Circulating PIWI-Interacting RNAs piR-5937 and piR-28876 Are Promising Diagnostic Biomarkers of Colon Cancer

Colon cancer accounts for approximately for 7% of all cancers, and it is one of the most common causes of cancer-related deaths. The prognosis of patients depends mostly on tumor–node–metastasis (TNM) stage at the time of diagnosis as well as on the possibility of curative surgical resection, which can be accomplished only in patients with localized disease (1). Thus, the early detection of colon cancer or, even better, precancerous lesions is one of the main prerequisites for mortality reduction and more successful treatment. In addition, novel biomarkers suitable for monitoring of disease progression as well as treatment response are in great demand.

Currently, fecal occult blood testing and endoscopic approaches are the predominant screening methods used for early detection of colon cancer. Although it has been shown that these methods significantly contribute to reduced risk of colon cancer–associated mortality (2), screening effectiveness is limited by test performance, high costs, and invasiveness as well as a suboptimal screening compliance. Thus, the development of a simple blood-based test, with a specimen drawn during the routine medical check-up, could improve the screening rates. Lately, various molecules including DNA (3), proteins (4), mRNA (5), or miRNAs (6, 7) have shown a great potential to serve as new molecular markers for the development of noninvasive and accurate tests for colon cancer screening.

Recently, several studies have indicated the abundance of novel class of small noncoding RNAs called PIWI-interacting RNAs (piRNA) in various types of body fluids (8, 9). These 26-32 nucleotide-long molecules have been shown to participate in the epigenetic regulation of cancer and other diseases and are key elements of cellular homeostasis (10). Furthermore, they play an important role in tumor formation, proliferation, and migration of the cells (11, 12). Similar to miRNAs, piRNAs posttranscriptional regulation occurs in the cytoplasm. The piRISC protects the integrity of the genome by binding the transposable elements as well as mRNAs or lncRNAs (13). Moreover, these molecules may regulate gene expression through histone modifications and DNA methylation (14). Unlike miRNAs, piRNAs are extremely diverse, with at least hundreds of thousands of mature species transcribed from thousands of genomic loci (15). However, similarly to miRNAs, they are short enough to not be easily degraded by ribonucleases, and they can pass through the cell membranes (16). Studies on the biological functions and possible clinical relevance of piRNAs in colorectal cancer (CRC) are still in the beginning. In 2015, Chu and colleagues (17) identified 7 common single-nucleotide polymorphisms in 9 known piRNAs. Further, they revealed that piR-015551 (DQ591252) may be generated from long noncoding RNA LNC00964-3, which is significantly downregulated in CRC tissues and may be involved in disease development. In addition, rs11776042 in piR-015551 was associated with a decreased risk of CRC. This year, piR-25447 (DQ558335), piR-23992 (DQ556880), and piR-1043 (DQ540931) were found to be overexpressed in tumor tissue compared with adjacent mucosa, whereas piR-28876 (DQ598676) was significantly underexpressed. Furthermore, 27 piRNAs were differentially expressed between adjacent tissue and CRC metastases (18). Recently, piR-823 (DQ571031) was found to be upregulated in CRC tissues, and its inhibition suppressed cell proliferation, arrested the cell cycle in G₁ phase, and induced apoptosis in DLD-1 and HCT-116 cells. Importantly, the authors further revealed that this piRNA plays a tumor-promoting role by upregulating phosphorylation and transcriptional activity of HSF1, the common transcription factor of heat shock proteins (19). Interestingly, piR-823 was also observed to be deregulated not only in tissue, but also in blood serum of patients with renal cell carcinoma (8) and gastric cancer (20). Currently, next-generation sequencing (NGS) is widely used to identify known as well as novel piRNAs with deregulated expression in cancer. In 2013, Huang and colleagues (21) demonstrated that a wide variety of RNA species including piRNAs are embedded in the circulating vesicles. This observation was confirmed 3 years later when Freedman and colleagues (22) found 144 different piRNAs to be stably present in human plasma. Up today, only one study analyzed the expression of circulating piRNAs in colorectal cancer (23). They found significant deregulation of piR-019825 (DQ597218) in plasma samples of patients compared with healthy donors.

In this three-phase study, large sets of serum specimens from patients with colon cancer as well as from healthy controls were analyzed using NGS and subsequent RT-qPCR validation with the aim to identify piRNAs with deregulated expression that could potentially serve as novel noninvasive biomarkers for early detection of the disease. During the study, piRNAs' labeling according to piRBase (24) is used together with unique GenBank accession number (DQ identifier) when mentioned first. Similarly, all names of cited piRNAs are accompanied by this identifier.

The study design consisted of three phases. All thresholds were determined to enable potential analytical applicability of the biomarker (Fig. 1). In total, a consecutive set of 403 patients with histopathologically verified colon cancer, who underwent resection from 2010 through 2014 at Masaryk Memorial Cancer Institute (MMCI, Brno, Czech Republic), was included in the study. These patients were further proportionally divided into screening, training, and validation sets based on TNM stage. In total, 144 cases and 96 controls were included in the screening phase, 80 cases and 80 controls were included in the training phase, and 179 cases and 100 controls were analyzed in the validation phase of the study. Clinical and pathologic characteristics are summarized in Table 1. The concentrations of CEA and CA19-9 were measured at Department of Laboratory Medicine at MMCI using electrochemiluminescence immunoassay and ELECSYS 2010 system (Roche), and during the study, cutoffs used in clinical routine were used (CEA: 5 ng·mL⁻¹; CA19-9: 27 U·mL⁻¹). All blood serum samples were collected prior to surgery. Serum samples from 276 healthy donors involved in this study were collected at the Department of Preventive Oncology (Masaryk Memorial Cancer Institute, Brno, Czech Republic); these donors had no prior diagnosis of any malignancy. The samples of cases and controls in all phases of the study were balanced regarding the age and sex. In addition, 20 paired samples from colon cancer patients before and 1 month after the surgery (7 men, 13 women; mean age 72 years) were included (Faculty Hospital Brno, Czech Republic). All subjects enrolled in the study were of the same ethnicity (European descent), and colon cancer patients did not receive any neoadjuvant treatment. Ten hemolytic serum samples were excluded from the study before the groups were selected. Written informed consent was obtained from all participants, and the study was conducted in accordance with Declaration of Helsinki and approved by the local Ethics Board at Masaryk Memorial Cancer Institute.

Before RNA extraction, all samples were checked for hemolysis using the Harboe's spectrophotometric method (25). Only the samples with hemoglobin concentration lower than 5 mg·dL⁻¹ were further used. Total RNA enriched for small RNAs was isolated from blood serum using Qiagen miRNeasy Serum/Plasma Kit (Qiagen, GmbH; catalog number 217184) according to the modified manufacturers' protocol. Briefly, 250 μL of serum was thawed on ice and centrifuged at 14,000 × g at 4°C for 5 minutes to remove cellular debris. Subsequently, 200 μL of supernatant was lysed with 1 mL of QIAzol Lysis Reagent. For each sample, 1.25 μL of 0.8 μg ⋅ μL⁻¹ MS2 RNA carrier (Roche; catalog number 10165948001) was added to 1 mL of QIAzol solution. The elution of RNA was performed twice with volumes of 20 μL (total volume of eluted RNA was 40 μL) using preheated Elution Solution. For the purposes of library preparation, RNA pools were used. Each RNA pool was gained using 12 serum samples of colon cancer patients or healthy donors (12 × 250 μL; in case of colon cancer patients, all 12 patients were of the same stage). The lysis with QIAzol was performed separately for each sample as described previously. After phase separation, upper aqueous phase from all 12 samples was combined, mixed with 1.5 volume of 100% ethanol and pipetted into one RNeasy MinElute spin column. Elution was performed using 14 μL of preheated Elution Solution. The concentration and purity of RNA were determined spectrophotometrically by measuring its optical density (A260/280 > 2.0; A260/230 > 1.8) using NanoDrop ND-1000 Spectrophotometer (Thermo Fisher Scientific). Further, the concentrations and quality of RNA of pooled samples for NGS were also measured using Qubit 2.0 Fluorometer (Thermo Fisher Scientific) and Agilent 2100 Bioanalyzer (Agilent Technologies).

In total, 144 cases and 96 controls were sequenced by preparing 20 libraries, each containing pooled RNA of 12 different cases (for a total of 12 libraries) or 12 controls (for a total of 8 libraries). All libraries were prepared using the Illumina TruSeq Small RNA Library Prep Kit (Illumina; catalog number RS-200-0012) following the manufacturer's instructions. The concentration of prepared libraries was measured using High Sensitivity DNA chip and Agilent 2100 Bioanalyzer. Equimolar amounts of each library were pooled at a final concentration of 2 nmol·L⁻¹ cDNA, and samples were sequenced on a flowcell with 50-bp single-end reads using MiSeq sequencer (Illumina).

Count-based piRNA expression data were generated by the CLC Genomic Workbench from fastq files. All sequences were adapter trimmed and mapped against piRBase (www.regulatoryrna.org/database/piRNA; ref. 24) allowing up to two mismatches per sequence. Further analyses were performed using R/Bioconductor packages. PiRNAs having less than 1 read per million in more than 17 pooled samples were dropped out. The read counts were prenormalized by adding normalization factors within edgeR package (26) and further between-sample normalized by the voom function in LIMMA package. After the normalized expression levels were determined, differentially expressed piRNAs were screened applying linear model fitting and a Bayes approach. The obtained P values were adjusted for multiple testing using the Benjamini–Hochberg method.

Complementary DNA was synthesized from total RNA using customized piRNA-specific primers (Supplementary Table S1) and 10 ng of RNA sample according to the TaqMan MicroRNA Assay protocol (TaqMan MicroRNA Reverse Transcription kit, Applied Biosystems; catalog number 4366597). Reaction mixtures were incubated for 30 minutes at 16°C, 30 minutes at 42°C, 5 minutes at 85°C, and then held at 4°C (T100 Thermal Cycler; Bio-Rad). Real-time PCR was performed using the TaqMan (NoUmpErase UNG) Universal PCR Master Mix (catalog number 4440040) and QuantStudio 12K Flex Real-Time PCR system (all from Applied Biosystems). Reactions were incubated in a 96-well optical plate at 95°C for 10 minutes, followed by 40 cycles at 95°C for 15 seconds and 60°C for 1 minute.

The threshold cycle data were calculated by QuantStudio 12K Flex software. All real-time PCR reactions were run in triplicates. The average expression levels of all measured piRNAs were normalized using piR-28131 (DQ597916) and subsequently analyzed by the 2^−ΔCt method. Statistical differences between the levels of analyzed piRNAs in serum samples of colon cancer patients and healthy donors were evaluated by two-tailed nonparametric Mann–Whitney test. Paired samples before and after the surgery were analyzed using two-tailed nonparametric Wilcoxon test for paired samples. Further, receiver operating curve (ROC) analyses were performed to ascertain the diagnostic performance of analyzed markers, and the maximum Youden index was used to obtain optimal cutoff values. The diagnostic score (DXscore) was established using logistic regression. The Spearman correlation was performed to assess the relation between analyzed piRNAs. All calculations were performed using GraphPad Prism version 5.00 (GraphPad Software) and R environment (R Development Core Team). P values of less than 0.05 were considered statistically significant.

In the screening phase of the study, small RNA sequencing of RNA isolated from blood serum samples from 144 colon cancer patients and 96 healthy controls was carried out using MiSeq sequencer (Illumina). In total, 20 small RNA libraries were prepared and sequenced (12 libraries per colon cancer patients, 8 libraries per healthy controls). On average, more than 96% of the reads had Q-score higher than 30, thus the obtained data were considered to be of high quality. The sequenced samples contained on average 8.925.000 ± 2.139.597 reads, and 8.219.664 ± 1.954.177 reads passed the filter. Mapping data to the piRBase, 163.355 ± 91.338 reads were annotated, being a proportion of 2.11 ± 1.31% of the total sequenced reads (Supplementary Table S2). Of the 854 different piRNAs that were found to be present in serum samples, 482 piRNAs had more than 1 read per million in more than 3 samples and were involved in subsequent analysis. In total, 48 piRNA were found to be significantly deregulated in serum samples of colon cancer patients compared with healthy donors (nonadjusted P < 0.01). Furthermore, 97 and 87 piRNAs were detectable in colon cancer patients and healthy donors, respectively, in more than 50 copies per 1 million of reads. From these, 31 piRNAs were found to be significantly deregulated (23 downregulated, 8 upregulated; Table 2A) in serum samples of colon cancer patients compared with healthy donors (nonadjusted P < 0.01; Supplementary Fig. S1). According to the criteria described in the study design (adjusted P < 0.005; at least 50 copies of piRNA in each pooled library; logFC > 1.5), five piRNAs (piR-5937 – DQ575659, piR-28876 – DQ598676, piR-23210 – DQ592932, piR-23209 – DQ592931, piR-32159 – DQ not available) were chosen for further evaluation in the training phase of the study.

Based on the results of NGS (fold change, SD, at least 500 copies per 1 million of reads, expressed in all prepared libraries), 3 piRNAs (piR-28131 – DQ597916, piR-27622 - DQ597347, piR-26131 – DQ595899) were chosen as potential endogenous controls for normalization of RT-qPCR data. First, expression of all 3 piRNAs was measured in randomly selected 40 serum samples of healthy donors and 40 serum samples of colon cancer patients from the training phase of the study. As the Cq values of piR-26131 were higher than 35 and this piRNA was not detected in all samples, it was eliminated from further analyses. Subsequently, piR-28131 was chosen as the best endogenous control for the normalization of data obtained by RT-qPCR. It was proved that its expression is highly stable in all analyzed sets of samples (Supplementary Table S3).

Serum samples from 80 colon cancer patients and 80 healthy donors were included in the training phase of the study. As shown in Table 2B, serum levels of piR-5937 (P < 0.0001; Fig. 2A), piR-28876 (P < 0.0001; Fig. 2D), piR-23210 (P = 0.0220), and piR-32159 (P = 0.0349) were significantly lower in colon cancer serum samples compared with healthy controls. These results are in agreement with sequencing data. The deregulation of piR-23209 (P = 0.3998) was not confirmed. In addition, the expression of piR-5937 and piR-28876 decreased significantly with advanced clinical stage (P < 0.0005; Fig. 2C and F), whereas there were no correlation between piRNAs expression and grade, location, and size of the tumor (P > 0.05). Furthermore, the ROC analysis was performed to determine the sensitivity and specificity of individual piRNAs for colon cancer detection. In case of piR-5937 (cutoff: 0.0655; Fig. 2B) and piR-28876 (cutoff: 0.0278; Fig. 2E), the AUC values were higher than 0.80, and the best results were reached for piR-28876 (AUC = 0.8065, sensitivity 75%, specificity 70%; Table 2B).

Based on the criteria mentioned in the study design (P < 0.01; Cq values < 35; detection rate > 80%), only two piRNAs (piR-5937 and piR-28876) were further analyzed in validation phase of the study that involved 179 serum samples of colon cancer patients and 100 serum samples of healthy donors. In addition, 20 paired samples of colon cancer patients before and 1 month after the surgery were analyzed in the third phase of the study. As shown in Table 2B, both piRNAs were significantly downregulated in serum samples of patients compared with healthy controls (P < 0.0001; Fig. 3A and D). However, there was no association between piRNAs' expression and clinical stage of the disease. Similarly to the results obtained in the training phase of the study, the AUC values were higher than 0.70 for both piRNAs when using ROC analysis (Fig. 3B and E). Moreover, in case that only the patients in stage I were included in ROC analysis, the diagnostic potential of analyzed piRNAs was still satisfactory (piR-5937: AUC = 0.8189, sensitivity = 71.4%, specificity = 66.3%; piR-28876: AUC = 0.7256, sensitivity = 64.3%, specificity = 65.3%). Furthermore, we wanted to know whether the combination of both piRNAs enables us more accurate detection of colon cancer patients. For this purpose, the diagnostic panel comprising of piR-5937 and piR-28876 was established, and diagnostic score was calculated according to the formula: DXscore = −1.268 + 15.246*piR-5937 + 3.811*piR-28876 (cutoff: –0.2339). Unfortunately, subsequent ROC analysis did not prove that usage of this two-piRNA–based panel would provide better results than piR-5937 or piR-28876 separately (Table 2B). Thus, the Spearman correlation analysis was performed to assess the relation between these two piRNAs. The results confirmed positive correlation in case of CRC patients (ρ = 0.8154; P < 0.0001) as well as in case of healthy controls (ρ = 0.7391; P < 0.0001). Finally, the expression of both piRNAs was measured in 20 paired serum samples from colon cancer patients before and 1 month after the surgery. As shown in Fig. 3C and F, the levels of analyzed piRNAs were significantly higher in postoperative samples (P < 0.05).

The capacity of piR-28876/piR-5937 and of CEA/CA19-9 markers to detect colon cancer was tested. In total, 138 colon cancer patients with known levels of CEA and CA19-9 were included in this analysis. As shown in Fig. 4A–D, CEA enabled to identify 66 colon cancer patients (48%; cutoff 5 ng·mL⁻¹), whereas CA19-9 only 36 (26%; cutoff 27 U·mL⁻¹), when cutoff values routinely used in our reference laboratory were applied. However, decreased expression of piR-5937 (cutoff: 0.0655) was observed in 98 of 138 colon cancer patients (71%), whereas the decreased levels of piR-28876 (cutoff: 0.0278) were noticed in 95 of 138 patients (69%). The highest diagnostic sensitivity was reached in case of combination of CEA, CA19-9, and both piRNAs (86%; Fig. 4E).

Recent studies indicated that noncoding RNAs play an important role in epigenetic regulation of cancers (27–29). Therefore, they may serve as novel biomarkers for patients with malignant diseases. As the tissue-based diagnosis remains invasive and time-consuming, minimally invasive techniques such as blood-based tests are highly requested. Since 2008, circulating miRNAs are largely analyzed for their potential to serve as novel noninvasive biomarkers in cancer patients (30–32). They were proved to be present in various body fluids including serum, plasma, urine, or saliva (33). In addition, they are extremely stable and resistant to degradation by ribonucleases (30); thus, they may be involved in cell-to-cell communication and other complex processes (34). Importantly, RNA sequencing revealed that not only miRNAs but also the other types of noncoding RNAs including piRNAs are stably present in human blood (21, 22). In 2015, Yang and colleagues (9) firstly described the presence of piR-57125 (DQ596014) in serum and plasma and proved that similarly to miRNAs, this piRNA remains extremely stable regardless of repetitive freeze-thawing or long-term incubation at room temperature. Since that time, several other articles described the presence of piRNAs in circulation and their deregulation in blood samples of cancer patients (8, 20, 35).

To our knowledge, this is the first study analyzing the expression of circulating piRNAs in serum samples of colon cancer patients using NGS and subsequent RT-qPCR validation. Firstly, small RNA sequencing was performed to assess the expression profile of piRNAs in serum samples of colon cancer patients and healthy donors. In total, 97 and 87 piRNAs were detectable in colon cancer patients and healthy donors, respectively, in more than 50 copies per 1 million of reads and the top 10 piRNAs accounted for 93% of all detected piRNAs. The most abundant piRNAs were piR-28131 (DQ597916), piR-1207 (DQ570956), and piR-28877 (DQ598677). Furthermore, NGS revealed 31 piRNAs to be significantly deregulated in serum samples of colon cancer patients compared with healthy donors. In 2016, Yuan and colleagues (23) performed RNA sequencing analysis using plasma extracellular vesicles derived from healthy controls, colorectal cancer patients, prostate cancer patients, and pancreatic cancer patients to thoroughly examine the extracellular RNA composition and distribution in human plasma. In accordance with our data, they identified 118 different piRNAs, and the top 10 piRNAs accounted for 96% of them. Importantly, among the top three most abundant piRNAs were again piR-000765 (DQ570956) and piR-020326 (DQ597916); thus, it seems that these piRNAs could play important roles in circulation. Interestingly, the most expressed piRNAs in both studies showed identical sequences and only differed at 5′ or 3′ ends by one base, suggesting that they are derived from the same precursor sequence.

Currently, RT-qPCR is the most commonly used approach for small noncoding RNA expression quantification. Nevertheless, in case of piRNAs, there are no well-established reference controls that could be used for expression normalization. Therefore, based on the results of NGS, we have chosen three piRNAs and tested their suitability to serve as normalization controls for piRNA expression. Finally, piR-28131 was identified as the best gene with high stability in all analyzed sets of samples. However, further confirmation is needed. Interestingly, this piRNA is thought to be one of the tRNA-derived piRNAs as its sequence overlaps with the 5′ end of 10 Gly tRNAs (36). This could be one of the reasons of its high abundance in circulation.

From the 31 piRNAs differentially expressed between serum samples of colon cancer patients and healthy donors, five were chosen for further validation (piR-5937, piR-28876, piR-23210, piR-23209, and piR-32159). During the training phase of the study, downregulated levels of piR-5937, piR-28876, piR-23210, and piR-32159 in serum samples of colon cancer patients were confirmed. In addition, the expression of all four piRNAs decreased significantly with advanced clinical stage. Because the blood is exposed to a wide variety of cell types, it is difficult to identify a tissue or organ of origin of these piRNAs. In addition, cell stress, external stimuli, or nutrition can affect the presence of small RNAs in circulation (37). As mentioned previously, piR-28876 was found to be significantly downregulated in tumor tissue of colorectal cancer patients compared with adjacent healthy tissue (18). Thus, it seems that this piRNA could potentially act as the tumor suppressor. Nevertheless, it will be necessary to confirm this assumption by detailed in vitro and in vivo studies. Concerning piR-23210, its levels were overexpressed in the metastatic tissue of colorectal cancer patients compared with the control benign tissue (18). Interestingly, piR-5937 overlaps significantly with the 5′ end of 8 Glu tRNAs and differs by only one base from piR-5938, which is one of the most abundant piRNAs in human serum (36). However, nothing is known about the source and function of this piRNAs in circulation. Concerning the piR-23209, its deregulated levels were previously found in plasma samples of pancreatic cancer patients as well as in prostate cancer (23); however, the training phase of this study did not confirm significant deregulation of this piRNA in colon cancer patients.

Finally, the validation phase of the study was carried out with the aim to further characterize the diagnostic potential of two selected piRNAs (piR-5937 and piR-28876) that enabled to differentiate colon cancer patients from healthy donors with the highest sensitivity and specificity. Similarly to the results obtained during the training phase of the study, both piRNAs were significantly downregulated in serum samples of colon cancer patients, and their diagnostic potential was high also in case that only the patients in clinical stage I were included. Unfortunately, the combined expression of both piRNAs did not provide better results than piR-5937 and piR-28876 separately as these markers were positively correlated. Pinsky and colleagues (38) proved that if the additional marker is positively correlated with the primary marker, then it is unlikely to increase the AUC, even when it has a good diagnostic ability on its own. Lastly, the comparison of both piRNAs with currently used biomarkers CEA and CA19-9 was performed. It was proved that although the elevated levels of CEA are detected in less than 50% of colon cancer patients, downregulation of piR-5937 and piR-28876 was observed in almost 70% of all tested samples. However, the highest diagnostic sensitivity was reached in case of the combination of all four markers. In addition, the levels of both piRNAs significantly increased in serum samples of patients 1 month after the surgery, which indicates that their levels are linked to the presence of the tumor in colon cancer patients. Considering these facts, it is obvious that mentioned piRNAs could serve as promising biomarkers for early colon cancer detection as well as potential novel biomarkers for the monitoring of patients after the surgical treatment. Nevertheless, it will be necessary to validate these data on larger and independent sets of patients. Further, we are aware of the fact that the use of a biomarker which is negatively correlated with the disease is difficult to translate into clinical use. Thus, we expect these piRNA biomarkers not to be used separately but in combination with other biomarkers upregulated in CRC serum samples to increase analytical performance and reduce the possibility of technical failure of the measurement.

We are also aware of potential limitations of this study, and it is evident that still many issues must be addressed in order to establish piRNAs as novel diagnostic tools. Firstly, expression profiling was performed using pooled samples of colon cancer patients/healthy donors as an insufficient amount of RNA was isolated from individual blood serum samples. Thus, because the quantity of free-circulating RNAs in body fluids can be very variable and a subject to interindividual variation, a bias could be introduced into the results using this kind of an approach. Today, improved kits for small RNA library preparation are available tagging efficiently in samples with low piRNA abundance and enabling to use as little as 1 ng of total RNA (39). Secondly, all patients were of the same ethnicity, and samples were enrolled in a single center. On the other hand, the dynamics of piRNAs prior and after the surgery was analyzed only in case of several patients. In addition, these samples were obtained from a different hospital with various times of sample storage, and although the methodology of serum samples collection and PCR measurement was the same in the whole course of the study, we have observed a small shift in piRNAs' expression. To establish final cutoff values for candidate piRNAs, our results need to be further validated in the multicenter prospective studies with independent cohorts of patients and laboratory facilities.

In summary, growing availability of NGS technologies enables the identification of novel classes of circulating noncoding RNAs. Up today, only limited number of studies analyzed the expression profiles of piRNAs in human blood. Our data underline the enormous potential for circulating piRNAs to serve as novel noninvasive biomarkers, and although their release mechanisms and biological significance require further study, their involvement in colon cancer pathogenesis is evident.

No potential conflicts of interest were disclosed.

Conception and design: P. Vychytilova-Faltejskova, M. Svoboda, O. Slaby

Development of methodology: P. Vychytilova-Faltejskova, M. Sachlova

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): P. Vychytilova-Faltejskova, M. Sachlova, Z. Kosarova, K. Slaba, Z. Kala, M. Svoboda, R. Vyzula

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): P. Vychytilova-Faltejskova, L. Radova, M. Sachlova, K. Slaba, Z. Kala, R. Vyzula, W.C. Cho, O. Slaby

Writing, review, and/or revision of the manuscript: P. Vychytilova-Faltejskova, Z. Kala, M. Svoboda, I. Kiss, R. Vyzula, W.C. Cho, O. Slaby

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): M. Sachlova, K. Slaba

Study supervision: O. Slaby

Other (made an analytic part): K. Stitkovcova

The work has been supported by Ministry of Health of the Czech Republic, grant no. 16-31765A (P. Vychytilova-Faltejskova, Z. Kala, I. Kiss, and O. Slaby) and MH CZ-DRO (MMCI, 00209805, to O. Slaby and M. Svoboda), by the Ministry of Education, Youth and Sports of the Czech Republic under the project CEITEC 2020 (LQ1601, O. Slaby).

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.