Comprehensive Genomic Analysis of Puerarin in Inhibiting Bladder Urothelial Carcinoma Cell Proliferation and Migration

Background: Bladder urothelial carcinoma (BUC) ranks second in the incidence of urogenital system tumors, and the treatment of BUC needs to be improved. Puerarin, a traditional Chinese medicine (TCM), has been shown to have various effects such as anti-cancer effects, the promotion of angiogenesis, and anti-inflammation. This study investigates the effects of puerarin on BUC and its molecular mechanisms. Methods: Through GeneChip experiments, we obtained differentially expressed genes (DEGs) and analyzed these DEGs using the Ingenuity® Pathway Analysis (IPA®), Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) pathway enrichment analyses. The Cell Counting Kit 8 (CCK8) assay was used to verify the inhibitory effect of puerarin on the proliferation of BUC T24 cells. String combined with Cytoscape® was used to create the Protein-Protein Interaction (PPI) network, and the MCC algorithm in cytoHubba plugin was used to screen key genes. Gene Set Enrichment Analysis (GSEA®) was used to verify the correlation between key genes and cell proliferation. Results: A total of 1617 DEGs were obtained by GeneChip. Based on the DEGs, the IPA® and pathway enrichment analysis showed they were mainly enriched in cancer cell proliferation and migration. CCK8 experiments proved that puerarin inhibited the proliferation of BUC T24 cells, and its IC50 at 48 hours was 218µmol/L. Through PPI and related algorithms, 7 key genes were obtained: ITGA1, LAMA3, LAMB3, LAMA4, PAK2, DMD, and UTRN. GSEA showed that these key genes were highly correlated with BUC cell proliferation. Survival curves showed that ITGA1 upregulation was associated with poor prognosis of BUC patients. Conclusion: Our findings support the potential antitumor activity of puerarin in BUC. To the best of our knowledge, bioinformatics investigation suggests that puerarin demonstrates anticancer mechanisms via the upregulation of ITGA1, LAMA3 and 4, LAMB3, PAK2, DMD, and UTRN, all of which are involved in the proliferation and migration of bladder urothelial cancer cells.


INTRODUCTION
A study by the American Cancer Society in 2021 estimated that bladder urothelial cancer (BUC) had the fourthhighest incidence and eighth-highest mortality among men in the United States [1].Due to high recurrence, BUC carries a large social burden, with over 430,000 men and women diagnosed worldwide yearly [2].BUC is a heterogeneous disease associated with various clinical outcomes, and conventional chemotherapy treatments are unsuccessful in curbing chemoresistance and BUC progression while having several adverse side effects [3,4].Therefore, finding a therapy to improve patient outcomes and reduce side effects and drug resistance is necessary.
Traditional Chinese medicine (TCM) advocates the treatment of diseases in the form of complexes, and multiple TCM complexes have been confirmed to have anticancer effects [5,6].Lately, a variety of TCM monomers, such as phenylpropanoids alkaloids, and flavonoids have been confirmed to have anti-tumor effects [7][8][9].TCM has previously been used for cancer treatment.It can not only limit the occurrence of tumors but also reduce the side effects of chemoradiotherapy due to its multi-component, and multi-target characteristics.In addition, TCM also has a positive effect in enhancing host immune function, prolonging patient survival, and reducing the risk of some chronic lung diseases [10].In clinical practice, TCM is commonly used as adjuvant chemotherapeutic drugs to enhance the efficacy of chemotherapeutic drugs and improve the quality of life of patients [11].
Puerarin is an isoflavone derivative isolated from the TCM Kudzu root that has effects on multiple systems of the human body.An in-vitro study found that puerarin alleviated nephrotoxicity by regulating Toll-like receptor 4 (TLR4)/ Nuclear factor-kappaB (NF-κB) signaling pathway [12].The T24 cell line belongs to that of metastatic urothelial carcinoma of the bladder and has a good representative role in advanced refractory bladder malignancies.Liu et al. found that puerarin could inhibit BUC cell proliferation, promote apoptosis, and block the NF-κB signaling pathway by upregulating the expression of mir-16 in T24 cells [13].This study predicts that puerarin may impart favorable outcome in BUC patients.It has been reported that puerarin has the capability of lowering blood pressure, reducing myocardial oxygen consumption, dilating coronary vessels, protecting the liver, controlling blood sugar, and suppressing cancers and ischemia-reperfusion injury [14].Most of the studies on the mechanism of action of puerarin in inhibiting BUC were not based on high-throughput basic experimental studies and their findings showed that puerarin restrained BUC cells by regulating The mechanistic target of rapamycin (mTOR) /p70S6K [15], circ_0020394/miR-328-3p/nuclear receptorbinding protein 1 (NRBP1) [16], silent information regulator sirtuin 1 (SIRT1)/p53 [17], and miR-16 [13].Although puerarin has been partially studied in BUC, the mechanism by which puerarin inhibits BUC cells remains to be explored.In this study, we used high-throughput and bioinformatics analysis to predict how puerarin inhibits BUC.We hope to provide some directions for subsequent studies.

Materials and Reagents
Puerarin was purchased from Solarbio Life Sciences Co., Ltd, Beijing, China, and was dissolved in Dimethyl sulfoxide (DMSO) to prepare an 800 µM stock solution stored at -20°C before use.The bladder urothelial cancer T24 cell line was purchased from the Cell Resource Center, Shanghai Institutes for Biological Sciences at the Chinese Academy of Sciences.An Agilent RNA 6000 Nano Kit was purchased from Agilent Technologies (Van Nuys, CA); an in vitro reverse transcription kit, GeneChip 3′ IVT Express Kit (US Affymetrix) for RNA purification, and a GeneChip Hybridization Wash and Stain Kit were purchased from Affymetrix.

Cell Culture
The BUC T24 cells were routinely cultured in a 1640 Dulbecco's modified Eagle medium (DMEM) containing 10% FBS in a 5%-CO 2 saturated humidity incubator at 37°C.When the cells were cultured in a 10 cm dish until the confluence reached 80% -90%, the culture medium was poured off and T24 cells were washed twice with 3 ml D-HANKS.Then, 1 ml of trypsin solution was added and mixed well to digest the T24 cells.When cells fell off in sheets, 3 ml of 1640 DMEM culture medium containing 10% FBS was added to terminate the digestion.The cells were blown to form single cells and then centrifuged at 1000 rpm, 4°C, for 5 minutes.After that, the supernatant was discarded and T24 cells were incubated in new Petri dishes and the complete medium was added.

Cell Viability Assay and IC 50
Cell viability assay with Cell Counting Kit-8 (CCK8) was used to quantify T24 cell viability.The T24 cells were seeded onto 96-well plates at a density of 4000 cells/well for 48 h and then incubated with RPMI-1640 medium containing various dilutions of puerarin (12.5, 25.0, 50.0, 100.0, 200.0, 300.0, 400.0, 600.0 and 800.0µmol/l) and negative control (medium only) at 37°C in a 5% CO 2 humidified atmosphere for 48 h.Following incubation for the indicated times, 10 µl CCK8 solution was added to each well and incubated for 2 h at 37°C to examine the effect of puerarin on BUC cell proliferation.The measurement of cell viability and IC 50 values via relative optical density was set at 450 nm.Every experiment was performed in triplicate.

Affymetrix Gene Expression Microarrays
RNA was tested for degradation and RNA concentration was determined using an Agilent ND-1000 before labeling the RNA.Samples were labeled using the Agilent Quick Amp Labeling kit, and hybridization experiments were performed using Agilent SureHyb.The chips were washed and then scanned by using an Agilent DNA Microarray Scanner.Chip probe signal values were acquired using Agilent Feature Extraction software (v11.0.0.1).Chip normalization and raw data were performed using Agilent GeneSpring GX v12.1 software.
We compared and analyzed the Affymetrix gene expression profiling microarray chip data of the puerarin treatment group and normal control group in T24 cells, and absolute expression signals as well as log transformation of fold change using base 2. Screening criteria were required to satisfy log 2 FC > 2 and a P-value < 0.05.Through the criteria, we obtained differentially expressed genes (DEGs).

Bioinformatic Analyses
The bioinformatic analyses were performed to find thepotential related pathways and genes related to the inhibitory effect of puerarin on T24 cells.The analyses primarily consisted of enrichment analyses of diseases and pathways, protein-protein interactions (PPI), gene set enrichment analysis and survival curves.

Volcano Plot
The volcano plot was made by R-package plot in Rstudio (2022.02.0) and was used to show the distribution of all genes, especially DEGs.The DEGs were defined as -1 < log2[Fold Change] < 1 and P-value < 0.05.

Ingenuity ® Pathway Analysis
According to the above screening method, the genes were divided into two groups of up-and down-regulated genes and introduced into Ingenuity ® pathway analysis (IPA ® ) which mainly includes four functions: diseases and functions, canonical pathway, upstream analysis and regulator effects.Through these functions, we can get which pathways and diseases the DEGs were enriched in and the upstream regulatory factors and relative networks.

Pathway Enrichment Analysis
Metascape ® (http://metascape.org) is one of the most commonly used bioinformatics websites that can make pathway enrichment analyses and transcription factor predictions [18].KEGG and GO pathway analysis, which is respectively based on the Kyoto Encyclopedia of Genes and Genomes (KEGG, https://www.genome.jp/kegg/)and Gene Ontology (GO, http://geneontology.org), are the two most familiar database that are accepted.Adobe Illustrator 20 and R-package "enrichplot," "ggplot2," "clusterProfiler," and "GOplot" in Rstudio software were performed for pathway enrichment analysis and graph modification.

Protein-protein Interactions
Protein-protein interactions (PPIs) were made by String (version 11.5, https://cn.string-db.org/),which aims to collect, score and integrate all publicly available sources of PPI information, and to complement these with computational predictions [19].Cytoscape ® (V3.9.1) is a software performed to further optimize PPIs and obtain key genes through algorithm MCC in plug-in cytohubba.GeneMANIA (http://genemania.org) was used to construct and analyze a functional network of key genes and their co-expressed genes.

Survival Curves
Gene expression profiling interactive analysis (GEPIA ® , http://gepia.cancer-pku.cn/) is a web server for cancer and normal gene expression profiling and interactive analyses based on TCGA and GTEx data.It provides key interactive and customizable functions, including differential expression analysis, profiling plotting, correlation analysis, patient sur-vival analysis, similar gene detection and dimensionality reduction analysis [20].The Methods and Cut Off were respectively chosen Overall Survival and Quartile.Both Hazards Ratio (HR) and 95% Confidence Interval were chosen to Yes, and Datasets Selection was set to BLCA.

Gene Set Enrichment Analysis
Gene Set Enrichment Analysis (GSEA ® , https://www.gsea-msigdb.org/gsea/index.jsp) is a computational method that determines whether 2 priori-defined set of genes shows statistical significance, and concordant differences between two biological states [21].The genome expression data was downloaded from GDC (https://portal.gdc.cancer.gov/).Gene set data named "GOBP_NEGATIVE_REGULATION_OF_EP ITHELIAL_CELL_PROLIFERATION" whose brief description was "Any process that stops, prevents or reduces the rate or extent of epithelial cell proliferation" was downloaded from GSEA ® .Here, GSEA was performed to identify whether a certain predefined gene set was enriched in the expected relevant pathway by using GSEA_4.2.3."collapse/remap to gene symbols" was set to "No_Collapse" and "Permutation type" was set to "Phenotype".

Statistical Analysis
Statistical analyses were performed by using IBM SPSS Statistics 27.Student's t-test was performed to compare the differences between the two groups.One-way ANOVA was performed to analyze the differences among multiple groups.Kaplan-Meier curves and log-rank tests were used for plotting survival curves.Statistical significance was set at p < 0.05.

Puerarin Inhibited the Viability of T24 Cells
To evaluate the effect of puerarin on cell viability, T24 cells were incubated with puerarin at a concentration of 0, 12.5, 25, 50, 100, 200, 300, 400, 600, and 800 µmol/L for 48 h.The results demonstrated a significant (p < 0.05) increase in cell death that occurred in a dose-dependent manner upon incubation with increasing concentrations of puerarin (100-800 µM) (Fig. 1A).The highest cell death was found at a puerarin concentration of 218 µM.

The DEGs in Cells Treated with or without Puerarin
To explore the mechanism by which puerarin inhibits BUC T24 cells, the whole genome Affymetrix microarray chip hybridization was adopted to discover the gene expression of T24 cells between the control groups and puerarintreated groups.The volcano plot showed that 1617 differentially expressed genes (DEGs), including 564 upregulated and 1053 downregulated genes, were identified (Fig. 1B).The details of DEGs are listed in Table S1.In addition, the flowchart of the study based on DEGs is shown in Fig. (2).

DEGs were Associated with Cell Proliferation and Migration
The DEGs were analyzed by Ingenuity ® pathway analysis (IPA ® ), which includes several functions such as diseases and functions, canonical pathway, upstream analysis and  regulator effects.Through these functions, we can know which pathways and diseases the DEGs are enriched with and the upstream regulatory factors and relative networks.

Diseases and Functions
Disease and function analysis with IPA ® assessed the positive association between puerarin and other diseases or functions.As shown in Fig. (3A), the results showed that DEGs were mostly enriched in some physiological and pathological conditions such as Cancer, Organismal Injury and Abnormalities, Cellular Movement, Cell Death and Survival, Tissue Morphology and a series of Systemic diseases.The information related to Diseases and Functions is shown in Table S2.Among these diseases and functions associated with malignant tumors were Cancer, Cellular Movement, Cell Death and Survival.

Canonical Pathway Analysis
Canonical pathway analysis of IPA ® elucidated on which pathways DEGs are enriched predominantly.A total of 55 classical pathways were obtained by the pathway analysis (p < 0.05).The  S3.

Upstream Analysis
Upstream analysis was able to predict the upstream regulatory factors of genes from DEGs.These factors could be chemical drugs, kinase, cytokine, enzyme, transcription regulator, complex, or others.Meanwhile, Upstream analysis generated many relationship graphs, each showing which genes from the DEGs are affected by an upstream regulator, with red representing genes up-regulated and green representing genes down-regulated.The predictive interactions were supported by literature based on the Ingenuity Pathway Knowledge Base (IPKB) [22].An overlap P-value was computed based on the significant overlap between genes in the DEGs, and the z-score was used to make predictions.As presented in Table 1, IPA ® predicted the most probable upstream    S4.

Regulator Effects
The regulatory effect network diagram shows the interactions between genes and regulators and functions in IPKB.The consistency score is a measure of the causal consistency and tight link between upstream regulatory factors and disease and function in IPKB.Usually, the more accurate the results of regulation prediction, the higher the consistency score is, and the more accurate the results of the prediction of the regulatory effects.From the results shown in Table 2, regulators affected diseases and functions consisting of Vascularization, Migration of cells, Morbidity and mortality, Cell viability, hypoplasia, vascular tumor and others.Obviously, they were predominantly related to angiogenesis and tumorigenesis.The details are listed in Table S5.

DEGs were Enriched in Proliferation and Migration
The results of KEGG and GO pathway enrichment analyses were obtained from Metascape ® , which is a website used to make a comprehensive informatics analysis.

KEGG Enrichment Analysis
Among the KEGG pathways, there were almost half of the pathways were tightly correlated with cancer and cell adhesion.These pathways included Pathways in cancer,  Focal adhesion, NOD-like receptor signaling pathway, MAPK Signaling pathway, cGMP-PKG signaling pathway, Transcriptional misregulation in cancer, Hippo signaling pathway, C-type lectin receptor signaling pathway, and FoxO signaling pathway (Fig. 5A).

GO Biological Process Analysis
The GO analyses in Fig. (5B) showed that six pathways were correlated with cell proliferation and locomotion.These pathways included regulation of cell adhesion, positive regu-lation of cell death, cell population proliferation, positive regulation of locomotion, regulation of MAPK cascade and negative regulation of cell population proliferation.

Protein-protein Interactions and Key Genes: ITGA1, LAMA3, LAMB3, LAMA4, PAK2, DMD and UTRN
To further understand the relationship among the DEGs, a website named String was used to obtain the list of PPIs.The minimum required interaction score was set to high confidence (0.900), and disconnected nodes in the network were   6) elucidated that ITGA1, LAMA3, LAMB3, LAMA4, PAK2, DMD and UTRN were the key genes of puerarin on BUC T24 cells.In addition, GeneChip analysis demonstrated that ITGA1, LAMA3, LAMB3, LA-MA4 and PAK2 were down-regulated whereas DMD and UTRN were up-regulated.
To explore the in-depth relationships, the 7 key genes and their co-expression genes were analyzed using Gene-MANIA.GeneMANIA constructed a network of 27 genes, including 20 related genes.A total of 7 hub genes showed a complex PPI network with a Pathway of 35.45%, Physical Interactions of 24.83%, shared protein domains of 24.81%, and Co-expression of 10.81%.The functional information in the network revealed the importance of collagen-containing extracellular matrix, basement membrane, cell-substrate adhesion, etc. (Fig. 6B).The complete function information is seen in Table S7.

Gene Set Enrichment Analysis (GSEA ® ) on Key Genes that Correlate with Cell Proliferation
GSEA was performed to observe the pertinence between key genes and cell proliferation.Gene expression and cell proliferation data were downloaded from GDC and GSEA ® .The results elucidated that key genes were significantly enriched in cell proliferation pathways (Fig. 7) and heatmap showed the expression of genes associated with proliferation (Fig. 7).Normalized Enrichment Score (NES) and False Discovery Rate (FDR) were listed in Table 3.In consideration of previous research results, puerarin inhibits the proliferation of BUC cells.

Survival Curve by Gene Expression Profiling Interactive Analysis (GEPIA®): ITGA1 was an Effective Target for Treatment
To verify whether patient survival is related to these genes gained from PPIs, the patient overall survival analysis in GEPIA ® was used to make a series of survival curves.The results in Fig. (8) showed that high expression of ITGA1 was associated with low survival in BUC patients.Additionally, both p-values of LAMA4 and DMD were less than 0.1, and they may be potentially effective therapeutic targets.

Separate Pathway Enrichment of Up and Downregulated Genes
The upregulated genes in GO pathways were enriched in protein refolding, inclusion body assembly, actomyosin, aggresome, unfolded protein binding and ATP-dependent protein folding chaperone; the downregulated genes in GO pathways were enriched in calcium ion transmembrane import into the cytosol, positive regulation of tumor necrosis factor superfamily cytokine production, nuclear chromosome, basement membrane, interleukin-6 receptor binding, and growth factor receptor binding (Fig. 9A).The upregulated genes in KEGG pathways were enriched in legionellosis and protein processing in the endoplasmic reticulum; the downregulated genes in KEGG pathways were enriched in ECM-receptor interaction and focal adhesion (Fig. 9B).

DISCUSSION
In this study, we investigated the effect of puerarin on the BUC T24 cell line using bioinformatics analyses.Our findings revealed that puerarin may inhibit the proliferation and migration of T24 cells through seven key genes, namely, ITGA1, LAMA3, LAMB3, LAMA4, PAK2, DMD, and UTRN.Further studies showed that ITGA1 upregulation was associated with poor prognosis of BUC patients.
The CCK8 assay showed that puerarin had a great inhibitory effect on the viability of T24 cells and the half-maximal inhibitory concentration was 218 µM.Ye et al. found that the inhibitory effect of puerarin on BUC T24 cells was only observed when the concentration of puerarin was greater than 100 µg/ml, i.e. 240 µM [17], which was similar to our study.Additionally, a study by Du et al. found significant cell viability inhibition by puerarin on the BUC T24 cells when its concentration was 100 mg/ml [16].Although puerarin had a more efficient viability inhibition of T24 cells at a concentration of   100 µg/ml, the study by Liu et al. concluded that the halfinhibitory concentration of puerarin on T24 cells was around 50 µg/ml [13].It is well believed that puerarin has a great anticancer effect and the optimal inhibitory concentration is 218 µM, though a few studies show discordance.
Puerarin is a TCM monomer extracted from the Kudzu root, which has a wide range of effects in a variety of systems, including the cardiovascular system, and can be used to treat a variety of diseases, including atherosclerosis, cardiomegaly, heart failure, and diabetic cardiovascular complications [23].A meta-analysis studying the efficacy and safety of puerarin injection in curing acute ischemic stroke by Zheng et al. showed that no significant adverse effects were observed when 600 mg of puerarin was administered intravenously [24], which was also supported by a study by Liu et al. [25].The normal blood volume of the human body is considered to be 4000-5000 ml, and assuming that the blood volume of the subject is 4500 ml, the blood concentration of puerarin after an injection of 600 mg is about 0.133 mg/ml, i.e., 320 μM.Thus, we think that the concentration of 218 μM puerarin has no significant negative effects on healthy cells and may be suitable for the human body.
Research on puerarin in BUC is limited, and existing studies have focused on the inhibitory effects of puerarin on the proliferative activity, migration and invasion of BUC cells with traditional methodology [16].In this study, we explore the effect of puerarin on BUC cells comprehensively in a high-throughput way combined with bioinformatics analyses.As shown by the results, puerarin inhibited the proliferation of BUC T24 cells and also had an inhibitory effect on the migration and invasion of cells, which is consistent with previous studies [16].In addition, IPA ® analysis showed that puerarin mainly mediated DNA damage in the nucleus of BUC cells, and biological target prediction analysis revealed that the possible key targets of puerarin were ITGA1, LAMA3, LAMB3, LAMA4, PAK2, DMD, and UTRN.
Integrins are transmembrane heterodimers presented on the surface of multitudinous cells and are composed of α and β subunits.Its function depends on calcium or magnesium ions.The widely known function of integrins is mediating mutual recognition and adhesion between cells and cells as well as between cells and extracellular matrix.The ITGA1 gene encodes the α 1 subunit of the integrin receptor, which heterodimerizes with the β1 subunit to form cell surface receptors for collagen and laminin, promoting tumorigenicity in malignant tumors [23].Based on the hub-gene analysis and related studies, we suggested that puerarin may have a prominent effect in inhibiting tumor migration and that this effect is related to the inhibition of ITGA1 expression.
Laminins, a family of extracellular matrix glycoproteins, are the major noncollagenous constituent of basement membranes and they have been implicated in a wide variety of biological processes, including cell adhesion, differentiation, migration, signaling, neurite outgrowth and tumor cell metastasis [24].LAMA located in chromosome 6q21, encodes the alpha chain isoform laminin, alpha 4 [25].LAMA4 is a cancer-treatment target and high expression of LAMA4 is associated with various malignant tumors such as hematolog-ic neoplasm [26], digestive system neoplasms [27,28] and genitourinary neoplasms [29,30].LAMA4 is a cancerassociated gene and a very promising malignant tumor therapeutic target.
In addition, high expression of other key genes like PAK2 [31], LAMB3 [32] and LAMA3 [33] is also associated with malignant tumors.It is well-known that DMD gene mutation can cause Duchenne and Becker muscular dystrophy.However, increasing evidence shows that DMD is associated with cancer development [34].DMD and UTRN negatively correlate with tumorigenesis [35,36].
Although important discoveries were revealed by these studies, there are also limitations.First, it is the selected DEGs rather than the total genes that were included in the analysis, which may lead to deviations in the study results from the actual situation.Second in this study, only some mechanisms were selected for detailed validation and exploration, and it did not prove that all study predictions were true and reliable.Lastly, we predicted the upstream regulators, comprehensive action pathways and targets of puerarin based on bioinformatics, but did not rule out certain errors with the actual situation, which requires further experimental verification.
This comprehensive prospective prediction study helps with the understanding of the cancer suppression mechanisms of puerarin and the role of puerarin in some systemic diseases.Overall, this study can provide a comprehensive, multi-faceted research direction for the subsequent study of puerarin in cancer.

CONCLUSION
Our findings support the potential antitumor activity of puerarin in BUC.To the best of our knowledge, bioinformatics investigation suggests that puerarin demonstrates anticancer mechanisms via the upregulation of ITGA1, LAMA3 and 4, LAMB3, PAK2, DMD, and UTRN, all of which are involved in the proliferation and migration of bladder urothelial cancer cells.

Fig. ( 1
Fig. (1).The DEGs. (A) The dose-response curve of puerarin on T24 cells demonstrates the IC 50 was 218 µmol/L.(B) The volcano plot of all genes contained in Affymetrix ® gene expression microarrays.The blue color on the left side represents 1053 downregulated genes, the red color on the right side represents 564 upregulated genes.(A higher resolution / colour version of this figure is available in the electronic copy of the article).

Fig. ( 2
Fig. (2).Flowchart of this study.The flowchart elucidates the research strategy of this study.(A higher resolution / colour version of this figure is available in the electronic copy of the article).
top 20 pathways ranked by z-score were shown in Fig. (3B) and there were 5 pathways that were highly related to tumorigenesis.These pathways include DNA Double-Strand Break Repair by Homologous Recombination (-log p = 4.06), Molecular Mechanisms of Cancer (-log p = 2.88), HIF1α Signaling (-log p = 2.84), Role of BRCA1 in DNA Damage Response (-log p = 2.59) and Wound Healing Signaling Pathway (-log p = 2.35).The information related to the Canonical pathway is listed in Table

Fig. ( 3 ).
Fig. (3).Diseases and functions and canonical pathway analysis by IPA ® .(A) The top 20 cluster of the differential genes in the disease and functional categories, which were ranked by -log (p-value).(B) The top 20 pathways in canonical pathway analysis by IPA ® .(A higher resolution / colour version of this figure is available in the electronic copy of the article).

Fig. ( 4 ).
Fig. (4).Upstream regulator (MAPK1) by IPA ® .Gene networks of upstream regulators related to tumorigenesis: MAPK1 network.The color shade and font size of a gene tag are proportional to the absolute value of its log (FC), with red representing its log (FC) as positive and green representing its log (FC) as negative.(A higher resolution / colour version of this figure is available in the electronic copy of the article).

Fig. ( 5 ).
Fig. (5).Pathway enrichment analyses by metascape ® showed that DEGs were enriched in pathways related to cancer, cell proliferation and migration.(A) The top 20 results of the KEGG pathway analysis ranked by -log p. (B) The top 20 results of biological processes ranked bylog p. (A higher resolution / colour version of this figure is available in the electronic copy of the article).

Fig. ( 6 )
Fig. (6).Protein-protein interaction network by String ® and Cytoscape ® .The PPI network of the top 50 genes in MCC and the color depth of the circle are proportional to the importance of corresponding genes.(A higher resolution / colour version of this figure is available in the electronic copy of the article).
hidden.Fig. (6) shows the Top 50 genes of MCC through CytoHubba in Cytoscape ® .The shade of circular color is directly proportional to its importance.The redder the color, the more important.The top 50 scores by MCC are listed in Table S5 and Fig. (

Fig. ( 7
Fig.(7).GSEA revealed that key genes were enriched in proliferation.Heatmap of gene expression abundance.Graph of GSEA enrichment analysis results.(A higher resolution / colour version of this figure is available in the electronic copy of the article).

Fig. ( 8 ).
Fig. (8).Survival curves by GEPIA ® .The overall survival curves of key genes show that BUC patients with high expression of ITGA1 was significantly related to low survival and high expression of LAMA4 and DMD was very likely associated with low survival.(A higher resolution / colour version of this figure is available in the electronic copy of the article).