Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2019 May 7.
Published in final edited form as: Int J Cancer. 2012 Dec 17;132(12):2901–2909. doi: 10.1002/ijc.27954

microRNA and inflammatory gene expression as prognostic marker for overall survival in esophageal squamous cell carcinoma

Yiqiang Zhao 1, Aaron J Schetter 1, Geoffrey B Yang 1,2, Giang Nguyen 1,2, Ewy A Mathé 1, Peng Li 3,4, Hong Cai 5, Lei Yu 1,6, Fangfang Liu 5, Dong Hang 5, Haijun Yang 7, Xin Wei Wang 1, Yang Ke 5, Curtis C Harris 1
PMCID: PMC6503976  NIHMSID: NIHMS1000535  PMID: 23175214

Abstract

MicroRNAs (miRNAs) and inflammatory genes have a role in the initiation and development of esophageal squamous cell carcinoma (ESCC). In our study, we examined the potential of using miRNA and inflammatory gene expression patterns as prognostic classifiers for ESCC. Five miRNAs and 25 inflammatory-related genes were measured by quantitative reverse transcriptase PCR in tumor tissues and adjacent noncancerous tissues from 178 Chinese patients with ESCC. The expression levels of miR-21 (p = 0.027), miR-181b (p = 0.002) and miR-146b (p = 0.021) in tumor tissue and miR-21 (p = 0.003) in noncancerous tissue were associated with overall survival of patients. These data were combined to generate a miRNA risk score that was significantly associated with worse prognosis (p = 0.0001), suggesting that these miRNAs may be useful prognostic classifiers for ESCC. To construct an inflammatory gene prognostic classifier, we divided the population into training (n = 124) and test cohorts (n = 54). The expression levels of CRY61, CTGF and IL-18 in tumor tissue and VEGF in adjacent noncancerous tissue were modestly associated with prognosis in the training cohort |Z-score| > 1.5 and were subsequently used to construct a Cox regression-based inflammatory risk score (IRS). IRS was significantly associated with survival in both the training cohort (p = 0.002) and the test cohort (p = 0.005). Furthermore, Cox regression models combining both miRNA risk score and IRS performed significantly better than models with either alone (p < 0.001 likelihood ratio test). Therefore, miRNA and inflammatory gene expression patterns, alone or in combination, have potential as prognostic classifiers for ESCC and may help to guide therapeutic decisions.

Keywords: esophageal squamous cell carcinoma, microRNA, prognostic classifier, inflammations

Esophageal cancer is the fifth leading cause of cancer-related deaths in men and the eighth leading cause in women worldwide.1 It is composed of two major histological subtypes: esophageal squamous cell carcinoma (ESCC) and esophageal adenocarcinoma (EADC). ESCC usually arises from the squamous cells in the middle or upper one-third of the esophagus. EADC arises from glandular cells in the lower one-third of the esophagus or at the junction between the esophagus and stomach.2 Incidence of esophageal cancer varies geographically, with the highest incidence in Southern and Eastern Africa and Eastern Asia and the lowest incidence in Western and Middle Africa and Central America.1 A relatively large region of Asia has been referred to as the “esophageal cancer belt,” and this stretches from northern Iran through the central Asian republics to North-Central China. Approximately 90% of cases from this region present as ESCC.3,4 The high rate of esophageal cancer in these populations demonstrate an urgent need to identify causes, molecular classifiers and therapeutic targets for ESCC that can be used to reduce the burden of ESCC in these populations.

The prognosis of ESCC is quite poor. Treatment options for ESCC can include surgery, radiation therapy and various chemotherapeutic regimes. Identifying genes whose expression is associated with poor prognosis of patients with ESCC may lead to clinically useful tools for the management of ESCC in at least two ways. First, these genes may be used to develop molecular classifiers that can identify subgroups of patients that would benefit from more aggressive therapeutic interventions. These classifiers may also be able to stratify patients into appropriate arms of clinical trials to aid in the development and testing of new therapeutics. Second, if genes associated with poor prognosis have a mechanistic role in the initiation or progression of ESCC, they may also serve as novel therapeutic targets.

MicroRNAs (miRNAs) are short, 20- to 24-nucleotide, noncoding RNAs that regulate the translation of specific genes. miRNAs have a causal role in many cancer types and can have either oncogenic or tumor suppressor functions. For example, abnormal expression of oncogenic miRNAs may decrease the translation of target tumor suppressor genes and contribute to the development or progression of cancer.5 There is interest in using miRNAs as both biomarkers and therapeutic targets for various cancer types. Expression levels of specific miRNAs have already shown potential as diagnostic and prognostic biomarkers for cancer.6,7 Our previous studies have found that altered expression of specific miRNAs is associated with prognosis of ESCC and adenocarcinoma of colon, lung and esophagus.8–11

Chronic inflammation also has a role in cancer initiation and progression. In the tumor microenvironment, cytokines and chemokines are produced by infiltrating immune cells, tumor supporting cells and tumor cells, and they regulate several important inflammatory pathways. These pathways may be either protumorigenic or antitumorigenic. In the esophageal mucosa, chronic irritation and inflammation, such as that from cigarette smoking and alcohol abuse, can lead to the initiation and development of ESCC.12,13 Other risk factors related to chronic irritation and ESCC are esophageal achalasia14 and HPV infection.15 We have recently demonstrated that the mRNA expression patterns of inflammation associated genes can be used as prognostic classifiers for liver cancer, lung cancer, colon cancer and esophageal adenocarcinoma.9,16–18 We have also demonstrated that miRNA and inflammatory gene classifiers were independently associated with prognosis for colon adenocarcinoma and EADC,9,17 suggesting that the combination of these classifiers could perform superior to either alone. Therefore, we hypothesized that a similar strategy could identify prognostic classifiers of ESCC.

In our study, we collected tumor and adjacent noncancerous tissues from 178 patients with ESCC from An Yang, Henan Province, China. The incidence of esophageal cancer in this region is 10-fold greater than the nationwide rate for China and 100-fold greater than the rate among Caucasian Americans.19,20 We focused exclusively on the squamous cell histology as it represents the majority of esophageal cancer in this region of the world. We measured the expression levels of five miRNAs and 25 inflammatory genes in these tissues to determine their potential as prognostic classifiers for ESCC. We hypothesized that the combination of these miRNAs and inflammatory genes could be prognostic classifiers of survival for patients with ESCC.

Patients and Methods

Clinical samples

Esophageal tumors and adjacent noncancerous specimens were collected from 185 patients who underwent surgical resection. Eligibility criteria for enrollment were as follows: (i) patients must be residents of An Yang, Henan Province, China, which is a high-risk area for ESCC; (ii) patients must be aged between 18 and 90 years and admitted to An Yang Cancer Hospital between April 30 and August 16, 2008; (iii) all tumors had to have a microscopically confirmed diagnosis of ESCC by two licensed pathologists and (iv) all patients must have signed informed consent forms. Our study was approved by the Medical Ethics Committee of Peking University (China) and the Institutional Review Boards of the National Institutes of Health (USA).

For each patient, primary tumor and its adjacent noncancerous specimens were collected during surgery, separated and cut by experienced pathologist. Tissues were immediately frozen and stored at ‒80°C until extraction of total RNA. The adjacent noncancerous tissues were obtained from tumor-free margins and were at least 5 cm away from the tumor site. For each patient, detailed follow-up and clinicopathological characteristics (including gender, age, histopathology, TNM stage, adjuvant therapy, alcohol and cigarette consumptions and survival information) were collected.

Total RNA isolation

Total RNA of the paired cancerous and adjacent noncancerous tissues were extracted using the standard TRIzol protocol (Invitrogen, Carlsbad, CA). The quality and integrity of the RNA were evaluated by the 2100 Bioanalyzer (Aligent Technologies, Santa Clara, CA), and all samples were required to have RNA integrity numbers of at least 7. A total of 178 patient samples were included, and seven samples were excluded based on this criterion. RNA concentration of each sample was determined using a Nanodrop 8000 Spectrophotometer (Thermo Scientific, Wilmington, DE).

qRT-PCR of inflammatory genes

cDNA was reverse transcribed using the High-Capacity cDNA Archive Kit (Applied Biosystems, Foster City, CA) using reverse transcription reaction volumes of 20 μl containing 1 μg of total RNA for each sample according to the manufacturer’s protocol. The TaqMan Gene Expression Assays (Applied Biosystems, Foster City, CA) that were used for our study are found in Supporting Information Table S1. The BioMark MX real-time PCR system Fluidigm, South San Francisco, CA was used to measure the expression levels of these genes according to the manufacturer’s instructions. Briefly, 14 cycles of preamplification were performed, and the amplified cDNA was loaded onto 96.96 dynamic array chips (Fluidigm, South San Francisco, CA), and then 40 cycles of qRT-PCR were performed. All inflammatory gene assays were performed in triplicate, including three endogenous controls (18S RNA, β-actin and GAPDH). Data processing took place using the Fluidigm real-time PCR analysis software (v. 2.1.1). Individual data with SDs for the triplicates above 1 CT value were excluded for quality control. The average of the expression data of three endogenous controls (18S RNA, β-actin GAPDH) was used to normalize the expression of each individual inflammatory gene in each sample. The inflammatory gene expression was calculated by the –ΔΔCt method, where Ct = threshold cycle and –ΔΔCt = (Ct endogenous control – Ct gene).

qRT-PCR of miRNAs

Reverse transcription of miR-21, miR-181b, miR-146b, miR223, miR-155 and RNU66 was performed according to the manufacturer’s instructions (Applied Biosystems, Foster City, CA) with a reaction volume of 30 μl containing 10 ng of total RNA. The real-time PCR was performed using the 7900HT Fast Real-Time PCR Systems (Applied Biosystems, Foster City, CA). Each miRNA and endogenous control (RUN66) was measured in triplicate, and the average of the triplicate data was used. As an overall quality control, CT values above 35 or measurements with standard deviations for the triplicates above 1 were excluded from further analysis.

Statistical analysis

A paired t-test was used to compare the differently expressed miRNAs and inflammatory genes in tumor and adjacent noncancerous tissues of all 178 patients with ESCC. To assess the association of miRNA and overall survival of patients with ESCC, miRNA expression data were binned into high and low based on median cutoff. Kaplan–Meier survival analysis was used to determine the association of miRNAs and overall survival (p < 0.05 assessed for significance by the log-rank test).

To develop a prognostic inflammatory gene classifier, all patients were randomly split into training (n = 124; 70% of all patients) and a test cohort (n = 54; 30% of all patients). More patients were included in the training cohort to make the risk model more robust. These two cohorts have similar clinicopathological characteristics (Table 1). Univariate Cox regression was used to select genes associated with overall survival in the training cohort using our previously reported criteria.17 In the training cohort, selected genes were used to build multivariate models for tumor and noncancerous tissues separately. We selected genes with |Z-score| > 1.5 as a cutoff to include in a multivariate Cox regression model that was used to generate a prognostic classifier using similar criteria as a previous study.21 Coefficients from these models were multiplied with gene expression values and summed to build risk scores. Individuals were defined as high risk if they had higher than median risk scores for both tumor and noncancerous models similar to our previous methodologies.17 The proportional hazards assumption was tested using Schoenfeld residuals and was met for all reported models. The Kaplan– Meier survival analysis was used to determine the inflammatory risk score (IRS) associated with survival in training cohort and test cohort, respectively, and p < 0.05 was assessed for significance by the log-rank test using GraphPad Prism 5 (Graphpad Software, La Jolla, CA). The data for final Cox regression analysis of IRS and the miRNA expression can be found at http://www3.cancer.gov/intra/lhc/zhao_sup_data.xlsx.

Table 1.

Characteristics of the patients

Cohort (n) All (n = 178) Training (n = 124) Test (n = 54) p-value1
Gender, n (%)
Male 108 (61) 77 (62) 31 (57) 0.62
Female 70 (39) 47 (38) 23 (43)
Age at enrollment (years)
Mean (SD) 62.2 (8.0) 62.7 (7.8) 61.2 (8.5) 0.25
Range 34–84 40–84 34–78
Alcohol consumption,2n (%)
Yes 23 (13) 16 (13) 7 (13) 1
No 155 (87) 108 (87) 47 (87)
Cigarette consumption,3n (%)
Yes 74 (42) 59 (48) 15 (28) 0.02
No 104 (58) 65 (52) 39 (72)
Survival,4n (%)
Yes 110 (62) 77 (62) 33 (61) 1
No 68 (38) 47 (38) 21 (39)
TNM staging,5n (%)
I 6 (3) 4 (3) 2 (4) 0.85
IIa 94 (53) 68 (55) 26 (48)
IIb 25 (14) 16 (13) 9 (17)
III 53 (30) 36 (29) 17 (31)
Open in a new tab
1

p-values are from Fisher’s exact test or t-test where appropriate, which compares training and test cohorts.

2

Alcohol consumption: drinking liquor at least twice per week for ≥12 months.

3

Cigarette consumption: at least one cigarette per day for ≥12 months or ≥180 packs for 1 year.

4

Data of 30 months follow-up.

5

TNM staging at the time of surgery was based on the 2009 World Health Organization Classification.

Results

Expression of miRNAs altered in tumors and associated with overall survival in patients with ESCC

We have previously reported that the expression levels of miR-21, miR-181b, miR-146b, miR-155 and miR-223 were elevated in tumor tissue of ESCC and associated with survival of patients.8 Therefore, the expression levels of these miRNAs were measured in tumor and adjacent noncancerous tissues from the 178 Chinese patients with ESCC. We found that miR-21 (fold change = 3.57; p < 0.0001), miR-181b (fold change = 1.64; p < 0.0001), miR-146b (fold change= 1.52; p < 0.0001), miR-155 (fold change = 1.52; p < 0.0001) and miR-223 (fold change = 1.41; p = 0.01) were all expressed at significantly higher levels in tumor tissues (Supporting Information Table S2). These results are consistent with a putative oncogenic role for these miRNAs.

To assess if the expression levels of any of these five miRNAs were associated with prognosis, we dichotomized the expression values of each miRNA into high and low groups based on median expression. High expression of miR-21 (p = 0.03; Kaplan–Meier log rank), miR-181b (p = 0.002; Kaplan–Meier log rank) and miR-146b (p = 0.02; Kaplan– Meier log rank) in tumor tissue was significantly associated with worse survival (Fig. 1 and Supporting Information Table S3). We also examined the expression patterns of miRNAs in noncancerous tissue, and patients with high miR-21 in noncancerous tissue had significantly worse survival prognosis (p = 0.003; Fig. 1 and Supporting Information Table S3). This result in noncancerous tissue is consistent with our previous report in patient cohorts from the United States and Japan.8

Figure 1.

Figure 1.

Open in a new tab

miRNAs associated with overall survival of patients with ESCC in tumor and adjacent noncancerous tissues. To assess the prognostic role of miR-21, miR-181b, miR-146b, miR-155 and miR-223 in Chinese patients with ESCC, we dichotomized the expression values of each miRNA into high and low groups based on median cutoff of their expression data. In tumor tissue, high expression of miR-21(p ¼ 0.03; Kaplan–Meier log rank), miR-181b (p ¼ 0.002; Kaplan–Meier log rank) and miR-146b (p ¼ 0.02; Kaplan–Meier log rank) was significantly associated with worse overall survival. In addition, high expression of miR-21 (p ¼ 0.003; Kaplan–Meier log rank) was associated with worse overall survival in noncancerous tissues of patients.

The expression levels of miR-21, miR-181b and miR-146b in tumor tissue and miR-21 in noncancerous tissue were each significantly associated with patient survival, demonstrating the potential for each of these as a prognostic classifier for ESCC. In an attempt to develop a more robust prognostic classifier, we combined these four statistically independent miRNA expression patterns into the miRNA risk score. The miRNA risk score was defined as high if two or more of the miRNA expression patterns were higher than median and low otherwise. Patients with high miRNA risk score had worse overall survival (p = 0.0001; Fig. 2), suggesting that the miRNA risk score may be a useful prognostic classifier for ESCC.

Figure 2.

Figure 2.

Open in a new tab

Combination of miRNA risk score (generated by miR-21, miR-181b and miR-146b in tumor and miR-21 in noncancerous tissue) is associated with overall survival of patients with ESCC. The combination of miR-21, miR-181b and miR-146b in tumor tissue and miR-21 in noncancerous tissue is defined as the miRNA risk score. Patients with high expression of two or more of the microRNA were defined as the high miRNA risk score group, and other patients were defined as the low miRNA risk score group. Patients with high miRNA risk score had worse overall survival (p= 0.0001).

Inflammatory genes are significantly altered in ESCC

We measured the expression level of 25 inflammatory-related genes in tumors and noncancerous tissues of 178 patients with ESCC. Among the 25 inflammatory genes, CYR61, CTGF, VEGF, MCP1, MIF, TGFA and TGFB were selected based on the reports linking their expression to ESCC tumoriogenesis and prognosis.22–26 The rest of the inflammatory genes were selected from our previous studies on liver, lung and esophageal adenocarcinoma.9,16,18

We have previously used expression patterns of inflammation-related genes to develop prognostic classifiers for esophageal adenocarcinoma. We followed a similar strategy to develop a similar classifier for ESCC. The expression patterns of many inflammation-related genes were systematically altered in tumor tissue, consistent with a role of an inflammatory response in ESCC. The expression levels of 10 inflammatory genes were significantly elevated in cancerous tissue (Supporting Information Table S1), including IL-8, foxp3, IFNG, IL-1B and IL-10. The expression of six inflammatory-related genes was significantly decreased in tumor tissues, including IL-12A, IL-18, ANXA1, IL-2, IL-1A and IL-17A (Supporting Information Table S1).

IRS is associated with survival in patients with ESCC

We evaluated the expression of the inflammation-related genes for associations with patient survival. The construction of a multigene classifier using several genes with moderate associations can provide stronger associations with prognosis compared to a model using a single gene. Therefore, we generated a prognostic classifier based on the expression of multiple inflammation-related genes. Our strategy is outlined in Supporting Information Figure S1. The 178 patients with ESCC were split into a training cohort (n = 124; 70% of all patients) and a test cohort (n = 54; 30% of all patients) as discussed above, and these cohorts were similar in clinicopathological characteristics (Table 1). The expression levels of the inflammatory genes were similar compared to each cohort (Supporting Information Table S4). In the training cohort, we performed univariate Cox regression and ranked the 25 inflammatory genes according to their Z-scores. We selected genes with |Z-score| > 1.5 as a cutoff to include in a multivariate Cox regression model that was used to generate a prognostic classifier using similar criteria as a previous study.21 The formulas to calculate the IRS for each patients of training cohort are as follows: in cancerous tissue, risk = (0.071 × CYR61) + (0.281 × CTGF) + (–0.170 × IL-18), and in adjacent noncancerous tissue, risk = (0.368 × VEGF). Patients whose risk was higher than the median value for both tumor and noncancerous tissues were classified as high IRS group, and all the others were defined as low IRS group. Kaplan– Meier log-rank analysis showed that patients with high IRS had worse survival than patients with low IRS in the training cohort (p = 0.002; Fig. 3). We then evaluated IRS in the test cohort. Similar results were found in the test cohort in that patients classified as high IRS group had worse survival outcomes than that of low IRS group (p = 0.005, test cohort; Fig. 3), suggesting that IRS may be a useful prognostic classifier.

Figure 3.

Figure 3.

Open in a new tab

CYR61, CTGF and IL-18 in tumor tissue and VEGF in adjacent noncancerous tissue were selected to generate inflammatory risk score (IRS) to predict survival of patients with ESCC in training and test cohort. To evaluate the association of inflammatory genes and overall survival of patients with ESCC, we first performed a univariate analysis of the expression data for the 25 genes. These genes were ranked based on their univariate Cox proportional hazard Z-scores for tumor (a) and nontumor (b) tissues. We selected Z-value of ±1.5 (p = 0.13) as cutoff to recruit the expression data of CTGF, CYR61 and IL-18 in tumor tissue and VEGF in adjacent noncancerous tissue to calculate the IRS for patients of the training cohort. In the training cohort (c), high IRS was associated with poor prognosis (p = 0.002; log-rank test), and the finding was validated in test cohort (d; p = 0.005; log-rank test).

Combination of miRNA score and IRS improves survival models

There was a modest, increased association between being classified as high miRNA score and high IRS score (odds ratio = 2.05; p = 0.048). We next determined if the combination of these two classifiers improved associations with prognosis. Multivariate analysis showed that IRS and miRNA risk score in all 178 patients with ESCC were independently associated with patient survival (Table 2). Likelihood ratio tests demonstrated that the Cox regression models using both IRS and miRNAs performed significantly better than models with only IRS or miRNA risk score alone (p < 0.001), suggesting that the combination of these classifiers is superior to either alone. Kaplan–Meier analysis gave similar results. Patients were classified based on both IRS and miRNA risk score. Patients who scored high for both were classified as high risk. Patients who scored high for either IRS or miRNA risk score were classified as intermediate. Those who were low for both IRS and miRNA risk score were classified as low. The intermediate group has significantly worse survival than the low-risk group (p = 0.04), whereas the high-risk group was significantly worse than the intermediate group (p < 0.0001) (Figure 4). These data suggest that miRNA risk score and IRS could be used in combination as a prognostic classifier for ESCC.

Table 2.

Univariate and multivariate Cox regression analyses on miR score and inflammatory risk score with overall survival in 178 patients with ESCC

Variable (comparison/referent) Univariate analysis
 Multivariate analysis1
HR (95% CI) p-value HR (95% CI) p-value
IRS2 (high/low) 2.88 (1.78–4.67) <0.0005 2.48 (1.48–4.17) 0.001
miR score3 (high/low) 2.89 (1.65–5.07) <0.0005 2.72 (1.55–4.78) 0.001
TNM stage (III and IIb/I and IIa) 1.34 (0.83–2.16) 0.230 1.02 (0.62–1.70) 0.93
Age4 (≥62 years/<62 years) 0.89 (0.56–1.44) 0.646
Gender (male/female) 1.51 (0.91–2.50) 0.115
Current alcohol consumption (yes/no) 1.77 (0.95–3.32) 0.072
Current smoker (yes/no) 1.12 (0.69–1.81) 0.640
Open in a new tab
1

Multivariate analysis including IRS, miR score, and TNM stage.

2

Inflammatory risk score classifier.

3

miR score is defined as high if two or more of the miRs were higher than median expression.

4

The median age was 62 years for the cohort, and therefore, we dichotomized age as higher or lower than 62 years.

Figure 4.

Figure 4.

Open in a new tab

Patients with high miRNA risk score and high inflammatory risk score (IRS) have worst over survival rate than others. The patients were split into three groups based on their miRNA risk score and IRS: patients with both high miRNA risk score and IRS had worst overall survival, and patients with both low miRNA risk score and IRS have best overall survival. The intermediate group was either high miRNA risk score and low IRS or low miRNA risk score and high IRS. The overall survival rate of these three groups was significantly different based on the log-rank test.

Discussion

In our study, we analyzed the expression levels of five miRNAs and 25 inflammatory-related genes for their association with survival in 178 Chinese patients with ESCC. The expression levels of miR-21, miR-181b, miR-146b, miR-155 and miR-223 were each elevated in cancerous tissues similar to our previous study on ESCC.8 These results are consistent with the suggested oncogenic roles for these miRNAs. For example, miR-21 is an oncogenic miRNA that is increased in most solid tumors, including ESCC.8 Increased expression of miR-21 in the absence of other genetic defects is sufficient at causing malignancies in mice,27 whereas deletion of miR-21 reduces KRAS-driven oncogenic transformation.28 miR-21 can target many important tumor suppressor genes in a variety of cancer cell lines, including PTEN,29 TPM1,30 PDCD431,32 and Sprouty2.33 The targeting of PTEN by miR21 is especially relevant to ESCC in that miR-21 expression can repress PTEN protein levels in an ESCC cell line and that PTEN protein levels were inversely correlated in ESCC tumors.34 miR-181b has been reported to be elevated in a variety of cancers11,35–39 and can target important oncogenes such as BCL240 and TIMP3.39 miR-155 is a highly studied miRNA that is implicated in both inflammation and cancer, and high levels of miR-155 can lead to lymphomas in mice.41 miR-146b was reported to be elevated in thyroid cancer42 and nonsmall cell lung cancer43 and can repress SMAD4.44 With the known mechanistic role of miRNAs in cancer, our results support that miR-21, miR-181b, miR-146b, miR-155 and miR-223 have a role in ESCC and therefore may be useful therapeutic targets for the treatment of ESCC.

Our previous study of populations from the United States and Japan suggested that the expression of miR-21, miR181b, miR-146b, miR-155 and miR-223 had potential as prognostic classifiers for ESCC.8 In our study, the increased expression of miR-21, miR-181b and miR-146b in tumors and the increased expression of miR-21 in noncancerous tissues were associated with prognosis in Chinese patients with ESCC. Increased expression of each of these miRNAs has also been reported to be associated with poor prognosis in other cancer types, which further demonstrates their potential as prognostic classifiers. Elevated miR-146b in lung squamous cell carcinomas was associated with worse survival prognosis,43 whereas increased miR-181b expression is associated with worse prognosis in gastric45 and colon adenocarcinomas.11 Elevated expression of miR-21 has been found to be associated with worse prognosis in at least 10 other cancer types, including colon cancer,11 lung cancer,46,47 breast cancer,48 pancreatic cancer,49 tongue cancer,50 gastric cancer,45 head and neck cancer,51 chronic lymphocytic leukemia,52 melanoma53 and astrocytomas.54 Interestingly, we validated the finding that the expression of miR-21 in nontumor tissue is associated with prognosis of ESCC. This suggests that miR-21 may have role in the tumor stroma. All of these results highlight the potential of using miRNA expression patterns as prognostic classifiers. The expression of miR-21 is also associated with therapeutic outcome in colon11 and gastric cancers,45 whereas miR-181b is associated with therapeutic outcome in gastric cancer.45 This highlights the potential of these miRNAs to guide therapeutic decisions. Importantly, we demonstrate here that the combination of miRNA expression pattern is a stronger prognostic classifier than each individual miRNA.

Inflammation has an etiological role in the development and progression of esophageal cancer.55 We developed a prognostic classifier of inflammatory-related genes (IRS) based on the expression of CTGF, CRY61, IL-18 and VEGF. These genes are also functionally linked to cancer. CTGF and CYR61 are two members of a group of matricellular proteins of extracellular matrix referred to as the CCN (Cyr61/CTGF/Nov) family. The CCN family has important roles in inflammation, wound healing and in cancer progression via several important signaling pathways, including insulin-like factor, transforming growth factor-β, and Wnt signaling.56,57 Elevated expression of CTGF has been reported in high tumor grade and metastatic ESCC tumor samples,58 whereas high expression of CYR61 can enhance ESCC tumor cell progression.59 IL-18 has duel effect on cancer. As known as an immune activator, IL-18 contributes to anticancer effect through activation of NK and T cells.60 VEGF can contribute to the tumor proliferation, angiogenesis and metastasis by promoting stromal degeneration, inducing endothelial cell proliferation and migration and enhancing vascular permeability.61–64 In ESCC, the expression of VEGF is associated with lymph node metastases, depth of tumor invasion, distance metastases, pathological grade of malignancy65–68 and prognosis.69–72

We developed two independent prognostic classifiers for ESCC based on the expression of inflammatory-related genes or miRNAs and demonstrated that these classifiers may be used in combination to better predict a patient’s survival risk. These associations were found in a relatively large cohort from rural China, none of which have received preoperative or postoperative chemotherapy. Therefore, it is unlikely that differences in therapy could confound these results. A limitation of our study is that a single portion of a tumor is used to classify each patient. Tumor heterogeneity and variable stromal content may confound some of these associations. One would expect that if multiple biopsies were sampled for each patient, one could improve the performance of the classifiers. Another limitation of our study is that follow-up time for these patients was only 30 months due to the recent accrual times of the tissues. It will be important for future studies to investigate additional populations for longer periods of time to determine if these associations are broadly applicable to all patients with ESCC. If so, the next step will be to determine the best ways these classifiers can be used to select proper postoperative therapies for ESCC or to assign individuals to appropriate arms of prospective clinical trials. Future work should also include sensitivity and specificity analyses of each classifier as well as determine the optimal way to combine these biomarkers to further improve sensitivity and specificity.

There is a need to develop prognostic biomarkers and identify therapeutic targets for ESCC. Our study suggests that expression patterns of miR-21, miR-181b and miR-146b, alone or in combination with IRS, can be used as prognostic classifiers for patients with ESCC. If these miRNAs or inflammatory genes are mechanistically involved in ESCC cancer progression, development of therapies based on these genes may be appropriate. Our study supports future work investigating these areas.

Supplementary Material

Figure 1
Table 1
Table 2
Table 3
Table 4

What’s new?

Esophageal cancer is the fifth leading cause of cancer-related deaths in men and the eighth leading cause in women worldwide. This study examined the potential of using microRNA and inflammatory gene expression patterns as prognostic classifiers for esophageal squamous cell carcinoma (ESCC). The data showed that microRNA and inflammatory gene expression patterns are associated with prognosis in ESCC, suggesting that they could be developed, alone or in combination, as prognostic classifiers to guide therapeutic decisions. If these microRNAs or inflammatory genes are mechanistically involved in ESCC cancer progression, development of therapies based on these genes may also be appropriate.

Acknowledgments

Grant sponsor: National Cancer Institute, Center for Cancer Research at the National Institutes of Health Grant sponsor: Natural Science Foundation of China Grant numbers: 30872937,30930102 Grant sponsor: National Ministry of Science and Technology (973 Project) Grant number: 2011CB504301 Grant sponsor: Natural Science Foundation of Beijing; Grant number: 7100001

Footnotes

Additional Supporting Information may be found in the online version of this article.

References

  • 1.Jemal A, Bray F, Center MM, et al. Global cancer statistics. CA Cancer J Clin 2011;61:69–90. [DOI] [PubMed] [Google Scholar]
  • 2.Jemal A, Center MM, DeSantis C, et al. Global patterns of cancer incidence and mortality rates and trends. Cancer Epidemiol Biomarkers Prev 2010;19:1893–907. [DOI] [PubMed] [Google Scholar]
  • 3.Gholipour C, Shalchi RA, Abbasi M. A histopathological study of esophageal cancer on the western side of the Caspian littoral from 1994 to 2003. Dis Esophagus 2008;21:322–7. [DOI] [PubMed] [Google Scholar]
  • 4.Tran GD, Sun XD, Abnet CC, et al. Prospective study of risk factors for esophageal and gastric cancers in the Linxian general population trial cohort in China. Int J Cancer 2005;113:456–63. [DOI] [PubMed] [Google Scholar]
  • 5.Esquela-Kerscher A, Slack FJ. Oncomirs— microRNAs with a role in cancer. Nat Rev Cancer 2006;6:259–69. [DOI] [PubMed] [Google Scholar]
  • 6.Manne U, Shanmugam C, Bovell L, et al. miRNAs as biomarkers for management of patients with colorectal cancer. Biomark Med 2010;4:761–70. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Grady WM, Tewari M. The next thing in prognostic molecular markers: microRNA signatures of cancer. Gut 2010;59:706–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Mathe EA, Nguyen GH, Bowman ED, et al. MiRNA expression in squamous cell carcinoma and adenocarcinoma of the esophagus and associations with survival. Clin Cancer Res 2009; 15:6192–200. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Nguyen GH, Schetter AJ, Chou DB, et al. Inflammatory and microRNA gene expression as prognostic classifier of Barrett’s-associated esophageal adenocarcinoma. Clin Cancer Res 2010;16:5824–34. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Saito M, Schetter AJ, Mollerup S, et al. The association of microRNA expression with prognosis and progression in early-stage, nonsmall cell lung adenocarcinoma: a retrospective analysis of three cohorts. Clin Cancer Res 2011; 17:1875–82. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Schetter AJ, Leung SY, Sohn JJ, et al. MicroRNA expression profiles associated with prognosis and therapeutic outcome in colon adenocarcinoma. JAMA 2008;299:425–36. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Wu AH, Wan P, Bernstein L. A multiethnic population-based study of smoking, alcohol and body size and risk of adenocarcinomas of the stomach and esophagus (United States). Cancer Causes Control 2001;12:721–32. [DOI] [PubMed] [Google Scholar]
  • 13.Brown LM, Hoover R, Silverman D, et al. Excess incidence of squamous cell esophageal cancer among US Black men: role of social class and other risk factors. Am J Epidemiol 2001;153: 114–22. [DOI] [PubMed] [Google Scholar]
  • 14.Sandler RS, Nyren O, Ekbom A, et al. The risk of esophageal cancer in patients with achalasia. A population-based study. JAMA 1995;274:1359–62. [PubMed] [Google Scholar]
  • 15.Antonsson A, Nancarrow DJ, Brown IS, et al. High-risk human papillomavirus in esophageal squamous cell carcinoma. Cancer Epidemiol Biomarkers Prev 2010;19:2080–7. [DOI] [PubMed] [Google Scholar]
  • 16.Budhu A, Forgues M, Ye QH, et al. Prediction of venous metastases, recurrence, and prognosis in hepatocellular carcinoma based on a unique immune response signature of the liver microenvironment. Cancer Cell 2006;10:99–111. [DOI] [PubMed] [Google Scholar]
  • 17.Schetter AJ, Nguyen GH, Bowman ED, et al. Association of inflammation-related and microRNA gene expression with cancer-specific mortality of colon adenocarcinoma. Clin Cancer Res 2009;15:5878–87. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Seike M, Yanaihara N, Bowman ED, et al. Use of a cytokine gene expression signature in lung adenocarcinoma and the surrounding tissue as a prognostic classifier. J Natl Cancer Inst 2007;99: 1257–69. [DOI] [PubMed] [Google Scholar]
  • 19.Blot WJ, Li JY. Some considerations in the design of a nutrition intervention trial in Linxian, People’s Republic of China. Natl Cancer Inst Monogr 1985;69:29–34. [PubMed] [Google Scholar]
  • 20.Li JY, Ershow AG, Chen ZJ, et al. A case–control study of cancer of the esophagus and gastric cardia in Linxian. Int J Cancer 1989;43:755–61. [DOI] [PubMed] [Google Scholar]
  • 21.Lossos IS, Czerwinski DK, Alizadeh AA, et al. Prediction of survival in diffuse large-B-cell lymphoma based on the expression of six genes. N Engl J Med 2004;350:1828–37. [DOI] [PubMed] [Google Scholar]
  • 22.Ohta M, Kitadai Y, Tanaka S, et al. Monocyte chemoattractant protein-1 expression correlates with macrophage infiltration and tumor vascularity in human esophageal squamous cell carcinomas. Int J Cancer 2002;102:220–4. [DOI] [PubMed] [Google Scholar]
  • 23.Ren Y, Law S, Huang X, et al. Macrophage migration inhibitory factor stimulates angiogenic factor expression and correlates with differentiation and lymph node status in patients with esophageal squamous cell carcinoma. Ann Surg 2005;242:55–63. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Sauter ER, Coia LR, Eisenberg BL, et al. Transforming growth factor a expression as a potential survival prognosticator in patients with esophageal adenocarcinoma receiving high-dose radiation and chemotherapy. Int J Radiat Oncol Biol Phys 1995;31:567–9. [DOI] [PubMed] [Google Scholar]
  • 25.Tanaka T, Ishiguro H, Kuwabara Y, et al. Vascular endothelial growth factor C (VEGF-C) in esophageal cancer correlates with lymph node metastasis and poor patient prognosis. J Exp Clin Cancer Res 2010;29:83. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Xie JJ, Xu LY, Wu JY, et al. Involvement of CYR61 and CTGF in the fascin-mediated proliferation and invasiveness of esophageal squamous cell carcinomas cells. Am J Pathol 2010;176:939–51. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Medina PP, Nolde M, Slack FJ. OncomiR addiction in an in vivo model of microRNA-21induced pre-B-cell lymphoma. Nature 2010;467: 86–90. [DOI] [PubMed] [Google Scholar]
  • 28.Hatley ME, Patrick DM, Garcia MR, et al. Modulation of K-Ras-dependent lung tumorigenesis by MicroRNA-21. Cancer Cell 2010;18:282–93. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Meng F, Henson R, Wehbe-Janek H, et al. MicroRNA-21 regulates expression of the PTEN tumor suppressor gene in human hepatocellular cancer. Gastroenterology 2007;133:647–58. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Zhu S, Si ML, Wu H, et al. MicroRNA-21 targets the tumor suppressor gene tropomyosin 1 (TPM1). J Biol Chem 2007;282:14328–36. [DOI] [PubMed] [Google Scholar]
  • 31.Asangani IA, Rasheed SA, Nikolova DA, et al. MicroRNA-21 (miR-21) post-transcriptionally downregulates tumor suppressor Pdcd4 and stimulates invasion, intravasation and metastasis in colorectal cancer. Oncogene 2008;27:2128–36. [DOI] [PubMed] [Google Scholar]
  • 32.Lu Z, Liu M, Stribinskis V, et al. MicroRNA-21 promotes cell transformation by targeting the programmed cell death 4 gene. Oncogene 2008;27: 4373–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Sayed D, Rane S, Lypowy J, et al. MicroRNA-21 targets Sprouty2 and promotes cellular outgrowths. Mol Biol Cell 2008;19:3272–82. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Feber A, Xi L, Luketich JD, et al. MicroRNA expression profiles of esophageal cancer. J Thorac Cardiovasc Surg 2008;135:255–60; discussion 60. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Nurul-Syakima AM, Yoke-Kqueen C, Sabariah AR, et al. Differential microRNA expression and identification of putative miRNA targets and pathways in head and neck cancers. Int J Mol Med 2011;28:327–36. [DOI] [PubMed] [Google Scholar]
  • 36.Slaby O, Lakomy R, Fadrus P, et al. MicroRNA181 family predicts response to concomitant chemoradiotherapy with temozolomide in glioblastoma patients. Neoplasma 2010;57:264–9. 37. [DOI] [PubMed] [Google Scholar]
  • 37.Visone R, Veronese A, Rassenti LZ, et al. miR181b is a biomarker of disease progression in chronic lymphocytic leukemia. Blood 2011;118: 3072–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Xu X, Jia R, Zhou Y, et al. Microarray-based analysis: identification of hypoxia-regulated microRNAs in retinoblastoma cells. Int J Oncol 2011;38:1385–93. [DOI] [PubMed] [Google Scholar]
  • 39.Wang B, Hsu SH, Majumder S, et al. TGFbmediated upregulation of hepatic miR-181b promotes hepatocarcinogenesis by targeting TIMP3. Oncogene 2010;29:1787–97. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Zhu W, Shan X, Wang T, et al. miR-181b modulates multidrug resistance by targeting BCL2 in human cancer cell lines. Int J Cancer 2010;127: 2520–9. [DOI] [PubMed] [Google Scholar]
  • 41.Costinean S, Zanesi N, Pekarsky Y, et al. Pre-B cell proliferation and lymphoblastic leukemia/ high-grade lymphoma in E(mu)-miR155 transgenic mice. Proc Natl Acad Sci USA 2006; 103:7024–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Nikiforova MN, Tseng GC, Steward D, et al. MicroRNA expression profiling of thyroid tumors: biological significance and diagnostic utility. J Clin Endocrinol Metab 2008;93:1600–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Raponi M, Dossey L, Jatkoe T, et al. MicroRNA classifiers for predicting prognosis of squamous cell lung cancer. Cancer Res 2009;69:5776–83. [DOI] [PubMed] [Google Scholar]
  • 44.Geraldo MV, Yamashita AS, Kimura ET. MicroRNA miR-146b-5p regulates signal transduction of TGF-b by repressing SMAD4 in thyroid cancer. Oncogene 2012;31:1910–22. [DOI] [PubMed] [Google Scholar]
  • 45.Jiang J, Zheng X, Xu X, et al. Prognostic significance of miR-181b and miR-21 in gastric cancer patients treated with S-1/Oxaliplatin or Doxifluridine/Oxaliplatin. PLoS One 2011;6: e23271. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46.Markou A, Tsaroucha EG, Kaklamanis L, et al. Prognostic value of mature microRNA-21 and microRNA-205 overexpression in non-small cell lung cancer by quantitative real-time RT-PCR. Clin Chem 2008;54:1696–704. [DOI] [PubMed] [Google Scholar]
  • 47.Yanaihara N, Caplen N, Bowman E, et al. Unique microRNA molecular profiles in lung cancer diagnosis and prognosis. Cancer Cell 2006;9: 189–98. [DOI] [PubMed] [Google Scholar]
  • 48.Yan LX, Huang XF, Shao Q, et al. MicroRNA miR-21 overexpression in human breast cancer is associated with advanced clinical stage, lymph node metastasis and patient poor prognosis. RNA 2008;14:2348–60. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Dillhoff M, Liu J, Frankel W, et al. MicroRNA-21 is overexpressed in pancreatic cancer and a potential predictor of survival. J Gastrointest Surg 2008;12:2171–6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.Li J, Huang H, Sun L, et al. MiR-21 indicates poor prognosis in tongue squamous cell carcinomas as an apoptosis inhibitor. Clin Cancer Res 2009;15:3998–4008. [DOI] [PubMed] [Google Scholar]
  • 51.Avissar M, McClean MD, Kelsey KT, et al. MicroRNA expression in head and neck cancer associates with alcohol consumption and survival. Carcinogenesis 2009;30:2059–63. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52.Rossi S, Shimizu M, Barbarotto E, et al. microRNA fingerprinting of CLL patients with chromosome 17p deletion identify a miR-21 score that stratifies early survival. Blood 2010;116: 945–52. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 53.Jiang L, Lv X, Li J, et al. The status of microRNA-21 expression and its clinical significance in human cutaneous malignant melanoma. Acta Histochem 2012;114:582–8. [DOI] [PubMed] [Google Scholar]
  • 54.Zhi F, Chen X, Wang S, et al. The use of hsamiR-21, hsa-miR-181b and hsa-miR-106a as prognostic indicators of astrocytoma. Eur J Cancer 2010;46:1640–9. [DOI] [PubMed] [Google Scholar]
  • 55.Abdel-Latif MM, Duggan S, Reynolds JV, et al. Inflammation and esophageal carcinogenesis. Curr Opin Pharmacol 2009;9:396–404. [DOI] [PubMed] [Google Scholar]
  • 56.Dhar A, Ray A. The CCN family proteins in carcinogenesis. Exp Oncol 2010;32:2–9. [PubMed] [Google Scholar]
  • 57.Jun JI, Lau LF. Taking aim at the extracellular matrix: CCN proteins as emerging therapeutic targets. Nat Rev Drug Discov 2011;10:945–63. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 58.Deng YZ, Chen PP, Wang Y, et al. Connective tissue growth factor is overexpressed in esophageal squamous cell carcinoma and promotes tumorigenicity through β-catenin-T-cell factor/Lef signaling. J Biol Chem 2007;282: 36571–81. [DOI] [PubMed] [Google Scholar]
  • 59.Xie JJ, Xu LY, Xie YM, et al. Involvement of Cyr61 in the growth, invasiveness and adhesion of esophageal squamous cell carcinoma cells. Int J Mol Med 2011;27:429–34. [DOI] [PubMed] [Google Scholar]
  • 60.Park S, Cheon S, Cho D. The dual effects of interleukin-18 in tumor progression. Cell Mol Immunol 2007;4:329–35. [PubMed] [Google Scholar]
  • 61.Dvorak HF. Vascular permeability factor/vascular endothelial growth factor: a critical cytokine in tumor angiogenesis and a potential target for diagnosis and therapy. J Clin Oncol 2002;20: 4368–80. [DOI] [PubMed] [Google Scholar]
  • 62.Fava RA, Olsen NJ, Spencer-Green G, et al. Vascular permeability factor/endothelial growth factor (VPF/VEGF): accumulation and expression in human synovial fluids and rheumatoid synovial tissue. J Exp Med 1994;180:341–6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 63.Roberts WG, Palade GE. Increased microvascular permeability and endothelial fenestration induced by vascular endothelial growth factor. J Cell Sci 1995;108 (Part 6):2369–79. [DOI] [PubMed] [Google Scholar]
  • 64.Nor JE, Christensen J, Mooney DJ, et al. Vascular endothelial growth factor (VEGF)-mediated angiogenesis is associated with enhanced endothelial cell survival and induction of Bcl-2 expression. Am J Pathol 1999; 154:375–84. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 65.Kitadai Y, Haruma K, Tokutomi T, et al. Significance of vessel count and vascular endothelial growth factor in human esophageal carcinomas. Clin Cancer Res 1998;4: 2195–200. [PubMed] [Google Scholar]
  • 66.Koide N, Nishio A, Kono T, et al. Histochemical study of vascular endothelial growth factor in squamous cell carcinoma of the esophagus. Hepatogastroenterology 1999;46:952–8. [PubMed] [Google Scholar]
  • 67.Sato F, Shimada Y, Watanabe G, et al. Expression of vascular endothelial growth factor, matrix metalloproteinase-9 and E-cadherin in the process of lymph node metastasis in oesophageal cancer. Br J Cancer 1999;80:1366–72. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 68.Wallner G, Ciechanski A, Dabrowski A, et al. Vascular endothelial growth factor and basic fibroblast growth factor in patients with squamous cell oesophageal cancer. Folia Histochem Cytobiol 2001;39 (Suppl 2):122–3. [PubMed] [Google Scholar]
  • 69.Inoue K, Ozeki Y, Suganuma T, et al. Vascular endothelial growth factor expression in primary esophageal squamous cell carcinoma. Association with angiogenesis and tumor progression. Cancer 1997;79:206–13. [DOI] [PubMed] [Google Scholar]
  • 70.Ogata Y, Fujita H, Yamana H, et al. Expression of vascular endothelial growth factor as a prognostic factor in node-positive squamous cell carcinoma in the thoracic esophagus: long-term follow-up study. World J Surg 2003; 27:584–9. [DOI] [PubMed] [Google Scholar]
  • 71.Shih CH, Ozawa S, Ando N, et al. Vascular endothelial growth factor expression predicts outcome and lymph node metastasis in squamous cell carcinoma of the esophagus. Clin Cancer Res 2000;6:1161–8. [PubMed] [Google Scholar]
  • 72.Shimada H, Takeda A, Nabeya Y, et al. Clinical significance of serum vascular endothelial growth factor in esophageal squamous cell carcinoma.Cancer 2001;92:663–9. [DOI] [PubMed] [Google Scholar]

Associated Data

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

Supplementary Materials

Figure 1
NIHMS1000535-supplement-Figure_1.pdf (90.9KB, pdf)
Table 1
NIHMS1000535-supplement-Table_1.pdf (119.3KB, pdf)
Table 2
NIHMS1000535-supplement-Table_2.pdf (134.3KB, pdf)
Table 3
NIHMS1000535-supplement-Table_3.pdf (126.3KB, pdf)
Table 4
NIHMS1000535-supplement-Table_4.pdf (134.6KB, pdf)

RESOURCES