1. Top k-gene markers for distinguishing cancers of high survival rates
Table 1 gives the top five one-gene special biomarkers across cancers of high survival rate. These genes can
be used as biomarkers for the cancers of high survival rate, and simultaneously they are found to be effective
discriminators for high survival rates and other survival rates cancers. S100B, KLHL21 and NAB1 are found to be
used as the biomarkers for breast cancer. Similarly, MMD and NAB1 can be used as one-gene discriminator for
prostate cancer. However, we didn’t find effective genes as biomarkers for thyroid cancer. The possible reason is
that the samples number is small. The top five two-gene special biomarkers across cancers of high survival rate is
showed in table 2. The combination of FBP1+S100B is found to be effective discriminator for three cancer types
which have high survival rate. Similarly, the combinations of S100B+VSNL1 and ITGB1BP1+S100B can be used
as two-gene discriminator for breast and thyroid cancers. Moreover, the combination of KLHL21+MMD is
effective two-gene discriminator for breast and prostate cancers. Table 3 indicates the top five three-gene special
biomarkers
across
cancers
of
high
survival
rate.
The
combinations
of
FBP1+S100B+VSNL1,
MMD+NT5E+VSNL1 and CEBPB+FBP1+S100B are found to be effective discriminators for three cancer types
which
have
high
survival
rate.
Moreover,
the
combinations
of
KLHL21+MMD+VSNL1
and
ARL4A+MMD+VSNL1 are good three-gene discriminator for breast cancer. The average classification accuracies
of top 100 k-gene special markers are showed in figure 1. We also can see that the good 3-gene discriminators for
cancer types of high survival rate have higher classification accuracies than the top 2-gene discriminators, and
similarly the 2-gene discriminators are better than 1-gene. However, the best discriminative capacity of cancers
which have high survival rate is not good enough. This may be because of the cancers of high survival rate have
small number of specific genes in cancers formation and progression.
As noted, S100B has been reported to play a key role in the regulation of a number of cellular processes such
as cell cycle progression and also relate with survival rate of cancers [1], KLHL21 is suggested to have a possible
role in cell division [2], MMD is involved in regulating ERK and Akt activation [3], LPIN1 is reported to have a
possible role in p53 regulation [4], NAB1, which contains an active transcriptional repression domain, is reported
as a co-repressor of EGR1 [5], and ITGB1BP1 is suggested to have a possible role in cell adhesion [6]. Moreover,
several of the top genes as biomarkers have been reported to be cancer relevant. For example, FBP1 is reported to
be down regulated in gastric carcinomas and used as a new biomarker for prognosis prediction of gastric cancer [7].
VSNL1 has been reporter to play a predictor role at the mRNA level of lymph node metastasis and poor prognosis
in colorectal cancer [8]. CEBPB is suggested to play an important mediator of metalloproteinase gene activation
during inflammatory responses in lung cancer cells [9].
Table 1: One-gene Marker for cancers of high survival rate
Markers
Breast
Prosate
Thyroid
Accuracy1
Accuracy2
Mean
S100B
76.84
60.56
68.33
70.85
66.78
68.81
KLHL21
74.27
67.40
49.58
67.91
69.40
68.65
MMD
67.16
71.24
56.67
66.36
69.94
68.15
LPIN1
61.75
69.72
67.08
64.90
69.17
67.03
NAB1
76.46
73.91
40.00
69.14
63.36
66.25
Table 2: Two-genes Marker for cancers of high survival rate
Markers
Breast
Prosate
Thyroid
Accuracy1
Accuracy2
Mean
FBP1+S100B
77.83
76.62
72.92
76.60
72.37
74.49
S100B+VSNL1
80.30
62.37
76.67
74.75
73.34
74.04
KLHL21+MMD
77.56
70.68
66.67
73.71
72.82
73.26
ITGB1BP1+S100B
77.77
66.92
75.83
74.46
71.84
73.15
MMD+VSNL1
77.59
67.50
62.08
72.02
73.67
72.84
Table 3: Three-genes Marker for cancers of high survival rate
Markers
Breast
Prosate
Thyroid
Accuracy1
Accuracy2
Mean
FBP1+S100B+VSNL1
81.94
75.38
77.92
79.42
76.15
77.79
KLHL21+MMD+VSNL1
82.92
70.40
66.67
76.55
78.26
77.40
MMD+NT5E+VSNL1
78.35
70.63
87.92
77.99
76.13
77.06
CEBPB+FBP1+S100B
76.03
76.31
77.92
76.45
76.85
76.65
ARL4A+MMD+VSNL1
80.25
68.94
76.25
76.44
76.70
76.57
1
1-gene
0.9
2-gene
3-gene
0.8
0.7
Accuracy
0.6
0.5
0.4
0.3
0.2
0.1
0
10
20
30
40
50
60
70
80
90
100
The number of genes
Figure 1: Classification accuracies by the top 100 k-gene markers
2. Top k-gene markers for distinguishing cancers of medium survival rates
Table 4 gives the top five one-gene special biomarkers across cancers of medium survival rate. These genes
can be used as biomarkers for the cancers of medium survival rate, and simultaneously they are found to be
effective discriminators for medium survival rates and other survival rates cancers. MTHFD1 and NEDD4L are
found to be used as the biomarkers for colorectal cancer. Similarly, DOCK4 and C11orf9 can be used as one-gene
discriminator for lung cancer. However, we didn’t find effective genes as biomarkers for gastric cancer. The
possible reason is the various types of gastric cancer. The top five two-gene special biomarkers across cancers of
medium survival rate is showed in table 5. The combinations of DOCK4+MTHFD1, EMP2+MTHFD1 and
ABCG1+MTHFD1 are found to be good discriminator for lung and colorectal cancers. Similarly, the combinations
of MTHFD1+NEDD4L and EDIL3+MTHFD1 can be used as two-gene discriminator for colorectal cancers. Table
6 indicates the top five three-gene special biomarkers across cancers of medium survival rate. The combinations of
CAPN9+EMP2+MTHFD1 and CAPN9+DOCK4+MTHFD1 are found to be effective discriminators for three
cancer types which have medium survival rate. Moreover, the combinations of EMP2+MTHFD1+NEDD4L,
DOCK4+GSTP1+MTHFD1 and EMP2+MTHFD1+TRIM2 are good three-gene discriminator for lung and
colorectal cancers. The average classification accuracies of top 100 k-gene special markers are showed in figure 2.
We also can see that the good 3-gene discriminators for cancer types of medium survival rate have higher
classification accuracies than the top 2-gene discriminators, and similarly the 2-gene discriminators are better than
1-gene.
As noted, DOCK4 is involved in regulation of adherens junctions between cells and regulates intercellular
junctions [10] TSPAN6 is reported to play a role in the regulation of cell development, activation, growth and
motility [11]. ABCG1 has been suggested to play an important role in cellular lipid/sterol regulation [12]. TRIM2
has been suggested to play a role in mediating the p42/p44 MAPK-dependent ubiquitination [13] Moreover,
several of the top genes as biomarkers have been reported to be cancer relevant. For example, MTHFD1 is
reported to play a key role among folate metabolism and colon cancer initiation and progression [14]. NEDD4L
has been reported to play a role in the progression of various cancers. The negative expression of the gene is
strongly related to the invasion and metastasis of gastric cancer [15]. EMP2 is suggested to be related with
endometrial and ovarian cancers [16, 17]. EDIL3 has reported to play a role in tumor angiogenesis and the
interaction between hepatocellular carcinoma cells and endothelial cells [18]. The gene CAPN9 is reported to be
suppressed or depleted with considerable frequency in gastric cancer [19]. GSTP1 has been suggested to relate
with cell death and play a role in susceptibility to colon cancer [20].
Table 4: One-gene Marker for cancers of medium survival rate
Markers
Lung
Colorectal
Gastric
Accuracy1
Accuracy2
Mean
MTHFD1
73.55
80.46
56.71
71.30
76.75
74.03
NEDD4L
69.64
87.08
65.92
74.17
70.22
72.19
DOCK4
87.47
64.00
62.71
73.54
70.25
71.90
C11orf9
82.91
57.33
67.91
70.89
68.64
69.77
TSPAN6
73.24
68.75
66.89
70.15
68.76
69.46
Table 5: Two-genes Marker for cancers of medium survival rate
Markers
Lung
Colorectal
Gastric
Accuracy1
Accuracy2
Mean
DOCK4+MTHFD1
90.14
84.59
63.85
81.47
79.40
80.44
EMP2+MTHFD1
89.26
82.27
56.73
78.49
80.76
79.63
MTHFD1+NEDD4L
79.80
92.51
63.65
79.57
78.45
79.01
EDIL3+MTHFD1
77.61
89.60
64.03
77.82
80.09
78.95
ABCG1+MTHFD1
80.67
81.33
59.28
75.25
82.11
78.68
Table 6: Three-genes Marker for cancers of medium survival rate
Markers
Lung
Colorectal
Gastric
Accuracy1
Accuracy2
Mean
CAPN9+EMP2+MTHFD1
89.18
86.85
75.87
84.94
83.52
84.23
CAPN9+DOCK4+MTHFD1
89.72
90.81
72.73
85.59
82.74
84.17
EMP2+MTHFD1+NEDD4L
92.12
91.44
65.71
84.96
82.34
83.65
DOCK4+GSTP1+MTHFD1
91.73
85.07
70.07
83.92
82.53
83.23
EMP2+MTHFD1+TRIM2
93.97
82.23
70.58
84.11
82.17
83.14
1
0.9
0.8
0.7
Accuracy
0.6
1-gene
0.5
2-gene
3-gene
0.4
0.3
0.2
0.1
0
10
20
30
40
50
60
70
80
90
100
The number of genes
Figure 2: Classification accuracies by the top 100 k-gene markers
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