Small RNA sequencing revealed aberrant piRNA expression profiles in colorectal cancer

  • Authors:
    • Jie Yin
    • Wei Qi
    • Chen‑Guang Ji
    • Dong‑Xuan Zhang
    • Xiao‑Li Xie
    • Qian Ding
    • Xiao‑Yu Jiang
    • Jing Han
    • Hui‑Qing Jiang
  • View Affiliations

  • Published online on: May 13, 2019     https://doi.org/10.3892/or.2019.7158
  • Pages: 263-272
Metrics: Total Views: 0 (Spandidos Publications: | PMC Statistics: )
Total PDF Downloads: 0 (Spandidos Publications: | PMC Statistics: )


Abstract

Piwi‑interacting RNAs (piRNAs), a novel class of non‑coding RNAs, are enriched in germ cells and implicated in spermatogenesis. Emerging evidence demonstrated deregulated expression of piRNAs in numerous tumor types. However, changes in piRNA expression profiles in colorectal cancer (CRC) have not yet been investigated. In the present study, small RNA sequencing was used to evaluate the differences in piRNA expression profiles between CRC and adjacent non‑tumor tissues, as well as to screen for differentially expressed piRNAs. The present results demonstrated that the percentage of unique piRNA reads had no notable difference between the paired CRC and adjacent non‑tumor samples (0.12% vs. 0.13%); however, the counts of total piRNA reads in CRC samples were increased, compared with those in adjacent non‑tumor samples (0.15% vs. 0.07%). Differential expression analysis identified 33 upregulated piRNAs and 2 downregulated piRNAs in CRC samples, among which piR‑18849, piR‑19521 and piR‑17724 were the top three upregulated piRNAs. Reverse transcription‑quantitative polymerase chain reaction further confirmed that the expression levels of piR‑18849, piR‑19521 and piR‑17724 were increased in 80 CRC tissues, compared with paired adjacent non‑tumor tissues. Furthermore, the high expression of piR‑18849 and piR‑19521 was associated with a poor degree of differentiation. The increased expression of piR‑18849 was also associated with high lymph node metastasis. However, no associations were determined between piR‑17724 expression and clinicopathological characteristics of patients. In summary, the present study is the first to provide an overview of the changes in piRNA expression patterns in CRC, shedding new light on the regulatory roles of piRNAs in colorectal carcinogenesis. piR‑18849 and piR‑19521 may be prognostic biomarkers for patients with CRC.

Introduction

Colorectal cancer (CRC), one of the most common cancer types worldwide, remains a serious threat to human health with high incidence and mortality globally (1). According to the 2018 statistics by the American Cancer Society, CRC ranks third in the morbidity and mortality among all malignancy types in the United States (2). CRC is difficult to detect at an early stage due to a lack of typical symptoms and signs; therefore, only 39% of patients with CRC have no metastasis at the time of diagnosis, with a 5-year survival rate of up to 90%; however, the majority of patients are diagnosed when the disease has distant metastasis, and the 5-year survival rate of these patients drops to 14%, according to data released by the National Cancer Institute (2006–2012) (3). Metastasis and recurrence are considered the primary causes of mortality in patients with CRC (4). Therefore, it is urgent and necessary to elucidate the molecular mechanism underlying the onset and progression (metastasis and recurrence), which will provide novel therapeutic strategies for CRC.

The onset and progression of CRC are multi-step processes, starting with hyper-proliferation of epithelial cells, then forming carcinoma in situ and eventually progressing to invasive and metastatic carcinoma (5). Gene mutations, including adenomatous polyposis coli, c-MYC and V-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog, and epigenetic changes, such as aberrant DNA methylation, serve key roles in these processes, and these alterations result in abnormalities in signaling pathways, including Wnt, mitogen-activated protein kinase/phosphatidylinositol 3-kinase, transforming growth factor-β and tumor protein P53, which will further influence a number of important biological functions of the cells (68). Notably, the nature of gene mutations and epigenetic changes is that both alter the expression of oncogenes or tumor suppressor genes (9,10). Gene expression is regulated at multiple levels, including transcription and translation levels (11). Previously, non-coding RNAs (ncRNAs), such as microRNAs (miRNAs) and long ncRNAs (lncRNAs), have been identified as crucial regulators of gene expression in numerous human diseases, particularly tumors (1215).

ncRNAs are generated from non-coding regions that were previously considered to be junk DNA, without the potential of translation into proteins (16). Previously, a novel class of ncRNAs has been identified, termed Piwi-interacting RNAs (piRNAs), which are characterized by a 3′-terminal 2′-O-methylation (17,18). piRNAs are named due to their characteristics of exclusive association with the Piwi subfamily, but not the Ago subfamily, and they maintain genome integrity by epigenetically silencing transposons (19,20). Although piRNAs were initially considered to be only expressed in germ cells, a growing number of studies identified piRNAs in various human tissues and cells, including brain tissues and cardiac progenitor cells (21,22), and they are also abnormally expressed in tumor cells (23,24), indicating that piRNAs may be involved in the onset and progression of tumors.

To date, it has been demonstrated that piRNAs are associated with gastric cancer, breast cancer, lung cancer, multiple myeloma and bladder cancer and piRNAs serve various roles in these tumor types, including tumor promotion and suppression in different tumor types (2529). Additionally, the mechanisms underlying piRNAs in tumors are also diverse and include epigenetic regulation, post-transcriptional regulation and post-translational regulation (30). These studies indicated that piRNAs have diverse functions and complex mechanisms in the tumor context. Although above studies have preliminarily elucidated the roles of piRNAs in a number of tumor types, the majority of studies only focused on a specific piRNA and did not depict the overall changes of piRNAs in tumors, which is not sufficient to fully understand the complex function of piRNAs in tumors.

In the present study, to improve the understanding of the biological function of piRNAs in CRC, the differences in piRNA expression profiles between CRC tissues and adjacent non-tumor tissues were compared using second-generation deep sequencing for small RNAs. Subsequently, the results were validated by reverse transcription-quantitative polymerase chain reaction (RT-qPCR). The potential clinical utility of CRC-associated piRNAs were also assessed by analyzing the association of piRNAs with clinicopathological features of patients with CRC. To the best of our knowledge, the present results provided, for the first time, an overall change of piRNA expression profiles in CRC and an outlook into clinical applications of piRNAs as a therapeutic target.

Materials and methods

Patients and samples

A total of 83 fresh CRC tissues and matched adjacent non-tumor tissues were collected from patients with CRC who underwent surgery from January 2016 to January 2018 at the Second Hospital of Hebei Medical University (Shijiazhuang, China). The diagnosis for CRC and histological evaluation were conducted by an experienced pathologist. All patients exhibited no primary tumors in other sites and did not receive chemoradiotherapy or biological therapy prior to surgery. Tissues were placed in RNAstore reagent (Tiangen Biotech Co., Ltd., Beijing, China) and then stored at −80°C after being resected from patients. Of the 83 pairs of samples, 3 pairs were used for deep sequencing for small RNAs, and the remaining 80 pairs were used for validation. The clinicopathological characteristics of patients with CRC for small RNA sequencing analysis and RT-qPCR validation analysis are listed in Tables I and II, respectively. Written informed consent was obtained from the recruited patients, and the present study was approved by the Ethics Committee of the Second Hospital of Hebei Medical University.

Table I.

Clinicopathological characteristics of patients with colorectal cancer for small RNA sequencing.

Table I.

Clinicopathological characteristics of patients with colorectal cancer for small RNA sequencing.

PatientsSexAgeTumor location DifferentiationT stageaLymph node metastasisAJCC stagea
1Male52RectumModeratelyT2NoI
2Male75RectumPoorlyT4YesIII
3Female62ColonPoorlyT3NoII

a AJCC 7th edition of T, nodes and metastasis staging system. T, tumor; AJCC, American Joint Committee on Cancer.

Table II.

Correlation of piRNA expression with clinicopathological characteristics of patients with colorectal cancer.

Table II.

Correlation of piRNA expression with clinicopathological characteristics of patients with colorectal cancer.

Clinicopathological characteristicsNo. (%)piR-18849 fold changeP-valuepiR-19521 fold changeP-valuepiR-17724 fold changeP-value
All cases80 (100)
Sex 0.235 0.849 0.647
  Male46 (57.50)2.27 (1.38-6.11) 2.03 (1.20-3.58) 1.25 (0.43-5.64)
  Female34 (42.50)3.10 (1.98-6.06) 2.10 (1.47-3.39) 1.83 (0.72-3.17)
Age 0.194 0.388 0.614
  ≥6049 (61.25)3.27 (1.74-6.88) 2.17 (1.42-3.61) 1.52 (0.56-4.15)
  <6031 (38.75)2.33 (1.45-4.45) 2.00 (1.38-3.27) 1.64 (0.42-3.24)
Tumor location 0.725 0.296 0.121
  Colon39 (48.75)3.15 (1.78-6.08) 2.23 (1.47-3.75) 0.97 (0.44-2.74)
  Rectum41 (51.25)2.84 (1.37-6.08) 2.06 (1.29-2.96) 1.91 (0.62-6.89)
Differentiation 0.001 0.001 0.329
  Well or Moderately56 (70.00)2.13 (1.35-4.73) 1.87 (1.12-2.60) 1.36 (0.45-3.22)
  Poorly24 (30.00)4.87 (2.64-7.99) 3.22 (1.80-7.89) 1.97 (0.59-8.07)
T stagea 0.794 0.618 0.321
  T1 or T216 (20.0)2.86 (1.75-7.98) 2.03 (1.40-2.97) 2.35 (1.06-3.77)
  T333 (41.25)2.84 (1.46-4.61) 2.07 (1.45-3.39) 1.15 (0.40-3.56)
  T431 (38.75)3.35 (1.59-6.94) 2.09 (1.21-3.68) 1.21 (0.50-3.15)
Lymph node metastasis 0.043 0.441 0.735
  No50 (62.50)2.32 (1.45-4.86) 2.05 (1.18-3.10) 1.65 (0.53-3.55)
  Yes30 (37.50)4.58 (1.81-7.28) 2.10 (1.48-4.24) 1.28 (0.43-3.59)
AJCC stagea 0.249 0.417 0.501
  I14 (17.50)2.86 (1.94-9.54) 2.18 (1.38-3.07) 1.98 (0.93-3.90)
  II35 (43.75)1.96 (1.32-4.45) 1.79 (1.16-3.04) 1.20 (0.42-3.24)
  III or IV31 (38.75)4.44 (1.81-7.14) 2.14 (1.48-4.36) 1.36 (0.44-3.31)

{ label (or @symbol) needed for fn[@id='tfn2-or-42-01-0263'] } Data are presented as n (%) or median with interquartile range.

a AJCC 7th edition of T, nodes and metastasis staging system. T, tumor; AJCC, American Joint Committee on Cancer.

Small RNA library construction, sequencing and data analysis

Total RNA was extracted from 3 pairs of CRC tissues and matched adjacent non-tumor tissues using TRIzol® reagent (Invitrogen; Thermo Fisher Scientific, Inc., Waltham, MA, USA). The concentration and quality of RNA were assessed by a NanoDrop ND-1000 spectrophotometer (NanoDrop Technologies; Thermo Fisher Scientific, Inc.) and an Agilent 2100 Bioanalyzer (Agilent Technologies, Inc., Santa Clara, CA, USA). Following passing the quality control tests, total RNAs from 3 CRC tissues and 3 matched adjacent non-tumor tissues were pooled separately in equal quantity (0.4 µg) to generate two sample pools [CRC (T) pool and adjacent non-tumor (N) pool] and then were sent to Beijing Genomics Institute (Shenzhen, China) for library preparation and sequencing. Briefly, small RNAs of 18–40 nt in length were purified from total RNA by size fractionation using 15% PAGE and sequentially ligated to 5′ and 3′ adaptors, followed by RT-PCR amplification to produce sequencing libraries using the TruSeq Small RNA Sample Prep Kit (Illumina, Inc., San Diego, CA, USA) according to the manufacturer's protocol. PCR products were gel-purified and sequenced using Illumina HiSeq 2000 (Illumina, Inc.). Clean reads were obtained by removing low-quality reads and adaptor sequences from raw reads. Subsequently, the length distribution of the clean reads and common and specific sequences between these two samples were summarized. Small RNA reads were aligned with piRNABank (http://pirnabank.ibab.ac.in/) to screen and annotate piRNAs using Bowtie (31). To identify differentially-expressed piRNAs between CRC samples and adjacent non-tumor samples, expression levels of each piRNA were normalized using the following formula: Normalized expression=actual piRNA count/total count of clean reads × 1,000,000. piRNAs with normalized expression values <1.0 in both samples were removed. piRNAs with fold change ≥2.0 (log2 ratio ≥1.0 or ≤-1.0) were considered as differentially expressed.

RT-qPCR of piRNA

Candidate differentially-expressed piRNAs were further validated in 80 pairs of CRC and adjacent non-tumor tissues by RT-qPCR. Briefly, total RNAs, including small RNAs, were isolated from tissues using a miRcute miRNA Isolation kit (Tiangen Biotech Co., Ltd.) and then reverse transcribed with a miScript Plant RT kit (Qiagen GmbH, Hilden, Germany), which is a kit specifically designed for small RNAs with the 2′-O-Me modification at their 3′ end. RT-qPCR was performed in triplicate with specific forward primers and universal reverse primers using a miScript SYBR® Green PCR kit (Qiagen GmbH), according to the manufacturer's protocols. The reverse transcription process was performed in two steps of ligation reaction catalyzed by ligase and reverse transcription reaction catalyzed by reverse transcriptase. RT-qPCR was performed on an ABI StepOne™ real-time PCR System (Applied Biosystems; Thermo Fisher Scientific, Inc.). Amplification conditions were pre-denaturation at 95°C for 15 min, followed by 40 cycles of denaturation at 94°C for 15 sec, annealing at 55°C for 30 sec and extension at 72°C for 30 sec. The expression of piRNAs was normalized against U6 small nuclear RNA levels and calculated using the 2−∆Cq method (32). Specific primers used in RT-qPCR are detailed in Table III.

Table III.

Primer sequences for reverse transcription-quantitative polymerase chain reaction analysis.

Table III.

Primer sequences for reverse transcription-quantitative polymerase chain reaction analysis.

NameSequence (5′-3′)
hsa-piR-19521 GAGTAGAGTGCTTAGTTGAACAG
hsa-piR-18849 TTTGGCAATGGTAGAACTCACAC
hsa-piR-17724 TTCCGTAGTGTAGTGGTTATCAC
U6 snRNA CTCGCTTCGGCAGCACATA

[i] piR, Piwi-interacting RNA; snRNA, small nuclear RNA.

Statistical analysis

Normally distributed data were expressed as the mean ± standard deviation and non-normally distributed data were expressed as median with interquartile range. Non-normally distributed data were analyzed using the Wilcoxon signed rank test (when paired) or Mann-Whitney U test (when unpaired). The correlation of piRNA expression with T stage and American Joint Committee on Cancer stage (7th edition) (33) was analyzed using Spearman's correlation analysis. P<0.05 was considered to indicate a statistically significant difference. Statistical analysis was performed using SPSS software version 17.0 (SPSS, Inc., Chicago, IL, USA).

Results

Common and specific reads analysis

To evaluate the overall differences of small RNAs in CRC and adjacent non-tumor tissues, the common and specific reads were analyzed between the CRC (T) library and adjacent non-tumor (N) library, including the number of unique reads (types of reads) and total reads. Small RNA sequencing yielded 570,231 unique reads with 114,209,517 total reads from these two libraries. Among these unique reads, only 67,866 (11.90%) unique reads were shared by the two libraries, whereas 297,988 (52.26%) and 204,377 (35.84%) unique reads were specific in the T library and N library, respectively (Fig. 1A). These specific reads were determined to not be abundant, with only 0.51% of total reads in the T library and 0.30% of total reads in the N library (Fig. 1B). These data indicate that CRC tissues and adjacent non-tumor tissues have diverse small RNA profiles and that specific small RNAs exhibit low expression levels.

Length distribution of small RNAs

In general, the length of small RNAs ranged from 18–35 nt, and the peak of length distribution was beneficial to identify the classes of small RNAs. miRNAs were concentrated in 21 or 22 nt, whereas piRNAs were concentrated in 26–32 nt (34). As depicted in Fig. 2, in the N and T libraries, the lengths of small RNAs were clustered in two ranges: 18–24 and 30–34 nt, and the most abundant cluster was 18–24 nt with a 22 nt peak point, the canonical length of miRNAs. The percentage of reads within the 18–24 nt cluster demonstrated no notable difference between these two libraries (82.64% vs. 95.19%), indicating that the abundance of miRNAs was not significantly changed in CRC. However, an increased 30–34 nt peak, primarily comprised of piRNAs, was determined in the T library compared with the N library (14.69% vs. 3.01%), indicating that the piRNA pathway is activated in human CRC.

piRNA annotation

To analyze the differentially expressed piRNAs between CRC and adjacent non-tumor tissues, piRNAs were first annotated and screened by mapping clean reads to the piRNA database. The results demonstrated that 367 unique reads in the N library and 423 unique reads in the T library, accounting for 0.13% and 0.12%, respectively, were aligned with piRNA sequences. Although the proportion of unique piRNA reads indicated no notable changes between these two libraries, the counts of total piRNA reads increased from 42,999 (0.07%) in the N library to 75,946 (0.15%) in the T library (Table IV), indicating that the overall expression of piRNAs was increased in CRC tissues, compared with adjacent non-tumor tissues, and further implying that piRNAs may be implicated in colorectal tumorigenesis.

Table IV.

Number of unique reads and total reads aligned to piRNA sequences in the N and T libraries.

Table IV.

Number of unique reads and total reads aligned to piRNA sequences in the N and T libraries.

N libraryT library


CategoriesUnique reads (%)Total reads (%)Unique reads (%)Total reads (%)
piRNA367 (0.13)42,999 (0.07)423 (0.12)75,946 (0.15)
Other271,876 (99.87)62,488,846 (99.93)365,431 (99.88)51,601,726 (99.85)
Total272,243 (100)62,531,845 (100)365,854 (100)51,677,672 (100)

[i] Data are presented as n (%). N, adjacent non-tumor; T, colorectal cancer; piRNA, Piwi-interacting RNA.

Differentially-expressed piRNAs between CRC and adjacent non-tumor tissues

Comparison of the expression levels of piRNAs in CRC and adjacent non-tumor tissues is beneficial for understanding the roles of piRNAs in the pathogenesis of CRC. In the present study, a total of 367 unique piRNA reads in the N library and 423 unique piRNA reads in the T library belonged to 141 registered piRNAs. Among these piRNAs, 87 piRNAs were excluded due <1.0 normalized expression values in both libraries. Therefore, 54 piRNAs were selected for further differential expression analysis. As depicted in Fig. 3, a total of 35 differentially-expressed piRNAs were identified, of which 33 piRNAs were upregulated in CRC tissues, whereas only 2 piRNAs were downregulated in CRC tissues. These differentially-expressed piRNAs are detailed in Table V.

Table V.

Differentially expressed piRNAs between colorectal cancer and adjacent non-tumor tissues by small RNA sequencing.

Table V.

Differentially expressed piRNAs between colorectal cancer and adjacent non-tumor tissues by small RNA sequencing.

piR-nameN-normalized expressionT-normalized expressionLog2(T/N)Regulation
hsa-piR-188490.14395.57305.28Up
hsa-piR-195210.431815.38385.15Up
hsa-piR-177242.926552.07284.15Up
hsa-piR-169700.09601.18043.62Up
hsa-piR-7940.75167.29523.28Up
hsa-piR-2036530.4165275.24463.18Up
has-piR-205480.25592.30273.17Up
hsa-piR-13120.94358.24343.13Up
hsa-piR-43092.766622.54363.03Up
hsa-piR-146200.57574.62483.01Up
hsa-piR-177910.75165.20532.79Up
hsa-piR-199146.316836.59222.53Up
has-piR-208295.101428.81322.50Up
hsa-piR-171841.63128.97872.46Up
hsa-piR-150260.20791.10302.41Up
hsa-piR-1771613.801064.43792.22Up
hsa-piR-2045010.762547.60282.15Up
hsa-piR-169452.03108.74652.11Up
hsa-piR-43070.95953.40571.83Up
hsa-piR-205001.11943.75401.75Up
hsa-piR-7530.51171.60611.65Up
hsa-piR-90512.63877.37261.48Up
hsa-piR-198251.15143.19291.47Up
hsa-piR-191020.43181.08361.33Up
hsa-piR-12070.49571.18041.25Up
hsa-piR-5520.75161.74161.21Up
hsa-piR-200098.331819.29271.21Up
hsa-piR-127532.67066.11481.20Up
hsa-piR-13464.12599.03681.13Up
hsa-piR-203880.54371.18041.12Up
hsa-piR-1673522.100747.71891.11Up
hsa-piR-1268134.382569.52711.02Up
hsa-piR-1878011.770023.60791.00Up
hsa-piR-18292113.84636.2632−1.65Down
hsa-piR-171942.78260.1161−4.58Down

[i] N, adjacent non-tumor; T, colorectal cancer; piRNA, Piwi-interacting RNA.

RT-qPCR validation

Since the vast majority of piRNAs were upregulated in CRC, the focus was on these upregulated piRNAs and the top three upregulated piRNAs (piR-18849, piR-19521 and piR-17724) were selected for further validation in 80 matched pairs of CRC and adjacent non-tumor tissues by RT-qPCR. The relative expression levels of these three piRNAs, calculated using the 2−∆Cq method, are depicted in Table VI. Consistent with the results from small RNA sequencing, the RT-qPCR results demonstrated that the expression levels of piR-18849, piR-19521 and piR-17724 were consistently increased in CRC tissues, compared with adjacent tissues (P<0.05; Fig. 4). Among these three piRNAs, piR-18849 was expressed at the lowest level but had the most notable difference between these two sets of samples. Notably, the fold changes from the RT-qPCR results were less than those from small RNA sequencing, which may be due to the increased sensitivity of deep sequencing.

Table VI.

Relative expression levels of the top three upregulated piRNAs in colorectal cancer.

Table VI.

Relative expression levels of the top three upregulated piRNAs in colorectal cancer.

Relative expression level

NameAdjacent tissuesColorectal cancer tissuesP-value
hsa-piR-188490.02 (0.01-0.04)0.06 (0.03-0.12)<0.001
hsa-piR-195210.10 (0.04-0.17)0.19 (0.09-0.30)<0.001
hsa-piR-177240.63 (0.24-1.39)0.92 (0.34-2.33)<0.05

[i] Data are presented as median with interquartile range. piRNA, Piwi-interacting RNA.

Association between the expression of piRNAs and clinicopathological features

To clarify the function of the three piRNAs in the onset and progression of CRC, the correlation of their expression with clinicopathological characteristics of patients with CRC was analyzed. As depicted in Table II, the expression of piR-18849 was positively correlated with lymph node metastasis potential and negatively correlated with the degree of tumor differentiation; additionally, CRC with poor differentiation and high lymph node metastasis had significantly increased levels of piR-18849 (P<0.05). The expression of piR-19521 was only negatively correlated with the degree of tumor differentiation (P=0.001). However, piR-17742 expression levels did not correlate with any clinicopathological features.

Discussion

It is well known that ncRNAs, as key molecules regulating gene expression, are widely distributed in various tissues (3537). Aberrant expression of ncRNAs is associated with numerous human disorders (3840). A large number of studies demonstrated that lncRNAs and miRNAs are implicated in a variety of tumor types and may serve as potential therapeutic targets or diagnostic markers for these tumor types, including CRC (4144). The rapid development of the second-generation deep sequencing technology has provided an unprecedented platform to comprehensively analyze non-coding transcriptomes as well as reveal a number of novel non-coding transcripts in various tissues, organs and disease models. Furthermore, second-generation sequencing has high sensitivity, which can avoid some minor differentially-expressed RNAs being missed. In view of the advantages of second-generation sequencing and our shortage of funds, studies from Huang et al (45), Wang et al (46) and Zhang et al (47) were referred to and only 3 pairs of CRC tissues and adjacent tissues were used for deep sequencing of small RNAs (≤40 nt). The present data demonstrated that only 11.90% of small RNAs were shared by CRC and adjacent tissues, indicating that the expression patterns of small RNAs in CRC and adjacent tissues were notably different. These observations further implied that small RNAs may be involved in colorectal carcinogenesis.

To date, the expression patterns of miRNAs and lncRNAs in tumors have been extensively investigated (48,49). However, piRNAs, as a class of newly identified small ncRNAs, and the expression patterns of piRNAs in tumors remain largely unknown, and the field remains in its infancy. Numerous studies demonstrated that a number of piRNAs are dysregulated in tumor types, including gastric cancer, multiple myeloma and bladder cancer, and these piRNAs are also involved in the onset and progression of these tumor types (25,28,29). However, the majority of these studies only focused on a particular piRNA or profiled piRNA expression patterns in tumor cell lines, but not in tumor tissues. Based on the current research status, the differences in piRNA expression patterns in CRC and adjacent non-tumor tissues were investigated using deep sequencing. The present results demonstrated that only low proportions of unique piRNA reads (0.12% vs. 0.13%) and total piRNA reads (0.15% vs. 0.07%) were identified in both the N and T libraries, indicating that the types of piRNAs were few and that their expression levels were also low in CRC and adjacent non-tumor tissues. Data from Yang et al (50) demonstrated that in normal human testis tissues, 25,845 unique reads corresponding to 1,051,404 total reads were matched to known piRNA sequences. Another study by Girard et al (18) identified 52,099 piRNAs in human testes. The aforementioned data indicate that despite piRNAs being expressed in somatic cells, including normal somatic cells and malignant cells, there are fewer types and their expression is reduced in somatic cells, compared with germ cells. The results were expected since piRNAs and Piwi are known to be germ cell-specific and serve key roles in germ cell development, stemness maintenance, meiosis and spermatogenesis (51).

It is notable that although piRNAs had fewer types and lower expression compared with miRNAs in CRC and adjacent tissues, the overall expression levels were notably different between these two types of tissues, indicating that piRNAs are dysregulated in CRC and further supporting that piRNAs are implicated in tumorigenesis (52). These data also indicate that low levels of piRNAs are sufficient to generate notable biological effects, similar to lncRNAs (53).

The signal transduction pathway and epigenetic status in tumors are similar to those in stem cells, indicating that tumors are an aberrant stem-like state (54). Therefore, Piwi and piRNAs that are highly enriched in germline stem cells may also be expressed in tumor cells. Indeed, studies disclosed that Piwi is overexpressed in all detected tumor types, as well as associated with tumor prognosis (5558). Consistent with the aforementioned data, the present small RNA sequencing results demonstrated that the overall expression levels of piRNAs in CRC were increased compared with adjacent non-tumor tissues. Furthermore, among 35 differentially-expressed piRNAs, 33 piRNAs were upregulated in CRC, indicating that the majority of piRNAs in CRC may serve tumor-driving roles, which is consistent with the role of Piwi in tumors. RT-qPCR further demonstrated that the expression levels of the top three upregulated piRNAs, piR-18849, piR-19521 and piR-17724, were increased in CRC, compared with adjacent non-tumor tissues. Notably, the increased levels of piR-18849 and piR-19521 were significantly correlated with a poorer degree of differentiation. This may be because piRNAs are highly enriched in germline stem cells (51), and tumor cells with a poorer degree of differentiation are more similar to stem cells (59,60). Therefore, as the degree of tumor differentiation decreases, the expression of piRNAs may increase.

In breast cancer, the upregulation of piR-4987 was associated with lymph node metastasis (45). The present study determined that in addition to the degree of tumor differentiation, the overexpression of piR-18849 was also associated with lymph node metastasis in patients with CRC. piRNAs thus may emerge not only as a potential therapeutic target but also as an indicator for prognosis in patients with CRC. However, in the present study, the prognostic value of identified piRNAs could not be verified due to the short follow-up period of patients recruited to the study. Additionally, the specific function of these piRNAs and their roles in the survival or prognosis of patients will be investigated in subsequent studies.

Collectively, to the best of our knowledge, the present study presented, for the first time, global piRNA expression profiles in CRC and adjacent non-tumor tissues by deep sequencing for small RNAs. Based on the small RNA sequencing data, it was determined that the overall expression levels of piRNAs in CRC tissues were increased, compared with adjacent tissues, implying that piRNAs may be involved in colorectal tumorigenesis. These observations will provide a theoretical basis for piRNA-targeted therapeutic strategies for CRC. However, only 3 pairs of samples were used for deep sequencing in the present study, which may cause a number of notable piRNAs to be omitted due to the limited samples. Another notable question was that the focus was only on those upregulated piRNAs, instead of those downregulated piRNAs. Therefore, determining what causes downregulation of a number of piRNAs in CRC, whether the downregulation of piRNAs is active or passive, and what roles these downregulated piRNAs serve in CRC will help the comprehensive understanding of the function of piRNAs in CRC.

Acknowledgements

Not applicable.

Funding

This work was supported by the National Natural Science Foundation of China (grant nos.81702324 and 81602529), the Natural Science Foundation of Hebei Province, China (grant no. H2017206141) and the Post-graduate's Innovation Fund Project of Hebei Province (grant no. CXZZBS2017103).

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Authors' contributions

JY, WQ and HQJ conceived and designed the experiments. JY and CGJ conducted the experiments. DXZ, QD and JH recruited patients and collected samples as well as patients' clinicopathological information. JY, WQ, XLX and XYJ analyzed and interpreted the data. JY drafted the manuscript. HQJ and XYJ revised the manuscript. HQJ supervised the whole project. All authors have read and approved the final manuscript.

Ethics approval and consent to participate

The present study was approved by the Ethics Committee of the Second Hospital of Hebei Medical University (Shijiazhuang, China). All patients provided written informed consent prior to participation in this study.

Patient consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Glossary

Abbreviations

Abbreviations:

piRNA

Piwi-interacting RNA

CRC

colorectal cancer

ncRNA

non-coding RNA

miRNA

microRNA

lncRNA

long non coding RNA

RT-qPCR

reverse transcription-quantitative polymerase chain reaction

References

1 

Ferlay J, Soerjomataram I, Dikshit R, Eser S, Mathers C, Rebelo M, Parkin DM, Forman D and Bray F: Cancer incidence and mortality worldwide: Sources, methods and major patterns in GLOBOCAN 2012. Int J Cancer. 136:E359–E386. 2015. View Article : Google Scholar : PubMed/NCBI

2 

Siegel RL, Miller KD and Jemal A: Cancer statistics, 2018. CA Cancer J Clin. 68:7–30. 2018. View Article : Google Scholar : PubMed/NCBI

3 

Howlader N, Noone A, Krapcho M, Miller D, Bishop K, Altekruse S, Kosary C, Yu M, Ruhl J and Tatalovich Z: SEER cancer statistics review. 1975-2013, National Cancer Institute; Bethesda, MD: https://seer.cancer.gov/archive/csr/1975_2013April. 2016

4 

Herszenyi L and Tulassay Z: Epidemiology of gastrointestinal and liver tumors. Eur Rev Med Pharmacol Sci. 14:249–258. 2010.PubMed/NCBI

5 

Lollini PL, De Giovanni C, Nicoletti G, Di Carlo E, Musiani P, Nanni P and Forni G: Immunoprevention of colorectal cancer: A future possibility? Gastroenterol Clin North Am. 31:1001–1014. 2002. View Article : Google Scholar : PubMed/NCBI

6 

Marmol I, Sanchez-de-Diego C, Pradilla Dieste A, Cerrada E and Rodriguez Yoldi MJ: Colorectal carcinoma: A general overview and future perspectives in colorectal cancer. Int J Mol Sci. 18:E1972017. View Article : Google Scholar : PubMed/NCBI

7 

Ashktorab H, Daremipouran M, Goel A, Varma S, Leavitt R, Sun X and Brim H: DNA methylome profiling identifies novel methylated genes in African American patients with colorectal neoplasia. Epigenetics. 9:503–512. 2014. View Article : Google Scholar : PubMed/NCBI

8 

Ashktorab H, Rahi H, Wansley D, Varma S, Shokrani B, Lee E, Daremipouran M, Laiyemo A, Goel A, Carethers JM and Brim H: Toward a comprehensive and systematic methylome signature in colorectal cancers. Epigenetics. 8:807–815. 2013. View Article : Google Scholar : PubMed/NCBI

9 

Pulford DJ, Falls JG, Killian JK and Jirtle RL: Polymorphisms, genomic imprinting and cancer susceptibility. Mutat Res. 436:59–67. 1999. View Article : Google Scholar : PubMed/NCBI

10 

Hatziapostolou M and Iliopoulos D: Epigenetic aberrations during oncogenesis. Cell Mol Life Sci. 68:1681–1702. 2011. View Article : Google Scholar : PubMed/NCBI

11 

Schwanhausser B, Busse D, Li N, Dittmar G, Schuchhardt J, Wolf J, Chen W and Selbach M: Global quantification of mammalian gene expression control. Nature. 473:337–342. 2011. View Article : Google Scholar : PubMed/NCBI

12 

Anastasiadou E, Jacob LS and Slack FJ: Non-coding RNA networks in cancer. Nat Rev Cancer. 18:5–18. 2018. View Article : Google Scholar : PubMed/NCBI

13 

Taft RJ, Pang KC, Mercer TR, Dinger M and Mattick JS: Non-coding RNAs: Regulators of disease. J Pathol. 220:126–139. 2010. View Article : Google Scholar : PubMed/NCBI

14 

Hayes J, Peruzzi PP and Lawler S: MicroRNAs in cancer: Biomarkers, functions and therapy. Trends Mol Med. 20:460–469. 2014. View Article : Google Scholar : PubMed/NCBI

15 

De Smet EG, Mestdagh P, Vandesompele J, Brusselle GG and Bracke KR: Non-coding RNAs in the pathogenesis of COPD. Thorax. 70:782–791. 2015. View Article : Google Scholar : PubMed/NCBI

16 

Goodrich JA and Kugel JF: From bacteria to humans, chromatin to elongation, and activation to repression: The expanding roles of noncoding RNAs in regulating transcription. Crit Rev Biochem Mol Biol. 44:3–15. 2009. View Article : Google Scholar : PubMed/NCBI

17 

Aravin A, Gaidatzis D, Pfeffer S, Lagos-Quintana M, Landgraf P, Iovino N, Morris P, Brownstein MJ, Kuramochi-Miyagawa S, Nakano T, et al: A novel class of small RNAs bind to MILI protein in mouse testes. Nature. 442:203–207. 2006. View Article : Google Scholar : PubMed/NCBI

18 

Girard A, Sachidanandam R, Hannon GJ and Carmell MA: A germline-specific class of small RNAs binds mammalian Piwi proteins. Nature. 442:199–202. 2006. View Article : Google Scholar : PubMed/NCBI

19 

Aravin AA, Sachidanandam R, Bourc'his D, Schaefer C, Pezic D, Toth KF, Bestor T and Hannon GJ: A piRNA pathway primed by individual transposons is linked to de novo DNA methylation in mice. Mol Cell. 31:785–799. 2008. View Article : Google Scholar : PubMed/NCBI

20 

Aravin AA and Bourc'his D: Small RNA guides for de novo DNA methylation in mammalian germ cells. Genes Dev. 22:970–975. 2008. View Article : Google Scholar : PubMed/NCBI

21 

Esposito T, Magliocca S, Formicola D and Gianfrancesco F: piR_015520 belongs to Piwi-associated RNAs regulates expression of the human melatonin receptor 1A gene. PLoS One. 6:e227272011. View Article : Google Scholar : PubMed/NCBI

22 

Vella S, Gallo A, Lo Nigro A, Galvagno D, Raffa GM, Pilato M and Conaldi PG: PIWI-interacting RNA (piRNA) signatures in human cardiac progenitor cells. Int J Biochem Cell Biol. 76:1–11. 2016. View Article : Google Scholar : PubMed/NCBI

23 

Law PT, Qin H, Ching AK, Lai KP, Co NN, He M, Lung RW, Chan AW, Chan TF and Wong N: Deep sequencing of small RNA transcriptome reveals novel non-coding RNAs in hepatocellular carcinoma. J Hepatol. 58:1165–1173. 2013. View Article : Google Scholar : PubMed/NCBI

24 

Cheng J, Guo JM, Xiao BX, Miao Y, Jiang Z, Zhou H and Li QN: piRNA, the new non-coding RNA, is aberrantly expressed in human cancer cells. Clin Chim Acta. 412:1621–1625. 2011. View Article : Google Scholar : PubMed/NCBI

25 

Cheng J, Deng H, Xiao B, Zhou H, Zhou F, Shen Z and Guo J: piR-823, a novel non-coding small RNA, demonstrates in vitro and in vivo tumor suppressive activity in human gastric cancer cells. Cancer Lett. 315:12–17. 2012. View Article : Google Scholar : PubMed/NCBI

26 

Hashim A, Rizzo F, Marchese G, Ravo M, Tarallo R, Nassa G, Giurato G, Santamaria G, Cordella A, Cantarella C and Weisz A: RNA sequencing identifies specific PIWI-interacting small non-coding RNA expression patterns in breast cancer. Oncotarget. 5:9901–9910. 2014. View Article : Google Scholar : PubMed/NCBI

27 

Peng L, Song L, Liu C, Lv X, Li X, Jie J, Zhao D and Li D: piR-55490 inhibits the growth of lung carcinoma by suppressing mTOR signaling. Tumour Biol. 37:2749–2756. 2016. View Article : Google Scholar : PubMed/NCBI

28 

Yan H, Wu QL, Sun CY, Ai LS, Deng J, Zhang L, Chen L, Chu ZB, Tang B, Wang K, et al: piRNA-823 contributes to tumorigenesis by regulating de novo DNA methylation and angiogenesis in multiple myeloma. Leukemia. 29:196–206. 2015. View Article : Google Scholar : PubMed/NCBI

29 

Chu H, Hui G, Yuan L, Shi D, Wang Y, Du M, Zhong D, Ma L, Tong N, Qin C, et al: Identification of novel piRNAs in bladder cancer. Cancer Lett. 356:561–567. 2015. View Article : Google Scholar : PubMed/NCBI

30 

Mei Y, Wang Y, Kumari P, Shetty AC, Clark D, Gable T, MacKerell AD, Ma MZ, Weber DJ, Yang AJ, et al: A piRNA-like small RNA interacts with and modulates p-ERM proteins in human somatic cells. Nat Commun. 6:73162015. View Article : Google Scholar : PubMed/NCBI

31 

Langmead B, Trapnell C, Pop M and Salzberg SL: Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10:R252009. View Article : Google Scholar : PubMed/NCBI

32 

Livak KJ and Schmittgen TD: Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) method. Methods. 25:402–408. 2001. View Article : Google Scholar : PubMed/NCBI

33 

Edge SB and Compton CC: The American Joint Committee on Cancer: The 7th edition of the AJCC cancer staging manual and the future of TNM. Ann Surg Oncol. 17:1471–1474. 2010. View Article : Google Scholar : PubMed/NCBI

34 

Williams Z, Morozov P, Mihailovic A, Lin C, Puvvula PK, Juranek S, Rosenwaks Z and Tuschl T: Discovery and characterization of piRNAs in the human fetal ovary. Cell Rep. 13:854–863. 2015. View Article : Google Scholar : PubMed/NCBI

35 

Kaikkonen MU, Lam MT and Glass CK: Non-coding RNAs as regulators of gene expression and epigenetics. Cardiovasc Res. 90:430–440. 2011. View Article : Google Scholar : PubMed/NCBI

36 

Sun Y, Koo S, White N, Peralta E, Esau C, Dean NM and Perera RJ: Development of a micro-array to detect human and mouse microRNAs and characterization of expression in human organs. Nucleic Acids Res. 32:e1882004. View Article : Google Scholar : PubMed/NCBI

37 

Sasaki YT, Sano M, Ideue T, Kin T, Asai K and Hirose T: Identification and characterization of human non-coding RNAs with tissue-specific expression. Biochem Biophys Res Commun. 357:991–996. 2007. View Article : Google Scholar : PubMed/NCBI

38 

de Almeida RA, Fraczek MG, Parker S, Delneri D and O'Keefe RT: Non-coding RNAs and disease: The classical ncRNAs make a comeback. Biochem Soc Trans. 44:1073–1078. 2016. View Article : Google Scholar : PubMed/NCBI

39 

Mestdagh P, Vandesompele J, Brusselle G and Vermaelen K: Non-coding RNAs and respiratory disease. Thorax. 70:388–390. 2015. View Article : Google Scholar : PubMed/NCBI

40 

Esteller M: Non-coding RNAs in human disease. Nat Rev Genet. 12:861–874. 2011. View Article : Google Scholar : PubMed/NCBI

41 

Di Leva G, Garofalo M and Croce CM: MicroRNAs in cancer. Annu Rev Pathol. 9:287–314. 2014. View Article : Google Scholar : PubMed/NCBI

42 

Zhang H, Chen Z, Wang X, Huang Z, He Z and Chen Y: Long non-coding RNA: A new player in cancer. J Hematol Oncol. 6:372013. View Article : Google Scholar : PubMed/NCBI

43 

Ma Y, Yang Y, Wang F, Moyer MP, Wei Q, Zhang P, Yang Z, Liu W, Zhang H, Chen N, et al: Long non-coding RNA CCAL regulates colorectal cancer progression by activating Wnt/β-catenin signalling pathway via suppression of activator protein 2α. Gut. 65:1494–1504. 2016. View Article : Google Scholar : PubMed/NCBI

44 

Yin Y, Zhang B, Wang W, Fei B, Quan C, Zhang J, Song M, Bian Z, Wang Q, Ni S, et al: miR-204-5p inhibits proliferation and invasion and enhances chemotherapeutic sensitivity of colorectal cancer cells by downregulating RAB22A. Clin Cancer Res. 20:6187–6199. 2014. View Article : Google Scholar : PubMed/NCBI

45 

Huang G, Hu H, Xue X, Shen S, Gao E, Guo G, Shen X and Zhang X: Altered expression of piRNAs and their relation with clinicopathologic features of breast cancer. Clin Transl Oncol. 15:563–568. 2013. View Article : Google Scholar : PubMed/NCBI

46 

Wang H, Zhao Y, Chen M and Cui J: Identification of novel long non-coding and circular RNAs in human papillomavirus- mediated cervical cancer. Front Microbiol. 8:17202017. View Article : Google Scholar : PubMed/NCBI

47 

Zhang Z, Song N, Wang Y, Zhong J, Gu T, Yang L, Shen X, Li Y, Yang X, Liu X, et al: Analysis of differentially expressed circular RNAs for the identification of a coexpression RNA network and signature in colorectal cancer. J Cell Biochem. 120:6409–6419. 2018. View Article : Google Scholar : PubMed/NCBI

48 

Chen Z, Li J, Tian L, Zhou C, Gao Y, Zhou F, Shi S, Feng X, Sun N, Yao R, et al: MiRNA expression profile reveals a prognostic signature for esophageal squamous cell carcinoma. Cancer Lett. 350:34–42. 2014. View Article : Google Scholar : PubMed/NCBI

49 

Su X, Malouf GG, Chen Y, Zhang J, Yao H, Valero V, Weinstein JN, Spano JP, Meric-Bernstam F, Khayat D and Esteva FJ: Comprehensive analysis of long non-coding RNAs in human breast cancer clinical subtypes. Oncotarget. 5:9864–9876. 2014. View Article : Google Scholar : PubMed/NCBI

50 

Yang Q, Hua J, Wang L, Xu B, Zhang H, Ye N, Zhang Z, Yu D, Cooke HJ, Zhang Y and Shi Q: MicroRNA and piRNA profiles in normal human testis detected by next generation sequencing. PLoS One. 8:e668092013. View Article : Google Scholar : PubMed/NCBI

51 

Thomson T and Lin H: The biogenesis and function of PIWI proteins and piRNAs: Progress and prospect. Annu Rev Cell Dev Biol. 25:355–376. 2009. View Article : Google Scholar : PubMed/NCBI

52 

Ng KW, Anderson C, Marshall EA, Minatel BC, Enfield KS, Saprunoff HL, Lam WL and Martinez VD: Piwi-interacting RNAs in cancer: Emerging functions and clinical utility. Mol Cancer. 15:52016. View Article : Google Scholar : PubMed/NCBI

53 

Derrien T, Johnson R, Bussotti G, Tanzer A, Djebali S, Tilgner H, Guernec G, Martin D, Merkel A, Knowles DG, et al: The GENCODE v7 catalog of human long noncoding RNAs: Analysis of their gene structure, evolution, and expression. Genome Res. 22:1775–1789. 2012. View Article : Google Scholar : PubMed/NCBI

54 

Dick JE: Stem cell concepts renew cancer research. Blood. 112:4793–4807. 2008. View Article : Google Scholar : PubMed/NCBI

55 

Liu X, Sun Y, Guo J, Ma H, Li J, Dong B, Jin G, Zhang J, Wu J, Meng L and Shou C: Expression of hiwi gene in human gastric cancer was associated with proliferation of cancer cells. Int J Cancer. 118:1922–1929. 2006. View Article : Google Scholar : PubMed/NCBI

56 

He W, Wang Z, Wang Q, Fan Q, Shou C, Wang J, Giercksky KE, Nesland JM and Suo Z: Expression of HIWI in human esophageal squamous cell carcinoma is significantly associated with poorer prognosis. BMC Cancer. 9:4262009. View Article : Google Scholar : PubMed/NCBI

57 

Jiang J, Zhang H, Tang Q, Hao B and Shi R: Expression of HIWI in human hepatocellular carcinoma. Cell Biochem Biophys. 61:53–58. 2011. View Article : Google Scholar : PubMed/NCBI

58 

Qiao D, Zeeman AM, Deng W, Looijenga LH and Lin H: Molecular characterization of hiwi, a human member of the piwi gene family whose overexpression is correlated to seminomas. Oncogene. 21:3988–3999. 2002. View Article : Google Scholar : PubMed/NCBI

59 

Hassiotou F and Geddes D: Anatomy of the human mammary gland: Current status of knowledge. Clin Anat. 26:29–48. 2013. View Article : Google Scholar : PubMed/NCBI

60 

Gisina AM, Lupatov AY, Karalkin PA, Mainovskaya OA, Petrov LO, Sidorov DV, Yarygin VN and Yarygin KN: Detection of minor subpopulations of colorectal adenocarcinoma cells expressing cancer stem cell markers. Bull Exp Biol Med. 151:234–238. 2011. View Article : Google Scholar : PubMed/NCBI

Related Articles

Journal Cover

July-2019
Volume 42 Issue 1

Print ISSN: 1021-335X
Online ISSN:1791-2431

Sign up for eToc alerts

Recommend to Library

Copy and paste a formatted citation
x
Spandidos Publications style
Yin J, Qi W, Ji CG, Zhang DX, Xie XL, Ding Q, Jiang XY, Han J and Jiang HQ: Small RNA sequencing revealed aberrant piRNA expression profiles in colorectal cancer. Oncol Rep 42: 263-272, 2019
APA
Yin, J., Qi, W., Ji, C., Zhang, D., Xie, X., Ding, Q. ... Jiang, H. (2019). Small RNA sequencing revealed aberrant piRNA expression profiles in colorectal cancer. Oncology Reports, 42, 263-272. https://doi.org/10.3892/or.2019.7158
MLA
Yin, J., Qi, W., Ji, C., Zhang, D., Xie, X., Ding, Q., Jiang, X., Han, J., Jiang, H."Small RNA sequencing revealed aberrant piRNA expression profiles in colorectal cancer". Oncology Reports 42.1 (2019): 263-272.
Chicago
Yin, J., Qi, W., Ji, C., Zhang, D., Xie, X., Ding, Q., Jiang, X., Han, J., Jiang, H."Small RNA sequencing revealed aberrant piRNA expression profiles in colorectal cancer". Oncology Reports 42, no. 1 (2019): 263-272. https://doi.org/10.3892/or.2019.7158