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American Journal of Translational Research logoLink to American Journal of Translational Research
. 2020 Nov 15;12(11):7395–7403.

Plasma circular RNA panel acts as a novel diagnostic biomarker for colorectal cancer detection

Jipeng Li 1,2,3, Yulan Song 3, Jianhua Wang 3, Jian Huang 1,2
PMCID: PMC7724351  PMID: 33312376

Abstract

Circular RNAs (circRNAs) can function as key regulators of oncogenic processes, making them ideal diagnostic biomarkers of many cancers. However, few studies to date have reported on plasma circRNA profiles associated with colorectal cancer (CRC). To that end, we herein employed microarray- and qRT-PCR-based approaches to evaluate circulating plasma circRNAs in CRC patients. Area under the receiver operating characteristic curve (AUC) values were then used to assess the diagnostic utility of these circRNAs. We ultimately determined that hsa_circ_0001900, hsa_circ_0001178, and hsa_circ_0005927 were upregulated in the plasma of CRC patients relative to healthy controls and were correlated with clinicopathological findings in these patients. We further established that a panel composed of these three circRNAs (CircPanel) was able to differentiate between patients with and without CRC more reliably than CEA (carcinoembryonic antigen) (AUC, 0.859 [95% confidence interval, CI: 0.805-0.903] vs. 0.698 [0.631-0.759], P=0.0003), enabling us to detect patients with CEA-negative CRC. In conclusion, our study reveals that CircPanel could serve as a promising potential biomarker for CRC diagnosis.

Keywords: Circular RNAs, colorectal cancer, biomarker, plasma

Introduction

According to the latest statistics of the American Cancer Society in 2020, colorectal cancer ranks the third in the number of new cases and mortality [1]. While many advances in CRC patient diagnosis and treatment have been made in recent years, patient prognosis remains poor, with 5-year survival rates varying substantially depending upon the disease stage at the time of diagnosis [2].

Imaging and endoscopic approaches are the most common and effective means of reliably diagnosing CRC [3]. These strategies, however, are expensive and invasive, leading to poor patient compliance and making them ill-suited for CRC screening in high-risk patient groups [4]. Researchers have identified non-invasive biomarkers that can be employed to screen for CRC including a fecal occult blood test (FOBT) and measurements of carcinoembryonic antigen (CEA) levels, but the sensitivity and specificity of these two approaches remain somewhat limited [5,6]. It is thus essential that novel non-invasive diagnostic biomarkers of CRC be identified to safely and effectively identify this disease during its earlier stages when it is more amenable to treatment.

Circular RNAs (circRNAs) are non-coding RNAs that form closed covalent loops lacking any 5’ capping or 3’-poly (A) tailing [7,8]. Owing to their unique structural properties, circRNAs are highly stable (>48 h half-life) and are highly conserved [9,10], making them ideal potential diagnostic biomarkers of CRC and other cancers. Consistent with such promise, circulating circRNAs have been shown to be of diagnostic value in prior studies of lung cancer, hepatocellular carcinoma (HCC), and CRC [11-13]. He et al. determined that plasma exosome circRNA_0056616 levels were correlated with tumor grade and with lymph node metastasis in patients with lung adenocarcinoma, an area under the receiver operating characteristic (ROC) curve (AUC) analysis of this circRNA as a tool for diagnosing lymph node metastasis yielding an AUC value of 0.812 (95% confidence interval [CI]: 0.720-0.903) with sensitivity and specificity values of 0.792 and 0.810, respectively [14]. Yin et al. employed a microarray-based approach to identify patterns of abnormal circRNA expression in the peripheral blood of breast cancer patients, leading them to identify hsa_circ_0001785 as a potentially valuable diagnostic biomarker in this cancer context [15]. Zhang et al. further determined that elevated plasma hsa_circ_0007534 levels in CRC patients were associated with tumor clinical classification, metastatic phenotype, and poor differentiation status [16].

Despite the above findings, the diagnostic value of circulating circRNAs in CRC remains poorly characterized. As such, we herein utilized microarray and qRT-PCR-based approaches in order to identify potential plasma circRNA biomarkers that may offer diagnostic utility in patients with CRC.

Materials and methods

Patients and samples

Samples utilized in this study were collected between 2017 and 2019 from patients at the Second Affiliated Hospital, Zhejiang University School of Medicine. CRC diagnosis was confirmed via pathological examination in appropriate patients. Healthy control samples were obtained from volunteers during routine physical examinations. Samples were stored at -80°C prior to use. For initial circRNA screening, plasma samples were collected from healthy controls and CRC patients (n=18 each), and tumor and matched paracancerous tissue samples were additionally collected from all 18 CRC patients. In addition, an independent validation cohort of plasma samples was collected that was comprised of samples from 80 healthy controls, 30 patients with precancerous lesions (colon adenomas and adenomatous polyps), and 102 CRC patients, including 20 patients from whom plasma was again collected 30 days following surgical tumor resection. The Ethics Committee of Second Affiliated Hospital, Zhejiang University School of Medicine approved this study, and patient clinicopathological characteristics are compiled in Table S1.

RNA preparation

TRIzol (Invitrogen, CA, USA) was used to extract RNA from tissue samples, while TRIzol™ LS (Invitrogen) was used to extract RNA from plasma samples based on provided directions. To enhance plasma RNA preparation, glycogen (ThermoFisher, final concentration: 100 μg/ml) was added during the isopropanol precipitation step. An ImProm-II Reverse Transcription System (Promega, WI, USA) was used based on provided instructions to prepare cDNA from extracted RNA.

Assessment of circRNA expression profiles

Initial microarray-based circRNA expression profiling was conducted by KangChen Biotech Company using plasma RNA samples prepared from CRC patients and healthy controls (n=3 each). Sample preparation and microarray hybridization were conducted according to provided directions (Arraystar, Inc). Briefly, samples were enriched for circRNAs by treating them with RNase R to degrade linear RNA, after which random primers were used to reverse transcribe enriched circRNAs into cDNA. Resultant labeled cDNAs were then hybridized onto an Arraystar human circRNA Array V2 (8×15 K), after which an Agilent Scanner G2505C was used to scan and analyze these arrays.

qRT-PCR

Power SYBR Green (Takara, Dalian, China) was used for qRT-PCR analyses of pairs of CRC and non-tumor tissues, with GAPDH used as a control for relative gene expression, which was assessed via the 2-ΔΔCq method.

No endogenous control RNA has, to date, been established as a reliable endogenous control when quantifying plasma circRNA levels. In light of prior studies [12], we thus utilized an absolute quantification approach to measuring the expression levels of these circRNAs in patient plasma. Briefly, we cloned PCR products corresponding to the three circRNAs of interest into separate pcDNA3.0 vectors and diluted these constructs to between 1×105 and 1×102 copies/ml. These recombinant plasmids were then run under identical qRT-PCR conditions to those above in parallel with plasma RNA samples in order to construct appropriate standard curves (Figure S1), which were in turn used to quantify absolute circRNA levels within patient plasma samples. Primer sequences used in these analyses are listed in Table S2.

Statistical analysis

SPSS 22.0 (SPSS, Inc., IL, USA) and GraphPad Prism 7.0 (GraphPad, Inc., CA, USA) were used for statistical testing. Data were compared using Student’s t-tests and Man-Whitney tests, as appropriate. Pearson’s correlation analyses were employed to assess relationships between variables. The CircPanel diagnostic model was developed through binary logistic regression analyses. ROC curve analyses were used to determine optimal plasma circRNA expression cutoff values in order to maximize diagnostic utility after using MedCalc 11.0 (MedCalc, Ostend, Belgium) to generate ROC curves. P<0.05 was the significance threshold in these analyses.

Results

Identification of CRC-related plasma circRNA expression profiles

We began by employing a microarray approach in order to detect circRNAs that were differentially expressed in the plasma of CRC patients and healthy controls. In total, we identified 226 circRNAs exhibiting >1.5-fold expression level differences between these two patient cohorts, including 88 and 138 that were up- and down-regulated, respectively (Figure 1A; Table S3). We then compared the 88 identified upregulated circRNAs with a previous dataset evaluating differential circRNA expression profiles in CRC patient tissues [17-19], leading us to identify six potential CRC-related candidate circRNAs (hsa_circ_0001900, hsa_circ_0036005, hsa_circ_0067185, hsa_circ_0005075, hsa_circ_0001178, and hsa_circ_0005927). Of these, we were able to confirm that three (hsa_circ_0001900, hsa_circ_0001178, and hsa_circ_0005927) were significantly differentially expressed in CRC patient plasma and CRC tumor tissues via qRT-PCR and Sanger sequencing (Figure S2A-C). We further confirmed that these putative circRNAs were RNase R-resistant, indicating that they adopt a circular rather than linear conformation (Figure 1B). We additionally found that these circRNAs did not undergo significant degradation when plasma was stored at room temperature for 24 h (Figure 1C), confirming that they are highly stable in this biological matrix. We further found that hsa_circ_0001900, hsa_circ_0001178, and hsa_circ_0005927 expression levels in CRC tissues were positively correlated with levels in patient plasma (Figure 1D-F), suggesting that these circRNAs may be secreted from tumors into circulation.

Figure 1.

Figure 1

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Candidate CRC-related circRNA identification. A. Differentially expressed circRNAs from the plasma of CRC patients and healthy controls (n=3) were subjected to hierarchical clustering analysis. B. qRT-PCR analysis of RNase R-resistant circRNAs, with GAPDH serving as a negative control. C. No changes in Ct values for these three circRNAs were detected following a 24 h incubation at room temperature, as measured via qRT-PCR. D-F. Correlations between plasma and intratumoral levels of these differentially expressed circRNAs in CRC patients (n=15).

Independent validation of candidate CRC-related circRNA expression profiles

To validate the clinical relevance of hsa_circ_0001900, hsa_circ_0001178, and hsa_circ_0005927 in the context of CRC, we next evaluated the expression of these circRNAs in plasma samples from 42 healthy controls, 30 patients with precancerous lesions, and 102 CRC patients. Furthermore pre- and post-operative plasma samples from 20 of the CRC patients were additionally compared in these analyses. We found that hsa_circ_0001900, hsa_circ_0001178, and hsa_circ_0005927 were all expressed at significantly higher levels in the plasma of CRC patients relative to levels in the plasma of healthy controls of patients with precancerous lesions (P<0.05, Figure 2A). We also found that hsa_circ_0001900 was differentially expressed when comparing plasma samples from healthy controls to those from patients with precancerous lesions (P=0.02, Figure 2A). We also found that the plasma levels of these three circRNAs decreased significantly in CRC patients following surgical tumor resection (Figure 2B). AS such, our findings strongly suggest that hsa_circ_0001900, hsa_circ_0001178, and hsa_circ_0005927 are primarily derived from tumor cells in CRC patients.

Figure 2.

Figure 2

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Validation of candidate CRC-related circulating circRNA profiles in an independent patient cohort. A. Levels of hsa_circ_0001900, hsa_circ_0001178, and hsa_circ_0005927 were assessed in the plasma of 102 CRC patients, 42 healthy controls, and 30 patients with precancerous lesions via qRT-PCR. B. Levels of hsa_circ_0001900, hsa_circ_0001178, and hsa_circ_0005927 were assessed via qRT-PCR in 20 paired plasma samples from CRC patients before and after surgery.

Correlations between candidate circRNA expression levels and CRC patient clinical characteristics

We next assess correlations between plasma levels of these three candidate circRNAs and clinicopathological findings in 102 CRC patients. In so doing, we found that hsa_circ_0001900 was correlated with tumor size (P=0.017), TNM stage (P=0.005), lymph node metastasis (P=0.013), and distant metastasis (P<0.0001), while hsa_circ_0001178 was associated with lymph node metastasis (P=0.0012) and distant metastasis (P=0.023), and hsa_circ_0005927 was correlated with tumor size (P=0.0006). None of these circRNAs were correlated with characteristics such as age, sex, or tumor site (Table 1).

Table 1.

Association between candidate circRNAs expression and clinicopathological characteristics

Characteristics Cases (n=102) hsa_circ_0001900 hsa_circ_0001178 hsa_circ_0005927
Mean ± SD P Mean ± SD P Mean ± SD P
Age 0.182 0.909 0.194
    <65 41 2076 ± 127.8 816.5 ± 74.3 798.5 ± 51.0
    ≥65 61 2293 ± 101.0 825.4 ± 39.9 881.1 ± 38.3
Gender 0.810 0.712 0.489
    Male 62 2190 ± 98.7 831.7 ± 39.9 863.1 ± 46.5
    Female 40 2230 ± 134.8 802.8 ± 75.3 820.5 ± 39.8
Tumor site 0.857 0.156 0.390
    Colon 50 2191 ± 115.3 768.4 ± 56.3 886.3 ± 49.3
    Rectum 52 2220 ± 111.0 876.6 ± 49.9 829.0 ± 44.2
Tumor size (cm) 0.017 0.082 0.0006
    <5 68 2072 ± 92.6 881.0 ± 45.8 774.6 ± 32.7
    ≥5 34 2471 ± 141.9 741.1 ± 65.4 994.5 ± 58.5
TNM stage 0.005 0.513 0.073
    I+II 54 1999 ± 100.5 796.8 ± 42.3 795.8 ± 40.7
    III+IV 48 2438 ± 118.1 846.9 ± 65.5 906.6 ± 45.9
Lymphatic metastasis 0.013 0.0012 0.162
    Postive 47 2418 ± 119.8 932.3 ± 54.0 888.0 ± 44.7
    Negative 55 2024 ± 101.0 689.4 ± 46.8 801.9 ± 41.5
Distal metastasis <0.0001 0.023 0.163
    Yes 14 2981 ± 203.7 1037 ± 126.2 956.1 ± 89.7
    No 88 2082 ± 79.1 786.0 ± 38.3 830.7 ± 32.6
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Assessment of the diagnostic utility of the three identified candidate circRNAs in CRC

We next gauged the diagnostic value of these three circRNAs using ROC curves. When distinguishing between CRC and non-CRC patient samples, we found that AUC values for hsa_circ_0001900, hsa_circ_0001178, and hsa_circ_0005927 were 0.722 (95% CI: 0.656-0.781), 0.718 (95% CI: 0.652-0.778), and 0.784 (95% CI: 0.722-0.837), respectively (Figure 3A-C). We then employed a binary logistic regression analysis in order to construct a diagnostic model incorporating all three of these circRNAs (CircPanel). The AUC, sensitivity, and specificity of CircPanel when distinguishing between CRC and non-CRC patient samples were 0.859 (95% CI: 0.805-0.903), 72.55%, and 82.73%, respectively, with these values being substantially higher than values for any individual circRNA (Figure 3D; Table S4). As such, we elected to focus on the diagnostic utility of CircPanel for the remainder of this study.

Figure 3.

Figure 3

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The diagnostic potential for three candidate circRNAs and the combination thereof as a means of differentiating between CRC and non-CRC patient samples. A. ROC curve corresponding to hsa_circ_0001900. B. ROC curve corresponding to hsa_circ_0001178. C. ROC curve corresponding to hsa_circ_0005927. D. ROC corresponding to the overall CircPanel.

The diagnostic performance of CircPanel and/or CEA in the detection of CRC

We next evaluated the relative value of CircPanel, the CRC protein biomarker CEA, or a combination of these two biomarkers (CircPanel+CEA) as tools for diagnosing CRC. We found that both CircPanel alone and CircPanel+CEA were more accurate than CEA alone as a means of differentiating between CRC and non-CRC patient samples (CircPanel vs. CEA: AUC 0.859 [0.805-0.903] vs. AUC 0.698 [0.631-0.759], P=0.0003); (CircPanel+CEA vs. CEA: AUC 0.881 [0.829-0.921] vs. AUC 0.698 [0.631-0.759], P<0.0001). However, we did not detect any significant differences in the relative abilities of CircPanel and CircPanel+CEA to differentiate between CRC and non-CRC patient samples (Figure 4A; Table 2). We further subdivided non-CRC patient samples into samples from healthy controls and samples from patients with precancerous lesions, and observed similar results when comparing samples from CRC patients and samples from patients with precancerous lesions (Figure 4B; Table 2). We also found that the diagnostic performance of CircPanel+CEA was superior to that of CircPanel alone as a means of differentiating between CRC patients and healthy controls (circPanel+CEA vs. CircPanel: AUC: 0.903 [0.860-0.947] vs. AUC 0.874 [0.816-0.918], P=0.0014) (Figure 4C; Table 2).

Figure 4.

Figure 4

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Assessment of the diagnostic utility of CircPanel, CEA, and the combination thereof. A. ROC curve analysis of the ability to differentiate between CRC and non-CRC patient samples. B. ROC curve analysis of the ability to differentiate between CRC and healthy patient samples. C. ROC curve of the ability to differentiate between samples from patients with CRC and patients with precancerous lesions.

Table 2.

The performance of CircPanel, CEA and their combination for the diagnosis of CRC

Groups AUC (95% CI) Sensitivity (%) Specificity (%) Comparation of AUC
Groups P value
CRC vs. Non-CRC
    circPanel 0.859 (0.805-0.903) 72.55 82.73 circPanel vs. CEA 0.0003
    CEA 0.698 (0.631-0.759) 60.78 75.45 circPanel+CEA vs. CEA <0.0001
    circPanel+CEA 0.881 (0.829-0.921) 82.35 80.91 circPanel vs. circPanel+CEA 0.095
CRC vs. Healthy
    circPanel 0.874 (0.816-0.918) 67.65 90.00 circPanel vs. CEA 0.0014
    CEA 0.724 (0.653-0.788) 63.73 80.00 circPanel+CEA vs. CEA <0.0001
    circPanel+CEA 0.903 (0.851-0.942) 82.35 83.75 circPanel vs. circPanel+CEA 0.0092
CRC vs. precancerous lesions
    circPanel 0.818 (0.741-0.880) 84.31 70.00 circPanel vs. CEA 0.0037
    CEA 0.626 (0.538-0.709) 41.18 86.67 circPanel+CEA vs. CEA 0.0002
    circPanel+CEA 0.820 (0.744-0.881) 91.18 70.00 circPanel vs. circPanel+CEA 0.9404
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CircPanel can effectively diagnose CEA-negative CRC

There is some clinical evidence that serum CEA positivity rates in CRC patients may be <50% [5]. As such, we specifically evaluated the ability of CircPanel to diagnose CEA-negative (<5 ng/ml) CRC. Overall, CircPanel achieved high diagnostic accuracy when differentiating between CRC and non-CRC patient samples, between CRC and precancerous samples, and between CRC and healthy samples that were CEA-negative (all AUCs >0.800; Figure 5; Table S5). These findings thus show that CircPanel may be a valuable biomarker that can be used to diagnose patients with CEA-negative CRC.

Figure 5.

Figure 5

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The utility of CircPanel as a means of diagnosing CEA-negative CRC. A. ROC curve assessment for CircPanel-mediated differentiation between CRC and non-CRC samples. B. ROC curve assessment for CircPanel-mediated differentiation between CRC and healthy patient samples. C. ROC curve assessment for CircPanel-mediated differentiation between patients with CRC and patients with precancerous lesions.

Discussion

Many studies to date have confirmed that circRNA dysregulation is closely linked to tumor development and progression in a range of tissue and cell types [20,21]. Many circRNAs can be stably detected in human peripheral blood under physiological and pathological conditions, making them ideal tumor biomarkers [22-24]. To evaluate the potential diagnostic utility of such circRNAs in the context of CRC, we employed a microarray-based approach that led us to detect 88 and 138 circRNAs that were up- and down-regulated in the plasma of CRC patients, respectively, when compared to the plasma of healthy control patients. After combining these data with published tissue circRNA profiles, we identified three circRNAs (hsa_circ_0001900, hsa_circ_0001178, and hsa_circ_0005927) as being ideal diagnostic candidates for the detection of CRC.

Recent studies have revealed that hsa_circ_0001900 and hsa_circ_0001178 are significantly increased in CRC tissues and correlated with poor prognosis of CRC patients [17,19]. Hsa_circ_0001900 acts as the sponge of miR-328-5p to promote CRC growth, while hsa_circ_0001178 facilitates CRC metastasis by upregulating transcription factors ZEB1. To date, the role of hsa_circ_0005927 in tumor is still unknown. In this study, ROC curve analysis was performed to evaluate the diagnostic value of the three plasma circRNAs in distinguishing CRC from non-CRC. The results showed AUC values for hsa_circ_0001900, hsa_circ_0001178, and hsa_circ_0005927 were 0.722, 0.718 and 0.784, respectively, indicating that the three circRNAs have certain diagnostic value for CRC.

Following logistic regression analyses, we established a CircPanel composed of these three circRNAs that we found to be more accurate than any of these individual circRNAs as a tool for differentiating between CRC and non-CRC patient samples. Non-CRC patient samples were further subdivided into those from healthy controls and those from patients with precancerous lesions. We found that analyzing a combination of plasma CircPanel expression and CEA levels (CircPanel+CEA) was sufficient to more effectively differentiate between CRC patients and healthy controls. However, we did not detect any differences in the ability of CircPanel+CEA and CircPanel alone to differentiate between CRC and non-CRC samples or CRC samples and samples from patients with precancerous lesions. Importantly, CircPanel alone exhibited robust diagnostic utility as a means of differentiating between CEA-negative CRC patient plasma samples and non-CRC patient plasma samples. Together, these findings underscore the utility of CircPanel as an ideal non-invasive diagnostic biomarker for the detection of CRC.

Herein, we further observed no significant differences in the expression of hsa_circ_0036005, hsa_circ_0067185, or hsa_circ_0005075, which are expressed at high levels in CRC tumor tissues, in CRC patient plasma relative to healthy control patient plasma (data not shown). Yu et al. have previously demonstrated that while hsa_circ_0139897 expression did not differ significantly between HCC patient tumor and paracancerous tissues, following tumor resection the plasma levels of this circRNA in HCC patients declined significantly [12]. Prior studies have also shown that circ-CCDC66 and circ-STIL upregulation occurs in CRC patient tissues, driving the proliferative and invasive activities of these tumor cells [25-27]. In contrast, Lin et al. found these two circRNAs to be downregulated in CRC patient plasma samples [11]. Together these findings emphasize that circulating circRNAs are likely secreted in a specific manner, although the mechanisms governing such specific circRNA secretion remain to be clarified.

In conclusion, in the present study we identified a panel of three circRNAs (hsa_circ_0001900, hsa_circ_0001178 and hsa_circ_0005927) that are differentially expressed in CRC patient plasma, and that therefore represent viable diagnostic biomarkers for the detection of this deadly form of cancer.

Acknowledgements

This work was supported by the National Natural Science Foundation of China (NO. 81930079 and 31471391).

Disclosure of conflict of interest

None.

Supporting Information

ajtr0012-7395-f6.pdf (620.4KB, pdf)

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