Original Article Differentially expressed genes profiling ...Clara, CA, USA) as a platform was used for analysis. Bioinformatics methods Two ClassDif: Two ClassDif was used to filter

Int J Clin Exp Med 2016;9(10):19313-19323www.ijcem.com /ISSN:1940-5901/IJCEM0029517

Original ArticleDifferentially expressed genes profiling in human esophageal squamous cell carcinoma: a small data of microarray and bioinformatics

Min Wang1,2,3,4,5, Changqin Xu6, Shuilong Guo1,2,3,4,5, Peng Li1,2,3,4,5, Shengtao Zhu1,2,3,4,5, Shutian Zhang1,2,3,4,5

1Department of Gastroenterology, Beijing Friendship Hospital, Capital Medical University, Beijing, China; 2National Clinical Research Center for Digestive Diseases, Beijing, China; 3Beijing Digestive Disease Center, Beijing, China; 4Faculty of Digestive Diseases, Capital Medical University, Beijing, China; 5Beijing Key Laboratory for Precancer-ous Lesion of Digestive Diseases, Beijing, China; 6Department of Gastroenterology, Provincial Hospital Affiliated to Shandong University, Jinan, Shandong, China

Received March 31, 2016; Accepted September 7, 2016; Epub October 15, 2016; Published October 30, 2016

Abstract: Esophageal squamous cell carcinoma (ESCC) is the predominant histologic type of esophageal cancer with high incidence and poor prognosis in China. Multiple heterogeneous genetic and epigenetic changes are de-tected frequently in ESCC. The purpose of this study was to identify potential differentially expressed genes (DEGs) and molecular biological processes in the occurrence and development of ESCC. An integrated analysis of microar-ray and bioinformatics technologies was use in the study. First, we constructed a small cDNA microarray dataset in eight cases of ESCC tissues compared with matched normal esophageal epithelium. Then, we performed a bioinformatics analysis by ways of Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, signal transduction pathways and gene co-expression networks. A total of 1208 genes DEGs in-cluding 529 up-regulated genes and 679 down-regulated genes were screened form this small microarray dataset. Gene function analysis showed that 347 of the functions of up-regulated DEGs and 203 down-regulated DEGs were explored, respectively. KEGG pathway analysis revealed that 52 and 51 signal transduction pathways were enriched in the up-regulated and down-regulated DEGs, respectively. Furthermore, some target DEGs (ie. PIK3R, JAM2, KIT, ITGA9, TJP1, ERBB3, PLCB4, ATCN1, FZD6 and HPGD) were examined which highly involved in ESCC tumorigenesis by the construction of gene-gene interaction network and gene co-expression network. Our findings provide the groundwork for understanding the molecular mechanism of ESCC with an integrated analysis of microarray and bioinformatics technologies.

Keywords: Esophageal squamous cell carcinoma, microarray, bioinformatics, differentially expressed genes

Introduction

Esophageal carcinoma (EC) is the sixth most prevalent malignancies, and the eight common cause of cancer related mortality worldwide [1]. China has a high incidence of EC, especially esophageal squamous cell carcinoma (ESCC), which is the predominant histological type [2, 3]. Despite obvious improvement of early diag-nosis and available combined therapy including surgical resection, chemotherapy, radio-therapy, the five-year survival rate of ESCC is still wandered 15% all the time, which threat-ened the people’s life and death [4]. The details of molecular mechanisms underlying ESCC is

complex and unclear. Multiple heterogeneous genetic and epigenetic changes are detected frequently and play roles in ESCC [5].

The microarray technology is a powerful tool for precisely, quickly and simultaneously ac- quiring information on expression of thousands of genes, and further allows a high-throughput identification of novel gene expression profiles as biomarkers in cancers [6, 7]. However, it gen-erates vast amounts of data; mining useful information from these data becomes current urgent problem. Bioinformatics, an emerging interdisciplinary combined with mathematics, computer science and biology, is more effective

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Differentially expressed genes profiling in human ESCC

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and suitable for genomics data mining to fur-ther understand the molecular mechanism of these DEGs in ESCC pathogenesis [8-10].

In the present study, we combined the two methods to identify potentially critical differentially expressed genes (DEGs) and molecular biological processes in ESCC tissues and compared with matched normal esophageal epithelium.

Materials and methods

Microarray dataset

Tissue samples: ESCC samples (the experi-ment group) and their matched normal epithe-lium tissues (the control group) were obtain- ed from eight patients with ESCC underwent oesophagectomy in Beijing Friendship Hospi- tal, Capital Medical University from May 2013 to September 2013 with informed consent. The squamous epithelial natural of all samples was confirmed by two dependent patho- logists. Matched normal tissues were taken at least 5 cm away from the tumor edge.

RNA isolation and microarray

Total RNA was extracted from the snap-frozen samples using TRIzol reagent according to the manufacturer’s protocol (Life Technology, Ro-

ckville, MD). The quantity and quality of total RNA were assessed with a NanoDrop® ND- 1000 (Sigma, USA). Affymetrix microarray chip U133 plus 2.0 Gene Chip (Affymetrix, Santa Clara, CA, USA) as a platform was used for analysis.

Bioinformatics methods

Two ClassDif: Two ClassDif was used to filter the DEGs for the control and experiment group. Due to limited number of testing samples, Random Variance Method (RVM) corrected t- test was conducted by a cut-off value of P < 0.05 and FDR (false discovery rate) < 0.05 [11, 12].

GO-analysis: GO-analysis was conducted to obtain targeted significant functions of the DEGs using Fisher testing and χ2 testing. This function analysis was according to the Gene Ontology (GO) which is the key functional clas-sification of NCBI, which can organize genes into hierarchical categories and uncover the gene regulatory network on the basis of biologi-cal process and molecular function [13, 14].

Pathway-analysis: Pathway-analysis was simul-taneously performed for the differentially expressed genes detected in the first step to

Table 1. The top twenty up-regulated DEGs Gene symbol Description P-value FDRMMP1 Matrix metallopeptidase 1 (interstitial collagenase) < 1e-07 < 1e-07MMP3 Matrix metallopeptidase 3 (stromelysin 1, progelatinase) 0.0000041 0.0000652SPP1 Secreted phosphoprotein 1 0.0000136 0.000142CTHRC1 Collagen triple helix repeat containing 1 0.0000032 0.0000545MMP12 Matrix metallopeptidase 12 (macrophage elastase) 0.0000052 0.0000783IL8 Interleukin 8 0.000086 0.000525CTHRC1 Collagen triple helix repeat containing 1 0.000006 0.0000857COL1A1 Collagen, type I, alpha 1 0.0000172 0.000166MMP10 Matrix metallopeptidase 10 (stromelysin 2) 0.000199 0.000989COL1A1 Collagen, type I, alpha 1 0.0000156 0.000154MAGEA3 Melanoma antigen family A, 3/melanoma antigen family A, 6 0.0056079 0.0129WDR66 WD repeat domain 66 < 1e-07 < 1e-07MMP13 Matrix metallopeptidase 13 (collagenase 3) 0.0015043 0.00449COL1A1 Collagen, type I, alpha 1 0.0001174 0.000661JUP Junction plakoglobin/keratin 17 0.0003991 0.0016LAMC2 Laminin, gamma 2 0.0001606 0.000831ADAM12 ADAM metallopeptidase domain 12 0.0000086 0.00011CXCL5 Chemokine (C-X-C motif) ligand 5 0.0008131 0.00274GAL Galanin prepropeptide 0.000433 0.0017POSTN Periostin, osteoblast specific factor 0.0004089 0.00164


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Table 2. The top twenty down-regulated DEGs Gene symbol Description P-value FDRCAPN14 Calpain 14 < 1e-07 < 1e-07CRISP3 Cysteine-rich secretory protein 3 < 1e-07 < 1e-07TMPRSS11B Transmembrane protease, serine 11B < 1e-07 < 1e-07MUC21 Mucin 21, cell surface associated < 1e-07 < 1e-07KRT4 Keratin 4 < 1e-07 < 1e-07CRNN Cornulin < 1e-07 < 1e-07SPINK7 Serine peptidase inhibitor, Kazal type 7 (putative) 0.0000009 0.0000195CLCA4 Chloride channel accessory 4 0.0000004 0.0000111MAL Mal, T-cell differentiation protein < 1e-07 < 1e-07CRNN Cornulin < 1e-07 < 1e-07KRT13 Keratin 13 0.0000001 0.0000038TGM3 Transglutaminase 3 (E polypeptide, protein-glutamine-gamma-glutamyltransferase) < 1e-07 < 1e-07SPINK5 Serine peptidase inhibitor, Kazal type 5 0.0000001 0.0000038SCEL Sciellin 0.0000012 0.0000246A2ML1 Alpha-2-macroglobulin-like 1 0.0000207 0.000187ENDOU Endonuclease, polyU-specific < 1e-07 < 1e-07SPRR3 Small proline-rich protein 3 0.0000287 0.000235FAM3B Family with sequence similarity 3, member B 0.0000005 0.000013SLURP1 Secreted LY6/PLAUR domain containing 1 0.0000083 0.000108HPGD Hydroxyprostaglandin dehydrogenase 15-(NAD) 0.0000002 0.00000629

identify the significant pathways based on the KEGG database. It contained genomic information with higher order functional informa-tion, which stored in the PATHWAY database [15-17].

Gene-gene interaction network: Gene-gene interaction network maps were constructed by using java that allows users to build and ana-lyze molecular networks. The considered evi-dence was the source of the interaction data-base from KEGG. Networks are stored and presented as graphs, where nodes were mainly genes (protein, compound, etc.) and edges rep-resent relation types between the nodes, e.g. activation or phosphorylation. The graph nature of Networks raised our interest to investigate them with powerful tools implemented in R. In gene networks, degree measures how corre- lated a gene was with all other network genes. For a gene in the network, the number of source genes of a gene was called the indegree of the gene and the number of target genes of a gene is its outdegree. The character of genes was described by betweenness centrality measures reflecting the importance of a node in a graph relative to other nodes [18, 19].

Gene coexpression networks: Gene coexpres-sion network was built according to the normalized signal intensity of specific expression genes. For each pair of genes, we calculated

the Pearson correlation and choose the signifi-cant correlation pairs with which to construct the network. Degree centrality and Clustering Coefficient were the most important measures of a gene centrality within a network that de- termining the relative importance. Degree cen-trality was defined as the link numbers one node has to the other. Clustering Coefficient is defined as the intensity of a gene and its neighboring genes. The Purpose of Network Structure Analysis was to locate core regulatory factors (genes). In one network, core regu-latory factors connect most adjacent genes and have the biggest degrees. While consider-ing different networks, Core regulatory factors were determined by the degree differences between two class samples. They always own the biggest degree differences [20, 21]. A p value < 0.05 was considered as significance.

Results

Identification of differentially expressed genes (DEGs) and hierarchical clustering analysis

A total of 1208 genes were screened to differ-entially expressed, of which 529 genes were up-regulated and 679 genes were down-regu-lated in the primary tissues compared with matched normal tissues. Tables 1 and 2 were listed the top up-regulated DEGs and the down-regulated DEGs, respectively.


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The heat map of hierarchical clustering of DEGs was seen in Figure 1. Here, these genes between ESCC tissues and matched normal tis-sues were significantly different.

Significant functions of differentially expressed genes

The detailed GO analysis identified 347 signifi-cant functions were found to be involved in up-regulated genes and 203 significant functions involved in down-regulation in ESCC tissues (P < 0.01). These genes were categorized into dif-ferent classes based on GO database.

The most significant functional process of up-regulated DEGs were extracellular matrix orga-nization and disassembly, collagen catabolic process, mitotic cell cycle (M phase, G1/S tran-sition), cell division, immune response, inflam-matory response, cell adhesion, cell prolifera-tion, response to virus and defense response, cytokine-mediated signaling pathways, cell- cell signaling, collagen fibril organization, type I interferon-mediated signaling pathway, and DNA replication (Figure 2A).

The most significant functional processes of down-regulated DEGs included small molecular metabolic processes, keratinocyte differentiation, epidemis development, xenobiotic met-abolic processes, arachidonic acid metabolic process, calcium-independent cell-cell adhe-sion, peptide cross-linking, epithelial cell differ-entiation, negative regulation of endopeptidase activity, signal transduction, positive regulation of transcription from RNA polymerase II pro-moter, negative regulation of peptidase activity, lipid metabolic processes, carnitine biosynth- etic process, cellular aldehyde metabolic pro-cess, negative regulation of epithelial cell pro- liferation, transmembrane transport, cyclooxy-genase pathway (Figure 2B).

Pathway

Based on KEGG database, 52 signal transduction pathways and 51 signal transduction

Figure 1. Hierarchical clustering analysis of differen-tially expressed genes in ESCC tissues (T) and normal esophageal epithelium (N) according to gene expres-sion profiles. Red and Green represent up-regulated genes and down-regulated genes, respectively. Black represents no change in gene expression between two groups.


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pathways were discovered to be associated with up-regulated genes and down-regulated genes, respectively (P < 0.01). The up-regulat-ed signal transduction pathways covered ECM-receptor interaction, Focal adhesion, amoebia-sis, cancer-related pathways, PI3K-Akt signal-ing pathway, small cell lung cancer pathway,

protein digestion and absorption, cell cycle, transcriptional misregulation in cancer, Toll-like receptor signaling pathway, cytokine-cytokine receptor interaction, phagosome, rheumatoid arthritis, NF-kappa B signaling pathway, leish-maniasis, regulation of actin cytoskeleton, sal-monella infection, chemokine signaling path-

Figure 2. GO functions Enrichment Analysis for DEGs. A. For up-regulated DEGs; B. For down-regulated genes Ab-scissa axis: the value of -LgP. Vertical axis: the significant GO function terms. -LgP: - lg 10 transformed of p-value.


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way, measles, and proteogly-cans in cancer (Figure 3A).

The down-regulated signal transduction pathways covered metabolic pathways, ara-chidonic acid metabolism, dr- ug metabolism by cytochrome P450, serotonergic synap- se, leukocyte transendothe- lial migration, retinol metabolism, beta-Alanine metabo-lism tight junction, metabo-lism of xenobiotics by cyto-chrome P450, chemical carci-nogenesis, histidine metabo-lism, fatty acid metabolism, cell adhesion molecules (CA- Ms), glycolysis/gluconeogene-sis, glycerolipid metabolism, tyrosine metabolism, mineral absorption, aldosterone-regu-lated sodium reabsorption, chemokine signaling pathway, amoebiasis (Figure 3B).

Analysis of gene-gene inter-action network

Gene-gene interaction net-work was constructed based on the data of differentially expressed genes (Figure 4). Some key DEGs determined by analyzing the between-ness centrality and the degree of each gene in the gene-gene interaction network were revealed in Table 3. As it shown that PPKCB, ITGB1, TJI and PLCB4 were the more important nodes and had more powerful ability to inter-act with other genes in the network.

Gene coexpression network

Gene coexpression network was built according to the normalized signal intensity of specific expression genes in the experimental group and the control group (supplemen-tary data). Based on the above gene coexpression net-work of DEGs in two groups,

Figure 3. The KEGG Pathway En-richment Analysis for DEGs. A. For up-regulated DEGs; B. For down-regulated genes Abscissa axis: the value of -LgP. Vertical axis: the significant pathways. -LgP: -lg 10 transformed of p-value.


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Figure 4. The gene-gene interac-tion network.


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Table 3. Parital important nodes of DEGs in gene-gene interaction networkGene symbol Description

Betweenness centrality Degree Indegree Outdegree

PRKCB Protein kinase C, beta 0.026239606 8 4 6ITGB1 Integrin, beta 1 (fibronectin receptor, beta polypeptide, antigen CD29 includes MDF2, MSK12) 0.017173828 23 23 4TJP1 Tight junction protein 1 (zona occludens 1) 0.015247486 10 10 8PLCB4 Phospholipase C, beta 4 0.010611205 9 8 3ACTN1 Actinin, alpha 1 0.009403161 7 7 7ITGB4 Integrin, beta 4 0.008510731 22 22 3PIK3R1 Phosphoinositide-3-kinase, regulatory subunit 1 (alpha) 0.008456314 11 10 3RRAS2 Related RAS viral (r-ras) oncogene homolog 2 0.007999216 2 1 1JAM2 Junctional adhesion molecule 2 0.007683601 3 3 2VAV3 Vav 3 guanine nucleotide exchange factor 0.002220191 2 2 2CALML3 Calmodulin-like 3 0.001893692 4 4 2MMP14 Matrix metallopeptidase 14 (membrane-inserted) 0.001730443 2 2 1EPN3 Epsin 3 0.001338644 5 5 5MET Met proto-oncogene (hepatocyte growth factor receptor) 0.001040442 6 4 3FZD10 Frizzled family receptor 10 0.001012146 4 2 2FZD6 Frizzled family receptor 6 0.001012146 4 2 2ERBB3 V-erb-b2 erythroblastic leukemia viral oncogene homolog 3 (avian) 0.000224196 3 2 2KIT V-kit Hardy-Zuckerman 4 feline sarcoma viral oncogene homolog 0.000224196 3 2 2ITGA3 Integrin, alpha 3 (antigen CD49C, alpha 3 subunit of VLA-3 receptor) 0.000206782 20 20 1ITGA6 Integrin, alpha 6 0.000206782 20 20 1ITGA8 Integrin, alpha 8 0.000206782 20 20 1ITGA9 Integrin, alpha 9 0.000206782 20 20 1CAV1 Caveolin 1, caveolae protein, 22 kDa 0.000130599 2 2 2FGFR3 Fibroblast growth factor receptor 3 7.18297E-05 3 3 1CXCL10 Chemokine (C-X-C motif) ligand 10 3.26499E-05 3 1 2CXCL11 Chemokine (C-X-C motif) ligand 11 3.26499E-05 3 1 2MMP2 Matrix metallopeptidase 2 (gelatinase A, 72 kDa gelatinase, 72 kDa type IV collagenase) 3.26499E-05 2 2 1ACVR1C Activin A receptor, type IC 6.52997E-06 2 2 1


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Table 4. Parital key DEGs in the gene relation network (Gene-Rel-Net)Gene symbol Description T_Degree T_K N_Degree N_K |DiffK|SLC27A6 Solute carrier family 27 (fatty acid transporter), member 6 46 1 1 0.058823529 0.941176471RAB11A RAB11A, member RAS oncogene family 4 0.086956522 17 1 0.913043478ABCA8 ATP-binding cassette, sub-family A (ABC1), member 8 45 0.97826087 2 0.117647059 0.860613811JAM2 Junctional adhesion molecule 2 40 0.869565217 1 0.058823529 0.810741688HNMT Histamine N-methyltransferase 39 0.847826087 2 0.117647059 0.730179028FMO2 Flavin containing monooxygenase 2 (non-functional) 36 0.782608696 1 0.058823529 0.723785166CD24 CD24 molecule 4 0.086956522 13 0.764705882 0.677749361CXCL12 Chemokine (C-X-C motif) ligand 12 39 0.847826087 3 0.176470588 0.671355499PIK3R1 Phosphoinositide-3-kinase, regulatory subunit 1 (alpha) 39 0.847826087 3 0.176470588 0.671355499CLDN5 Claudin 5 32 0.695652174 1 0.058823529 0.636828645CCNB2 Cyclin B2 5 0.108695652 12 0.705882353 0.597186701KIT V-kit Hardy-Zuckerman 4 feline sarcoma viral oncogene homolog 41 0.891304348 5 0.294117647 0.597186701CDS1 CDP-diacylglycerol synthase (phosphatidate cytidylyltransferase) 1 3 0.065217391 11 0.647058824 0.581841432IL18 Interleukin18 (interferon-gamma-inducing factor) 3 0.065217391 11 0.647058824 0.581841432ITGA9 Integrin, alpha 9 40 0.869565217 5 0.294117647 0.57544757ITPR2 Inositol 1,4,5-trisphosphate receptor, type 2 9 0.195652174 13 0.764705882 0.569053708TFPI Tissue factor pathway inhibitor 34 0.739130435 3 0.176470588 0.562659847CLDN4 Claudin 4 4 0.086956522 11 0.647058824 0.560102302NEGR1 Neuronal growth regulator 1 42 0.913043478 6 0.352941176 0.560102302CTSL1 Cathepsin L1 2 0.043478261 10 0.588235294 0.544757033C7 Complement component 7 41 0.891304348 6 0.352941176 0.538363171CCL4 Chemokine (C-C motif) ligand 4 8 0.173913043 12 0.705882353 0.531969309CYP4F3 Cytochrome P450, family 4, subfamily F, polypeptide 3 3 0.065217391 10 0.588235294 0.523017903CCL4L1 Chemokine (C-C motif) ligand 4-like 1/2 3 0.065217391 10 0.588235294 0.523017903NCF2 Neutrophil cytosolic factor 2 9 0.195652174 12 0.705882353 0.510230179FPR2 Formyl peptide receptor 2 1 0.02173913 9 0.529411765 0.507672634CADM1 Cell adhesion molecule 1 26 0.565217391 1 0.058823529 0.506393862DHRS3 Dehydrogenase/reductase (SDR family) member 3 4 0.086956522 10 0.588235294 0.501278772HMGA2 High mobility group AT-hook 2 4 0.086956522 10 0.588235294 0.501278772RRM2 Ribonucleotide reductase M2 7 0.152173913 11 0.647058824 0.49488491T = The Experiment group, N = The Control group, K = Relative degree.


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the core regulatory genes were determined according to the coexpression changes of a gene, which was measured by the degree dif-ferences between the two class samples. These core DEGs including SLC27A6, ABCA8, NEGR1, CDC20, KIT and JAM2 always own the biggest degree differences in the gene coex-pression network (Table 4).

Finally, combined with the above two networks, PIK3R, JAM2, KIT, ITGA9, TJP1, ERBB3, PLCB4, ATCN1, FZD6 and HPGD) were explored as tar-gets genes in the future research on ESCC.

Discussion

ESCC is estimated to be one of the most prevalent gastrointestinal malignancies and highly aggressive with poor prognosis in China. Study on the pathogenesis is great of signifi-cance for diagnosis and therapy of ESCC in the long ran. It is regarded that the formation and development of cancer including ESCC is a multi-step dynamic biological process with alteration of many tumor-associated genes.

Microarray technology is a powerful tool that can realize high-throughput screening of genes and provide a great number of basic informa-tion on the expression of these genes. Applica- tion of bioinformatics technique takes advan-tages of mining data form such a large amount of basic data, selectively exploring potential differentially expressed genes, biological func-tions, pathways and interconnections with each other. In our study, we used both normal and tumor tissues with ESCC patients, and per-formed an integrated analysis of microarray and bioinformatics techniques for exploring the molecular mechanism of ESCC initiation and development.

Our findings suggested that 1208 genes were identified as differentially expressed in ESCC tissues, consisting of 529 up-regulated genes and 679 down-regulated genes. These DEGs screened in this study were involved in many biological processes. GO enrichment indicated that up-regulated genes were significantly enriched in processes, such as cell matrix organization, cell matrix disassembly, collagen catabolic process, mitotic cell cycle, cell division, while down-regulated DEGs were enriched in small molecular metabolic process keratino-cyte differentiation aracidonic acid metabolic processes. Further KEGG pathway analysis re-

vealed that 52 signal transduction pathways and 51 signal transduction pathways were sig-nificant enriched in the up-regulated DEGs and down-regulated DEGs, respectively. Path- ways including ECM-receptor interaction, focal adhesion, and pathway in cancer archidomic acid metabolism were likely to essential roles in ESCC tumorgenesis. In addition, signal net-work and co-expression network of DEGs al- lowed us to investigate potential target DEGs, including PIK3R, JAM2, KIT, ITGA9, TJP1, ERB- B3, PLCB4, ATCN1, FZD6 and HPGD closely associated with ESCC.

Admittedly, this present work is limited by small samples size. Besides, information on the expression of DEGs merely stays at an mRNA expression level without experimental validation.

In conclusion, our findings provided the ground-work for understanding the molecular mecha-nism of ESCC with integrated analysis of microarray and bioinformatics technologies. Fur- ther experiments of these target genes are underway, and further studies are necessary to for improving the early diagnosis, therapy and prognosis of ESCC.

Acknowledgements

This study was supported by the National Na- tural Science Foundation of China (Grant No. 81272447) and Beijing Municipal Administra- tion of Hospitals Clinical Medicine Develop- ment of Special Funding Support (ZY201308).

Disclosure of conflict of interest

None.

Address correspondence to: Dr. Shutian Zhang, De- partment of Gastroenterology, Beijing Friendship Hospital, Capital Medical University, 95 Yong’an Road, Xicheng District, Beijing 100050, China. Tel: 86-10-63139206; Fax: 86-10-63139206; E-mail: [email protected]

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Documents

Original Article Differentially expressed genes profiling ...Clara, CA, USA) as a platform was used for analysis. Bioinformatics methods Two ClassDif: Two ClassDif was used to filter