Molecular expression profiling with respect to KEGG hsa05219 pathway.

One of the most promising avenues for interpreting large datasets of molecular expression profiles involves pathway-based analysis. Pathways are collection of genes and proteins that perform a well-defined biological task. These pathways have been established through decades of molecular biology research and are collected in a variety of public pathway repositories (KEGG and Reactome Pathway database). Understanding the complexity of these pathways is critical for understanding normal biological conditions and disease states and also since the number of known pathways within the cells is significantly smaller than the number of genes that is typically profiled, the transformation of data from a gene-centric view to a pathway-centred one represents a dramatic reduction in the number of dimensions. Such reduction allows a biologist to interpret and understand the data in a manner that is not possible when it is viewed as a collection of individual genes.

Introduction

Gene expression studies are used as an independent predictive method for prognosis. In cancer genomic studies, tremendous effort has been devoted to pathway-based analysis. Pathway analysis is a promising tool to identify the mechanisms that underlie disease, adaptive physiological compensatory responses and new avenues for investigation. Different pathways have different biological functions. Thus, it is reasonable to study each pathway separately. Among the many pathways, only a few have predictive power for cancer development. Among genes within predictive pathways, there are subsets having small to moderate predictive power, whereas the remaining are noisy genes [1-6].

Background

Genes have the inherent pathway structure, where pathways are composed of multiple genes with coordinated functions. The aim of this study was to identify genetic signatures associated with disease prognosis in bladder cancer with respect to hsa05219 pathway obtained from Kyoto Encyclopedia of Genes and Genomes (KEGG).

Methods

Microarray data files were taken from Gene Expression Omnibus (GEO), accession number GSE7476. Four different types of data files were generated from GSE7476 experiment by analyzing gene expression profiles in normal bladder tissues (controls), low grade superficial tumour samples (pathologically classified as Ta low grade, named as Ta), high grade superficial tumours with an unclear clinical behaviour (T1 high grade, named as T1) and high grade muscle invasive tumours (pathologically classified as T2, T3 or T4, named as T2+).

Data files representing controls and high grade invasive tumours (T2+) were compared for the current review work.

Affymetrix data files

Affymetrix gene expression chip was used for their study and the intensity values seen in the data file were log transformed values. One would subtract the control value from the experimental value to find the significant change in expression level. ‘Researchers generally’ use a cut off at least 2-fold change (linear value) between control and experiment to ‘screen significantly differentially expressed genes’.

Affymetrix does not have a certain threshold cut off that it recommends. Researchers in the community have seemed to adopt a value of about 100 (linear) so ∼6.65 (log). However, this does not mean that a value of 6.8 is expressed in a sample; this is just a general guideline that many researchers have adopted to filter out a bulk of the probe sets. Most researchers will first analyze the data based on fold change, then filter on intensity when a probe set is <100 in both samples. A change from 3.5 to 4.8 in a sample could very well be just a background, where as a change from 6.0 to 7.4 might be real.

hsa05219 pathway

Pathway hsa05219 referring to bladder cancer was selected from KEGG pathway database (section 6.1 cancers http://www.genome.jp/kegg/pathway.html). There are totally 42 genes listed in hsa05219 pathway which are believed to be involved in causing bladder cancer (Tables 1–3).

Table 1:

Gene list from hsa05219 pathway showing affymetrix ID, gene name, and gene ID

Sl no.	Affy ID	Gene name	Gene ID
1	201109_s_at	THBS1	7057
2	204633_s_at	RPS6KA5	9252
3	202284_s_at	CDKN1A	1026
4	209946_at	VEGFC	7424
5	202431_s_at	MYC	4609
6	204346_s_at	RASSF1	11186
7	203683_s_at	VEGFB	7423
8	224621_at	MAPK1	5594
9	206742_at	FIGF	2277
10	243829_at	BRAF	673
11	212046_x_at	MAPK3	5595
12	206324_s_at	DAPK2	23604
13	215179_x_at	PGF	5228
14	1566678_at	MMP2	4313
15	206254_at	EGF	1950
16	203132_at	RB1	5925
17	212983_at	HRAS	3265
18	201895_at	ARAF	369
19	202424_at	MAP2K2	5605
20	203891_s_at	DAPK3	1613
21	211506_s_at	IL8	3576
22	201244_s_at	RAF1	5894
23	202670_at	MAP2K1	5604
24	204947_at	E2F1	1869
25	203936_s_at	MMP9	4318
26	211607_x_at	EGFR	1956
27	228361_at	E2F2	1870
28	202246_s_at	CDK4	1019
29	202647_s_at	NRAS	4893
30	225160_x_at	MDM2	4193
31	216836_s_at	ERBB2	2064
32	204858_s_at	TYMP	1890
33	201131_s_at	CDH1	999
34	211300_s_at	TP53	7157
35	203139_at	DAPK1	1612
36	208712_at	CCND1	595
37	204379_s_at	FGFR3	2261
38	214352_s_at	KRAS	3845
39	203693_s_at	E2F3	1871
40	209644_x_at	CDKN2A	1029
41	211527_x_at	VEGFA	7422
42	204475_at	MMP1	4312

Table 2:

Displaying affymetrix grades and ID along with T2+ and control mean values

Sl no.	Affy grades	Affy ID	T2+ average	C average	Difference
1	A	201109_s_at	7.197941667	9.268797667	−2.070856
2	A	204633_s_at	5.538698	6.974227667	−1.435529667
3	A	202284_s_at	8.292146333	9.426557667	−1.134411333
4	A	209946_at	4.972374667	5.932246333	−0.959871667
5	A	202431_s_at	9.570811333	10.20563933	−0.634828
6	A	204346_s_at	5.68049	6.300829667	−0.620339667
7	A	203683_s_at	4.670015333	5.256459	−0.586443667
8	A	224621_at	8.188972333	8.710883	−0.521910667
9	A	206742_at	3.331917667	3.622964	−0.291046333
10	A	243829_at	5.140807	5.374049333	−0.233242333
11	A	212046_x_at	7.135151667	7.303775667	−0.168624
12	A	206324_s_at	4.684244667	4.824944667	− 0.1407
13	A	215179_x_at	7.105821667	7.123966	− 0.018144333
14	A	1566678_at	3.752619667	3.757442667	−0.004823
15	A	206254_at	3.159667333	3.112292333	0.047375
16	A	203132_at	6.140205333	6.082435667	0.057769667
17	A	212983_at	6.390220333	6.299536	0.090684333
18	A	201895_at	7.477000333	7.315159	0.161841333
19	A	202424_at	8.049549333	7.880261667	0.169287667
20	A	203891_s_at	6.282708	6.019414667	0.263293333
21	A	211506_s_at	7.225747667	6.952306667	0.273441
22	A	201244_s_at	8.239435333	7.863649667	0.375785667
23	A	202670_at	7.602015	7.090296333	0.511718667
24	A	204947_at	5.903176333	5.286578333	0.616598
25	A	203936_s_at	6.152433667	5.477768667	0.674665
26	A	211607_x_at	5.508033333	4.786028667	0.722004667
27	A	228361_at	5.898566667	5.131460667	0.767106
28	A	202246_s_at	8.944539333	8.051693	0.892846333
29	A	202647_s_at	6.692538	5.796430333	0.896107667
30	A	225160_x_at	7.044023333	6.121776667	0.922246667
31	A	216836_s_at	9.437352333	8.389638667	1.047713667
32	A	204858_s_at	6.671381333	5.556419	1.114962333
33	A	201131_s_at	10.37200433	9.242416333	1.129588
34	A	211300_s_at	5.945064333	4.792846667	1.152217667
35	A	203139_at	8.916442667	7.673051333	1.243391333
36	A	208712_at	8.620222333	7.024218667	1.596003667
37	A	204379_s_at	9.858403	8.250372333	1.608030667
38	A	214352_s_at	9.427273667	7.638116667	1.789157
39	A	203693_s_at	7.203678333	5.31342	1.890258333
40	A	209644_x_at	7.779116333	5.662257333	2.116859
41	A	211527_x_at	8.164427333	6.013361333	2.151066
42	A	204475_at	8.622475667	4.105322667	4.517153

Log difference between the control and study subjects which exceeds more than 1.0 or less than −1.0 were first screened. Downregulated genes are marked in green and upregulated genes are marked in red with respect to tumour samples.

Table 3:

List displaying up and downregulated genes, downregulated genes are marked in green colour and up regulated genes are marked in red colour with respect to tumour samples

Sl No	Gene name	Difference	Pathways involved
1	THBS1	−2.07086	Angiogenesis
2	RPS6KA5	−1.43553	MAPK signaling pathway
3	CDKN1A	−1.13441	Cell cycle
4	ERBB2	1.047714	ErbB signaling pathway
5	TYMP	1.114962	Nucleotide metabolism
6	CDH1	1.129588	Adherens junction
7	TP53	1.152218	p53 pathway—tumour suppressor
8	DAPK1	1.243391	MAPK signaling pathway
9	CCND1	1.596004	Cell cycle
10	FGFR3	1.608031	MAPK signaling pathway
11	KRAS	1.789157	MAPK signaling pathway
12	E2F3	1.890258	Cell cycle
13	CDKN2A	2.116859	Cell cycle—tumour suppressor
14	VEGFA	2.151066	Angiogenesis
15	MMP1	4.517153	Angiogenesis

MAPK, mitogen-activated protein kinase.

Conclusion

We have taken the list of genes associated with bladder cancer pathway from KEGG database. Log difference between the control and study subjects which exceeds more than 1.0 or less than −1.0 were first screened. THBS1, RPS6KA5 and CDKN1A are the genes which are highly expressed in control when compared with study subjects (T2+). These genes are associated with ‘angiogenesis’, ‘mitogen-activated protein kinase (MAPK) signaling pathway’ and ‘cell cycle’, respectively. ERBB2, TYMP, CDH1, TP53, DAPK1, CCND1, FGFR3, KRAS, E2F3, CDKN2A, VEGFA, MMP1 are the genes which are highly expressed in study when compared to control and these genes are associated with ‘ErbB signaling pathway’, ‘nucleotide metabolism’ ‘adherens junction’, ‘p53 pathway’, ‘cell cycle’, ‘MAPK signaling pathway’ and ‘angiogenesis’. By this current pathway analysis approach to the GSE7476 bladder cancer datasets, we can say that genes like ERBB2, TYMP, CDH1, TP53, DAPK1, CCND1, FGFR3, KRAS, E2F3, CDKN2A, VEGFA, MMP1 can be used as prognosis markers for bladder cancer gene expression study. Association of above 12 sets of genes for causing cancer was confirmed from Online Mendelian Inheritance in Man (OMIM) and articles from PubMed database. Further research is needed to evaluate whether the same gene signatures result from other bladder cancer profiling experiments (Table 4).

Table 4:

Genes from the hsa05219 pathway involved in different cancers

Gene name	Diff (N-T2+)	From reference article
THBS1	−2.070856	Not related to any cancer
RPS6KA5	−1.435529667	Not related to any cancer
CDKN1A	−1.134411333	Cervical cancer
VEGFC	−0.959871667	Gastric cancer
MYC	−0.634828	Acute lymphoblastic leukaemia (ALL) (precursor B lymphoblastic leukaemia), ALL (precursor T lymphoblastic leukaemia), Burkitt lymphoma, multiple myeloma, small cell lung cancer, oral cancer, penile cancer, ovarian cancer, choriocarcinoma, breast cancer, osteosarcoma, Kaposi’s sarcoma, laryngeal cancer
RASSF1	−0.620339667	Non-small cell lung cancer, bladder cancer, nasopharyngeal cancer
VEGFB	−0.586443667	Gastric cancer
MAPK1	−0.521910667	Not related to any cancer
FIGF	−0.291046333	Gastric cancer
BRAF	−0.233242333	Thyroid and malignant cancer
MAPK3	−0.168624	Not related to any cancer
DAPK2	−0.1407	Not related to any cancer
PGF	−0.018144333	Not related to any cancer
MMP2	−0.004823	Choriocarcinoma
EGF	0.047375	Gastric cancer
RB1	0.057769667	Chronic myeloid leukaemia (CML), small cell lung cancer, oesophageal cancer, breast cancer, osteosarcoma, glioma, hepatocellular carcinoma
HRAS	0.090684333	Bladder, penile, cervical, thyroid cancer, squamous cell carcinoma, hepatocellular carcinoma
ARAF	0.161841333	Not related to any cancer
MAP2K2	0.169287667	Not related to any cancer
DAPK3	0.263293333	Not related to any cancer
IL8	0.273441	Not related to any cancer
RAF1	0.375785667	Not related to any cancer
MAP2K1	0.511718667	Not related to any cancer
E2F1	0.616598	Not related to any cancer
MMP9	0.674665	Penile cancer
EGFR	0.722004667	Oral cancer, oesophageal, gastric, bladder, cervical, laryngeal cancer, glioma and choriocarcinoma
E2F2	0.767106	Not related to any cancer
CDK4	0.892846333	Cervical cancer, malignant melanoma, glioma
NRAS	0.896107667	Acute myeloid leukaemia (AML), multiple myeloma, oral cancer, thyroid cancer, adrenal carcinoma, malignant melanoma, hepatocellular carcinoma, autoimmune lymphoproliferative syndromes
MDM2	0.922246667	Penile cancer, choriocarcinoma, osteosarcoma, alveolar rhabdmycosarcoma and glioma
ERBB2	1.047713667	Gastric, pancreatic, bladder, endometrial, ovarian, cervical, breast cancer, choriocarcinoma, cholangiocarcinoma
TYMP	1.114962333	Not related to any cancer
CDH1	1.129588	Gastric, penile, breast, thyroid, nasopharyngeal cancer and hepatocellular carcinoma
TP53	1.152217667	MAPK signaling pathway, cell cycle, p53 signaling pathway, apoptosis, Wnt signaling pathway, neurotrophin signaling pathway, amyotrophic lateral sclerosis, Huntington’s disease, pathways in cancer, colorectal cancer, pancreatic cancer, endometrial cancer, glioma, prostate cancer, thyroid cancer, basal cell carcinoma, melanoma, bladder cancer, CML, small cell lung cancer, non-small cell lung cancer
DAPK1	1.243391333	Bladder cancer
CCND1	1.596003667	Hairy-cell leukemia, multiple myeloma, oral cancer, oesophageal cancer, breast cancer, laryngeal cancer
FGFR3	1.608030667	Multiple myeloma and bladder cancer
KRAS	1.789157	AML, multiple myeloma, non-small cell lung cancer, oral cancer, gastric cancer, pancreatic cancer, colorectal cancer, endometrial cancer, ovarian cancer, cervical cancer, thyroid cancer, squamous cell carcinoma, Kaposi’s sarcoma, cholangiocarcinoma, gallbladder cancer, hepatocellular carcinoma
E2F3	1.890258333	Not related to any cancer
CDKN2A	2.116859	CML, Burkitt lymphoma, adult T-cell leukemia, non-small cell lung cancer, malignant pleural mesothelioma, oral cancer, oesophageal cancer, pancreatic cancer, bladder cancer, penile cancer, osteosarcoma, malignant melanoma, squamous cell carcinoma, glioma, malignant islet cell carcinoma, cholangiocarcinoma, gallbladder cancer, hepatocellular carcinoma, nasopharyngeal cancer, laryngeal cancer, type II diabetes mellitus
VEGFA	2.151066	Gastric cancer
MMP1	4.517153	Choriocarcinoma

Log difference between the control and study subjects which exceeds more than 1.0 or less than −1.0 were first screened. Downregulated genes are marked in green and upregulated genes are marked in red with respect to tumour samples.

Difference in expression level

Significant findings

According to KEGG pathway, ‘hsa05219’ for bladder cancer, RB, CDKN2A and p53 are considered as tumour suppressor genes and FGFR3 and HRAS as oncogenes. Two of the tumour suppressor genes CDKN2A and p53 were expressed in significant level when compared with normal tissue samples. FGFR3 which is an oncogene is highly expressed in tumour samples compared to control tissue samples. CDKN2A, p53 and FGFR3 along with the other genes ERBB2, TYMP, CDH1, DAPK1, CCND1, KRAS, E2F3, VEGFA and MMP1 are unregulated in tumour tissue samples. All these genes play an important role in ErbB signaling pathway, nucleotide metabolism, adherens junction, p53 pathway, MAPK signaling pathway, cell cycle and angiogenesis. THBS1, RPS6KA5 and CDKN1A are downregulated in tumour tissue when compared with control tissue samples. Out of the 42 genes listed in the KEGG bladder cancer pathway, only eight genes RASSF1, RB1, HRAS, EGFR, ERBB2, DAPK1, FGFR3 and CDKN2A have reference support to prove their involvement in causing bladder cancer. This current review suggest the lack of research/involvement of the other genes in the pathway to cause bladder cancer. Pathway analysis of affymetrix data file shows upregulation of four genes ERBB2, DAPK1, FGFR3 and CDKN2A which have reference to prove their involvement in causing bladder cancer.