Computational Molecular Biology 2015, Vol. 5, No. 5, 1-9
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Table 3 Differentially expressed genes is significantly associated with genetic mutation of colon cancer
Genes
pValue
FDRB&H
FDRB&Y
Bonferroni
MMP1,MAOB,SPRR1A,
SPRR3,PHLDB2
1.08E-04
2.82E-01
1.09E-02
9.16E-02
SPRR1A,SPRR1B
1.38E-04
3.61E-01
1.18E-02
9.92E-02
EMP1,MAOB,DCLK1,AKAP12
2.70E-04
7.04E-01
1.46E-02
1.23E-01
PROM1
1.18E-03
6.49E+00
3.26E-03
2.99E-02
KRAS
1.18E-03
6.49E+00
3.26E-03
2.99E-02
Table 4 Significant genes in colon cancer that is involved in gene-gene interaction
Gene Ranking
Gene ID
Mean Diff
Express UP/Down
Redundant
1
205064_at
2.385
UP
FALSE
2
205488_at
-2.2332
DOWN
FALSE
3
206505_at
-2.1166
DOWN
TRUE
4
226517_at
0.9886
UP
FALSE
5
227529_s_at
1.0332
UP
FALSE
6
224009_x_at
1.5783
UP
TRUE
7
1557796_at
1.1084
UP
TRUE
8
210517_s_at
1.7642
UP
TRUE
9
214452_at
0.8011
UP
TRUE
10
1559203_s_at
0.9882
UP
TRUE
Table 5 Differentially expressed genes is significantly associated with genetic mutation of Ovarian cancer
Genes
FDRB&H
FDRB&Y
Bonferroni
NOS3,EDN2
1.13E+01
1.33E-02
1.16E-01
PTEN,MLH1,PMS2,APC,MSH2
8.71E-03
1.53E-04
1.23E-03
BAX,FANCC,MLH1,VDR,APC
1.94E+01
2.65E-02
2.32E-01
FAS
4.51E+01
4.70E-02
4.12E-01
BAX,PTEN,FANCC,MLH1,VDR,
APC,TP73
2.38E+01
3.07E-02
2.69E-01
CDKN1A,PTEN,MDM2,DDB2,
GADD45A,FANCC,HRAS,MLH1,
DNMT1,VDR,PMS2,APC,TP53I3,
MSH2,IGFBP3,EGFR,TP53
1.38E-14
1.97E-15
2.27E-14
PTEN,MDM2,TP53
3.36E-03
1.10E-05
1.27E-04
with ovarian cancer such as CDKN1A, PTEN, MDM2,
DDB2, GADD45A, FANCC, HRAS, MLH1, DNMT1,
VDR, PMS2, APC, TP53I3, MSH2, IGFBP3, EGFR,
APC, MSH2, MET, CHMP4C, BIRC5, EGFR, TP53
TP63 (Figure: 6).
Conclusion
A statistical methods to predict differential gene
express ion analys is that significantly associated
with cancer and control samples of breast, colo and
ovarian cancer cell types. A different computational
protocols for predicting biomarkers in cancer tissues
that corresponds w ith individual gene markers.
Us ing functional enrichment analys is across all
cancer types have identified different functional
characters of genes that spec ifically helps for
biomarkers. Using this application is helps to
identify biomarkers for further diagnostics to identify
disease in early stage of infection and disease
progress ion. The information provided on individual
genes that should provide useful information to
elucidating pathways in cancer as well as expeditig the
search for potential drug targets for specific cancer.
