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Expression profile of microRNAs in serum: a fingerprint for esophageal squamous cell carcinoma.

Esophageal cancer is one of the most common tumors and is the fourth-leading cause of cancer-related death in China. The incidence of high-risk esophageal cancer in northern China exceeds 130 per 100 000 individuals (1, 2). In Asia, 90% of esophageal cancer has the histologic type of esophageal squamous cell carcinoma (ESCC). [10] Currently, the most effective treatment is surgical resection, which prolongs the survival of patients with resectable disease. The 5-year survival rate is 20%-30% for patients who have curative surgery and do not develop lymph node metastases; however, the survival rate is only 13% for patients with at least 1 lymph node metastasis (3). Unfortunately, most early-stage ESCCs are asymptomatic and difficult to detect (4, 5). Endoscopic evaluation with or without chromoendoscopy and random biopsies can diagnose a subset of patients in the early stages of ESCC, but the invasiveness of these diagnostic procedures and the potential for sampling error with random endoscopic biopsy limit their effectiveness. These factors help explain why ESCC has an extremely poor prognosis and a high mortality rate (5, 6). Therefore, novel biomarkers and diagnostic methods for the early detection of ESCC are urgently needed to reduce disease morbidity and mortality. Because serum and plasma are accessed with relative ease, circulating biomarkers are one of the most promising means of diagnosis. To date, however, a sensitive and specific circulating biomarker for ESCC has not been discovered.

MicroRNAs (miRNAs) are small noncoding RNAs 19 -24 nucleotides in length that are thought to be involved in the development of cancer (7). The discovery of a unique miRNA profile in a human cancer such as esophageal cancer could potentially assist with tumor diagnosis and cancer treatment (1, 8-11).Ina recent study, we determined that human serum contains numerous stable miRNAs and that the concentration profile of serum miRNAs is altered in such cancers as lung cancer and colorectal cancer (12). Similar distinctive patterns of circulating miRNAs in plasma or serum samples have been reported for other cancers, such as prostate cancer and pancreatic cancer (13-17); however, the global miRNA pattern in the sera of ESCC patients has not been determined. In the present study, we used high-throughput Solexa (Illumina) sequencing scanning followed by a stem-loop quantitative reverse-transcription PCR (RT-qPCR) assay that uses a hydrolysis probe to systematically and comprehensively evaluate miRNA concentrations in sera from ESCC patients and matched cancer-free control individuals. We determined that the concentrations of particular serum miRNAs were altered in ESCC samples compared with control samples. A statistical analysis revealed a profile of 7 serum miRNAs that may prove useful as a biomarker for ESCC detection.

Materials and Methods


All samples were collected from consenting individuals according to protocols approved by the ethics committee of each participating institution. We included 290 patients with primary ESCC and 140 control individuals in our study. A multiphase, case-control study was designed to identify serum miRNAs as surrogate markers for ESCC (Fig. 1). In the initial biomarker-screening stage, pooled serum samples from 141 ESCC patients who were treated at Jinling Hospital, the Cancer Hospital of Xuzhou, or the Cancer Hospital of Jiangsu Province underwent Solexa sequencing (miRBase 12.0; total, 692 miRNAs) to identify miRNAs that showed significant differences between the ESCC cases and matched controls. Of the 141 patients, 86 had nonmetastatic disease (as evidenced by histologic analysis of surgically excised tumors); the remaining 55 patients had vascular metastasis, lymph node metastasis, or other distant metastasis (see Table 1 in the Data Supplement that accompanies the online version of this article at issue12). We subsequently performed a biomarker confirmation analysis with a hydrolysis probe-based RT-qPCR assay to refine the number of serum miRNAs in the ESCC signature. This analysis was carried out in 2 phases: (a) the biomarker-selection phase, in which serum samples from 36 ESCC patients treated at Jinling Hospital or Yanggongjing Hospital formed the training set, and (b) the biomarker-validation phase, in which serum samples from an additional 113 ESCC patients from Jinling Hospital or the Cancer Hospital of Jiangsu Province formed the validation set. All patients received a diagnosis of esophageal carcinoma between 2008 and 2009, and blood samples were collected before any therapeutic procedures, such as surgery, chemotherapy, and radiotherapy. Patient histopathology results were confirmed by surgical resection of the tumors, and tumor stage was defined by the operative findings. For patients who were unsuitable for surgical management, histopathology characteristics and tumor stage were confirmed by histobiopsy and imaging technology. Table 1 summarizes the demographic and clinical features of the patients. Control participants were recruited from a large pool of individuals seeking a routine health checkup at the Healthy Physical Examination Centre of Jinling Hospital. People who showed no evidence of disease were selected as noncancer controls. Controls were matched to the patients by age, sex, and ethnicity.



The methods used for sample processing are given in the Supplemental Data file in the online Data Supplement. For the Solexa sequencing assay, equal volumes of sera from 86 patients with nonmetastatic ESCC (1.28 mL each), 55 patients with metastatic ESCC (2.00 mL each), and 40 controls with similar age and sex distributions (2.75 mL each) were pooled separatelyto form case and control sample pools. TRIzol reagent (Invitrogen) was used according to the manufacturer's instructions with minor modification to extract total RNA from each pool of serum samples (approximately 110 mL). The aqueous phase was subjected to 3 steps of acid phenol/chloroform purification to eliminate protein residues before isopropyl alcohol precipitation. The resulting RNA pellet was dissolved in 30 [micro]L diethylpyrocarbonate-treated water and stored at -80 [degrees]C until further analysis. For the RT-qPCR assay, total RNA was extracted from 250 [micro]L serum with a 1step phenol/chloroform purification protocol. In brief, 250 [micro]L serum was mixed with 250 [micro]L acid phenol, 250 [micro]L chloroform, and 250 [micro]L diethylpyrocarbonate-treated water. The mixture was vortex-mixed vigorously and incubated at room temperature for 15 min. After phase separation, the aqueous layer was mixed with 1.5 volumes of isopropyl alcohol and 0.1 volumes of 3 mol/L sodium acetate (pH 5.3). This solution was stored at -20 [degrees]C for 1 h. The RNA pellet was collected by centrifugation at 16 000g for 20 min at 4 [degrees]C. The resulting RNA pellet was washed once with 750 mL/L ethanol and dried for 10 min at room temperature. Finally, the pellet was dissolved in 20 [micro]L of ribonuclease-free water and stored at -80 [degrees]C until further analysis.


Solexa sequencing was performed as previously described (12). For more details, see the Supplemental Data file in the online Data Supplement.


Because U6 RNA and 5S rRNA are degraded in serum samples and a consensus housekeeping miRNA is lacking for RT-qPCR analysis of serum miRNAs, we normalized miRNA concentration to serum volume. A hydrolysis probe-based RT-qPCR assay was performed according to the manufacturer's instructions (7300 Sequence Detection System, Applied Biosystems) with a minor modification (see Methods in the Supplemental Data file in the online Data Supplement). This system is highly specific for the target miRNA but not for longer preprocessed precursors or for other highly homologous miRNAs, which may differ in sequence by as little as 1 nucleotide (18, 19). We also assessed the detection limits of the RT-qPCR assay and its dynamic range, and we calculated the absolute concentrations of target miRNAs from calibration curves developed with corresponding synthetic miRNA oligonucleotides (see Methods in the Supplemental Data file in the online Data Supplement). We also assessed assay imprecision for the target miRNAs (see Methods in the Supplemental Data file in the online Data Supplement).


Statistical analysis was performed with SAS software (version 9.1.3; SAS Institute). Data are presented as the mean (SD). Nonparametric Mann-Whitney U-tests were used to compare differences in serum miRNA concentrations between the cancer group and the healthy control group. A P value <0.05 was considered statistically significant. We used Cluster 3.0 software (version; Stanford University; http://rana. Stanford.EDU/software) with the complete linkage method to perform hierarchical clustering. For each miRNA, we constructed the ROC curve and calculated the area under the ROC curve (AUC) to evaluate the specificity and sensitivity of ESCC prediction.

We performed risk score analysis to evaluate the associations between ESCC and serum miRNA concentrations (see Methods in the Supplemental Data file in the online Data Supplement).



Our examination of miRNA concentrations in pooled serum samples from 86 nonmetastatic patients, 55 metastatic patients, and 40 healthy individuals by Solexa sequencing showed that miRNAs were the major components of small RNAs in serum (see Table 2 in the online Data Supplement). Of the 692 serum miRNAs that were scanned by Solexa sequencing, 316, 315, and 268 miRNAs were detected in healthy controls, nonmetastatic patients, and metastatic patients, respectively (see Tables 3 and 4 and Fig. 1 in the online Data Supplement). An miRNA was considered altered if Solexa sequencing detected 100 copies in the patient group and the miRNA showed at least a 2-fold difference in concentration between the patient and control groups. We constructed a list of 25 differentially produced miRNAs (see Table 5 in the online Data Supplement), 6 of which showed a difference in concentration between the nonmetastatic and metastatic patient groups.


We confirmed the concentrations of the 25 candidate miRNAs selected from the Solexa sequencing with a hydrolysis probe-based RT-qPCR assay of samples from 149 patients with a clinical and pathologic diagnosis of ESCC and from 100 healthy control individuals. We found no significant differences between the cancer patients and control individuals in age distribution, sex, smoking status, alcohol consumption, and other diseases.

The RT-qPCR assay for measuring serum miRNA concentration was reliable and reproducible. Semilogarithmic plots of the calibration curves for various concentrations of the synthetic single-strand miRNA calibrators were linear from 10 fmol/L to [10.sup.4] pmol/L (see Fig. 2, A-G, in the online Data Supplement). Threshold cycle (Cq) values of replicate assays were very similar ([r.sup.2] = 0.990), indicating that the RNA extraction method was reproducible (see Fig. 3A in the online Data Supplement). Furthermore, the analytical reproducibility of the RT-qPCR assay was also very good ([r.sup.2] = 0.997) (see Fig. 3B in the online Data Supplement). The mean CVs for the RT-qPCR assays (including the RNA extraction step) for miR-10a, miR-22, miR-100, miR-148b, miR-133a, miR-127-3p, and miR-223 were 9.9%, 8.7%, 8.0%, 9.4%, 7.2%, 8.5%, and 7.7%, respectively.

In the training set, miRNAs were measured in a separate set of individual serum samples from 36 ESCC patients and 33 healthy controls; only miRNAs with a mean change [greater than or equal to] 1.5-fold and a P value <0.001 were selected for further analysis. Moreover, miRNAs with a Cq value >35 and a detection rate <75% in either the ESCC group or the control group were excluded from further analysis. We used these criteria to generate a list of 7 miRNAs (miR-10a, miR-22, miR-100, miR-148b, miR-223, miR-133a, and miR-127-3p) that showed a difference in miRNA patterns between ESCC patients and controls (Table 2).

These 7 miRNAs were further examined by RTqPCR in a larger cohort consisting of 113 ESCC patients and 67 matched controls. Consistent with the results from the training set, the serum concentrations of the 7 miRNAs were significantly higher in the cancer cases than in the control individuals. The changes in concentration ranged from 1.85-fold to 2.54-fold (Table 2). Fig. 2 shows the differences in concentration for the 7 miRNAs in the 149 ESCC patients and 100 control individuals enrolled in the training and validation sets.


We used an unsupervised clustering method that was unbiased to the clinical annotations to investigate the different concentration patterns for several miRNA panels in the ESCC and control serum samples. We found that both a 7-member panel of serum miRNAs (miR-10a, miR-22, miR-100, miR-148b, miR-223, miR-133a, and miR-127-3p) (see Fig. 4 in the online Data Supplement) and a 5-member panel (miR-10a, miR-22, miR-148b, miR-223, and miR-127-3p) (see Fig. 5 in the online Data Supplement) were able to reliably discriminate ESCC samples from control samples, with the former panel having a lower misclassification rate than the latter. The 7-member serum miRNA profile correctly classified 31 (86.1%) of 36 ESCC cases and 26 (78.8%) of 33 control samples in the training set (see Fig. 4A in the online Data Supplement). In the validation set, 113 ESCC cases and 67 controls were separated into 2 main classes; only 15 ESCC cases and 13 controls were misclassified (see Fig. 4B in the online Data Supplement). Because patients with cancers in tumor, node, metastasis (TNM) stage I or II can undergo complete resection of tumors and because early detection of this cancer will most likely improve survival rate, we performed a separate analysis with only stage I/II ESCC patients. The dendrogram generated by the cluster analysis showed a clear separation of stage I/II ESCC samples from controls in both the training set and the validation set: The 7-member serum miRNA profile correctly classified 95 (89.6%) of 106 early-stage ESCC samples and 79 (79.0%) of 100 control samples (see Fig. 4C in the online Data Supplement).


ROC curves constructed to compare the relative concentrations of the 7 miRNAs for the ESCC patients and the healthy controls yielded the following AUCs: miR10a, 0.886 (95% CI, 0.843-0.930); miR-22,0.949 (95% CI, 0.925-0.974); miR-100, 0.817 (95% CI, 0.7630-870); miR-148b, 0.855 (95% CI, 0.810-0.901); mi-R223, 0.911 (95% CI, 0.876-0.945); miR-133a, 0.830 (95% CI, 0.781-0.880); and miR-127-3p, 0.899 (95% CI, 0.860-0.938) (Fig. 3). The AUCs for the 7miRNAs were markedly higher than the AUC for carcinoembryonic antigen (0.549; 95% CI, 0.475-0.623) (P < 0.0005). Using the optimal cutoff value, we obtained the following sensitivity and specificity values: miR-10a, 81.2% and 80.0%, respectively; miR-22, 88.6% and 86.0%; miR-100, 63.8% and 81.0%; miR-148b, 66.4% and 87.0%; miR-223, 83.2% and 83.0%; miR-133a, 65.1% and 83.0%; and miR-127-3p, 78.5% and 87.0%. By comparison, the sensitivity and specificity for carcinoembryonic antigen were 13.4% and 100%, respectively (3.05 [micro]g/L as the cutoff value).


To further evaluate the diagnostic value of the 7-member miRNA profiling system, we used a risk score formula to calculate the risk score function (RSF) for ESCC samples and control samples. Samples were ranked according to their RSF and then divided into a high-risk group, representing the predicted ESCC cases, and a low-risk group, representing the predicted control individuals. The frequency table and the ROC curves were then used to evaluate the diagnostic effects of the 7-miRNA profiling system and to find the appropriate cutoff point.

Fig. 3 shows that the AUC for the RSF was 0.929 (95% CI, 0.899-0.960). With an optimal cutoff value (RSF = 8.10), in which the sum of the sensitivity and specificity was maximal, the specificity was 96.0%, and the sensitivity was 78.5%. At this cutoff, 96 of the 100 controls had RSF values <8.10, whereas 116 of the 149 ESCC samples had a risk score >8.10 (see Table 6 in the online Data Supplement). Subdividing by TNF stage shows that use of this cutoff value was able to correctly predict 85 (80.2%) of the 106 patients in stage I or II and 32 (74.4%) of the 43 patients in stage III or IV (see Table 6 in the online Data Supplement).



Our investigation of the use of unsupervised clustering and a Student t-test to determine whether a distinctive concentration pattern existed for the 7 miRNAs in ESCC at different stages of the disease revealed no significant differences in the serum concentrations of the 7 chosen miRNAs in the cancer patients at different clinical stages (see Table 7 in the online Data Supplement). Furthermore, we used RT-qPCR analysis to investigate the concentrations for 4 miRNAs (miR423-5p, miR-483-5p, miR-501-3p, and miR-874) that showed a greater than 2-fold difference in concentration between the metastatic and nonmetastatic ESCC cases in the Solexa analysis (see Table 5 in the online Data Supplement) for the serum samples from ESCC patients who were enrolled in the training and validation sets. As assessed by the RT-qPCR assay, the nonmetastatic and metastatic cancer cases showed no significant differences in concentration for any of the 4 miRNAs. These observations suggest that the concentration profile for the 7 serum miRNAs is a biomarker for ESCC of various stages.



Stratifying the ESCC cases by such clinical features as sex, age, smoking history, and alcohol consumption revealed no significant effects on serum miRNA concentrations except for sex (see Table 7 in the online Data Supplement). We suspect that the sex effect was a consequence of the relatively small sample size for females in the study.


We have demonstrated in this global analysis of miRNA concentration in the serum of ESCC patients that the concentration profile of 7 serum miRNAs can serve as a noninvasive, accurate biomarker for ESCC diagnosis. In addition, we have developed a reliable strategy that uses Solexa sequencing of pooled serum samples followed by multiple RT-qPCR to determine disease-associated serum miRNA profiles.

Numerous stable miRNAs are present in plasma and serum. They exhibit distinctive miRNA concentration profiles in patients with various cancers, including lung, colorectal, prostate, and pancreatic cancers (12-17). These findings highlight the potential of a plasma or serum miRNA panel to serve as a reliable noninvasive biomarker for the detection of cancer. In the present study, we focused on the serum miRNA profile of patients with ESCC, a cancer with an extremely poor prognosis and a very high incidence and mortality rate in China (1, 2). As an initial screening stage, we performed a high-throughput Solexa sequencing assay with a serum small-RNA library (which excludes possible contamination by other small RNA and DNA molecules). This stage was followed by multiple RTqPCR assays with individual serum samples from the patient and control groups. We systematically measured the concentrations of serum miRNAs in ESCC patients and identified 7 serum miRNAs (miR-10a, miR-22, miR-100, miR-148b, miR-223, miR-133a, and miR-127-3p) that were significantly upregulated in the sera of ESCC patients compared with control individuals. Our study demonstrated that the 7-miRNA profile may be used as a biomarker for ESCC, and, importantly, has the potential to predict ESCC at a relative early stage, as exhibited by the clear separation of stage I/II ESCC samples from the control samples in the cluster analysis dendrogram. Furthermore, we have demonstrated that the panel of 7 serum miRNAs is a much more sensitive indicator of ESCC than the conventional carcinoembryonic antigen biomarker.

Because of the similarities among various tumors, such as unlimited proliferation and rapid metastasis, the upregulation of some of these miRNAs is likely to be observed in the sera of patients with other types of tumors. Therefore, we compared the 7 upregulated serum miRNAs in ESCC with the miRNA profiles for other cancers. Except for miR-100, which is shared by ESCC and prostate cancer panels (13), the other 6 serum miRNAs have shown no altered production for any other cancers, such as colorectal cancer, ovarian cancer, and pancreatic ductal adenocarcinoma (15-17). Nevertheless, future studies are necessary to clarify whether the concentration profile for these 7 serum miRNAs is capable of discriminating ESCC from other types of tumors.

Our laboratory and other laboratories have confirmed that miRNA concentrations in human serum and plasma are quite stable (12, 13, 20). One possible explanation for the remarkable stability of miRNAs in serum and plasma is that they are protected by binding proteins or microvesicles (12, 13, 20). In addition, serum miRNAs might be chemically modified (e.g., methylation), making them resistant to ribonuclease activity (12, 13). The source of circulating miRNAs is not clear, however. Our previous study demonstrated that serum miRNAs were derived not only from circulating blood cells but also from other tissues affected by disease (12). Furthermore, other investigators have reported that miRNAs are stored in microvesicles derived from various cell types (12, 13, 20), suggesting that active secretion by cells is a major source of serum and plasma miRNAs. Such findings further support the conclusion that the serum miRNA profile is an indicator of biological function.

Because U6 RNA and 5S rRNA are degraded in serum samples and a consensus housekeeping miRNA is lacking for the RT-qPCR analysis of serum miRNAs, we normalized miRNA concentration to serum volume in this study. Several miRNAs, including miR-16 (21), have recently been used in the normalization of serum miRNAs; however, one of our previous studies (12) revealed that the concentration of miR-16 itself is altered in certain diseases and cannot serve as an internal control for normalization of serum miRNAs. We speculate that normalizing the concentration of circulating miRNAs by the volume of serum or plasma samples may currently be the most feasible way to solve the problem.

Functional studies of miRNAs in tumor tissue may be helpful for evaluating serum miRNAs as indicators of various types of cancer. Of the 7 serum miRNAs selected in the ESCC patients, several are involved in general tumorigenesis. For instance, miR-223 is upregulated in the tissue samples of some digestive system neoplasms, including gastric cancer, colon cancer, pancreatic cancer, and so forth (22, 23). Increased production of miR-10a and miR-100, on the other hand, has been observed in gastric cancer, pancreatic cancer, and other cancers (23-27). Future studies are necessary, however, to identify the target genes of circulating miRNAs and the mechanism that regulates miRNA biogenesis.

Circulating miRNAs as a class have enormous potential as ideal cancer biomarkers, for the following reasons: (a) They are remarkably stable and reproducible; (b) their concentration profiles are specifically correlated with certain types of cancer; and (c) they are easily accessible, are sampled in a relatively noninvasive manner, and are readily detected by RT-qPCR assay, a technique widely used in clinical laboratories. Whether this 7-member serum miRNA profile can be established as a routine biomarker for ESCC diagnosis in the clinical laboratory will require much more investigation and the testing of a large number of ESCC samples from multiple centers.

In summary, we have defined a distinctive serum miRNA signature in ESCC patients. In particular, we have demonstrated for the first time that the profile of 7 serum miRNAs has potential to serve as a noninvasive biomarker for diagnosing ESCC. These results may provide an impetus for future evaluations of the clinical value of serum miRNAs to predict therapeutic efficacy and to forecast ESCC recrudescence.

Author Contributions: All authors confirmed they have contributed to the intellectual content of this paper and have met the following 3 requirements: (a) significant contributions to the conception and design, acquisition of data, or analysis and interpretation of data; (b) drafting or revising the article for intellectual content; and (c) final approval of the published article.

Authors' Disclosures or Potential Conflicts of Interest: Upon manuscript submission, all authors completed the Disclosures of Potential Conflict of Interest form. Potential conflicts of interest:

Employment or Leadership: None declared.

Consultant or Advisory Role: None declared.

Stock Ownership: None declared.

Honoraria: None declared.

Research Funding: C.-Y. Zhang and K. Zen, grants from the National Natural Science Foundation of China (nos. 30730080, 30772484, and 30872411) and the National Basic Research Program of China (973 Program) (nos. 2006CB503903, 2004CB518603, and 2002CB512902); C. Zhang, the National Natural Science Foundation of China (no. 30950100).

Expert Testimony: None declared.

Role of Sponsor: The funding organizations played no role in the design of study, choice of enrolled patients, review and interpretation of data, or preparation or approval of manuscript.


(1.) Guo Y, Chen ZL, Zhang L, Zhou F, Shi SS, Feng XL, et al. Distinctive microRNA profiles relating to patient survival in esophageal squamous cell carcinoma. Cancer Res 2008;68:26-33.

(2.) Wobst A, Audisio RA, Colleoni M, Geraghty JG. Oesophageal cancer treatment: studies, strategies and facts. Ann Oncol 1998;9:951-62.

(3.) Pisani P, Parkin DM, Bray F, Ferlay J. Estimates of the worldwide mortality from 25 cancers in 1990. Int J Cancer 1999;83:18-29.

(4.) Earlam R, Cunha-Melo JR. Oesophageal squamous cell carcinoma: I. A critical review of surgery. Br J Surg 1980;67 :381-90.

(5.) Campbell F, Lauwers GY, Williams GT. Tumors of the esophagus and stomach. In: Fletcher CDM, ed. Diagnostic histopathology of tumors. Churchill Livingstone Elsevier; 2007. p 328-9.

(6.) Anderson LL, Lad TE. Autopsy findings in squamous-cell carcinoma of the esophagus. Cancer 1982;50:1587-90.

(7.) Bartel DP. MicroRNAs: genomics, biogenesis, mechanism, and function. Cell 2004;116:281-97.

(8.) Esquela-Kerscher A, Slack FJ. Oncomirs--microRNAs with a role in cancer. Nat Rev Cancer 2006;6:259-69.

(9.) Calin GA, Croce CM. MicroRNA signatures in human cancers. Nat Rev Cancer 2006;6:857-66.

(10.) Feber A, Xi LQ, Luketich JD, Pennathur A, Landreneau RJ, Wu MX, et al. MicroRNA expression profiles of esophageal cancer. J Thorac Cardiovasc Surg 2008;135:255-60.

(11.) Hiyoshi Y, Kamohara H, Karashima R, Sato N, Imamura Y, Nagai Y, et al. MicroRNA-21 regulates the proliferation and invasion in esophageal squamous cell carcinoma. Clin Cancer Res 2009; 15:1915-22.

(12.) Chen X, Ba Y, Ma LJ, Cai X, Yin Y, Wang KH, et al. Characterization of microRNAs in serum: a novel class of biomarkers for diagnosis of cancer and other diseases. Cell Res 2008;18:997-1006.

(13.) Mitchell PS, Parkin RK, Kroh EM, Fritz BR, Wyman SK, Pogosova-Agadanyan EL, et al. Circulating microRNAs as stable blood-based markers for cancer detection. Proc Natl Acad Sci USA 2008; 105:10513-8.

(14.) Gilad S, Meiri E, Yogev Y, Benjamin S, Lebanony D, Yerushalmi N, et al. Serum microRNAs are promising novel biomarkers. PLoS One 2008;3: e3148.

(15.) Ng EK, Chong WW, Jin H, Lam EK, Shin VY, Yu J, et al. Differential expression of microRNAs in plasma of patients with colorectal cancer: a potential marker for colorectal cancer screening. Gut 2009;58:1375-81.

(16.) Resnick KE, Alder H, Hagan JP, Richardson DL, Croce CM, Cohn DE. The detection of differentially expressed microRNAs from the serum of ovarian cancer patients using a novel real-time PCR platform. Gynecol Oncol 2009;112:55-9.

(17.) Wang J, Chen J, Chang P, LeBlanc A, Li D, Abbruzzesse JL, et al. MicroRNAs in plasma of pancreatic ductal adenocarcinoma patients as novel blood-based biomarkers of disease. Cancer Prev Res 2009;2:807-13.

(18.) Chen C, Ridzon DA, Broomer AJ, Zhou Z, Lee DH, Nguyen JT, et al. Real-time quantification of microRNAs by stem-loop RT-PCR. Nucleic Acids Res 2005;33:e179.

(19.) Tang F, Hajkova P, Barton SC, Lao K, Surani MA. MicroRNA expression profiling of single whole embryonic stem cells. Nucleic Acids Res 2006;34: e9.

(20.) Chim SS, Shing TK, Hung EC, Leung TY, Chiu RW, Lo YM. Detection and characterization of placental microRNAs in maternal plasma. Clin Chem 2008;54:482-90.

(21.) Lawrie CH, Gal S, Dunlop HM, Pushkaran B, Liggins AP, Pulford K, et al. Detection of elevated levels of tumor-associated microRNAs in serum of patients with diffuse large B-cell lymphoma. Br J Haematol 2008;141:672-5.

(22.) Volinia S, Calin GA, Liu CG, Ambs S, Cimmino A, Petrocca F, et al. A microRNA expression signature of human solid tumors defines cancer gene targets. Proc Natl Acad Sci USA 2006;103: 2257-61.

(23.) Bloomston M, Frankel WL, Petrocca F, Volinia S, Alder H, Hagan JP, et al. MicroRNA expression patterns to differentiate pancreatic adenocarcinoma from normal pancreas and chronic pancreatitis. JAMA 2007;297:1901-8.

(24.) Ueda T, Volinia S, Okumura H, Shimizu M, Taccioli C, Rossi S, et al. Relation between microRNA expression and progression and prognosis of gastric cancer: a microRNA expression analysis. Lancet Oncol 2009;11:136-46.

(25.) Lee EJ, Gusev Y, Jiang J, Nuovo GJ, Lermer MR, Frankel WL, et al. Expression profiling identifies microRNA signature in pancreatic cancer. Int J Cancer 2007;120:1046-54.

(26.) Garofalo M, Quintavalle C, Di Leva G, Zanca C, Romano G, Taccioli C, et al. MicroRNA signatures of TRAIL resistance in human non-small cell lung cancer. Oncogene 2008;27:3845-55.

(27.) Dahiya N, Sherman-Baust CA, Wang TL, Davidson B, Shih leM, Zhang Y, et al. MicroRNA expression and identification of putative miRNA targets in ovarian cancer. PLoS One 2008;3:e2436.

Chunni Zhang, [1] * [[dagger]] Cheng Wang, [1] [[dagger]] Xi Chen, [2] [dagger] Cuihua Yang, [1] Ke Li, [1] Junjun Wang, [1] Juncheng Dai, [3] Zhibin Hu, [3] Xiaojun Zhou, [4] Longbang Chen, [5] Yanni Zhang, [6] Yanfang Li, [6] Hong Qiu, [7] Jicheng Xing, [7] Zhichao Liang, [8] Binhui Ren, [9] Chen Yang, [1] Ke Zen, [2] * and Chen-Yu Zhang [2] *

[1] Department of Clinical Laboratory, Jinling Hospital, Clinical School of Medical College, Nanjing University, Nanjing, China; [2] Jiangsu Diabetes Center, State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing, China; [3] Department of Epidemiology and Biostatistics, Cancer Center, Nanjing Medical University, Nanjing, China; Departments of [4] Pathology and [5] Medical Oncology, Jinling Hospital, Clinical School of Medical College, Nanjing University, Nanjing, China; [6] Department of Medical Oncology, Cancer Hospital of Xuzhou, Xuzhou, China; [7] Department of Biochemistry, Yanggongjing Hospital, Nanjing, China; Departments of 8 Clinical Laboratory and [9] Cardio-thoracic Surgery, Cancer Hospital of Jiangsu Province, Nanjing, China.

[10] Nonstandard abbreviations: ESCC, esophageal squamous cell carcinoma; miRNA, microRNA; RT-qPCR, quantitative reverse-transcription PCR; AUC, the area under the ROC curve; Cq, threshold cycle; TNM, tumor, node, metastasis (TNM Staging System); RSF, risk score function.

* Address correspondence to: C.Z. at Department of Clinical Laboratory, Jinling Hospital, Clinical School of Medical College, Nanjing University, 305 E. Zhongshan Rd., Nanjing, 210002, China. Fax +86-25-80863082; e-mail zchunni27@ K.Z. at State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, 22 Hankou Rd., Nanjing, 210093, China. Fax +86-25-83686234; e-mail C.-Y.Z. at State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, 22 Hankou Rd., Nanjing, 210093, China. Fax +86-25-83686234; e-mail

[[dagger]] These authors contributed equally to this work.

Received March 27, 2010; accepted September 20, 2010.

Previously published online at DOI: 10.1373/clinchem.2010.147553
Table 1. Demographic and clinical features of the ESCC patients
and healthy individuals in the training and validation sets.

Variable Cases Controls P
 (n = 149) (n = 100)

Age, years (a) 61.3 (8.6) 60.0 (10.3) 0.2538 (b)

Age group, n 0.6172 (c)

 [less than or equal 64 (43%) 47 (47%)
 to]59 years
 >59 years 85 (57%) 53 (53%)

Sex, n 0.5831 (c)

 Male 116(77.9%) 74 (74%)
 Female 33 (22.1%) 26 (26%)

Smoking status, n 0.6840 (c)

 Ever and current 38 (25.5%) 23 (23%)
 Never 106 (71.1%) 76 (76%)
 Unknown 5 (3.4%) 1 (1%)

Alcohol consumption, 0.7493 (c)

 Ever and current 34 (22.8%) 26 (26%)
 Never 110(73.8%) 73 (73%)
 Unknown 5 (3.4%) 1 (1%)

Histological type, n

 High 26(17.4%)
 Middle 77 (51.7%)
 Low 42 (28.2%)
 Unknown 4(2.7%)

TNM stage, n

 I 18(12.1%)
 II 81 (54.4%)
 III 31 (20.8%)
 IV 11 (7.4%)
 0 2 (1.3%)
 Unknown 6 (4%)

Family history
of ESCC, n

 Yes 13(8.7%)
 No 136 (91.3%)

Significant cardiac 0.5397 (c)
dysfunction, n

 Yes 4(2.7%) 5 (5%)
 No 145 (97.3%) 95 (95%)

Neurologic disease 0.4036 (c)
or diabetes, n

 Yes 3 (2%) 0 (0%)
 No 146 (98%) 100 (100%)

(a) Age data are presented as the mean (SD).

(b) Student t-test.

(c) Two-sided [chi square] test.

Table 2. miRNA concentrations in ESCC serum
samples and control samples in the training
set and the validation set. (a)

 Training set

miRNA Controls ESCC cases
 (n = 33), fmol/L (n = 36), fmol/L

miR-10a 56.17 (36.50) 149.95 (88.04)
miR-22 645.50 (166.42) 1176.88 (294.03)
miR-100 75.18 (36.03) 148.38 (67.18)
miR-148b 153.60 (56.11) 280.97 (81.90)
miR-223 20 213.98 36 907.69
 (8983.64) (14 303.04)
miR-133a 41 125.38 82 441.92
 (18 153.54) (40 611.98)
miR-127-3p 274.16 (127.84) 511.67 (206.42)

 Training set

miRNA -Fold
 change P

miR-10a 2.67 4.92 X [10.sup.-9]
miR-22 1.82 9.51 X [10.sup.-11]
miR-100 1.97 1.63 X [10.sup.-7]
miR-148b 1.83 3.68 X [10.sup.-9]
miR-223 1.83 1.17 X [10.sup.-7]
miR-133a 2.01 9.02 X [10.sup.-8]
miR-127-3p 1.87 1.98 X [10.sup.-7]

 Validation set

miRNA Controls ESCC cases
 (n = 67), fmol/L (n = 113), fmol/L

miR-10a 66.23 (54.75) 168.41 (100.77)
miR-22 547.23 (165.05) 1277.56 (572.91)
miR-100 81.70 (59.60) 168.94 (101.45)
miR-148b 184.18 (85.17) 349.58 (143.66)
miR-223 26 598.57 66 443.38
 (10 600.23) (29 440.87)
miR-133a 50 763.30 94 066.56
 (23 171.08) (45 458.16)
miR-127-3p 215.22 (102.67) 513.02 (226.65)

 Validation set

miRNA -Fold
 change P

miR-10a 2.54 4.01 X [10.sup.-17]
miR-22 2.33 1.45 X [10.sup.-24]
miR-100 2.07 1.56 X [10.sup.-11]
miR-148b 1.90 3.39 X [10.sup.-14]
miR-223 2.50 1.40 X [10.sup.-23]
miR-133a 1.85 2.08 X [10.sup.-13]
miR-127-3p 2.38 6.88 X [10.sup.-21]

(a) miRNA concentrations are presented as the mean (SD).
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Title Annotation:Cancer Diagnostics
Author:Zhang, Chunni; Wang, Cheng; Chen, Xi; Yang, Cuihua; Li, Ke; Wang, Junjun; Dai, Juncheng; Hu, Zhibin;
Publication:Clinical Chemistry
Date:Dec 1, 2010
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