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. 2018 Oct 1;10:4051–4064. doi: 10.2147/CMAR.S174452

Development and validation of an immunity-related classifier of nine chemokines for predicting recurrence in stage I–III patients with colorectal cancer after operation

Guozeng Xu 1,2,*, Yuehan Zhou 3,*, Fuxiang Zhou 1,2,✉
PMCID: PMC6173492  PMID: 30323661

Abstract

Introduction

Chemokines are closely related with tumor immunity, progression, and metastasis. We aimed to construct a multi-RNA classifier of chemokine family genes for predicting tumor recurrence in stage I–III patients with colorectal cancer (CRC) after operation.

Patients and methods

By analyzing microarray data, the Cox regression analysis was conducted to determine survival-related chemokine family genes and develop a multi-RNA classifier in the training set. The prognostic value of this multi-RNA classifier was further validated in the internal validation and external independent sets. Receiver operating characteristic curves were used to compare the prediction ability of the combined model of this multi-RNA classifier and stage, and this multi-RNA classifier and stage alone.

Results

Nine survival-related chemokines were identified in the training set. We identified a nine-chemokine classifier and classified the patients as high-risk or low-risk. Compared with CRC patients with high-risk scores, CRC patients with low-risk scores had longer disease-free survival in the training (HR=2.353, 95% CI=1.480–3.742, P<0.001), internal validation (HR=2.389, 95% CI=1.428–3.996, P<0.001), and external independent (HR=3.244, 95% CI=1.813–5.807, P<0.001) sets. This nine-chemokine classifier was an independent prognostic factor in these datasets (P<0.05). The combined model of this nine-chemokine classifier and tumor stage may tend to have higher accuracy than stage alone in the training (area under curve 0.727 vs 0.626, P<0.01), internal validation (0.668 vs 0.584, P=0.03), and external independent (0.704 vs 0.678, P>0.05) sets. This nine-chemokine classifier may only be applied in Marisa’s C2, C5, and C6 subtypes patients.

Conclusion

Our nine-chemokine classifier is a reliable prognostic tool for some specific biological subtypes of CRC patients. It might contribute to guide the personalized treatment for high-risk patients.

Keywords: classifier, colorectal cancer, chemokine, survival analysis, risk classification, microarray

Introduction

CRC is the third most common malignancy and the fourth leading cause of mortality worldwide.1 Risk stratification of CRC may require the combined multiple approaches, including analysis of molecular biomarkers and clinical data, and thorough experimental studies. In the past two decades, study results have confirmed that tumor is a heterogeneity disease.2 The combined model of multiple clinical and molecular parameters could be a much more objective prognostic tool for CRC patients.

Chemokines are a group of small molecular weight proteins that bind to the G protein-coupled chemokine receptors.3 Chemokines and their receptors are divided into four subfamilies (CXC, CC, CX3C, and C) based on the pattern of cysteine residues, where C represents the cysteine and X represents noncysteine amino acids.4,5 Chemokine family genes are closely related with senescence, angiogenesis, epithelial mesenchymal transition, proliferation, immune evasion, and tumor metastasis.6 The altered expression of chemokines and their receptors is found in many malignancies and may lead to aberrant chemokine receptor signaling.7–20 The chemokine ligand–receptor interactions were closely related with tumor immunity, progression, and metastasis in CRC patients, including the CXCL1– CXCR2,7 CXCL9/10–CXCR3,8,9 CXCL12–CXCR4,10–14 CCL2–CCR2,15,16 CCL5–CCR5,17 CCL15–CCR1,18 CCL20– CCR6,19 and CX3CL1–CX3CR120 axes. Although small sample studies showed that some chemokines could be used as the single biomarker for CRC, the values of these genes were not further validated in other validation sets.8,11,13,16,21–23 Until now, there is no multi-RNA classifier of chemokine family genes for predicting tumor recurrence in CRC patients. As a widely used microarray platform, Affymetrix human genome U133 plus 2.0 array included all probes of human genome U133 set and about 6,500 new genes.24 Almost all the mRNA-level data of chemokine family genes could be obtained from this microarray platform. Laetitia Marisa defined six molecular subtypes for CRC based on the main biological characteristics, including one subtype with downregulated immune pathways (C1), one deficient mismatch repair subtype (C2), one KRAS mutant subtype (C3), one cancer stem cell subtype (C4), one subtype with the upregulated Wnt pathway (C5), and one subtype with a normal-like gene expression profile (C6).25 Therefore, we performed this study to develop and validate a multi-RNA prognostic classifier of chemokine family genes for predicting recurrence in stage I–III patients with CRC by analyzing the microarray data. These multi-RNA signatures were further validated in CRC patients with different Marisa’s biological subtypes (C1–C6).25

Materials and methods

Datasets preparation

Gene expression of chemokine family genes and corresponding clinical data were obtained from the GEO database. All log2-transformed expression data of chemokines and their receptors were obtained from Affymetrix human genome U133 plus 2.0 array (GSE39582 and GSE14333). The expression values of genes with multiple probes were calculated by using the median values of multiple probes. After filtering out CRC patients without DFS and clinical data, there were a total of 718 stage I–III patients with CRC, including 492 from GSE39582 and 226 from GSE14333. The 492 patients from GSE39582 were randomly divided into training (n=246, GSE39582) and internal validation (n=246, GSE39582) sets. The 226 patients from GSE14333 were used for the external independent set.

Statistical analysis

The survival analysis was performed by the “survival” package of R software (version 3.4.3). In the training set, the association between the expression level of chemokine family genes and CRC patients’ DFS was evaluated using a univariate Cox regression analysis. Those chemokine family genes were considered to be significant if their P-values were less than 0.05. Then, the selected chemokine family genes were fitted in a multivariate Cox regression analysis in the training set. Risk scores were calculated by the selected chemokine family genes and their regression coefficients in the multivariate Cox regression analysis,26–28 as follows:

RiskScore=∑i=1kCi×Vi

where k is the number of prognostic chemokines, Ci represents the coefficient of the ith chemokine in the multivariate Cox regression analysis, Vi is the expression value of the ith chemokine. Using the upper quartile value of risk scores in the training set as the cutoff point, CRC patients in the training, internal validation, and external independent sets were classified as low-risk or high-risk correspondingly. Survival differences between low-risk and high-risk groups were assessed by the Kaplan–Meier estimator and log-rank test. The multivariate Cox regression analysis was performed to assess whether this risk score was independent of the clinical characteristics such as stage, age, gender, and adjuvant chemotherapy. Additionally, ROC curves were used to compare the predictive value of DFS by the combined model of risk score and stage, and this risk score model and stage alone. The ROC curve analysis was performed using the “pROC” package of R software (version 3.4.3). To generate the ROC curves, those CRC patients whose durations were less than the 5-year DFS needed to be excluded, if they still did not recur at last follow-up. The remaining CRC patients were classified as having either shorter or longer than the recurrence-free survival of 60 months.28 The prognostic values of this multi-RNA prognostic classifier were further validated in different Marisa’s biological subtypes (C1–C6).25 The log-rank test, Cox regression analysis, and ROC analysis were considered to be significant if their P-values were less than 0.05.

Functional enrichment analysis

To evaluate the functional implication of these nine chemokines, functional enrichment analyses for GO and KEGG category were performed with the GeneCodis web tool (http://genecodis.cnb.csic.es/).29–31 GO and KEGG category enrichments were based on the threshold of P-value <0.05. Significant enrichment results were visualized using R software (version 3.4.3).

Results

Identification of survival-related chemokines in the training set

Figure 1 shows the study flow for the development and validation of the nine-chemokine classifier. By using microarray data, we identified 59 chemokine family genes from GSE39582 and GSE14333. We further analyzed these 59 genes by the univariate Cox regression analysis in the training set (n=246, GSE39582; Table S1). Consequently, we identified nine chemokines that were significantly correlated with DFS in CRC patients (shown in Table 1). The positive coefficients for three chemokines (CCL1, CCL14, and CXCL14) indicated that their higher levels of expression were associated with worse prognosis. The negative coefficients for the remaining six genes (CXCL1, CXCL3, CXCL9, CXCL10, CXCL11, and CXCL13) indicated that their higher levels of expression were associated with better prognosis.

Figure 1.

Figure 1

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Study flow for the development and validation of the nine-chemokine classifier.

Abbreviation: CRC, colorectal cancer.

Table 1.

The characteristics of nine chemokines associated with DFS in the training set of 246 CRC patients (n=246, GSE39582)

Gene symbol Type HR (95% CI) Coefficients P-value Putative function
CCL1 Ligand 2.729 (1.118–6.662) 1.004 0.027 Risky
CCL14 Ligand 1.400 (1.043–1.879) 0.336 0.025 Risky
CXCL1 Ligand 0.824 (0.697–0.974) −0.194 0.024 Protective
CXCL3 Ligand 0.836 (0.708–0.986) −0.179 0.034 Protective
CXCL9 Ligand 0.840 (0.722–0.977) −0.174 0.024 Protective
CXCL10 Ligand 0.832 (0.712–0.972) −0.184 0.021 Protective
CXCL11 Ligand 0.878 (0.774–0.995) −0.131 0.041 Protective
CXCL13 Ligand 0.870 (0.763–0.992) −0.140 0.037 Protective
CXCL14 Ligand 1.146 (1.008–1.302) 0.136 0.037 Risky
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Abbreviation: CRC, colorectal cancer.

Survival comparisons between low-risk and high-risk groups in the training, internal validation, and external independent sets

According to these chemokines and their regression coefficients in the multivariate Cox model, we calculated the risk scores for every patient in the training (n=246, GSE39582), internal validation (n=246, GSE39582), and external independent (n=226, GSE14333) sets. Using the cutoff value of risk scores (1.559), CRC patients were classified into low-risk group and high-risk group for the training (low-risk/high-risk: 185/61), internal validation (low-risk/high-risk: 196/50), and external independent (low-risk/high-risk: 185/41) sets. Figure 2 shows the distributions of this risk score and the DFS status in these three sets. As shown in Figure 2, CRC patients with high-risk scores tended to have higher risk of treatment relapse.

Figure 2.

Figure 2

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The distributions of risk score and DFS status in the training (n=246, GSE39582), internal validation (n=246, GSE39582), and external independent (n=226, GSE14333) sets.

Abbreviation: DFS, disease-free survival.

The clinical characteristics of low-risk group and high-risk group patients with CRC in these three sets are shown in Table 2. There were no differences for clinical characteristics (age, gender, stage, and adjuvant chemotherapy) between low-risk group and high-risk group (all P>0.05).

Table 2.

Clinical characteristics of CRC patients according to the nine-chemokine classifier in the training (n=246, GSE39582), internal validation (n=246, GSE39582), and external independent (n=226, GSE14333) sets

Characteristics Training set (n= 246, GSE39582)
Internal validation set (n=246, GSE39582)
External independent set (n =226, GSE14333)
High-risk
(n=61)		 Low-risk
(n=185)		 P-value High-risk
(n=50)		 Low-risk
(n=196)		 P-value High-risk
(n=41)		 Low-risk
(n=185)		 P-value
Age (years)
 <66 30 68 0.085 17 72 0.719 20 81 0.560
 ≥66 31 117 33 124 21 104
Gender
 Male 32 105 0.558 29 106 0.644 26 94 0.143
 Female 29 80 21 89 15 91
Stage
 I 3 14 0.727 1 13 0.331 3 38 0.122
 II 33 102 24 101 18 76
 III 25 69 25 82 20 71
Adjuvant chemotherapy
 Yes 28 67 0.078 24 83 0.472 21 118 0.135
 No 32 118 26 113 20 67
 Unknown 1 0 0 0 0 0
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Abbreviation: CRC, colorectal cancer.

Figure 3 and Table 3 show the DFS differences between high-risk and low risk groups in these three sets. The log-rank test showed that CRC patients with low-risk scores had significantly longer DFS than those with high-risk scores in the training set (HR=2.353, 95% CI=1.480–3.742, P<0.001) and internal validation (HR=2.389, 95% CI=1.428–3.996, P<0.001), and external independent (HR=3.244, 95% CI=1.813–5.807, P<0.001) sets.

Figure 3.

Figure 3

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Kaplan–Meier curves of disease-free survival according to the nine-chemokine classifier in the training (n=246, GSE39582), internal validation (n=246, GSE39582), and external independent (n=226, GSE14333) sets.

Table 3.

Log-rank test of disease-free survival according to the nine-chemokine classifier in the training (n=246, GSE39582), internal validation (n=246, GSE39582), and external independent (n=226, GSE14333) sets

Datasets Risk group (n) Disease-free survival
1-year 3-year 5-year HR (95% CI) P-value
Training set (n=246) High-risk (n=61) 91.0% 55.9% 50.1% 2.353 (1.480–3.742) <0.001
Low-risk (n=185) 92.6% 80.2% 74.8%
Validation set (n=246) High-risk (n=50) 77.5% 55.4% 55.4% 2.389 (1.428–3.996) <0.001
Low-risk (n=196) 90.4% 79.0% 75.5%
Independent set (n=226) High-risk (n=41) 84.7% 56.5% 56.5% 3.244 (1.813–5.807) <0.001
Low-risk (n=185) 95.0% 84.9% 80.7%
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Multivariate Cox regression analysis in the training, internal validation, and external independent sets

Table 4 shows the multivariate Cox regression analysis results of the nine-chemokine classifier, gender, age, stage, adjuvant chemotherapy, and DFS in the training, internal validation, and external independent sets. Both the nine-chemokine classifier and stages were significantly associated with CRC patients’ DFS in these datasets (all P<0.05).

Table 4.

Multivariate Cox regression analysis of the nine-chemokine classifier, gender, age, stage, adjuvant chemotherapy, and disease-free survival in the training (n=246, GSE39582), internal validation (n=246, GSE39582), and external independent (n=226, GSE14333) sets

Datasets Variable Disease-free survival
HR (95% CI) P-value
Training set (n=246) Nine-chemokine classifier (high- vs low-risk) 2.107 (1.313–3.382) 0.002
Age (≥66 years vs <66 years) 0.944 (0.567–1.571) 0.824
Gender (female vs male) 1.023 (0.644–1.626) 0.923
Tumor stage (III vs II vs I) 1.763 (1.083–2.869) 0.023
Adjuvant chemotherapy (unknown/no vs yes) 0.761 (1.425–1.362) 0.358
Validation set (n=246) Nine-chemokine classifier (high- vs low-risk) 2.298 (1.370–3.855) 0.002
Age (≥66 years vs <66 years) 1.181 (0.695–2.004) 0.538
Gender (female vs male) 0.624 (0.377–1.035) 0.068
Tumor stage (III vs II vs I) 2.001 (1.186–3.378) 0.009
Adjuvant chemotherapy (unknown/no vs yes) 1.296 (0.726–2.317) 0.380
Independent set (n=226) Nine-chemokine classifier (high- vs low-risk) 2.914 (1.599–5.311) <0.001
Age (≥66 years vs <66 years) 0.666 (0.374–1.186) 0.167
Gender (female vs male) 1.102 (0.621–1.954) 0.740
Tumor stage (III vs II vs I) 3.235 (1.845–5.673) <0.001
Adjuvant chemotherapy (no vs yes) 0.672 (0.350–1.290) 0.232
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ROC analysis in the training, internal validation, and external independent sets

Figure 4 shows the ROC curves for predicting DFS in the training, internal validation, and external independent sets. AUC of the nine-chemokine classifier is similar with that of stage alone in the training (0.673 vs 0.626, P=0.343), internal validation (0.651 vs 0.584, P=0.332), and external independent (0.609 vs 0.678, P=0.888) sets. AUC of the combined model (nine-chemokine classifier and tumor stage) may tend to be higher than that of stage alone in the training (0.727 vs 0.626, P=0.001), internal validation (0.668 vs 0.584, P=0.030), and external independent (0.704 vs 0.678, P=0.298) sets.

Figure 4.

Figure 4

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Receiver operating characteristics curves of the combined model of the nine-chemokine classifier and stage, the nine-chemokine classifier and stage alone for predicting disease-free survival in the training (n=246, GSE39582), internal validation (n=246, GSE39582), and external independent (n=226, GSE14333) sets.

Abbreviation: AUC, area under the curve.

Survival analysis between low-risk and high-risk groups in the combined training and validation set

Using the same cutoff point (1.559), CCR patients were categorized into low-risk group (n=381) and high-risk group (n=111) in the combined training and validation set. CRC patients with low-risk scores had significantly longer DFS (HR=2.383, 95% CI=1.690–3.359, P<0.001) and OS (HR=1.603, 95% CI=1.115–2.305, P=0.010) than those with high-risk scores (shown in Table 5). Table 6 shows the multivariate Cox regression analysis results of the nine-chemokine classifier, gender, age, stage, adjuvant chemotherapy, and survival in the combined training and validation set (n=492, GSE39582). The multivariate Cox analysis showed that the nine-chemokine classifier was significantly associated with patients’ DFS (HR=2.292, 95% CI=1.622–3.239, P<0.001) and OS (HR=1.640, 95% CI=1.139–2.362, P=0.008; Table 6). Subgroup analysis showed that stage II–III patients with low-risk scores had significantly longer DFS (P<0.05) than those with high-risk scores (shown in Figure 5).

Table 5.

Comparison of the survival of colorectal cancer patients according to the nine-chemokine classifier in the combined training and validation set (n=492, GSE39582)

Set Risk group (n) Disease-free survival
Overall survival
1-year 3-year 5-year HR (95% CI) P-value 1-year 3-year 5-year HR (95% CI) P-value
The combined set (n=492) High-risk (n=111) 79.8% 55.6% 52.3% 2.383 <0.001 93.6% 77.1% 63.5% 1.603 0.010
Low-risk (n=381) 90.7% 79.6% 75.2% (1.690–3.359) 96.5% 85.2% 76.1% (1.115–2.305)
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Table 6.

Multivariate Cox regression analysis of the nine-chemokine classifier, gender, age, stage, adjuvant chemotherapy, and survival in the combined training and validation set (n=492, GSE39582)

Datasets Variable Disease-free survival
Overall survival
HR (95% CI) P-value HR (95% CI) P-value
The combined set (n=492) Nine-chemokine classifier (high- vs low-risk) 2.292 (1.622–3.239) <0.001 1.640 (1.139–2.362) 0.008
Age (≥66 years vs <66 years) 1.019 (0.714–1.454) 0.919 1.395 (0.967–2.031) 0.075
Gender (female vs male) 0.806 (0.573–1.132) 0.213 0.768 (0.549–1.074) 0.122
Tumor stage (III vs II vs I) 1.867 (1.303–2.674) 0.001 1.122 (0.804–1.565) 0.497
Adjuvant chemotherapy (unknown/no vs yes) 0.995 (0.662–1.495) 0.980 1.198 (0.798–1.796) 0.384
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Figure 5.

Figure 5

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Kaplan–Meier curves of disease-free survival according to the nine-chemokine classifier for different stage patients with CRC in the combined training and validation set (n=492, GSE39582).

Abbreviation: CRC, colorectal cancer.

The prediction values of the nine-chemokine classifier for different biological subtypes in the combined training and validation set

To verify the value of the nine-chemokine classifier, we further validated our findings in different molecular subtypes of Laetitia Marisa in the combined training and validation set (n=460, GSE39582).

We further validated the values of the nine-chemokine classifier for different biological subtypes in the combined training and validation set (n=492, GSE39582). Patients with high-risk scores had similar DFS (all P>0.05) with those with low-risk scores in the subtypes C1 (one subtype with downregulated immune pathways) and C3 (one KRAS mutant subtype) (shown in Figure 6). Patients with high-risk scores had significantly shorter DFS (all P>0.05) than those with low-risk scores in the subtypes C2 (one deficient mismatch repair subtype), C4 (one cancer stem cell subtype), C5 (one subtype with the upregulated Wnt pathway), and C6 (one subtype with a normal-like gene expression profile) (shown in Figure 6). But the Cox regression analysis showed that the integrated lncRNA-mRNA classifier was not significantly associated with the subtypes C1, C3, and C4 patients’ DFS (all P≥0.05). The integrated lncRNA-mRNA classifier was an independent prognostic factor for the subtypes C2, C5, and C6 patients’ DFS (all P<0.05).

Figure 6.

Figure 6

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Kaplan–Meier curves of disease-free survival according to the nine-chemokine classifier for different biological subtypes in the combined training and validation set (n=492, GSE39582).

Functional enrichment analysis

To explore the functional implication of nine chemokines, we performed functional category enrichment analysis. Functional enrichment analysis showed that the nine chemokine family genes were significantly enriched in 55 GO terms and 9 KEGG pathways (shown in Figure 7). The functional categories are mainly involved in eight GO terms, including immune response (GO:0006955), inflammatory response (GO:0006954), signal transduction (GO:0007165), chemotaxis (GO:0006935), cell–cell signaling (GO:0007267), extracellular space (GO:0005615), extracellular region (GO:0005576), and chemokine activity (GO:0008009). The mainly involved KEGG pathways included cytokine–cytokine receptor interaction (KEGG:04060), chemokine signaling pathway (KEGG:04062), and toll-like receptor signaling pathway (KEGG:04620).

Figure 7.

Figure 7

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Results of functional enrichment analyses for GO (A) and KEGG (B) category.

Discussion

Although the TNM staging system is widely used as the risk stratification of CRC patients, it is insufficient in the prediction of prognosis and estimation for some patients.32,33 Conflict clinical outcomes may exist among some CRC patients with the same stage.31,32 To date, there are no clinically utilized prognostic biomarkers in CRC patients. An increasing amount of evidence demonstrates that chemokines and their receptors play an important role in tumor immunity, progression, and metastasis of CRC patients.7–20 The discovery and application of a multiple-chemokine biomarker will promote the evaluation and identification of potential high-risky recurrence in CRC patients. To identify the prognostic chemokines, we profiled chemokines by mining the existing microarray data of Affymetrix human genome U133 plus 2.0 array. We applied a univariate Cox regression analysis to select DFS-related chemokines. Based on this data-mining method, we have developed and validated a nine-chemokine classifier. The utility of this nine-chemokine classifier may add to the prognostic value of the TNM stage system. Furthermore, the clinical application of this nine-chemokine classifier might stratify CRC patients with the same stage into low-risk and high-risk groups of recurrence after operation. CRC patients of high-risk group had shorter DFS than those of low-risk group in stage II and III patients. The nine-chemokine classifier may provide an additional biomarker for identifying potential candidates for aggressive treatment strategies. The molecular subtype should be considered before the clinical application of the prognostic signatures. Subgroup showed that the nine-chemokine classifier was not an independent prognostic factor for DFS in Marisa’s C1, C3, and C4 subtypes patients. Therefore, the nine-chemokine classifier may only be applied in Marisa’s C2, C5, and C6 subtypes patients.

The nine-chemokine classifier included three risky genes (CCL1, CCL14, and CXCL14) and six protective genes (CXCL1, CXCL3, CXCL9, CXCL10, CXCL11, and CXCL13). The previous study showed that high tissue levels of CXCL14 was associated with increased risk of recurrence and mortality among CRC patients.34 However, high expressions of CXCL1,CXCL9, CXCL10, and CXCL13 may be correlated with better prognosis of CRC patients.23,35–37 Moreover, the prognostic values of these five chemokines were not further confirmed in another validation sets. The relationship of the remaining four chemokines (CCL1, CCL14, CXCL3, CXCL11) and CRC patients’ prognosis should be further studied.

Although our study possessed the larger sample size for developing and validating this nine-chemokine classifier, we should acknowledge certain potential limitations. First, preliminary functional enrichment analysis indicated that the nine-chemokine classifier was mainly involved in immune response, inflammatory response, signal transduction, chemotaxis, and cell–cell signaling. But the mechanism of these nine chemokines has not been confirmed through experimental studies. Further experimental studies may provide potential therapeutic targets for CRC patients. Second, Affymetrix human genome U133 plus 2.0 array was used for obtaining the mRNA level data of chemokine family genes in this study. But the mRNA level data of some chemokines (CCL6, CCL12, CXCL7, and CXCL15) were not available in this microarray platform. The link between the mRNA levels of these chemokines and survival of CRC patients should be further investigated by experimental studies. Third, this multi-RNA classifier was only developed and validated by the mRNA-level data. The value of this multi-RNA classifier was not further confirmed by the protein-level data. Moreover, future analysis of protein level on additional independent datasets would contribute to determine the potential importance of population and geographical differences.

In conclusion, we performed a comprehensive analysis of chemokine expression levels and corresponding survival information of CRC patients. We have successfully developed and validated a nine-chemokine classifier that may be a useful prognostic biomarker for the personalized treatment. It was the first study demonstrating a link between a multiple-chemokine classifier and tumor recurrence in CRC patients. This nine-chemokine classifier may provide an effective risk stratification of disease-free survival in CRC patients with the same stage, especially for stage II–III patients. But we should acknowledge that the protein-level data of this nine-chemokine classifier should be further validated before its clinical application.

Supplementary material

Table S1.

Univariate Cox regression analysis of chemokine family genes associated with disease-free survival in the training set (n=246, GSE39582)

Gene symbol Type Sub-family HR (95% CI) Coefficient P-value
CCL1 Ligand CC 2.729 (1.118–6.662) 1.004 0.027
CCL2 Ligand CC 1.169 (0.970–1.410) 0.156 0.102
CCL3 Ligand CC 1.004 (0.844–1.194) 0.004 0.962
CCL4 Ligand CC 1.019 (0.823–1.261) 0.019 0.863
CCL5 Ligand CC 0.918 (0.757–1.114) −0.085 0.387
CCL7 Ligand CC 1.118 (0.728–1.719) 0.112 0.610
CCL8 Ligand CC 0.993 (0.830–1.190) −0.007 0.943
CCL11 Ligand CC 1.019 (0.868–1.196) 0.018 0.822
CCL13 Ligand CC 1.152 (0.743–1.784) 0.141 0.527
CCL14 Ligand CC 1.400 (1.043–1.879) 0.336 0.025
CCL15 Ligand CC 1.051 (0.823–1.344) 0.050 0.688
CCL16 Ligand CC 1.556 (0.916–2.643) 0.442 0.102
CCL17 Ligand CC 1.240 (0.699–2.203) 0.215 0.462
CCL18 Ligand CC 0.987 (0.853–1.142) −0.013 0.861
CCL19 Ligand CC 1.063 (0.902–1.252) 0.061 0.467
CCL20 Ligand CC 0.885 (0.771–1.015) −0.122 0.082
CCL21 Ligand CC 1.124 (0.924–1.366) 0.117 0.242
CCL22 Ligand CC 0.877 (0.538–1.430) −0.131 0.600
CCL23 Ligand CC 1.211 (0.701–2.090) 0.191 0.492
CCL24 Ligand CC 1.132 (0.919–1.395) 0.124 0.243
CCL25 Ligand CC 0.739 (0.466–1.172) −0.302 0.199
CCL26 Ligand CC 0.938 (0.705–1.250) −0.064 0.664
CCL27 Ligand CC 1.507 (0.673–3.373) 0.410 0.319
CCL28 Ligand CC 1.058 (0.821–1.363) 0.056 0.664
CXCL1 Ligand CXC 0.824 (0.697–0.974) −0.194 0.024
CXCL2 Ligand CXC 0.870 (0.608–1.243) −0.140 0.444
CXCL3 Ligand CXC 0.836 (0.708–0.986) −0.179 0.034
CXCL5 Ligand CXC 0.939 (0.824–1.071) −0.062 0.349
CXCL6 Ligand CXC 0.951 (0.810–1.115) −0.051 0.534
CXCL8 Ligand CXC 0.933 (0.810–1.073) −0.070 0.329
CXCL9 Ligand CXC 0.840 (0.722–0.977) −0.174 0.024
CXCL10 Ligand CXC 0.832 (0.712–0.972) −0.184 0.021
CXCL11 Ligand CXC 0.878 (0.774–0.995) −0.131 0.041
CXCL12 Ligand CXC 1.107 (0.907–1.352) 0.102 0.319
CXCL13 Ligand CXC 0.870 (0.763–0.992) −0.140 0.037
CXCL14 Ligand CXC 1.146 (1.008–1.302) 0.136 0.037
CXCL16 Ligand CXC 0.901 (0.650–1.249) −0.104 0.531
CXCL17 Ligand CXC 0.814 (0.525–1.262) −0.206 0.357
XCL1 Ligand XC 0.923 (0.524–1.627) −0.080 0.782
XCL2 Ligand XC 1.004 (0.744–1.355) 0.004 0.978
CX3CL1 Ligand CX3C 0.896 (0.613–1.312) −0.109 0.574
CCR1 Receptor CC 1.079 (0.786–1.481) 0.076 0.638
CCR2 Receptor CC 0.965 (0.632–1.475) −0.035 0.870
CCR3 Receptor CC 1.001 (0.553–1.815) 0.001 0.996
CCR4 Receptor CC 1.356 (0.600–3.062) 0.304 0.464
CCR5 Receptor CC 0.861 (0.595–1.244) −0.15 0.425
CCR6 Receptor CC 0.937 (0.622–1.411) −0.065 0.756
CCR7 Receptor CC 0.880 (0.667–1.161) −0.128 0.364
CCR8 Receptor CC 1.127 (0.429–2.960) 0.119 0.808
CCR9 Receptor CC 1.190 (0.391–3.620) 0.174 0.759
CCR10 Receptor CXC 1.239 (0.880–1.745) 0.214 0.220
CXCR1 Receptor CXC 0.511 (0.211–1.240) −0.671 0.138
CXCR2 Receptor CXC 1.005 (0.746–1.355) 0.005 0.973
CXCR3 Receptor CXC 0.732 (0.373–1.438) −0.312 0.365
CXCR4 Receptor CXC 0.979 (0.793–1.208) −0.021 0.842
CXCR5 Receptor CXC 0.966 (0.412–2.267) −0.034 0.937
CXCR6 Receptor CXC 0.803 (0.490–1.317) −0.219 0.385
XCR1 Receptor XC 1.648 (0.515–5.274) 0.499 0.400
CX3CR1 Receptor CX3C 1.569 (0.864–2.849) 0.45 0.139
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Acknowledgments

The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Abbreviations

AUC

area under the curve

CRC

colorectal cancer

DFS

disease-free survival

GEO

Gene Expression Omnibus

GO

Gene Ontology

KEGG

Kyoto Encyclopedia of Genes and Genomes OS, overall survival

ROC

receiver operating characteristics

Footnotes

Disclosure

The authors report no conflicts of interest in this work.

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Associated Data

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

Supplementary Materials

Table S1.

Univariate Cox regression analysis of chemokine family genes associated with disease-free survival in the training set (n=246, GSE39582)

Gene symbol Type Sub-family HR (95% CI) Coefficient P-value
CCL1 Ligand CC 2.729 (1.118–6.662) 1.004 0.027
CCL2 Ligand CC 1.169 (0.970–1.410) 0.156 0.102
CCL3 Ligand CC 1.004 (0.844–1.194) 0.004 0.962
CCL4 Ligand CC 1.019 (0.823–1.261) 0.019 0.863
CCL5 Ligand CC 0.918 (0.757–1.114) −0.085 0.387
CCL7 Ligand CC 1.118 (0.728–1.719) 0.112 0.610
CCL8 Ligand CC 0.993 (0.830–1.190) −0.007 0.943
CCL11 Ligand CC 1.019 (0.868–1.196) 0.018 0.822
CCL13 Ligand CC 1.152 (0.743–1.784) 0.141 0.527
CCL14 Ligand CC 1.400 (1.043–1.879) 0.336 0.025
CCL15 Ligand CC 1.051 (0.823–1.344) 0.050 0.688
CCL16 Ligand CC 1.556 (0.916–2.643) 0.442 0.102
CCL17 Ligand CC 1.240 (0.699–2.203) 0.215 0.462
CCL18 Ligand CC 0.987 (0.853–1.142) −0.013 0.861
CCL19 Ligand CC 1.063 (0.902–1.252) 0.061 0.467
CCL20 Ligand CC 0.885 (0.771–1.015) −0.122 0.082
CCL21 Ligand CC 1.124 (0.924–1.366) 0.117 0.242
CCL22 Ligand CC 0.877 (0.538–1.430) −0.131 0.600
CCL23 Ligand CC 1.211 (0.701–2.090) 0.191 0.492
CCL24 Ligand CC 1.132 (0.919–1.395) 0.124 0.243
CCL25 Ligand CC 0.739 (0.466–1.172) −0.302 0.199
CCL26 Ligand CC 0.938 (0.705–1.250) −0.064 0.664
CCL27 Ligand CC 1.507 (0.673–3.373) 0.410 0.319
CCL28 Ligand CC 1.058 (0.821–1.363) 0.056 0.664
CXCL1 Ligand CXC 0.824 (0.697–0.974) −0.194 0.024
CXCL2 Ligand CXC 0.870 (0.608–1.243) −0.140 0.444
CXCL3 Ligand CXC 0.836 (0.708–0.986) −0.179 0.034
CXCL5 Ligand CXC 0.939 (0.824–1.071) −0.062 0.349
CXCL6 Ligand CXC 0.951 (0.810–1.115) −0.051 0.534
CXCL8 Ligand CXC 0.933 (0.810–1.073) −0.070 0.329
CXCL9 Ligand CXC 0.840 (0.722–0.977) −0.174 0.024
CXCL10 Ligand CXC 0.832 (0.712–0.972) −0.184 0.021
CXCL11 Ligand CXC 0.878 (0.774–0.995) −0.131 0.041
CXCL12 Ligand CXC 1.107 (0.907–1.352) 0.102 0.319
CXCL13 Ligand CXC 0.870 (0.763–0.992) −0.140 0.037
CXCL14 Ligand CXC 1.146 (1.008–1.302) 0.136 0.037
CXCL16 Ligand CXC 0.901 (0.650–1.249) −0.104 0.531
CXCL17 Ligand CXC 0.814 (0.525–1.262) −0.206 0.357
XCL1 Ligand XC 0.923 (0.524–1.627) −0.080 0.782
XCL2 Ligand XC 1.004 (0.744–1.355) 0.004 0.978
CX3CL1 Ligand CX3C 0.896 (0.613–1.312) −0.109 0.574
CCR1 Receptor CC 1.079 (0.786–1.481) 0.076 0.638
CCR2 Receptor CC 0.965 (0.632–1.475) −0.035 0.870
CCR3 Receptor CC 1.001 (0.553–1.815) 0.001 0.996
CCR4 Receptor CC 1.356 (0.600–3.062) 0.304 0.464
CCR5 Receptor CC 0.861 (0.595–1.244) −0.15 0.425
CCR6 Receptor CC 0.937 (0.622–1.411) −0.065 0.756
CCR7 Receptor CC 0.880 (0.667–1.161) −0.128 0.364
CCR8 Receptor CC 1.127 (0.429–2.960) 0.119 0.808
CCR9 Receptor CC 1.190 (0.391–3.620) 0.174 0.759
CCR10 Receptor CXC 1.239 (0.880–1.745) 0.214 0.220
CXCR1 Receptor CXC 0.511 (0.211–1.240) −0.671 0.138
CXCR2 Receptor CXC 1.005 (0.746–1.355) 0.005 0.973
CXCR3 Receptor CXC 0.732 (0.373–1.438) −0.312 0.365
CXCR4 Receptor CXC 0.979 (0.793–1.208) −0.021 0.842
CXCR5 Receptor CXC 0.966 (0.412–2.267) −0.034 0.937
CXCR6 Receptor CXC 0.803 (0.490–1.317) −0.219 0.385
XCR1 Receptor XC 1.648 (0.515–5.274) 0.499 0.400
CX3CR1 Receptor CX3C 1.569 (0.864–2.849) 0.45 0.139
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