We use a systems biology approach to construct protein-protein interaction networks (PPINs) for early and late stage bladder cancer. By comparing the networks of these two stages, we find that both networks showed very significantly different mechanisms. To obtain the differential network structures between cancer and noncancer PPINs, we constructed cancer PPIN and noncancer PPIN network structures for the two bladder cancer stages using microarray data from cancer cells and their adjacent noncancer cells, respectively. With their carcinogenesis relevance values (CRVs), we identified 152 and 50 significant proteins and their PPI networks (network markers) for early and late stage bladder cancer by statistical assessment. To investigate the evolution of network biomarkers in the carcinogenesis process, primary pathway analysis showed that the significant pathways of early stage bladder cancer are related to ordinary cancer mechanisms, while the ribosome pathway and spliceosome pathway are most important for late stage bladder cancer. Their only intersection is the ubiquitin mediated proteolysis pathway in the whole stage of bladder cancer. The evolution of network biomarkers from early to late stage can reveal the carcinogenesis of bladder cancer. The findings in this study are new clues specific to this study and give us a direction for targeted cancer therapy, and it should be validated in vivo or in vitro in the future.
Cancer is the leading cause of death worldwide and its etiology occurs at the DNA, RNA, or protein level. It is a very complex disease involving cascades of spatial and temporal changes in the genetic network and metabolic pathways [
Biomarker discovery of various cancers is one of the key topic areas of cancer research. It can aid investigations into carcinogenesis and novel drug designs for cancer therapy. Several bioinformatics methods have been developed and applied to compare normal tissue with cancerous tissue to determine what cancer driving genes can act as cancer biomarkers [
Genes and proteins function cooperatively to regulate common biological cell processes by coregulating each other [
Based on their PPI information and the gene expression profiles from cancer and surrounding normal samples, two PPI networks with quantitative protein association abilities for each cancer stage (early stage and late stage) and the surrounding noncancerous tissue are constructed, respectively. For each stage, the network structure and protein association abilities of the cancer and noncancer PPI networks are then compared to obtain sets of significant proteins which play important roles in the carcinogenesis process of bladder cancer.
Recently, PPI targets seem to have become a paradigm for the drug discovery of cancer therapy and precision medicine [
Therefore, future research directed at PPI target discovery, PPI interface characterization, and PPI-focused chemical libraries are expected to accelerate the development of the next generation of PPI-based anticancer agents. However, the PPI networks of cancer are very complex and quite differ between early and late stage cancer. In such circumstances, we will focus on the PPI network markers with their significant carcinogenesis relevance value (CRV) to exploit the important targets and their PPI interface for early and late stage cancer characterization. Then, we will not only gain insight into the crucial common pathways involved in bladder carcinogenesis, but we will also obtain a highly promising PPI target for bladder cancers. If we are then able to develop various combined anticancer strategies to target PPIs in the early and late stage network markers in the future, it may provide emerging opportunities for anticancer therapeutic approaches.
Chen et al. developed a dynamical network biomarker (DNB) that can serve as a general early warning signal to indicate an imminent bifurcation or sudden deterioration before the critical transition occurs; that means it can identify predisease state by time series microarray data. We use different approach from their methods by sample microarray data from bladder cancer patients of different stages. Our approach could also be extended to predict some similar results as their research. That is, in this study, we simply divided the cancer into early and late stages, but there are more stages of cancer, such as stages I, II, III, and IV. If we could observe the time evolution of the cancer biomarkers at these more different stages, we could also predict the predisease state by comparing it with these cancer biomarkers at different stages [
A flowchart representing the construction of network biomarkers for early and late stage bladder cancer is shown in Figure
The flowchart of constructing both stages of network marker of bladder cancer and the investigation of the carcinogenesis mechanisms. We integrate microarray data, GO database, and PPI information to construct the PPI network. These data are used for pool selection, and then the selected proteins and the microarray data are used for the contribution of protein-protein interaction network (PPIN) by maximum likelihood estimation and model order detection method, resulting in bladder cancer PPIN (CPPIN) and noncancer PPIN (NPPIN) of early and late stage. The two constructed PPINs can be used for the determination of significant proteins of tumorigenesis by the difference between two PPI matrices of two constructed PPINs. With the help of the differential PPI matrix (network) between CPPIN and NPPIN, carcinogenesis relevance value (CRV) is computed for each protein, and significant proteins in carcinogenesis are determined based on
The microarray gene expression dataset of bladder cancer was obtained from the NCBI gene expression omnibus (GEO) [
Descriptive information on datasets extracted from the GEO database used in this study.
Cancer | GEO |
Early |
Late |
Adjacent normal | Platform |
---|---|---|---|---|---|
Bladder cancer | GSE13507 | 106 | 37 | 58 | GPL6102 |
Cases are grouped by cancer and surrounding normal tissues came from human patients of early stage and late bladder cancer.
The PPI data for
To integrate gene expression with PPI data to construct the corresponding CPPINs and NPPINs, we set up a protein pool containing differentially expressed proteins. The gene expression values were reasonably assumed to correlate with protein expression levels. We used one-way analysis of variance (ANOVA) to analyze the expression of each protein and select for proteins with differential expression levels. This method allowed determination of significant differences between cancer and noncancer datasets. The null hypothesis (Ho) was based on the assumption that the mean protein expression levels of cancer and noncancer sets are the same. Bonferroni adjustment [
On the strength of the significant pool and PPI information, candidate PPI networks for early and late stage bladder cancer were constructed for bladder cancer and noncancer by linking the proteins that interacted with each other. In other words, the proteins that had PPI information through the pool were linked together, resulting in candidate PPI networks.
As the candidate PPIN included all possible PPIs under various environments, different organisms, and experimental conditions, the candidate PPIN needed to be further confirmed by microarray data to identify appropriate PPIs according to the biological processes that are relevant to cancer. To remove false positive PPIs from each candidate PPIN for different biological conditions, we used both a PPI model and a model order detection method to prune each candidate PPIN using the corresponding microarray data to approach the actual PPIN. Here, the PPIs of a target protein
After constructing (
Once the association parameters for all proteins in the candidate PPI network were identified for each protein, the significant protein associations were determined using the interaction model order detection method based on the estimated association abilities. The Akaike information criterion (AIC) [
After
If there is no PPI between proteins
The different matrix
The
In order to investigate what proteins are more likely involved in the
Based on the
The intersections of these significant proteins in the early and late stages of bladder cancer and their PPIs are known as the core network markers appearing in all stages of bladder cancer. In contrast, the unique significant proteins and their PPIs in each stage of bladder cancers are known as the specific network markers for each stage of cancer. We found that there were 18 significant proteins that could be classified as a core network marker in the whole carcinogenesis process of bladder cancer. We also found 134 significant proteins in the specific network marker of early stage bladder cancer and 32 significant proteins in the specific network marker of late stage bladder cancer.
Much valuable cellular information can be found in the known pathways, which are useful for describing most “normal” biological phenomena. All of these known pathways are the result of repeated testing and verification and the entire pathway network has given definitions for most links. Therefore, the proteins we identified to be significant in the above network markers were mapped onto the known pathway networks (e.g., the KEGG or PANTHER pathway) to investigate significant pathways with the network marker and to explore the relationships between these pathways and the carcinogenesis of bladder cancer. This approach supports the view that systems biology can help identify significant network biomarkers in both normal and cancerous pathways to their roles in the pathogenesis of cancer.
Together with comprehensive pathway databases such as the Kyoto Encyclopedia of Genes and Genomes (KEGG), we used a series of bioinformatics pathway analysis tools to identify biologically relevant pathway networks [
Our cancer PPI model is constructed from the differential expression of cancer and noncancer microarray data and data mining of PPI information from BioGRID database. So, the early and late stage bladder cancer CPPINs (cancer PPI networks) and NPPINs (noncancer PPI networks) are the results of our systems biology model using the original microarray data and PPI databases. There are three key factors that will affect the final results. The effect of different microarray data: we know that the microarray data has the shortage of irreproducible. That means even in the same case the microarray data does not promise to produce the same result as the previous ones. Also, for the same cancers, patients of different ethnics, different age, or different sex will give the different microarray data. This is the first factor to affect the final results. The effect of different original PPI databases: we know that PPI databases, such as BioGRID and MIPS, are constructed from putative and validated by wet-lab experiments. Due to the advances of many high-throughput experimental skills, the original PPI databases are evolved with time growing. The new updated original PPI databases are the second factor to affect the final results. The effect of systems biology model: microarray data, PPI databases, and PPI interaction model in (
The constructed cancer PPIN (CPPIN) and noncancer PPIN (NPPIN) for early and late stage bladder cancer. The protein association numbers of CPPIN and NPPIN with respect to early and late bladder cancers are listed below (CPPIN/NPPIN): early stage bladder cancer (3388/3151) and late stage bladder cancer (634/1185). The figures are created using Cytoscape.
We also know that the biosystems are evolved with time. It is obvious that the early stage and late stage patients have very different symptoms; they are the key features for us to classify early and late stage bladder cancers. Since the two stage bladder cancer patients have great different symptoms, it is undoubted that the microarray data of these two stage patients will show to be quite different. As described above, the protein expression from microarray data is one of the key factors of our systems biology model to give the final CPPINs and NPPINs. And the CPPINs and NPPINs give the final network biomarkers from our systems biology model. So, the most important thing for the network biomarkers evolving is due to the evolution of microarray data at both stages of bladder cancer, which is inherent in the exhibition of cancer-related genes due to DNA mutations in the carcinogenesis process.
In the first instance, we built the CPPIN and NPPIN for early and late stage bladder cancer (Figure
After
The 18 identified significant proteins of core network marker in both early and late stage bladder cancers.
Common network marker of early and late stage bladder cancer | ||||
---|---|---|---|---|
Protein | CRV-early |
|
CRV-late |
|
UBC | 29.91709 | <1 |
158.5321 | <1 |
CUL3 | 27.96694 | <1 |
13.0117 | <1 |
CUL5 | 14.97713 | <1 |
4.834916 | 0.002872 |
RPL22 | 10.47367 | <1 |
8.110447 | <1 |
SUMO2 | 8.391421 | <1 |
10.34113 | <1 |
APP | 6.933807 | <1 |
11.47363 | <1 |
SH3KBP1 | 6.765387 | <1 |
4.447911 | 0.00619 |
PTBP1 | 6.740458 | <1 |
4.511016 | 0.005506 |
ELAVL1 | 6.635085 | <1 |
10.77056 | <1 |
SIRT7 | 5.55441 | 3.13 |
7.656515 | <1 |
MYC | 4.6109 | 0.00072 | 13.0423 | <1 |
HSP90AA1 | 4.60136 | 0.00072 | 6.513345 | 0.000123 |
COPS5 | 3.898548 | 0.003163 | 6.601779 | 9.22 |
ESR1 | 3.873735 | 0.003383 | 5.6189 | 0.000614 |
BRCA1 | 3.788256 | 0.00415 | 11.5863 | <1 |
TERF2IP | 3.680287 | 0.00487 | 5.202998 | 0.00149 |
SOX2 | 3.534024 | 0.007219 | 5.495338 | 0.00076 |
CUL1 | 3.521039 | 0.00736 | 13.2669 | <1 |
The identified top 20 significant proteins in both early and late stage bladder cancer individually.
Early stage bladder cancer ( |
Late stage bladder cancer ( |
||||
---|---|---|---|---|---|
CRV | Name |
|
CRV | Name |
|
UBC | 29.91709 | <1 |
UBC | 158.5321 | <1 |
CUL3 | 27.96694 | <1 |
VCAM1 | 20.98798103 | <1 |
RIOK2 | 16.02326 | <1 |
RPS13 | 20.09693015 | <1 |
CUL5 | 14.97713 | <1 |
TP53 | 19.5883 | <1 |
RPS23 | 12.13218 | <1 |
HDAC1 | 19.2879 | <1 |
RPL12 | 10.87102 | <1 |
HSPA8 | 17.24137906 | <1 |
RPL22 | 10.47367 | <1 |
RPS27A | 17.23738059 | <1 |
RANBP2 | 9.8086 | <1 |
TUBB | 17.03734405 | <1 |
PAN2 | 9.521207 | <1 |
CDK2 | 16.7366 | <1 |
DHX9 | 9.47832 | <1 |
VIM | 15.89214155 | <1 |
RPS8 | 8.722495 | <1 |
KIAA0101 | 15.8188 | <1 |
RPL27 | 8.641642 | <1 |
ITGA4 | 15.69058519 | <1 |
SUMO2 | 8.391421 | <1 |
GSK3B | 15.44597966 | <1 |
HNRNPH3 | 8.011681 | <1 |
EEF1A1 | 14.21690842 | <1 |
CDC5L | 7.950851 | <1 |
RUVBL2 | 13.63207486 | <1 |
RUVBL1 | 7.887244 | <1 |
PCNA | 13.3217 | <1 |
SF3A1 | 7.468209 | <1 |
CUL1 | 13.2669 | <1 |
APP | 6.933807 | <1 |
MYC | 13.0423 | <1 |
CCT3 | 6.860228 | <1 |
CUL3 | 13.0117 | <1 |
SH3KBP1 | 6.765387 | <1 |
HNRNPA0 | 12.15264603 | <1 |
We analyzed the pathway of early stage bladder cancer using the DAVID database. Our initial observation revealed that several cancer pathways were hit by the 152 key proteins, including 11 genes in hsa05200: pathways in cancer (Figure
Overview of significant pathways in network marker of early stage bladder cancer. Among these KEGG pathways via DAVID tool (Table
The proteins in the early stage bladder cancer network marker are enriched in “hsa05200:Pathways in cancer” (Rank 2 in Table
The proteins in the early stage bladder cancer network marker are enriched in “hsa05219:Bladder cancer” (Rank 7 in Table
The proteins in the early stage bladder cancer network marker are enriched in “hsa04110:Cell cycle” (Rank 1 in Table
The proteins in the early stage bladder cancer network marker are enriched in “hsa04110:Wnt signaling pathway” (Rank 5 in Table
The proteins in the early stage bladder cancer network marker are enriched in “hsa04120:Ubiquitin mediated proteolysis pathway” (Rank 13 in Table
Next, we proceeded to analyze the important pathways related to early stage bladder cancer (Table
(a) The pathways analysis for 152 early stage significant proteins in carcinogenesis. (b) The pathway analysis and gene set enrichment analysis of the top 20 proteins of early stage bladder cancer on (
Rank | Term | Count | Symbol |
|
---|---|---|---|---|
1 | hsa04110:Cell cycle | 13 | YWHAZ, CREBBP, TP53, PRKDC, RB1, CDK2, HDAC2, EP300, HDAC1, PCNA, MDM2, MYC, andCUL1 | 1.50 |
2 | hsa05200:Pathways in cancer | 11 | TRAF2, EP300, HDAC2, HDAC1, CREBBP, TP53, MDM2, RB1, MYC, CDK2, and CTNNB1 | 3.51 |
3 | hsa05215:Prostate cancer | 7 | EP300, CREBBP, TP53, MDM2, RB1, CDK2, and CTNNB1 | 1.45 |
4 | hsa05220:Chronic myeloid leukemia | 6 | HDAC2, HDAC1, TP53, MDM2, RB1, and MYC | 1.32 |
5 | hsa04310:Wnt signaling pathway | 6 | EP300, CREBBP, TP53, MYC, CUL1, and CTNNB1 | 3.78 |
6 | hsa05222:Small cell lung cancer | 5 | TRAF2, TP53, RB1, MYC, and CDK2 | 4.04 |
7 | hsa05219:Bladder cancer | 4 | TP53, MDM2, RB1, and MYC | 7.24 |
8 | hsa04330:Notch signaling pathway | 4 | EP300, HDAC2, HDAC1, and CREBBP | 0.001008 |
9 | hsa04520:Adherens junction | 4 | EP300, CREBBP, SRC, and CTNNB1 | 0.00418 |
10 | hsa04350:TGF-beta signaling pathway | 4 | EP300, CREBBP, MYC, and CUL1 | 0.005889 |
11 | hsa05016:Huntington's disease | 5 | EP300, HDAC2, HDAC1, CREBBP, and TP53 | 0.006736 |
12 | hsa05216:Thyroid cancer | 3 | TP53, MYC, and CTNNB1 | 0.00676 |
13 | hsa04120:Ubiquitin mediated proteolysis | 4 | CUL3, MDM2, BRCA1, and CUL1 | 0.020254 |
14 | hsa05213:Endometrial cancer | 3 | TP53, MYC, and CTNNB1 | 0.020793 |
15 | hsa05214:Glioma | 3 | TP53, MDM2, and RB1 | 0.029762 |
16 | hsa04115:p53 signaling pathway | 3 | TP53, MDM2, and CDK2 | 0.034267 |
17 | hsa05218:Melanoma | 3 | TP53, MDM2, and RB1 | 0.037091 |
18 |
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19 |
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20 |
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21 |
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22 |
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23 |
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The significant pathways via DAVID Bioinformatics database are selected for the 152 significant proteins in carcinogenesis. Black background indicates
GO:term |
|
Corrected |
|
|
|
|
Term name |
---|---|---|---|---|---|---|---|
( |
|||||||
0044260 | 5.3 |
1.7 |
14791 | 19 | 3428 | 18 | Cellular macromolecule metabolic process |
0043170 | 7.3 |
2.4 |
14791 | 19 | 3975 | 18 | Macromolecule metabolic process |
0044237 | 3.6 |
1.2 |
14791 | 19 | 4963 | 18 | Cellular metabolic process |
0006414 | 1.4 |
4.7 |
14791 | 19 | 101 | 5 | Translational elongation |
0019538 | 1.0 |
3.2 |
14791 | 19 | 2528 | 13 | Protein metabolic process |
0008152 | 1.1 |
3.6 |
14791 | 19 | 6033 | 18 | Metabolic process |
0044238 | 1.7 |
5.6 |
14791 | 19 | 5258 | 17 | Primary metabolic process |
0016071 | 4.4 |
0.0014 | 14791 | 19 | 364 | 6 | mRNA metabolic process |
0044267 | 3.3 |
0.0110 | 14791 | 19 | 1883 | 10 | Cellular protein metabolic process |
0009987 | 1.2 |
0.0406 | 14791 | 19 | 9216 | 19 | Cellular process |
|
|||||||
( |
|||||||
0030529 | 7.9 |
7.8 |
16768 | 18 | 510 | 9 | Ribonucleoprotein complex |
0032991 | 1.8 |
1.8 |
16768 | 18 | 3312 | 14 | Macromolecular complex |
0043228 | 1.2 |
1.2 |
16768 | 18 | 2051 | 11 | Non-membrane-bounded organelle |
0043232 | 1.2 |
1.2 |
16768 | 18 | 2051 | 11 | Intracellular non-membrane-bounded organelle |
0005840 | 1.5 |
1.5 |
16768 | 18 | 196 | 5 | Ribosome |
0005829 | 2.0 |
2.0 |
16768 | 18 | 1269 | 9 | Cytosol |
0043229 | 8.3 |
8.2 |
16768 | 18 | 8759 | 18 | Intracellular organelle |
0043226 | 8.5 |
8.4 |
16768 | 18 | 8773 | 18 | Organelle |
0044445 | 1.7 |
0.0016 | 16768 | 18 | 150 | 4 | Cytosolic part |
0033279 | 2.8 |
0.0287 | 16768 | 18 | 123 | 3 | Ribosomal subunit |
|
|||||||
( |
|||||||
0003735 | 1.0 |
1.1 |
15767 | 19 | 161 | 5 | Structural constituent of ribosome |
0003723 | 1.8 |
0.0193 | 15767 | 19 | 755 | 6 | RNA binding |
0005198 | 8.0 |
0.0825 | 15767 | 19 | 643 | 5 | Structural molecule activity |
0031625 | 0.0013 | 0.1360 | 15767 | 19 | 45 | 2 | Ubiquitin protein ligase binding |
0003678 | 0.0013 | 0.1360 | 15767 | 19 | 45 | 2 | DNA helicase activity |
0004535 | 0.0024 | 0.2480 | 15767 | 19 | 2 | 1 | Poly(A)-specific ribonuclease activity |
0033130 | 0.0048 | 0.4956 | 15767 | 19 | 4 | 1 | Acetylcholine receptor binding |
0008201 | 0.0079 | 0.8140 | 15767 | 19 | 112 | 2 | Heparin binding |
0030332 | 0.0096 | 0.9890 | 15767 | 19 | 8 | 1 | Cyclin binding |
0004386 | 0.0129 | 1 | 15767 | 19 | 145 | 2 | Helicase activity |
The Wnt/
Other pathways identified in early stage bladder cancer, such as the Notch signaling pathway, adherens junctions, the TGF-
Overview of significant pathways in network marker of late stage bladder cancer. Among these KEGG pathways via DAVID tool (Table
The proteins in the late stage bladder cancer network marker are enriched in “hsa03010:Ribosome” (Rank 1 in Table
The proteins in the late stage bladder cancer network marker are enriched in “hsa03040:Ribosome” (Rank 2 in Table
The proteins in the early stage bladder cancer network marker are enriched in “hsa04120:Ubiquitin mediated proteolysis pathway” (Rank 3 in Table
The NOA analysis results of the pathway and gene enrichment analysis of the early stage bladder cancer is shown in Table
The most important results in this study as compared to our previous work are that we reveal related pathways of late stage bladder cancer in comparison to early stage cancer to reveal the evolution of network biomarkers in the carcinogenesis process. From Table
(a) The pathways analysis for 50 significant proteins in late stage bladder cancer carcinogenesis. (b) The pathway analysis and gene set enrichment analysis of the top 20 proteins of late stage bladder cancer on (
Rank | Term | Count | Symbol |
|
---|---|---|---|---|
1 | hsa03010:Ribosome | 8 | RPS28, RPS16, RPL22, RPL27, RPL12, RPS6, RPS8, and RPS23 | 2.26 |
2 | hsa03040:Spliceosome | 5 | HSPA1L, CDC5L, SF3A1, SNRPE, and HNRNPU | 0.004054716 |
3 | hsa04120:Ubiquitin mediated proteolysis | 4 | CUL3, CUL5, BRCA1, and CUL1 | 0.034906958 |
The significant pathways via DAVID Bioinformatics database are selected for the 50 significant proteins in carcinogenesis.
GO:term |
|
Corrected |
|
|
|
|
Term name |
---|---|---|---|---|---|---|---|
( |
|||||||
GO:0045786 | 1.8 |
0.0010 | 14791 | 18 | 178 | 5 | Negative regulation of cell cycle |
GO:0022402 | 2.4 |
0.0014 | 14791 | 18 | 562 | 7 | Cell cycle process |
GO:0007050 | 8.4 |
0.0049 | 14791 | 18 | 111 | 4 | Cell cycle arrest |
GO:0051726 | 8.5 |
0.0049 | 14791 | 18 | 435 | 6 | Regulation of cell cycle |
GO:0060710 | 1.3 |
0.0080 | 14791 | 18 | 5 | 2 | Chorioallantoic fusion |
GO:0044260 | 1.4 |
0.0081 | 14791 | 18 | 3428 | 13 | Cellular macromolecule metabolic process |
GO:0051052 | 1.5 |
0.0091 | 14791 | 18 | 130 | 4 | Regulation of DNA Metabolic process |
GO:0008629 | 2.6 |
0.0155 | 14791 | 18 | 49 | 3 | Induction of apoptosis by intracellular signals |
GO:0006917 | 2.8 |
0.0162 | 14791 | 18 | 313 | 5 | Induction of apoptosis |
GO:0012502 | 2.8 |
0.0165 | 14791 | 18 | 314 | 5 | Induction of programmed cell death |
|
|||||||
( |
|||||||
GO:0032991 | 5.2 |
5.5 |
16768 | 18 | 3312 | 16 | Macromolecular complex |
GO:0005829 | 1.4 |
1.5 |
16768 | 18 | 1269 | 10 | Cytosol |
GO:0043234 | 2.5 |
2.6 |
16768 | 18 | 2748 | 12 | Protein complex |
GO:0005654 | 6.1 |
6.4 |
16768 | 18 | 465 | 6 | Nucleoplasm |
GO:0044428 | 6.4 |
0.0067 | 16768 | 18 | 1932 | 9 | Nuclear part |
GO:0000307 | 9.8 |
0.0102 | 16768 | 18 | 14 | 2 | Cyclin-dependent protein kinase holoenzyme complex |
GO:0030529 | 1.5 |
0.0163 | 16768 | 18 | 510 | 5 | Ribonucleoprotein complex |
GO:0031461 | 4.9 |
0.0516 | 16768 | 18 | 31 | 2 | Cullin-RING ubiquitin ligase complex |
GO:0022627 | 7.4 |
0.0777 | 16768 | 18 | 38 | 2 | Cytosolic small ribosomal subunit |
GO:0043626 | 0.0010 | 0.1116 | 16768 | 18 | 1 | 1 | PCNA complex |
|
|||||||
( |
|||||||
GO:0019899 | 3.1 |
0.0037 | 15767 | 18 | 584 | 6 | Enzyme binding |
GO:0005515 | 1.1 |
0.0129 | 15767 | 18 | 8097 | 17 | Protein binding |
GO:0030337 | 0.0011 | 0.1335 | 15767 | 18 | 1 | 1 | DNA polymerase processivity factor activity |
GO:0000701 | 0.0011 | 0.1335 | 15767 | 18 | 1 | 1 | Purine-specific mismatch base pair DNA N-glycosylase activity |
GO:0031625 | 0.0011 | 0.1384 | 15767 | 18 | 45 | 2 | Ubiquitin protein ligase binding |
GO:0000166 | 0.0021 | 0.2510 | 15767 | 18 | 2283 | 8 | Nucleotide binding |
GO:0035033 | 0.0022 | 0.2669 | 15767 | 18 | 2 | 1 | Histone deacetylase regulator activity |
GO:0004696 | 0.0022 | 0.2669 | 15767 | 18 | 2 | 1 | Glycogen synthase kinase 3 activity |
GO:0000700 | 0.0022 | 0.2669 | 15767 | 18 | 2 | 1 | Mismatch base pair DNA N-glycosylase activity |
GO:0005200 | 0.0031 | 0.3705 | 15767 | 18 | 74 | 2 | Structural constituent of cytoskeleton |
The nucleolus is the site of ribosome biogenesis (Figure
Alternative splicing is a modification of the premessenger RNA (pre-mRNA) transcript in which internal noncoding regions of pre-mRNA (introns) are removed and then the remaining segments (exons) are joined (Figure
The NOA analysis results of the pathway and gene enrichment analysis of the late stage bladder cancer is shown in Table
The only pathway to intersect between early and late stage bladder cancer is the ubiquitin-mediated proteolysis pathway (Table
The pathways analysis for 18 significant proteins in early and late stage bladder cancer carcinogenesis.
Rank | Term | Count | Symbol |
|
---|---|---|---|---|
1 | hsa03010:Ribosome | 4 | CUL3, CUL5, BRCA1, and CUL1 | 1.4 |
Bladder cancer is among the 10 most common forms of carcinoma in the USA and worldwide. It is a lethal disease like other cancers and understanding the carcinogenesis mechanism can help to develop new therapeutic strategy. Identifying the PPI interface to develop small molecule inhibitors has become a new direction for targeted cancer therapy. This study, which follows from our prior work, analyzes the carcinogenesis mechanism from early to late stage bladder cancer using a network-based biomarker evolution approach. Other research studies do not distinguish network markers between these two stages of bladder cancer. Thus, our approach is advantageous in that it can provide added insight into the significant network marker evolution of the carcinogenesis process of bladder cancer. The network markers and their related pathways identified in early stage bladder cancer are mostly related to ordinary cancer mechanisms, which just show a highly active state of the early stage and cannot reveal additional novel results. All of these results should be validated in vivo or in vitro in the future. However, from the two specific and significant pathways identified in late stage bladder cancer, ribosome pathway and spliceosome pathway, we identified a novel result, which has potential to become a target for cancer therapy. The only core pathway in these two stages is the ubiquitin-mediated proteolysis pathway, which is a significant cue of carcinogenesis from early to late stage bladder cancer. Applying our method to study more cancers and more classification groups (such as stage, age, ethics, and sex) will give us further insight into the various pathogenesis mechanisms.
The authors declare that there is no conflict of interests regarding the publication of this paper.
The authors are grateful for the support provided by the Ministry of Science and Technology (NSC-102-2745-E-007-001-ASP).