· Web viewThese genes also deregulated a number of biological pathways including: Cell cycle,DNA replication, Fanconi anemia pathway, p53 signaling pathway, Homologous recombination,

Bioinformatics analysis and identification of potential genes related to

pathogenesis of cervical intraepithelial neoplasia

Xue Zhang#1, Jian Bai#2, Cheng Yuan1 ,Long Long1, Zhewen Zheng1, Qingqing Wang1,

Fengxia Chen1 , Yunfeng Zhou1△

1Department of Radiation and Medical Oncology, Zhongnan Hospital, Wuhan

University, Wuhan, Hubei 430071, P.R. China2Department of Gastrointestinal Surgery and Department of Gastric and Colorectal

Surgical Oncology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China

#contributed equally to this work.

△Correspondence to: Pro. Yunfeng ZhouInstitution: Department of Radiation and Medical Oncology, Zhongnan Hospital,

Wuhan University Email: [email protected]

Abstract

The aim of this study was to explore and identify the key genes and signal

pathways contributing to cervical intraepithelial neoplasia (CIN). The gene expression

profiles of GSE63514 were downloaded from Gene Expression Omnibus database.

Differentially expressed genes (DEGs) were screened performing with packages in

software R. After Gene ontology terms, Kyoto Encyclopedia of Genes and Genomes

(KEGG) pathway enrichment analyzing, and Gene set enrichment analysis (GSEA),

weighted gene co-expression network analysis (WGCNA) was used to analyze these

genes. Then sub-modules were subsequently analyzed base CIN grade, and protein–

protein interaction (PPI) network of DEGs were constructed. 537 DEGs were

screened in total, consisting 331 up-regulated genes and 206 down-regulated genes in

CIN samples compared to normal samples. The most DEGs were enriched in

chromosomal region in cellular component (CC), organelle fission inbiological

process (BP) and ATPase activity in molecular function (MF). KEGG pathway

enrichment analyzing found the DEGs were mainly concentrated in 10 pathways. The

results of GSEA mainly enriched in 4 functional sets: E2F-Targets, G2M-Checkpoint,

Mitotic-Spindle and Spermatogenesis. A total of 6 modules were identified by

WCGNA. Subsequently, grey module was the highest correlation (Cor=0.78, P=5e-

22) and 31 genes were taken as candidate hub genes for CIN high grade risk

(weighted correlation coefficients >0.80). Finally, diagnostic analysis showed that in

addition to CCDC7, the expression levels of the remaining 13 DEGs have a high

diagnostic value (AUC>0.8 and P<0.05). These findings provided a new sight into the

understanding of molecular functions for CIN.

Keywords: cervical intraepithelial neoplasia, bioinformatical analysis, microarray,

differentially expressed genes

Introduction

The formation of cervical cancer is a continuous process from inflammation to

cervical intraepithelial neoplasia (CIN), and finally to invasive cancer, which takes 10

to 25 years1-3. CIN is regarded as a potentially premalignant transformation of

squamous cells of the cervix. According to the composite data for the natural history

of CIN, CIN1 is likely to regress in 60% of cases, persist in 30%, progress to CIN 3 in

10%, and progress to invasion in 1%4. Two high-risk HPV subtypes (types 16 and 18)

themselves produce two proto-oncoproteins, E6 and E7, which are key to their

disease5.

In recent years, many studies have focused on the diversity or heterogeneity of

various solid tumor types6,7. Gene expression network patterns are more complex in

cancer cells and tumors than in normal cells and organs8-11. In the study of Banerji et

al8, signaling entropy has been found to be significantly higher in cancer cells,

especially cancer stem cells, than in normal cells, thereby helping to distinguish them.

In this study, genes from CIN and normol samples were analyzed and screened

for differentially expression, from microarray datasets (GSE63514), using

bioinformatics. Functions and signal pathway enrichments of differentially expressed

genes (DEGs) were analyzed. Moreover, WGCNA explored the genes modules were

associated with CIN grade. Finally, identifying the biological function of the hub

genes and pathways, this study may offer a better insight of potential molecular

mechanisms to explore novel therapeutic strategies for CIN.

Methods

Data Procession

The gene expression profiles of GSE63514

(https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE63514) submitted by den

Boon J et al. was downloaded from the Gene Expression Omnibus (GEO) database.

The GSE63514 was an expression profiling based on GPL570 platform (Affymetrix

Human Genome U133 Plus 2.0 Array) and contained 128 samples (24 normal

samples, 76 CIN samples and 28 cervical cancer samples). All samples were taken

from flash-frozen biopsy and cryosectioned. This study mainly focused on the

Screening for differentially expressed genes (DEGs) between CIN and normal

samples, therefore, the 28 cervical cancer samples were not included.

Prior to bioinformatics analysis, we first mapped the array probes to the

respective Gene ID by using the array annotations. If a probe matches multiple genes,

the probe will be deleted. If a gene matches multiple probes, we will calculate its

average value. A proper threshold was settled based on the amount of genes filtered

out. A workflow of this study was indicated in Fig. 1.

Analysis of microarray datasetsLimma package12 in R/Bioconductor software was used to compare CIN sample

with its normal sample. In addition, normalization and log2 conversion were carried

out for each GEO dataset to filter out the final DEGs. The filtration conditions are as

follows: |log2FC| ≥1 and adjust P‐value(AdjP-value) < 0.05.

Enrichment analysis of gene function and pathwaysThe ClusterProfiler is an ontology-based R package, it applies the biological

terms classification and enrichment analysis to the comparison of gene clusters to

better understand the higher order functions of biological system13. DAVID14

(http://david.abcc.ncifcrf.gov/), a common functional annotation tool of

bioinformatics resources was utilized to distinguish the biological attributes such as

biological process (BP), cellular component (CC) and molecular function (MF) of

important DEGs. Moreover, Kyoto Encyclopedia of Genes and Genomes (KEGG)15

(http://www.genome.jp/kegg/) pathway enrichment analysis was used to discern the

crucial pathways significantly. AdjP-value <0.05 was set as the cut-off criterion for

the significant enrichment.

Gene set enrichment analysis (GSEA)

The enrichment analyses were conducted to detect whether a series of priori

defined biological processes was enriched. The enriched pathways were arranged in

the order of their normalized enrichment scores (NESs), and FDR < 0.05 was chosen

as the cut-off criteria.

Construction of gene co-expression network Firstly, the quality of the DEGs of GSE63514 was checked through R package.

Then, the scale-free gene co-expression network was constructed through the

“WGCNA” package. Pearson’s correlation matrices were calculated and a weighted

adjacency matrix was constructed through a power function amn = |cmn|β (cmn

means Pearson's correlation between gene m and gene n; amn = adjacency between

gene m and gene n). Afterwards, the most appropriate soft - thresholding parameter

(β) was chosen to transform the adjacency matrix into a topological overlap matrix

(TOM), so that modules including similar genes were identified. Module eigengenes

(MEs) was defined as the most principal component and clarify all genes into a single

characteristic expression profile. The correlation between module exigencies (MEs)

was defined as the dominating component of gene module and clinical traits to

identify the correlative module. The module highly related to given clinical

characteristics was selected for further analysis.

Results

Identification of DEGsThe clinical parameters are shown in Tab S1. A total of 100 tissues were divided

into 76 CIN and 24 normal samples in GSE53757. After integrated analysis, 537

DEGs (|log2 FC| ≥ 1 and AdjP-value < 0.05) were screened in total, consisting 331

up-regulated genes and 206 down-regulated genes in CIN samples compared to

normal samples. Volcano plots (Fig. S1) were visualized to show the correlation

between DEGs.

GO, pathway enrichment analysis and GSEA of DEGsAll DEGs were uploaded to the online website DAVID to discern GO

classfication. The terms for each GO category were shown in Fig 2 and Fig S2. The

most DEGs were enriched in chromosomal region in CC (Fig. 2A), organelle fission

in BP (Fig. 2B) and ATPase activity in MF (Fig. 2C). The results of pathway

enrichment analysis were shown in Fig. 3.

To identify potential function of the hub genes, GSEA was conducted

respectively to search “All gene sets” enriched in the samples with the gene highly

expressed. The DEGs are mainly enriched in 4 functional sets: E2F-Targets, G2M-

Checkpoint, Mitotic-Spindle and Spermatogenesis (Fig. 4 and 5).

Co-expression network construction and key modules identification

The DEGs with similar expression patterns were grouped into modules via the

average linkage hierarchical clustering, calculated by “WGCNA” package. A total of

6 modules were identified (Fig. 6A). Subsequently, we calculated the correlation

between gene module and CIN grade. Grey module has the highest correlation

(Cor=0.78, P=5e-22; Fig. 6B). Therefore, 31 genes with the high connectivity in grey

module were taken as candidate hub genes for CIN high grade risk in the module

(weighted correlation coefficients >0.80, Tab S2). The analysis of protein interaction

network suggested that 14 of these genes might interact more closely in CIN

classification (Fig. 7 and Tab S3). Diagnostic analysis results showed that in addition

to CCDC7, the expression levels of the remaining 13 genes have a high diagnostic

value (AUC>0.8 and P<0.05; Fig. 8).

Conclusion

DEGs in CIN samples can be used to diagnose the progressing disease before it

leads to cancer. A combinatorial approach utilizing gene expression profile, PPI

network, hubs, modules and motifs was employed to identify potential prognostic

markers capable of distinguishing progressing cervical disease. A total of 537 DEGs

(331 up-regulated genes and 206 down-regulated genes) were identified in CIN

samples by gene expression profiling. These genes also deregulated a number of

biological pathways including: Cell cycle, DNA replication, Fanconi anemia

pathway, p53 signaling pathway, Homologous recombination, Oocyte meiosis,

Mismatch repair, Pyrimidine metabolism, Progesterone-mediated oocyte

maturation and Drug metabolism - other enzymes. In addition, 4 functional gene sets

were enriched: E2F-Targets, G2M-Checkpoint, Mitotic-Spindle and Spermatogenesis.

31 DEGs out of 537 were found as candidate hub genes for CIN high grade risk.

Among them, 13 genes might interact more closely in CIN classification and have a

high diagnostic value.

The most DEGs were enriched in chromosomal region in CC, organelle fission in

BP and ATPase activity in MF. Chromosomal instability is a crucial sign of

malignancy. Kudela E et al16 focused on chromosomal changes in the process of

cervical carcinogenesis and CIN. This study indicated the amplification of

chromosomal regions increases with the degree of dysplasia toward the invasive

disease. Increasing in the amplification of 3q26 is noticeable already at CIN

2 + lesions, and 5p15 amplification is shifted up toward CIN 3. At present, organelle

fission focuses on mitochondrial fission. Mitochondria are highly dynamic organelles,

and mitochondrial fission is a crucial step of apoptosis17. Mitochondrial fragmentation

is involved in the apoptotic process of cervical cancer17. However, whether this is

related to CIN has not yet been clarified. As a condition in which cells change their

chromosomal content at a high rate, chromosomal instability is a consistent feature of

the majority of solid tumours18, and chromosomal instability plays an important role

in cervical disease, and is significantly associated with patient outcome. KEGG

results showed that most DEGs enrichment pathways were related to cell cycle. Ki67

is a marker of cell proliferation, and the increased expression of Ki67 is correlated

with higher cervical CIN grade and is a highly sensitive biomarker for differentiating

between CIN1 and CIN2/319,20. In addition, high-risk HPV E7 oncoproteins bind and

inactivate pRb, leading to abnormal cell proliferation21.

Previous studies have focused on different types of solid tumors (cervical cancer),

such as genetic instability at gene locus 1p36, which may be a feature of cervical

cancer22; decreased expression of cytokeratin 7 may lead to poor prognosis of cervical

cancer23; HPV infection is an important potential biomarker of cervical cancer24;

neutrophil ratio and white matter cell count can be used as a prognostic factor for

recurrence of cervical cancer25. However, the continuous process from inflammation

to CIN to invasive cancer is often overlooked. Since CIN is the most important

precancerous lesion of cervical cancer, we focus more on the progress from normal

cervical epithelial tissue to CIN, which is closely related to the occurrence and

progression of cancer. Our results show that TOP2A and RFC4 play an important role

in this process. TOP2A is regarded as a biomarker for the improved diagnosis of

CIN26. Recent study has shown that TOP2A protein is expressed in cells with aberrant

S-phases and including HPV-transformed cells in association with elevated expression

of the HPV E6/E7 proteins27. It is worth noting that many studies have shown that

TOP2A expression level is significantly correlated with CIN grade26,28. In addition,

RFC4 accelerated G1 to S phase progression, and promoted the proliferation of

cervical cancer cells and the growth of cervical cancer29. However, our study screened

13 DEGs related to CIN grade. At present, there is not enough evidence to support the

association with CIN grade except TOP2A and RFC4. The research of gene

bioinformatics provides a possible molecular targeting mechanism for the treatment of

progressive cervical diseases. Therefore, subsequent studies will focus on validating

these DEGs.

The limitation of this study is that the data used in this study are from public

databases, so the quality cannot be evaluated. In addition, we did not further study the

differential expression of CIN to cervical cancer.

Sum up, this study used bioinformation-based methods to reveal DEGs related to

CIN. This study is a gene analysis with a large sample size that integrates microarray

data from GEO databases. Then the functional and pathway enrichment analysis of

DEGs was carried out. In addition, the WGCNA method was used to analyze the

clinical data graed related to CIN. Therefore, this research provided a new sight into

the understanding of molecular functions for CIN. However, further experiments are

required to confirm and validate these predicted results.

Acknowledgments

National Natural Science Foundation Youth Project (81701768)

Conflict of interest

The authors report no conflicts of interest.

Reference

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Figure Legends

Figure 1 The flowchart of the integrated analysis and functional validation.

Figure 2 GO analysis and the significantly terms of differentially expressed genes

(DEGs) in CIN.

Figure 3 Significantly signaling pathway analysis of differentially expressed genes

(DEGs) related to CIN performing with KEGG pathway website and software R. (A)

The network of pathways and genes, blue represents pathways, green is the down-

regulated gene, red is the up-regulated gene. (B) Pathway enrichment analysis based

on differentially expressed genes (DEGs). GeneRatio = count/setsize.

Figure 4 GESA Constructs function set and genes network. Yellow represents

functional sets, the number on the outer edge of the network represents entrezID.

Figure 5 Gene set enrichment analysis (GSEA). (A) E2F-Targets (B) G2M-

Checkpoint (C) Mitotic-Spindle (D) Spermatogenesis

Figure 6 Results of the co-expression network.(A) Dendrogram of the differentially

expressed genes (DEGs) of GEO datasets clustered. (B) The correlation between the

module eigengenes and the CIN grade.

Figure 7 Protein–protein interaction (PPI) network of differentially expressed genes

(DEGs)

Figure 8 ROC diagnosis analysis of the differentially expressed genes (DEGs) for

CIN

Figure S1 Volcano plot of differentially expressed genes (DEGs). Red, green and

black denote up-regulated, down-regulated and non-significantly expressed genes,

respectively.

Figure S2 Gene and functional network constructed by GO enrichment analysis.

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Figure 7

Figure 8

Figure S1

Figure S2

Table S1 Clinical evaluation of cervical specimens

Normal (n=24) CIN1 (n=14) CIN2 (n=22) CIN3 (n=40)

Median age 28.5 27 24 30.5

HPV genotype

HPV 16 16 (16.2) 12 (12.1) 20 (20.2) 32 (32.3)

HPV 18 1 (25.0) 0 0 2 (50.0)

other high-risk HPV 5 (25.0) 2 (10.0) 2 (10.0) 6 (25.0)

other HPV 2 (40.0) 0 0 1 (20.0)

Study cytology

HSIL+ 16 (14.3) 13 (11.6) 22 (19.6) 37 (32.1)

LSIL 3 (50.0) 1 (16.7) 0 2 (33.3)

ASC-US 2 (40.0) 0 0 2 (40.0)

Normal 3 (100.0) 0 0 0

Missing 0 0 0 0

Colposcopy impression

Cancer 0 0 1 (3.2) 8 (25.8)

High grade 7 (29.2) 0 7 (29.2) 9 (37.5 )

Low grade 13 (23.6) 11 (20.0) 14 (25.5) 15 (25.5)

Metaplasia & Equivocal 3 (25.0) 2 (16.7) 0 6 (50.0)

Normal 1 (100.0) 0 0 0

Missing 0 1 (20.0) 0 3 (60.0)

HSIL = high grade squamous intraepithelial lesion; LSIL = low grade squamous intraepithelial lesion; ASC-US = atypical

squamous cells of undetermined significance

Roemeling C A V , Radisky D C , Marlow L A , et al. Neuronal pentraxin 2 supports clear cell renal cell carcinoma by activating the AMPA-selective glutamate receptor-4[J]. Cancer Research, 2014, 74(17):4796-810.

Table S2 31 genes with the high connectivity in grey module were taken as candidate hub genes for CIN high grade risk

GenecolorNEW

kMEblue kMEbrown kMEgreen kMEgrey kMEturquoise kMEyellow

SYCP2 grey -0.31933 0.813579 0.748354 0.902993 0.792075 0.674302TCAM1P grey -0.56294 0.687622 0.70513 0.890289 0.765379 0.66542DCLRE1B grey -0.57014 0.753084 0.756259 0.87688 0.789789 0.769592CDC7 grey -0.26862 0.860968 0.809275 0.872573 0.84401 0.704296TOPBP1 grey -0.30903 0.832814 0.742776 0.863863 0.871869 0.642696ACTL6A grey -0.60488 0.653026 0.693032 0.86191 0.78691 0.641038CKLF grey -0.48131 0.737526 0.716072 0.86049 0.822085 0.667799C18orf54 grey -0.5212 0.699997 0.663395 0.859988 0.783101 0.671595CENPQ grey -0.62882 0.688158 0.671624 0.858939 0.817186 0.651964ATAD2 grey -0.23736 0.772421 0.788452 0.8506 0.79 0.547155PRIM2 grey -0.34016 0.786596 0.746414 0.848956 0.796391 0.641971RAD54L grey -0.30243 0.886984 0.84222 0.842679 0.750092 0.825727EXO1 grey -0.63054 0.740341 0.723948 0.840726 0.760152 0.790412KIAA0101 grey -0.51815 0.739371 0.812399 0.83823 0.820286 0.697761HENMT1 grey -0.14286 0.777 0.733399 0.837402 0.73288 0.58583TOP2A grey -0.47398 0.750826 0.848469 0.834757 0.841481 0.689668NCAPH grey -0.40566 0.811809 0.873767 0.826243 0.818959 0.701439ZWILCH grey -0.31397 0.783499 0.825587 0.825152 0.823406 0.622159LINC00925 grey -0.42662 0.649481 0.661172 0.823625 0.630425 0.627539CENPO grey -0.39311 0.832394 0.781135 0.820124 0.715549 0.843433ZFR2 grey -0.38531 0.633341 0.665261 0.818176 0.741588 0.468172RIBC2 grey -0.71674 0.551642 0.599487 0.816547 0.654397 0.6715SYNGR3 grey -0.53921 0.535436 0.609433 0.814336 0.677193 0.509219ASPM grey -0.46706 0.67391 0.728141 0.814137 0.717365 0.671502LOC375196 grey -0.2865 0.664535 0.677685 0.814128 0.662071 0.560577RFC4 grey -0.09753 0.868167 0.855795 0.813682 0.8086 0.610759DHFR grey -0.18802 0.815199 0.854834 0.810412 0.830256 0.555871CDKN2A grey -0.32164 0.646347 0.717055 0.806906 0.749467 0.439761MLF1 grey -0.25365 0.691977 0.622876 0.804038 0.674678 0.53789CDC45 grey -0.21746 0.869529 0.888799 0.803326 0.789017 0.706893

Table S3 Protein to Protein Interaction with high interaction score

node1 node2neighborhood_on

_chromosomegene_fusion

phylogenetic_

cooccurrencehomology coexpression

experimentally_dete

rmined_interactiondatabase_annotated

CENPO CENPQ 0 0 0 0 0.107 0.917 0.9

CDC45 CDC7 0 0 0 0 0.431 0.264 0.9

PRIM2 CDC45 0 0 0 0 0.863 0 0.9

PRIM2 RFC4 0 0 0 0 0.874 0.157 0.9

RFC4 EXO1 0 0 0 0 0.74 0.186 0.9

RFC4 TOPBP1 0 0 0 0 0.589 0.18 0.9

CDC45 TOPBP1 0 0 0 0 0.26 0.829

EXO1 TOPBP1 0 0 0 0 0.271 0 0.9

RFC4 KIAA0101 0 0 0 0 0.757 0 0.9

TOP2A ASPM 0 0 0 0 0.967 0

TOP2A KIAA0101 0 0 0 0 0.949 0

TOP2A NCAPH 0 0 0 0 0.775 0.572

CENPO ZWILCH 0 0 0 0 0.098 0 0.9

CENPQ ZWILCH 0 0 0 0 0.194 0 0.9

CDC45 EXO1 0 0 0 0 0.902 0

CDC45 RFC4 0 0 0 0 0.852 0.064

PRIM2 CDC7 0 0 0 0 0.213 0 0.9

RAD54L EXO1 0 0 0 0 0.75 0.147

ASPM EXO1 0 0 0 0 0.918 0.104

ASPM NCAPH 0 0 0 0 0.903 0

ASPM KIAA0101 0 0 0 0 0.903 0