Bladder cancer (BC) is one of the most prevalent genitourinary cancers. Despite the growing research interest in BC, the molecular mechanisms underlying its carcinogenesis remain poorly understood. The microarray datasets GSE38264 and GSE61615 obtained from the Gene Expression Omnibus (GEO) database were analyzed and differentially expressed genes (DEGs) were identified, which were then verified using a dataset from The Cancer Genome Atlas (TCGA). By taking the intersection of the two microarray datasets, the common DEGs were identified and these were selected as candidate genes associated with BC. The DEGs were further subjected to Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis, and the protein-protein interaction network was constructed. Further module analysis was performed using STRING and Cytoscape. A total of 362 DEGs were identified, including 13 hub genes, and the GO analysis revealed that these genes were mainly enriched in extracellular matrix organization, positive regulation of cell proliferation, angiogenesis and peptidyl-tyrosine phosphorylation. The expression changes of PTPRC, PDGFRA, CASQ2, TGFBI, KLRD1 and MT1X in the different datasets indicated that these genes were involved in the development of BC. Next, the differential expression of these genes was verified in the TCGA dataset, and ultimately, these 13 genes were determined to be related to the occurrence and development of BC. Finally, the cancer tissues and adjacent tissues of patients with BC were collected and subjected to reverse transcription-quantitative PCR, the results of which were consistent with the bioinformatics prediction. The present findings provide several vital genes for the clinical diagnosis and treatment of BC.
Bladder cancer (BC) is the second most frequently occurring urinary system tumor and the mortality rate of BC is gradually increasing worldwide (
In recent years, microarray technology has been widely used in studies related to gene expression. Its application complements the methods of gene expression studies and strengthens research on disease susceptibility and disease pathology. After detecting the differences in gene expression, the next step is to find the biological functions of these differences and use bioinformatics analysis to screen for gene changes at the genomic level, so as to identify differentially expressed genes (DEG) and functional pathways involved in the occurrence and development of liver cancer. However, the analysis of a single microarray data set has limitations, and its results require to be further verified. Therefore, in the present study, following the method of Li
In the present study, the expression of BC-related genes and their impact on progression, malignancy and prognosis were examined. Through the analysis of two mRNA microarray data sets, a total of 362 DEGs, comprising 315 upregulated and 47 downregulated DEGs, were identified. Subsequently, 13 central genes were identified by using the Cancer Genome Atlas (TCGA) database and protein-protein interaction (PPI) network analysis. In conclusion, 362 DEGs and 13 hub genes were identified. Through various software analyses, it was indicated that these genes may be candidate biomarkers of BC. Among these hub genes, platelet-derived growth factor receptor α (PDGFRA) had the highest degree of connectivity.
GEO (
GEO2R (
DAVID (
The STRING database, an online resource dedicated to organism-wide protein association networks (
TCGA clinical data were downloaded from the Genomic Data Commons data portal (
The plug-in biological network ontology tool (Bingo) (version 3.0.3) in Cytoscape was used to analyze the hub gene and visualize its biological processes (
Tumor and normal tissue samples were provided by three patients with squamous cell carcinoma of the bladder. In March 2022, three male patients aged 57, 54 and 59 years were hospitalized at the First Affiliated Hospital of Xinjiang Medical University (Urumqi, China), all from Xinjiang, China. All 3 patients had painless and complete hematuria.
The total RNA in the sample to be tested was extracted with TRIzol (Thermo Fisher Scientific, Inc.) and the purity and concentration of RNA were detected by a spectrophotometer. RNA was reverse transcribed into cDNA with an RT kit (Thermo Fisher Scientific, Inc.) according to the manufacturer's instructions. Real-time qPCR was performed with SYBR green real-time PCR reagent (Thermo Fisher Scientific, Inc.) according to the manufacturer's instructions and the reaction time and temperature had been determined in a preliminary experiment. The PCR amplification conditions were as follows: Initial denaturation at 95˚C for 5 min, followed by 40 times cycles of 95˚C for 10 sec, 58˚C for 20 sec and 72˚C for 30 sec. GAPDH was used as the internal reference and the relative mRNA expression level of the gene to be tested was analyzed using the 2-∆∆Cq method (
Paraffin-embedded tissues were sliced and dewaxed (10 min for xylene I/II; 5 min for 100% ethanol I/II; 10 sec for 95, 90, 85 and 75% ethanol. They were incubated with 3% H2O2 for 5-10 min at room temperature to eliminate the activity of endogenous peroxidase. Following rinsing with distilled water, they were soaked in PBS for 5 min, blocked with 5-10% normal goat serum (Shanghai Suolaibao Biological Co.) in PBS at room temperature for 10 min and the serum was drained off. The primary antibody to TGFBI (cat. no. PA5-82358; Thermo Fisher Scientific, Inc.) working solution (diluted with PBS at 1:200) was added dropwise and incubated at 4˚C overnight. After washing with PBS, an appropriate amount of biotin-labeled secondary antibody conjugated to HRP (cat. no. A-11001; Thermo Fisher Scientific, Inc.) working solution was added and samples were incubated at 37˚C for 30 min. Following washing with PBS for 5 min, an appropriate amount of horseradish enzyme (Shanghai Suolaibao Biological Co.) working solution was added with incubation at 37˚C for 10-30 min. Samples were washed with PBS for 5 min and the chromogenic agent diaminobenzidine was added for 3-15 min. Samples were fully rinsed with tap water, re-dyed with hematoxylin, dehydrated, cleared with xylene and sealed with neutral balsam. Slides were then observed under an inverted microscope (WMJ-9590; Nikon Corporation).
Paraffin sections are dewaxed and rehydrated as follows: They dewaxed with xylene and rehydrated with an ethanol gradient and then distilled water. Hematoxylin was then used to stain the nuclei: The slices were stained with Harris hematoxylin for 3-8 min, washed with tap water, differentiated with 1% hydrochloric acid alcohol for several seconds, washed with tap water, turned back to blue with 0.6% ammonia and washed with running water. The sections were then stained with eosin for 1-3 min. Subsequently, the samples were dehydrated with an ethanol gradient, cleared with xylene. The slices were then slightly dried and sealed with neutral balsam, followed by observation under a microscope.
Statistical analysis was performed using R software (4.1.0) and GraphPad (version 8.0; GraphPad Software, Inc.). All data were expressed as the mean ± standard deviation and statistical analysis among different groups was performed by SPSS 24.0 software (IBM Corporation). Differences between groups were evaluated using one-way ANOVA with Tukey's post-hoc test. P<0.05 was considered to indicate a statistically significant difference.
After standardizing gene expression values in the GeneChip datasets GSE38264 and GSE6165, 4,414 and 494 DEGs were screened, respectively. As indicated in the Venn diagram (
The DEGs were analyzed using functional analysis with the Web tool DAVID. GO analysis indicated that the changes in the category molecular function mainly included heparin binding, calcium ion binding, protein homodimerization activity, scavenger receptor activity and sequence-specific DNA binding (
Cytoscape was used to construct a PPI network of the different DEGs (
Using Cytoscape Mcode, a total of 13 hub genes were selected. These 13 genes are listed in
RT-qPCR was used to detect the expression of hub genes in cancerous and paracancerous tissues of patients with BC. The results indicated that the expression levels of the hub genes KLRD1, MT1X and PDGFRA in cancer tissues were significantly lower than those in adjacent tissues (
Immunohistochemical analysis of TGFBI protein indicated that the positive expression rate in tumor tissue was high (
BC is one of the 10 most common tumor types. In recent years, mortalities from BC have increased (
Through the analysis of two mRNA microarray datasets, a total of 362 DEGs, comprising 315 upregulated and 47 downregulated DEG, were identified in the present study. Enrichment analysis using GO and KEGG was performed to explore the interactions between DEGs. DEGs were mainly enriched in extracellular matrix organization, heparin binding and plasma membrane. In previous studies, the extracellular matrix has been found to have an important role in the occurrence and development of tumors, and may cause tumor invasion and migration (
In the present study, 13 DEGs were selected as the central genes with a degree of connectivity of ≥10. Among these central genes, PDGFRA had the highest nodal degree (
ENPP3 is a molecular therapeutic target for renal cell carcinoma. It is expressed in renal tubules, activated basophils and mast cells. In cancer, ENPP3 is expressed in most clear-cell histologies (94%), such as bladder tissue and kidney tissue. However, it still requires to be proven whether ENPP3 may be used as a molecular therapeutic target for BC (
In conclusion, the present study set out to identify DEGs that may be associated with BC. A total of 13 hub genes were identified and through various bioinformatics analyses, these genes were determined to serve as potential diagnostic markers of BC; however, the biological function of these genes in BC still requires further investigation.
Not applicable.
TCGA mRNA expression and clinical data were downloaded from the TCGA public database (
LW and XY completed the experiments. BS and LL were involved in the study conception and design. BS and LW performed the bioinformatics analysis. LL and XY wrote and edited the manuscript. BS and LL checked and confirm the authenticity of all the raw data. All authors have read and approved the final version of the manuscript.
Written informed consent was provided by the three patients who donated their BC tissues. Ethical review was performed and the protocol was approved by the ethics committee of Xinjiang Medical University (Urumqi, China; review no. XJYKDXR20220106001).
Not applicable.
The authors declare that they have no competing interests.
Venn diagram, PPI network and the most important modules of DEGs. (A) DEGs were selected from the mRNA expression datasets GSE38264 and GSE61615 using the selection criteria of fold change >2 and P-value <0.01. The two data sets had 362 overlapping DEGs. (B) The PPI network of DEGs was built using Cytoscape. The upregulated genes are marked in red and the downregulated genes in light blue. (C) The most important module was obtained from the PPI network with 12 nodes. PPI, protein-protein interaction; DEG, differentially expressed gene.
Interaction network and biological process analysis of hub genes. (A) The network of the hub gene and its co-expressed genes were analyzed using Cytoscape. (B) Biological process analysis for determining the central genes by using the plug-in biological network ontology tool (version 3.0.3) in Cytoscape. The color depth of the node refers to the corrected P-value of the body. The size of the nodes refers to the number of genes involved in the body. P<0.01 was considered to be statistically significant. (C) The hierarchical clustering of central genes was performed using the University of California Santa Cruz website. The upregulated genes are displayed in red and the downregulated ones in blue. In the category ‘sample type’, pink bars indicate non-cancerous samples and blue bars indicate breast cancer samples. In the category ‘clinical T stage’, light green to dark red represents clinical stage T0-T4. In ‘days to death’, light green to dark red represents the time of death from short to long.
Kaplan-Meier plotter online platform was used to analyze overall survival associated with central genes. P<0.05 was considered statistically significant. Survival analysis for (A) PDGFRA, (B) TLR1, (C) CASQ2, (D) BOC, (E) TGFBI, (F) KLRD1, (G) ADAP2, (H) ITGA4, (I) ENPP3, (J) MT1X, (K) IGSF10 and (L) CRYAB in bladder cancer. HR, hazard ratio (presented with 95% CI).
Hub gene expression in Blaveri Bladder, Dyrskjot Bladder, Sanchez-Carbayo Bladder and Stransky bladder datasets. (A) PTPRC in Blaveri Bladder and Sanchez-Carbayo Bladder datasets, (B) PDGFRA in Blaveri Bladder, Sanchez-Carbayo Bladder and Stransky bladder datasets, (C) CASQ2 in Blaveri Bladder, Sanchez-Carbayo Bladder and Stransky bladder datasets, (D) TGFBI in Blaveri Bladder, Dyrskjot Bladder, Sanchez-Carbayo Bladder and Stransky bladder datasets, (E) KLRD1 in Blaveri Bladder, Dyrskjot Bladder, Sanchez-Carbayo Bladder and Stransky bladder datasets, (F) MT1X in Blaveri Bladder, Dyrskjot Bladder, Sanchez-Carbayo Bladder and Stransky bladder datasets. Heat maps of PTPRC, PDGFRA, CASQ2, TGFBI, KLRD1 and MT1X gene expression in clinical bladder cancer samples vs. normal tissues. P<0.05 was considered statistically significant. 1-4 in the figure are respectively quoted from refs. 69-72. Data source cited in figure.
In The Cancer Genome Atlas dataset for clinical patients with bladder cancer, the expression of each gene was compared among different tumor stages. (A) PTPRC (P=0.006), (B) PDGFRA (P=0.045), (C) CASQ2, (D) BOC, (E) KLRD1 (P=0.005), (F) ADAP2 (P=0.007), (G) ITGA4 (P=0.021), (H) IGSF10 (P=0.023) and (I) CRYAB (P=0.008).
RT-qPCR was used to verify Hub genes. RT-qPCR was used to verify the expression of the differential genes (A) KLRD1, (B) MT1X, (C) PDGFRA, (D) PTPRC and (E) TGFBI in BC and normal tissues. (F) Immunohistochemistry was used to verify the expression of TGFBI in BC and normal tissues (scale bars, 200 µm). (G) H&E staining was used to compare the difference between BC and normal tissues (scale bars, 200 µm). ***P<0.001. BC, bladder cancer.
Primer sequences.
Gene/direction | Primer sequence (5'-3') |
---|---|
GAPDH | |
Forward | TGCACCACCAACTGCTTAGC |
Reverse | GGCATGGACTGTGGTCATGAG |
KLRD1 | |
Forward | GTGAACAGAAAACTTGGAACGA |
Reverse | ATAGATACTGGGAGAGTGCAGA |
MT1X | |
Forward | CCTGCAAGAAGAGCTGCTGC |
Reverse | GCAGCTGCACTTGTCTGACG |
PDGFRA | |
Forward | GAAAATGAAAAGGTTGTGCAGC |
Reverse | CTCTTCTTCAGACATGGGGTAC |
PTPRC | |
Forward | AAGTGCGGAAACAGAAGAGGTAGTG |
Reverse | CAGGGTAGGTGCTGGCAATGAC |
TGFBI | |
Forward | ACTCAGCCAAGACACTATTTGA |
Reverse | CTTGTATGGGCATCAATTGGAG |
GO and KEGG pathway enrichment analysis of DEGs in BC samples.
A, GO | |||
---|---|---|---|
Term | Description | Count in gene set | P-value |
GO:0030198 | Extracellular matrix organization | 17 | 1.01x10-6 |
GO:0008284 | Positive regulation of cell proliferation | 24 | 2.81x10-5 |
GO:0001525 | Angiogenesis | 15 | 9.06x10-5 |
GO:0018108 | Peptidyl-tyrosine phosphorylation | 12 | 1.58x10-4 |
GO:0008201 | Heparin binding | 18 | 6.71x10-9 |
GO:0005509 | Calcium ion binding | 36 | 1.50x10-7 |
GO:0042803 | Protein homodimerization activity | 29 | 2.09x10-4 |
GO:0005044 | Scavenger receptor activity | 6 | 1.84x10-3 |
GO:0043565 | Sequence-specific DNA binding | 17 | 3.11x10-2 |
GO:0005887 | Integral component of plasma membrane | 61 | 2.29x10-9 |
GO:0005886 | Plasma membrane | 125 | 8.24x10-9 |
GO:0005576 | Extracellular region | 61 | 2.42x10-7 |
GO:0016021 | Integral component of membrane | 142 | 2.48x10-7 |
B, KEGG | |||
Term | Description | Count in gene set | P-value |
Hsa04015 | Rapl signaling pathway | 14 | 3.24x10-4 |
Hsa05200 | Pathways in cancer | 19 | 9.61x10-4 |
Hsa04151 | PI3K-Akt signaling pathway | 15 | 1.02x10-2 |
Hsa05205 | Proteoglycans in cancer | 10 | 2.05x10-2 |
Hsa04010 | MAPK signaling pathway | 12 | 1.38x10-2 |
GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes.
GO pathway enrichment analysis of differentially expressed genes in the most significant module.
Pathway ID | Pathway description | Count in gene set | P-value |
---|---|---|---|
GO:1990405 | Protein antigen binding | 2 | 0.003254 |
GO:0005886 | Plasma membrane | 8 | 0.008012 |
GO:0005515 | Protein binding | 11 | 0.008428 |
GO:0005887 | Integral component of plasma membrane | 5 | 0.010799 |
GO:0046872 | Metal ion binding | 5 | 0.036499 |
GO:0007155 | Cell adhesion | 3 | 0.041053 |
GO:0009986 | Cell surface | 3 | 0.047841 |
GO, Gene Ontology.
Functional roles of 13 hub genes with degree ≥10.
Gene symbol | Full name | Function |
---|---|---|
PTPRC | Protein tyrosine phosphatase receptor type C | Essential regulator of T- and B-cell antigen receptor signaling |
PDGFRA | Platelet-derived growth factor receptor α | Mutations in this gene have been associated with idiopathic hypereosinophilic syndrome, somatic and familial gastrointestinal stromal tumors and a variety of other cancers |
TLR1 | Toll-like receptor 1 | Associated with nasopharyngeal cancer |
CASQ2 | Calsequestrin 2 | Mutations in this gene cause stress-induced polymorphic ventricular tachycardia |
BOC | BOC cell adhesion-associated oncogene regulated | Component of a cell-surface receptor complex that mediates cell-cell interactions between muscle precursor cells, and promotes myogenic differentiation |
TGFBI | Transforming growth factor β-induced | Mutations in this gene are associated with multiple types of corneal dystrophy |
KLRD1 | Killer cell lectin like receptor D1 | Several transcript variants encoding different isoforms have been found for this gene |
ADAP2 | ArfGAP with dual PH domains | The gene is able to block the entry of certain RNA viruses |
ITGA4 | Integrin subunit α4 | This gene is associated with gastrointestinal stromal tumors |
ENPP3 | Ectonucleotide pyrophosphatase/phosphodiesterase 3 | Antibody drugs of ENPP3 may be used to treat advanced renal cell carcinoma |
MT1X | Metallothionein 1X | High expression of this gene is related to the progression of hepatocellular carcinoma |
IGSF10 | Immunoglobulin superfamily member 10 | High expression of this gene is related to the occurrence and development of breast cancer |
CRYAB | Crystallin αB | CRYAB inhibits migration and invasion of bladder cancer cells through the PI3K/AKT and ERK pathways |
All information is from the National Center for Biotechnology Information database.