Skip to main content
NCBI home page
Molecular Oncology logoLink to Molecular Oncology
. 2018 Feb 16;12(4):463–475. doi: 10.1002/1878-0261.12175

A robust gene signature for the prediction of early relapse in stage I–III colon cancer

Weixing Dai 1,2,†, Yaqi Li 1,2,†, Shaobo Mo 1,2,†, Yang Feng 1,2, Long Zhang 3, Ye Xu 1,2, Qingguo Li 1,2,✉, Guoxiang Cai 1,2,✉
PMCID: PMC5891048  PMID: 29377588

Abstract

Colon cancer patients experiencing early relapse consistently exhibited poor survival. The aim of our study was to develop an mRNA signature that can help to detect early relapse cases in stage I–III colon cancer. Public microarray datasets of stage I–III colon cancer samples were extracted from the Gene Expression Omnibus database. Propensity score matching analysis was performed between patients in the early relapse group and the long‐term survival group from GSE39582 discovery series (N = 386), and patients were 1 : 1 matched. Global mRNA expression changes were then analyzed between the paired groups to identify the differentially expressed genes. Lasso Cox regression modeling analysis was conducted for the selection of prognostic mRNA. Fifteen mRNA were finally identified to build an early relapse classifier. With specific risk score formula, patients were classified into a high‐risk group and a low‐risk group. Relapse‐free survival was significantly different between the two groups in every series, including discovery [hazard ratio (HR): 2.547, 95% confidence interval (CI): 1.708–3.797, P < 0.001)], internal validation (HR: 5.146, 95% CI: 1.968–13.457, P < 0.001), and external validation (HR: 1.977, 95% CI: 1.295–3.021, P < 0.001) sets of patients. Time‐dependent receiver‐operating characteristic at 1 year suggested more prognostic accuracy of the classifier [area under curve (AUC = 0.703)] than the American Joint Commission on Cancer tumor–node–metastasis staging system (AUC = 0.659) in all 951 patients. In conclusion, we developed a robust mRNA signature that can effectively classify colon cancer patients into groups with low and high risks of early relapse. This mRNA signature may help select high‐risk colon cancer patients who require more aggressive therapeutic intervention.

Keywords: colon cancer, early relapse, Gene Expression Omnibus database, mRNA signature, propensity score

Abbreviations

AJCC

American Joint Commission on Cancer

ARP3

actin‐related protein 3 homolog B

AUC

area under curve

BLMH

bleomycin hydrolase

CCL20

C‐C motif chemokine ligand 20

CI

confidence interval

CMPK2

cytidine/uridine monophosphate kinase 2

CRC

colorectal cancer

CTCs

circulating tumor cells

DEGs

differentially expressed genes

ECM1

extracellular matrix protein 1

GEO

Gene Expression Omnibus

gPCA

guided PCA

GZMB

granzyme B

HES6

hes family BHLH transcription factor 6

HR

hazard ratio

IL7

interleukin 7

KLK10

kallikrein‐related peptidase 10

KRT6A

keratin 6A

LIMMA

linear models for microarray data

MMP9

matrix metallopeptidase 9

MSLN

mesothelin

OAS1

2′‐5′‐oligoadenylate synthetase 1

PS

propensity score

PUS7

pseudouridylate synthase 7

ROC

receiver‐operating characteristic

TNM

tumor–node–metastasis

UICC

International Union against Cancer

ZNF426

zinc finger protein 426

1. Introduction

Colorectal cancer (CRC) is a worldwide common malignant tumor and also a major cause of cancer‐related deaths (Siegel et al., 2016). The implement of curative resection and advancement of adjuvant chemotherapy have witnessed a moderate improvement on the overall prognosis of colon cancer (Shi et al., 2013; Wilkinson et al., 2010). However, relapse following surgery is still a major and ultimate cause of deaths. Approximately 25–40% of patients would develop recurrence or metastases after primary radical resection, of which one‐third were local recurrence and the others were distal metastases (Becker, 1995; Tsai et al., 2009b; Van Cutsem et al., 2006). For the most part, the relapse of colon cancer is time‐related. Almost 40–50% of relapse emerged within the first year after initial primary resection, and 90% within the first 3 years (Longo and Johnson, 2002; Seo et al., 2013). Early relapse in colon cancer patients is attributed mainly to poor clinicopathological features (such as deeper tumor invasion, regional lymph nodes metastasis, poor differentiation, and worse histopathological type) and poor response to adjuvant chemotherapy. Those cases who developed early relapse consistently tended to have poorer long‐term survival rates. Consequently, more valuable predictive factors are urgently needed to detect the early postoperative relapse.

For decades, the most significant risk factor for predicting early relapse in colon cancer is based on American Joint Commission on Cancer/International Union against Cancer (AJCC/UICC) tumor–node–metastasis (TNM) staging system. However, for the great heterogeneity of colon cancer, prognosis varies significantly in colon cancer patients with same tumor stage and comparable clinicopathological features. Therefore, researchers are searching extensively for the ideal biomarker or indicator for predicting early relapse in colon cancer patients (Hwang et al., 2011; Lu et al., 2011; Yang et al., 2012). Although substantial efforts have been made to explore gene‐based molecular biomarker to predict the early relapse, no molecular prognostic classifiers have been established. Recent studies in many malignancies, including colon cancer, suggested that multigene expression patterns or gene signature can make a good prediction of cancer prognosis (Catto et al., 2010; Chen et al., 2011; Lee et al., 2015b; Tan and Tan, 2011). But, few precious gene profiling has been applied to detect the early relapse‐associated genes in colon cancer. Searching a gene signature might be of concrete predictive value in the prediction of early relapse in colon cancer patients.

In this study, a large group of mRNA‐specific probes were fortuitously represented on the commonly used microarray platform (Affymetrix HG‐U133 plus 2.0). We adopted previously published gene expression microarray data from the Gene Expression Omnibus (GEO) and conducted mRNA profiling on large cohorts of colon cancer patients. Using the sample‐splitting method and Cox regression analysis, a prognostic 15‐mRNA signature was identified from the discovery set in GSE39582 and validated in the internal validation series and another four GEO cohorts. This mRNA signature may help identify the subset of colon cancer patients at high risk of early relapse. Extensive postoperative management and surveillance may be needed for these patients.

2. Materials and methods

2.1. Preprocessing of microarray data

Raw microarray colon cancer datasets were obtained from the GEO database (http://www.ncbi.nlm.nih.gov/geo/) and were normalized using Robust Multichip Average (Irizarry et al., 2003). All datasets were produced by the Affymetrix HG‐U133 plus 2.0 platform. All probes were mapped based on their own EntrezGeneID. When multiple probes were mapped to the same EntrezGeneID, the mean value was used to represent its average expression level.

2.2. Dataset selection

The selection criterion for CRC datasets were as follows: (a) All sets were created by Affymetrix HG‐U133 plus 2.0 platform; (b) all sets should have basic clinical information of stage, relapse‐free survival (RFS) interval and RFS status; (c) datasets with larger sample size were preferred to be chosen, and we limited sample size to ~ 100. Datasets missing necessary clinicopathological or follow‐up data were excluded. Finally, CRC datasets of GSE39582, GSE14333, GSE17538, GSE33113, and GSE37892 were identified in this study to construct and validate the prognostic value of gene signature. Table S1 was used to describe the GEO datasets that were excluded in our study. All the samples were further filtered based on the criterion of stage I–III colon cancer and the availability of clinical outcome data. GSE39582 is the largest set consisting of 497 stage I–III colon cancer, and hence, it was assigned to a discovery series and an internal validation series. GSE17538, GSE14333, GSE33113, and GSE37892 were combined and set as external validation series. Analyses of the probe cell intensity (CEL) files suggested that there was extensive overlap between samples (H. Lee Moffitt Cancer Center) in the GSE17538 and GSE14333 series (Sveen et al., 2012). Therefore, the samples from Moffitt Cancer Center (N = 138, stages I–III) in GSE17538 were excluded. ComBat method was used to remove the internal batch effects among 10 batches in GSE39582 and external batch effects among different GEO datasets. This method was implemented in the SVA R package, and the corresponding R‐code can be found as Data S1. The guided PCA (gPCA) method was used to evaluate the success of batch effect correction (Reese et al., 2013).

2.3. Identification of early relapse‐associated genes

Early relapse was defined as the locoregional recurrence or distant metastasis within 1 year after primary resection (Lu et al., 2011). Samples in the discovery set from GSE39582 were selected and divided into early relapse group and long‐term survival group (no relapse after a minimum of 5 years follow‐up). Propensity score (PS) matching analysis was performed between the two groups to adjust for stage and adjuvant chemotherapy, which were the most significant clinical factors associated with early relapse. All patients were matched 1 : 1. Finally, 45 paired patients in the discovery set were identified to identify the changes of global gene expression profile between early relapse group and long‐term survival groups. The analysis of differentially expressed genes (DEGs) between early relapse and long‐term survival samples was conducted using the linear models for microarray data (LIMMA) method (Smyth, 2005). The threshold for identification of DEGs was set as P < 0.05 and fold change ≥ 1.25. Lastly, LASSO Cox regression model (Tibshirani, 1997) was used to select the most significantly relapse‐associated mRNA of all the DEGs.

2.4. Development of risk score and statistical analysis

Using LASSO Cox regression analysis, we identified a panel of genes and constructed a multi‐mRNA‐based classifier for predicting the early relapse in patients with stage I–III colon cancer in the discovery set. With specific risk score formula, patients from different sets were divided into high‐risk and low‐risk groups using the median risk score of the discovery set as the cutoff point. Survival differences between the low‐risk and high‐risk groups in each set were assessed by the Kaplan–Meier estimate and compared using the log‐rank test. Multivariate Cox regression analysis and data stratification analysis were performed to test the independent prognostic role of risk score in predicting RFS. Time‐dependent receiver‐operating characteristic (ROC) analysis was used to investigate the prognostic or predictive accuracy of each feature and signature. All statistical analyses were performed with use of r (version 2.15.0, http://www.r-project.org). All statistical tests were two‐sided, and P values < 0.05 were considered statistically significant.

3. Results

3.1. Preparation of colon cancer datasets

A total of 951 patients were identified and fully studied, which included 497 patients from GSE39582 (386 patients from the discovery set and 111 from the internal validation set), 234 patients from GSE17538 and GSE14333, 90 patients from GSE33113, and 130 patients from GSE37892. Plots of the first versus the second principal components before and after removing batch effects are shown in Fig. S1. The original data of the all patients included in analysis are listed in Table S2. The baseline clinical information for patients in external validation sets, GSE14333, GSE17538, GSE33113, and GSE37892, is shown in Table S3.

3.2. Development of early relapse signature from the discovery series

Samples in discovery set were divided into early relapse group and long‐term survival group. Patients' clinicopathological features before and after PS matching are described in Table 1. Before the implement of PS analysis, it is noticeable that tumor stage in early relapse group was significantly higher than that in long‐term survival group. After PS matching, there were no significant differences in age, AJCC stage, tumor location, and adjuvant chemotherapy between early relapse and long‐term survival groups in each set (Table 1). Changes of global mRNA expression profiles were analyzed between early relapse and long‐term survival groups. One hundred and seven of them were differentially expressed between the two groups (P < 0.05, fold change ≥ 1.25; Fig. 1A). LASSO coefficient profiles of the 107 mRNA are shown in Fig. 1B. A coefficient profile plot was produced against the log (λ) sequence. Vertical line was drawn at the value selected using 10‐fold cross‐validation, and the minimize λ method resulted in 15 optimal coefficients. Of these, 11 mRNA were downregulated and four were upregulated in early relapse group compared with long‐term survival group (Table S4). Using Lasso Cox regression modeling, we derived a 15‐mRNA signature to calculate the risk score for every patient based on the expression levels of the 15 RNA weighted by their regression coefficients: risk score = (− 0.052 × expression level of ACTR3B) + (− 0.116 × expression level of BLMH) + (− 0.047 × expression level of CCL20) + (− 0.121 × expression level of CMPK2) + (0.259 × expression level of ECM1) + (0.043 × expression level of GZMB) + (− 0.287 × expression level of HES6) + (− 0.102 × expression level of IL7) + (0.201 × expression level of KLK10) + (− 0.015 × expression level of KRT6A) + (− 0.302 × expression level of MMP9) + (0.038 × expression level of MSLN) + (− 0.217 × expression level of OAS1) + (− 0.236 × expression level of PUS7) + (− 0.168 × expression level of ZNF426).

Table 1.

Clinical–pathological features of patients in early relapse and long‐term survival groups before and after PS matching

Variable Discovery set
Before matching After matching
Early relapse Long‐term survival P Early relapse Long‐term survival P
Age (mean, IQR) 68.2 (58.5–78.0) 65.2 (57.5–73) 0.17 68.2 (58.0–78.0) 66.4 (60.0–73.0) 0.52
Gender
Male 20 66 0.75 20 19 0.83
Female 25 74 25 26
Stage
I 0 8 0.08 0 0 1
II 20 76 20 20
III 25 56 25 25
T stage
T1 0 3 0.03 0 1 0.16
T2 2 11 2 3
T3 25 108 25 35
T4 13 18 13 6
NA 5 0 5 0
N stage
N0 24 84 0.01 16 20 0.09
N1 11 39 11 19
N2 13 17 13 6
NA 1 0 1 0
Tumor location
Proximal 17 54 0.92 17 19 0.67
Distal 28 86 28 26
Adjuvant chemotherapy
No 20 80 0.14 20 20 1
Yes 25 60 25 25
Total 45 140 45 45
Open in a new tab

Figure 1.

Figure 1

Open in a new tab

(A) Heat map showed eighteen differentially expressed mRNA in colon cancer between early relapse and long‐term survival group both in discovery set. (B) LASSO coefficient profiles of the 107 early relapse‐associated mRNA. A vertical line is drawn at the value chosen by 10‐fold cross‐validation.

3.3. The prognostic value of 15‐mRNA signature in discovery, internal validation, and external validation series

Patients in discovery set were divided into low‐risk group (N = 193) or high‐risk group (N = 193) using the median risk score as cutoff point. The distribution of risk scores and survival status is shown in Fig. 2A (left panel), which suggested that patients with lower risk scores generally had better survival than those with higher risk scores. Time‐dependent ROC analyses at 1, 3, and 5 years were conducted to assess the prognostic accuracy of the 15‐mRNA‐based classifier (Fig. 2A, middle panel). The RFS rates for patients with low‐risk scores were 93.6% at 1 year, 86.6% at 3 years, and 81.0% at 5 years, compared with 83.4%, 62.7%, and 57.9% in patients with high‐risk scores, respectively [hazard ratio (HR): 2.547, 95% confidence interval (CI): 1.708–3.797, P < 0.001, Fig. 2A, right panel].

Figure 2.

Figure 2

Open in a new tab

Distribution of risk score, time‐dependent ROC curves at 1, 3, and 5 years and Kaplan–Meier survival analysis between patients at low and high risks of relapse in discovery set (A), internal validation set (B), external validation set (C), and entire dataset (D).

We then did the same analyses in the internal validation cohort. In the internal validation series, 1‐, 3‐, and 5‐year RFS was 84.6%, 58.0%, and 56.0% for the high‐risk group, and 95.9%, 89.5%, and 89.5% for the low‐risk group (HR: 5.146, 95% CI: 1.968–13.457, P < 0.001, Fig. 2B).

To confirm that the 15‐mRNA‐based classifier had similar prognostic value in different populations, we combined the samples in GSE17538, GSE14333, and GSE33113, and a total of 324 colon cancer patients were further studied to validate the 15‐mRNA signature. Using the established cutoff point, 193 (42.5%) patients were classified as low risk, and 261 (57.5%) as high risk. Five‐year disease‐free survival was 68.1% for the high‐risk group and 83.9% for the low‐risk group (HR: 1.977, 95% CI: 1.295–3.021; P < 0.001; Fig. 2C).

In the entire dataset analysis, risk score‐based classification yielded similar results (Fig. 2C). Colon cancer patients can be divided into low‐ and high‐risk patients with significantly different RFS, and the signature showed the highest predicting accuracy at 1 year after surgery.

3.4. Independence and accuracy of the signature in predicting RFS

After multivariate analysis adjusted by clinicopathological variables, the 15‐mRNA‐based classifier remained a powerful and independent factor in the discovery, internal validation, and external validation sets (Table 2). Stratified analysis suggested that the 15‐mRNA‐based classifier was still a clinically and statistically significant prognostic model in stage II, stage III, patients with or without adjuvant chemotherapy and patients with or without KRAS mutation (Fig. 3). Samples from the entire dataset were then separated into five risk groups based on their relapse‐free status and time: group A (relapse within 1 year), group B (relapse within 3 years), group C (relapse after 3 years), group D (no relapse within 5 years), group E (no relapse after minimum 5 years). The distribution of risk score among five risk groups are shown in Fig. S2. As expected, group A showed the highest risk score, while group E showed the lowest.

Table 2.

Univariable and multivariable Cox regression analysis in colon cancer

Variables Univariate analysis Multivariate analysis
HR (95% CI) P HR (95% CI) P
Discovery set (N = 386)
Age 1.01 (0.99 to 1.02) 0.438 1.01 (0.99 to 1.03) 0.132
15 gene risk score 2.58 (2.04 to 3.28) < 0.001 2.52 (1.97 to 3.23) < 0.001
Gender
Female 1 0.109 1 0.167
Male 1.36 (0.93 to 2.01) 1.31 (0.99 to 1.02)
Stage
I 1 0.005 1 0.161
II 6.44 (0.88 to 46.67) 5.45 (0.75 to 39.70)
III 10.43 (1.44 to 75.29) 6.67 (0.89 to 49.98)
Tumor location
Proximal 1 0.52 1 0.179
Distal 1.13 (0.77 to 1.66) 0.76 (0.51 to 1.15)
Adjuvant chemotherapy
No 1 0.001 1 0.296
Yes 1.85 (1.27 to 2.70) 1.30 (0.79 to 2.14)
Internal validation set (N = 111)
Age 1.02 (0.98 to 1.05) 0.221 1.01 (0.97 to 1.05) 0.324
15 gene risk score 3.57 (1.97 to 6.45) < 0.001 2.86 (1.51 to 5.40) < 0.001
Gender
Female 1 0.442 1 0.612
Male 1.34 (0.63 to 2.87) 1.24 (0.53 to 2.87)
Stage
I 1 0.003 1 0.023
II > 1000 (0 to > 1000) > 1000 (0 to > 1000)
III > 1000 (0 to > 1000) > 1000 (0 to > 1000)
Tumor location
Proximal 1 0.798 1 0.367
Distal 1.10 (0.51 to 2.36) 0.69 (0.30 to 1.56)
Adjuvant chemotherapy
No 1 0.237 1 0.257
Yes 1.54 (0.75 to 3.15) 0.56 (0.20 to 1.53)
External validation set (N = 454)
Age 0.99 (0.97 to 1.00) 0.116 0.99 (0.98 to 1.01) 0.863
15 gene risk score 1.79 (1.45 to 2.21) < 0.001 1.60 (1.29 to 1.98) < 0.001
Gender
Female 1 0.798 1 0.972
Male 1.05 (0.71 to 1.55) 1.01 (0.67 to 1.50)
Stage
I 1 < 0.001 1 < 0.001
II 5.72 (0.78 to 41.7) 5.20 (1.25 to 21.64)
III 18.95 (2.62 to 136.6) 11.94 (2.90 to 49.13)
Open in a new tab

Figure 3.

Figure 3

Open in a new tab

Kaplan–Meier survival analysis for the entire dataset with stage I–III colon cancer (N = 951) based on the 15‐mRNA‐based signature stratified by clinicopathological risk factors.

To confirm that the 15‐mRNA‐based classifier had higher efficacy in predicting early relapse, time‐dependent ROC was used, which suggested that the 15‐mRNA‐based classifier had significantly higher prognostic accuracy than tumor stage at 1 year (Fig. 4).

Figure 4.

Figure 4

Open in a new tab

Time‐dependent ROC curves at 1 year compare the prognostic accuracy in predicting early relapse of the 15‐mRNA signature with TNM staging system in the entire cohorts with stage I–III colon cancer (N = 951).

3.5. Identification of 15‐mRNA signature‐associated biological signaling pathway

We performed GSEA in dataset GSE39582 to identify the 15‐mRNA‐associated biological signaling pathway. Significant gene sets (FDR < 5%) were visualized as Enrichment Map (Fig. S3). The risk score was accompanied with exceptional regulation of several important cancer‐related networks, namely apical junction, hypoxia, Hedgehog signaling, epithelial–mesenchymal transition, G2M checkpoint, interferon GAMMA response.

4. Discussion

To be noted, tumor relapse within the first year after initial resection still accounted for almost half of all tumor relapse, indicating that potential micrometastases, incomplete clinicopathological factors, or inherent heterogeneity may be critical factors in promoting tumor recurrence or distant metastasis (Steinert et al., 2008; Zhang et al., 2005). Postoperative relapse after radical surgery, ascribed to tumor cell dissemination, is closely related to survival outcomes, which is mainly evaluated by current AJCC/UICC TNM staging system. However, colon cancer patients within the same pathologic staging vary considerably in disease progression and prognosis due to their different genetic and epigenetic backgrounds, resulting in the unsatisfactory situation of current methods (Bathe and Farshidfar, 2014). Despite the continuous improvement of treatment strategies, patients with early postoperative relapse in colon cancer suffer from significantly inferior overall survival rates, in comparison with those without early relapse (Hwang et al., 2011). Simple and reliable biomarkers for the detection of early postoperative relapse would make up for the deficiency of standard TNM classification, and thereby assisting physicians in formulating more efficient therapeutic strategies at an earlier stage of a patient's treatment (Cho, 2010; Tsai et al., 2013a).

Previous studies have tried to identify postoperative molecular markers for detection of early relapse in colon cancer. In 2009, Tsai et al. (Tsai et al., 2009a) indicated that vascular invasion, perineural invasion, and postoperative CEA level may be significant factors for postoperative early relapse in UICC stage I–III colon cancer. Besides, it is also demonstrated in 2013 that activation of VEGF, an important predictor of early postoperative relapse in patients with stage I–III CRC, may help identify patients who would benefit from intensive follow‐up and therapeutic programs (Tsai et al., 2013b). Recently, another research conducted by Lu et al. (2011) revealed that molecular detection of persistent postoperative circulating tumor cells (CTCs) is a prognostic predictor of early relapse in UICC stage II/III colon cancer patients. Nonetheless, although the association between molecular markers and early postoperative relapse in colon cancer has been explored in relevant literatures, most work has focused on analyzing the function of one or two biomarkers. Little attention has been paid to mRNA expression pattern and its involvement in the prediction of early relapse in stage I–III colon cancer using high‐throughput expression profile datasets.

In the current study, a novel prognostic classifier based on 15 mRNA was developed to improve the prediction of early relapse and prediction of RFS for colon cancer after surgical resection. By applying the 15‐mRNA signature to the GSE39582 discovery set patients, a clear separation was observed in the survival curves between low‐ and high‐risk patients. And it was internally validated in the validation series of GSE39582 patients and the external cohorts of GSE17538, GSE14333, GSE33113, and GSE37892, indicating the good reproducibility of this signature in colon cancer. Stratified by AJCC stage, the 15‐mRNA‐based signature remains a good prognostic model, implying that the mRNA signature can be used to refine the current staging system. Furthermore, the time‐dependent ROC at 1 year suggested that this 15‐mRNA signature has considerable prognostic accuracy in predicting tumor relapse within the first year after initial resection of colon cancer. Therefore, our study identified a 15‐mRNA signature that could help identify patients with high risk of early relapse and guide individualized treatment of patients with colon cancer, which is credible to be applied to clinic.

Most of genes included in the signature have been experimentally demonstrated to be linked with cancer. Of these, six mRNA including ECM1, GZMB, KLK10, CCL20, MMP9, and IL7 have been previously reported to have a prognostic role in colon cancer. Extracellular matrix protein 1 (ECM1) is a secreted protein that has been implicated with cell proliferation, angiogenesis, and differentiation (Lee et al., 2015a). Previous studies suggested that ECM1 tends to be preferentially expressed by metastatic CRC (Wang et al., 2003). Granzyme B (GZMB) is a serine protease expressed by cytotoxic T lymphocytes and natural killer cells (Dahl et al., 1990). Patients with low expression of GZMB have been proved to have poor disease‐free survival (Tosolini et al., 2011). Kallikrein‐related peptidase 10 (KLK10) is homologue to KLK3 and encodes the prostate‐specific antigen, which is a widely used biomarker for the detection and monitoring of prostate cancer (Sardana et al., 2007). The mRNA expression level of KLK10 has been previously suggested to be negatively associated with prognosis in CRC (Alexopoulou et al., 2013). Several recent studies suggested that CC‐chemokine cysteine motif chemokine ligand 20 (CCL20) and its physiological sole receptor CCR6 played a role in the development and metastatic spread of CRC (Ghadjar et al., 2009; Iwata et al., 2013). However, this hypothesis was warranted to be further validated by functional studies and the results from Ghadjar et al. (2006) did not support it. Matrix metalloproteinase (MMP) played an important role in degradation of extracellular matrix and basement membranes, and previous studies indicated that the overexpression of matrix metallopeptidase 9 (MMP9) was associated with deep tumor invasion, lymph‐node metastasis, and advanced TNM stage in CRC (Lee et al., 2014; Matsuyama et al., 2002). But a recent study revealed that overexpression of MMP9 can predict good response to chemotherapy in patients with CRC (Yang et al., 2017). Therefore, we hypothesized that in patients with comparable clinicopathological features, those with high expression level of MMP9 may exhibit better survival, which has been suggested by the results in this study. Interleukin‐7 (IL‐7) is a cytokine that has been known since long in immunology, and recent studies found the role of IL‐7 was far beyond the field of immunology and it might have direct or indirect effect on cancer (Lin et al., 2017). However, its prognostic and biological effects varied significantly among different studies and cancer types (Berghella et al., 2002; Fritzell et al., 2013; Liu et al., 2014; Lynch et al., 1991). In our study, we found the expression of IL7 was upregulated in long‐term survival group and may exert antitumor effect. Bleomycin hydrolase (BLMH) is a kind of drug‐metabolizing enzymes that were highly expressed in drug‐resistant colon cancer stem cells, but no previous studies was conducted to detect the prognostic role of BLMH in colon cancer (Emmink et al., 2013). As for the rest eight genes integrated in our signature, further clinical and basic research should be conducted to explore their value in colon cancer.

To date, several multigene assays have been developed like Oncotype DX (Webber et al., 2010), ColoPrint (Salazar et al., 2011) and ColDX (Kennedy et al., 2011), in hopes of providing prognostic and predictive information to aid in decisions regarding adjuvant therapy in patients with stage II or III colon cancer. However, before the signature can be applied as a clinical‐grade assay, further steps are needed according to the established guidelines (Altman et al., 2012): firstly, identification of an appropriate approach to quantify expression (microarray); secondly, design of specific probes based on the sequences tested in the microarray chips; thirdly, validation in independent cohorts of patients with full clinical annotation available. We will firstly validate the prognostic value of this classifier in our center. Although not controversially applicable worldwide in the present form, we do believe the multigene classifier established in this study bears promising translational value.

Inevitably, there are some limitations in our study. Firstly, our study was based on the data from a publicly available datasets without testing prospectively in a clinical trial. Furthermore, the information of several other important clinicopathological features, like differentiation and number of lymph nodes, was not available in these datasets. Finally, mechanisms of the identified genes on the early relapse in colon cancer are still needed to be further explored.

5. Conclusions

In conclusion, we developed a robust mRNA signature consisting of both up‐ and downregulated mRNA that can effectively classify colon cancer patients into groups with low and high risks of early relapse postoperatively. Further validation in prospective clinical trials could verify the clinical significance of this mRNA signature in detecting postoperative early relapse in colon cancer patients.

Author contributions

WXD and GXC had the idea for this study. QGL supervised the acquisition of the data. YF undertook the statistical analysis, and YX and LZ provided statistical advice. All authors contributed to interpretation of the results. WXD, YQL, and SBM wrote the article, and other authors contributed to the content. All authors approved the final version of the manuscript, including the authorship list.

Supporting information

Fig. S1. Principal components plot of first two principal components from gPCA.

Click here for additional data file. (418.3KB, pdf)

Fig. S2. The distribution of risk score among five risk groups in the entire dataset with stage I–III colon cancer (N = 951).

Click here for additional data file. (194.5KB, pdf)

Fig. S3. Gene set enrichment analysis delineates biological pathways associated with risk score.

Click here for additional data file. (1.1MB, pdf)

Table S1. Description of GEO datasets that were excluded in our study.

Click here for additional data file. (14.1KB, docx)

Table S2. Clinicopathological features of patients in GSE39582, GSE1433, GSE33113, GSE17538 and GSE37892.

Click here for additional data file. (81.1KB, xlsx)

Table S3. Baseline information for patients in GSE14333, GSE33113, GSE17538 and GSE37892.

Click here for additional data file. (14.3KB, docx)

Table S4. Detailed information of eighteen gene identified from discovery series in GSE39582

Click here for additional data file. (15KB, docx)

Data S1. R code for batch effect removing.

Click here for additional data file. (3.2KB, txt)

Acknowledgements

We thank the GEO database for providing their platforms and contributors for their valuable datasets. This study was supported by the National Key R&D Program of China (No. 2016YFC0905300 and 2016YFC0905301), the Grant of Science and Technology Commission of Shanghai Municipality (No. 16401970502), the Grant of National Natural Science Foundation of China (No. 81572351), and Shanghai Shenkang Program (No.SHDC12014206).

Contributor Information

Qingguo Li, Email: oncosurgeonli@sohu.com.

Guoxiang Cai, Email: gxcaifuscc@163.com.

References

  1. Alexopoulou DK, Papadopoulos IN and Scorilas A (2013) Clinical significance of kallikrein‐related peptidase (KLK10) mRNA expression in colorectal cancer. Clin Biochem 46, 1453–1461. [DOI] [PubMed] [Google Scholar]
  2. Altman DG, McShane LM, Sauerbrei W and Taube SE (2012) Reporting Recommendations for Tumor Marker Prognostic Studies (REMARK): explanation and elaboration. PLoS Med 9, e1001216. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Bathe OF and Farshidfar F (2014) From genotype to functional phenotype: unraveling the metabolomic features of colorectal cancer. Genes 5, 536–560. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Becker H (1995) Surgery of colorectal carcinoma. Praxis 84, 1371–1372. [PubMed] [Google Scholar]
  5. Berghella AM, Contasta I, Pellegrini P, Del Beato T and Adorno D (2002) Peripheral blood immunological parameters for use as markers of pre‐invasive to invasive colorectal cancer. Cancer Biother Radiopharm 17, 43–50. [DOI] [PubMed] [Google Scholar]
  6. Catto JW, Abbod MF, Wild PJ, Linkens DA, Pilarsky C, Rehman I, Rosario DJ, Denzinger S, Burger M, Stoehr R et al (2010) The application of artificial intelligence to microarray data: identification of a novel gene signature to identify bladder cancer progression. Eur Urol 57, 398–406. [DOI] [PubMed] [Google Scholar]
  7. Chen DT, Hsu YL, Fulp WJ, Coppola D, Haura EB, Yeatman TJ and Cress WD (2011) Prognostic and predictive value of a malignancy‐risk gene signature in early‐stage non‐small cell lung cancer. J Natl Cancer Inst 103, 1859–1870. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Cho WC (2010) MicroRNAs: potential biomarkers for cancer diagnosis, prognosis and targets for therapy. Int J Biochem Cell Biol 42, 1273–1281. [DOI] [PubMed] [Google Scholar]
  9. Dahl CA, Bach FH, Chan W, Huebner K, Russo G, Croce CM, Herfurth T and Cairns JS (1990) Isolation of a cDNA clone encoding a novel form of granzyme B from human NK cells and mapping to chromosome 14. Hum Genet 84, 465–470. [DOI] [PubMed] [Google Scholar]
  10. Emmink BL, Verheem A, Van Houdt WJ, Steller EJ, Govaert KM, Pham TV, Piersma SR, Borel Rinkes IH, Jimenez CR and Kranenburg O (2013) The secretome of colon cancer stem cells contains drug‐metabolizing enzymes. J Proteomics 91, 84–96. [DOI] [PubMed] [Google Scholar]
  11. Fritzell S, Eberstal S, Sanden E, Visse E, Darabi A and Siesjo P (2013) IFNgamma in combination with IL‐7 enhances immunotherapy in two rat glioma models. J Neuroimmunol 258, 91–95. [DOI] [PubMed] [Google Scholar]
  12. Ghadjar P, Coupland SE, Na IK, Noutsias M, Letsch A, Stroux A, Bauer S, Buhr HJ, Thiel E, Scheibenbogen C et al (2006) Chemokine receptor CCR6 expression level and liver metastases in colorectal cancer. J Clin Oncol 24, 1910–1916. [DOI] [PubMed] [Google Scholar]
  13. Ghadjar P, Rubie C, Aebersold DM and Keilholz U (2009) The chemokine CCL20 and its receptor CCR6 in human malignancy with focus on colorectal cancer. Int J Cancer 125, 741–745. [DOI] [PubMed] [Google Scholar]
  14. Hwang CC, Chai HT, Chen HW, Tsai HL, Lu CY, Yu FJ, Huang MY and Wang JY (2011) S100B protein expressions as an independent predictor of early relapse in UICC stages II and III colon cancer patients after curative resection. Ann Surg Oncol 18, 139–145. [DOI] [PubMed] [Google Scholar]
  15. Irizarry RA, Hobbs B, Collin F, Beazer‐Barclay YD, Antonellis KJ, Scherf U, Speed TP (2003) Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics (Oxford, England) 4, 249–264. [DOI] [PubMed] [Google Scholar]
  16. Iwata T, Tanaka K, Inoue Y, Toiyama Y, Hiro J, Fujikawa H, Okugawa Y, Uchida K, Mohri Y and Kusunoki M (2013) Macrophage inflammatory protein‐3 alpha (MIP‐3a) is a novel serum prognostic marker in patients with colorectal cancer. J Surg Oncol 107, 160–166. [DOI] [PubMed] [Google Scholar]
  17. Kennedy RD, Bylesjo M, Kerr P, Davison T, Black JM, Kay EW, Holt RJ, Proutski V, Ahdesmaki M, Farztdinov V et al (2011) Development and independent validation of a prognostic assay for stage II colon cancer using formalin‐fixed paraffin‐embedded tissue. J Clin Oncol 29, 4620–4626. [DOI] [PubMed] [Google Scholar]
  18. Lee YK, Jee BA, Kwon SM, Yoon YS, Xu WG, Wang HJ, Wang XW, Thorgeirsson SS, Lee JS, Woo HG et al (2015b) Identification of a mitochondrial defect gene signature reveals NUPR1 as a key regulator of liver cancer progression. Hepatology (Baltimore, MD) 62, 1174–1189. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Lee KM, Nam K, Oh S, Lim J, Kim RK, Shim D, Choi JH, Lee SJ, Yu JH, Lee JW et al (2015a) ECM1 regulates tumor metastasis and CSC‐like property through stabilization of beta‐catenin. Oncogene 34, 6055–6065. [DOI] [PubMed] [Google Scholar]
  20. Lee MA, Park JH, Rhyu SY, Oh ST, Kang WK and Kim HN (2014) Wnt3a expression is associated with MMP‐9 expression in primary tumor and metastatic site in recurrent or stage IV colorectal cancer. BMC Cancer 14, 125. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Lin J, Zhu Z, Xiao H, Wakefield MR, Ding VA, Bai Q and Fang Y (2017) The role of IL‐7 in Immunity and Cancer. Anticancer Res 37, 963–967. [DOI] [PubMed] [Google Scholar]
  22. Liu ZH, Wang MH, Ren HJ, Qu W, Sun LM, Zhang QF, Qiu XS and Wang EH (2014) Interleukin 7 signaling prevents apoptosis by regulating bcl‐2 and bax via the p53 pathway in human non‐small cell lung cancer cells. Int J Clin Exp Pathol 7, 870–881. [PMC free article] [PubMed] [Google Scholar]
  23. Longo WE and Johnson FE (2002) The preoperative assessment and postoperative surveillance of patients with colon and rectal cancer. Surg Clin North Am 82, 1091–1108. [DOI] [PubMed] [Google Scholar]
  24. Lu CY, Uen YH, Tsai HL, Chuang SC, Hou MF, Wu DC, Juo SH, Lin SR and Wang JY (2011) Molecular detection of persistent postoperative circulating tumour cells in stages II and III colon cancer patients via multiple blood sampling: prognostic significance of detection for early relapse. Br J Cancer 104, 1178–1184. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Lynch DH, Namen AE and Miller RE (1991) In vivo evaluation of the effects of interleukins 2, 4 and 7 on enhancing the immunotherapeutic efficacy of anti‐tumor cytotoxic T lymphocytes. Eur J Immunol 21, 2977–2985. [DOI] [PubMed] [Google Scholar]
  26. Matsuyama Y, Takao S and Aikou T (2002) Comparison of matrix metalloproteinase expression between primary tumors with or without liver metastasis in pancreatic and colorectal carcinomas. J Surg Oncol 80, 105–110. [DOI] [PubMed] [Google Scholar]
  27. Reese SE, Archer KJ, Therneau TM, Atkinson EJ, Vachon CM, de Andrade M, Kocher JP and Eckel‐Passow JE (2013) A new statistic for identifying batch effects in high‐throughput genomic data that uses guided principal component analysis. Bioinformatics 29, 2877–2883. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Salazar R, Roepman P, Capella G, Moreno V, Simon I, Dreezen C, Lopez‐Doriga A, Santos C, Marijnen C, Westerga J et al (2011) Gene expression signature to improve prognosis prediction of stage II and III colorectal cancer. J Clin Oncol 29, 17–24. [DOI] [PubMed] [Google Scholar]
  29. Sardana G, Marshall J and Diamandis EP (2007) Discovery of candidate tumor markers for prostate cancer via proteomic analysis of cell culture‐conditioned medium. Clin Chem 53, 429–437. [DOI] [PubMed] [Google Scholar]
  30. Seo SI, Lim SB, Yoon YS, Kim CW, Yu CS, Kim TW, Kim JH and Kim JC (2013) Comparison of recurrence patterns between </=5 years and >5 years after curative operations in colorectal cancer patients. J Surg Oncol 108, 9–13. [DOI] [PubMed] [Google Scholar]
  31. Shi Q, Andre T, Grothey A, Yothers G, Hamilton SR, Bot BM, Haller DG, Van Cutsem E, Twelves C, Benedetti JK et al (2013) Comparison of outcomes after fluorouracil‐based adjuvant therapy for stages II and III colon cancer between 1978 to 1995 and 1996 to 2007: evidence of stage migration from the ACCENT database. J Clin Oncol 31, 3656–3663. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Siegel RL, Miller KD and Jemal A (2016) Cancer statistics, 2016. CA Cancer J Clin 66, 7–30. [DOI] [PubMed] [Google Scholar]
  33. Smyth GK (2005) Limma: Linear Models for Microarray Data, Bioinformatics and Computational Biology Solutions Using R and Bioconductor, pp. 397–420. Springer, New York, NY. [Google Scholar]
  34. Steinert R, Hantschick M, Vieth M, Gastinger I, Kuhnel F, Lippert H, Reymond MA (2008) Influence of subclinical tumor spreading on survival after curative surgery for colorectal cancer. Arch Surg (Chicago, Ill.: 1960) 143, 143, 122–128. [DOI] [PubMed] [Google Scholar]
  35. Sveen A, Agesen TH, Nesbakken A, Meling GI, Rognum TO, Liestol K, Skotheim RI and Lothe RA (2012) ColoGuidePro: a prognostic 7‐gene expression signature for stage III colorectal cancer patients. Clin Cancer Res 18, 6001–6010. [DOI] [PubMed] [Google Scholar]
  36. Tan IB and Tan P (2011) Genetics: an 18‐gene signature (ColoPrint(R)) for colon cancer prognosis. Nat Rev Clin Oncol 8, 131–133. [DOI] [PubMed] [Google Scholar]
  37. Tibshirani R (1997) The lasso method for variable selection in the Cox model. Stat Med 16, 385–395. [DOI] [PubMed] [Google Scholar]
  38. Tosolini M, Kirilovsky A, Mlecnik B, Fredriksen T, Mauger S, Bindea G, Berger A, Bruneval P, Fridman WH, Pages F et al (2011) Clinical impact of different classes of infiltrating T cytotoxic and helper cells (Th1, th2, treg, th17) in patients with colorectal cancer. Can Res 71, 1263–1271. [DOI] [PubMed] [Google Scholar]
  39. Tsai HL, Chu KS, Huang YH, Su YC, Wu JY, Kuo CH, Chen CW and Wang JY (2009a) Predictive factors of early relapse in UICC stage I‐III colorectal cancer patients after curative resection. J Surg Oncol 100, 736–743. [DOI] [PubMed] [Google Scholar]
  40. Tsai HL, Yang IP, Huang CW, Ma CJ, Kuo CH, Lu CY, Juo SH and Wang JY (2013a) Clinical significance of microRNA‐148a in patients with early relapse of stage II stage and III colorectal cancer after curative resection. Transl Res 162, 258–268. [DOI] [PubMed] [Google Scholar]
  41. Tsai HL, Yang IP, Lin CH, Chai CY, Huang YH, Chen CF, Hou MF, Kuo CH, Juo SH and Wang JY (2013b) Predictive value of vascular endothelial growth factor overexpression in early relapse of colorectal cancer patients after curative resection. Int J Colorectal Dis 28, 415–424. [DOI] [PubMed] [Google Scholar]
  42. Tsai HL, Yeh YS, Yu FJ, Lu CY, Chen CF, Chen CW, Chang YT and Wang JY (2009b) Predicting factors of postoperative relapse in T2‐4N0M0 colorectal cancer patients via harvesting a minimum of 12 lymph nodes. Int J Colorectal Dis 24, 177–183. [DOI] [PubMed] [Google Scholar]
  43. Van Cutsem E, Nordlinger B, Adam R, Kohne CH, Pozzo C, Poston G, Ychou M, Rougier P (2006) Towards a pan‐European consensus on the treatment of patients with colorectal liver metastases. Eur J Cancer (Oxford, England: 1990), 42, 2212–2221. [DOI] [PubMed] [Google Scholar]
  44. Wang L, Yu J, Ni J, Xu XM, Wang J, Ning H, Pei XF, Chen J, Yang S, Underhill CB et al (2003) Extracellular matrix protein 1 (ECM1) is over‐expressed in malignant epithelial tumors. Cancer Lett 200, 57–67. [DOI] [PubMed] [Google Scholar]
  45. Webber EM, Lin JS, Evelyn PW (2010) Oncotype DX tumor gene expression profiling in stage II colon cancer. Application: prognostic, risk prediction. PLoS Curr 2, pii: RRN1177. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Wilkinson NW, Yothers G, Lopa S, Costantino JP, Petrelli NJ and Wolmark N (2010) Long‐term survival results of surgery alone versus surgery plus 5‐fluorouracil and leucovorin for stage II and stage III colon cancer: pooled analysis of NSABP C‐01 through C‐05. A baseline from which to compare modern adjuvant trials. Ann Surg Oncol 17, 959–966. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Yang XZ, Cui SZ, Zeng LS, Cheng TT, Li XX, Chi J, Wang R, Zheng XF and Wang HY (2017) Overexpression of Rab1B and MMP9 predicts poor survival and good response to chemotherapy in patients with colorectal cancer. Aging 9, 914–931. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Yang IP, Tsai HL, Hou MF, Chen KC, Tsai PC, Huang SW, Chou WW, Wang JY and Juo SH (2012) MicroRNA‐93 inhibits tumor growth and early relapse of human colorectal cancer by affecting genes involved in the cell cycle. Carcinogenesis 33, 1522–1530. [DOI] [PubMed] [Google Scholar]
  49. Zhang XW, Yang HY, Fan P, Yang L and Chen GY (2005) Detection of micrometastasis in peripheral blood by multi‐sampling in patients with colorectal cancer. World J Gastroenterol 11, 436–438. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Fig. S1. Principal components plot of first two principal components from gPCA.

Click here for additional data file. (418.3KB, pdf)

Fig. S2. The distribution of risk score among five risk groups in the entire dataset with stage I–III colon cancer (N = 951).

Click here for additional data file. (194.5KB, pdf)

Fig. S3. Gene set enrichment analysis delineates biological pathways associated with risk score.

Click here for additional data file. (1.1MB, pdf)

Table S1. Description of GEO datasets that were excluded in our study.

Click here for additional data file. (14.1KB, docx)

Table S2. Clinicopathological features of patients in GSE39582, GSE1433, GSE33113, GSE17538 and GSE37892.

Click here for additional data file. (81.1KB, xlsx)

Table S3. Baseline information for patients in GSE14333, GSE33113, GSE17538 and GSE37892.

Click here for additional data file. (14.3KB, docx)

Table S4. Detailed information of eighteen gene identified from discovery series in GSE39582

Click here for additional data file. (15KB, docx)

Data S1. R code for batch effect removing.

Click here for additional data file. (3.2KB, txt)

Articles from Molecular Oncology are provided here courtesy of Wiley

RESOURCES