Abstract
Background
Cancer remains a leading cause of global morbidity and mortality, underscoring the need for innovative diagnostic and prognostic biomarkers. PIWI-interacting RNAs (piRNAs) have emerged as critical regulators of gene expression and genomic stability, with increasing evidence linking their dysregulation to oncogenesis. piRNAs can modulate DNA methylation through interactions with DNA methyltransferases (DNMTs), contributing to tumor suppressor gene silencing and broader epigenetic reprogramming. However, the functional implications of piRNA-mediated methylation changes in cancer are not fully elucidated. This study investigates the circulating levels of piR-823 and piR-651, their associations with DNMT3B expression and global DNA methylation, and their correlations with clinical and molecular markers in colorectal, breast, and prostate cancers.
Materials and methods
A total of 300 participants, including 150 patients with histologically confirmed cancers and 150 age- and sex-matched healthy controls, were enrolled. Plasma piRNA levels were measured via qRT-PCR, while global 5-methylcytosine (5mC) and DNMT3B concentrations were quantified using ELISA. Immunohistochemistry was performed on resected tumor tissues to assess DNMT3B, Ki-67, p53, E-cadherin, vimentin, HER2, and androgen receptor (AR). Correlations were analyzed using Pearson’s test and multivariate regression models. Diagnostic performance was evaluated via Receiver Operating Characteristic (ROC) curves.
Results
piR-823 was significantly upregulated in colorectal and breast cancers (p < 0.001), while piR-651 was markedly downregulated in prostate cancer (p = 0.002). Cancer patients exhibited elevated levels of DNMT3B and 5mC (both p < 0.001), which correlated strongly with Ki-67 and p53 expression (r > 0.7, p < 0.001). E-cadherin expression was inversely correlated with DNMT3B and piR-823, consistent with a role in epithelial–mesenchymal transition (EMT). cfDNA levels were also significantly elevated in cancer cases (p < 0.001), suggesting potential utility as a non-invasive biomarker.
Conclusion
The observed associations between piRNA dysregulation, epigenetic markers, and tumor aggressiveness indicators support the potential of circulating piRNAs—particularly piR-823 and piR-651—as novel biomarkers for cancer detection and progression monitoring. These findings warrant further mechanistic studies to validate their functional roles in tumor epigenetics.
Keywords: PIWI-interacting RNAs, DNA methylation, Cancer biomarkers, CfDNA, Epithelial–mesenchymal transition, Epigenetics
Introduction
Cancer remains one of the leading causes of morbidity and mortality worldwide, necessitating the continuous search for innovative diagnostic, prognostic, and therapeutic strategies. Traditional cancer biomarkers, including protein-coding genes and epigenetic modifications, have been widely studied, yet emerging evidence highlights the significance of small non-coding RNAs, such as PIWI-interacting RNAs (piRNAs), in cancer pathophysiology [1–4].
piRNAs are a distinct class of small regulatory RNAs that primarily function in transposable element suppression and genomic stability maintenance [5, 6]. Initially thought to be exclusive to germline cells, piRNAs have been increasingly detected in somatic tissues, particularly in cancerous cells, where they appear to exert oncogenic or tumor-suppressive effects depending on the cellular context [7, 8]. Recent data suggests that piRNAs interact with PIWI proteins to modulate gene expression via epigenetic mechanisms, including DNA methylation and histone modifications [9, 10]. Through the regulation of DNA methyltransferases (DNMTs), piRNAs influence gene silencing pathways that are frequently disrupted in cancer [9].
DNA methylation, one of the most well-characterized epigenetic modifications, is orchestrated by DNMTs, which catalyze the addition of methyl groups to cytosine residues in CpG dinucleotides [11, 12]. Aberrant methylation patterns, including hypermethylation of tumor suppressor genes and global hypomethylation, have been implicated in oncogenesis and cancer progression [13]. Given the critical role of DNMTs in establishing and maintaining these methylation landscapes, understanding how piRNAs regulate DNMT activity may provide novel insights into tumor epigenetics.
Recent studies have highlighted the potential of piRNAs as cancer biomarkers due to their stable presence in biofluids such as plasma and their distinct expression profiles in different malignancies [14, 15]. Moreover, emerging evidence suggests that specific piRNAs are differentially expressed in various cancer types, influencing key oncogenic pathways and potentially serving as therapeutic targets [15, 16].
Despite these advances, the precise role of piRNAs in cancer epigenetics remains poorly understood. There is a need for further investigation into the interplay between piRNAs, DNMTs, and DNA methylation in solid tumors. This study aims to bridge this gap by systematically analyzing the expression of key piRNAs and their correlation with DNA methylation markers in colorectal, breast, and prostate cancers. By integrating molecular profiling techniques with immunohistochemical validation, we seek to elucidate the epigenetic mechanisms underlying piRNA regulation and assess their potential as clinically relevant biomarkers for cancer detection and prognosis.
Materials and methods
This study conducted from 1st March 2024 to 1st November 2024, included a total of 300 participants (Table 1), comprising 50 cases of colorectal cancer (25 females and 25 males), 50 cases of breast cancer, 50 cases of prostate cancer, and a control group of 150 healthy individuals (75 females and 75 males). Blood samples were collected from all participants, with the cancer cases being histologically confirmed prior (from 1st March 2024 to 1st June 2024) to the study. Detailed clinical parameters were collected for each patient, including tumor stage (TNM classification), histologic grade, metastatic status, and receptor status (ER, PR, HER2 for breast cancer; AR for prostate cancer). These data were extracted from relevant pathology reports and anonymized before analysis (Table 2). Tumor tissue samples were obtained from the same patients enrolled in the study during surgical resection. All tumor samples were formalin fixed and paraffin-embedded (FFPE) and collected prior to the initiation of systemic treatment, ensuring that all patients were treatment-naïve at the time of tissue and blood sampling. Immunohistochemical analysis was performed to access the expression of clinically and biologically relevant markers (Ki-67, p53, E-cadherin, vimentin, HER2, ER, PR, and AR). All IHC staining was scored by experienced pathologist blinded to data of this study. Immunohistochemical scoring was based on established criteria: Ki-67 and p53 were scored according to the percentage of positively stained nuclei; E-cadherin and vimentin were scored semi-quantitatively based on staining intensity and distribution (0–3 + scale); hormone receptors (ER, PR, HER2, AR) were scored using the Allred or CAP/ASCO criteria, with HER2 positivity confirmed by IHC 3 + or 2 + with FISH amplification. Ethical approval was granted by the institutional review board, and written informed consent was obtained from all participants at blood samples collection moment. Study didn’t include minors. All data were fully anonymized before access by the research team, ensuring that no personally identifiable information could be linked to the participants. The process of obtaining written informed consent included providing participants with detailed information about the study’s purpose, procedure, risks, and benefits, and allowing sufficient time for questions before signing the consent form. An impartial witness was present during the consent process to confirm that participants understood the study and voluntarily agreed to participate.
Table 1.
Sample demographics with age
| Group | Total (n) | Female (%) | Male (%) | Age range (years) | Mean ± SD age |
|---|---|---|---|---|---|
| Colorectal cancer | 50 | 25 (50%) | 25 (50%) | 45–75 | 61.4±8.6 |
| Breast cancer | 50 | 50 (100%) | 0 (0%) | 30–70 | 54.8±9.4 |
| Prostate cancer | 50 | 0 (0%) | 50 (100%) | 50–80 | 66.9±7.8 |
| Control group | 150 | 75 (50%) | 75 (50%) | 30–80 | 59.2±10.3 |
Table 2.
Clinical characteristics of cancer patients
| Parameter | Colorectal cancer (n = 50) | Breast cancer (n = 50) | Prostate cancer (n = 50) |
|---|---|---|---|
| Stage I-II | 22 (44%) | 18 (36%) | 25 (50%) |
| Stage III-IV | 28 (56%) | 32 (64%) | 25 (50%) |
| Grade G1/G2/G3 | 10/28/12 | 6/24/20 | 8/30/12 |
| Metastatic disease | 18 (36%) | 14 (28%) | 20 (40%) |
| ER+/PR+/HER2+ (Breast) | – | 32/28/20 | – |
| AR+ (prostate) | – | – | 45 (90%) |
| Treatment-naïve (100%) | Yes | Yes | Yes |
Blood samples (10 mL) were collected in EDTA tubes from each participant. The samples were centrifuged at 2000g for 10 min to separate plasma. Although a two-step centrifugation is commonly recommended for cfDNA extraction, in our protocol, an immediate one-step spin at 2000g was chosen to minimize cell lysis and preserve cfDNA integrity, in line with previous studies validating this approach under controlled pre-analytical conditions. Plasma was promptly aliquoted and stored at − 80 °C within 1 h of collection. All procedures were conducted under sterile conditions to preserve the integrity of the samples. RNA was extracted from plasma using the QIAamp Circulating Nucleic Acid Kit (Qiagen), following the manufacturer’s protocol for plasma RMA extraction. The purity and concentration of RNA were measured using a Nanodrop spectrophotometer, ensuring an A260/A280 ratio between 1.8 and 2.0 for high-quality RNA. The levels of piRNA were assessed using qRT-PCR. Complementary DNA (cDNA) was synthesized from 500 ng of total RNA using a reverse transcription kit (PrimeScript™ RT reagent Kit, for research use only). qRT-PCR was conducted using SYBR Green Master Mix on a QuantStudio 3 Real-Time PCR System. Specific primers (Table 3) for piRNAs (piR-651, piR-823), along with GAPDH as the reference gene [17], were used. Relative expression levels were calculated using the 2(− ΔΔCt) method [18]. DNA methylation and epigenetic biomarker levels were quantified using ELISA-based assays. Plasma levels of global 5-methylcytosine (5mC), DNA methyltransferase 3B (DNMT3B), and circulating free DNA (cfDNA) were measured using individual ELISA kits specific to each analyte (Fine Biotech Co., Ltd, China). The 5mC ELISA detects methylated cytosine residues in total cfDNA extracted from plasma, providing a global estimate of DNA methylation levels in circulating nucleic acids rather than in tumor tissue directly. This interpretation is consistent with studies that have utilized cfDNA as a non-invasive proxy for assessing systemic epigenetic alterations. The cfDNA assay employed a DNA-binding protein detection method that recognizes fragmented double-stranded DNA without the need for amplification or labeling. This method has been validated in previous studies as suitable for measuring cfDNA directly from plasma under tightly controlled pre-analytical conditions, without the need for column-based extraction. To ensure cfDNA stability and consistency, blood samples were processed within one hour of collection, and plasma was immediately aliquoted and stored at − 80 °C. All ELISA assays were performed in duplicate. Absorbance was measured at 450 nm using a calibrated microplate reader, and concentrations were calculated based on standard curves provided by the manufacturer. RNA extraction and piRNA quantification were performed independently from cfDNA assessment and did not interfere with DNA measurements.
Table 3.
Specific primers for piR-651, piR-823, GADPH
| piR-651 |
F: 5′-CTCAACTGGTGTCGTGGAGTCGGCAATTCAGTTGAGACCAGCATGGG-3′ R: 5′-ACGTAGGCAGAGGAGGATTGA-3′ |
| piR-823 |
F: 5′-GCGGTGTAGTCCAGGAGGTA-3′ R: 5′-ATCCAGTGCAGGGTCCGAGG-3′ |
| GADPH |
F: 5′-GAAGGTGAAGGTCGGAGTC-3′ R: 5′-GAAGATGGTGATGGGATTTC-3’ |
All statistical analyses were performed using SPSS v25.0. Pearson’s correlation and ANOVA were conducted to determine biomarker associations. Multivariate regression models adjusted for confounding variables such as age, sex, and cancer stage. Receiver Operating Characteristic (ROC) curve analysis evaluated the diagnostic accuracy of piRNAs, cfDNA, and DNMT3B levels (Fig. 1). Wilcoxon rank-sum and t-tests compared biomarker levels between cancer and control groups, while Kaplan-Meier survival analysis assessed the prognostic relevance of biomarker expression.
Fig. 1.
ROC curves for diagnostic performance
Results
The analysis revealed significant differences in biomarker expression between cancer patients and healthy controls. The relative expression of piR-823, calculated using the 2(−ΔΔCt) method was significantly upregulated in colorectal and breast cancer patients compared to healthy controls. Specifically, the mean expression was 8.6 ± 1.4 in colorectal cancer and 7.8 ± 1.2 in breast cancer, whereas in controls it was 3.5 ± 1.1 (p < 0.001 for both comparisons. Conversely the relative expression of piR-651, also determined using the 2(−ΔΔCt) method, was significantly downregulated in prostate cancer patients compared to controls. The mean expression level in prostate cancer patients was 2.9 ± 0.8, while the control group showed a mean expression of 5.6 ± 1.2; p = 0.002. This difference suggests a potential role for piR-651 as a suppressive regulatory factor in prostate carcinogenesis.
DNMT3B expression, quantified using a specific ELISA kit (Fine Biotech Co, Ltd) was significantly higher in cancer patients (mean: 10.5 ± 2.1) than in controls (mean: 4.2 ± 1.3; p < 0.001). Similarly, plasma levels of global 5-methylcytosine (5mC), measured via ELISA-based quantification, were elevated in the cancer group (mean: 7.9 ± 1.7) compared to healthy individuals (mean: 3.3 ± 1.0; p < 0.001). cfDNA levels were also significantly increased in cancer patients (mean: 12.1 ± 2.4 vs. 5.4 ± 1.5 in controls; p < 0.001), as determined by a cfDNA-specific quantitative ELISA platform employing a DNA-binding protein detection system (Table 4). Clinical stratification showed no statistically significant differences in piRNA or DNMT3B expression across tumor stages (p > 0.05), although higher levels were observed in advanced stage (III/IV) cases. Similarly, hormone receptor status (e.g., HER2 positivity in breast cancer, AR in prostate cancer) showed moderate correlation withpiR-823 and cfDNA levels (r = 0.52–0.66, p < 0.01), suggesting potential interactions between hormonal signaling and epigenetic regulation.
Table 4.
Biomarker expression in cancer and control groups
| Biomarker | Cancer group (mean ± SD) | Control group (mean ± SD) | p-value |
|---|---|---|---|
| piR-823 | 8.2 ± 1.3 | 3.5 ± 1.1 | < 0.001 |
| piR-651 | 2.9 ± 0.8 | 5.6 ± 1.2 | 0.002 |
| DNMT3B | 10.5 ± 2.1 | 4.2 ± 1.3 | < 0.001 |
| 5mC | 7.9 ± 1.7 | 3.3 ± 1.0 | < 0.001 |
| cfDNA | 12.1 ± 2.4 | 5.4 ± 1.5 | < 0.001 |
When stratified by cancer type (Fig. 2), piR-823 levels were significantly elevated in both colorectal cancer (mean: 8.6 ± 1.4) and breast cancer (mean: 7.8 ± 1.2) compared to controls (mean: 3.5 ± 1.1; p < 0.001 for both), while prostate cancer patients showed moderate upregulation (mean: 5.1 ± 1.0; p = 0.03). Conversely, piR-651 was significantly downregulated in prostate cancer (mean: 2.9 ± 0.8) compared to controls (5.6 ± 1.2; p = 0.002), with no significant changes in breast or colorectal cancer. DNMT3B expression showed the highest levels in colorectal cancer (mean: 11.2 ± 2.2), followed by prostate (10.3 ± 1.9) and breast cancer (9.8 ± 2.0), all significantly elevated relative to controls (mean: 4.2 ± 1.3; p < 0.001). Similarly, 5mC levels were most increased in colorectal cancer (8.3 ± 1.6), followed by breast (7.5 ± 1.8) and prostate (7.2 ± 1.5) cancers. cfDNA levels were highest in colorectal cancer (13.1 ± 2.5), followed by breast (11.6 ± 2.2) and prostate (11.4 ± 2.3), again significantly higher than in the control group (5.4 ± 1.5; all p < 0.001).
Fig. 2.
Biomarker expression by cancer type
In addition to plasma-based biomarker profiling, IHC analysis was performed on tumor tissue samples collected from the same patients. These samples were obtained during diagnostic surgical procedures prior to initiation of any systemic therapy, ensuring a drug-naïve status. IHC markers included Ki-67, p53, Ecadherin, vimentin, HER2 (breast cancer), and androgen receptor (prostate cancer). As shown in Table 5, high positivity rates were observed for Ki-67, p53, and vimentin across cancer types, supporting their relevance in tumor proliferation and epithelial–mesenchymal transition (EMT). Notably, HER2 and AR were expressed in 40% of breast cancer cases and 90% of prostate cancer cases, respectively, confirming consistency with clinical profiles.
Table 5.
Immunohistochemistry (IHC) data specific to cancer cases
| Marker | Cancer type | Role/significance | Positive cases (%) | Scoring criteria |
|---|---|---|---|---|
| Ki-67 | Colorectal | Proliferation marker; indicates tumor growth and aggressiveness | 84% (42/50) | % of nuclear staining: low, moderate, high |
| Breast | 90% (45/50) | |||
| Prostate | 80% (40/50) | |||
| p53 | Colorectal | Tumor suppressor; mutations lead to loss of cell cycle control | 70% (35/50) | Intensity and distribution of nuclear staining |
| Breast | 60% (30/50) | |||
| Prostate | – | |||
| E-cadherin | Colorectal | Epithelial marker; loss indicates epithelia–mesenchymal transition (EMT) | 76% (38/50) | Completeness and intensity of membrane staining |
| Breast | 64% (32/50) | |||
| Prostate | – | |||
| Vimentin | Colorectal | Mesenchymal marker; increased levels indicate EMT and invasiveness | 60% (30/50) | Cytoplasmic staining intensity (low, medium, high) |
| Breast | 70% (35/50) | |||
| Prostate | 76% (38/50) | |||
| ER/PR | Colorectal | Hormone receptors; guide treatment decisions and prognosis | – | Positive/negative staining and intensity |
| Breast | 64% (32/50) | |||
| Prostate | – | |||
| HER2 | Colorectal | Growth factor receptor; amplification indicates poor prognosis | – | Scored 0–3+: 3 + indicates positivity |
| Breast | 40% (20/50) | |||
| Prostate | – | |||
| Androgen receptor | Colorectal | Hormone receptor; critical for prostate cancer growth | – | Percentage and intensity of nuclear staining |
| Breast | – | |||
| Prostate | 90% (45/50) |
Further analysis showed strong correlations between biomarkers and tumor aggressiveness. piR-823 exhibited a significant positive correlation with Ki-67 (r = 0.74, p < 0.001) and p53 (r = 0.68, p < 0.001), indicating a relationship with tumor proliferation. DNMT3B expression was highly correlated with Ki-67 (r = 0.81, p < 0.001) and p53 (r = 0.75, p < 0.001), further suggesting its role in tumor growth. E-cadherin expression was inversely correlated with piR-823 (r = − 0.61, p < 0.001) and DNMT3B (r = − 0.69, p < 0.001), while vimentin showed a strong positive correlation (r = 0.72, p < 0.001), indicating EMT involvement (Table 6; Fig. 3). Pearson correlation analysis revealed strong positive correlations between cfDNA and DNMT3B (r = 0.78, p < 0.001), Ki-67 (r = 0.79, p < 0.001), and p53 (r = 0.73, p < 0.001). piR-823 expression similarly correlated with DNMT3B (r = 0.76, p < 0.001) and Ki-67 (r = 0.72, p < 0.001), while piR-651 was inversely associated with E-cadherin (r = − 0.68, p < 0.001).
Table 6.
Correlation between biomarkers and tumor markers
| Biomarker | Ki-67 (r) | p53 (r) | E-cadherin (r) | Vimentin (r) | HER2 (r) | AR (r) |
|---|---|---|---|---|---|---|
| piR-823 | 0.74 | 0.68 | − 0.61 | 0.72 | 0.64 | 0.59 |
| piR-651 | − 0.63 | − 0.55 | 0.58 | − 0.66 | − 0.49 | − 0.52 |
| DNMT3B | 0.81 | 0.75 | − 0.69 | 0.77 | 0.72 | 0.66 |
| cfDNA | 0.79 | 0.73 | − 0.65 | 0.74 | 0.71 | 0.68 |
Fig. 3.
Correlation matrix of biomarkers vs. tumor markers
Subgroup analysis revealed that piR-823 and DNMT3B expression levels were significantly higher in stage III–IV tumors compared to stage I–II in all cancer types (p < 0.01). Similarly, patients with high-grade (G3) tumors exhibited elevated cfDNA and 5mC levels compared to those with lower-grade malignancies (p < 0.05). Hormonal receptor positivity was moderately correlated with increased expression of piR-823 and DNMT3B in breast cancer (HER2+, ER+, PR+) and with piR-823 and cfDNA in AR + prostate cancer (r = 0.58–0.66; p < 0.01). Cross-cancer comparison revealed that the strength of correlation between piR-823 and proliferation markers (e.g., Ki-67, p53) was highest in colorectal cancer (r = 0.74 and 0.71), whereas in prostate cancer, piR-651 was inversely correlated with both EMT markers and androgen receptor expression (r = − 0.66 to − 0.52). This suggests cancer-specific regulatory roles of piRNAs and DNA methylation pathways. These findings indicate that piRNA dysregulation and DNA methylation alterations contribute to EMT and metastasis, reinforcing their relevance as potential biomarkers for cancer progression and prognosis.
In multivariate linear regression models adjusted for age, sex, and cancer stage, cfDNA (β = 0.41, 95% CI 0.25–0.57, p < 0.001) and piR-823 (β = 0.35, 95% CI 0.19–0.52, p < 0.001) remained independently associated with DNMT3B levels. ROC curve analysis demonstrated that piR-823 yielded an AUC of 0.88 (95% CI 0.82–0.94, p < 0.001), while cfDNA showed an AUC of 0.91 (95% CI 0.86–0.96, p < 0.001), indicating high diagnostic performance for cancer detection. Kaplan–Meier curves demonstrated that patients with high cfDNA levels had significantly worse overall survival compared to those with low levels (median survival: 18 vs. 32 months, p = 0.004, log-rank test). piR-823 high expression was also associated with shorter survival (HR = 2.1, 95% CI 1.3–3.4, p = 0.002).
Discussion
There is a growing body of evidence implicating PIWI-interacting RNAs (piRNAs) in the regulation of gene expression, epigenetic modifications, and maintenance of genomic integrity, particularly in germline cells. More recently, their aberrant expression has been observed in various cancers, suggesting emerging roles in somatic tumorigenesis. Among these, piR-823 and piR-651 have attracted attention due to their consistent dysregulation across multiple malignancies. piR-823 has been shown to influence DNA methylation by regulating DNMT3B, promote cancer cell proliferation, and suppress mitophagy, especially in colorectal, gastric, and breast cancer tissues. In contrast, piR-651 is generally considered an oncogenic piRNA, with reported overexpression in lung, gastric, and breast cancers and mechanistic links to the suppression of tumor suppressor genes such as PTEN via DNMT1-mediated promoter hypermethylation. These functional associations, supported by both in vitro and patient-derived data, provided the rationale for selecting piR-823 and piR-651 for this study. Our inclusion of three biologically and clinically distinct tumor types- colorectal, breast, and prostate cancers -was intended to evaluate whether circulating levels of these piRNAs reflect shared oncogenic mechanisms across epithelial malignancies or exhibit cancer-type specificity. While this broad approach limits organ-specific mechanistic interpretation, it enhances the potential translational relevance of our findings by identifying pan-cancer biomarkers with diagnostic and prognostic utility. The observed correlations between circulating piRNAs and established tumor proliferation and EMT markers across cancer types suggest that these piRNAs may participate in common epigenetic programs driving tumor progression. By integrating piRNA expression data with global methylation (5mC), DNMT3B levels, and phenotypic markers (e.g., Ki-67, p53, E-cadherin, vimentin), this study contributes to the expanding understanding of circulating piRNAs as regulators and biomarkers of oncogenic transformation. Although causality cannot be inferred due to the observational design, our results provide a foundation for future mechanistic and longitudinal studies aimed at dissecting piRNA-mediated epigenetic reprogramming in cancer. Importantly, this study underscores the feasibility of non-invasive plasma-based detection of piRNA signatures, which may complement existing biomarker platforms and contribute to earlier diagnosis, risk stratification, and treatment monitoring in solid tumors.
The findings of this study highlight the significant role of piRNAs and DNA methylation markers in cancer progression. The upregulation of piR-823 and downregulation of piR-651 observed in cancer patients support previous studies linking piRNA expression with tumorigenesis [9, 19, 20]. There is growing evidence linking piRNAs, including piR-823 and piR-651, to cancer development and progression. piR-823 has been shown to promote tumorigenesis by regulating DNA methylation and influencing cancer cell proliferation. For instance, piR-823 enhances DNMT3B expression, contributing to aberrant DNA methylation in multiple myeloma [21]. Similarly, its upregulation has been reported in gastric cancer, where it modulates cancer cell growth and migration by suppressing mitophagy and affecting DNA methylation pathways [22]. Moreover, elevated serum piR-823 has demonstrated high diagnostic value in colorectal and renal cancers, supporting its utility as a circulating biomarker of tumor progression [23]. In contrast, piR-651 is often described as an oncogenic piRNA, with studies showing its overexpression in breast, lung, and gastric cancers, correlating with poor prognosis and higher tumor grade. Its oncogenic potential has been attributed to epigenetic silencing of tumor suppressors, as shown by reduced PTEN expression through DNMT1-mediated promoter methylation in gastric cancer [24]. Together, these findings support the biological plausibility of our observed correlations between piR-823/piR-651 and tumor aggressiveness indicators such as Ki-67, p53, and EMT markers, reinforcing the relevance of these circulating piRNAs in cancer biology. The elevated DNMT3B expression and increased 5mC levels in cancer cases reinforce the role of aberrant DNA methylation in oncogenesis [25, 26]. The finding of elevated 5mC levels in plasma cfDNA contrasts with the well-documented global hypomethylation observed in tumor genomic DNA. This discrepancy may reflect the heterogeneity of cfDNA sources, which include contributions from both tumor and normal cells. As such, cfDNA-based 5mC measures may not directly reflect intratumoral DNA methylation status but rather systemic epigenetic alterations occurring in response to tumor burden or inflammatory signaling. Furthermore, recent studies have proposed that cfDNA methylation profiling, although not identical to tumor methylomes, retains diagnostic and prognostic utility in multiple cancers.
The strong positive correlations between piR-823, DNMT3B, and tumor proliferation markers such as Ki-67 and p53 suggest that these epigenetic regulators contribute to tumor aggressiveness [21, 27]. Additionally, the inverse relationship between E-cadherin and DNMT3B/piR-823, coupled with the positive association with vimentin, supports the hypothesis that these markers play a role in promoting epithelial–mesenchymal transition (EMT) and metastasis [28, 29].
While our study focuses on circulating levels of piR-823 and piR-651 in plasma, the observed associations with tumor proliferation markers (e.g., Ki-67, p53) and EMT indicators (e.g., E-cadherin, vimentin) support their relevance to tumor biology. Although tumor tissue expression of these piRNAs was not directly assessed in our cohort, previous studies have demonstrated their dysregulation within cancerous tissues. For instance, piR-823 has been reported to be overexpressed in colorectal and breast tumor specimens, where it promotes tumorigenesis through DNMT3B-mediated epigenetic modulation and transcriptional activation of HSF1 [21, 22]. Similarly, piR-651 expression has been detected in tumor tissues from patients with breast, gastric, and lung cancers, where it facilitates oncogenic processes via DNMT1-dependent silencing of tumor suppressors like PTEN [24]. These findings suggest that the plasma levels we observed may reflect active dysregulation of these piRNAs within tumor microenvironments, possibly due to secretion via exosomes or passive release during cell turnover. Nonetheless, future studies incorporating paired plasma and tumor tissue analysis will be essential to validate this assumption and further elucidate the origin and functional implications of circulating piRNA alterations.
The elevated cfDNA levels in cancer patients validates this biomarkers importance and significance for detecting tumor-derived DNA alterations [30, 31]. Given its strong correlations with key proliferation and epigenetic markers—such as Ki-67 (r = 0.79), p53 (r = 0.73), and DNMT3B (r = 0.78)—cfDNA may serve as a complementary non-invasive biomarker for monitoring cancer progression and tumor burden [31]. These correlations underscore the biological relevance of cfDNA as a surrogate for tumor proliferative activity and epigenetic dysregulation. The cfDNA detection approach used in this study is based on direct plasma measurement via ELISA without conventional DNA extraction. While this may raise concerns about specificity, previous investigations have demonstrated that protein-based detection methods reliably quantify total cfDNA in plasma under controlled sample processing protocols [32]. Moreover, these assays have been applied in several cancer biomarker studies where RNA and cfDNA were simultaneously profiled from the same plasma fraction [ref]. Nevertheless, we acknowledge that the exact DNA species represented—whether tumor-derived or derived from other cell types—cannot be fully resolved with this approach. Future studies incorporating fragmentomic analysis or targeted cfDNA sequencing may provide deeper insight into the origin and functional relevance of these circulating DNA species.
While our findings reveal strong correlations between plasma piRNA levels, DNA methylation markers, and tumor aggressiveness indicators, we acknowledge the limitations of our study design in establishing causality. This study was observational and did not involve functional or mechanistic experiments. Therefore, although the data support the potential role of piR-823 and piR-651 in cancer-related epigenetic regulation, we cannot conclusively determine the mechanistic pathways underlying these associations. Further in vitro or in vivo studies—such as piRNA knockdown or overexpression experiments in cancer cell lines—are needed to clarify whether these molecules directly regulate DNA methylation machinery or contribute to tumor progression. Accordingly, we have refined the scope of our conclusions to emphasize correlative relationships rather than mechanistic causation.
Conclusion
This study provides compelling evidence of the interplay between piRNAs, DNA methylation, and tumor progression. The findings suggest that piRNA dysregulation and aberrant methylation patterns could serve as novel biomarkers for cancer detection and prognosis.
Limitations
This study has several limitations. Its cross-sectional design prevents establishing causal links between piRNA dysregulation, DNA methylation, and oncogenesis. Longitudinal studies are needed to track dynamic changes over time. The sample size (n = 300) may not fully capture the heterogeneity of piRNA expression across cancer subtypes and stages. Larger, multi-center cohorts would improve generalizability.
Plasma-based piRNA analysis does not account for tissue-specific expression patterns. Integrating tumor tissue profiling and functional validation studies (in vitro/in vivo) would strengthen mechanistic insights. ELISA and qRT-PCR methods, while sensitive, may not fully capture piRNA-methylation interactions at the transcriptomic and epigenomic levels. High-throughput sequencing and multi-omics approaches could provide deeper insights. Residual confounding from factors like age and comorbidities cannot be ruled out despite statistical adjustments. Case-control studies with functional genomics integration will help refine causal inferences.
Acknowledgements
Not applicable.
Author contributions
E.K. is responsible for research design, E.K. and E.D. performed activities, I.S. made statistical analysis. A.I was responsible for methodology development, M.T. sample processing. All authors reviewed the manuscript. All authors have read and approved the final version of the manuscript. Corresponding author has full access to all of the data in this study and takes complete responsibility for the integrity of the data and the accuracy of the data analysis.
Funding
This study was supported by the Shota Rustaveli National Science Foundation of Georgia, the project FR-22-6827: The aberrant expression of regulatory factors as the stimuli of oncogenesis.
Data availability
The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.
Declarations
Ethics approval and consent to participate
Ethical clearance was secured from the Bioethics International Committee of the Petre Shotadze Tbilisi Medical Academy (IRB20042004/1, Tbilisi, Georgia). All procedures adhered to the Helsinki Declaration of 1975, revised in 2013, with participants receiving comprehensive study information and providing written informed consent prior to inclusion.
Consent for publication
Not applicable.
Declaration of Generative AI and AI-assisted Technologies
During the preparation of this paper the authors used ChatGPT (https://chat.openai.com/) for stylistic purpose, checking grammar and spelling. After using this tool the authors reviewed and edited the content, they take full responsibility for the content of the publication.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.