Female1939iib27,295No393127
97Male1945ib24,810Yes951
98Female1950iib23,218No313155
99Female1950iib22,413Yes4
100Female19422011iib25,312No224
101Female19482009iib21,611No741
102Male19552007iib19,287No61
103Female19552009iib19,718No486
104Male1945iib24,864Yes431
105Male1939iib25,809Yes289
106Male1950iib22,433No36624
107Male19362013iib28,239No95
108Female1943iib25,412No1794
109Female19262012iib31,393No481
110Male1946iib24,589Yes737
111Female19332011iib28,353No702
112Female1958iib20,366Yes33
113Female1950iib23,306No230179
114Male1954iib21,024No5188
115Male19452009iia23,703No117
116Female19222007iib31,074No155
117Male1950iia22,283Yes1,216
118Female1954iv21,501No5455
119Male19312012iib29,674No120
120Male1957iia20,607Yes498
121Female19352012iib27,957No695
122Female1956iib20,858Yes395
123Femaleiia17,628Yes584
124Female19492013iib23,622No239
125Male1934iia28,317Yes482
126Male1946iia23,760Yes314
127Male19462010iib23,443No12
128Male19372009iv26,216No619
129Male19302010iib29,319No123
130Female1946ia24,174Yes1,021
131Female1924iib32,475No421233
132Male1944ib23,791Yes1,854
133Male19522009iib20,984No334
134Male1950iia22,425Yes1,287
135Female1951iib22,329Yes289
136Female1949ib23,685Yes95
137Male1935iib28,454No3080
138Male1946iib24,576Yes338
139Male1952Not reported21,175Yes1,794
140Female19562012ib20,760No219
141Male1965iib16,766Yes1,323
142Male1970iib15,869Yes440
143Female1932iib28,554Yes1,257
144Female1943iib25,214No37816
145Male1939iib26,573Yes969
146Male1964iia17,649No353166
147Female1955iib21,484Yes463
148Female19632011iib16,126No1,502
149Male1941iib26,188Yes484
150Male19552012iib20,618No684
151Male19372012iia27,600No293
152Male1942iia25,768Yes252
153Female1946ib22,799Yes2,084
154Female1940iia26,311Yes232
155Male1948iib23,801Yes287
156Male1942iii25,227Yes706
157Male19672009iib15,188No666
158Female1938Not reported26,859Yes388
159Male19472007iib22,148No145
160Male19392013iib26,745No430
161Male1954Not reported20,451Yes1,942
162Male1954iib21,792Yes350
163Female19282002iii26,881No541
164Male19622012iia18,475No128
165Female19422011iia24,117No1,332
166Female19502013iib22,400No738
167Female1932iib29,585No46636
168Male1937iib28,013Yes8
169Female1949iia23,624Yes379
170Male1954iib21,277Yes416
171Female1962iib18,129Yes1,116
172Male1940ib26,167No2360
173Female1959ib19,707Yes720
174Male1958iib19,315Yes1,383
175Male1939iia26,943Yes676
176Male1941iib26,129No365329
177Male19372013iia26,234No2,182
178Male1940iib26,322Yes978

[i] Tumor stage was determined according to the 7th Edition of the American Joint Committee on Cancer Staging Manual (61).

Promoter analysis of DEGs

Ensemble (http://www.ensembl.org/index.html) is an online website that was used to perform promoter analysis of the DEGs. The eligible transcript of every DEG associated with prognosis was selected and then the 3,000 base pairs 5′ upstream were selected as the promoter. Subsequently, the transcription factors (TFs) site analysis function of Genomatix (http://www.genomatix.de/solutions/genomatix-software-suite.html) was used to predict potential TF families and TF binding sites by analyzing the sequence of promoter obtained from Ensemble. Core similarity was set as 1 for an accurate prediction.

Results

Microarray data and validation

As demonstrated in the Fig. 1, ten genes that were shared between the original microarray data downloaded from the GEO database and our own microarray data regarding β-catenin knockdown, including CGN, CNN3, DZIP1, EGR1, FBXL17, MDM2, SRXN1, HMOX1 and TMEM2, were selected to confirm the results from microarray data downloaded from the GEO database. The results obtained for samples with β-catenin siRNA transfection and samples treated with FH535 exhibited consistent trends, with the exception of the results for IL32, HMOX1 and TMEM2.

Identification of DEGs and clustering analysis

A total of 1,784 DEGs, including, 812 upregulated and 972 downregulated genes, were identified from GSE90926 regarding TCF7L1 knockdown. A total of 2,013 DEGs, including 1,000 upregulated and 1,013 downregulated genes, were identified from GSE57728 regarding β-catenin knockdown. Among these DEGs, 88 DEGs were screened out as shared by the two datasets. The upregulated and downregulated DEGs were considered separately when selecting the shared genes. As a result, 37 upregulated and 51 downregulated DEGs were identified (Fig. 2A and B; Table II). The respective heatmaps of the 88 DEGs were generated by R software (Fig. 2C and D).

Table II.

Identification of differentially expressed genes.

Table II.

Identification of differentially expressed genes.

RegulationGenes name
UpregulatedMMP19, OBSL1, KIT, PDSS1, SYT5, KLHL9, KCNT2, PPL, KRT7, FBXL17, SH2D3C, MR1, C10orf54, IL32, FLG-AS1, SLC9A1, TDRP, GPSM3, CGN, FKBP1A-SDCBP2, CASK, WDFY2, SLC35F3, SLC7A2, EBF4, KCTD18, SLITRK6, IRF9, STC1, CLIC3, SLC6A6, CYP1A1, GATSL2, NOTUM, TP53INP1, CACNA2D1, SPOCK3.
DownregulatedPOLR3G, MNS1, ZMAT1, CXCL5, PMP2, DEPDC1, TRIM24, SRXN1, CYP27C1, GPR180, OSBPL6, DNAI1, DCLRE1A, POLR3B, PCDHGA1, CLUL1, C3orf14, SMC5, EGR1, PDK4, RPS6KA5, CLEC2B, SFXN2, HAGLR, PDCD4, RHEBL1, RRBP1, NFIB, DHX34, UBE2Q2L, EOMES, MDM2, FUBP1, DNAH1, DSTYK, ESX1, TET1, ODF2L, NSD1, SSH2, PTX3, LINC00173, MYCL, TMEM2, GRB14, TNFRSF19, CDK14, FRA10AC1, SOX17, PXYLP1, ZNF618.
Functional and pathway enrichment analysis, and PPI network construction

To investigate the function of the DEGs, functional enrichment analysis was performed. Analysis using FunRich software indicated that the DEGs were predominantly enriched in the following biological process terms: Transport, amino acid transport, transcription, cytokine and chemokine mediated signaling pathway, and carbohydrate metabolism (Fig. 3A and B). In addition, the DEGs were predominantly enriched in following cell component terms: Cytoplasmic cyclin-dependent protein kinase holoenzyme complex, interacted disc, M band and DNA-directed RNA polymerase III complex (Fig. 3C and D). Furthermore, for molecular function, the DEGs were enriched in the following terms: Transcription factor activity, DNA-directed RNA polymerase activity, amino acid transporter activity, transcription and lipid binding (Fig. 3E and F).

Using the DAVID database, GO analysis identified that the DEGs were enriched in the following terms: Negative regulation of myofibroblast differentiation, stem cell population maintenance and cellular response to antibiotic (Fig. 3G; Table III).

Table III.

GO analysis of differentially expressed genes in pancreatic cancer.

Table III.

GO analysis of differentially expressed genes in pancreatic cancer.

CategoryTermCount% P-valuea
GOTERM_BP_DIRECTGO:1904761~negative regulation of myofibroblast differentiation22.2727272730.009268837
GOTERM_BP_DIRECTGO:0019827~stem cell population maintenance33.4090909090.02259656
GOTERM_MF_DIRECTGO:0002039~p53 binding33.4090909090.033191281
GOTERM_BP_DIRECTGO:0071236~cellular response to antibiotic22.2727272730.036569485
GOTERM_BP_DIRECTGO:0001706~endoderm formation22.2727272730.054355943
GOTERM_BP_DIRECT GO:0003351~epithelial cilium movement22.2727272730.058751666
GOTERM_BP_DIRECTGO:0071391~cellular response to estrogen stimulus22.2727272730.058751666
GOTERM_MF_DIRECTGO:0000977~RNA polymerase II regulatory region sequence-specific DNA binding44.5454545450.059249025
GOTERM_BP_DIRECT GO:0070498~interleukin-1-mediated signaling pathway22.2727272730.063127217
GOTERM_BP_DIRECT GO:0006885~regulation of pH22.2727272730.071818168
GOTERM_BP_DIRECTGO:0090280~positive regulation of calcium ion import22.2727272730.071818168
GOTERM_BP_DIRECTGO:0071456~cellular response to hypoxia33.4090909090.07344514
GOTERM_MF_DIRECTGO:0001056~RNA polymerase III activity22.2727272730.074087687
GOTERM_CC_DIRECT GO:0005666~DNA-directed RNA polymerase III complex22.2727272730.079265755
GOTERM_BP_DIRECTGO:0045089~positive regulation of innate immune response22.2727272730.08042952
GOTERM_BP_DIRECTGO:0002690~positive regulation of leukocyte chemotaxis22.2727272730.08042952
GOTERM_BP_DIRECTGO:0045892~negative regulation of transcription, DNA-templated66.8181818180.082369804
GOTERM_BP_DIRECTGO:0045944~positive regulation of transcription from RNA polymerase II promoter910.227272730.084587325
GOTERM_CC_DIRECTGO:0036126~sperm flagellum22.2727272730.087239628
GOTERM_BP_DIRECT GO:0006366~transcription from RNA polymerase II promoter66.8181818180.090139483

a P<0.05 was set as the cut-off criterion. Count, the number of enriched genes in each term; GO, gene ontology; BP, biological processes; CC, cell component; MF, molecular function.

KEGG pathway analysis using KOBAS revealed that the DEGs were significantly enriched in the following terms: RNA polymerase, Wnt signaling pathway and cytokine-cytokine receptor interaction (Fig. 3H; Table IV).

Table IV.

Kyoto Encyclopedia of Genes and Genomes signaling pathway enrichment analysis of differentially expressed genes in pancreatic cancer.

Table IV.

Kyoto Encyclopedia of Genes and Genomes signaling pathway enrichment analysis of differentially expressed genes in pancreatic cancer.

Pathway IDTermCount P-valuea
hsa03020RNA polymerase20.002583461
hsa05219Bladder cancer20.004105174
hsa04261Adrenergic signaling in cardiomyocytes30.004711319
hsa04623Cytosolic DNA-sensing pathway20.009436485
hsa05169Epstein-Barr virus infection30.010969532
hsa05205Proteoglycans in cancer30.011112587
hsa04260Cardiac muscle contraction20.013627503
hsa04060Cytokine-cytokine receptor interaction30.021708158
hsa00240Pyrimidine metabolism20.023537332
hsa04668TNF signaling pathway20.025617518
hsa04919Thyroid hormone signaling pathway20.029094794
hsa05206MicroRNAs in cancer30.029496436
hsa04120Ubiquitin mediated proteolysis20.038049532
hsa04530Tight junction20.039046349
hsa04310Wnt signaling pathway20.041069627
hsa00900Terpenoid backbone biosynthesis10.049591361

a P<0.05 was set as the cut-off criterion. Count, the number of enriched genes in each term.

The PPI network of DEGs consisted of 58 nodes and 171 edges, including 24 upregulated genes and 34 downregulated genes (Fig. 3I). As aforementioned, the shared 88 DEGs sorted from the two GSE datasets included 37 upregulated and 51 downregulated genes; however, all shared DEGs were not included in the PPI network as certain genes that were isolated at the edge were removed. Therefore, as presented in Fig. 3I, 58 shared DEGs were included in the PPI network, in which the red nodes represent the upregulated genes and the green nodes represent the downregulated genes. Furthermore, the most significant hub genes were selected as those with the highest numbers of edges. A total of 15 genes were selected as hub genes, including WDFY2, KIT, EGR1, NSD1, DSTYK, CDK14, MDM2, RPS6KA5, CYP1A1, POLR3B, SMC5, DNAI1, SSH2, TRIM24 and CASK.

OS analysis

OS analysis was performed using R software to investigate the prognostic value of the 88 DEGs and the results were presented as Kaplan-Meier curves. Among the 37 upregulated DEGs, CASK, IL32, and KRT7 were significantly associated with prognosis. In addition, among the 51 downregulated DEGs, the expression levels of CDK14, CXCL5, CYP27C1, DNAI1, FUBP1, TRIM24 and ZMAT1 were identified to be significantly associated with prognosis (Fig. 4). Furthermore, among the downregulated DEGs, high expression levels of CXCL5, CYP27C1, FUBP1, CDK14 and TRIM24 were associated with significantly worse overall survival (Fig. 4), which suggests inhibition of the β-catenin-TCF7L1 complex may result in the downregulation of these five potential oncogenic genes. Notably, CDK14 and TRIM24 were identified as hub genes in the PPI network, which indicates these genes may be the key downstream regulators of the β-catenin-TCF7L1 complex.

Promoter analysis of DEGs

Promoter analysis of DEGs performed using the Ensemble and Genomatix databases revealed that the predicted TFs of the five DEGs associated with poor OS, including CXCL5, CYP27C1, FUBP1, CDK14 and TRIM24, covered different TF families. Only TFs associated with TCF7L1 were selected to obtain a precise result. As presented in the Fig. 5, TCF7L1 was identified as a TF of four of the DEGs but not CXCL5. This result suggests that TCF7L1 may not be a TF of CXCL5, however, certain unavoidable errors of the prediction may have occurred. Furthermore, the locations of predicted TF sites of each promoter are demonstrated distinctly in Fig. 5. Two DEGs, including CDK14 and FUBP1, exhibited only one TF site, whereas, TRIM24 and CYP27C1 possessed two different sites. In addition, the locations of the two TF sites of TRIM24 were separated by <5 base pairs (Fig. 5).

Discussion

Pancreatic cancer is a highly lethal type of tumor of the digestive tract as its mortality rate is closely associated with the incidence rate (19). The majority of patients with pancreatic cancer exhibit no clinical signs until the disease reaches an advanced stage (20). Despite rapid developments in treatment strategies, effective early detective tests and drug targets for pancreatic cancer remain limited (21). Therefore, further understanding of the mechanisms underlying pancreatic cancer carcinogenesis is essential to improve prognosis and reduce the mortality rate. With developments in microarray technology, it can be useful to determine the general genetic alterations associated with disease progression, which may provide beneficial insight into the diagnosis, treatment and prognosis of the disease (22).

The present study selected two datasets of pancreatic cancer in which β-catenin and TCF7L1 knockdown had been performed separately to identify DEGs. A total of 88 shared DEGs were screened out consisting of 37 upregulated and 51 downregulated DEGs. According to functional and pathway enrichment analysis, the shared DEGs were predominantly involved in transport, transcription, and the cytokine and chemokine mediated signaling pathway process. Furthermore, a PPI network was constructed and 15 genes were selected as hub genes, including WDFY2, KIT, EGR1, NSD1, DSTYK, CDK14, MDM2, RPS6KA5, CYP1A1, POLR3B, SMC5, DNAI1, SSH2, TRIM24 and CASK. According to OS analysis, high expression levels of CXCL5, CYP27C1, FUBP1, CDK14 and TRIM24, which were downregulated by inhibition of the β-catenin-TCF7L1 complex, were associated with worse prognosis. Notably, both CDK14 and TRIM24 were identified as hub genes in the PPI network and were negatively associated with OS, which suggests these two genes may serve key roles downstream of β-catenin-TCF7L1 complex.

CDK14, a member of the cyclin-dependent kinases, is a cdc2-associated serine/threonine protein kinase, which serves a vital role in normal cell cycle progression (23). It has been reported that CDK14 may interact with cyclin D3 and human cyclin Y to regulate cell cycle and cell proliferation (24,25). Furthermore, certain reports have suggested that CDK14 also regulates a number of pathways, including the Wnt/β-catenin signaling pathway and phosphoinositide 3-kinase (PI3K)/Akt signaling pathway, and cellular mechanisms to act as an oncogene (26,27). It is understood that the Wnt/β-catenin signaling pathway is a conserved signaling pathway associated with cell proliferation, migration, apoptosis, differentiation and normal stem cell self-renewal (28). In the absence of Wnt signaling, the mitosis-specific CDK14-Cyclin Y kinase complex phosphorylates Ser-1490 of LRP5/6, which are co-receptors for Wnt ligands at the G2/M stage, thereby triggering the receptor for Wnt-induced phosphorylation (29,30). Furthermore, a previous study has identified that CDK14 is highly expressed in pancreatic cancer, which promotes the proliferation, migration and invasion of cancer cells (31). In addition, this high expression has been observed in a number of other types of malignant tumor, including hepatocellular carcinoma, gastric cancer and breast cancer (26,32,33). By contrast, knockout or inhibition of CDK14 has been demonstrated to exhibit a benefit on the prognosis of cancer types, including ovarian cancer and breast cancer (32,34). Furthermore, the PI3K/Akt signaling pathway also serves a vital role in cell proliferation, migration, apoptosis and differentiation, and dysregulation of this pathway is common in pancreatic cancer. A previous study demonstrated that knockdown of CDK14 inhibited the proliferation and invasion of pancreatic cancer cells, in addition to the epithelial-to-mesenchymal transition, by suppressing the PI3K/Akt signaling pathway (31).

TRIM24, also termed transcription intermediary factor 1-α, is a member of the transcription intermediary factor family and has been confirmed to serve a key role in tumor development and progression (35,36). Furthermore, previous studies have demonstrated that TRIM24 is upregulated in several types of cancer and involved in numerous pathways. For example, certain studies have identified that TRIM24 is overexpressed, and promotes cancer cell growth and invasion in bladder cancer and cervical cancer, possibly via the nuclear factor-κB and PI3K/Akt signaling pathways (36,37). Similarly, it has been reported that TRIM24 can accelerate cell growth and facilitate gastric cancer progression by activation of the Akt pathway (37) and the Wnt/β-catenin signaling pathway (38). Notably, in contrast to the aforementioned studies that suggest TRIM24 is an important oncogene in tumor development, TRIM24 has been identified to suppress the progression of murine hepatocellular carcinoma (39). Therefore, the contradictory role or TRIM24 requires further investigation.

In addition to CDK14 and TRIM24, three other genes downstream of β-catenin-TCF7L1 were revealed to be negatively associated with prognosis including, CXCL5, CYP27C1 and FUBP1. CXCL5, CYP27C1 and FUBP1 were not identified as hub genes in the PPI network; however, these genes may also be target genes that affect OS and respond to the β-catenin-TCF7L1 complex.

FUBP1 encodes far upstream element-binding protein 1; a single stranded DNA-binding protein containing three domains that contribute to c-myc transcriptional regulation by binding to the far upstream element (40,41). As a member of the myc oncoprotein family, c-myc has been confirmed to be associated with oncogenesis (42,43). Therefore, it is not surprising that FUBP1 has also been revealed to be expressed in many types of malignant tissue and promote tumor proliferation and migration, and led to poor prognosis (44,45), which is consistent with the previous study. In addition, FUBP1 has been identified to function as an oncogene by regulating c-myc transcription in tumor progression (46). By contrast, the role of FUBP1 tumorigenesis may be c-myc independent, as a previous report demonstrated that knockdown of FUBP1 had no effect on the level of c-myc in hepatocellular carcinoma (44). In summary, FUBP1 may serve as a potential drug target due to its significant role in tumorigenesis. A recent study revealed that camptothecin and its analog SN-38, the active metabolite of irinotecan, may serve as a novel therapy for hepatocellular carcinoma by targeting FUBP1 (47). In addition, a previous study suggested that miR-16 may suppress FUBP1, both of which were associated with the trastuzumab response in ErbB-2-positive primary breast cancer (48).

CXCL5 is a member of the CXC subfamily of chemokines, which are produced locally in tissues. These chemokines function by interacting with specific G protein-coupled receptors, which are mainly expressed on leukocytes (49). It is well understood that chemokines serve a key role in infection and inflammation. Similarly, a number of reports have suggested that CXCL5 may contribute to pathogen- and autoimmune-induced inflammatory reactions, and angiogenesis by driving neutrophil recruitment (50,51). Furthermore, CXCL5 has also been confirmed to participate in cancer progression. Previous studies have demonstrated that overexpression of CXCL5 mediates neutrophil infiltration, and promotes cell proliferation and invasion in different types of tumor, including hepatocellular carcinoma and colorectal cancer, which suggests a poor prognosis (52,53). Knockdown of CXCL5 has been revealed to inhibit the proliferation and migration of human bladder cancer T24 cells (54). Furthermore, CXCL5 is associated with the PI3K/Akt/glycogen synthase kinase-3β/Snail signaling pathway (55,56) and epidermal growth factor (EGF)-EGF receptor signaling pathway (57), which have been demonstrated to serve significant roles in tumorigenesis.

CYP27C1 belongs to the cytochrome P450 superfamily of enzymes, which is understood to catalyze a number of reactions associated with drug metabolism (58). However, the number of studies regarding CYP27C1 is very limited. Certain studies have revealed that CYP27C1 can convert vitamin A1 into A2, which could be a switch for visual sensitivity (59,60). However, the other functions of this gene require further investigation.

In conclusion, the genes identified in the current study may serve as potential targets in pancreatic cancer. Furthermore, the associated functions and pathways may also provide information that can assist with the diagnosis and treatment of patients with pancreatic cancer. However, it is undeniable that there is a limitation of the present study due to the lack of experimental validation. In the future, the results predicted by bioinformatics analysis may be verified by advanced research and technology to provide benefits for the clinical outcome of patients with pancreatic cancer. In summary, the genes identified in the present study may provide potential targets for the diagnosis and treatment of pancreatic cancer, and they need to be validated prior to clinical use.

Acknowledgements

Not applicable.

Funding

No funding was received.

Availability of data and materials

All data generated or analyzed during the present study are included in this published article.

Authors' contributions

YHY, JZ and YZ participated in the design of this study and performed the statistical analysis. JZ collected important background information and contributed to the data acquisition. YZ carried out the study and contributed to figure preparation. YHY drafted the manuscript. MDX, JW and WL contributed to data acquisition, data analysis and statistical analysis. MYW and DML made great contributions to the original conception of the study and perfomed part of the data analysis. In addition, MYW and DML also performed manuscript review and critically revised the manuscript for important intellectual content. All authors read and approved the final manuscript.

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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Yuan YH, Zhou J, Zhang Y, Xu MD, Wu J, Li W, Wu MY and Li DM: Identification of key genes and pathways downstream of the β‑catenin‑TCF7L1 complex in pancreatic cancer cells using bioinformatics analysis. Oncol Lett 18: 1117-1132, 2019
APA
Yuan, Y., Zhou, J., Zhang, Y., Xu, M., Wu, J., Li, W. ... Li, D. (2019). Identification of key genes and pathways downstream of the β‑catenin‑TCF7L1 complex in pancreatic cancer cells using bioinformatics analysis. Oncology Letters, 18, 1117-1132. https://doi.org/10.3892/ol.2019.10444
MLA
Yuan, Y., Zhou, J., Zhang, Y., Xu, M., Wu, J., Li, W., Wu, M., Li, D."Identification of key genes and pathways downstream of the β‑catenin‑TCF7L1 complex in pancreatic cancer cells using bioinformatics analysis". Oncology Letters 18.2 (2019): 1117-1132.
Chicago
Yuan, Y., Zhou, J., Zhang, Y., Xu, M., Wu, J., Li, W., Wu, M., Li, D."Identification of key genes and pathways downstream of the β‑catenin‑TCF7L1 complex in pancreatic cancer cells using bioinformatics analysis". Oncology Letters 18, no. 2 (2019): 1117-1132. https://doi.org/10.3892/ol.2019.10444