A nine-gene signature to improve prognosis prediction of colon carcinoma

Figures & data

Figure 1. Screening of candidate genes for the prognostic model

(a) The volcano diagram shows the differential genes in colon carcinoma patients in GSE39582; (b) LASSO coefficient in regression analysis; C Tuning parameter (lambda) in LASSO model selected through 10-fold cross validation.

Table 1. Screening of survival-related genes via univariate COX regression

ID	HR	HR.95 L	HR.95 H	pvalue
TMEM97	0.675739	0.550013	0.830206	0.00019
CD36	1.307265	1.130283	1.511959	0.000306
TNFRSF11A	0.816795	0.724316	0.921081	0.000964
PIGR	0.903402	0.849195	0.96107	0.001292
UBD	0.842175	0.756314	0.937785	0.001744
GABBR1	0.842175	0.756314	0.937785	0.001744
CXCL13	0.858961	0.78012	0.94577	0.001968
KLK10	1.146712	1.049712	1.252676	0.002399
HOXB8	1.202441	1.064334	1.358468	0.003061
BIRC5	0.733502	0.592677	0.907789	0.00438
FKBP10	1.355012	1.099003	1.670657	0.004461
AURKA	0.742826	0.600911	0.918256	0.00599
CXCL10	0.860215	0.770493	0.960385	0.00738
LGR5	0.891805	0.819603	0.970368	0.007854
GZMB	0.85544	0.760034	0.962823	0.009656

Table 2. The optimal multivariate COX regression model identified based on the AIC

ID	Coef	HR	HR.95 L	HR.95 H	pvalue
TNFRSF11A	−0.121389445175388	0.885688965124658	0.785150170108015	0.999101793273088	0.0483156910287385
TMEM97	−0.26170873519764	0.769735187827784	0.616417141289096	0.961187189151184	0.0209289615789062
LGR5	−0.104060462086471	0.901170809106597	0.823948154444505	0.98563098030525	0.0228097763436299
KLK10	0.0973223345182819	1.1022155985013	0.99949272151635	1.21549582045626	0.0512001563910653
HOXB8	0.16551493029547	1.18000058034526	1.03722734056514	1.34242640466505	0.011887708223002
FKBP10	0.227030481375778	1.25486811746397	1.00946095693161	1.55993550955557	0.0408770899335053
CXCL13	−0.131201933501283	0.877040652618485	0.779880650869619	0.98630515513848	0.028513243512523
CXCL10	−0.131644181871515	0.876652868573988	0.762822646982128	1.00746910834309	0.0635816602757651
CD36	0.259343262573961	1.29607862376328	1.10819636261264	1.51581421456379	0.00117172466920451

Figure 2. The optimal 9 survival-related genes in multivariate COX regression analysis

Figure 3. Analysis of the prognostic performance of the 9-gene model in the training dataset, testing dataset and TCGA-COAD independent dataset

A-B RFS curves of patients in the training dataset (a) and the testing dataset (b) drawn by Kaplan-Meier; C-E OS curves of patients in the high- and low-risk groups in the training dataset (c), testing dataset (d) and TCGA-COAD independent dataset (e) drawn by Kaplan-Meier; F-H ROC curves were plotted to testify the performance of the 9-gene model in predicting the prognosis of colon carcinoma patients in the training dataset (f), testing dataset (g) and TCGA-COAD independent dataset (h), respectively.

Figure 4. The expression of the 9 model genes, the riskscore distribution and survival status of patients in the training dataset and testing dataset

A, B show the heatmap of the 9 model genes in patients in the training dataset (a) and the testing dataset (b); C, D present the riskscore distribution of patients in the training dataset (c) and the testing dataset (d); E, F display the survival status of patients in the training dataset (e) and the testing dataset (f); G, H, I suggest significant differences in ECM RECEPTOR INTERACTION (g), DNA REPLICATION (h), CELL CYCLE (i) pathways between high-risk group and low-risk group.

Table 3. Statistics of the basic clinical date of patients in the train dataset

	Low risk	High risk	P-value
	(n = 187)	(n = 182)
Gender
Female	92 (49.2%)	80 (44.0%)	0.366
Male	95 (50.8%)	102 (56.0%)
Event
Yes	40 (21.4%)	84 (46.2%)	<0.001
No	147 (78.6%)	98 (53.8%)
Age_diag
<65	73 (39.0%)	72 (39.6%)	1
>65	114 (61.0%)	110 (60.4%)
T
T1	4 (2.1%)	2 (1.1%)	0.042
T2	17 (9.1%)	10 (5.5%)
T3	134 (71.7%)	118 (64.8%)
T4	32 (17.1%)	52 (28.6%)
N
N0	113 (60.4%)	93 (51.1%)	0.195
N1	44 (23.5%)	47 (25.8%)
N2	27 (14.4%)	40 (22.0%)
N3	3 (1.6%)	2 (1.1%)
M
M0	173 (92.5%)	150 (82.4%)	0.005
M1	14 (7.5%)	32 (17.6%)
Tumor_stage
Stage I	12 (6.4%)	6 (3.3%)	0.015
Stage II	100 (53.5%)	81 (44.5%)
Stage III	61 (32.6%)	64 (35.2%)
Stage IV	14 (7.5%)	31 (17.0%)
Tumor.location
Distal	112 (59.9%)	111 (61.0%)	0.913
Proximal	75 (40.1%)	71 (39.0%)

Table 4. Statistics of the basic clinical data of patients in the test dataset

	Low risk	High risk	P-value
	(n = 95)	(n = 64)
Gender
Female	39 (41.1%)	31 (48.4%)	0.449
Male	56 (58.9%)	33 (51.6%)
Event
Yes	21 (22.1%)	27 (42.2%)	0.011
No	74 (77.9%)	37 (57.8%)
Age_diag
<65	35 (36.8%)	22 (34.4%)	0.881
>65	60 (63.2%)	42 (65.6%)
T
T1	4 (4.2%)	1 (1.6%)	0.005
T2	15 (15.8%)	1 (1.6%)
T3	63 (66.3%)	44 (68.8%)
T4	13 (13.7%)	18 (28.1%)
N
N0	54 (56.8%)	33 (51.6%)	0.482
N1	26 (27.4%)	16 (25.0%)
N2	15 (15.8%)	15 (23.4%)
M
M0	90 (94.7%)	55 (85.9%)	0.102
M1	5 (5.3%)	9 (14.1%)
Tumor_stage
Stage I	13 (13.7%)	0 (0%)	0.006
Stage II	39 (41.1%)	30 (46.9%)
Stage III	38 (40.0%)	25 (39.1%)
Stage IV	5 (5.3%)	9 (14.1%)
Tumor.location
Distal	64 (67.4%)	34 (53.1%)	0.1
Proximal	31 (32.6%)	30 (46.9%)

Figure 5. The prognostic performance of the 9-gene model in patients with different clinical subtypes in the training dataset and testing dataset

A-J The differences of OS in the high- and low-risk group in the training dataset and testing dataset (age>65 (a), age <65 (b), tumor stage I/II (c), tumor stage III/IV (d), T1&T2 (e), T3&T4 (f), N0&N1 (g), N2&N3 (h), M0 (i), M1 (j)) shown by Kaplan-Meier; K Nomogram quantitatively predicts the OS of patients.