Biochemistry Research International

Biochemistry Research International / 2019 / Article

Research Article | Open Access

Volume 2019 |Article ID 6469420 | 13 pages | https://doi.org/10.1155/2019/6469420

Integrated Analysis of Oncogenic Networks in Colorectal Cancer Identifies GUCA2A as a Molecular Marker

Academic Editor: Robert Speth
Received09 Mar 2019
Accepted12 Jun 2019
Published28 Jul 2019

Abstract

Colorectal cancer (CRC) is one of the most common and deadly malignancies in the world. In China, the morbidity rate of CRC has increased during the period 2000 to 2011. Biomarker detection for early CRC diagnosis can effectively reduce the mortality of patients with CRC. To explore the underlying mechanisms of effective biomarkers and identify more of them, we performed weighted correlation network analysis (WGCNA) on a GSE68468 dataset generated from 378 CRC tissue samples. We screened the gene set (module), which was significantly associated with CRC histology, and analyzed the hub genes. The key genes were identified by obtaining six colorectal raw data (i.e., GSE25070, GSE44076, GSE44861, GSE21510, GSE9348, and GSE21815) from the GEO database (https://www.ncbi.nlm.nih.gov/geo). The robust differentially expressed genes (DEGs) in all six datasets were calculated and obtained using the library “RobustRankAggreg” package in R 3.5.1. An integrated analysis of CRC based on the top 50 downregulated DEGs and hub genes in the red module from WGCNA was conducted, and the intersecting genes were screened. The Kaplan–Meier plot was further analyzed, and the genes associated with CRC prognosis based on patients from the TCGA database were determined. Finally, we validated the candidate gene in our clinical CRC specimens. We postulated that the candidate genes screened from the database and verified by our clinical pathological data may contribute to understanding the molecular mechanisms of tumorigenesis and may serve as potential biomarkers for CRC diagnosis and treatment.

1. Introduction

Colorectal cancer (CRC) is a malignant tumor that ranks third in terms of incidence and second in terms of mortality worldwide [1]. Similarly, the incidence and mortality of CRC rank fifth in China [2]. Despite dramatic reduction in the overall CRC incidence and mortality [3, 4], the morbidity rate in China is still rising from 2000 to 2011 [2]. Hence, further research is desperately needed to elucidate the causes for the increasing burden of CRC and to advance treatments for tumor subtypes with low response rates to current therapies. Treatment according to the distinctive tumor biology is an effective means to reduce the mortality of patients with CRC, as well as the detection of biomarkers that enable the stratification of patients with CRC into different prognostic subgroups and in relation therapeutic response [5]. Advances in RNA sequencing technologies and bioinformatics analysis provide novel potential biomarkers and drug targets for tumor treatment [6]. Weighted gene coexpression network analysis (WGCNA), as a systems biology algorithm, can enable the identification of highly coexpressed gene clusters (modules) [7]. Then, such interest modules and hub genes related to clinical traits can be screened out and used to identify candidate biomarkers [8]. The robust rank aggregation (RRA) method can be used to integrate multiple sets of chip data gene lists and to perform comprehensive reordering to find the most significant difference genes [9]. RRA prevents cross-platform standardization processing, and the number of samples per chip has no strict limit, which is of great significance for the effective evaluation of the results of different gene expression profiles [10].

In our study, we performed WGCNA on GSE68468 and screened out the key gene modules and hub genes significantly associated with the CRC histology. Additionally, we conducted RRA on six raw data (i.e., GSE25070, GSE44076, GSE44861, GSE21510, GSE9348, and GSE21815) and calculated the top 50 robust DEGs in all the six data by using the library “RobustRankAggreg” package. We analyzed the integrated genes between DEGs and hub genes in the key module by using Kaplan–Meier analysis in the TCGA database and obtained the candidate genes associated with OS. Finally, we validated the candidate gene in our clinical CRC specimens. The candidate genes screened from the database and verified using our clinical pathological data might have significant clinical implications for CRC diagnosis, treatment, and prognosis prediction.

2. Results

2.1. Weighted Gene Coexpression Network Construction and Module Detection

We performed WGCNA to find the highly correlated genes. The sample dendrogram and trait heatmap are shown in Figure S1A. As shown in Figure S1B, power value 4 was set to guarantee the high-scale independence and low mean connectivity of 13515 genes. We set the dissimilarity as 0.25 to merge similar modules (Figure S1C), and 22 modules were generated (Figure 1(a)). Furthermore, the interaction relationship network of 22 modules was plotted (Figure 1(b)). From those obtained modules, the red module had the deepest association with tumor histology (cor = −0.76, ), which was selected for further analysis (Figure 1(c)). Additionally, the module memberships in the red module and the gene significance had a high correlation (0.89) and a high value (<1e − 200) (Figure 1(d)), suggesting its suitability for identifying the hub genes associated with CRC occurrence and metastasis.

2.2. Coexpression Network Construction and Hub Gene Identification

In our study, we obtained 616 genes in the red modules. The hub genes in the red module were filtered by a condition of the weight value being greater than 0.15, and a total of 37 edges were obtained and are visualized in Figure 2. The top 10 hub genes were CA2, MS4A12, DHRS11, GUCA2A, ETHE1, CLCA4, TSPAN1, HSD11B2, AQP8, and CHP2.

2.3. RRA Analysis

We calculated DEGs expressed between the cancerous and adjacent tissues in each dataset and displayed the results by using volcano maps (Figures S2af). Then, DEGs in all six data were recalculated and reorganized using the library “RobustRankAggreg” package. A total of 464 robust DEGs were identified, including 176 upregulated genes and 288 downregulated genes (Table 1), by using and |logFC| ≥ 1 as the cutoff criteria. The 50 most prominent up- and downregulated genes were screened and are visualized in Figures 3(a) and 3(b).


Name valueLogFC

Upregulated DEGs
MMP71.04E − 162.71E − 123.717319
THBS23.32E − 168.67E − 122.821997
TGFBI7.96E − 162.08E − 112.584099
MMP31.59E − 154.15E − 112.998933
DPEP14.59E − 151.20E − 103.250292
CDH34.60E − 141.20E − 093.266416
KRT231.55E − 134.05E − 093.658825
NFE2L31.32E − 123.45E − 082.149137
GZMB1.36E − 123.55E − 082.078558
COL11A13.42E − 128.95E − 082.728714
CXCL13.70E − 129.65E − 082.210194
CEMIP4.96E − 121.30E − 072.894223
FAP5.19E − 121.36E − 072.449763
SLCO1B36.55E − 121.71E − 072.197776
CLDN17.48E − 121.95E − 072.916603
MYC1.15E − 112.99E − 071.831720
IFITM11.15E − 112.99E − 071.625712
TESC2.53E − 116.61E − 072.108654
CXCL112.95E − 117.70E − 071.989137
EDNRA3.02E − 117.89E − 071.496142
MMP13.29E − 118.60E − 072.399938
FOXQ15.40E − 111.41E − 062.749133
TPX25.42E − 111.41E − 061.634694
COL4A15.77E − 111.51E − 061.630033
TCN17.96E − 112.08E − 061.926601
EGFL69.09E − 112.38E − 061.752245
TRIP139.50E − 112.48E − 061.729510
PMAIP11.01E − 102.63E − 061.621771
CXCL31.19E − 103.10E − 061.933844
PSAT11.33E − 103.48E − 061.749995
CTHRC11.36E − 103.56E − 062.547903
PLS31.76E − 104.61E − 061.561012
INHBA1.82E − 104.76E − 062.470231
IFITM22.22E − 105.81E − 061.477155
PHLDA12.77E − 107.25E − 062.025229
CSE1L2.77E − 107.25E − 061.509054
BMP44.28E − 101.12E − 051.311002
CRNDE4.93E − 101.29E − 052.196882
S100A95.05E − 101.32E − 051.793278
CDK46.20E − 101.62E − 051.268501
XPOT6.54E − 101.71E − 051.350678
LY6E6.54E − 101.71E − 051.773015
CCL208.45E − 102.21E − 051.681642
TSPAN58.63E − 102.25E − 051.300671
CDC25B9.62E − 102.51E − 051.477785
LGR51.03E − 092.69E − 051.765688
ASCL21.06E − 092.78E − 052.270850
IFITM31.12E − 092.94E − 051.535829
PLAU1.35E − 093.52E − 051.449998
RIPK21.40E − 093.65E − 051.304180
COL5A21.42E − 093.72E − 051.709418
CXCL101.45E − 093.78E − 051.832510
DUSP141.49E − 093.89E − 051.604280
TRIB31.52E − 093.97E − 052.009118
CKMT21.71E − 094.46E − 052.047320
CEL1.77E − 094.62E − 051.921534
TGIF21.82E − 094.74E − 051.594622
HOMER11.89E − 094.94E − 051.595741
SRPX21.97E − 095.15E − 051.759897
SLC7A52.05E − 095.37E − 051.907805
UBD2.06E − 095.38E − 051.848031
GPSM22.90E − 097.58E − 051.310352
AZGP13.52E − 099.20E − 052.058214
AURKA3.81E − 099.96E − 051.248453
STC23.90E − 090.0001021.838371
SULF13.90E − 090.0001022.003268
ATAD24.09E − 090.0001071.500415
TMEM1584.11E − 090.0001071.212075
PPAT4.12E − 090.0001081.305250
SPP14.13E − 090.0001082.205887
CXCL24.36E − 090.0001141.826375
COL1A24.44E − 090.0001161.882105
SLC7A115.29E − 090.0001381.208023
TIMP15.69E − 090.0001491.661168
MMP126.08E − 090.0001591.933314
SCD6.11E − 090.0001601.564105
SOX96.43E − 090.0001681.352557
CXCL96.92E − 090.0001811.461166
HSPH18.01E − 090.0002091.238503
GDPD58.12E − 090.0002121.308392
CXCL88.27E − 090.0002162.588362
COL1A18.38E − 090.0002191.769908
FABP68.52E − 090.0002232.079844
PMEPA18.52E − 090.0002231.333566
ANXA99.83E − 090.0002571.608179
TDGF1P31.15E − 080.0003011.841176
CEP551.22E − 080.0003191.470764
COL10A11.27E − 080.0003311.780470
MCM101.37E − 080.0003581.211513
MPP61.51E − 080.0003961.432320
PROX11.58E − 080.0004131.364230
TEAD41.65E − 080.0004311.447381
CLDN21.72E − 080.0004491.698260
ODAM1.79E − 080.0004681.471032
MTHFD21.83E − 080.0004781.111261
KRT6B1.85E − 080.0004841.485079
HILPDA1.87E − 080.0004881.488635
DACH12.14E − 080.0005591.610398
ESM12.17E − 080.0005681.708858
CYP4X12.22E − 080.0005811.382114
GNG42.25E − 080.0005881.178933
ARID3A2.35E − 080.0006131.275140
PPBP2.38E − 080.0006211.748166
EPHX42.42E − 080.0006332.381989
LRP82.67E − 080.0006971.522085
PTP4A32.67E − 080.0006971.183443
SERPINE22.67E − 080.0006971.154912
AGT2.81E − 080.0007341.405610
C22.84E − 080.0007431.372973
RRM22.86E − 080.0007481.196973
NEBL3.49E − 080.0009121.521795
RPP403.49E − 080.0009121.285132
ARNTL23.77E − 080.0009841.419157
AHCY3.84E − 080.0010041.237807
GTF2IRD13.95E − 080.0010321.371062
WNT24.00E − 080.0010441.452032
TOP2A4.04E − 080.0010571.372624
PUS74.09E − 080.0010691.249772
NMU4.30E − 080.0011221.284463
COL6A34.44E − 080.0011601.270615
RNF434.57E − 080.0011951.397306
COL5A14.69E − 080.0012261.191502
CDK14.70E − 080.0012281.317782
ETV44.86E − 080.0012712.204983
PTGS25.04E − 080.0013171.233879
CHEK15.20E − 080.0013581.126745
MMP105.41E − 080.0014141.195256
FZD35.52E − 080.0014421.089222
UBE2C5.81E − 080.0015181.241558
RNASEH2A6.16E − 080.0016091.071208
ENC16.27E − 080.0016371.375197
IGF2BP36.37E − 080.0016651.082776
VSNL16.75E − 080.0017641.786437
GALNT66.79E − 080.0017741.266153
ERP276.97E − 080.0018211.218536
PROCR7.73E − 080.0020201.376152
PAFAH1B37.88E − 080.0020581.100418
RFC38.14E − 080.0021261.372173
NKRF8.14E − 080.0021271.043072
CXCL59.69E − 080.0025311.294918
SQLE1.03E − 070.0027001.308167
EXOSC51.08E − 070.0028281.132422
S100P1.13E − 070.0029441.551099
ELOVL51.27E − 070.0033311.176539
ZAK1.29E − 070.0033601.541699
TCFL51.45E − 070.0037861.270140
LAPTM4B1.67E − 070.0043761.026874
CEBPB1.70E − 070.0044311.333258
SLCO4A11.83E − 070.0047901.606904
TTK1.89E − 070.0049341.386804
TMEM971.98E − 070.0051791.082142
CHI3L12.01E − 070.0052391.621308
PPM1H2.10E − 070.0054751.353835
SORD2.14E − 070.0055911.037586
CST12.20E − 070.0057471.894531
CENPF2.46E − 070.0064221.024242
GTF3A2.47E − 070.0064511.215577
APCDD12.59E − 070.0067721.580775
KIF4A2.59E − 070.0067721.267447
MFAP22.65E − 070.0069141.397671
CCNB12.77E − 070.0072411.203637
GDF152.88E − 070.0075141.563948
NXT12.93E − 070.0076671.046948
SHMT22.95E − 070.0076971.002641
SNTB12.96E − 070.0077261.291950
TACSTD22.97E − 070.0077541.640872
SNX102.99E − 070.0078141.021758
SLC12A23.04E − 070.0079331.134681
TNFRSF12A3.13E − 070.0081901.332447
SLC5A63.25E − 070.0084881.064032
RUVBL13.26E − 070.0085131.066507
FXYD53.32E − 070.0086801.172860
ZNF2393.35E − 070.0087451.158161
REG1A3.40E − 070.0088761.168719
OLFML2B3.57E − 070.0093261.332885
ERCC6L3.63E − 070.0094721.325393

Downregulated DEGs
GUCA2B4.94E − 211.29E − 16−5.47325
GUCA2A5.63E − 201.47E − 15−4.98756
CA49.10E − 202.38E − 15−4.93587
MS4A122.15E − 195.61E − 15−5.22647
CA23.16E − 198.27E − 15−4.64843
AQP87.18E − 191.88E − 14−5.22336
CLCA11.67E − 174.37E − 13−4.41598
CLCA42.30E − 176.01E − 13−5.78604
AKR1B103.47E − 179.06E − 13−3.66480
CA11.37E − 163.58E − 12−4.85127
HSD17B21.42E − 153.72E − 11−3.65299
GCG1.86E − 154.87E − 11−4.24507
CWH433.10E − 158.09E − 11−3.35700
MT1M6.63E − 151.73E − 10−3.74527
CHP26.63E − 151.73E − 10−3.59700
FCGBP9.37E − 152.45E − 10−3.35836
ADH1C1.89E − 144.93E − 10−3.66678
CD1777.18E − 141.88E − 09−3.56989
CA121.04E − 132.73E − 09−2.48416
CHGA1.04E − 132.73E − 09−3.21194
SLC26A31.09E − 132.85E − 09−3.26689
CEACAM71.16E − 133.02E − 09−3.26188
PLAC81.75E − 134.58E − 09−2.93747
CLDN81.79E − 134.67E − 09−4.07661
PCK13.95E − 131.03E − 08−2.41432
BEST25.80E − 131.52E − 08−2.88517
GBA36.08E − 131.59E − 08−3.15495
MT1H7.81E − 132.04E − 08−2.45079
HMGCS21.28E − 123.35E − 08−2.63704
SI1.53E − 123.99E − 08−3.18335
GCNT31.86E − 124.87E − 08−2.55705
EDN32.02E − 125.29E − 08−2.36855
SLC26A22.58E − 126.74E − 08−2.74311
TSPAN13.02E − 127.89E − 08−2.43173
UGT2B173.26E − 128.51E − 08−2.68448
BCAS13.43E − 128.96E − 08−2.14870
LRRC194.61E − 121.20E − 07−2.60514
LGALS25.96E − 121.56E − 07−2.50281
ANPEP8.52E − 122.23E − 07−2.96251
VSIG28.52E − 122.23E − 07−2.90829
MT1E9.09E − 122.37E − 07−2.30341
MT1G9.49E − 122.48E − 07−2.13170
NXPE49.66E − 122.53E − 07−2.93788
KRT209.69E − 122.53E − 07−2.12657
CA71.29E − 113.38E − 07−2.72816
MT1F1.32E − 113.45E − 07−2.15911
KLF41.38E − 113.59E − 07−2.38010
AHCYL21.43E − 113.74E − 07−2.24169
ITLN11.74E − 114.55E − 07−3.42798
HSD11B22.10E − 115.49E − 07−2.34259
NR3C22.53E − 116.60E − 07−2.37317
EPB41L32.57E − 116.72E − 07−2.39867
ITM2C2.67E − 116.97E − 07−2.12884
MMP282.86E − 117.48E − 07−1.93771
ZG163.09E − 118.06E − 07−4.23120
ABCG23.75E − 119.80E − 07−3.20141
SLC4A44.13E − 111.08E − 06−3.28247
SCGB2A14.33E − 111.13E − 06−2.48958
MALL4.61E − 111.20E − 06−2.29999
TUBAL34.92E − 111.29E − 06−2.39086
RUNDC3B5.33E − 111.39E − 06−1.89913
NR1H45.50E − 111.44E − 06−2.46055
SLC30A105.68E − 111.48E − 06−3.22414
PIGR6.05E − 111.58E − 06−2.18018
MUC26.24E − 111.63E − 06−2.62907
HPGD6.74E − 111.76E − 06−2.64497
TSPAN76.84E − 111.79E − 06−2.30396
PAPSS21.30E − 103.39E − 06−1.78856
SLC17A41.31E − 103.43E − 06−2.31184
DHRS91.36E − 103.56E − 06−2.74003
CAPN91.65E − 104.32E − 06−1.77713
PTPRH1.70E − 104.43E − 06−1.93474
SST1.81E − 104.73E − 06−2.35448
HEPACAM21.82E − 104.76E − 06−2.85009
SMPDL3A2.03E − 105.31E − 06−1.85516
UGT2A32.16E − 105.65E − 06−2.42200
TRPM62.42E − 106.34E − 06−2.40017
CES22.58E − 106.74E − 06−1.80950
NR5A22.64E − 106.91E − 06−1.65550
DHRS112.77E − 107.25E − 06−2.40964
ENTPD52.88E − 107.52E − 06−1.91140
PLCL22.98E − 107.79E − 06−1.71267
MYO1A2.98E − 107.79E − 06−1.82841
BTNL83.28E − 108.58E − 06−2.68136
PLCD13.40E − 108.88E − 06−1.69423
ADTRP3.71E − 109.69E − 06−2.66899
ARL144.37E − 101.14E − 05−2.10661
PDZD34.78E − 101.25E − 05−1.73340
SCNN1B5.25E − 101.37E − 05−2.51853
MEP1A5.25E − 101.37E − 05−2.15953
C2orf885.40E − 101.41E − 05−2.60606
GPA335.69E − 101.49E − 05−1.85470
SCIN6.20E − 101.62E − 05−2.19971
MAOA6.61E − 101.73E − 05−1.79573
PIGZ6.89E − 101.80E − 05−1.67717
PYY6.96E − 101.82E − 05−2.53788
C4orf197.26E − 101.90E − 05−1.57357
TNFRSF177.47E − 101.95E − 05−2.36650
ADAMDEC17.47E − 101.95E − 05−2.39500
TEX118.71E − 102.28E − 05−2.24440
CDHR29.44E − 102.47E − 05−1.71114
TMEM1719.64E − 102.52E − 05−2.11860
STMN21.28E − 093.36E − 05−2.05581
PKIB1.45E − 093.78E − 05−2.98037
CLIC51.57E − 094.11E − 05−1.55858
BMP21.60E − 094.19E − 05−1.76798
RETSAT1.63E − 094.26E − 05−1.68389
PADI21.69E − 094.42E − 05−2.10023
SLC9A21.80E − 094.70E − 05−1.55732
CYP2C182.16E − 095.65E − 05−1.71017
C1orf1152.32E − 096.05E − 05−1.74460
CEACAM12.42E − 096.31E − 05−1.84011
PDE9A2.44E − 096.37E − 05−2.05665
SLCO2A12.58E − 096.75E − 05−1.53024
BCAR32.63E − 096.87E − 05−1.50716
METTL7A2.65E − 096.92E − 05−1.62515
DSC22.65E − 096.92E − 05−1.44448
IL1R22.75E − 097.19E − 05−2.16852
CCDC682.75E − 097.19E − 05−1.89156
GDPD32.78E − 097.28E − 05−2.08667
PDE6A2.81E − 097.34E − 05−1.93721
EPHX23.12E − 098.16E − 05−1.49186
SPIB3.35E − 098.76E − 05−2.27898
ITPKA3.41E − 098.91E − 05−1.62863
SCUBE23.66E − 099.57E − 05−1.51582
AMPD14.13E − 090.000108−1.81548
SELENBP14.61E − 090.000120−2.04099
TFCP2L15.10E − 090.000133−1.16817
BTNL35.13E − 090.000134−2.10512
VIPR15.45E − 090.000142−1.57631
ZZEF15.53E − 090.000144−1.27744
NAT25.61E − 090.000147−1.61058
CDHR55.81E − 090.000152−1.97788
PTGDR6.44E − 090.000168−2.08658
HHLA27.58E − 090.000198−1.91952
PLCE17.60E − 090.000199−1.63459
BEST47.87E − 090.000206−2.37175
FUCA18.12E − 090.000212−1.41919
FGFBP18.12E − 090.000212−1.45128
MT1X8.24E − 090.000215−1.87572
SLC16A98.52E − 090.000223−1.97426
SEMA6A8.52E − 090.000223−1.66469
FXYD38.58E − 090.000224−1.39744
DEFB18.94E − 090.000233−1.76340
ACADS9.22E − 090.000241−1.70299
UGDH9.50E − 090.000248−1.45862
HRASLS21.06E − 080.000276−1.48365
CYP4F121.09E − 080.000284−1.26493
JCHAIN1.10E − 080.000288−2.33454
ABHD31.13E − 080.000295−1.54939
VWA5A1.23E − 080.000321−1.12976
LDHD1.27E − 080.000332−2.10259
XDH1.28E − 080.000334−1.53406
INSL51.29E − 080.000337−2.42430
SPINK51.38E − 080.000359−2.43812
VILL1.50E − 080.000391−1.41689
SQRDL1.51E − 080.000396−1.38225
FABP21.52E − 080.000398−1.88374
LGALS41.61E − 080.000421−1.42830
DNASE1L31.65E − 080.000431−1.82008
SLC44A41.66E − 080.000434−1.59136
GOLM11.68E − 080.000439−1.20581
RNF1861.72E − 080.000450−1.21896
GDPD21.85E − 080.000484−1.90186
SLC22A18AS1.85E − 080.000484−1.62665
MGLL1.91E − 080.000499−1.32867
B3GNT61.92E − 080.000501−1.59112
IL6R1.92E − 080.000502−1.86011
RAPGEFL11.92E − 080.000502−1.32000
TRPM41.94E − 080.000508−1.34414
ETFDH2.00E − 080.000522−1.52397
TMPRSS22.05E − 080.000536−1.39816
APOBR2.08E − 080.000544−1.69871
APPL22.11E − 080.000551−1.46951
IQGAP22.17E − 080.000566−1.55628
FAM83E2.17E − 080.000568−1.17487
ETHE12.29E − 080.000599−1.61458
FLVCR22.39E − 080.000624−1.01211
ALDH1A12.41E − 080.000631−1.28928
DENND2A2.51E − 080.000657−1.32793
HRCT12.57E − 080.000671−1.58787
ATP8B12.69E − 080.000703−1.00964
A1CF2.74E − 080.000715−1.36877
ELOVL62.96E − 080.000773−1.08274
C11orf863.02E − 080.000789−2.11210
TMEM1003.14E − 080.000821−1.69604
ASAP33.27E − 080.000854−1.44474
S100A143.29E − 080.000859−1.21353
PHLPP23.42E − 080.000892−1.45264
LIMA13.47E − 080.000907−1.20588
FABP13.51E − 080.000916−1.62316
FMO53.62E − 080.000947−1.41152
LRMP3.64E − 080.000950−1.84646
UGP23.68E − 080.000961−1.28750
ATP2A33.74E − 080.000978−1.51001
SLC22A53.74E − 080.000978−1.30616
FAM46C3.78E − 080.000989−1.41521
LXN3.97E − 080.001037−1.10935
CDKN2B3.98E − 080.001039−2.23550
HMOX13.99E − 080.001043−1.23547
NEDD4L4.03E − 080.001054−1.23680
CLDN74.18E − 080.001093−1.29905
PARM14.21E − 080.001099−1.52095
HSD3B24.29E − 080.001120−2.26639
KIAA05134.39E − 080.001146−1.22322
ACADVL4.50E − 080.001176−1.23429
SYTL24.74E − 080.001238−1.07640
SLC1A14.79E − 080.001251−1.40176
CNTN34.81E − 080.001256−2.13362
CNNM44.97E − 080.001300−1.36807
CASP75.04E − 080.001317−1.34691
GLTP5.30E − 080.001385−1.40471
PGM15.49E − 080.001435−1.23037
ERN25.60E − 080.001464−1.10843
NAAA5.72E − 080.001494−1.45647
GNA116.10E − 080.001593−1.40416
CCL236.37E − 080.001665−1.50079
STYK16.40E − 080.001673−1.56848
CFD6.56E − 080.001715−1.80374
C15orf486.82E − 080.001783−1.45026
OASL6.89E − 080.001800−1.13508
GPT6.99E − 080.001827−1.44289
CLDN237.12E − 080.001859−1.93577
MB7.62E − 080.001991−1.42542
HIGD1A7.65E − 080.002000−1.40813
EMP18.37E − 080.002188−1.46200
ADH1B8.46E − 080.002211−2.01430
LPAR18.49E − 080.002219−1.26559
GPAT38.70E − 080.002272−2.01808
CHGB8.90E − 080.002324−1.55043
P3H29.09E − 080.002376−1.48288
PRKACB9.28E − 080.002424−1.46492
OSBPL1A9.84E − 080.002572−1.16940
SPINK41.04E − 070.002716−1.69181
MT2A1.04E − 070.002726−1.55590
SULT1B11.07E − 070.002807−1.66731
NAT11.08E − 070.002828−1.17391
ADRA2A1.11E − 070.002892−1.54370
MEP1B1.13E − 070.002960−2.16734
LPCAT41.18E − 070.003077−1.02702
PCSK71.18E − 070.003077−1.00185
ST6GALNAC11.18E − 070.003088−1.65355
SGK21.21E − 070.003160−1.41158
GRAMD31.26E − 070.003287−1.25518
RIOK31.27E − 070.003331−1.21281
CITED21.29E − 070.003374−1.11561
CXCL121.41E − 070.003684−1.54414
ITM2A1.45E − 070.003786−1.47880
SEPP11.51E − 070.003956−1.10723
KBTBD111.53E − 070.004006−1.05544
RHOF1.55E − 070.004040−1.11757
GSTA11.57E − 070.004105−1.34829
GALNT121.61E − 070.004214−1.23449
BCHE1.70E − 070.004446−1.57410
HGD1.78E − 070.004657−1.24750
HOXD11.83E − 070.004793−1.79058
FGL21.90E − 070.004977−1.31339
ATP8A12.03E − 070.005307−1.05015
HIST1H1C2.06E − 070.005393−1.25170
MYOT2.10E − 070.005480−1.72660
PBLD2.11E − 070.005502−1.58127
RBM472.15E − 070.005614−1.08864
CES32.18E − 070.005700−1.45856
CD1D2.18E − 070.005704−1.29631
FAM150B2.19E − 070.005719−1.50655
RARRES12.23E − 070.005819−1.46770
LGALS92.24E − 070.005842−1.01981
KLK12.27E − 070.005936−1.29450
TOX2.27E − 070.005942−1.29132
ACVRL12.36E − 070.006175−1.13083
CPT22.44E − 070.006372−1.22566
SGK12.57E − 070.006718−1.64587
ALPI2.62E − 070.006834−1.09455
SLC51B2.65E − 070.006919−2.27581
SLC20A12.71E − 070.007076−1.03315
SCGN2.74E − 070.007164−1.86108
ASPA2.75E − 070.007186−1.45497
FAM107B2.81E − 070.007337−1.24898
AGR32.82E − 070.007358−1.80558
HOXA52.85E − 070.007437−1.09160
NPY1R2.97E − 070.007754−1.92966
TNFSF102.97E − 070.007754−1.20956
FAM107A3.18E − 070.008318−1.40373
MUC43.26E − 070.008520−1.83234
TST3.31E − 070.008645−1.30505
NXPE13.57E − 070.009326−1.90526
SPON13.60E − 070.009400−1.78810
ST6GALNAC63.78E − 070.009874−1.79857

FC: fold change.
2.4. Survival Analysis in the TCGA Database and Validation in the GEO Database

By taking the intersection of the 50 downregulated DEGs and hub genes in the red module from WGCNA (Figure 4(a)), we obtained 19 interacting genes.

K-M analysis was conducted to evaluate the relationship between gene expression and the overall survival (OS) of CRC, and only GUCA2A was clearly related to the OS of CRC patients in the TCGA database. Patients with a lower GUCA2A expression had a significantly shorter OS than those with a higher expression () (Figure 4(b)). Obviously, GUCA2A was abnormally expressed in tumor tissues and was significantly different in TCGA and GEO databases (Figures 4(c) and 4(d)). We further validated the aberrant expression of GUCA2A in GSE68468, which contains both CRC tissues and cellular RNA-Seq data. Compared with adjacent normal tissues, the expression of GUCA2A in tumor and metastatic tissues was significantly low, and the normal liver and lung tissues had the lowest expression value () (Figure 4(e)). The colonic epithelial cell (NCM460) has the highest GUCA2A expression compared with other CRC cells (Figure 4(f)).

2.5. Human Tissue Samples and Quantitative Real-Time PCR

We performed real-time PCR to examine the levels of GUCA2A in 31 paired CRC/adjacent tissues to further validate the dysregulated expression of GUCA2A (Figure 4(g)). Then, we further evaluated the diagnostic value of GUCA2A for CRC tissues and normal counterparts using ROC curve analyses. The results showed that the area under the ROC curve (AUC) was 0.835 (; sensitivity: 0.806; specificity: 0.903). These results suggest that GUCA2A downregulation may play an important role in colorectal tumorigenesis and has a potential diagnostic value for CRC patients.

2.6. Pathway Analysis

The pathway enrichment analysis of GUCA2A coexpressed genes was conducted in the ConsensusPathDB human database. From the 16 pathways shown in Figure 5, the transport of small molecules and metabolism are prominent.

3. Discussion

Early detection and complete resection before metastasis are considered the only curative therapy for CRC [11]. The five-year survival rate of CRC patients was obviously better when the localized disease was detected at an early stage than that of CRC patients with distant metastasis. Cancer is a molecularly heterogeneous disease, and none of the currently identified biomarkers are sensitive and specific enough for reliable CRC screening in the clinical setting. Thus, identifying novel molecular biomarkers has significant clinical benefits.

In our study, we first performed WGCNA for GSE68468 and screened the pathologically related hub genes. We conducted RRA analysis for six datasets and screened the top 100 robust DEGs, which had a high or low expression in all expression profiles. By taking the intersection, we obtained 19 candidate genes, and only the expression of GUCA2A was associated with the OS of CRC patients in the TCGA database. We found that GUCA2A was prominent and top-ranking in both the hub gene network (Figure 2) and robust DEGs (Figure 3(b)), indicating its value in tumorigenesis.

Guanylin (GUCA2A) and uroguanylin (GUCA2B) are two peptide hormones that function as paracrine endogenous ligands for the guanylate cyclase-C (GUCY2C) receptor [12]. A previous study indicated the role of GUCY2C signaling in facilitating mucosal wounding and inflammation mediation, in part, through the control of resistin-like molecule β production [13]. GUCA2A, GUCA2B, and GUCY2C are downregulated in inflammatory bowel disease [14], which may have implications in inflammatory bowel disease pathogenesis. A recent study suggests that GUCY2C can suppress tumor progress [15] in the intestine, and the loss of the GUCY2C signaling cascade increases CRC susceptibility [16]. Intestinal homeostasis disruption and CRC tumorigenesis are associated with a fairly common loss of GUCA2A and GUCA2B [1719]. Bashir et al. revealed the possibility of GUCA2A loss silencing GUCY2C, which leads to microsatellite instability tumor [20].

Consistent with the expression of GUCA2A in our study, the expression of GUCA2B was significantly downregulated in CRC tissues and had a relative high weight in the red module, which further indicates that GUCA2A and GUCA2B may play a consistent role in CRC neoplasia.

In conclusion, we revealed that GUCA2A was downregulated in CRC tissues. Aberrantly expressed GUCA2A can be a candidate marker of poor prognosis in patients with CRC, which may be a therapeutic target for precision medicine in cervical cancer. However, further studies are still needed to explore the underlying molecular mechanism through which GUCA2A plays a role in CRC. However, future in vivo and in vitro experiments are still required to explore the mechanisms underlying the roles of GUCA2A in CRC.

4. Methods

4.1. WGCNA Construction and Module Detection

We performed WGCNA to microarray data GSE68468 generated from 378 CRC tissue samples, and the “WGCNA” package in R 3.5.1 was used to construct a coexpression network. WGCNA seeks to identify modules of densely interconnected genes by searching for genes with similar patterns of connection strengths or high topological overlap. For each dataset, Pearson correlation coefficients were calculated for all pairwise comparisons of expressed genes across all samples. Genes with similar expression profiles were classified into modules based on the TOM dissimilarity with a minimum size of 30 for the gene dendrogram and visualized via hierarchical clustering [7]. Then, the modules whose eigengenes were highly correlated (correlation above 0.7) were merged. The gene network was visualized with randomly selected 400 genes.

The resulting Pearson correlation matrix was transformed into a matrix of connection strengths (that is, an adjacency matrix) by using a power function (connection). All the modules were assigned to the corresponding color. The relationships of modules and clinical traits (i.e., disease status, histology, and organism part) were calculated. Among these clinical traits, pathology, including normal mucosa, polyps, CRC tissue, CRC with metastases, and normal lung/liver tissues, can reflect the occurrence and metastasis of CRC. The associations of individual genes with the clinical trait, namely, gene significance (GS), and the module eigengenes, namely, module membership (MM), were evaluated. Then, the correlation between GS and MM was calculated, and the highly correlated interest module can be used to construct the coexpression network and identify the hub genes.

4.2. Coexpression Network Construction and Hub Gene Identification

The genes in the key modules screened from WGCNA were further analyzed.

We filtered the hub genes by a condition of the weight value (>0.15) and visualized them using Cytoscape 3.6.0 [21].

4.3. Robust Rank Aggregation (RRA) Analysis

To further increase the reliability of the results and screen out the ideal candidate, we enrolled six published colorectal microarray data (i.e., GSE25070, GSE44076, GSE44861, GSE21510, GSE9348, and GSE21815) [2231] from the GEO database, which have 530 CRC tissues and 50 normal tissues in our study (Table 2). After screening DEGs ( and |logFC| ≥ 2) in each dataset by using the “limma” package [32], the RRA method was used to identify significantly robust DEGs by using the library “RobustRankAggreg” package in R 3.5.1. The statistically significant DEGs were defined to have and |logFC| ≥ 1. Finally, the top 50 up- and downregulated DEGs were selected and visualized by using a heatmap.


ReferenceTissueGEOPlatformNormalTumor

Hinoue et al. [22]CRCGSE25070GPL68835151
Cordero et al. [2326]CRCGSE44076GPL1366714898
Ryan et al. [27]CRCGSE44861GPL39215556
Tsukamoto et al. [28]CRCGSE21510GPL57025123
Hong et al. [29]CRCGSE9348GPL5701270
Mimori et al. [30, 31]CRCGSE21815GPL64809132

4.4. Survival Analysis in the TCGA Database

In order to increase the reliability of the results, the intersection of hub genes in the red module from WGCNA and 50 downregulated robust DEGs was performed and analyzed. Kaplan–Meier (K-M) analysis was conducted to evaluate the relationship between gene expression and the overall survival (OS) of 617 CRC patients in The Cancer Genome Atlas (TCGA) database (https://cancergenome.nih.gov/). Patients were classified into high- or low-expression groups according to the median value. Then, genes associated with CRC survival were screened.

4.5. Human Tissue Samples and Quantitative Real-Time PCR

Overall, the 31 CRC and adjacent normal tissues obtained at Jiangsu Cancer Hospital (Nanjing, China) were frozen immediately after surgical resection and kept at 80°C until further analysis. Tumor histopathology was classified according to the World Health Organization Classification of Tumors system. The present study was done with the approval of the local ethics committee.

RNA isolation (Takara, Dalian, China) was performed according to the manufacturer’s instructions. Reverse transcription was conducted with a PrimeScript RT reagent kit (Takara, Kusatsu City, Shiga, Japan) and the fluorescent quantitative experiment with ABI qPCR 7300 (Torrance, CA). The PCR reactant mix consisted of 2 µl cDNA solution, 10 μl 2× PowerUp™ SYBR™ Green Master Mix (Thermo Fisher Scientific, Carlsbad, CA), 0.5 μl of 10 μM forward and reverse PCR primer, and 7 μl DNA template Nuclease-Free Water. The PCR conditions were set as follows: denaturation at 50°C for 2 min, 95°C for 2 min, 95°C for 15 s, and 60°C for 1 min with 40 cycles. A GAPDH primer set was used as an internal control.

4.6. Pathway Analysis

We performed pathway enrichment analysis for the coexpressed genes to explore the possible mechanism of the candidate gene in CRC. The coexpressed genes were obtained from the TCGA database, and the top 100 genes with the highest Pearson correlation coefficient were considered to be significantly coexpressed (Table 3). Pathway enrichment analysis was performed by using the ConsensusPathDB human database (http://cpdb.molgen.mpg.de/) [33]. The overrepresentation gene set analysis was utilized, and the following pathway databases were enrolled in our analysis: Manual upload, NetPath, SignaLink, PID, EHMN, HumanCyc, INOH, KEGG, BioCarta, WikiPathways, SMPDB, and PharmGKB. Minimum overlap input list >5 and value cutoff <0.01 were considered significant enrichment.


GenesCorrelation value

GUCA2A10
TMIGD10.810349486.78E − 145
CA40.789886421.02E − 132
GUCA2B0.780067962.39E − 127
SCNN1B0.769221029.92E − 122
CDKN2B-AS10.759760204.48E − 117
LINC009740.728269625.10E − 103
CA10.725897784.85E − 102
OTOP20.725246368.98E − 102
B3GNT70.698740831.59E − 91
CA20.698728131.60E − 91
CLDN80.691622555.79E − 89
CLCA40.687243812.01E − 87
MS4A120.670846126.81E − 82
CEACAM70.656977021.74E − 77
BTNL30.646726062.24E − 74
SLC25A340.644570729.77E − 74
C11orf860.640759411.28E − 72
CLDN230.640148631.93E − 72
EDN30.627552267.21E − 69
RP11-35P15.10.623587708.90E − 68
B3GALT5-AS10.620606195.75E − 67
SULT1A20.615915171.04E − 65
PYY0.614455372.53E − 65
ZG160.614344902.71E − 65
ALPI0.611098441.93E − 64
SLC25A47P10.607426161.73E − 63
BTNL80.606198783.58E − 63
BEST40.593442115.70E − 60
CD1770.593226866.44E − 60
ST6GALNAC60.590744292.60E − 59
CDHR50.585388735.07E − 58
TRPM60.577707393.26E − 56
SEPP10.577570173.51E − 56
KRTAP13-20.571862097.21E − 55
SLC4A40.568536734.08E − 54
RP11-203J24.90.558282487.58E − 52
MYPN0.555890622.50E − 51
RP11-202A13.10.549936514.66E − 50
SLC30A100.546408582.57E − 49
SLC26A30.542793351.45E − 48
SMPD10.541420832.77E − 48
CDKN2B-AS0.539029048.55E − 48
RN7SKP1270.537698071.59E − 47
HHLA20.536442052.86E − 47
TMEM820.530293074.85E − 46
LYPD80.528801799.56E − 46
CD177P10.527335511.85E − 45
SCNN1G0.525216704.81E − 45
ABCC130.523582779.98E − 45
GDPD20.519421046.29E − 44
TTC220.519325996.56E − 44
B3GALT50.517775311.29E − 43
C2orf880.517552171.43E − 43
DHRS90.515397703.64E − 43
TSPAN10.515368623.69E − 43
INSL50.512807011.11E − 42
TMEM2530.507852209.21E − 42
SDCBP20.507172571.23E − 41
SLC17A40.505157642.86E − 41
PLAC80.501314561.42E − 40
MMP280.498304224.90E − 40
PPY0.497967695.62E − 40
PLA2G100.495550281.51E − 39
GLRA40.494930811.94E − 39
ITM2C0.492310375.57E − 39
TRIM400.491906146.55E − 39
SMPDL3A0.491887556.60E − 39
MALL0.491206428.68E − 39
BCAS10.490192321.30E − 38
CDKN2B0.489258531.89E − 38
SLC51B0.487089764.46E − 38
BMP30.485363668.79E − 38
AMPD10.482028233.23E − 37
ENTPD50.470547772.56E − 35
PTPRH0.468825334.87E − 35
RPL12P140.467596687.68E − 35
RP11-92A5.20.466521361.14E − 34
AC106869.20.466401391.19E − 34
MUC120.465785241.50E − 34
CASC180.465703351.55E − 34
PEX260.460718459.52E − 34
TMEM720.460261231.12E − 33
GLDN0.459956771.25E − 33
TEX110.459949531.26E − 33
NAAA0.457411963.13E − 33
TRANK10.456657194.10E − 33
SMIM60.455729855.71E − 33
LINC005070.448726416.70E − 32
AOC10.448214428.01E − 32
CDHR20.446639311.38E − 31
APPL20.440910229.81E − 31
TMPRSS20.438620052.12E − 30
CCDC1520.438559032.17E − 30
TRPV30.436054035.02E − 30
NPY2R0.436037505.04E − 30
GNA110.433891071.03E − 29
ACVRL10.430219413.44E − 29
SEMA6D0.429473384.39E − 29
GPR150.428717495.62E − 29
RP1-117B12.40.408553403.19E − 26

Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Acknowledgments

This study was supported in part by grants from the Jiangsu Provincial Key Research Development Program (BE2016794 to Jifeng Feng and BE2016795 to Jianzhong Wu).

Supplementary Materials

S1: sample dendrogram and soft-thresholding value estimation. (A) Sample dendrogram and trait heatmap. The three traits correspond to the disease status, histology, and organism part, respectively. (B) Scale independence and mean connectivity of soft-thresholding values (β). (C) Clustering of module eigengenes. The dissimilarity was set as 0.25 to merge the similar modules. S2: volcano plots of DEGs between cancerous and adjacent tissues in six microarray data. (Supplementary Materials)

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Copyright © 2019 Hui Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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