Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Classification of Benign and Malignant Thyroid Nodules Using a Combined Clinical Information and Gene Expression Signatures

  • Bing Zheng ,

    Contributed equally to this work with: Bing Zheng, Jun Liu

    Affiliations Shanghai Institute of Medical Genetics, Shanghai Children’s Hospital, Shanghai Jiao Tong University, Shanghai, China, Department of Laboratory Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China

  • Jun Liu ,

    Contributed equally to this work with: Bing Zheng, Jun Liu

    Affiliations Department of Otolaryngology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China, Department of Otolaryngology-Head and Neck Surgery, Xinhua Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China, Ear Institute, Shanghai Jiaotong University, Shanghai, China

  • Jianlei Gu,

    Affiliations Shanghai Institute of Medical Genetics, Shanghai Children’s Hospital, Shanghai Jiao Tong University, Shanghai, China, Key Laboratory of Molecular Embryology, Ministry of Health and Shanghai Key Laboratory of Embryo and Reproduction Engineering, Shanghai, China

  • Jing Du,

    Affiliation Department of Ultrasonography, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China

  • Lin Wang,

    Affiliation Department of Ultrasonography, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China

  • Shengli Gu,

    Affiliation Department of Ultrasonography, Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China

  • Juan Cheng,

    Affiliation Department of Ultrasonography, Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China

  • Jun Yang ,

    hui_genomehealth@qq.com (HL); otology-xinhua@hotmail.com (JY)

    Affiliations Department of Otolaryngology-Head and Neck Surgery, Xinhua Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China, Ear Institute, Shanghai Jiaotong University, Shanghai, China

  • Hui Lu

    hui_genomehealth@qq.com (HL); otology-xinhua@hotmail.com (JY)

    Affiliations Shanghai Institute of Medical Genetics, Shanghai Children’s Hospital, Shanghai Jiao Tong University, Shanghai, China, Key Laboratory of Molecular Embryology, Ministry of Health and Shanghai Key Laboratory of Embryo and Reproduction Engineering, Shanghai, China, Department of Bioengineering, University of Illinois at Chicago, Chicago, Illinois, United States of America

Classification of Benign and Malignant Thyroid Nodules Using a Combined Clinical Information and Gene Expression Signatures

  • Bing Zheng, 
  • Jun Liu, 
  • Jianlei Gu, 
  • Jing Du, 
  • Lin Wang, 
  • Shengli Gu, 
  • Juan Cheng, 
  • Jun Yang, 
  • Hui Lu
PLOS
x

Abstract

Background

A key challenge in thyroid carcinoma is preoperatively diagnosing malignant thyroid nodules. A novel diagnostic test that measures the expression of a 3-gene signature (DPP4, SCG5 and CA12) has demonstrated promise in thyroid carcinoma assessment. However, more reliable prediction methods combining clinical features with genomic signatures with high accuracy, good stability and low cost are needed.

Methodology/Principal Findings

25 clinical information were recorded in 771 patients. Feature selection and validation were conducted using random forest. Thyroid samples and clinical data were obtained from 142 patients at two different hospitals, and expression of the 3-gene signature was measured using quantitative PCR. The predictive abilities of three models (based on the selected clinical variables, the gene expression profile, and integrated gene expression and clinical information) were compared. Seven clinical characteristics were selected based on a training set (539 patients) and tested in three test sets, yielding predictive accuracies of 82.3% (n = 232), 81.4% (n = 70), and 81.9% (n = 72). The predictive sensitivity, specificity, and accuracy were 72.3%, 80.5% and 76.8% for the model based on the gene expression signature, 66.2%, 81.8% and 74.6% for the model based on the clinical data, and 83.1%, 84.4% and 83.8% for the combined model in a 10-fold cross-validation (n = 142).

Conclusions

These findings reveal that the integrated model, which combines clinical data with the 3-gene signature, is superior to models based on gene expression or clinical data alone. The integrated model appears to be a reliable tool for the preoperative diagnosis of thyroid tumors.

Introduction

The incidence of thyroid carcinoma has substantially increased in the United States in recent years. In 2008, the estimated number of new cases of thyroid cancer was 37,340, in contrast to the estimated 60,220 new cases in 2013, which indicates that the incidence nearly doubled in five years [1, 2]. This dramatic increase in the number of new cases is mainly the result of the gradual increase in the use of ultrasound in routine physical examinations [3, 4].

A key challenge in thyroid cancer research lies in distinguishing benign thyroid nodules from malignant tumors [57]. The problem is that most thyroid nodules are benign, with only 5–15% being malignant. The current cornerstone of preoperative thyroid nodule character evaluation is ultrasonographically guided fine needle aspiration (FNA). However, a clear limitation of this approach is that approximately 15–30% of FNAs reveal indeterminate or suspicious cytological findings [8]. Moreover, only one-fifth of the indeterminate FNAs are found to be malignant after diagnostic surgical operations [7]. Therefore, there is a compelling case for developing more practical and accurate diagnostic methods to preoperatively evaluate the characteristics of thyroid nodules, which could play an important role in the management of patients with benign lesions to avoid unnecessary thyroid lobectomies.

We hypothesis that the integration of small gene signatures and clinical data would substantially impact the predictive accuracy of thyroid tumor models and have promise or application in routine clinical practice. First, clinical data, including patient history, medical examination results and ultrasound imaging analysis, are typically available and form the foundation of day-to-day clinical decisions. Furthermore, data on clinically important variables have the distinct advantage of a relatively low noise level, which is important for cancer prediction accuracy.

Second, gene expression profiles have demonstrated the power to help determine the heterogeneity of various tumors [6, 9, 10]. Even patients with similar symptoms may have a distinct treatment response or prognosis, which is the foundation for advocating personalized medicine. Therefore, molecular biomarkers may offer an alternative means to indicate tumor behavior and enhance the predictive ability of models integrating multiple forms of clinical data. However, it is essential to select a small number of genes that can be easily assayed via quantitative PCR (qPCR) to predict thyroid tumors in clinical applications rather than to use a microarray analysis, which is more complex, expensive, and hard to interpret [11].

In our previous study, we identified a three-gene signature—DPP4, SCG5 and CA12—that is measured via qPCR and performed well in distinguishing benign and malignant thyroid nodules[12]. Consistent with the integrative viewpoint, our present analysis focuses on the selection of relevant clinical information to develop an integrated clinico-genomic model for further improvement of thyroid cancer prediction.

Materials and Methods

Patient clinical information and tissue samples

To select and validate clinical variables that are significantly correlated with thyroid tumor malignancy, we collected data from 771 patients with clinically significant thyroid nodules who had undergone thyroid ultrasonography and were ultimately diagnosed with a thyroid tumor after partial or total thyroidectomy operations. The patients had received treatment in 2011–2012 at the Otolaryngology Department in Shanghai Renji Hospital, China. The mean age ± standard deviation of the 771 patients, including 202 males (26.2%) and 569 females (73.8%), was 47.4 ± 12.5 years (range: 13–77 years). Thyroid ultrasonography examinations were performed and recorded by two radiologists who were professionals in thyroid sonography using a L12-5 transducer (Siemens, Germany) on an S2000 scanner (Siemens, Germany). Two additional independent cohorts, which include 70 (Cohort 2) [12] and 72 (Cohort 3) randomly selected patients in 2013 from two centers (Shanghai Renji Hospital and Xinhua Hospital), were analyzed for validation of the model based on clinical information and an integrated model that combined clinical information with gene expression. Tissue samples preserved in the RNALater reagent during the operation and immediately transferred to -80°C to stabilize the RNA, together with the clinical data collected from the 142 patients. The demographic, clinical and ultrasound characteristics for each patient in the three cohorts are summarized in Table 1. The distributions of the histopathological subtypes of benign and malignant thyroid nodules in the three cohorts are shown in Table A in S1 File. All patient personal information was de-identified and is unknown to the authors, and the sample collections were approved by the institutional review boards of the Renji and Xinhua Hospitals.

thumbnail
Table 1. Demographic, clinical and ultrasound characteristics of 913 patients.

https://doi.org/10.1371/journal.pone.0164570.t001

RNA extraction and qPCR

Extraction and analysis of RNA were performed as previously described (our paper). To validate the expression levels of the three genes—DPP4, SCG5 and CA12 (which were selected by a Bayesian model averaging (BMA) algorithm in our previous research and predict thyroid tumors with good accuracy), reverse transcription real-time qPCR was performed using the SYBR Green method and the previously reported primer sets [12]. Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) was used as a reference gene, The relative gene expression level was calculated as follows: 2-ΔCt×100 (ΔCt = Ct target gene−Ct GAPDH) [13].

Feature selection in clinical data using random Forest (RF) models

Five hundred and thirty-nine patients (70% of the patients in Cohort 1; data set: Cohort 1-TR), which included 241 benign and 298 malignant samples, were randomly stratified as the training set to construct the prediction model. Two hundred and thirty-two patients (30% of the patients in Cohort 1; data set: Cohort 1-TE), which included 104 benign and 128 malignant nodules, comprised the test data.

The original RF model contains 23 variables (Table 1), including eleven categorical variables: (1) Sex (male = 0, female = 1), (2) Mobility of mass upon physical examination (nonpalpable = 0, fixed = 1, mobile = 2), (3) Texture of mass upon physical examination (nonpalpable = 0, soft = 1, medium = 2, hard = 3), (4) Max nodule diameter in ultrasound (<1.00 cm = 1, 1.00–1.99 cm = 2, 2.00–2.99 cm = 3, 3 cm = 4), (5) Nodule number in ultrasound (single = 1, germination = 2, multiple = 3), (6) Nodule echo type in ultrasound with respect to normal thyroid parenchyma (hypoechoic = 1, isoechoic = 2, hyperechoic = 3, mixed echogenicity = 4), (7) Ultrasound lymph node number (without lymph node involvement = 0, single lymph node = 1, two lymph nodes = 2, multiple lymph nodes = 3), (8) Ultrasound lymph node morphology (without lymph node involvement = 0, morphology regular = 1, morphology irregular = 2), (9) Ultrasound lymph node boundary (without lymph node involvement = 0, boundary clear = 1, boundary vague = 2), (10) Ultrasound lymph node veins (without lymph node involvement = 0, veins abundant = 1, veins scarce = 2), and (11) Calcification (No calcification = 0, calcification = 1, micro-calcification = 2). The ultrasound images in our study were reviewed by two experienced sonographers (J.D. and L.W) with more than 5 years’ experience. As for the inconsistent results, the two examiners with another expert examiner (J.X.) who has more than 15 years’ experience would review and reach a consensus by discussion.

The random forest algorithm [14] has commonly been used for descriptor selection because it provides information regarding variable importance for the classifier. The percent increased mean square error (%IncMSE) was calculated, which indicates the declension of the predictive ability of the model when each descriptor is permuted in turn by random noise. An increased %IncMSE typically indicates a greater role for the variable in the prediction model. In this study, we set the cut-off value of %IncMSE to 10, which indicates that if the %IncMSE value of a descriptor is 10 or higher, it can be selected as a variable to distinguish benign from malignant nodules.

Data mining algorithm based on clinical data and cross-validation

The most commonly used classification methods can be divided into two groups: one category comprises the strong classification algorithms, such as support vector machine (SVM) and Naïve Bayes (NB); the other category comprises the combination classification algorithms, which are also referred to as weak classification algorithms, such as random forest (RF). SVM and RF are the most widely used classifiers in bioscience; thus, comparisons between the performances of these two methods have been performed many times [1518]. RF is a useful classifier with the following unique advantages over SVM: it provides the importance of the variable, exhibits good tolerance to noise in the data and little or no overfitting and is applicable to several data types. Thus, we employed RF for classification analyses in this study. The RF algorithm was implemented by the R package ‘randomForest’[19] with its default parameters was applied.

The arrangement of the training and test sets to construct and validate the model based on clinical information is summarized in Fig 1. Ten-fold cross-validation in the training set was used to assess the robustness and the predictive results of the models. Thus, the training set was randomly split into 10 equal parts, in which 9/10 of the data were used to develop the model and the remaining 1/10 of the data were evaluated as the independent test data. Furthermore, three independent data sets, including Cohort 2 [12], Cohort 3 and 30% of the samples randomly selected from Cohort 1, comprised the test set. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and predictive accuracy were calculated to evaluate the predictive performance.

thumbnail
Fig 1. Workflow of this study.

(a) Flow diagram of feature selection and validation of clinical data. Cohort 1 comprised 771 samples that were randomly divided into the training (539 samples) and test (232 samples) sets. Two additional independent data sets, Cohorts 2 and 3, included 70 and 72 samples, respectively, from Renji and Xinhua Hospital and were also employed as test sets to validate the predictive accuracy of the classification based on clinical data. (b) Flow diagram for the comparison between the classifier models based on the three gene expression levels, the clinical information, and integrating the gene expression with clinical data.

https://doi.org/10.1371/journal.pone.0164570.g001

Integrated clinical-gene-expression models and cross-validation

One hundred and forty-two thyroid tissue samples, including 70 samples from Cohort 2 and 72 samples from Cohort 3, had both clinical and gene expression information. The relative mRNA expression levels of the three genes (DPP4, SCG5 and CA12) measured by real-time qPCR, the clinical predictive factors previously described, and the combined gene signature and clinical variables were applied to the random forest classification with default parameters. The information obtained from Cohorts 2 and 3 was from two centers; thus, one cohort was used as the training set, and the other cohort was used as the test set. In addition, 10-fold cross-validation of the 142 samples (together with Cohorts 2 and 3) was conducted to assess the integrated model. The work flow for the comparison of the classifier models based on the three-gene signature, the clinical information, and the integration of gene expression with clinical data is summarized in Fig 1b.

Results

Univariate analysis

The study population of Cohort 1, which included 848 patients, was recruited between January 2011 and December 2012 at Shanghai Renji Hospital to assess the important clinical characteristics in thyroid cancer prediction. Certain patients were excluded because of incomplete data records (n = 57), a delay in undergoing surgery (n = 11; more than 120 days from ultrasound examination), data input errors (n = 7), and disagreements in the histopathologic diagnosis (n = 2). Thus, 771 patients (90.9%) were included and were randomly divided into the training set (Cohort 1-TR, 539 samples, 241 benign nodules and 298 malignant tumors) and the test set (Cohort 1-TE, 232 samples, 104 benign nodules and 128 malignant tumors). Furthermore, the clinical data from two independent data sets, which included Cohort 2 [12] and Cohort 3, were recorded as two additional test sets for model validation (Fig 1). Data from 539 samples from data set Cohort 1-TR (70%) were available for feature selection and for the development of the clinical data cancer prediction model. All the relevant data for Cohort 1, 2 and 3 was available in S2 File.

To evaluate the potential risk factors involved in diagnosing malignant thyroid tumor, univariate analyses of the demographic data, physical examination and ultrasound-based variables in the training set were performed; the abbreviations for the clinical variables are listed in Table 2 (p<0.01 for all parameters). All variables except the two ultrasound-based variables (nodule number and position) were significantly different between benign and malignant thyroid nodules.

thumbnail
Table 2. Comparison of the characteristics of benign and malignant tumors in the training set following histologic classification of thyroid nodules.

https://doi.org/10.1371/journal.pone.0164570.t002

Feature selection of clinical data by random Forest

The feature selection procedure was completed using the R package ‘randomForest.’ There are two main parameters in random Forest: mtry, which represents the number of different descriptors tried at each split; and ntree, which represents the number of trees in the forest. For the ntree parameter, we employed default values (ntree = 500); for mtry, we utilized a 10-fold cross-validation to test the cross-validated prediction performance of the models with a sequentially reduced number of predictors. As shown in S1 Fig, when mtry = 7, the model attained the optimum mtry with the lowest MSE at each step. Because the %IncMSE is consistent with the importance of the descriptors, we determined the most important parameters based on the %IncMSE values (S2 Fig). We employed the top seven features as the significant variables with a cutoff of ten for the %IncMSE value to train the clinical model, which included five ultrasound variables (Nodule morphology, Echo type, Calcification, Cystic lesion and Nodule boundary), one demographic variable (Age) and one physical examination variable (Texture of mass in physical examination).

Validation of the predictive performance of the clinical information model

Seven variables were employed in the reduced random forest model with the training set of Cohort 1-TR, and three independent data sets were evaluated as the validation data set. For the test set Cohort 1-TE, which comprised 232 samples, the clinical information classifier accurately recognized 107 of the 128 malignant thyroid nodules, a sensitivity of 83.6% (95% CI, 77.2–90.0), and 84 of the 104 benign nodules, a specificity of 80.7% (95% CI, 73.2–88.3). The predictive accuracy was 82.3% (95% CI, 77.4–87.2). For the test set Cohort 2, which comprised 70 samples, the model correctly identified 23 of the 31 malignant samples, a sensitivity of 74.2% (95% CI, 58.8–89.6), and 34 of the 39 benign samples, a specificity of 87.1% (95% CI, 76.7–97.7). The predictive accuracy was 81.4% (95% CI, 72.3–90.5). For the test set Cohort 3, which comprised 72 samples from Xinhua Hospital, the classifier discriminated 29 of the 34 malignant nodules, a sensitivity of 85.3% (95% CI, 73.4–97.2), and 30 of the 38 benign samples, a specificity of 78.9% (95% CI, 66.0–91.9). The predictive accuracy was 81.9% (95% CI, 73.1–90.8). The predictive performances on the three data sets are summarized in Table 3 and indicate that the model with the 7 clinical characteristics was effective at distinguishing malignant thyroid nodules from benign nodules.

thumbnail
Table 3. Predictive performance of three independent data sets using the clinical information model.

https://doi.org/10.1371/journal.pone.0164570.t003

Combined model for thyroid tumor prediction

Although the predictive performance based on the 7 clinical variables was good in the random forest algorithm, a subset of patients with malignant tumors would still be misclassified into the benign group because of similar clinical symptoms. This finding suggests that gene expression level variables should be integrated to further improve the predictive accuracy and to analyze the mechanism of thyroid tumor at the gene level. In the combined model, the expression levels of the three genes selected by an iterative BMA method in our previous study [12] was combined with 7 clinical variables and simultaneously entered into the model classification.

The gene expression levels of DPP4, SCG5 and CA12, which were measured by qPCR in thyroid tissue samples, combined with the 7 selected significant clinical variables were detected and recorded for 142 patients, including 70 patients in Cohort 2 and 72 patients in Cohort 3. The relative gene expression levels of all the 142 patients are summarized in Table B in S1 File and Fig 2. In Cohorts 2 and 3, the gene expression levels of DPP4 and SCG5 were significantly different, whereas CA12 exhibited little expression difference between benign and malignant thyroid nodules; these findings are consistent with our previous research [12].

thumbnail
Fig 2. Histogram of the relative gene expression levels of DPP4, SCG5 and CA12 in malignant and benign thyroid nodules.

**P<0.01 by two-tailed t test between benign and malignant thyroid tumor types. *P<0.05 by two-tailed t test between benign and malignant thyroid tumor types.

https://doi.org/10.1371/journal.pone.0164570.g002

The two data sets originated from different centers. Thus, Cohorts 2 and 3 were utilized as training and test sets, respectively, to compare the predictive performances of the random Forest model based on the three-gene signature, the 7 clinical variables, and the combination of gene expression and clinical data (Fig 1b). To further validate the superiority of the model combining clinical information and gene expression, we combined Cohorts 2 and 3 and performed a 10-fold cross-validation for the 142 patients. The input variables comprised the 3-gene signature, 7 clinical variables, and the combined gene expression and clinical data. The predictive performance was evaluated according to the sensitivity, specificity and predictive accuracy. As shown in Table 4, the predictive sensitivity using clinical data was lower than that of the model based on the expression levels of the three genes; however, the specificity of the prediction based on the clinical data was superior to that of the other two models. These findings indicate that the predictive abilities of gene expression and clinical data complement each other. Regardless of the cohort used as the test set or in the 10-fold cross-validation, the predictive accuracy was highest when the model was based on the combination of gene expression and clinical data.

thumbnail
Table 4. Comparison of thyroid cancer predictive performance based on the gene expression, clinical data, or integrated model.

https://doi.org/10.1371/journal.pone.0164570.t004

Discussion

The diagnosis of thyroid cancer remains a common problem in routine physical examination. It is important to preoperatively identify malignant thyroid nodules. With the development of genomic technology and advances in analyzing complex biomedical information, many investigators have focused on the molecular classification of thyroid nodules based on oligonucleotide microarray gene expression patterns using tissues or FNA samples [57, 20, 21]. However, the use of these methods for the prediction of thyroid nodule malignancy in routine clinical practice is impractical because 1) the quantitative measurement of vast, complex microarray gene expression patterns increases the cost for patients with thyroid nodules, and most genes are irrelevant to thyroid nodule characteristics [22]; 2) the analysis of a large number of genes requires clinical specialists to master complex statistical or computational tools [2224]; 3) the results of microarrays may exhibit poor reproducibility and significant interbatch variability [11]; and 4) the various microarray platforms lead to differences in gene expression profiling [25].

To some extent, clinical data can address the deficiencies of genomic data. The routine clinical data used to aid in the diagnosis of thyroid tumors include the demographic data, physical examination data and ultrasound characteristics of patients with thyroid nodules. Recent studies have been conducted to distinguish malignant thyroid nodules according to characteristics evaluated by high-resolution thyroid ultrasonography [2629]. Currently, microcalcifications, irregular nodule morphologies, hypoechogenicity of the nodules, and a blurred margin and shape have demonstrated strong relationships with malignant thyroid neoplasms [2629]. Unfortunately, there are no uniform sonographic criteria for accurately predicting malignancy in thyroid nodules. Benign and malignant thyroid nodules can be difficult to clinically differentiate owing to their overlapping ultrasound patterns and the differences in the sensitivity of contrast-enhanced ultrasound in various ultrasonic testing devices. Thus, in addition to the ultrasonographic characteristics, other clinical data, such as the hormonal status or palpation of the nodules, should be integrated to further improve the accuracy of predicting malignancy in thyroid nodules.

In our study, 23 clinical variables that were considered to have correlations with malignant thyroid tumors were selected by the consensus of senior otolaryngologists and sonologists. A RF method was utilized to simplify the variables in the prediction model, and 7 variables were selected to distinguish between the benign and malignant groups in the training set, which included 1 demographic, 1 clinical and 5 ultrasound variables. Several features, including (1) irregular nodule morphologies, (2) nodules with hypoechogenicity, (3) nodules with calcification or micro-calcifications, (4) a lack of cyst formation, (5) patients at a young age, (6) palpate hard nodules upon physical examination, and (7) unclear boundaries of a nodule, were vital characteristics of malignant thyroid tumors and were included in our prediction model based on the clinical data. The distributions of the 23 descriptors were compared between the benign and malignant thyroid nodules. There were no significant differences in the number or position of the nodules between the benign and malignant thyroid tumors. Apart from these two characteristics, the remaining variables comprised risk factors for malignant tumors.

The model based on the 7 clinical variables performed well and was stable. In three independent data sets from two centers, the predictive accuracies were all greater than 80% using a random forest model, a value superior to those reported in previous studies that only used ultrasound parameters to predict thyroid nodule characteristics [26, 2831]. Therefore, we are optimistic that ultrasound examination integrated with other demographic or clinical data will facilitate the development of a model superior to the application of ultrasound diagnosis alone.

Our study further confirms previous evidence that predictive accuracy can be enhanced by integrating clinical variables with genomic data [3235]. However, the high cost, equipment, analytical procedures and critical need for precision in the operating steps to obtain reproducible results has restricted the application of microarray analysis in daily clinical use [3638]. In contrast, qPCR is a practical and economic method in routine clinical use that can measure the fold changes in the expression levels of individual genes with high sensitivity and reproducibility [36, 39]. Therefore, in our previous study [12], we analyzed the public microarray data sets from the Gene Expression Omnibus, selected specific genes from overall gene expression profiling and constructed a qPCR-based model that included a three-gene signature (DPP4, SCG5 and CA12).

To our knowledge, there are no publicly available databases that contain both gene expression data and relatively complete clinical information for thyroid tumors. In the current study, we measured the expression of the three previously identified genes via qPCR in 142 patients from two centers and then combined these data with 7 clinical factors selected in the current research to construct an integrated model to predict malignant thyroid nodules. To further reduce the impact of the unbalanced distribution from the two centers, we utilized a 10-fold cross-validation in addition to a 2-fold cross-validation (one data set as the training set and the other data set as the test set). It is clear that regardless of the validation set that was used, the best predictive performances were achieved by the random forest model based on the integrated clinical and molecular variables, which outperformed models that used clinical or genomic data alone. The gene expression model had a higher sensitivity than the clinical information model but a relatively low specificity in our study, and the clinical information model showed the reverse performance. Thus, in combination, these methods complement each other to produce an increased predictive accuracy.

However, we should note that this study comprised a pilot study. With advances in molecular medicine and improvements in personalized medical databases, more complex clinical factors and other clinical factors (such as the characteristics of ultrasound elastography or the levels of other serum biomarkers of thyroid tumors) and omics data (such as single-nucleotide polymorphisms, protein pattern concentrations, and metabolite analysis) may be further studied to improve the model and facilitate its day-to-day clinical application in thyroid cancer management.

Supporting Information

S1 Fig. Variations in the corresponding vector of error (error.cv) when different numbers of variables were employed at each split.

https://doi.org/10.1371/journal.pone.0164570.s001

(TIF)

S2 Fig. Percent increased mean square error (%IncMSE) of the variables calculated by the random Forest analysis.

https://doi.org/10.1371/journal.pone.0164570.s002

(TIF)

S1 File. Supporting tables.

Table A. Histopathological subtype distribution in three cohorts. Cohort 1 comprised 711 patients from Renji Hospital with clinical information. Cohort 2 comprised 70 patients from Renji Hospital with clinical and gene expression information. Cohort 3 comprised 72 patients from Xinhua Hospital with clinical and gene expression information. Table B. Summary of the qPCR results for the expression of DPP4, SCG5 and CA12 in Cohort 2 and Cohort 3.

https://doi.org/10.1371/journal.pone.0164570.s003

(DOCX)

S2 File. Relevant data underlying the results described in manuscript.

https://doi.org/10.1371/journal.pone.0164570.s004

(XLSX)

Acknowledgments

The authors thank Yitao Zeng, Fanyi Zeng, Shuzhen Huang, Chunmei Ying and Fenghua Li for helpful discussion.

Author Contributions

  1. Conceived and designed the experiments: BZ JL JY HL.
  2. Performed the experiments: BZ JL.
  3. Analyzed the data: BZ JG.
  4. Contributed reagents/materials/analysis tools: HL.
  5. Wrote the paper: BZ JL JY HL.
  6. Recorded clinical data: JL JD LW SG JC JY.

References

  1. 1. Jemal A, Siegel R, Ward E, Hao Y, Xu J, Murray T, et al. Cancer statistics, 2008. CA: a cancer journal for clinicians. 2008;58(2):71–96.
  2. 2. Siegel R, Naishadham D, Jemal A. Cancer statistics, 2013. CA: a cancer journal for clinicians. 2013;63(1):11–30.
  3. 3. Davies L, Welch HG. Current thyroid cancer trends in the United States. (2168-619X (Electronic)).
  4. 4. Hoang JK, Nguyen XV, Davies L. Overdiagnosis of thyroid cancer: answers to five key questions. (1878–4046 (Electronic)).
  5. 5. Alexander EK, Kennedy GC, Baloch ZW, Cibas ES, Chudova D, Diggans J, et al. Preoperative diagnosis of benign thyroid nodules with indeterminate cytology. New England Journal of Medicine. 2012;367(8):705–15. pmid:22731672
  6. 6. Mazzanti C, Zeiger MA, Costourous N, Umbricht C, Westra WH, Smith D, et al. Using gene expression profiling to differentiate benign versus malignant thyroid tumors. Cancer Research. 2004;64(8):2898–903. pmid:15087409
  7. 7. Lubitz CC, Ugras SK, Kazam JJ, Zhu B, Scognamiglio T, Chen Y-T, et al. Microarray analysis of thyroid nodule fine-needle aspirates accurately classifies benign and malignant lesions. The Journal of Molecular Diagnostics. 2006;8(4):490–8. pmid:16931590
  8. 8. Cooper DS, Doherty GM, Haugen BR, Kloos RT, Lee SL, Mandel SJ, et al. Revised american thyroid association management guidelines for patients with thyroid nodules and differentiated thyroid cancer: the american thyroid association (ATA) guidelines taskforce on thyroid nodules and differentiated thyroid cancer. Thyroid. 2009;19(11):1167–214. pmid:19860577
  9. 9. Carson MB, Gu J, Yu G, Lu H. Identification of cancer-related genes and motifs in the human gene regulatory network. (1751–8849 (Print)).
  10. 10. Qin W, Liu C, Sodhi M, Lu H. Meta-analysis of sex differences in gene expression in schizophrenia. (1752–0509 (Electronic)). doi: D—NLM: PMC4895727 EDAT- 2016/01/29 06:00 MHDA- 2016/01/29 06:00 CRDT- 2016/01/29 06:00 AID—10.1186/s12918-015-0250-3 AID— [pii] PST—epublish. pmid:26818902
  11. 11. Chen H-Y, Yu S-L, Chen C-H, Chang G-C, Chen C-Y, Yuan A, et al. A five-gene signature and clinical outcome in non–small-cell lung cancer. New England Journal of Medicine. 2007;356(1):11–20. pmid:17202451
  12. 12. Zheng B, Liu J, Gu J, Lu Y, Zhang W, Li M, et al. A three-gene panel that distinguishes benign from malignant thyroid nodules. International Journal of Cancer. 2015;136(7):1646–54. pmid:25175491
  13. 13. Oehler VG, Yeung KY, Choi YE, Bumgarner RE, Raftery AE, Radich JP. The derivation of diagnostic markers of chronic myeloid leukemia progression from microarray data. Blood. 2009;114(15):3292–8. pmid:19654405
  14. 14. L B. Random Forests. Mach Learn. 2001;45:5–32.
  15. 15. Maroco J, Silva D, Rodrigues A, Guerreiro M, Santana I, de Mendonca A. Data mining methods in the prediction of Dementia: A real-data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random forests. BMC research notes. 2011;4:299. pmid:21849043; PubMed Central PMCID: PMC3180705.
  16. 16. Ogutu JO, Piepho HP, Schulz-Streeck T. A comparison of random forests, boosting and support vector machines for genomic selection. BMC proceedings. 2011;5 Suppl 3:S11. pmid:21624167; PubMed Central PMCID: PMC3103196.
  17. 17. Statnikov A, Aliferis CF. Are random forests better than support vector machines for microarray-based cancer classification? AMIA Annual Symposium proceedings / AMIA Symposium AMIA Symposium. 2007:686–90. pmid:18693924; PubMed Central PMCID: PMC2655823.
  18. 18. Pang H, Lin AP, Holford M, Enerson BE, Lu B, Lawton MP, et al. Pathway analysis using random forests classification and regression. Bioinformatics. 2006;22(16):2028–36. WOS:000239900200014. pmid:16809386
  19. 19. Liaw A, M W. Classification and Regression by randomForest. Rnews. 2002;2:18–22.
  20. 20. Chudova D, Wilde JI, Wang ET, Wang H, Rabbee N, Egidio CM, et al. Molecular classification of thyroid nodules using high-dimensionality genomic data. Journal of Clinical Endocrinology & Metabolism. 2010;95(12):5296–304.
  21. 21. Finley DJ, Zhu B, Barden CB, Fahey TJ III. Discrimination of benign and malignant thyroid nodules by molecular profiling. Annals of surgery. 2004;240(3):425. pmid:15319714
  22. 22. Ramaswamy S. Translating cancer genomics into clinical oncology. New England Journal of Medicine. 2004;350(18):1814–6. pmid:15115827
  23. 23. Gu J-l, Lu Y, Liu C, Lu H. Multiclass classification of sarcomas using pathway based feature selection method. Journal of theoretical biology. 2014;362:3–8. pmid:25014475
  24. 24. Langlois RE, Lu H, editors. Intelligible machine learning with malibu. Engineering in Medicine and Biology Society, 2008 EMBS 2008 30th Annual International Conference of the IEEE; 2008: IEEE.
  25. 25. Lossos IS, Czerwinski DK, Alizadeh AA, Wechser MA, Tibshirani R, Botstein D, et al. Prediction of survival in diffuse large-B-cell lymphoma based on the expression of six genes. New England Journal of Medicine. 2004;350(18):1828–37. pmid:15115829
  26. 26. Tae HJ, Lim DJ, Baek KH, Park WC, Lee YS, Choi JE, et al. Diagnostic value of ultrasonography to distinguish between benign and malignant lesions in the management of thyroid nodules. Thyroid. 2007;17(5):461–6. pmid:17542676
  27. 27. Shimura H, Haraguchi K, Hiejima Y, Fukunari N, Fujimoto Y, Katagiri M, et al. Distinct diagnostic criteria for ultrasonographic examination of papillary thyroid carcinoma: a multicenter study. Thyroid. 2005;15(3):251–8. pmid:15785244
  28. 28. Koike E, Noguchi S, Yamashita H, Murakami T, Ohshima A, Kawamoto H, et al. Ultrasonographic characteristics of thyroid nodules: prediction of malignancy. Archives of surgery. 2001;136(3):334. pmid:11231857
  29. 29. Moon W-J, Jung SL, Lee JH, Na DG, Baek J-H, Lee YH, et al. Benign and Malignant Thyroid Nodules: US Differentiation—Multicenter Retrospective Study1. Radiology. 2008;247(3):762–70. pmid:18403624
  30. 30. Watters DA, Ahuja AT, Evans RM, Chick W, King WW, Metreweli C, et al. Role of ultrasound in the management of thyroid nodules. The American journal of surgery. 1992;164(6):654–7. pmid:1463118
  31. 31. Shweel M, Mansour E. Diagnostic performance of combined elastosonography scoring and high-resolution ultrasonography for the differentiation of benign and malignant thyroid nodules. European journal of radiology. 2013.
  32. 32. Pittman J, Huang E, Dressman H, Horng C-F, Cheng SH, Tsou M-H, et al. Integrated modeling of clinical and gene expression information for personalized prediction of disease outcomes. Proceedings of the National Academy of Sciences of the United States of America. 2004;101(22):8431–6. pmid:15152076
  33. 33. Stephenson AJ, Smith A, Kattan MW, Satagopan J, Reuter VE, Scardino PT, et al. Integration of gene expression profiling and clinical variables to predict prostate carcinoma recurrence after radical prostatectomy. Cancer. 2005;104(2):290–8. pmid:15948174
  34. 34. Li L, Chen L, Goldgof D, George F, Chen Z, Rao A, et al., editors. Integration of clinical information and gene expression profiles for prediction of chemo-response for ovarian cancer. Engineering in Medicine and Biology Society, 2005 IEEE-EMBS 2005 27th Annual International Conference of the; 2006: IEEE.
  35. 35. Nevins JR, Huang ES, Dressman H, Pittman J, Huang AT, West M. Towards integrated clinico-genomic models for personalized medicine: combining gene expression signatures and clinical factors in breast cancer outcomes prediction. Human molecular genetics. 2003;12(suppl 2):R153–R7.
  36. 36. Lee E-S, Son D-S, Kim S-H, Lee J, Jo J, Han J, et al. Prediction of recurrence-free survival in postoperative non–small cell lung cancer patients by using an integrated model of clinical information and gene expression. Clinical Cancer Research. 2008;14(22):7397–404. pmid:19010856
  37. 37. Pollack JR. A perspective on DNA microarrays in pathology research and practice. The American journal of pathology. 2007;171(2):375–85. pmid:17600117
  38. 38. Jaluria P, Konstantopoulos K, Betenbaugh M, Shiloach J. A perspective on microarrays: current applications, pitfalls, and potential uses. Microbial Cell Factories. 2007;6(1):4.
  39. 39. Wong ML, Medrano JF. Real-time PCR for mRNA quantitation. Biotechniques. 2005;39(1):75. pmid:16060372