Biochemistry Research International
Volume 2019 (2019), Article ID 6469420, 13 pages
https://doi.org/10.1155/2019/6469420

Research Article

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

Hui Zhang,¹ Yuanyuan Du,¹ Zhuo Wang,¹ Rui Lou,² Jianzhong Wu,¹ and Jifeng Feng¹

¹Research Center for Clinical Oncology, The Affiliated Cancer Hospital of Nanjing Medical University, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing 210000, Jiangsu, China
²Department of Oncology, The Affiliated Hospital of Jiangnan University, Wuxi 214000, Jiangsu, China

Correspondence should be addressed to Jifeng Feng; jifeng_feng@163.com

Received 9 March 2019; Accepted 12 June 2019; Published 28 July 2019

Academic Editor: Robert Speth

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.

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.

(a)

(b)

(c)

(d)

Figure 1: Modules defined by the WGCNA. (a) Dendrogram tree represents the clustering dendrogram of genes in GSE68468, and the bottom colorful bands represent 22 modules assigned by genes. (b) A network heatmap plot of 400 genes. The intensity of the red color indicates the strength of the correlation between pairs of modules on a linear scale. (c) Correlation between modules and clinicopathologic features. The correlation coefficient and corresponding value of each module are shown in each cell. (d) Correlation (Pearson) between module membership and gene significance for the red module.

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.

Figure 2: Coexpression network of hub genes. The hub genes in the red modules were filtered by a condition of the weight value (>0.15) and visualized using Cytoscape 3.6.0. The size and color of the edge represent the intensity of weight interaction in the network.

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 S2a–f). 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).

Table 1: Whole DEGs (176 upregulated genes and 288 downregulated genes) identified in the six microarray data from the GEO database by using and |logFC| ≥ 1 as the cutoff criteria.

Name
value

LogFC

Upregulated DEGs

MMP7
1.04E − 16
2.71E − 12
3.717319

THBS2
3.32E − 16
8.67E − 12
2.821997

TGFBI
7.96E − 16
2.08E − 11
2.584099

MMP3
1.59E − 15
4.15E − 11
2.998933

DPEP1
4.59E − 15
1.20E − 10
3.250292

CDH3
4.60E − 14
1.20E − 09
3.266416

KRT23
1.55E − 13
4.05E − 09
3.658825

NFE2L3
1.32E − 12
3.45E − 08
2.149137

GZMB
1.36E − 12
3.55E − 08
2.078558

COL11A1
3.42E − 12
8.95E − 08
2.728714

CXCL1
3.70E − 12
9.65E − 08
2.210194

CEMIP
4.96E − 12
1.30E − 07
2.894223

FAP
5.19E − 12
1.36E − 07
2.449763

SLCO1B3
6.55E − 12
1.71E − 07
2.197776

CLDN1
7.48E − 12
1.95E − 07
2.916603

MYC
1.15E − 11
2.99E − 07
1.831720

IFITM1
1.15E − 11
2.99E − 07
1.625712

TESC
2.53E − 11
6.61E − 07
2.108654

CXCL11
2.95E − 11
7.70E − 07
1.989137

EDNRA
3.02E − 11
7.89E − 07
1.496142

MMP1
3.29E − 11
8.60E − 07
2.399938

FOXQ1
5.40E − 11
1.41E − 06
2.749133

TPX2
5.42E − 11
1.41E − 06
1.634694

COL4A1
5.77E − 11
1.51E − 06
1.630033

TCN1
7.96E − 11
2.08E − 06
1.926601

EGFL6
9.09E − 11
2.38E − 06
1.752245

TRIP13
9.50E − 11
2.48E − 06
1.729510

PMAIP1
1.01E − 10
2.63E − 06
1.621771

CXCL3
1.19E − 10
3.10E − 06
1.933844

PSAT1
1.33E − 10
3.48E − 06
1.749995

CTHRC1
1.36E − 10
3.56E − 06
2.547903

PLS3
1.76E − 10
4.61E − 06
1.561012

INHBA
1.82E − 10
4.76E − 06
2.470231

IFITM2
2.22E − 10
5.81E − 06
1.477155

PHLDA1
2.77E − 10
7.25E − 06
2.025229

CSE1L
2.77E − 10
7.25E − 06
1.509054

BMP4
4.28E − 10
1.12E − 05
1.311002

CRNDE
4.93E − 10
1.29E − 05
2.196882

S100A9
5.05E − 10
1.32E − 05
1.793278

CDK4
6.20E − 10
1.62E − 05
1.268501

XPOT
6.54E − 10
1.71E − 05
1.350678

LY6E
6.54E − 10
1.71E − 05
1.773015

CCL20
8.45E − 10
2.21E − 05
1.681642

TSPAN5
8.63E − 10
2.25E − 05
1.300671

CDC25B
9.62E − 10
2.51E − 05
1.477785

LGR5
1.03E − 09
2.69E − 05
1.765688

ASCL2
1.06E − 09
2.78E − 05
2.270850

IFITM3
1.12E − 09
2.94E − 05
1.535829

PLAU
1.35E − 09
3.52E − 05
1.449998

RIPK2
1.40E − 09
3.65E − 05
1.304180

COL5A2
1.42E − 09
3.72E − 05
1.709418

CXCL10
1.45E − 09
3.78E − 05
1.832510

DUSP14
1.49E − 09
3.89E − 05
1.604280

TRIB3
1.52E − 09
3.97E − 05
2.009118

CKMT2
1.71E − 09
4.46E − 05
2.047320

CEL
1.77E − 09
4.62E − 05
1.921534

TGIF2
1.82E − 09
4.74E − 05
1.594622

HOMER1
1.89E − 09
4.94E − 05
1.595741

SRPX2
1.97E − 09
5.15E − 05
1.759897

SLC7A5
2.05E − 09
5.37E − 05
1.907805

UBD
2.06E − 09
5.38E − 05
1.848031

GPSM2
2.90E − 09
7.58E − 05
1.310352

AZGP1
3.52E − 09
9.20E − 05
2.058214

AURKA
3.81E − 09
9.96E − 05
1.248453

STC2
3.90E − 09
0.000102
1.838371

SULF1
3.90E − 09
0.000102
2.003268

ATAD2
4.09E − 09
0.000107
1.500415

TMEM158
4.11E − 09
0.000107
1.212075

PPAT
4.12E − 09
0.000108
1.305250

SPP1
4.13E − 09
0.000108
2.205887

CXCL2
4.36E − 09
0.000114
1.826375

COL1A2
4.44E − 09
0.000116
1.882105

SLC7A11
5.29E − 09
0.000138
1.208023

TIMP1
5.69E − 09
0.000149
1.661168

MMP12
6.08E − 09
0.000159
1.933314

SCD
6.11E − 09
0.000160
1.564105

SOX9
6.43E − 09
0.000168
1.352557

CXCL9
6.92E − 09
0.000181
1.461166

HSPH1
8.01E − 09
0.000209
1.238503

GDPD5
8.12E − 09
0.000212
1.308392

CXCL8
8.27E − 09
0.000216
2.588362

COL1A1
8.38E − 09
0.000219
1.769908

FABP6
8.52E − 09
0.000223
2.079844

PMEPA1
8.52E − 09
0.000223
1.333566

ANXA9
9.83E − 09
0.000257
1.608179

TDGF1P3
1.15E − 08
0.000301
1.841176

CEP55
1.22E − 08
0.000319
1.470764

COL10A1
1.27E − 08
0.000331
1.780470

MCM10
1.37E − 08
0.000358
1.211513

MPP6
1.51E − 08
0.000396
1.432320

PROX1
1.58E − 08
0.000413
1.364230

TEAD4
1.65E − 08
0.000431
1.447381

CLDN2
1.72E − 08
0.000449
1.698260

ODAM
1.79E − 08
0.000468
1.471032

MTHFD2
1.83E − 08
0.000478
1.111261

KRT6B
1.85E − 08
0.000484
1.485079

HILPDA
1.87E − 08
0.000488
1.488635

DACH1
2.14E − 08
0.000559
1.610398

ESM1
2.17E − 08
0.000568
1.708858

CYP4X1
2.22E − 08
0.000581
1.382114

GNG4
2.25E − 08
0.000588
1.178933

ARID3A
2.35E − 08
0.000613
1.275140

PPBP
2.38E − 08
0.000621
1.748166

EPHX4
2.42E − 08
0.000633
2.381989

LRP8
2.67E − 08
0.000697
1.522085

PTP4A3
2.67E − 08
0.000697
1.183443

SERPINE2
2.67E − 08
0.000697
1.154912

AGT
2.81E − 08
0.000734
1.405610

C2
2.84E − 08
0.000743
1.372973

RRM2
2.86E − 08
0.000748
1.196973

NEBL
3.49E − 08
0.000912
1.521795

RPP40
3.49E − 08
0.000912
1.285132

ARNTL2
3.77E − 08
0.000984
1.419157

AHCY
3.84E − 08
0.001004
1.237807

GTF2IRD1
3.95E − 08
0.001032
1.371062

WNT2
4.00E − 08
0.001044
1.452032

TOP2A
4.04E − 08
0.001057
1.372624

PUS7
4.09E − 08
0.001069
1.249772

NMU
4.30E − 08
0.001122
1.284463

COL6A3
4.44E − 08
0.001160
1.270615

RNF43
4.57E − 08
0.001195
1.397306

COL5A1
4.69E − 08
0.001226
1.191502

CDK1
4.70E − 08
0.001228
1.317782

ETV4
4.86E − 08
0.001271
2.204983

PTGS2
5.04E − 08
0.001317
1.233879

CHEK1
5.20E − 08
0.001358
1.126745

MMP10
5.41E − 08
0.001414
1.195256

FZD3
5.52E − 08
0.001442
1.089222

UBE2C
5.81E − 08
0.001518
1.241558

RNASEH2A
6.16E − 08
0.001609
1.071208

ENC1
6.27E − 08
0.001637
1.375197

IGF2BP3
6.37E − 08
0.001665
1.082776

VSNL1
6.75E − 08
0.001764
1.786437

GALNT6
6.79E − 08
0.001774
1.266153

ERP27
6.97E − 08
0.001821
1.218536

PROCR
7.73E − 08
0.002020
1.376152

PAFAH1B3
7.88E − 08
0.002058
1.100418

RFC3
8.14E − 08
0.002126
1.372173

NKRF
8.14E − 08
0.002127
1.043072

CXCL5
9.69E − 08
0.002531
1.294918

SQLE
1.03E − 07
0.002700
1.308167

EXOSC5
1.08E − 07
0.002828
1.132422

S100P
1.13E − 07
0.002944
1.551099

ELOVL5
1.27E − 07
0.003331
1.176539

ZAK
1.29E − 07
0.003360
1.541699

TCFL5
1.45E − 07
0.003786
1.270140

LAPTM4B
1.67E − 07
0.004376
1.026874

CEBPB
1.70E − 07
0.004431
1.333258

SLCO4A1
1.83E − 07
0.004790
1.606904

TTK
1.89E − 07
0.004934
1.386804

TMEM97
1.98E − 07
0.005179
1.082142

CHI3L1
2.01E − 07
0.005239
1.621308

PPM1H
2.10E − 07
0.005475
1.353835

SORD
2.14E − 07
0.005591
1.037586

CST1
2.20E − 07
0.005747
1.894531

CENPF
2.46E − 07
0.006422
1.024242

GTF3A
2.47E − 07
0.006451
1.215577

APCDD1
2.59E − 07
0.006772
1.580775

KIF4A
2.59E − 07
0.006772
1.267447

MFAP2
2.65E − 07
0.006914
1.397671

CCNB1
2.77E − 07
0.007241
1.203637

GDF15
2.88E − 07
0.007514
1.563948

NXT1
2.93E − 07
0.007667
1.046948

SHMT2
2.95E − 07
0.007697
1.002641

SNTB1
2.96E − 07
0.007726
1.291950

TACSTD2
2.97E − 07
0.007754
1.640872

SNX10
2.99E − 07
0.007814
1.021758

SLC12A2
3.04E − 07
0.007933
1.134681

TNFRSF12A
3.13E − 07
0.008190
1.332447

SLC5A6
3.25E − 07
0.008488
1.064032

RUVBL1
3.26E − 07
0.008513
1.066507

FXYD5
3.32E − 07
0.008680
1.172860

ZNF239
3.35E − 07
0.008745
1.158161

REG1A
3.40E − 07
0.008876
1.168719

OLFML2B
3.57E − 07
0.009326
1.332885

ERCC6L
3.63E − 07
0.009472
1.325393

Downregulated DEGs

GUCA2B
4.94E − 21
1.29E − 16
−5.47325

GUCA2A
5.63E − 20
1.47E − 15
−4.98756

CA4
9.10E − 20
2.38E − 15
−4.93587

MS4A12
2.15E − 19
5.61E − 15
−5.22647

CA2
3.16E − 19
8.27E − 15
−4.64843

AQP8
7.18E − 19
1.88E − 14
−5.22336

CLCA1
1.67E − 17
4.37E − 13
−4.41598

CLCA4
2.30E − 17
6.01E − 13
−5.78604

AKR1B10
3.47E − 17
9.06E − 13
−3.66480

CA1
1.37E − 16
3.58E − 12
−4.85127

HSD17B2
1.42E − 15
3.72E − 11
−3.65299

GCG
1.86E − 15
4.87E − 11
−4.24507

CWH43
3.10E − 15
8.09E − 11
−3.35700

MT1M
6.63E − 15
1.73E − 10
−3.74527

CHP2
6.63E − 15
1.73E − 10
−3.59700

FCGBP
9.37E − 15
2.45E − 10
−3.35836

ADH1C
1.89E − 14
4.93E − 10
−3.66678

CD177
7.18E − 14
1.88E − 09
−3.56989

CA12
1.04E − 13
2.73E − 09
−2.48416

CHGA
1.04E − 13
2.73E − 09
−3.21194

SLC26A3
1.09E − 13
2.85E − 09
−3.26689

CEACAM7
1.16E − 13
3.02E − 09
−3.26188

PLAC8
1.75E − 13
4.58E − 09
−2.93747

CLDN8
1.79E − 13
4.67E − 09
−4.07661

PCK1
3.95E − 13
1.03E − 08
−2.41432

BEST2
5.80E − 13
1.52E − 08
−2.88517

GBA3
6.08E − 13
1.59E − 08
−3.15495

MT1H
7.81E − 13
2.04E − 08
−2.45079

HMGCS2
1.28E − 12
3.35E − 08
−2.63704

SI
1.53E − 12
3.99E − 08
−3.18335

GCNT3
1.86E − 12
4.87E − 08
−2.55705

EDN3
2.02E − 12
5.29E − 08
−2.36855

SLC26A2
2.58E − 12
6.74E − 08
−2.74311

TSPAN1
3.02E − 12
7.89E − 08
−2.43173

UGT2B17
3.26E − 12
8.51E − 08
−2.68448

BCAS1
3.43E − 12
8.96E − 08
−2.14870

LRRC19
4.61E − 12
1.20E − 07
−2.60514

LGALS2
5.96E − 12
1.56E − 07
−2.50281

ANPEP
8.52E − 12
2.23E − 07
−2.96251

VSIG2
8.52E − 12
2.23E − 07
−2.90829

MT1E
9.09E − 12
2.37E − 07
−2.30341

MT1G
9.49E − 12
2.48E − 07
−2.13170

NXPE4
9.66E − 12
2.53E − 07
−2.93788

KRT20
9.69E − 12
2.53E − 07
−2.12657

CA7
1.29E − 11
3.38E − 07
−2.72816

MT1F
1.32E − 11
3.45E − 07
−2.15911

KLF4
1.38E − 11
3.59E − 07
−2.38010

AHCYL2
1.43E − 11
3.74E − 07
−2.24169

ITLN1
1.74E − 11
4.55E − 07
−3.42798

HSD11B2
2.10E − 11
5.49E − 07
−2.34259

NR3C2
2.53E − 11
6.60E − 07
−2.37317

EPB41L3
2.57E − 11
6.72E − 07
−2.39867

ITM2C
2.67E − 11
6.97E − 07
−2.12884

MMP28
2.86E − 11
7.48E − 07
−1.93771

ZG16
3.09E − 11
8.06E − 07
−4.23120

ABCG2
3.75E − 11
9.80E − 07
−3.20141

SLC4A4
4.13E − 11
1.08E − 06
−3.28247

SCGB2A1
4.33E − 11
1.13E − 06
−2.48958

MALL
4.61E − 11
1.20E − 06
−2.29999

TUBAL3
4.92E − 11
1.29E − 06
−2.39086

RUNDC3B
5.33E − 11
1.39E − 06
−1.89913

NR1H4
5.50E − 11
1.44E − 06
−2.46055

SLC30A10
5.68E − 11
1.48E − 06
−3.22414

PIGR
6.05E − 11
1.58E − 06
−2.18018

MUC2
6.24E − 11
1.63E − 06
−2.62907

HPGD
6.74E − 11
1.76E − 06
−2.64497

TSPAN7
6.84E − 11
1.79E − 06
−2.30396

PAPSS2
1.30E − 10
3.39E − 06
−1.78856

SLC17A4
1.31E − 10
3.43E − 06
−2.31184

DHRS9
1.36E − 10
3.56E − 06
−2.74003

CAPN9
1.65E − 10
4.32E − 06
−1.77713

PTPRH
1.70E − 10
4.43E − 06
−1.93474

SST
1.81E − 10
4.73E − 06
−2.35448

HEPACAM2
1.82E − 10
4.76E − 06
−2.85009

SMPDL3A
2.03E − 10
5.31E − 06
−1.85516

UGT2A3
2.16E − 10
5.65E − 06
−2.42200

TRPM6
2.42E − 10
6.34E − 06
−2.40017

CES2
2.58E − 10
6.74E − 06
−1.80950

NR5A2
2.64E − 10
6.91E − 06
−1.65550

DHRS11
2.77E − 10
7.25E − 06
−2.40964

ENTPD5
2.88E − 10
7.52E − 06
−1.91140

PLCL2
2.98E − 10
7.79E − 06
−1.71267

MYO1A
2.98E − 10
7.79E − 06
−1.82841

BTNL8
3.28E − 10
8.58E − 06
−2.68136

PLCD1
3.40E − 10
8.88E − 06
−1.69423

ADTRP
3.71E − 10
9.69E − 06
−2.66899

ARL14
4.37E − 10
1.14E − 05
−2.10661

PDZD3
4.78E − 10
1.25E − 05
−1.73340

SCNN1B
5.25E − 10
1.37E − 05
−2.51853

MEP1A
5.25E − 10
1.37E − 05
−2.15953

C2orf88
5.40E − 10
1.41E − 05
−2.60606

GPA33
5.69E − 10
1.49E − 05
−1.85470

SCIN
6.20E − 10
1.62E − 05
−2.19971

MAOA
6.61E − 10
1.73E − 05
−1.79573

PIGZ
6.89E − 10
1.80E − 05
−1.67717

PYY
6.96E − 10
1.82E − 05
−2.53788

C4orf19
7.26E − 10
1.90E − 05
−1.57357

TNFRSF17
7.47E − 10
1.95E − 05
−2.36650

ADAMDEC1
7.47E − 10
1.95E − 05
−2.39500

TEX11
8.71E − 10
2.28E − 05
−2.24440

CDHR2
9.44E − 10
2.47E − 05
−1.71114

TMEM171
9.64E − 10
2.52E − 05
−2.11860

STMN2
1.28E − 09
3.36E − 05
−2.05581

PKIB
1.45E − 09
3.78E − 05
−2.98037

CLIC5
1.57E − 09
4.11E − 05
−1.55858

BMP2
1.60E − 09
4.19E − 05
−1.76798

RETSAT
1.63E − 09
4.26E − 05
−1.68389

PADI2
1.69E − 09
4.42E − 05
−2.10023

SLC9A2
1.80E − 09
4.70E − 05
−1.55732

CYP2C18
2.16E − 09
5.65E − 05
−1.71017

C1orf115
2.32E − 09
6.05E − 05
−1.74460

CEACAM1
2.42E − 09
6.31E − 05
−1.84011

PDE9A
2.44E − 09
6.37E − 05
−2.05665

SLCO2A1
2.58E − 09
6.75E − 05
−1.53024

BCAR3
2.63E − 09
6.87E − 05
−1.50716

METTL7A
2.65E − 09
6.92E − 05
−1.62515

DSC2
2.65E − 09
6.92E − 05
−1.44448

IL1R2
2.75E − 09
7.19E − 05
−2.16852

CCDC68
2.75E − 09
7.19E − 05
−1.89156

GDPD3
2.78E − 09
7.28E − 05
−2.08667

PDE6A
2.81E − 09
7.34E − 05
−1.93721

EPHX2
3.12E − 09
8.16E − 05
−1.49186

SPIB
3.35E − 09
8.76E − 05
−2.27898

ITPKA
3.41E − 09
8.91E − 05
−1.62863

SCUBE2
3.66E − 09
9.57E − 05
−1.51582

AMPD1
4.13E − 09
0.000108
−1.81548

SELENBP1
4.61E − 09
0.000120
−2.04099

TFCP2L1
5.10E − 09
0.000133
−1.16817

BTNL3
5.13E − 09
0.000134
−2.10512

VIPR1
5.45E − 09
0.000142
−1.57631

ZZEF1
5.53E − 09
0.000144
−1.27744

NAT2
5.61E − 09
0.000147
−1.61058

CDHR5
5.81E − 09
0.000152
−1.97788

PTGDR
6.44E − 09
0.000168
−2.08658

HHLA2
7.58E − 09
0.000198
−1.91952

PLCE1
7.60E − 09
0.000199
−1.63459

BEST4
7.87E − 09
0.000206
−2.37175

FUCA1
8.12E − 09
0.000212
−1.41919

FGFBP1
8.12E − 09
0.000212
−1.45128

MT1X
8.24E − 09
0.000215
−1.87572

SLC16A9
8.52E − 09
0.000223
−1.97426

SEMA6A
8.52E − 09
0.000223
−1.66469

FXYD3
8.58E − 09
0.000224
−1.39744

DEFB1
8.94E − 09
0.000233
−1.76340

ACADS
9.22E − 09
0.000241
−1.70299

UGDH
9.50E − 09
0.000248
−1.45862

HRASLS2
1.06E − 08
0.000276
−1.48365

CYP4F12
1.09E − 08
0.000284
−1.26493

JCHAIN
1.10E − 08
0.000288
−2.33454

ABHD3
1.13E − 08
0.000295
−1.54939

VWA5A
1.23E − 08
0.000321
−1.12976

LDHD
1.27E − 08
0.000332
−2.10259

XDH
1.28E − 08
0.000334
−1.53406

INSL5
1.29E − 08
0.000337
−2.42430

SPINK5
1.38E − 08
0.000359
−2.43812

VILL
1.50E − 08
0.000391
−1.41689

SQRDL
1.51E − 08
0.000396
−1.38225

FABP2
1.52E − 08
0.000398
−1.88374

LGALS4
1.61E − 08
0.000421
−1.42830

DNASE1L3
1.65E − 08
0.000431
−1.82008

SLC44A4
1.66E − 08
0.000434
−1.59136

GOLM1
1.68E − 08
0.000439
−1.20581

RNF186
1.72E − 08
0.000450
−1.21896

GDPD2
1.85E − 08
0.000484
−1.90186

SLC22A18AS
1.85E − 08
0.000484
−1.62665

MGLL
1.91E − 08
0.000499
−1.32867

B3GNT6
1.92E − 08
0.000501
−1.59112

IL6R
1.92E − 08
0.000502
−1.86011

RAPGEFL1
1.92E − 08
0.000502
−1.32000

TRPM4
1.94E − 08
0.000508
−1.34414

ETFDH
2.00E − 08
0.000522
−1.52397

TMPRSS2
2.05E − 08
0.000536
−1.39816

APOBR
2.08E − 08
0.000544
−1.69871

APPL2
2.11E − 08
0.000551
−1.46951

IQGAP2
2.17E − 08
0.000566
−1.55628

FAM83E
2.17E − 08
0.000568
−1.17487

ETHE1
2.29E − 08
0.000599
−1.61458

FLVCR2
2.39E − 08
0.000624
−1.01211

ALDH1A1
2.41E − 08
0.000631
−1.28928

DENND2A
2.51E − 08
0.000657
−1.32793

HRCT1
2.57E − 08
0.000671
−1.58787

ATP8B1
2.69E − 08
0.000703
−1.00964

A1CF
2.74E − 08
0.000715
−1.36877

ELOVL6
2.96E − 08
0.000773
−1.08274

C11orf86
3.02E − 08
0.000789
−2.11210

TMEM100
3.14E − 08
0.000821
−1.69604

ASAP3
3.27E − 08
0.000854
−1.44474

S100A14
3.29E − 08
0.000859
−1.21353

PHLPP2
3.42E − 08
0.000892
−1.45264

LIMA1
3.47E − 08
0.000907
−1.20588

FABP1
3.51E − 08
0.000916
−1.62316

FMO5
3.62E − 08
0.000947
−1.41152

LRMP
3.64E − 08
0.000950
−1.84646

UGP2
3.68E − 08
0.000961
−1.28750

ATP2A3
3.74E − 08
0.000978
−1.51001

SLC22A5
3.74E − 08
0.000978
−1.30616

FAM46C
3.78E − 08
0.000989
−1.41521

LXN
3.97E − 08
0.001037
−1.10935

CDKN2B
3.98E − 08
0.001039
−2.23550

HMOX1
3.99E − 08
0.001043
−1.23547

NEDD4L
4.03E − 08
0.001054
−1.23680

CLDN7
4.18E − 08
0.001093
−1.29905

PARM1
4.21E − 08
0.001099
−1.52095

HSD3B2
4.29E − 08
0.001120
−2.26639

KIAA0513
4.39E − 08
0.001146
−1.22322

ACADVL
4.50E − 08
0.001176
−1.23429

SYTL2
4.74E − 08
0.001238
−1.07640

SLC1A1
4.79E − 08
0.001251
−1.40176

CNTN3
4.81E − 08
0.001256
−2.13362

CNNM4
4.97E − 08
0.001300
−1.36807

CASP7
5.04E − 08
0.001317
−1.34691

GLTP
5.30E − 08
0.001385
−1.40471

PGM1
5.49E − 08
0.001435
−1.23037

ERN2
5.60E − 08
0.001464
−1.10843

NAAA
5.72E − 08
0.001494
−1.45647

GNA11
6.10E − 08
0.001593
−1.40416

CCL23
6.37E − 08
0.001665
−1.50079

STYK1
6.40E − 08
0.001673
−1.56848

CFD
6.56E − 08
0.001715
−1.80374

C15orf48
6.82E − 08
0.001783
−1.45026

OASL
6.89E − 08
0.001800
−1.13508

GPT
6.99E − 08
0.001827
−1.44289

CLDN23
7.12E − 08
0.001859
−1.93577

MB
7.62E − 08
0.001991
−1.42542

HIGD1A
7.65E − 08
0.002000
−1.40813

EMP1
8.37E − 08
0.002188
−1.46200

ADH1B
8.46E − 08
0.002211
−2.01430

LPAR1
8.49E − 08
0.002219
−1.26559

GPAT3
8.70E − 08
0.002272
−2.01808

CHGB
8.90E − 08
0.002324
−1.55043

P3H2
9.09E − 08
0.002376
−1.48288

PRKACB
9.28E − 08
0.002424
−1.46492

OSBPL1A
9.84E − 08
0.002572
−1.16940

SPINK4
1.04E − 07
0.002716
−1.69181

MT2A
1.04E − 07
0.002726
−1.55590

SULT1B1
1.07E − 07
0.002807
−1.66731

NAT1
1.08E − 07
0.002828
−1.17391

ADRA2A
1.11E − 07
0.002892
−1.54370

MEP1B
1.13E − 07
0.002960
−2.16734

LPCAT4
1.18E − 07
0.003077
−1.02702

PCSK7
1.18E − 07
0.003077
−1.00185

ST6GALNAC1
1.18E − 07
0.003088
−1.65355

SGK2
1.21E − 07
0.003160
−1.41158

GRAMD3
1.26E − 07
0.003287
−1.25518

RIOK3
1.27E − 07
0.003331
−1.21281

CITED2
1.29E − 07
0.003374
−1.11561

CXCL12
1.41E − 07
0.003684
−1.54414

ITM2A
1.45E − 07
0.003786
−1.47880

SEPP1
1.51E − 07
0.003956
−1.10723

KBTBD11
1.53E − 07
0.004006
−1.05544

RHOF
1.55E − 07
0.004040
−1.11757

GSTA1
1.57E − 07
0.004105
−1.34829

GALNT12
1.61E − 07
0.004214
−1.23449

BCHE
1.70E − 07
0.004446
−1.57410

HGD
1.78E − 07
0.004657
−1.24750

HOXD1
1.83E − 07
0.004793
−1.79058

FGL2
1.90E − 07
0.004977
−1.31339

ATP8A1
2.03E − 07
0.005307
−1.05015

HIST1H1C
2.06E − 07
0.005393
−1.25170

MYOT
2.10E − 07
0.005480
−1.72660

PBLD
2.11E − 07
0.005502
−1.58127

RBM47
2.15E − 07
0.005614
−1.08864

CES3
2.18E − 07
0.005700
−1.45856

CD1D
2.18E − 07
0.005704
−1.29631

FAM150B
2.19E − 07
0.005719
−1.50655

RARRES1
2.23E − 07
0.005819
−1.46770

LGALS9
2.24E − 07
0.005842
−1.01981

KLK1
2.27E − 07
0.005936
−1.29450

TOX
2.27E − 07
0.005942
−1.29132

ACVRL1
2.36E − 07
0.006175
−1.13083

CPT2
2.44E − 07
0.006372
−1.22566

SGK1
2.57E − 07
0.006718
−1.64587

ALPI
2.62E − 07
0.006834
−1.09455

SLC51B
2.65E − 07
0.006919
−2.27581

SLC20A1
2.71E − 07
0.007076
−1.03315

SCGN
2.74E − 07
0.007164
−1.86108

ASPA
2.75E − 07
0.007186
−1.45497

FAM107B
2.81E − 07
0.007337
−1.24898

AGR3
2.82E − 07
0.007358
−1.80558

HOXA5
2.85E − 07
0.007437
−1.09160

NPY1R
2.97E − 07
0.007754
−1.92966

TNFSF10
2.97E − 07
0.007754
−1.20956

FAM107A
3.18E − 07
0.008318
−1.40373

MUC4
3.26E − 07
0.008520
−1.83234

TST
3.31E − 07
0.008645
−1.30505

NXPE1
3.57E − 07
0.009326
−1.90526

SPON1
3.60E − 07
0.009400
−1.78810

ST6GALNAC6
3.78E − 07
0.009874
−1.79857

FC: fold change.

(a)

(b)

Figure 3: Top 50 robust DEGs identified in the six microarray data (i.e., GSE25070, GSE44076, GSE44861, GSE21510, GSE9348, and GSE21815) from the GEO database: (a) upregulated genes; (b)downregulated genes. These genes were ranked by .

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.

(a)

(b)

(c)

(d)

(e)

(f)

(g)

(h)

Figure 4: Aberrant expression of GUCA2A can be a candidate prognostic biomarker in CRC. (a) Venn diagram of the robust DEGs and genes in the red module revealed 19 interacting genes. Set 1: hub genes in the red module; Set 2: downregulated DEGs. (b) Five-year OS of CRC patients from the TCGA database (). Patients with a low expression of GUCA2A have a significantly shorter OS. (c) GUCA2A expression in the TCGA database. (d) GUAC2A expression between CRC tissues and normal tissues in the six datasets. (e) GUAC2A expression in different tissue histology. (f) CRC cells in GSE68468. (g) GUCA2A expression levels in human CRC and their matched adjacent normal tissues. (h) ROC curves of tissue GUCA2A expression for differentiating CRC tissue from normal tissue. .

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.

Figure 5: Pathway enrichment analysis for GUCA2A coexpressed genes.

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 [17–19]. 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) [22–31] 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.

Table 2: Detailed information on the six microarrays from the GEO database.

Reference
Tissue
GEO
Platform
Normal
Tumor

Hinoue et al. [22]
CRC
GSE25070
GPL6883
51
51

Cordero et al. [23–26]
CRC
GSE44076
GPL13667
148
98

Ryan et al. [27]
CRC
GSE44861
GPL3921
55
56

Tsukamoto et al. [28]
CRC
GSE21510
GPL570
25
123

Hong et al. [29]
CRC
GSE9348
GPL570
12
70

Mimori et al. [30, 31]
CRC
GSE21815
GPL6480
9
132

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.

Table 3: GUCA2A coexpressed genes in the TCGA database.

Genes
Correlation
value

GUCA2A
1
0

TMIGD1
0.81034948
6.78E − 145

CA4
0.78988642
1.02E − 132

GUCA2B
0.78006796
2.39E − 127

SCNN1B
0.76922102
9.92E − 122

CDKN2B-AS1
0.75976020
4.48E − 117

LINC00974
0.72826962
5.10E − 103

CA1
0.72589778
4.85E − 102

OTOP2
0.72524636
8.98E − 102

B3GNT7
0.69874083
1.59E − 91

CA2
0.69872813
1.60E − 91

CLDN8
0.69162255
5.79E − 89

CLCA4
0.68724381
2.01E − 87

MS4A12
0.67084612
6.81E − 82

CEACAM7
0.65697702
1.74E − 77

BTNL3
0.64672606
2.24E − 74

SLC25A34
0.64457072
9.77E − 74

C11orf86
0.64075941
1.28E − 72

CLDN23
0.64014863
1.93E − 72

EDN3
0.62755226
7.21E − 69

RP11-35P15.1
0.62358770
8.90E − 68

B3GALT5-AS1
0.62060619
5.75E − 67

SULT1A2
0.61591517
1.04E − 65

PYY
0.61445537
2.53E − 65

ZG16
0.61434490
2.71E − 65

ALPI
0.61109844
1.93E − 64

SLC25A47P1
0.60742616
1.73E − 63

BTNL8
0.60619878
3.58E − 63

BEST4
0.59344211
5.70E − 60

CD177
0.59322686
6.44E − 60

ST6GALNAC6
0.59074429
2.60E − 59

CDHR5
0.58538873
5.07E − 58

TRPM6
0.57770739
3.26E − 56

SEPP1
0.57757017
3.51E − 56

KRTAP13-2
0.57186209
7.21E − 55

SLC4A4
0.56853673
4.08E − 54

RP11-203J24.9
0.55828248
7.58E − 52

MYPN
0.55589062
2.50E − 51

RP11-202A13.1
0.54993651
4.66E − 50

SLC30A10
0.54640858
2.57E − 49

SLC26A3
0.54279335
1.45E − 48

SMPD1
0.54142083
2.77E − 48

CDKN2B-AS
0.53902904
8.55E − 48

RN7SKP127
0.53769807
1.59E − 47

HHLA2
0.53644205
2.86E − 47

TMEM82
0.53029307
4.85E − 46

LYPD8
0.52880179
9.56E − 46

CD177P1
0.52733551
1.85E − 45

SCNN1G
0.52521670
4.81E − 45

ABCC13
0.52358277
9.98E − 45

GDPD2
0.51942104
6.29E − 44

TTC22
0.51932599
6.56E − 44

B3GALT5
0.51777531
1.29E − 43

C2orf88
0.51755217
1.43E − 43

DHRS9
0.51539770
3.64E − 43

TSPAN1
0.51536862
3.69E − 43

INSL5
0.51280701
1.11E − 42

TMEM253
0.50785220
9.21E − 42

SDCBP2
0.50717257
1.23E − 41

SLC17A4
0.50515764
2.86E − 41

PLAC8
0.50131456
1.42E − 40

MMP28
0.49830422
4.90E − 40

PPY
0.49796769
5.62E − 40

PLA2G10
0.49555028
1.51E − 39

GLRA4
0.49493081
1.94E − 39

ITM2C
0.49231037
5.57E − 39

TRIM40
0.49190614
6.55E − 39

SMPDL3A
0.49188755
6.60E − 39

MALL
0.49120642
8.68E − 39

BCAS1
0.49019232
1.30E − 38

CDKN2B
0.48925853
1.89E − 38

SLC51B
0.48708976
4.46E − 38

BMP3
0.48536366
8.79E − 38

AMPD1
0.48202823
3.23E − 37

ENTPD5
0.47054777
2.56E − 35

PTPRH
0.46882533
4.87E − 35

RPL12P14
0.46759668
7.68E − 35

RP11-92A5.2
0.46652136
1.14E − 34

AC106869.2
0.46640139
1.19E − 34

MUC12
0.46578524
1.50E − 34

CASC18
0.46570335
1.55E − 34

PEX26
0.46071845
9.52E − 34

TMEM72
0.46026123
1.12E − 33

GLDN
0.45995677
1.25E − 33

TEX11
0.45994953
1.26E − 33

NAAA
0.45741196
3.13E − 33

TRANK1
0.45665719
4.10E − 33

SMIM6
0.45572985
5.71E − 33

LINC00507
0.44872641
6.70E − 32

AOC1
0.44821442
8.01E − 32

CDHR2
0.44663931
1.38E − 31

APPL2
0.44091022
9.81E − 31

TMPRSS2
0.43862005
2.12E − 30

CCDC152
0.43855903
2.17E − 30

TRPV3
0.43605403
5.02E − 30

NPY2R
0.43603750
5.04E − 30

GNA11
0.43389107
1.03E − 29

ACVRL1
0.43021941
3.44E − 29

SEMA6D
0.42947338
4.39E − 29

GPR15
0.42871749
5.62E − 29

RP1-117B12.4
0.40855340
3.19E − 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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