Droplet digital PCR-based circulating microRNA detection serve as a promising diagnostic method for gastric cancer

Gastric cancer is second most common cancer in terms of incidence and mortality in China, according to the most recent cancer statistics [1]. With the improvement of surgical technique, radiotherapy and chemotherapy in recent years, patients in the early stage of GC had a significant increased 5-year survival rate, but the prognosis for advanced GC remains poor [2, 3]. Thus, it is important to diagnose GC in the early stage thus yielding better outcome. Gastroscopy is the gold standard test for GC diagnosis, but it is invasive and couldn’t be frequently used as regular health examination. Carcinoembryonic antigen (CEA), α-fetoprotein (AFP) and carbohydrate antigen 19–9 (CA19–9) are widely used as non-invasive markers in clinical, but their sensitivities and specificities are not enough for early diagnosis of GC [4]. Therefore, novel non-invasive biomarkers with better sensitivities and specificities are urgently needed.

MicroRNAs (miRNAs) are small noncoding RNAs, about 22–24 bases long, that inhibit their target mRNAs translation by inducing mRNA degradation or translational repression [5, 6]. Up to now, there are thousands of miRNAs have been reported to be associated with tumor growth, invasion, metastasis and apoptosis [7]. Several studies have demonstrated that circulating miRNAs can serve as biomarkers for GC diagnosis. For example, miR-223, miR-16 and miR-100 were highly expressed in the serum of GC patients, and positively associated with TNM stage, metastatic status, tumor size and differentiation grade [8]. The level of let-7a expression in the plasma of GC patients was significant lower, and the value of the area under the receiver-operating characteristic curve was 0.879 for the miR-106a/let-7a ratio in GC patients and healthy volunteers [9]. Thus, miRNAs in peripheral blood have great potential for helping early diagnosis of GC.

Although the results of previous studies are promising, their clinical transferability remains uncertain, which mainly due to the lack of uniformity and reproducibility in the criteria for determining the circulating miRNA levels by quantitative real-time PCR (qPCR). Besides, several variables such as sample storage, RNA isolation, PCR inhibitors and normalization could affect final results [10]. The droplet digital PCR (ddPCR) technique is increasingly considered to be the gold standard in the application of liquid biopsy, because it has shown superior precision and sensitivity, being less affected by PCR inhibitors, and unnecessity of internal/external normalization while detecting low concentration of target nucleic acids molecules [11, 12]. In this study, we used the ddPCR technique to explore the circulating miRNA signatures which could be potential biomarkers for GC diagnosis, and discriminating GC patients with different TNM stage. Four miRNAs, miR-21, miR-93, miR-106a and miR-106b, which have been most reported to be closely correlated with GC in tissue and plasma of patients and represent as candidate biomarkers for human GC, were examined by novel technique of ddPCR. [9, 13‐15].

Furthermore, without the assist of tissue biopsy and imaging examinations, it would be difficult for clinicians to make diagnosis and tumor staging for GC, because there are many factors could probably influence the results. To improve the precision and accuracy of diagnosing disease, new approaches such as machine learning which is the main technical basis for data mining, provide an effective solution [16]. Several studies have been reported to use machine learning tools for data mining to diagnose disease or predict prognosis [17‐19]. In this study, we explored the use of random forest model based learning for GC diagnosis, by using circulating miRNA expressions and clinical parameters such as age, gender, CEA and CA19–9.

Early diagnosis could greatly improve the survival rates of GC patients. However, the currently used diagnostic methods are either invasive or insensitive, thus limited their application in clinic. In recent years, a number of circulating miRNAs, which are notably stable in the circulation of body fluids [20, 21], are suggested as promising non-invasive diagnostic markers for GC [9, 15, 22‐24]. Unfortunately, since circulating miRNAs exist in blood at extremely low concentrations [25], the test results would be made poorly repeatable due to the interference of several variables, such as sample processing protocols, RNA isolation and so on [10, 26]. Most importantly, quantitative real-time PCR is most commonly used but must rely on the use of external calibrators, because it lacks reliable endogenous reference miRNA for normalization of results in plasma or serum. Therefore, the data which produced by a variety of normalization methods in different studies, become non-comparable or difficult to compare. This is a major obstacle for their translation into clinically useful applications [10, 27].

The present study, to our best knowledge, is the first to evaluate the diagnostic value of circulating miRNAs for GC patients using the ddPCR technique. ddPCR is a recently introduced technology which can achieve absolute quantification of nucleic acids based on the principles of sample portioning, end-point PCR and Poisson statistics [28, 29]. Thus, it overcomes the normalization and calibrator issues [30]. Besides, it has shown better precision and sensitivity while detecting low concentration of target nucleic acids molecules [31, 32]. More importantly, ddPCR can tolerate PCR inhibitors which could influence the efficiency of PCR amplification, without affecting the quantitative results of the target [11].

Using ddPCR, we analyzed the levels of circulating miR-21, miR-93, miR-106a and miR-106b in the plasma of GC patients and healthy volunteers. Similar to previous studies [9, 14, 15], we found the significantly increased levels of these miRNAs in GC patients compared with healthy controls, and some miRNAs were associated with advanced TNM stage. ROC curve analysis showed that each miRNA had higher diagnostic sensitivity and specificity than CEA and CA19–9 which were widely used in clinic. Furthermore, through a combination of the expression levels of four validated miRNAs, a patient will be considered to have GC if the predicted probability is higher than the threshold set (0.315 with a sensitivity of 84.8% and specificity of 79.2%) in the model. An AUC of 0.887 (95% CI, 0.83–0.943) and P-values< 0.001 indicate the great potential value of these miRNAs as GC biomarkers.

Based on the results above, we further evaluate the potential use of these miRNAs in discriminating GC with different TNM stage. First, GC patients with TNM stage I and II were combined as one group, as well as stage III and IV, because there was no statistically significant difference between these groups. Then, our results showed that the levels of circulating miR-21, miR-93 and miR-106b in the plasma of GC patients were significantly higher in TNM stage III and IV than stage I and II, except for the miR-106a. As usual, a combination of four miRNAs showed better capability to discriminate GC with different TNM stage. A patient will be considered to have GC with TNM stage III or IV if the risk score is higher than 0.534 (a sensitivity of 78.3% and specificity of 70.7%). ROC analysis also showed an AUC of 0.809 (95% CI, 0.723–0.896) and P-values< 0.001. To our knowledge, this study is the first to demonstrate that these miRNAs might be also used as biomarkers to discriminate GC with TNM stage I and II from stage III and IV.

In the search of possible correlations with clinicopathological features, it was noteworthy that the presence of lymph node metastases was significantly correlated with increased levels of circulating miR-21, miR-93 and miR-106b. Moreover, a high level of circulating miR-21 was significantly related to a bigger tumor size (≥5 cm). These results indicate that these miRNAs might represent biomarkers of tumor aggressiveness, which further improved their value for discriminating GC with different TNM stage. Some studies have reported that high levels of miR-21 expression may induce tumor proliferation, migration and invasion via the downregulation of Noxa or PTEN expressions in GC cells [33, 34]. And miR-93 could promote proliferation and metastasis of GC via targeting TIMP2 or inactivation of the Hippo signaling pathway [35, 36]. In cancer-associated fibroblasts from GC, miR-106b could promote cell migration and invasion by targeting PTEN [37]. And it could also promote cell cycling of GC cells through regulation of p21 and E2F5 target gene expression [38]. These might be the mechanism of its correlation with lymph node metastases and tumor size. However, although it was reported that miR-106a could also regulate invasion and metastasis of GC via targeting TIMP2 [39, 40], and may inhibit extrinsic apoptotic pathway through targeting FAS [41], our results demonstrated that miR-106a expression was not associated with the lymph node metastases and tumor size. Further studies are required.

In clinic, due to the numerous factors that influence the precision and accuracy of diagnosing diseases or predicting of patients’ prognosis, more and more studies are applying machine learning algorithms to medical data, including the detection of GC [20, 42, 43]. There are several algorithms such as random forest, support vector machine and neural networks were commonly used [43, 44]. Here, we chose random forest model since it is easy to interpret, and allowed us to estimate the importance of a variable. After the random forest model was established in the training stage, when we tested the predictive value of this model using the testing data set, our results showed that it correctly discriminated 14 out of 17 healthy volunteers (false rate, 17.6%), 4 out of 5 GC patients with TNM stage I or II (false rate, 20%), and 5 out of 6 GC patients with TNM stage III or IV (false rate, 16.7%). However, the number of cases included in the present study is still far from sufficient to develop a reliable model, and we also didn’t have enough cases to test and validate the model. Further studies with much more cases are urgently required, to improve their application in clinic. Moreover, despite our results and accumulating evidences suggested that circulating miRNAs stably existed in circulation and can indeed be used as biomarkers to identify and monitor a variety of cancers and other diseases, it is still unknown how and why GC causes changes in the levels of these four circulating miRNAs, and whether or how they play roles in physiology. Further studies are also needed.

Overall, the present study demonstrated that by using the ddPCR technique, circulating miR-21, miR-93, miR-106a and miR-106b could be used as diagnostic plasma biomarkers in gastric cancer patients.