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Mathematical Biosciences and Engineering

2023, Volume 20, Issue 6: 11313-11327. doi: 10.3934/mbe.2023501
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Research article Special Issues

Efficient motion capture data recovery via relationship-aggregated graph network and temporal pattern reasoning

  • 1.
    Information Center, Xiamen Medical College, Xiamen, China
  • 2.
    Department of Artificial Intelligence, Huaqiao University, Xiamen, China

  • Academic Editor: Yiu-ming Cheung

  • Human motion capture (mocap) data is of crucial importance to the realistic character animation, and the missing optical marker problem caused by marker falling off or occlusions often limit its performance in real-world applications. Although great progress has been made in mocap data recovery, it is still a challenging task primarily due to the articulated complexity and long-term dependencies in movements. To tackle these concerns, this paper proposes an efficient mocap data recovery approach by using Relationship-aggregated Graph Network and Temporal Pattern Reasoning (RGN-TPR). The RGN is comprised of two tailored graph encoders, local graph encoder (LGE) and global graph encoder (GGE). By dividing the human skeletal structure into several parts, LGE encodes the high-level semantic node features and their semantic relationships in each local part, while the GGE aggregates the structural relationships between different parts for whole skeletal data representation. Further, TPR utilizes self-attention mechanism to exploit the intra-frame interactions, and employs temporal transformer to capture long-term dependencies, whereby the discriminative spatio-temporal features can be reasonably obtained for efficient motion recovery. Extensive experiments tested on public datasets qualitatively and quantitatively verify the superiorities of the proposed learning framework for mocap data recovery, and show its improved performance with the state-of-the-arts.

    Citation: Chuanqin Zheng, Qingshuang Zhuang, Shu-Juan Peng. Efficient motion capture data recovery via relationship-aggregated graph network and temporal pattern reasoning[J]. Mathematical Biosciences and Engineering, 2023, 20(6): 11313-11327. doi: 10.3934/mbe.2023501

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  • Human motion capture (mocap) data is of crucial importance to the realistic character animation, and the missing optical marker problem caused by marker falling off or occlusions often limit its performance in real-world applications. Although great progress has been made in mocap data recovery, it is still a challenging task primarily due to the articulated complexity and long-term dependencies in movements. To tackle these concerns, this paper proposes an efficient mocap data recovery approach by using Relationship-aggregated Graph Network and Temporal Pattern Reasoning (RGN-TPR). The RGN is comprised of two tailored graph encoders, local graph encoder (LGE) and global graph encoder (GGE). By dividing the human skeletal structure into several parts, LGE encodes the high-level semantic node features and their semantic relationships in each local part, while the GGE aggregates the structural relationships between different parts for whole skeletal data representation. Further, TPR utilizes self-attention mechanism to exploit the intra-frame interactions, and employs temporal transformer to capture long-term dependencies, whereby the discriminative spatio-temporal features can be reasonably obtained for efficient motion recovery. Extensive experiments tested on public datasets qualitatively and quantitatively verify the superiorities of the proposed learning framework for mocap data recovery, and show its improved performance with the state-of-the-arts.


    1.   Introduction

    Colorectal cancer (CRC) is one of the leading causes of death associated with cancer worldwide, which has been estimated to cause 50,000 deaths per year in America [1,2]. The pathogenesis of CRC is a complicated process involved in mutations of various oncogenes and tumor suppressor genes that pinpoint multiple cellular events, such as endoplasmic reticulum stress [3], oxidative stress [4], epithelial-mesenchymal transition (EMT) [5], abnormal cell proliferation and apoptosis [6].

    Although surgery combined with adjuvant radiotherapy or chemotherapy has effectively improved the prognosis of CRC, metastasis or neoplasm recurrence still occur in more than 50% of the CRC patients who have received a definite diagnosis or underwent intended curative treatment, contributing to a relatively poor 5-year survival rate [7]. Additionally, it is difficult for doctors to achieve early diagnosis of CRC for the reason that specificity of auxiliary examination indices is lacking in clinic, so that many patients may eventually miss the optional chances for surgery, thereby increasing the risk of death [8]. Hence, the detection of early-stage biomarkers and identification of core therapeutic target appear significant to decrease CRC-related deaths.

    Recent years, the high-throughput gene microarray has been widely used to screen the differentially expressed genes (DEGs) between normal samples and tumor samples in human beings and animal models, which makes it accessible for us to further explore the entire molecular alterations of tumors at multiple levels involving DNA, RNA, proteins, epigenetic alterations, and metabolism [9,10]. However, it still has a long way to go to put these microarrays in application in clinic because the DEGs identified by gene profiling were countless and the statistical analyses were also too complicated [11]. Therefore, it is urgent to verify a proper number of genes and develop a suitable approach which can be operated by routine assay in clinic.

    In this study, we selected the GSE32323 from Gene Expression Omnibus (GEO) and applied bioinformatics analysis to screen the DEGs in CRC. Subsequently, we used STRING to construct the protein-protein interaction (PPI) network to identify the hub genes with top15 degree of connectivity in CRC. Moreover, we made the analysis of these DEGs, including biological process (BP), molecular function (MF), cellular component (CC) and KEGG pathways. We chose top5 up-regulated and top5 down-regulated DEGs to make the overall survival (OS) analysis, aiming to observe the associations between the DEGs and the prognosis of CRC. Finally, we confirmed the mRNA expression level of the top5 up-regulated and top5 down-regulated DEGs by Q-PCR in samples obtained from patients with CRC in our hospital. These genes will assist us to screen and identify significant biomarkers and therapeutic targets of CRC in the near future.

    2.   Materials and method

    2.1.   Data source

    The heading levels should not be more than 4 levels. The fond of heading and subheadings should be 12 point normal Times New Roman. The first letter of headings and subheadings should be capitalized. The gene expression profile of GSE32323 was downloaded from the GEO database, which was a free and publicly available database. 17 pairs of cancer and non-cancerous tissues from patients with CRC in this dataset were detected by Affymetrix HG-U133 Plus 2.0 arrays by Ahmed K [12]. We also downloaded the raw Series Matrix File of GSE32323 from the GEO database.

    2.2.   DEGs analysis

    In our study, the online software GEO2R was employed to analyze the tissue samples in GSE32323 dataset. GEO2R is an online software by which users can divide the samples into two and more groups and select out the DEGs. We used the Benjamini and Hochberg methods by default to discover false rate and used the adjust P value to reduce the errors of false positive. The choice criterion contains the adjust P value < 0.05 and |logFC| ≥ 2.

    2.3.   Gene ontology and KEGG pathway analysis of DEGs

    Gene ontology analysis (GO), serving as a useful approach to annotate genes and gene products, could also identify characteristic biological attributing to high-throughput genome or transcriptome data. Kyoto Encyclopedia of Genes and Genomes (KEGG) is a collection of databases, aiming to handle biological pathways, genomes, diseases, chemical substances and drugs. We divided these DEGs into up-regulated group and down-regulated group. The Database for Annotation, Visualization and Integrated Discovery (DAVID, https://david.ncifcrf.gov/), an online web based on the bioinformatics, is routinely applied for annotating genes and protein function [13]. We input these DEGs separately into DAVID to acquire the GO and KEGG analysis, P < 0.05 as choice criterion. Finally, we used the ImageGP (http://www.ehbio.com/ImageGP/index.php/Home/Index/index.html) to visualize the major biological processes (BP), molecular function (MF) and cell composition (CC) of these DEGs.

    2.4.   Comparison of the top5 upregulated and top5 downregulated DEGs

    GEPIA (http://gepia.cancer-pku.cn/index.html), designed by Chenwei Li, Zefang Tang, and Boxi Kang of Zhang Lab, Peking University, is a newly developed interactive web server aiming at analyzing the RNA sequencing expression data of 9736 tumors and 8587 normal samples from the GTEx and TCGA projects in a standard processing pipeline [14]. In this study, we employed the boxplot to visualize the mRNA expression of top5 up-regulated and top5 down-regulated DEGs in CRC tissues and and normal colorectum tissues. Transcripts per million (TPM) is the number of transcripts from this particular gene normalized first by gene length, and then by sequencing depth (in millions) in the sample. GEPIA also provides the transcripts per million (TPM) of these DEGs to display their relative expression levels.

    2.5.   Comparison of the top5 upregulated and top5 downregulated DEGs The overall survival (OS) of the top5 upregulated and top5 downregulated DEGs

    Similarly, we used the GEPIA database to get the overall survival information of these DEGs. The logrank P value and hazard ratio (HR) with 95% confidence intervals were showed on the plot. P < 0 05 was statistically significant.

    2.6.   Comparison of the top5 upregulated and top5 downregulated DEGs Sample Collection and Quantitative Real-time PCR (Q-PCR)

    We enrolled 15 male patients who were diagnosed with CRC by pathology reports in our hospital. The samples from patients with CRC for the validation of these DEGs were supported by the ethics committee in our hospital. The paired carcinoma and adjacent tissues were firstly isolated. Then total RNA was isolated using the TRIzol (Invitrogen, Carlsbad, CA, USA) kit, the concentrations and purities of which were quantified by an ultraviolet spectrophotometer. The RNA was then reversely transcribed according to the previous literature [15]. The expression levels of top5 up-regulated genes and top5 down-regulated genes were normalized to GAPDH. Relative mRNA expression levels were analyzed by 2-∆∆cycle threshold (CT) method.

    2.7.   Comparison of the top5 upregulated and top5 downregulated DEGs Establishing the PPI network

    Search Tool for the Retrieval of Interacting Genes (STRING) is an online app for evaluating PPI network [16]. For finding the potential correlation of DEGs, we used online app STRING to map the DEGs, subsequently the Cytoscape software was utilized to construct a PPI network. The confidence score ≥ 0.4 and maximum number of interactors = 0 were set as the criterion.

    3.   Results

    3.1.   Data source

    The overall flow diagram of our study is presented in Figure 1. In our study, a total of 17 paired carcinoma and adjacent tissue from patients with CRC were analyzed. We applied the GEO2R online analysis tool with default parameters to screen the DEGs, using adjusted P value < 0.05 and logFC ≤ −2 or logFC ≥ 2 as the cut-off criteria. We captured 451 DEGs including 145 up-regulated DEGs and 306 downregulated DEGs. Whereafter, the DEGs were presented in the form of a heatmap and a volcano plot (Figure 2A-B). In the heatmap, the top25 up-regulated genes and top25 down-regulated genes between carcinoma and adjacent tissue were presented. Among the 451 DEGs, the top5 up-regulated genes involved DPEP1, KRT23, CLDN1, LGR5 and FOXQ1, while the top5 down-regulated genes were CLCA4, ZG16, SLC4A4, ADH1B and GCG. The gene tiles and biological functions of top5 upregulated and top5 down regulated genes were displayed in Table 1.

    Figure 1.  Flow diagram of the analysis procedure: data collection, preprocessing, analysis and validation.
    DownLoad: Full-Size Img PowerPoint
    Figure 2.  Volcano plot and heatmap of the differentially expressed genes (DEGs) between adjacent mucosa and carcinoma tissues from patients with colorectal cancer (CRC). (A) Volcano plot of genes detected in CRC. green means up-regulated DEGs; Red means down-regulated DEGs; blue means no difference. (B) Heatmap of top 25 up-regulated DEGs and top25 down-regulated DEGs between normal and CRC tissues.
    DownLoad: Full-Size Img PowerPoint
    Table 1.  The top 5 up-regulated and down-regulated differentially expressed genes in patients with colorectal cancer.
    DEGs Gene title Gene symbol LogFC Biological function
    Down-regulated Chloride channel accessory 4 CLCA4 4.91 Calcium sensitive chloride conductance protein
    Zymogen granule protein 16 ZG16 4.45 Promoting cell growth and stemness.
    Solute carrier family 4 member 4 SLC4A4 4.43 Sodium bicarbonate cotransporter
    Alcohol dehydrogenase 1B (class I), beta polypeptide ADH1B 4.29 Metabolizing ethanol, retinol, and aliphatic alcohol
    Glucagon GCG 4.15 Counteracting the action of insulin in the bloodstream.
    Up-regulated Dipeptidase 1 DPEP1 −4.14 Kidney membrane enzyme
    Keratin 23 KRT23 −4.28 Intermediate filament proteins
    Claudin 1 CLDN1 −4.46 Tight junctions
    Leucine rich repeat containing G protein-coupled receptor 5 LGR5 −4.51 Leucine-rich repeat-containing receptor
    Forkhead box Q1 FOXQ1 −5.55 Embryonic development and cell cycle regulation
     | Show Table
    DownLoad: CSV

    3.2.   Re-identification of DEGs by Q-PCR

    To ensure the credibility and reliability of the microarray of GSE32323 and get further credible analysis, we re-identified the top5 up-regulated genes and top5 down-regulated genes via Q-PCR. Q-PCR (Figure 5A-B) showed that the mRNA expression levels of DPEP1, KRT23, CLDN1, LGR5 and FOXQ1 were significantly higher in carcinoma group compared with adjacent tissue group (P < 0.05). Meantime, the mRNA expression levels of CLCA4, ZG16, SLC4A4, ADH1B and GCG were obviously down-regulated in carcinoma tissues from patients with CRC (P < 0.05).

    3.3.   Validation of DEGs by TCGA

    To ensure the credibility of the microarray of GSE32323 and proceed further credible analysis, we validated the top5 up-regulated genes and top5 down-regulated genes based on TCGA database via GEPIA. The results showed that the mRNA expression levels of CLCA4, ZG16, SLC4A4, ADH1B and GCG were significantly lower in carcinoma group compared to adjacent tissue group while the mRNA expression level of DPEP1, KRT23, CLDN1, LGR5 and FOXQ1 in carcinoma group were statistically higher than the adjacent tissue group (P < 0.05) (Figure 3A-J). Meanwhile, the TPM analysis demonstrated that the relative expressions of these DEGs were consist with those based on GSE32323 (P < 0.05) (Figure 4A-J).

    Figure 3.  The mRNA expression of top5 up-regulated and top5 down-regulated genes based on TCGA database. (A-J) represents CLCA4, ZG16, SLC4A4, ADH1B, GCG, DPEP1, KRT23, CLDN1, LGR5, and FOXQ1. T: tumor; N: Normal. * P < 0.05 versus normal group. P < 0 05 was regarded statistically different.
    DownLoad: Full-Size Img PowerPoint
    Figure 4.  The transcripts per million (TPM) of top5 up-regulated and top5 down-regulated genes based on TCGA database. (A-J) represents CLCA4, ZG16, SLC4A4, ADH1B, GCG, DPEP1, KRT23, CLDN1, LGR5, and FOXQ1. T: tumor; N: Normal. * P < 0.05 versus normal group. P < 0 05 was regarded statistically different.
    DownLoad: Full-Size Img PowerPoint

    3.4.   Overall survival (OS)

    Furthermore, we analyzed the potential association between the expression levels of top5 upregulated genes as well as top5 downregulated genes and the OS of patients with CRC. The Kaplan-Meier showed that only 1(ZG16) of the 10 genes displayed significantly correlation with the OS of patients with CRC (Figure 6A-J). The high level of ZG16 may contribute to a poorer prognosis of CRC (Logrank p = 0.044, HR = 0.61).

    Figure 5.  Validation of top5 up-regulated and top5 down-regulated DEGs in the samples from patients with CRC. (A) CLCA4, ZG16, SLC4A4, ADH1B and GCG were significantly down-regulated in carcinoma group. (B) DPEP1, KRT23, CLDN1, LGR5, and FOXQ1 were significantly up-regulated in carcinoma group. * P < 0.05 versus adjacent tissue group. P < 0 05 was regarded statistically different.
    DownLoad: Full-Size Img PowerPoint
    Figure 6.  Prognostic value of top5 up-regulated and top5 down-regulated genes. (A-J) represents CLCA4, ZG16, SLC4A4, ADH1B, GCG, DPEP1, KRT23, CLDN1, LGR5, and FOXQ1. P < 0 05 was regarded statistically different.
    DownLoad: Full-Size Img PowerPoint

    3.5.   GO enrichment analysis

    The results (Table2 & Figure 7A-C) from GO term enrichment analysis varied from expression levels and GO classification of the DEGs. By analyzing GO enrichment of these up-regulated and down-regulated DEGs via DAVID, we found that the up-regulated DEGs in BP were mainly enriched in negative regulation of cell proliferation, positive regulation of transcription from RNA polymerase Ⅱ promoter, proteolysis, positive regulation of transcription, DNA-templated and negative regulation of transcription from RNA polymerase Ⅱ promoter while the down-regulated DEGs in BP were mainly concentrated on cell adhesion, negative regulation of cell proliferation, positive regulation of cell proliferation, response to drug and nervous system development. As for CC, the up-regulated DEGs were principally enriched in extracellular region, extracellular space, integral component of plasma membrane, proteinaceous extracellular matrix and cell surface while the down-regulated DEGs were enriched in integral component of membrane, plasma membrane, extracellular exosome, extracellular space and extracellular region. Additionally, MF analysis uncovered that the up-regulated DEGs were mainly enriched in chemokine activity, CXCR chemokine receptor binding, serine-type endopeptidase activity, extracellular matrix binding and Wnt-protein binding while the down-regulated DEGs are responsible for carbonate dehydratase activity, heparin binding, structural constituent of cytoskeleton, hormone activity and zinc ion binding.

    Table 2.  Gene ontology analysis of differentially expressed genes associated with colorectal cancer.
    Expression Category Term Count % P-Value FDR
    Down-regulated GOTERM_BP_DIRECT GO:0007155~cell adhesion 13 0.038728513 0.013503078 19.70156803
    GOTERM_BP_DIRECT GO:0008285~negative regulation of cell proliferation 12 0.035749397 0.011658571 17.24378143
    GOTERM_BP_DIRECT GO:0008284~positive regulation of cell proliferation 11 0.03277028 0.07067658 69.36458376
    GOTERM_BP_DIRECT GO:0042493~response to drug 10 0.029791164 0.014890967 21.50558949
    GOTERM_BP_DIRECT GO:0007399~nervous system development 9 0.026812048 0.028891454 37.69742039
    GOTERM_CC_DIRECT GO:0016021~integral component of membrane 83 0.247266661 0.004198766 5.157367461
    GOTERM_CC_DIRECT GO:0005886~plasma membrane 71 0.211517264 0.001689338 2.105290204
    GOTERM_CC_DIRECT GO:0070062~extracellular exosome 67 0.199600798 6.59E-08 8.30E-05
    GOTERM_CC_DIRECT GO:0005615~extracellular space 43 0.128102005 1.82E-08 2.29E-05
    GOTERM_CC_DIRECT GO:0005576~extracellular region 34 0.101289957 0.002466544 3.060090837
    GOTERM_MF_DIRECT GO:0004089~carbonate dehydratase activity 5 0.014895582 1.89E-05 0.0259938
    GOTERM_MF_DIRECT GO:0008201~heparin binding 9 0.026812048 7.38E-04 1.012297275
    GOTERM_MF_DIRECT GO:0005200~structural constituent of cytoskeleton 7 0.020853815 0.002187539 2.974720065
    GOTERM_MF_DIRECT GO:0005179~hormone activity 6 0.017874698 0.005649705 7.515474579
    GOTERM_MF_DIRECT GO:0008270~zinc ion binding 25 0.07447791 0.007595654 9.980321245
    Up-regulated GOTERM_BP_DIRECT GO:0008285~negative regulation of cell proliferation 12 0.072115385 4.45E-05 0.070505424
    GOTERM_BP_DIRECT GO:0045944~positive regulation of transcription from RNA polymerase Ⅱ promoter 12 0.072115385 0.047923342 54.04862787
    GOTERM_BP_DIRECT GO:0006508~proteolysis 10 0.060096154 0.004653246 7.118885476
    GOTERM_BP_DIRECT GO:0045893~positive regulation of transcription, DNA-templated 10 0.060096154 0.005625306 8.544770506
    GOTERM_BP_DIRECT GO:0000122~negative regulation of transcription from RNA polymerase Ⅱ promoter 10 0.060096154 0.040288919 47.85424336
    GOTERM_CC_DIRECT GO:0005576~extracellular region 25 0.150240385 2.24E-05 0.026796109
    GOTERM_CC_DIRECT GO:0005615~extracellular space 23 0.138221154 1.30E-05 0.015593466
    GOTERM_CC_DIRECT GO:0005887~integral component of plasma membrane 17 0.102163462 0.0098955 11.22182273
    GOTERM_CC_DIRECT GO:0005578~proteinaceous extracellular matrix 10 0.060096154 3.33E-05 0.039831238
    GOTERM_CC_DIRECT GO:0009986~cell surface 8 0.048076923 0.044084961 41.70407607
    GOTERM_MF_DIRECT GO:0008009~chemokine activity 6 0.036057692 1.24E-05 0.016078978
    GOTERM_MF_DIRECT GO:0045236~CXCR chemokine receptor binding 4 0.024038462 1.86E-05 0.024112254
    GOTERM_MF_DIRECT GO:0004252~serine-type endopeptidase activity 8 0.048076923 0.00101144 1.305200327
    GOTERM_MF_DIRECT GO:0050840~extracellular matrix binding 3 0.018028846 0.01109514 13.48485303
    GOTERM_MF_DIRECT GO:0017147~Wnt-protein binding 3 0.018028846 0.01556121 18.42243874
    GO: Gene Ontology; FDR: False Discovery Rate.
     | Show Table
    DownLoad: CSV
    Figure 7.  Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of CRC. (A) The enriched GO terms in the biological process (BP); (B) The enriched GO terms in the cellular component (CC); (C) The enriched GO terms in the molecular function (MF); (D) The enriched KEGG pathway in CRC.
    DownLoad: Full-Size Img PowerPoint

    3.6.   KEGG pathway analysis

    To obtain a more comprehensive information regarding to the critical pathways of those selected DEGs, KEGG pathways analysis were also carried out via DAVID. The results in Table3 and Figure 7D disclosed the most vital KEGG pathways of the down-regulated and up-regulated DEGs. The down-regulated DEGs were mainly enriched in mineral absorption, pancreatic secretion, nitrogen metabolism, aldosterone-regulated sodium reabsorption and bile secretion. By contrast, the up-regulated DEGs were mainly responsible for chemokine signaling pathway, pathways in cancer, transcriptional misregulation in cancer, PPAR signaling pathway and rheumatoid arthritis.

    Table 3.  KEGG pathway analysis of differentially expressed genes associated with colorectal cancer.
    Category Term Count % P-Value Genes FDR
    Down-regulatedDEGs hsa04978:Mineral absorption 6 0.017874698 4.69E-04 TRPM6, MT1M, MT1E, MT1H, MT1X, MT1F 0.546769342
    hsa04972:Pancreatic secretion 6 0.017874698 0.010495925 KCNMA1, CLCA1, CLCA4, ATP2A3, CA2, SLC4A4 11.59221263
    hsa00910:Nitrogen metabolism 5 0.014895582 8.18E-05 CA12, CA7, CA4, CA2, CA1 0.095443878
    hsa04960:Aldosterone-regulated sodium reabsorption 5 0.014895582 0.002219912 SGK1, NR3C2, HSD11B2, SCNN1G, SCNN1B 2.561720438
    hsa04976:Bile secretion 5 0.014895582 0.016846119 AQP8, SLC51B, CA2, SLC4A4, ABCG2 17.99502607
    Up-regulatedDEGs hsa04062:Chemokine signaling pathway 7 0.042067308 0.001681492 CXCL1, PLCB4, PPBP, CXCL5, CXCL3, CXCL8, CXCL11 1.951113014
    hsa05200:Pathways in cancer 7 0.042067308 0.053070195 BMP4, PLCB4, MET, CXCL8, AXIN2, MYC, MMP1 47.1893587
    hsa05202:Transcriptional misregulation in cancer 6 0.036057692 0.005889645 MET, CXCL8, MMP3, MYC, WT1, PLAU 6.682415976
    hsa03320:PPAR signaling pathway 4 0.024038462 0.011041824 SCD, ACSL6, MMP1, FABP6 12.19046418
    hsa05323:Rheumatoid arthritis 4 0.024038462 0.022861328 CXCL5, CXCL8, MMP3, MMP1 23.72114711
    KEGG: Kyoto Encyclopedia of Genes and Genomes; FDR: False Discovery Rat
     | Show Table
    DownLoad: CSV

    3.7.   Identification of hub genes

    Applying the STRING online tool, 213 nodes with 264 PPI relationships were found, accounting for about 77.8% of these selected DEGs. According to the degree of connectivity of these DEGs, we constructed the PPI network and selected the top 15 hub genes (Table 4). The top 15 hub genes, possessing high degree of connectivity in CRC are as follows, MYC, CXCR1, TOP2A, CXCL12, SST, TIMP1, SPP1, PPBP, CDK1, THBS1, CXCL1, PYY, LPAR1, BMP2 and MMP3. Among these 15 hub genes: MYC, CXCR1, TOP2A, SPP1, PPBP, CDK1, CXCL1 and MMP3 were significantly up-regulated while CXCL12, SST, TIMP1, THBS1, PYY, LPAR1 and BMP2 significantly down-regulated (P < 0.05). The 15 hub genes could interact with 243 genes directly, and MYC acted as the most intensive gene which could interact with 46 up-regulated genes and 31 down-regulated genes. Intriguingly, among these hub genes, there also displayed very strong interactions (Figure 8).

    Table 4.  Top 15 hub genes with higher degree of connectivity.
    Gene Degree of connectivity Adjusted P value
    MYC 34 9.25E-11
    CXCR1 28 1.87E-03
    TOP2A 23 2.90E-02
    CXCL12 20 1.95E-08
    SST 18 7.36E-09
    TIMP1 17 1.05E-02
    SPP1 17 3.95E-02
    PPBP 17 9.73E-04
    CDK1 16 1.13E-02
    THBS1 16 2.99E-08
    CXCL1 15 1.91E-03
    PYY 15 1.77E-08
    LPAR1 15 8.36E-11
    BMP2 15 1.06E-05
    MMP3 14 7.22E-05
     | Show Table
    DownLoad: CSV
    Figure 8.  Protein-protein interaction (PPI) network. The PPI network of top15 hub genes with high connectivity degree.
    DownLoad: Full-Size Img PowerPoint

    4.   Discussion

    CRC is a one of the serious public health problems, which is also being the third most commonly diagnosed cancer and the major factor resulting in cancer-related deaths around the world [17,18]. CRC screening has been demonstrated to greatly decrease the morbidity and the mortality in a great many longstanding or newly economically developed countries [19]. However, at present, there is no an efficient and specific diagnostic methodology and treatment strategy for CRC, which is mainly attributed to the intricate pathogenesis, and its symptoms that are difficult to diagnose in the first several years [20]. Our study mainly focused on the expression profiling selected from microarray studies of CRC. In this study, a total of 17 pairs of cancer and non-cancerous tissues isolated from patients with CRC were analyzed. 451 DEGs including 145 up-regulated DEGs and 306 downregulated DEGs were screened. To obtain a comprehensive understanding of these DEGs, we performed GO function and KEGG pathway analysis.

    4.1.   DEGs may serve as potential biomarkers for the diagnosis of CRC

    Our analysis screened 451 DEGs with a quadruple fold change between carcinoma and adjacent tissue from patients with CRC. In our heat map, a total of 50 DEGs including top25 up-regulated genes and top25 down-regulated genes were displayed. From our perspective, these DEGs would be promised to be possible candidates for the diagnosis of CRC in future. Currently, some of these DEGs, in fact, have been already disclosed to be novel indicators of CRC. H Meng et al. [21] found that ZG16 exhibited an extremely high expression in normal epithelial cells from rectum, colon and small intestine in an organ-specific fashion while its protein expression was completely lost in CRC tissues. Furthermore, another study proved that the gene expression and copy number alterations of ZG16 significantly correlated with MLH1 silencing, microsatellite instability (MSI), hyper-mutation status, presence of synchronous adenomas, CpG island methylator phenotype, histological type and gender. Survival analysis showed that patients with higher ZG16 gene expression level displayed longer overall survival and progression-free survival compared with those having relatively lower level. Another study showed that the expression of DPEP1 was strikingly up-regulated in human CRC tissue than that in normal mucosa. Meanwhile, high level of DPEP1 was found to be significantly associated with localisation and histological stage of CRC [22]. Our study screened the DEGs of CRC from the angle of bioinformatics for the first time, however, the clinical practice of these DEGs needs to be further investigated.

    4.2.   Hub genes are possible therapeutic targets in CRC

    In our study, we picked out 15 hub genes of CRC, all of which were located in the core nodes in PPI network, meaning that these genes could be critical therapeutic targets to protect against CRC. For instance, somatostatin (SST), encoding a well-characterized growth-regulatoryand gastrointestinal neuroendocrine peptide, has been proved to be a tumor suppressor gene and own potent anti-tumor effects. SST could suppress tumors through direct and indirect mechanisms. Direct anti-tumor effects involve inhibiting cell proliferation; inducing cell cycle arrest; promoting apoptosis; and suppressing cell invasion while indirect anti-tumor effects are blocking the synthesis and secretion of growth factors as well as growth-promoting hormones, meanwhile stopping angiogenesis which is vital in neoplasm growth and spread [23]. Corroborative evidence has unveiled the decreased protein and mRNA expression of SST in CRC tissues [24]. One of the possible reason contributing to the down-regulation of SST was attributed to the higher SST methylation levels in tumor tissues. As expected, another study showed that pretreatment of demethylation agents into SST methylated CRC cells induced the up-regulation of SST expression in vitro [25]. Clinical research demonstrated that serum methylation level of SST in patients with CRC were obviously predictive of tumor recurrence and cancer-related death [26]. Collectively, our study proved that up-regulating SST in CRC cells may exert potent anti-tumor activity.

    4.3.   Chemokines may play essential roles in the development of CRC

    An interesting finding of our study was that many DEGs in CRC were related with chemokines and their receptors. To be more specific, GO analysis showed that up-regulated DEGs enriched in MF were mainly responsible for chemokine activity and CXCR chemokine receptor binding. Also, KEGG pathways analysis also disclosed that the up-regulated genes including CXCL1, PLCB4, PPBP, CXCL5, CXCL3, CXCL8, CXCL11 are implicated with chemokine signaling pathway. Additionally, 3 of the top15 hub genes based PPI network were also chemokines and their receptors, namely CXCR1, CXCL12 and CXCL1. Mountainous evidence proved that the interactions between chemokines as well as chemokine receptors could mediate the recruitment of different immune cell subsets into tumour microenvironment where the host immune system and tumour cells could interact [27]. On the one hand, chemokines regulate the molecules affecting tumour immunity as well as therapeutic outcomes in patients [28,29]. On the other one hand, chemokines could also target stromal cells and tumor cells, thereby regulating tumour stemness, angiogenesis [30], proliferation [31], apoptosis [32], and survival [33]. These findings suggested that therapies targeting chemokines in CRC may synergize with current cancer therapies, especially immunotherapies by affecting tumour immunological phenotypes, and improving antitumour immune responses.

    5.   Conclusion

    In conclusion, we provided a comprehensive and novel analysis of gene expression profiles patients with CRC. Particularly, the top5 down-regulated genes including CLCA4, ZG16, SLC4A4, ADH1B and GCG and the top5 up-regulated genes including DPEP1, KRT23, CLDN1, LGR5 and FOXQ1, which are expected to sensitive biomarkers in diagnosis of CRC. Meanwhile, we also screened the top 15 hub genes involving MYC, CXCR1, TOP2A, CXCL12, SST, TIMP1, SPP1, PPBP, CDK1, THBS1, CXCL1, PYY, LPAR1, BMP2 and MMP3, which could be promising therapeutic targets of CRC. Additionally, genes involved in chemokines pathways were also significantly altered in patients with CRC. Anyway, this analysis may offer the powerful evidence and clues for the future genomic individualized treatment of CRC.

    Acknowledgments

    This work was supported by the Science and Technology Project of Zhejiang Province (2014C33147), High-level Talent Project of Lishui City (2016RC29), Zhejiang Province Medical and Health Care Project (2017KY728) and National Natural Science Foundation of China (81803778).

    Conflict of interest

    All authors declare no conflicts of interest in this paper.



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